diff options
152 files changed, 5883 insertions, 5612 deletions
diff --git a/.eslintrc.js b/.eslintrc.js index cf839769..9c70eff8 100644 --- a/.eslintrc.js +++ b/.eslintrc.js @@ -86,8 +86,6 @@ module.exports = { // imageviewer.js modalPrevImage: "readonly", modalNextImage: "readonly", - // token-counters.js - setupTokenCounters: "readonly", // localStorage.js localSet: "readonly", localGet: "readonly", diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml index 3dafaf8d..f42e4758 100644 --- a/.github/workflows/run_tests.yaml +++ b/.github/workflows/run_tests.yaml @@ -20,6 +20,12 @@ jobs: cache-dependency-path: | **/requirements*txt launch.py + - name: Cache models + id: cache-models + uses: actions/cache@v3 + with: + path: models + key: "2023-12-30" - name: Install test dependencies run: pip install wait-for-it -r requirements-test.txt env: @@ -33,6 +39,8 @@ jobs: TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu WEBUI_LAUNCH_LIVE_OUTPUT: "1" PYTHONUNBUFFERED: "1" + - name: Print installed packages + run: pip freeze - name: Start test server run: > python -m coverage run @@ -49,7 +57,7 @@ jobs: 2>&1 | tee output.txt & - name: Run tests run: | - wait-for-it --service 127.0.0.1:7860 -t 600 + wait-for-it --service 127.0.0.1:7860 -t 20 python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test - name: Kill test server if: always() @@ -37,3 +37,4 @@ notification.mp3 /node_modules /package-lock.json /.coverage* +/test/test_outputs diff --git a/CHANGELOG.md b/CHANGELOG.md index 67429bbf..f0c65981 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,134 @@ +## 1.8.0-RC
+
+### Features:
+* Update torch to version 2.1.2
+* Soft Inpainting ([#14208](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14208))
+* FP8 support ([#14031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031), [#14327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14327))
+* Support for SDXL-Inpaint Model ([#14390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14390))
+* Use Spandrel for upscaling and face restoration architectures ([#14425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14425), [#14467](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14467), [#14473](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14473), [#14474](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14474), [#14477](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14477), [#14476](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14476), [#14484](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14484), [#14500](https://github.com/AUTOMATIC1111/stable-difusion-webui/pull/14500), [#14501](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14501), [#14504](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14504), [#14524](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14524), [#14809](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14809))
+* Automatic backwards version compatibility (when loading infotexts from old images with program version specified, will add compatibility settings)
+* Implement zero terminal SNR noise schedule option (**[SEED BREAKING CHANGE](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Seed-breaking-changes#180-dev-170-225-2024-01-01---zero-terminal-snr-noise-schedule-option)**, [#14145](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14145), [#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979))
+* Add a [✨] button to run hires fix on selected image in the gallery (with help from [#14598](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14598), [#14626](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14626), [#14728](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14728))
+* [Separate assets repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets); serve fonts locally rather than from google's servers
+* Official LCM Sampler Support ([#14583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14583))
+* Add support for DAT upscaler models ([#14690](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14690), [#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
+* Extra Networks Tree View ([#14588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14588), [#14900](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14900))
+* NPU Support ([#14801](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14801))
+* Propmpt comments support
+
+### Minor:
+* Allow pasting in WIDTHxHEIGHT strings into the width/height fields ([#14296](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14296))
+* add option: Live preview in full page image viewer ([#14230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14230), [#14307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14307))
+* Add keyboard shortcuts for generate/skip/interrupt ([#14269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14269))
+* Better TCMALLOC support on different platforms ([#14227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14227), [#14883](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14883), [#14910](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14910))
+* Lora not found warning ([#14464](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14464))
+* Adding negative prompts to Loras in extra networks ([#14475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14475))
+* xyz_grid: allow varying the seed along an axis separate from axis options ([#12180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12180))
+* option to convert VAE to bfloat16 (implementation of [#9295](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9295))
+* Better IPEX support ([#14229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14229), [#14353](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14353), [#14559](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14559), [#14562](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14562), [#14597](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14597))
+* Option to interrupt after current generation rather than immediately ([#13653](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13653), [#14659](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14659))
+* Fullscreen Preview control fading/disable ([#14291](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14291))
+* Finer settings freezing control ([#13789](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13789))
+* Increase Upscaler Limits ([#14589](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14589))
+* Adjust brush size with hotkeys ([#14638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14638))
+* Add checkpoint info to csv log file when saving images ([#14663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14663))
+* Make more columns resizable ([#14740](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14740), [#14884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14884))
+* Add an option to not overlay original image for inpainting for #14727
+* Add Pad conds v0 option to support same generation with DDIM as before 1.6.0
+* Add "Interrupting..." placeholder.
+* Button for refresh extensions list ([#14857](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14857))
+* Add an option to disable normalization after calculating emphasis. ([#14874](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14874))
+* When counting tokens, also include enabled styles (can be disabled in settings to revert to previous behavior)
+* Configuration for the [📂] button for image gallery ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947))
+* Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973))
+* support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002))
+
+### Extensions and API:
+* Removed packages from requirements: basicsr, gfpgan, realesrgan; as well as their dependencies: absl-py, addict, beautifulsoup4, future, gdown, grpcio, importlib-metadata, lmdb, lpips, Markdown, platformdirs, PySocks, soupsieve, tb-nightly, tensorboard-data-server, tomli, Werkzeug, yapf, zipp, soupsieve
+* Enable task ids for API ([#14314](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14314))
+* add override_settings support for infotext API
+* rename generation_parameters_copypaste module to infotext_utils
+* prevent crash due to Script __init__ exception ([#14407](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14407))
+* Bump numpy to 1.26.2 ([#14471](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14471))
+* Add utility to inspect a model's dtype/device ([#14478](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14478))
+* Implement general forward method for all method in built-in lora ext ([#14547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14547))
+* Execute model_loaded_callback after moving to target device ([#14563](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14563))
+* Add self to CFGDenoiserParams ([#14573](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14573))
+* Allow TLS with API only mode (--nowebui) ([#14593](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14593))
+* New callback: postprocess_image_after_composite ([#14657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14657))
+* modules/api/api.py: add api endpoint to refresh embeddings list ([#14715](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14715))
+* set_named_arg ([#14773](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14773))
+* add before_token_counter callback and use it for prompt comments
+* ResizeHandleRow - allow overriden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
+
+### Performance
+* Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528))
+* Reduce unnecessary re-indexing extra networks directory ([#14512](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14512))
+* Avoid unnecessary `isfile`/`exists` calls ([#14527](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14527))
+
+### Bug Fixes:
+* fix multiple bugs related to styles multi-file support ([#14203](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14203), [#14276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14276), [#14707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14707))
+* Lora fixes ([#14300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14300), [#14237](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14237), [#14546](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14546), [#14726](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14726))
+* Re-add setting lost as part of e294e46 ([#14266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14266))
+* fix extras caption BLIP ([#14330](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14330))
+* include infotext into saved init image for img2img ([#14452](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14452))
+* xyz grid handle axis_type is None ([#14394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14394))
+* Update Added (Fixed) IPV6 Functionality When there is No Webui Argument Passed webui.py ([#14354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14354))
+* fix API thread safe issues of txt2img and img2img ([#14421](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14421))
+* handle selectable script_index is None ([#14487](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14487))
+* handle config.json failed to load ([#14525](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14525), [#14767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14767))
+* paste infotext cast int as float ([#14523](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14523))
+* Ensure GRADIO_ANALYTICS_ENABLED is set early enough ([#14537](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14537))
+* Fix logging configuration again ([#14538](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14538))
+* Handle CondFunc exception when resolving attributes ([#14560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14560))
+* Fix extras big batch crashes ([#14699](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14699))
+* Fix using wrong model caused by alias ([#14655](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14655))
+* Add # to the invalid_filename_chars list ([#14640](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14640))
+* Fix extension check for requirements ([#14639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14639))
+* Fix tab indexes are reset after restart UI ([#14637](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14637))
+* Fix nested manual cast ([#14689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14689))
+* Keep postprocessing upscale selected tab after restart ([#14702](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14702))
+* XYZ grid: filter out blank vals when axis is int or float type (like int axis seed) ([#14754](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14754))
+* fix CLIP Interrogator topN regex ([#14775](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14775))
+* Fix dtype error in MHA layer/change dtype checking mechanism for manual cast ([#14791](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14791))
+* catch load style.csv error ([#14814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14814))
+* fix error when editing extra networks card
+* fix extra networks metadata failing to work properly when you create the .json file with metadata for the first time.
+* util.walk_files extensions case insensitive ([#14879](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14879))
+* if extensions page not loaded, prevent apply ([#14873](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14873))
+* call the right function for token counter in img2img
+* Fix the bugs that search/reload will disappear when using other ExtraNetworks extensions ([#14939](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14939))
+* Gracefully handle mtime read exception from cache ([#14933](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14933))
+* Only trigger interrupt on `Esc` when interrupt button visible ([#14932](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14932))
+* Disable prompt token counters option actually disables token counting rather than just hiding results.
+* avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
+* Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
+* fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
+* Fix resize-handle for mobile ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010), [#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
+
+### Other:
+* Assign id for "extra_options". Replace numeric field with slider. ([#14270](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14270))
+* change state dict comparison to ref compare ([#14216](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14216))
+* Bump torch-rocm to 5.6/5.7 ([#14293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14293))
+* Base output path off data path ([#14446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14446))
+* reorder training preprocessing modules in extras tab ([#14367](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14367))
+* Remove `cleanup_models` code ([#14472](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14472))
+* only rewrite ui-config when there is change ([#14352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14352))
+* Fix lint issue from 501993eb ([#14495](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14495))
+* Update README.md ([#14548](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14548))
+* hires button, fix seeds ()
+* Logging: set formatter correctly for fallback logger too ([#14618](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14618))
+* Read generation info from infotexts rather than json for internal needs (save, extract seed from generated pic) ([#14645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14645))
+* improve get_crop_region ([#14709](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14709))
+* Bump safetensors' version to 0.4.2 ([#14782](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14782))
+* add tooltip create_submit_box ([#14803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14803))
+* extensions tab table row hover highlight ([#14885](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14885))
+* Always add timestamp to displayed image ([#14890](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14890))
+* Added core.filemode=false so doesn't track changes in file permission… ([#14930](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14930))
+* Normalize command-line argument paths ([#14934](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14934), [#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035))
+* Use original App Title in progress bar ([#14916](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14916))
+* register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012))
+
## 1.7.0
### Features:
@@ -41,6 +172,7 @@ * add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046))
* support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126))
* allow use of mutiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
+* make extra network card description plaintext by default, with an option (Treat card description as HTML) to re-enable HTML as it was (originally by [#13241](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13241))
### Extensions and API:
* update gradio to 3.41.2
@@ -1,5 +1,5 @@ # Stable Diffusion web UI
-A browser interface based on Gradio library for Stable Diffusion.
+A web interface for Stable Diffusion, implemented using Gradio library.
@@ -151,11 +151,12 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al - Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
-- GFPGAN - https://github.com/TencentARC/GFPGAN.git
-- CodeFormer - https://github.com/sczhou/CodeFormer
-- ESRGAN - https://github.com/xinntao/ESRGAN
-- SwinIR - https://github.com/JingyunLiang/SwinIR
-- Swin2SR - https://github.com/mv-lab/swin2sr
+- Spandrel - https://github.com/chaiNNer-org/spandrel implementing
+ - GFPGAN - https://github.com/TencentARC/GFPGAN.git
+ - CodeFormer - https://github.com/sczhou/CodeFormer
+ - ESRGAN - https://github.com/xinntao/ESRGAN
+ - SwinIR - https://github.com/JingyunLiang/SwinIR
+ - Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
diff --git a/configs/sd_xl_inpaint.yaml b/configs/sd_xl_inpaint.yaml new file mode 100644 index 00000000..3bad3721 --- /dev/null +++ b/configs/sd_xl_inpaint.yaml @@ -0,0 +1,98 @@ +model: + target: sgm.models.diffusion.DiffusionEngine + params: + scale_factor: 0.13025 + disable_first_stage_autocast: True + + denoiser_config: + target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser + params: + num_idx: 1000 + + weighting_config: + target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting + scaling_config: + target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling + discretization_config: + target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization + + network_config: + target: sgm.modules.diffusionmodules.openaimodel.UNetModel + params: + adm_in_channels: 2816 + num_classes: sequential + use_checkpoint: True + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [4, 2] + num_res_blocks: 2 + channel_mult: [1, 2, 4] + num_head_channels: 64 + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16 + context_dim: 2048 + spatial_transformer_attn_type: softmax-xformers + legacy: False + + conditioner_config: + target: sgm.modules.GeneralConditioner + params: + emb_models: + # crossattn cond + - is_trainable: False + input_key: txt + target: sgm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: hidden + layer_idx: 11 + # crossattn and vector cond + - is_trainable: False + input_key: txt + target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2 + params: + arch: ViT-bigG-14 + version: laion2b_s39b_b160k + freeze: True + layer: penultimate + always_return_pooled: True + legacy: False + # vector cond + - is_trainable: False + input_key: original_size_as_tuple + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: crop_coords_top_left + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + # vector cond + - is_trainable: False + input_key: target_size_as_tuple + target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND + params: + outdim: 256 # multiplied by two + + first_stage_config: + target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + attn_type: vanilla-xformers + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [1, 2, 4, 4] + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py index 6021fd8d..b8fd9194 100644 --- a/extensions-builtin/Lora/network.py +++ b/extensions-builtin/Lora/network.py @@ -3,6 +3,9 @@ import os from collections import namedtuple
import enum
+import torch.nn as nn
+import torch.nn.functional as F
+
from modules import sd_models, cache, errors, hashes, shared
NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
@@ -115,6 +118,29 @@ class NetworkModule: if hasattr(self.sd_module, 'weight'):
self.shape = self.sd_module.weight.shape
+ self.ops = None
+ self.extra_kwargs = {}
+ if isinstance(self.sd_module, nn.Conv2d):
+ self.ops = F.conv2d
+ self.extra_kwargs = {
+ 'stride': self.sd_module.stride,
+ 'padding': self.sd_module.padding
+ }
+ elif isinstance(self.sd_module, nn.Linear):
+ self.ops = F.linear
+ elif isinstance(self.sd_module, nn.LayerNorm):
+ self.ops = F.layer_norm
+ self.extra_kwargs = {
+ 'normalized_shape': self.sd_module.normalized_shape,
+ 'eps': self.sd_module.eps
+ }
+ elif isinstance(self.sd_module, nn.GroupNorm):
+ self.ops = F.group_norm
+ self.extra_kwargs = {
+ 'num_groups': self.sd_module.num_groups,
+ 'eps': self.sd_module.eps
+ }
+
self.dim = None
self.bias = weights.w.get("bias")
self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
@@ -137,7 +163,7 @@ class NetworkModule: def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
- updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
updown = updown.reshape(output_shape)
if len(output_shape) == 4:
@@ -155,5 +181,10 @@ class NetworkModule: raise NotImplementedError()
def forward(self, x, y):
- raise NotImplementedError()
+ """A general forward implementation for all modules"""
+ if self.ops is None:
+ raise NotImplementedError()
+ else:
+ updown, ex_bias = self.calc_updown(self.sd_module.weight)
+ return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs)
diff --git a/extensions-builtin/Lora/network_full.py b/extensions-builtin/Lora/network_full.py index bf6930e9..f221c95f 100644 --- a/extensions-builtin/Lora/network_full.py +++ b/extensions-builtin/Lora/network_full.py @@ -18,9 +18,9 @@ class NetworkModuleFull(network.NetworkModule): def calc_updown(self, orig_weight):
output_shape = self.weight.shape
- updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ updown = self.weight.to(orig_weight.device)
if self.ex_bias is not None:
- ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
+ ex_bias = self.ex_bias.to(orig_weight.device)
else:
ex_bias = None
diff --git a/extensions-builtin/Lora/network_glora.py b/extensions-builtin/Lora/network_glora.py index 492d4870..efe5c681 100644 --- a/extensions-builtin/Lora/network_glora.py +++ b/extensions-builtin/Lora/network_glora.py @@ -22,12 +22,12 @@ class NetworkModuleGLora(network.NetworkModule): self.w2b = weights.w["b2.weight"] def calc_updown(self, orig_weight): - w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype) - w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype) - w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) - w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + w1a = self.w1a.to(orig_weight.device) + w1b = self.w1b.to(orig_weight.device) + w2a = self.w2a.to(orig_weight.device) + w2b = self.w2b.to(orig_weight.device) output_shape = [w1a.size(0), w1b.size(1)] - updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a)) + updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a)) return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_hada.py b/extensions-builtin/Lora/network_hada.py index 5fcb0695..d95a0fd1 100644 --- a/extensions-builtin/Lora/network_hada.py +++ b/extensions-builtin/Lora/network_hada.py @@ -27,16 +27,16 @@ class NetworkModuleHada(network.NetworkModule): self.t2 = weights.w.get("hada_t2")
def calc_updown(self, orig_weight):
- w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
- w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
- w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
- w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1a = self.w1a.to(orig_weight.device)
+ w1b = self.w1b.to(orig_weight.device)
+ w2a = self.w2a.to(orig_weight.device)
+ w2b = self.w2b.to(orig_weight.device)
output_shape = [w1a.size(0), w1b.size(1)]
if self.t1 is not None:
output_shape = [w1a.size(1), w1b.size(1)]
- t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
+ t1 = self.t1.to(orig_weight.device)
updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
output_shape += t1.shape[2:]
else:
@@ -45,7 +45,7 @@ class NetworkModuleHada(network.NetworkModule): updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
if self.t2 is not None:
- t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+ t2 = self.t2.to(orig_weight.device)
updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
else:
updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
diff --git a/extensions-builtin/Lora/network_ia3.py b/extensions-builtin/Lora/network_ia3.py index 7edc4249..96faeaf3 100644 --- a/extensions-builtin/Lora/network_ia3.py +++ b/extensions-builtin/Lora/network_ia3.py @@ -17,7 +17,7 @@ class NetworkModuleIa3(network.NetworkModule): self.on_input = weights.w["on_input"].item()
def calc_updown(self, orig_weight):
- w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
+ w = self.w.to(orig_weight.device)
output_shape = [w.size(0), orig_weight.size(1)]
if self.on_input:
diff --git a/extensions-builtin/Lora/network_lokr.py b/extensions-builtin/Lora/network_lokr.py index 340acdab..fcdaeafd 100644 --- a/extensions-builtin/Lora/network_lokr.py +++ b/extensions-builtin/Lora/network_lokr.py @@ -37,22 +37,22 @@ class NetworkModuleLokr(network.NetworkModule): def calc_updown(self, orig_weight):
if self.w1 is not None:
- w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1 = self.w1.to(orig_weight.device)
else:
- w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
- w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w1a = self.w1a.to(orig_weight.device)
+ w1b = self.w1b.to(orig_weight.device)
w1 = w1a @ w1b
if self.w2 is not None:
- w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2 = self.w2.to(orig_weight.device)
elif self.t2 is None:
- w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
- w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ w2a = self.w2a.to(orig_weight.device)
+ w2b = self.w2b.to(orig_weight.device)
w2 = w2a @ w2b
else:
- t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
- w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
- w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+ t2 = self.t2.to(orig_weight.device)
+ w2a = self.w2a.to(orig_weight.device)
+ w2b = self.w2b.to(orig_weight.device)
w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py index 26c0a72c..4cc40295 100644 --- a/extensions-builtin/Lora/network_lora.py +++ b/extensions-builtin/Lora/network_lora.py @@ -61,13 +61,13 @@ class NetworkModuleLora(network.NetworkModule): return module
def calc_updown(self, orig_weight):
- up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
- down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ up = self.up_model.weight.to(orig_weight.device)
+ down = self.down_model.weight.to(orig_weight.device)
output_shape = [up.size(0), down.size(1)]
if self.mid_model is not None:
# cp-decomposition
- mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+ mid = self.mid_model.weight.to(orig_weight.device)
updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
output_shape += mid.shape[2:]
else:
diff --git a/extensions-builtin/Lora/network_norm.py b/extensions-builtin/Lora/network_norm.py index ce450158..d25afcbb 100644 --- a/extensions-builtin/Lora/network_norm.py +++ b/extensions-builtin/Lora/network_norm.py @@ -18,10 +18,10 @@ class NetworkModuleNorm(network.NetworkModule): def calc_updown(self, orig_weight): output_shape = self.w_norm.shape - updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype) + updown = self.w_norm.to(orig_weight.device) if self.b_norm is not None: - ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype) + ex_bias = self.b_norm.to(orig_weight.device) else: ex_bias = None diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py index fa647020..7821a8a7 100644 --- a/extensions-builtin/Lora/network_oft.py +++ b/extensions-builtin/Lora/network_oft.py @@ -1,6 +1,5 @@ import torch import network -from lyco_helpers import factorization from einops import rearrange @@ -22,20 +21,28 @@ class NetworkModuleOFT(network.NetworkModule): self.org_module: list[torch.Module] = [self.sd_module] self.scale = 1.0 + self.is_R = False + self.is_boft = False - # kohya-ss + # kohya-ss/New LyCORIS OFT/BOFT if "oft_blocks" in weights.w.keys(): - self.is_kohya = True self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size) - self.alpha = weights.w["alpha"] # alpha is constraint + self.alpha = weights.w.get("alpha", None) # alpha is constraint self.dim = self.oft_blocks.shape[0] # lora dim - # LyCORIS + # Old LyCORIS OFT elif "oft_diag" in weights.w.keys(): - self.is_kohya = False + self.is_R = True self.oft_blocks = weights.w["oft_diag"] # self.alpha is unused self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size) + # LyCORIS BOFT + if self.oft_blocks.dim() == 4: + self.is_boft = True + self.rescale = weights.w.get('rescale', None) + if self.rescale is not None: + self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1)) + is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear] is_conv = type(self.sd_module) in [torch.nn.Conv2d] is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported @@ -47,36 +54,65 @@ class NetworkModuleOFT(network.NetworkModule): elif is_other_linear: self.out_dim = self.sd_module.embed_dim - if self.is_kohya: - self.constraint = self.alpha * self.out_dim - self.num_blocks = self.dim - self.block_size = self.out_dim // self.dim - else: + self.num_blocks = self.dim + self.block_size = self.out_dim // self.dim + self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim + if self.is_R: self.constraint = None - self.block_size, self.num_blocks = factorization(self.out_dim, self.dim) + self.block_size = self.dim + self.num_blocks = self.out_dim // self.dim + elif self.is_boft: + self.boft_m = self.oft_blocks.shape[0] + self.num_blocks = self.oft_blocks.shape[1] + self.block_size = self.oft_blocks.shape[2] + self.boft_b = self.block_size def calc_updown(self, orig_weight): - oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) - eye = torch.eye(self.block_size, device=self.oft_blocks.device) - - if self.is_kohya: - block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix - norm_Q = torch.norm(block_Q.flatten()) - new_norm_Q = torch.clamp(norm_Q, max=self.constraint) - block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) + oft_blocks = self.oft_blocks.to(orig_weight.device) + eye = torch.eye(self.block_size, device=oft_blocks.device) + + if not self.is_R: + block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix + if self.constraint != 0: + norm_Q = torch.norm(block_Q.flatten()) + new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device)) + block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8)) oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse()) - R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) - - # This errors out for MultiheadAttention, might need to be handled up-stream - merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size) - merged_weight = torch.einsum( - 'k n m, k n ... -> k m ...', - R, - merged_weight - ) - merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') - - updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight + R = oft_blocks.to(orig_weight.device) + + if not self.is_boft: + # This errors out for MultiheadAttention, might need to be handled up-stream + merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size) + merged_weight = torch.einsum( + 'k n m, k n ... -> k m ...', + R, + merged_weight + ) + merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') + else: + # TODO: determine correct value for scale + scale = 1.0 + m = self.boft_m + b = self.boft_b + r_b = b // 2 + inp = orig_weight + for i in range(m): + bi = R[i] # b_num, b_size, b_size + if i == 0: + # Apply multiplier/scale and rescale into first weight + bi = bi * scale + (1 - scale) * eye + inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b) + inp = rearrange(inp, "(d b) ... -> d b ...", b=b) + inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp) + inp = rearrange(inp, "d b ... -> (d b) ...") + inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b) + merged_weight = inp + + # Rescale mechanism + if self.rescale is not None: + merged_weight = self.rescale.to(merged_weight) * merged_weight + + updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype) output_shape = orig_weight.shape return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py index 629bf853..83ea2802 100644 --- a/extensions-builtin/Lora/networks.py +++ b/extensions-builtin/Lora/networks.py @@ -1,3 +1,4 @@ +import gradio as gr
import logging
import os
import re
@@ -259,11 +260,11 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No loaded_networks.clear()
- networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+ networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
if any(x is None for x in networks_on_disk):
list_available_networks()
- networks_on_disk = [available_network_aliases.get(name, None) for name in names]
+ networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
failed_to_load_networks = []
@@ -314,7 +315,12 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No emb_db.skipped_embeddings[name] = embedding
if failed_to_load_networks:
- sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
+ lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
+ sd_hijack.model_hijack.comments.append(lora_not_found_message)
+ if shared.opts.lora_not_found_warning_console:
+ print(f'\n{lora_not_found_message}\n')
+ if shared.opts.lora_not_found_gradio_warning:
+ gr.Warning(lora_not_found_message)
purge_networks_from_memory()
@@ -389,18 +395,26 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn if module is not None and hasattr(self, 'weight'):
try:
with torch.no_grad():
- updown, ex_bias = module.calc_updown(self.weight)
+ if getattr(self, 'fp16_weight', None) is None:
+ weight = self.weight
+ bias = self.bias
+ else:
+ weight = self.fp16_weight.clone().to(self.weight.device)
+ bias = getattr(self, 'fp16_bias', None)
+ if bias is not None:
+ bias = bias.clone().to(self.bias.device)
+ updown, ex_bias = module.calc_updown(weight)
- if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+ if len(weight.shape) == 4 and weight.shape[1] == 9:
# inpainting model. zero pad updown to make channel[1] 4 to 9
updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
- self.weight += updown
+ self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
if ex_bias is not None and hasattr(self, 'bias'):
if self.bias is None:
- self.bias = torch.nn.Parameter(ex_bias)
+ self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
else:
- self.bias += ex_bias
+ self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
except RuntimeError as e:
logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
@@ -444,23 +458,23 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn self.network_current_names = wanted_names
-def network_forward(module, input, original_forward):
+def network_forward(org_module, input, original_forward):
"""
Old way of applying Lora by executing operations during layer's forward.
Stacking many loras this way results in big performance degradation.
"""
if len(loaded_networks) == 0:
- return original_forward(module, input)
+ return original_forward(org_module, input)
input = devices.cond_cast_unet(input)
- network_restore_weights_from_backup(module)
- network_reset_cached_weight(module)
+ network_restore_weights_from_backup(org_module)
+ network_reset_cached_weight(org_module)
- y = original_forward(module, input)
+ y = original_forward(org_module, input)
- network_layer_name = getattr(module, 'network_layer_name', None)
+ network_layer_name = getattr(org_module, 'network_layer_name', None)
for lora in loaded_networks:
module = lora.modules.get(network_layer_name, None)
if module is None:
diff --git a/extensions-builtin/Lora/preload.py b/extensions-builtin/Lora/preload.py index 50961be3..52fab29b 100644 --- a/extensions-builtin/Lora/preload.py +++ b/extensions-builtin/Lora/preload.py @@ -1,7 +1,8 @@ import os
from modules import paths
+from modules.paths_internal import normalized_filepath
def preload(parser):
- parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
- parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))
+ parser.add_argument("--lora-dir", type=normalized_filepath, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
+ parser.add_argument("--lyco-dir-backcompat", type=normalized_filepath, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index ef23968c..1518f7e5 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -39,6 +39,8 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
"lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
"lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
+ "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"),
+ "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"),
}))
diff --git a/extensions-builtin/Lora/ui_edit_user_metadata.py b/extensions-builtin/Lora/ui_edit_user_metadata.py index c7011909..3160aecf 100644 --- a/extensions-builtin/Lora/ui_edit_user_metadata.py +++ b/extensions-builtin/Lora/ui_edit_user_metadata.py @@ -54,12 +54,13 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor) self.slider_preferred_weight = None
self.edit_notes = None
- def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
+ def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes):
user_metadata = self.get_user_metadata(name)
user_metadata["description"] = desc
user_metadata["sd version"] = sd_version
user_metadata["activation text"] = activation_text
user_metadata["preferred weight"] = preferred_weight
+ user_metadata["negative text"] = negative_text
user_metadata["notes"] = notes
self.write_user_metadata(name, user_metadata)
@@ -127,6 +128,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor) gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
user_metadata.get('activation text', ''),
float(user_metadata.get('preferred weight', 0.0)),
+ user_metadata.get('negative text', ''),
gr.update(visible=True if tags else False),
gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
]
@@ -162,7 +164,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor) self.taginfo = gr.HighlightedText(label="Training dataset tags")
self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
-
+ self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts")
with gr.Row() as row_random_prompt:
with gr.Column(scale=8):
random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
@@ -198,6 +200,7 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor) self.taginfo,
self.edit_activation_text,
self.slider_preferred_weight,
+ self.edit_negative_text,
row_random_prompt,
random_prompt,
]
@@ -211,7 +214,9 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor) self.select_sd_version,
self.edit_activation_text,
self.slider_preferred_weight,
+ self.edit_negative_text,
self.edit_notes,
]
+
self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py index df02c663..66d15dd0 100644 --- a/extensions-builtin/Lora/ui_extra_networks_lora.py +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -24,13 +24,16 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): alias = lora_on_disk.get_alias()
+ search_terms = [self.search_terms_from_path(lora_on_disk.filename)]
+ if lora_on_disk.hash:
+ search_terms.append(lora_on_disk.hash)
item = {
"name": name,
"filename": lora_on_disk.filename,
"shorthash": lora_on_disk.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
- "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
+ "search_terms": search_terms,
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": lora_on_disk.metadata,
"sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
@@ -45,6 +48,11 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): if activation_text:
item["prompt"] += " + " + quote_js(" " + activation_text)
+ negative_prompt = item["user_metadata"].get("negative text")
+ item["negative_prompt"] = quote_js("")
+ if negative_prompt:
+ item["negative_prompt"] = quote_js('(' + negative_prompt + ':1)')
+
sd_version = item["user_metadata"].get("sd version")
if sd_version in network.SdVersion.__members__:
item["sd_version"] = sd_version
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index 167d2f64..fe5e5a19 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -1,16 +1,9 @@ import sys import PIL.Image -import numpy as np -import torch -from tqdm import tqdm import modules.upscaler -from modules import devices, modelloader, script_callbacks, errors -from scunet_model_arch import SCUNet - -from modules.modelloader import load_file_from_url -from modules.shared import opts +from modules import devices, errors, modelloader, script_callbacks, shared, upscaler_utils class UpscalerScuNET(modules.upscaler.Upscaler): @@ -42,100 +35,37 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scalers.append(scaler_data2) self.scalers = scalers - @staticmethod - @torch.no_grad() - def tiled_inference(img, model): - # test the image tile by tile - h, w = img.shape[2:] - tile = opts.SCUNET_tile - tile_overlap = opts.SCUNET_tile_overlap - if tile == 0: - return model(img) - - device = devices.get_device_for('scunet') - assert tile % 8 == 0, "tile size should be a multiple of window_size" - sf = 1 - - stride = tile - tile_overlap - h_idx_list = list(range(0, h - tile, stride)) + [h - tile] - w_idx_list = list(range(0, w - tile, stride)) + [w - tile] - E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device) - W = torch.zeros_like(E, dtype=devices.dtype, device=device) - - with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar: - for h_idx in h_idx_list: - - for w_idx in w_idx_list: - - in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] - - out_patch = model(in_patch) - out_patch_mask = torch.ones_like(out_patch) - - E[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch) - W[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch_mask) - pbar.update(1) - output = E.div_(W) - - return output - def do_upscale(self, img: PIL.Image.Image, selected_file): - devices.torch_gc() - try: model = self.load_model(selected_file) except Exception as e: print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr) return img - device = devices.get_device_for('scunet') - tile = opts.SCUNET_tile - h, w = img.height, img.width - np_img = np.array(img) - np_img = np_img[:, :, ::-1] # RGB to BGR - np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW - torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore - - if tile > h or tile > w: - _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device) - _img[:, :, :h, :w] = torch_img # pad image - torch_img = _img - - torch_output = self.tiled_inference(torch_img, model).squeeze(0) - torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any - np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy() - del torch_img, torch_output + img = upscaler_utils.upscale_2( + img, + model, + tile_size=shared.opts.SCUNET_tile, + tile_overlap=shared.opts.SCUNET_tile_overlap, + scale=1, # ScuNET is a denoising model, not an upscaler + desc='ScuNET', + ) devices.torch_gc() - - output = np_output.transpose((1, 2, 0)) # CHW to HWC - output = output[:, :, ::-1] # BGR to RGB - return PIL.Image.fromarray((output * 255).astype(np.uint8)) + return img def load_model(self, path: str): device = devices.get_device_for('scunet') if path.startswith("http"): # TODO: this doesn't use `path` at all? - filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") + filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") else: filename = path - model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) - model.load_state_dict(torch.load(filename), strict=True) - model.eval() - for _, v in model.named_parameters(): - v.requires_grad = False - model = model.to(device) - - return model + return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet') def on_ui_settings(): import gradio as gr - from modules import shared shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling")) shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam")) diff --git a/extensions-builtin/ScuNET/scunet_model_arch.py b/extensions-builtin/ScuNET/scunet_model_arch.py deleted file mode 100644 index b51a8806..00000000 --- a/extensions-builtin/ScuNET/scunet_model_arch.py +++ /dev/null @@ -1,268 +0,0 @@ -# -*- coding: utf-8 -*- -import numpy as np -import torch -import torch.nn as nn -from einops import rearrange -from einops.layers.torch import Rearrange -from timm.models.layers import trunc_normal_, DropPath - - -class WMSA(nn.Module): - """ Self-attention module in Swin Transformer - """ - - def __init__(self, input_dim, output_dim, head_dim, window_size, type): - super(WMSA, self).__init__() - self.input_dim = input_dim - self.output_dim = output_dim - self.head_dim = head_dim - self.scale = self.head_dim ** -0.5 - self.n_heads = input_dim // head_dim - self.window_size = window_size - self.type = type - self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) - - self.relative_position_params = nn.Parameter( - torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)) - - self.linear = nn.Linear(self.input_dim, self.output_dim) - - trunc_normal_(self.relative_position_params, std=.02) - self.relative_position_params = torch.nn.Parameter( - self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1, - 2).transpose( - 0, 1)) - - def generate_mask(self, h, w, p, shift): - """ generating the mask of SW-MSA - Args: - shift: shift parameters in CyclicShift. - Returns: - attn_mask: should be (1 1 w p p), - """ - # supporting square. - attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device) - if self.type == 'W': - return attn_mask - - s = p - shift - attn_mask[-1, :, :s, :, s:, :] = True - attn_mask[-1, :, s:, :, :s, :] = True - attn_mask[:, -1, :, :s, :, s:] = True - attn_mask[:, -1, :, s:, :, :s] = True - attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)') - return attn_mask - - def forward(self, x): - """ Forward pass of Window Multi-head Self-attention module. - Args: - x: input tensor with shape of [b h w c]; - attn_mask: attention mask, fill -inf where the value is True; - Returns: - output: tensor shape [b h w c] - """ - if self.type != 'W': - x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2)) - - x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size) - h_windows = x.size(1) - w_windows = x.size(2) - # square validation - # assert h_windows == w_windows - - x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size) - qkv = self.embedding_layer(x) - q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0) - sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale - # Adding learnable relative embedding - sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q') - # Using Attn Mask to distinguish different subwindows. - if self.type != 'W': - attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2) - sim = sim.masked_fill_(attn_mask, float("-inf")) - - probs = nn.functional.softmax(sim, dim=-1) - output = torch.einsum('hbwij,hbwjc->hbwic', probs, v) - output = rearrange(output, 'h b w p c -> b w p (h c)') - output = self.linear(output) - output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size) - - if self.type != 'W': - output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2)) - - return output - - def relative_embedding(self): - cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)])) - relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 - # negative is allowed - return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()] - - -class Block(nn.Module): - def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): - """ SwinTransformer Block - """ - super(Block, self).__init__() - self.input_dim = input_dim - self.output_dim = output_dim - assert type in ['W', 'SW'] - self.type = type - if input_resolution <= window_size: - self.type = 'W' - - self.ln1 = nn.LayerNorm(input_dim) - self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.ln2 = nn.LayerNorm(input_dim) - self.mlp = nn.Sequential( - nn.Linear(input_dim, 4 * input_dim), - nn.GELU(), - nn.Linear(4 * input_dim, output_dim), - ) - - def forward(self, x): - x = x + self.drop_path(self.msa(self.ln1(x))) - x = x + self.drop_path(self.mlp(self.ln2(x))) - return x - - -class ConvTransBlock(nn.Module): - def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None): - """ SwinTransformer and Conv Block - """ - super(ConvTransBlock, self).__init__() - self.conv_dim = conv_dim - self.trans_dim = trans_dim - self.head_dim = head_dim - self.window_size = window_size - self.drop_path = drop_path - self.type = type - self.input_resolution = input_resolution - - assert self.type in ['W', 'SW'] - if self.input_resolution <= self.window_size: - self.type = 'W' - - self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path, - self.type, self.input_resolution) - self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) - self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True) - - self.conv_block = nn.Sequential( - nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), - nn.ReLU(True), - nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False) - ) - - def forward(self, x): - conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1) - conv_x = self.conv_block(conv_x) + conv_x - trans_x = Rearrange('b c h w -> b h w c')(trans_x) - trans_x = self.trans_block(trans_x) - trans_x = Rearrange('b h w c -> b c h w')(trans_x) - res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) - x = x + res - - return x - - -class SCUNet(nn.Module): - # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256): - def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256): - super(SCUNet, self).__init__() - if config is None: - config = [2, 2, 2, 2, 2, 2, 2] - self.config = config - self.dim = dim - self.head_dim = 32 - self.window_size = 8 - - # drop path rate for each layer - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] - - self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] - - begin = 0 - self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], - 'W' if not i % 2 else 'SW', input_resolution) - for i in range(config[0])] + \ - [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] - - begin += config[0] - self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], - 'W' if not i % 2 else 'SW', input_resolution // 2) - for i in range(config[1])] + \ - [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] - - begin += config[1] - self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], - 'W' if not i % 2 else 'SW', input_resolution // 4) - for i in range(config[2])] + \ - [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] - - begin += config[2] - self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin], - 'W' if not i % 2 else 'SW', input_resolution // 8) - for i in range(config[3])] - - begin += config[3] - self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \ - [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin], - 'W' if not i % 2 else 'SW', input_resolution // 4) - for i in range(config[4])] - - begin += config[4] - self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \ - [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin], - 'W' if not i % 2 else 'SW', input_resolution // 2) - for i in range(config[5])] - - begin += config[5] - self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \ - [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin], - 'W' if not i % 2 else 'SW', input_resolution) - for i in range(config[6])] - - self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] - - self.m_head = nn.Sequential(*self.m_head) - self.m_down1 = nn.Sequential(*self.m_down1) - self.m_down2 = nn.Sequential(*self.m_down2) - self.m_down3 = nn.Sequential(*self.m_down3) - self.m_body = nn.Sequential(*self.m_body) - self.m_up3 = nn.Sequential(*self.m_up3) - self.m_up2 = nn.Sequential(*self.m_up2) - self.m_up1 = nn.Sequential(*self.m_up1) - self.m_tail = nn.Sequential(*self.m_tail) - # self.apply(self._init_weights) - - def forward(self, x0): - - h, w = x0.size()[-2:] - paddingBottom = int(np.ceil(h / 64) * 64 - h) - paddingRight = int(np.ceil(w / 64) * 64 - w) - x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0) - - x1 = self.m_head(x0) - x2 = self.m_down1(x1) - x3 = self.m_down2(x2) - x4 = self.m_down3(x3) - x = self.m_body(x4) - x = self.m_up3(x + x4) - x = self.m_up2(x + x3) - x = self.m_up1(x + x2) - x = self.m_tail(x + x1) - - x = x[..., :h, :w] - - return x - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py index ae0d0e6a..16bf9b79 100644 --- a/extensions-builtin/SwinIR/scripts/swinir_model.py +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -1,20 +1,15 @@ +import logging import sys -import platform -import numpy as np import torch from PIL import Image -from tqdm import tqdm -from modules import modelloader, devices, script_callbacks, shared -from modules.shared import opts, state -from swinir_model_arch import SwinIR -from swinir_model_arch_v2 import Swin2SR +from modules import devices, modelloader, script_callbacks, shared, upscaler_utils from modules.upscaler import Upscaler, UpscalerData SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth" -device_swinir = devices.get_device_for('swinir') +logger = logging.getLogger(__name__) class UpscalerSwinIR(Upscaler): @@ -37,26 +32,28 @@ class UpscalerSwinIR(Upscaler): scalers.append(model_data) self.scalers = scalers - def do_upscale(self, img, model_file): - use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \ - and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows" - current_config = (model_file, opts.SWIN_tile) + def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image: + current_config = (model_file, shared.opts.SWIN_tile) - if use_compile and self._cached_model_config == current_config: + if self._cached_model_config == current_config: model = self._cached_model else: - self._cached_model = None try: model = self.load_model(model_file) except Exception as e: print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr) return img - model = model.to(device_swinir, dtype=devices.dtype) - if use_compile: - model = torch.compile(model) - self._cached_model = model - self._cached_model_config = current_config - img = upscale(img, model) + self._cached_model = model + self._cached_model_config = current_config + + img = upscaler_utils.upscale_2( + img, + model, + tile_size=shared.opts.SWIN_tile, + tile_overlap=shared.opts.SWIN_tile_overlap, + scale=model.scale, + desc="SwinIR", + ) devices.torch_gc() return img @@ -69,115 +66,22 @@ class UpscalerSwinIR(Upscaler): ) else: filename = path - if filename.endswith(".v2.pth"): - model = Swin2SR( - upscale=scale, - in_chans=3, - img_size=64, - window_size=8, - img_range=1.0, - depths=[6, 6, 6, 6, 6, 6], - embed_dim=180, - num_heads=[6, 6, 6, 6, 6, 6], - mlp_ratio=2, - upsampler="nearest+conv", - resi_connection="1conv", - ) - params = None - else: - model = SwinIR( - upscale=scale, - in_chans=3, - img_size=64, - window_size=8, - img_range=1.0, - depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], - embed_dim=240, - num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8], - mlp_ratio=2, - upsampler="nearest+conv", - resi_connection="3conv", - ) - params = "params_ema" - pretrained_model = torch.load(filename) - if params is not None: - model.load_state_dict(pretrained_model[params], strict=True) - else: - model.load_state_dict(pretrained_model, strict=True) - return model - - -def upscale( - img, - model, - tile=None, - tile_overlap=None, - window_size=8, - scale=4, -): - tile = tile or opts.SWIN_tile - tile_overlap = tile_overlap or opts.SWIN_tile_overlap - - - img = np.array(img) - img = img[:, :, ::-1] - img = np.moveaxis(img, 2, 0) / 255 - img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype) - with torch.no_grad(), devices.autocast(): - _, _, h_old, w_old = img.size() - h_pad = (h_old // window_size + 1) * window_size - h_old - w_pad = (w_old // window_size + 1) * window_size - w_old - img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :] - img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad] - output = inference(img, model, tile, tile_overlap, window_size, scale) - output = output[..., : h_old * scale, : w_old * scale] - output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() - if output.ndim == 3: - output = np.transpose( - output[[2, 1, 0], :, :], (1, 2, 0) - ) # CHW-RGB to HCW-BGR - output = (output * 255.0).round().astype(np.uint8) # float32 to uint8 - return Image.fromarray(output, "RGB") - - -def inference(img, model, tile, tile_overlap, window_size, scale): - # test the image tile by tile - b, c, h, w = img.size() - tile = min(tile, h, w) - assert tile % window_size == 0, "tile size should be a multiple of window_size" - sf = scale - - stride = tile - tile_overlap - h_idx_list = list(range(0, h - tile, stride)) + [h - tile] - w_idx_list = list(range(0, w - tile, stride)) + [w - tile] - E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img) - W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir) - - with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: - for h_idx in h_idx_list: - if state.interrupted or state.skipped: - break - - for w_idx in w_idx_list: - if state.interrupted or state.skipped: - break - - in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] - out_patch = model(in_patch) - out_patch_mask = torch.ones_like(out_patch) - - E[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch) - W[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch_mask) - pbar.update(1) - output = E.div_(W) - - return output + model_descriptor = modelloader.load_spandrel_model( + filename, + device=self._get_device(), + prefer_half=(devices.dtype == torch.float16), + expected_architecture="SwinIR", + ) + if getattr(shared.opts, 'SWIN_torch_compile', False): + try: + model_descriptor.model.compile() + except Exception: + logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True) + return model_descriptor + + def _get_device(self): + return devices.get_device_for('swinir') def on_ui_settings(): @@ -185,8 +89,7 @@ def on_ui_settings(): shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling"))) shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling"))) - if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows - shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) + shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py deleted file mode 100644 index 93b93274..00000000 --- a/extensions-builtin/SwinIR/swinir_model_arch.py +++ /dev/null @@ -1,867 +0,0 @@ -# ----------------------------------------------------------------------------------- -# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 -# Originally Written by Ze Liu, Modified by Jingyun Liang. -# ----------------------------------------------------------------------------------- - -import math -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.checkpoint as checkpoint -from timm.models.layers import DropPath, to_2tuple, trunc_normal_ - - -class Mlp(nn.Module): - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): - super().__init__() - out_features = out_features or in_features - hidden_features = hidden_features or in_features - self.fc1 = nn.Linear(in_features, hidden_features) - self.act = act_layer() - self.fc2 = nn.Linear(hidden_features, out_features) - self.drop = nn.Dropout(drop) - - def forward(self, x): - x = self.fc1(x) - x = self.act(x) - x = self.drop(x) - x = self.fc2(x) - x = self.drop(x) - return x - - -def window_partition(x, window_size): - """ - Args: - x: (B, H, W, C) - window_size (int): window size - - Returns: - windows: (num_windows*B, window_size, window_size, C) - """ - B, H, W, C = x.shape - x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) - windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) - return windows - - -def window_reverse(windows, window_size, H, W): - """ - Args: - windows: (num_windows*B, window_size, window_size, C) - window_size (int): Window size - H (int): Height of image - W (int): Width of image - - Returns: - x: (B, H, W, C) - """ - B = int(windows.shape[0] / (H * W / window_size / window_size)) - x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) - x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) - return x - - -class WindowAttention(nn.Module): - r""" Window based multi-head self attention (W-MSA) module with relative position bias. - It supports both of shifted and non-shifted window. - - Args: - dim (int): Number of input channels. - window_size (tuple[int]): The height and width of the window. - num_heads (int): Number of attention heads. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set - attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 - proj_drop (float, optional): Dropout ratio of output. Default: 0.0 - """ - - def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): - - super().__init__() - self.dim = dim - self.window_size = window_size # Wh, Ww - self.num_heads = num_heads - head_dim = dim // num_heads - self.scale = qk_scale or head_dim ** -0.5 - - # define a parameter table of relative position bias - self.relative_position_bias_table = nn.Parameter( - torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH - - # get pair-wise relative position index for each token inside the window - coords_h = torch.arange(self.window_size[0]) - coords_w = torch.arange(self.window_size[1]) - coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww - coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww - relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 - relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 - relative_coords[:, :, 1] += self.window_size[1] - 1 - relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 - relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww - self.register_buffer("relative_position_index", relative_position_index) - - self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) - self.attn_drop = nn.Dropout(attn_drop) - self.proj = nn.Linear(dim, dim) - - self.proj_drop = nn.Dropout(proj_drop) - - trunc_normal_(self.relative_position_bias_table, std=.02) - self.softmax = nn.Softmax(dim=-1) - - def forward(self, x, mask=None): - """ - Args: - x: input features with shape of (num_windows*B, N, C) - mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None - """ - B_, N, C = x.shape - qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) - q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) - - q = q * self.scale - attn = (q @ k.transpose(-2, -1)) - - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( - self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww - attn = attn + relative_position_bias.unsqueeze(0) - - if mask is not None: - nW = mask.shape[0] - attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) - attn = attn.view(-1, self.num_heads, N, N) - attn = self.softmax(attn) - else: - attn = self.softmax(attn) - - attn = self.attn_drop(attn) - - x = (attn @ v).transpose(1, 2).reshape(B_, N, C) - x = self.proj(x) - x = self.proj_drop(x) - return x - - def extra_repr(self) -> str: - return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' - - def flops(self, N): - # calculate flops for 1 window with token length of N - flops = 0 - # qkv = self.qkv(x) - flops += N * self.dim * 3 * self.dim - # attn = (q @ k.transpose(-2, -1)) - flops += self.num_heads * N * (self.dim // self.num_heads) * N - # x = (attn @ v) - flops += self.num_heads * N * N * (self.dim // self.num_heads) - # x = self.proj(x) - flops += N * self.dim * self.dim - return flops - - -class SwinTransformerBlock(nn.Module): - r""" Swin Transformer Block. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - num_heads (int): Number of attention heads. - window_size (int): Window size. - shift_size (int): Shift size for SW-MSA. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float, optional): Stochastic depth rate. Default: 0.0 - act_layer (nn.Module, optional): Activation layer. Default: nn.GELU - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., - act_layer=nn.GELU, norm_layer=nn.LayerNorm): - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.num_heads = num_heads - self.window_size = window_size - self.shift_size = shift_size - self.mlp_ratio = mlp_ratio - if min(self.input_resolution) <= self.window_size: - # if window size is larger than input resolution, we don't partition windows - self.shift_size = 0 - self.window_size = min(self.input_resolution) - assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" - - self.norm1 = norm_layer(dim) - self.attn = WindowAttention( - dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, - qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) - - self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() - self.norm2 = norm_layer(dim) - mlp_hidden_dim = int(dim * mlp_ratio) - self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) - - if self.shift_size > 0: - attn_mask = self.calculate_mask(self.input_resolution) - else: - attn_mask = None - - self.register_buffer("attn_mask", attn_mask) - - def calculate_mask(self, x_size): - # calculate attention mask for SW-MSA - H, W = x_size - img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 - h_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - w_slices = (slice(0, -self.window_size), - slice(-self.window_size, -self.shift_size), - slice(-self.shift_size, None)) - cnt = 0 - for h in h_slices: - for w in w_slices: - img_mask[:, h, w, :] = cnt - cnt += 1 - - mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 - mask_windows = mask_windows.view(-1, self.window_size * self.window_size) - attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) - attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) - - return attn_mask - - def forward(self, x, x_size): - H, W = x_size - B, L, C = x.shape - # assert L == H * W, "input feature has wrong size" - - shortcut = x - x = self.norm1(x) - x = x.view(B, H, W, C) - - # cyclic shift - if self.shift_size > 0: - shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - else: - shifted_x = x - - # partition windows - x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C - x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C - - # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size - if self.input_resolution == x_size: - attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C - else: - attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) - - # merge windows - attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) - shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C - - # reverse cyclic shift - if self.shift_size > 0: - x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - else: - x = shifted_x - x = x.view(B, H * W, C) - - # FFN - x = shortcut + self.drop_path(x) - x = x + self.drop_path(self.mlp(self.norm2(x))) - - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ - f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" - - def flops(self): - flops = 0 - H, W = self.input_resolution - # norm1 - flops += self.dim * H * W - # W-MSA/SW-MSA - nW = H * W / self.window_size / self.window_size - flops += nW * self.attn.flops(self.window_size * self.window_size) - # mlp - flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio - # norm2 - flops += self.dim * H * W - return flops - - -class PatchMerging(nn.Module): - r""" Patch Merging Layer. - - Args: - input_resolution (tuple[int]): Resolution of input feature. - dim (int): Number of input channels. - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - """ - - def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): - super().__init__() - self.input_resolution = input_resolution - self.dim = dim - self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) - self.norm = norm_layer(4 * dim) - - def forward(self, x): - """ - x: B, H*W, C - """ - H, W = self.input_resolution - B, L, C = x.shape - assert L == H * W, "input feature has wrong size" - assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." - - x = x.view(B, H, W, C) - - x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C - x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C - x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C - x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C - x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C - x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C - - x = self.norm(x) - x = self.reduction(x) - - return x - - def extra_repr(self) -> str: - return f"input_resolution={self.input_resolution}, dim={self.dim}" - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.dim - flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim - return flops - - -class BasicLayer(nn.Module): - """ A basic Swin Transformer layer for one stage. - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - """ - - def __init__(self, dim, input_resolution, depth, num_heads, window_size, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): - - super().__init__() - self.dim = dim - self.input_resolution = input_resolution - self.depth = depth - self.use_checkpoint = use_checkpoint - - # build blocks - self.blocks = nn.ModuleList([ - SwinTransformerBlock(dim=dim, input_resolution=input_resolution, - num_heads=num_heads, window_size=window_size, - shift_size=0 if (i % 2 == 0) else window_size // 2, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop, attn_drop=attn_drop, - drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, - norm_layer=norm_layer) - for i in range(depth)]) - - # patch merging layer - if downsample is not None: - self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) - else: - self.downsample = None - - def forward(self, x, x_size): - for blk in self.blocks: - if self.use_checkpoint: - x = checkpoint.checkpoint(blk, x, x_size) - else: - x = blk(x, x_size) - if self.downsample is not None: - x = self.downsample(x) - return x - - def extra_repr(self) -> str: - return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" - - def flops(self): - flops = 0 - for blk in self.blocks: - flops += blk.flops() - if self.downsample is not None: - flops += self.downsample.flops() - return flops - - -class RSTB(nn.Module): - """Residual Swin Transformer Block (RSTB). - - Args: - dim (int): Number of input channels. - input_resolution (tuple[int]): Input resolution. - depth (int): Number of blocks. - num_heads (int): Number of attention heads. - window_size (int): Local window size. - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. - qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. - drop (float, optional): Dropout rate. Default: 0.0 - attn_drop (float, optional): Attention dropout rate. Default: 0.0 - drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 - norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm - downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. - img_size: Input image size. - patch_size: Patch size. - resi_connection: The convolutional block before residual connection. - """ - - def __init__(self, dim, input_resolution, depth, num_heads, window_size, - mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., - drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False, - img_size=224, patch_size=4, resi_connection='1conv'): - super(RSTB, self).__init__() - - self.dim = dim - self.input_resolution = input_resolution - - self.residual_group = BasicLayer(dim=dim, - input_resolution=input_resolution, - depth=depth, - num_heads=num_heads, - window_size=window_size, - mlp_ratio=mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop, attn_drop=attn_drop, - drop_path=drop_path, - norm_layer=norm_layer, - downsample=downsample, - use_checkpoint=use_checkpoint) - - if resi_connection == '1conv': - self.conv = nn.Conv2d(dim, dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(dim // 4, dim, 3, 1, 1)) - - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, - norm_layer=None) - - self.patch_unembed = PatchUnEmbed( - img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, - norm_layer=None) - - def forward(self, x, x_size): - return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x - - def flops(self): - flops = 0 - flops += self.residual_group.flops() - H, W = self.input_resolution - flops += H * W * self.dim * self.dim * 9 - flops += self.patch_embed.flops() - flops += self.patch_unembed.flops() - - return flops - - -class PatchEmbed(nn.Module): - r""" Image to Patch Embedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - if norm_layer is not None: - self.norm = norm_layer(embed_dim) - else: - self.norm = None - - def forward(self, x): - x = x.flatten(2).transpose(1, 2) # B Ph*Pw C - if self.norm is not None: - x = self.norm(x) - return x - - def flops(self): - flops = 0 - H, W = self.img_size - if self.norm is not None: - flops += H * W * self.embed_dim - return flops - - -class PatchUnEmbed(nn.Module): - r""" Image to Patch Unembedding - - Args: - img_size (int): Image size. Default: 224. - patch_size (int): Patch token size. Default: 4. - in_chans (int): Number of input image channels. Default: 3. - embed_dim (int): Number of linear projection output channels. Default: 96. - norm_layer (nn.Module, optional): Normalization layer. Default: None - """ - - def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): - super().__init__() - img_size = to_2tuple(img_size) - patch_size = to_2tuple(patch_size) - patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] - self.img_size = img_size - self.patch_size = patch_size - self.patches_resolution = patches_resolution - self.num_patches = patches_resolution[0] * patches_resolution[1] - - self.in_chans = in_chans - self.embed_dim = embed_dim - - def forward(self, x, x_size): - B, HW, C = x.shape - x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C - return x - - def flops(self): - flops = 0 - return flops - - -class Upsample(nn.Sequential): - """Upsample module. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - """ - - def __init__(self, scale, num_feat): - m = [] - if (scale & (scale - 1)) == 0: # scale = 2^n - for _ in range(int(math.log(scale, 2))): - m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(2)) - elif scale == 3: - m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) - m.append(nn.PixelShuffle(3)) - else: - raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.') - super(Upsample, self).__init__(*m) - - -class UpsampleOneStep(nn.Sequential): - """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) - Used in lightweight SR to save parameters. - - Args: - scale (int): Scale factor. Supported scales: 2^n and 3. - num_feat (int): Channel number of intermediate features. - - """ - - def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): - self.num_feat = num_feat - self.input_resolution = input_resolution - m = [] - m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1)) - m.append(nn.PixelShuffle(scale)) - super(UpsampleOneStep, self).__init__(*m) - - def flops(self): - H, W = self.input_resolution - flops = H * W * self.num_feat * 3 * 9 - return flops - - -class SwinIR(nn.Module): - r""" SwinIR - A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. - - Args: - img_size (int | tuple(int)): Input image size. Default 64 - patch_size (int | tuple(int)): Patch size. Default: 1 - in_chans (int): Number of input image channels. Default: 3 - embed_dim (int): Patch embedding dimension. Default: 96 - depths (tuple(int)): Depth of each Swin Transformer layer. - num_heads (tuple(int)): Number of attention heads in different layers. - window_size (int): Window size. Default: 7 - mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 - qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True - qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None - drop_rate (float): Dropout rate. Default: 0 - attn_drop_rate (float): Attention dropout rate. Default: 0 - drop_path_rate (float): Stochastic depth rate. Default: 0.1 - norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. - ape (bool): If True, add absolute position embedding to the patch embedding. Default: False - patch_norm (bool): If True, add normalization after patch embedding. Default: True - use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False - upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction - img_range: Image range. 1. or 255. - upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None - resi_connection: The convolutional block before residual connection. '1conv'/'3conv' - """ - - def __init__(self, img_size=64, patch_size=1, in_chans=3, - embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6), - window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None, - drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, - norm_layer=nn.LayerNorm, ape=False, patch_norm=True, - use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv', - **kwargs): - super(SwinIR, self).__init__() - num_in_ch = in_chans - num_out_ch = in_chans - num_feat = 64 - self.img_range = img_range - if in_chans == 3: - rgb_mean = (0.4488, 0.4371, 0.4040) - self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) - else: - self.mean = torch.zeros(1, 1, 1, 1) - self.upscale = upscale - self.upsampler = upsampler - self.window_size = window_size - - ##################################################################################################### - ################################### 1, shallow feature extraction ################################### - self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) - - ##################################################################################################### - ################################### 2, deep feature extraction ###################################### - self.num_layers = len(depths) - self.embed_dim = embed_dim - self.ape = ape - self.patch_norm = patch_norm - self.num_features = embed_dim - self.mlp_ratio = mlp_ratio - - # split image into non-overlapping patches - self.patch_embed = PatchEmbed( - img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - num_patches = self.patch_embed.num_patches - patches_resolution = self.patch_embed.patches_resolution - self.patches_resolution = patches_resolution - - # merge non-overlapping patches into image - self.patch_unembed = PatchUnEmbed( - img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, - norm_layer=norm_layer if self.patch_norm else None) - - # absolute position embedding - if self.ape: - self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) - trunc_normal_(self.absolute_pos_embed, std=.02) - - self.pos_drop = nn.Dropout(p=drop_rate) - - # stochastic depth - dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule - - # build Residual Swin Transformer blocks (RSTB) - self.layers = nn.ModuleList() - for i_layer in range(self.num_layers): - layer = RSTB(dim=embed_dim, - input_resolution=(patches_resolution[0], - patches_resolution[1]), - depth=depths[i_layer], - num_heads=num_heads[i_layer], - window_size=window_size, - mlp_ratio=self.mlp_ratio, - qkv_bias=qkv_bias, qk_scale=qk_scale, - drop=drop_rate, attn_drop=attn_drop_rate, - drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results - norm_layer=norm_layer, - downsample=None, - use_checkpoint=use_checkpoint, - img_size=img_size, - patch_size=patch_size, - resi_connection=resi_connection - - ) - self.layers.append(layer) - self.norm = norm_layer(self.num_features) - - # build the last conv layer in deep feature extraction - if resi_connection == '1conv': - self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) - elif resi_connection == '3conv': - # to save parameters and memory - self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), - nn.LeakyReLU(negative_slope=0.2, inplace=True), - nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) - - ##################################################################################################### - ################################ 3, high quality image reconstruction ################################ - if self.upsampler == 'pixelshuffle': - # for classical SR - self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), - nn.LeakyReLU(inplace=True)) - self.upsample = Upsample(upscale, num_feat) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR (to save parameters) - self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, - (patches_resolution[0], patches_resolution[1])) - elif self.upsampler == 'nearest+conv': - # for real-world SR (less artifacts) - self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1), - nn.LeakyReLU(inplace=True)) - self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - if self.upscale == 4: - self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) - self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) - self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) - else: - # for image denoising and JPEG compression artifact reduction - self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) - - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - @torch.jit.ignore - def no_weight_decay(self): - return {'absolute_pos_embed'} - - @torch.jit.ignore - def no_weight_decay_keywords(self): - return {'relative_position_bias_table'} - - def check_image_size(self, x): - _, _, h, w = x.size() - mod_pad_h = (self.window_size - h % self.window_size) % self.window_size - mod_pad_w = (self.window_size - w % self.window_size) % self.window_size - x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') - return x - - def forward_features(self, x): - x_size = (x.shape[2], x.shape[3]) - x = self.patch_embed(x) - if self.ape: - x = x + self.absolute_pos_embed - x = self.pos_drop(x) - - for layer in self.layers: - x = layer(x, x_size) - - x = self.norm(x) # B L C - x = self.patch_unembed(x, x_size) - - return x - - def forward(self, x): - H, W = x.shape[2:] - x = self.check_image_size(x) - - self.mean = self.mean.type_as(x) - x = (x - self.mean) * self.img_range - - if self.upsampler == 'pixelshuffle': - # for classical SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.conv_last(self.upsample(x)) - elif self.upsampler == 'pixelshuffledirect': - # for lightweight SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.upsample(x) - elif self.upsampler == 'nearest+conv': - # for real-world SR - x = self.conv_first(x) - x = self.conv_after_body(self.forward_features(x)) + x - x = self.conv_before_upsample(x) - x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - if self.upscale == 4: - x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) - x = self.conv_last(self.lrelu(self.conv_hr(x))) - else: - # for image denoising and JPEG compression artifact reduction - x_first = self.conv_first(x) - res = self.conv_after_body(self.forward_features(x_first)) + x_first - x = x + self.conv_last(res) - - x = x / self.img_range + self.mean - - return x[:, :, :H*self.upscale, :W*self.upscale] - - def flops(self): - flops = 0 - H, W = self.patches_resolution - flops += H * W * 3 * self.embed_dim * 9 - flops += self.patch_embed.flops() - for layer in self.layers: - flops += layer.flops() - flops += H * W * 3 * self.embed_dim * self.embed_dim - flops += self.upsample.flops() - return flops - - -if __name__ == '__main__': - upscale = 4 - window_size = 8 - height = (1024 // upscale // window_size + 1) * window_size - width = (720 // upscale // window_size + 1) * window_size - model = SwinIR(upscale=2, img_size=(height, width), - window_size=window_size, img_range=1., depths=[6, 6, 6, 6], - embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect') - print(model) - print(height, width, model.flops() / 1e9) - - x = torch.randn((1, 3, height, width)) - x = model(x) - print(x.shape) diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py deleted file mode 100644 index dad22cca..00000000 --- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py +++ /dev/null @@ -1,1017 +0,0 @@ -# -----------------------------------------------------------------------------------
-# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
-# Written by Conde and Choi et al.
-# -----------------------------------------------------------------------------------
-
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-class WindowAttention(nn.Module):
- r""" Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
- pretrained_window_size=(0, 0)):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.pretrained_window_size = pretrained_window_size
- self.num_heads = num_heads
-
- self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
- # mlp to generate continuous relative position bias
- self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
- nn.ReLU(inplace=True),
- nn.Linear(512, num_heads, bias=False))
-
- # get relative_coords_table
- relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
- relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
- relative_coords_table = torch.stack(
- torch.meshgrid([relative_coords_h,
- relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
- if pretrained_window_size[0] > 0:
- relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
- else:
- relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
- relative_coords_table *= 8 # normalize to -8, 8
- relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
- torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
- self.register_buffer("relative_coords_table", relative_coords_table)
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=False)
- if qkv_bias:
- self.q_bias = nn.Parameter(torch.zeros(dim))
- self.v_bias = nn.Parameter(torch.zeros(dim))
- else:
- self.q_bias = None
- self.v_bias = None
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv_bias = None
- if self.q_bias is not None:
- qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
- qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
- qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- # cosine attention
- attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
- logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
- attn = attn * logit_scale
-
- relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
- relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
- def extra_repr(self) -> str:
- return f'dim={self.dim}, window_size={self.window_size}, ' \
- f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
- def flops(self, N):
- # calculate flops for 1 window with token length of N
- flops = 0
- # qkv = self.qkv(x)
- flops += N * self.dim * 3 * self.dim
- # attn = (q @ k.transpose(-2, -1))
- flops += self.num_heads * N * (self.dim // self.num_heads) * N
- # x = (attn @ v)
- flops += self.num_heads * N * N * (self.dim // self.num_heads)
- # x = self.proj(x)
- flops += N * self.dim * self.dim
- return flops
-
-class SwinTransformerBlock(nn.Module):
- r""" Swin Transformer Block.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resulotion.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- pretrained_window_size (int): Window size in pre-training.
- """
-
- def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- if min(self.input_resolution) <= self.window_size:
- # if window size is larger than input resolution, we don't partition windows
- self.shift_size = 0
- self.window_size = min(self.input_resolution)
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
- pretrained_window_size=to_2tuple(pretrained_window_size))
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- if self.shift_size > 0:
- attn_mask = self.calculate_mask(self.input_resolution)
- else:
- attn_mask = None
-
- self.register_buffer("attn_mask", attn_mask)
-
- def calculate_mask(self, x_size):
- # calculate attention mask for SW-MSA
- H, W = x_size
- img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- return attn_mask
-
- def forward(self, x, x_size):
- H, W = x_size
- B, L, C = x.shape
- #assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = x.view(B, H, W, C)
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- else:
- shifted_x = x
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
- if self.input_resolution == x_size:
- attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
- else:
- attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
- x = x.view(B, H * W, C)
- x = shortcut + self.drop_path(self.norm1(x))
-
- # FFN
- x = x + self.drop_path(self.norm2(self.mlp(x)))
-
- return x
-
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
- f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
- def flops(self):
- flops = 0
- H, W = self.input_resolution
- # norm1
- flops += self.dim * H * W
- # W-MSA/SW-MSA
- nW = H * W / self.window_size / self.window_size
- flops += nW * self.attn.flops(self.window_size * self.window_size)
- # mlp
- flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
- # norm2
- flops += self.dim * H * W
- return flops
-
-class PatchMerging(nn.Module):
- r""" Patch Merging Layer.
- Args:
- input_resolution (tuple[int]): Resolution of input feature.
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.input_resolution = input_resolution
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(2 * dim)
-
- def forward(self, x):
- """
- x: B, H*W, C
- """
- H, W = self.input_resolution
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
- assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
- x = x.view(B, H, W, C)
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.reduction(x)
- x = self.norm(x)
-
- return x
-
- def extra_repr(self) -> str:
- return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
- def flops(self):
- H, W = self.input_resolution
- flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
- flops += H * W * self.dim // 2
- return flops
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resolution.
- depth (int): Number of blocks.
- num_heads (int): Number of attention heads.
- window_size (int): Local window size.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- pretrained_window_size (int): Local window size in pre-training.
- """
-
- def __init__(self, dim, input_resolution, depth, num_heads, window_size,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
- drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
- pretrained_window_size=0):
-
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
- num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop, attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer,
- pretrained_window_size=pretrained_window_size)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x, x_size):
- for blk in self.blocks:
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x, x_size)
- else:
- x = blk(x, x_size)
- if self.downsample is not None:
- x = self.downsample(x)
- return x
-
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
- def flops(self):
- flops = 0
- for blk in self.blocks:
- flops += blk.flops()
- if self.downsample is not None:
- flops += self.downsample.flops()
- return flops
-
- def _init_respostnorm(self):
- for blk in self.blocks:
- nn.init.constant_(blk.norm1.bias, 0)
- nn.init.constant_(blk.norm1.weight, 0)
- nn.init.constant_(blk.norm2.bias, 0)
- nn.init.constant_(blk.norm2.weight, 0)
-
-class PatchEmbed(nn.Module):
- r""" Image to Patch Embedding
- Args:
- img_size (int): Image size. Default: 224.
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
- self.img_size = img_size
- self.patch_size = patch_size
- self.patches_resolution = patches_resolution
- self.num_patches = patches_resolution[0] * patches_resolution[1]
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
- if norm_layer is not None:
- self.norm = norm_layer(embed_dim)
- else:
- self.norm = None
-
- def forward(self, x):
- B, C, H, W = x.shape
- # FIXME look at relaxing size constraints
- # assert H == self.img_size[0] and W == self.img_size[1],
- # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
- if self.norm is not None:
- x = self.norm(x)
- return x
-
- def flops(self):
- Ho, Wo = self.patches_resolution
- flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
- if self.norm is not None:
- flops += Ho * Wo * self.embed_dim
- return flops
-
-class RSTB(nn.Module):
- """Residual Swin Transformer Block (RSTB).
-
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resolution.
- depth (int): Number of blocks.
- num_heads (int): Number of attention heads.
- window_size (int): Local window size.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- img_size: Input image size.
- patch_size: Patch size.
- resi_connection: The convolutional block before residual connection.
- """
-
- def __init__(self, dim, input_resolution, depth, num_heads, window_size,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
- drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
- img_size=224, patch_size=4, resi_connection='1conv'):
- super(RSTB, self).__init__()
-
- self.dim = dim
- self.input_resolution = input_resolution
-
- self.residual_group = BasicLayer(dim=dim,
- input_resolution=input_resolution,
- depth=depth,
- num_heads=num_heads,
- window_size=window_size,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop, attn_drop=attn_drop,
- drop_path=drop_path,
- norm_layer=norm_layer,
- downsample=downsample,
- use_checkpoint=use_checkpoint)
-
- if resi_connection == '1conv':
- self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
- elif resi_connection == '3conv':
- # to save parameters and memory
- self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
- nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
- self.patch_embed = PatchEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
- norm_layer=None)
-
- self.patch_unembed = PatchUnEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
- norm_layer=None)
-
- def forward(self, x, x_size):
- return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
- def flops(self):
- flops = 0
- flops += self.residual_group.flops()
- H, W = self.input_resolution
- flops += H * W * self.dim * self.dim * 9
- flops += self.patch_embed.flops()
- flops += self.patch_unembed.flops()
-
- return flops
-
-class PatchUnEmbed(nn.Module):
- r""" Image to Patch Unembedding
-
- Args:
- img_size (int): Image size. Default: 224.
- patch_size (int): Patch token size. Default: 4.
- in_chans (int): Number of input image channels. Default: 3.
- embed_dim (int): Number of linear projection output channels. Default: 96.
- norm_layer (nn.Module, optional): Normalization layer. Default: None
- """
-
- def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
- self.img_size = img_size
- self.patch_size = patch_size
- self.patches_resolution = patches_resolution
- self.num_patches = patches_resolution[0] * patches_resolution[1]
-
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- def forward(self, x, x_size):
- B, HW, C = x.shape
- x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
- return x
-
- def flops(self):
- flops = 0
- return flops
-
-
-class Upsample(nn.Sequential):
- """Upsample module.
-
- Args:
- scale (int): Scale factor. Supported scales: 2^n and 3.
- num_feat (int): Channel number of intermediate features.
- """
-
- def __init__(self, scale, num_feat):
- m = []
- if (scale & (scale - 1)) == 0: # scale = 2^n
- for _ in range(int(math.log(scale, 2))):
- m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(2))
- elif scale == 3:
- m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(3))
- else:
- raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
- super(Upsample, self).__init__(*m)
-
-class Upsample_hf(nn.Sequential):
- """Upsample module.
-
- Args:
- scale (int): Scale factor. Supported scales: 2^n and 3.
- num_feat (int): Channel number of intermediate features.
- """
-
- def __init__(self, scale, num_feat):
- m = []
- if (scale & (scale - 1)) == 0: # scale = 2^n
- for _ in range(int(math.log(scale, 2))):
- m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(2))
- elif scale == 3:
- m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
- m.append(nn.PixelShuffle(3))
- else:
- raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
- super(Upsample_hf, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
- """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
- Used in lightweight SR to save parameters.
-
- Args:
- scale (int): Scale factor. Supported scales: 2^n and 3.
- num_feat (int): Channel number of intermediate features.
-
- """
-
- def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
- self.num_feat = num_feat
- self.input_resolution = input_resolution
- m = []
- m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
- m.append(nn.PixelShuffle(scale))
- super(UpsampleOneStep, self).__init__(*m)
-
- def flops(self):
- H, W = self.input_resolution
- flops = H * W * self.num_feat * 3 * 9
- return flops
-
-
-
-class Swin2SR(nn.Module):
- r""" Swin2SR
- A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
-
- Args:
- img_size (int | tuple(int)): Input image size. Default 64
- patch_size (int | tuple(int)): Patch size. Default: 1
- in_chans (int): Number of input image channels. Default: 3
- embed_dim (int): Patch embedding dimension. Default: 96
- depths (tuple(int)): Depth of each Swin Transformer layer.
- num_heads (tuple(int)): Number of attention heads in different layers.
- window_size (int): Window size. Default: 7
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- drop_rate (float): Dropout rate. Default: 0
- attn_drop_rate (float): Attention dropout rate. Default: 0
- drop_path_rate (float): Stochastic depth rate. Default: 0.1
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
- patch_norm (bool): If True, add normalization after patch embedding. Default: True
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
- upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
- img_range: Image range. 1. or 255.
- upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
- resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
- """
-
- def __init__(self, img_size=64, patch_size=1, in_chans=3,
- embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
- window_size=7, mlp_ratio=4., qkv_bias=True,
- drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
- norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
- use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
- **kwargs):
- super(Swin2SR, self).__init__()
- num_in_ch = in_chans
- num_out_ch = in_chans
- num_feat = 64
- self.img_range = img_range
- if in_chans == 3:
- rgb_mean = (0.4488, 0.4371, 0.4040)
- self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
- else:
- self.mean = torch.zeros(1, 1, 1, 1)
- self.upscale = upscale
- self.upsampler = upsampler
- self.window_size = window_size
-
- #####################################################################################################
- ################################### 1, shallow feature extraction ###################################
- self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
- #####################################################################################################
- ################################### 2, deep feature extraction ######################################
- self.num_layers = len(depths)
- self.embed_dim = embed_dim
- self.ape = ape
- self.patch_norm = patch_norm
- self.num_features = embed_dim
- self.mlp_ratio = mlp_ratio
-
- # split image into non-overlapping patches
- self.patch_embed = PatchEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
- num_patches = self.patch_embed.num_patches
- patches_resolution = self.patch_embed.patches_resolution
- self.patches_resolution = patches_resolution
-
- # merge non-overlapping patches into image
- self.patch_unembed = PatchUnEmbed(
- img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
- norm_layer=norm_layer if self.patch_norm else None)
-
- # absolute position embedding
- if self.ape:
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
-
- # build Residual Swin Transformer blocks (RSTB)
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = RSTB(dim=embed_dim,
- input_resolution=(patches_resolution[0],
- patches_resolution[1]),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=self.mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
- norm_layer=norm_layer,
- downsample=None,
- use_checkpoint=use_checkpoint,
- img_size=img_size,
- patch_size=patch_size,
- resi_connection=resi_connection
-
- )
- self.layers.append(layer)
-
- if self.upsampler == 'pixelshuffle_hf':
- self.layers_hf = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = RSTB(dim=embed_dim,
- input_resolution=(patches_resolution[0],
- patches_resolution[1]),
- depth=depths[i_layer],
- num_heads=num_heads[i_layer],
- window_size=window_size,
- mlp_ratio=self.mlp_ratio,
- qkv_bias=qkv_bias,
- drop=drop_rate, attn_drop=attn_drop_rate,
- drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
- norm_layer=norm_layer,
- downsample=None,
- use_checkpoint=use_checkpoint,
- img_size=img_size,
- patch_size=patch_size,
- resi_connection=resi_connection
-
- )
- self.layers_hf.append(layer)
-
- self.norm = norm_layer(self.num_features)
-
- # build the last conv layer in deep feature extraction
- if resi_connection == '1conv':
- self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
- elif resi_connection == '3conv':
- # to save parameters and memory
- self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
- nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
- nn.LeakyReLU(negative_slope=0.2, inplace=True),
- nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
- #####################################################################################################
- ################################ 3, high quality image reconstruction ################################
- if self.upsampler == 'pixelshuffle':
- # for classical SR
- self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.upsample = Upsample(upscale, num_feat)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- elif self.upsampler == 'pixelshuffle_aux':
- self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
- self.conv_before_upsample = nn.Sequential(
- nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- self.conv_after_aux = nn.Sequential(
- nn.Conv2d(3, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.upsample = Upsample(upscale, num_feat)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
- elif self.upsampler == 'pixelshuffle_hf':
- self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.upsample = Upsample(upscale, num_feat)
- self.upsample_hf = Upsample_hf(upscale, num_feat)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
- self.conv_before_upsample_hf = nn.Sequential(
- nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
- elif self.upsampler == 'pixelshuffledirect':
- # for lightweight SR (to save parameters)
- self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
- (patches_resolution[0], patches_resolution[1]))
- elif self.upsampler == 'nearest+conv':
- # for real-world SR (less artifacts)
- assert self.upscale == 4, 'only support x4 now.'
- self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
- nn.LeakyReLU(inplace=True))
- self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
- self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
- self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
- self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
- self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
- else:
- # for image denoising and JPEG compression artifact reduction
- self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'absolute_pos_embed'}
-
- @torch.jit.ignore
- def no_weight_decay_keywords(self):
- return {'relative_position_bias_table'}
-
- def check_image_size(self, x):
- _, _, h, w = x.size()
- mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
- mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
- x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
- return x
-
- def forward_features(self, x):
- x_size = (x.shape[2], x.shape[3])
- x = self.patch_embed(x)
- if self.ape:
- x = x + self.absolute_pos_embed
- x = self.pos_drop(x)
-
- for layer in self.layers:
- x = layer(x, x_size)
-
- x = self.norm(x) # B L C
- x = self.patch_unembed(x, x_size)
-
- return x
-
- def forward_features_hf(self, x):
- x_size = (x.shape[2], x.shape[3])
- x = self.patch_embed(x)
- if self.ape:
- x = x + self.absolute_pos_embed
- x = self.pos_drop(x)
-
- for layer in self.layers_hf:
- x = layer(x, x_size)
-
- x = self.norm(x) # B L C
- x = self.patch_unembed(x, x_size)
-
- return x
-
- def forward(self, x):
- H, W = x.shape[2:]
- x = self.check_image_size(x)
-
- self.mean = self.mean.type_as(x)
- x = (x - self.mean) * self.img_range
-
- if self.upsampler == 'pixelshuffle':
- # for classical SR
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.conv_before_upsample(x)
- x = self.conv_last(self.upsample(x))
- elif self.upsampler == 'pixelshuffle_aux':
- bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
- bicubic = self.conv_bicubic(bicubic)
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.conv_before_upsample(x)
- aux = self.conv_aux(x) # b, 3, LR_H, LR_W
- x = self.conv_after_aux(aux)
- x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
- x = self.conv_last(x)
- aux = aux / self.img_range + self.mean
- elif self.upsampler == 'pixelshuffle_hf':
- # for classical SR with HF
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x_before = self.conv_before_upsample(x)
- x_out = self.conv_last(self.upsample(x_before))
-
- x_hf = self.conv_first_hf(x_before)
- x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
- x_hf = self.conv_before_upsample_hf(x_hf)
- x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
- x = x_out + x_hf
- x_hf = x_hf / self.img_range + self.mean
-
- elif self.upsampler == 'pixelshuffledirect':
- # for lightweight SR
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.upsample(x)
- elif self.upsampler == 'nearest+conv':
- # for real-world SR
- x = self.conv_first(x)
- x = self.conv_after_body(self.forward_features(x)) + x
- x = self.conv_before_upsample(x)
- x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
- x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
- x = self.conv_last(self.lrelu(self.conv_hr(x)))
- else:
- # for image denoising and JPEG compression artifact reduction
- x_first = self.conv_first(x)
- res = self.conv_after_body(self.forward_features(x_first)) + x_first
- x = x + self.conv_last(res)
-
- x = x / self.img_range + self.mean
- if self.upsampler == "pixelshuffle_aux":
- return x[:, :, :H*self.upscale, :W*self.upscale], aux
-
- elif self.upsampler == "pixelshuffle_hf":
- x_out = x_out / self.img_range + self.mean
- return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
-
- else:
- return x[:, :, :H*self.upscale, :W*self.upscale]
-
- def flops(self):
- flops = 0
- H, W = self.patches_resolution
- flops += H * W * 3 * self.embed_dim * 9
- flops += self.patch_embed.flops()
- for layer in self.layers:
- flops += layer.flops()
- flops += H * W * 3 * self.embed_dim * self.embed_dim
- flops += self.upsample.flops()
- return flops
-
-
-if __name__ == '__main__':
- upscale = 4
- window_size = 8
- height = (1024 // upscale // window_size + 1) * window_size
- width = (720 // upscale // window_size + 1) * window_size
- model = Swin2SR(upscale=2, img_size=(height, width),
- window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
- embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
- print(model)
- print(height, width, model.flops() / 1e9)
-
- x = torch.randn((1, 3, height, width))
- x = model(x)
- print(x.shape)
diff --git a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js index 45c7600a..df60c1a1 100644 --- a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js +++ b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js @@ -218,6 +218,8 @@ onUiLoaded(async() => { canvas_hotkey_fullscreen: "KeyS", canvas_hotkey_move: "KeyF", canvas_hotkey_overlap: "KeyO", + canvas_hotkey_shrink_brush: "KeyQ", + canvas_hotkey_grow_brush: "KeyW", canvas_disabled_functions: [], canvas_show_tooltip: true, canvas_auto_expand: true, @@ -227,6 +229,8 @@ onUiLoaded(async() => { const functionMap = { "Zoom": "canvas_hotkey_zoom", "Adjust brush size": "canvas_hotkey_adjust", + "Hotkey shrink brush": "canvas_hotkey_shrink_brush", + "Hotkey enlarge brush": "canvas_hotkey_grow_brush", "Moving canvas": "canvas_hotkey_move", "Fullscreen": "canvas_hotkey_fullscreen", "Reset Zoom": "canvas_hotkey_reset", @@ -686,7 +690,9 @@ onUiLoaded(async() => { const hotkeyActions = { [hotkeysConfig.canvas_hotkey_reset]: resetZoom, [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, - [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen + [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen, + [hotkeysConfig.canvas_hotkey_shrink_brush]: () => adjustBrushSize(elemId, 10), + [hotkeysConfig.canvas_hotkey_grow_brush]: () => adjustBrushSize(elemId, -10) }; const action = hotkeyActions[event.code]; diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py index 2d8d2d1c..89b7c31f 100644 --- a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py +++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py @@ -4,6 +4,8 @@ from modules import shared shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), { "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"), + "canvas_hotkey_shrink_brush": shared.OptionInfo("Q", "Shrink the brush size"), + "canvas_hotkey_grow_brush": shared.OptionInfo("W", "Enlarge the brush size"), "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"), "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "), "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"), @@ -11,5 +13,5 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"), "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"), "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"), - "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}), + "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size","Hotkey enlarge brush","Hotkey shrink brush","Moving canvas","Fullscreen","Reset Zoom","Overlap"]}), })) diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py index ac2c3de4..4c10d9c7 100644 --- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py +++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py @@ -1,7 +1,7 @@ import math
import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
+from modules import scripts, shared, ui_components, ui_settings, infotext_utils
from modules.ui_components import FormColumn
@@ -25,7 +25,7 @@ class ExtraOptionsSection(scripts.Script): extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img")
- mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
+ mapping = {k: v for v, k in infotext_utils.infotext_to_setting_name_mapping}
with gr.Blocks() as interface:
with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):
diff --git a/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py new file mode 100644 index 00000000..d9024344 --- /dev/null +++ b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py @@ -0,0 +1,747 @@ +import numpy as np +import gradio as gr +import math +from modules.ui_components import InputAccordion +import modules.scripts as scripts + + +class SoftInpaintingSettings: + def __init__(self, + mask_blend_power, + mask_blend_scale, + inpaint_detail_preservation, + composite_mask_influence, + composite_difference_threshold, + composite_difference_contrast): + self.mask_blend_power = mask_blend_power + self.mask_blend_scale = mask_blend_scale + self.inpaint_detail_preservation = inpaint_detail_preservation + self.composite_mask_influence = composite_mask_influence + self.composite_difference_threshold = composite_difference_threshold + self.composite_difference_contrast = composite_difference_contrast + + def add_generation_params(self, dest): + dest[enabled_gen_param_label] = True + dest[gen_param_labels.mask_blend_power] = self.mask_blend_power + dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale + dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation + dest[gen_param_labels.composite_mask_influence] = self.composite_mask_influence + dest[gen_param_labels.composite_difference_threshold] = self.composite_difference_threshold + dest[gen_param_labels.composite_difference_contrast] = self.composite_difference_contrast + + +# ------------------- Methods ------------------- + +def processing_uses_inpainting(p): + # TODO: Figure out a better way to determine if inpainting is being used by p + if getattr(p, "image_mask", None) is not None: + return True + + if getattr(p, "mask", None) is not None: + return True + + if getattr(p, "nmask", None) is not None: + return True + + return False + + +def latent_blend(settings, a, b, t): + """ + Interpolates two latent image representations according to the parameter t, + where the interpolated vectors' magnitudes are also interpolated separately. + The "detail_preservation" factor biases the magnitude interpolation towards + the larger of the two magnitudes. + """ + import torch + + # NOTE: We use inplace operations wherever possible. + + # [4][w][h] to [1][4][w][h] + t2 = t.unsqueeze(0) + # [4][w][h] to [1][1][w][h] - the [4] seem redundant. + t3 = t[0].unsqueeze(0).unsqueeze(0) + + one_minus_t2 = 1 - t2 + one_minus_t3 = 1 - t3 + + # Linearly interpolate the image vectors. + a_scaled = a * one_minus_t2 + b_scaled = b * t2 + image_interp = a_scaled + image_interp.add_(b_scaled) + result_type = image_interp.dtype + del a_scaled, b_scaled, t2, one_minus_t2 + + # Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.) + # 64-bit operations are used here to allow large exponents. + current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001) + + # Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1). + a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_( + settings.inpaint_detail_preservation) * one_minus_t3 + b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_( + settings.inpaint_detail_preservation) * t3 + desired_magnitude = a_magnitude + desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation) + del a_magnitude, b_magnitude, t3, one_minus_t3 + + # Change the linearly interpolated image vectors' magnitudes to the value we want. + # This is the last 64-bit operation. + image_interp_scaling_factor = desired_magnitude + image_interp_scaling_factor.div_(current_magnitude) + image_interp_scaling_factor = image_interp_scaling_factor.to(result_type) + image_interp_scaled = image_interp + image_interp_scaled.mul_(image_interp_scaling_factor) + del current_magnitude + del desired_magnitude + del image_interp + del image_interp_scaling_factor + del result_type + + return image_interp_scaled + + +def get_modified_nmask(settings, nmask, sigma): + """ + Converts a negative mask representing the transparency of the original latent vectors being overlayed + to a mask that is scaled according to the denoising strength for this step. + + Where: + 0 = fully opaque, infinite density, fully masked + 1 = fully transparent, zero density, fully unmasked + + We bring this transparency to a power, as this allows one to simulate N number of blending operations + where N can be any positive real value. Using this one can control the balance of influence between + the denoiser and the original latents according to the sigma value. + + NOTE: "mask" is not used + """ + import torch + return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale) + + +def apply_adaptive_masks( + settings: SoftInpaintingSettings, + nmask, + latent_orig, + latent_processed, + overlay_images, + width, height, + paste_to): + import torch + import modules.processing as proc + import modules.images as images + from PIL import Image, ImageOps, ImageFilter + + # TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control. + latent_mask = nmask[0].float() + # convert the original mask into a form we use to scale distances for thresholding + mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2)) + mask_scalar = (0.5 * (1 - settings.composite_mask_influence) + + mask_scalar * settings.composite_mask_influence) + mask_scalar = mask_scalar / (1.00001 - mask_scalar) + mask_scalar = mask_scalar.cpu().numpy() + + latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1) + + kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2) + + masks_for_overlay = [] + + for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)): + converted_mask = distance_map.float().cpu().numpy() + converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, + percentile_min=0.9, percentile_max=1, min_width=1) + converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, + percentile_min=0.25, percentile_max=0.75, min_width=1) + + # The distance at which opacity of original decreases to 50% + half_weighted_distance = settings.composite_difference_threshold * mask_scalar + converted_mask = converted_mask / half_weighted_distance + + converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast) + converted_mask = smootherstep(converted_mask) + converted_mask = 1 - converted_mask + converted_mask = 255. * converted_mask + converted_mask = converted_mask.astype(np.uint8) + converted_mask = Image.fromarray(converted_mask) + converted_mask = images.resize_image(2, converted_mask, width, height) + converted_mask = proc.create_binary_mask(converted_mask, round=False) + + # Remove aliasing artifacts using a gaussian blur. + converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) + + # Expand the mask to fit the whole image if needed. + if paste_to is not None: + converted_mask = proc.uncrop(converted_mask, + (overlay_image.width, overlay_image.height), + paste_to) + + masks_for_overlay.append(converted_mask) + + image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height)) + image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), + mask=ImageOps.invert(converted_mask.convert('L'))) + + overlay_images[i] = image_masked.convert('RGBA') + + return masks_for_overlay + + +def apply_masks( + settings, + nmask, + overlay_images, + width, height, + paste_to): + import torch + import modules.processing as proc + import modules.images as images + from PIL import Image, ImageOps, ImageFilter + + converted_mask = nmask[0].float() + converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2) + converted_mask = 255. * converted_mask + converted_mask = converted_mask.cpu().numpy().astype(np.uint8) + converted_mask = Image.fromarray(converted_mask) + converted_mask = images.resize_image(2, converted_mask, width, height) + converted_mask = proc.create_binary_mask(converted_mask, round=False) + + # Remove aliasing artifacts using a gaussian blur. + converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) + + # Expand the mask to fit the whole image if needed. + if paste_to is not None: + converted_mask = proc.uncrop(converted_mask, + (width, height), + paste_to) + + masks_for_overlay = [] + + for i, overlay_image in enumerate(overlay_images): + masks_for_overlay[i] = converted_mask + + image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height)) + image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"), + mask=ImageOps.invert(converted_mask.convert('L'))) + + overlay_images[i] = image_masked.convert('RGBA') + + return masks_for_overlay + + +def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0): + """ + Generalization convolution filter capable of applying + weighted mean, median, maximum, and minimum filters + parametrically using an arbitrary kernel. + + Args: + img (nparray): + The image, a 2-D array of floats, to which the filter is being applied. + kernel (nparray): + The kernel, a 2-D array of floats. + kernel_center (nparray): + The kernel center coordinate, a 1-D array with two elements. + percentile_min (float): + The lower bound of the histogram window used by the filter, + from 0 to 1. + percentile_max (float): + The upper bound of the histogram window used by the filter, + from 0 to 1. + min_width (float): + The minimum size of the histogram window bounds, in weight units. + Must be greater than 0. + + Returns: + (nparray): A filtered copy of the input image "img", a 2-D array of floats. + """ + + # Converts an index tuple into a vector. + def vec(x): + return np.array(x) + + kernel_min = -kernel_center + kernel_max = vec(kernel.shape) - kernel_center + + def weighted_histogram_filter_single(idx): + idx = vec(idx) + min_index = np.maximum(0, idx + kernel_min) + max_index = np.minimum(vec(img.shape), idx + kernel_max) + window_shape = max_index - min_index + + class WeightedElement: + """ + An element of the histogram, its weight + and bounds. + """ + + def __init__(self, value, weight): + self.value: float = value + self.weight: float = weight + self.window_min: float = 0.0 + self.window_max: float = 1.0 + + # Collect the values in the image as WeightedElements, + # weighted by their corresponding kernel values. + values = [] + for window_tup in np.ndindex(tuple(window_shape)): + window_index = vec(window_tup) + image_index = window_index + min_index + centered_kernel_index = image_index - idx + kernel_index = centered_kernel_index + kernel_center + element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)]) + values.append(element) + + def sort_key(x: WeightedElement): + return x.value + + values.sort(key=sort_key) + + # Calculate the height of the stack (sum) + # and each sample's range they occupy in the stack + sum = 0 + for i in range(len(values)): + values[i].window_min = sum + sum += values[i].weight + values[i].window_max = sum + + # Calculate what range of this stack ("window") + # we want to get the weighted average across. + window_min = sum * percentile_min + window_max = sum * percentile_max + window_width = window_max - window_min + + # Ensure the window is within the stack and at least a certain size. + if window_width < min_width: + window_center = (window_min + window_max) / 2 + window_min = window_center - min_width / 2 + window_max = window_center + min_width / 2 + + if window_max > sum: + window_max = sum + window_min = sum - min_width + + if window_min < 0: + window_min = 0 + window_max = min_width + + value = 0 + value_weight = 0 + + # Get the weighted average of all the samples + # that overlap with the window, weighted + # by the size of their overlap. + for i in range(len(values)): + if window_min >= values[i].window_max: + continue + if window_max <= values[i].window_min: + break + + s = max(window_min, values[i].window_min) + e = min(window_max, values[i].window_max) + w = e - s + + value += values[i].value * w + value_weight += w + + return value / value_weight if value_weight != 0 else 0 + + img_out = img.copy() + + # Apply the kernel operation over each pixel. + for index in np.ndindex(img.shape): + img_out[index] = weighted_histogram_filter_single(index) + + return img_out + + +def smoothstep(x): + """ + The smoothstep function, input should be clamped to 0-1 range. + Turns a diagonal line (f(x) = x) into a sigmoid-like curve. + """ + return x * x * (3 - 2 * x) + + +def smootherstep(x): + """ + The smootherstep function, input should be clamped to 0-1 range. + Turns a diagonal line (f(x) = x) into a sigmoid-like curve. + """ + return x * x * x * (x * (6 * x - 15) + 10) + + +def get_gaussian_kernel(stddev_radius=1.0, max_radius=2): + """ + Creates a Gaussian kernel with thresholded edges. + + Args: + stddev_radius (float): + Standard deviation of the gaussian kernel, in pixels. + max_radius (int): + The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2. + The kernel is thresholded so that any values one pixel beyond this radius + is weighted at 0. + + Returns: + (nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2)) + """ + + # Evaluates a 0-1 normalized gaussian function for a given square distance from the mean. + def gaussian(sqr_mag): + return math.exp(-sqr_mag / (stddev_radius * stddev_radius)) + + # Helper function for converting a tuple to an array. + def vec(x): + return np.array(x) + + """ + Since a gaussian is unbounded, we need to limit ourselves + to a finite range. + We taper the ends off at the end of that range so they equal zero + while preserving the maximum value of 1 at the mean. + """ + zero_radius = max_radius + 1.0 + gauss_zero = gaussian(zero_radius * zero_radius) + gauss_kernel_scale = 1 / (1 - gauss_zero) + + def gaussian_kernel_func(coordinate): + x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0 + x = gaussian(x) + x -= gauss_zero + x *= gauss_kernel_scale + x = max(0.0, x) + return x + + size = max_radius * 2 + 1 + kernel_center = max_radius + kernel = np.zeros((size, size)) + + for index in np.ndindex(kernel.shape): + kernel[index] = gaussian_kernel_func(vec(index) - kernel_center) + + return kernel, kernel_center + + +# ------------------- Constants ------------------- + + +default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2) + +enabled_ui_label = "Soft inpainting" +enabled_gen_param_label = "Soft inpainting enabled" +enabled_el_id = "soft_inpainting_enabled" + +ui_labels = SoftInpaintingSettings( + "Schedule bias", + "Preservation strength", + "Transition contrast boost", + "Mask influence", + "Difference threshold", + "Difference contrast") + +ui_info = SoftInpaintingSettings( + "Shifts when preservation of original content occurs during denoising.", + "How strongly partially masked content should be preserved.", + "Amplifies the contrast that may be lost in partially masked regions.", + "How strongly the original mask should bias the difference threshold.", + "How much an image region can change before the original pixels are not blended in anymore.", + "How sharp the transition should be between blended and not blended.") + +gen_param_labels = SoftInpaintingSettings( + "Soft inpainting schedule bias", + "Soft inpainting preservation strength", + "Soft inpainting transition contrast boost", + "Soft inpainting mask influence", + "Soft inpainting difference threshold", + "Soft inpainting difference contrast") + +el_ids = SoftInpaintingSettings( + "mask_blend_power", + "mask_blend_scale", + "inpaint_detail_preservation", + "composite_mask_influence", + "composite_difference_threshold", + "composite_difference_contrast") + + +# ------------------- Script ------------------- + + +class Script(scripts.Script): + def __init__(self): + self.section = "inpaint" + self.masks_for_overlay = None + self.overlay_images = None + + def title(self): + return "Soft Inpainting" + + def show(self, is_img2img): + return scripts.AlwaysVisible if is_img2img else False + + def ui(self, is_img2img): + if not is_img2img: + return + + with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled: + with gr.Group(): + gr.Markdown( + """ + Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity. + **High _Mask blur_** values are recommended! + """) + + power = \ + gr.Slider(label=ui_labels.mask_blend_power, + info=ui_info.mask_blend_power, + minimum=0, + maximum=8, + step=0.1, + value=default.mask_blend_power, + elem_id=el_ids.mask_blend_power) + scale = \ + gr.Slider(label=ui_labels.mask_blend_scale, + info=ui_info.mask_blend_scale, + minimum=0, + maximum=8, + step=0.05, + value=default.mask_blend_scale, + elem_id=el_ids.mask_blend_scale) + detail = \ + gr.Slider(label=ui_labels.inpaint_detail_preservation, + info=ui_info.inpaint_detail_preservation, + minimum=1, + maximum=32, + step=0.5, + value=default.inpaint_detail_preservation, + elem_id=el_ids.inpaint_detail_preservation) + + gr.Markdown( + """ + ### Pixel Composite Settings + """) + + mask_inf = \ + gr.Slider(label=ui_labels.composite_mask_influence, + info=ui_info.composite_mask_influence, + minimum=0, + maximum=1, + step=0.05, + value=default.composite_mask_influence, + elem_id=el_ids.composite_mask_influence) + + dif_thresh = \ + gr.Slider(label=ui_labels.composite_difference_threshold, + info=ui_info.composite_difference_threshold, + minimum=0, + maximum=8, + step=0.25, + value=default.composite_difference_threshold, + elem_id=el_ids.composite_difference_threshold) + + dif_contr = \ + gr.Slider(label=ui_labels.composite_difference_contrast, + info=ui_info.composite_difference_contrast, + minimum=0, + maximum=8, + step=0.25, + value=default.composite_difference_contrast, + elem_id=el_ids.composite_difference_contrast) + + with gr.Accordion("Help", open=False): + gr.Markdown( + f""" + ### {ui_labels.mask_blend_power} + + The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas). + This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step. + This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation. + + - **Below 1**: Stronger preservation near the end (with low sigma) + - **1**: Balanced (proportional to sigma) + - **Above 1**: Stronger preservation in the beginning (with high sigma) + """) + gr.Markdown( + f""" + ### {ui_labels.mask_blend_scale} + + Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content. + This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength. + + - **Low values**: Favors generated content. + - **High values**: Favors original content. + """) + gr.Markdown( + f""" + ### {ui_labels.inpaint_detail_preservation} + + This parameter controls how the original latent vectors and denoised latent vectors are interpolated. + With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors. + This can prevent the loss of contrast that occurs with linear interpolation. + + - **Low values**: Softer blending, details may fade. + - **High values**: Stronger contrast, may over-saturate colors. + """) + + gr.Markdown( + """ + ## Pixel Composite Settings + + Masks are generated based on how much a part of the image changed after denoising. + These masks are used to blend the original and final images together. + If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process. + """) + + gr.Markdown( + f""" + ### {ui_labels.composite_mask_influence} + + This parameter controls how much the mask should bias this sensitivity to difference. + + - **0**: Ignore the mask, only consider differences in image content. + - **1**: Follow the mask closely despite image content changes. + """) + + gr.Markdown( + f""" + ### {ui_labels.composite_difference_threshold} + + This value represents the difference at which the original pixels will have less than 50% opacity. + + - **Low values**: Two images patches must be almost the same in order to retain original pixels. + - **High values**: Two images patches can be very different and still retain original pixels. + """) + + gr.Markdown( + f""" + ### {ui_labels.composite_difference_contrast} + + This value represents the contrast between the opacity of the original and inpainted content. + + - **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting. + - **High values**: Ghosting will be less common, but transitions may be very sudden. + """) + + self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label), + (power, gen_param_labels.mask_blend_power), + (scale, gen_param_labels.mask_blend_scale), + (detail, gen_param_labels.inpaint_detail_preservation), + (mask_inf, gen_param_labels.composite_mask_influence), + (dif_thresh, gen_param_labels.composite_difference_threshold), + (dif_contr, gen_param_labels.composite_difference_contrast)] + + self.paste_field_names = [] + for _, field_name in self.infotext_fields: + self.paste_field_names.append(field_name) + + return [soft_inpainting_enabled, + power, + scale, + detail, + mask_inf, + dif_thresh, + dif_contr] + + def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + # Shut off the rounding it normally does. + p.mask_round = False + + settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) + + # p.extra_generation_params["Mask rounding"] = False + settings.add_generation_params(p.extra_generation_params) + + def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf, + dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + if mba.is_final_blend: + mba.blended_latent = mba.current_latent + return + + settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) + + # todo: Why is sigma 2D? Both values are the same. + mba.blended_latent = latent_blend(settings, + mba.init_latent, + mba.current_latent, + get_modified_nmask(settings, mba.nmask, mba.sigma[0])) + + def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf, + dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + nmask = getattr(p, "nmask", None) + if nmask is None: + return + + from modules import images + from modules.shared import opts + + settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) + + # since the original code puts holes in the existing overlay images, + # we have to rebuild them. + self.overlay_images = [] + for img in p.init_images: + + image = images.flatten(img, opts.img2img_background_color) + + if p.paste_to is None and p.resize_mode != 3: + image = images.resize_image(p.resize_mode, image, p.width, p.height) + + self.overlay_images.append(image.convert('RGBA')) + + if len(p.init_images) == 1: + self.overlay_images = self.overlay_images * p.batch_size + + if getattr(ps.samples, 'already_decoded', False): + self.masks_for_overlay = apply_masks(settings=settings, + nmask=nmask, + overlay_images=self.overlay_images, + width=p.width, + height=p.height, + paste_to=p.paste_to) + else: + self.masks_for_overlay = apply_adaptive_masks(settings=settings, + nmask=nmask, + latent_orig=p.init_latent, + latent_processed=ps.samples, + overlay_images=self.overlay_images, + width=p.width, + height=p.height, + paste_to=p.paste_to) + + def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale, + detail_preservation, mask_inf, dif_thresh, dif_contr): + if not enabled: + return + + if not processing_uses_inpainting(p): + return + + if self.masks_for_overlay is None: + return + + if self.overlay_images is None: + return + + ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index] + ppmo.overlay_image = self.overlay_images[ppmo.index] diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html index 39674666..f1d959a6 100644 --- a/html/extra-networks-card.html +++ b/html/extra-networks-card.html @@ -1,14 +1,9 @@ -<div class='card' style={style} onclick={card_clicked} data-name="{name}" {sort_keys}> +<div class="card" style="{style}" onclick="{card_clicked}" data-name="{name}" {sort_keys}> {background_image} - <div class="button-row"> - {metadata_button} - {edit_button} - </div> - <div class='actions'> - <div class='additional'> - <span style="display:none" class='search_term{search_only}'>{search_term}</span> - </div> - <span class='name'>{name}</span> - <span class='description'>{description}</span> + <div class="button-row">{copy_path_button}{metadata_button}{edit_button}</div> + <div class="actions"> + <div class="additional">{search_terms}</div> + <span class="name">{name}</span> + <span class="description">{description}</span> </div> </div> diff --git a/html/extra-networks-copy-path-button.html b/html/extra-networks-copy-path-button.html new file mode 100644 index 00000000..8083bb03 --- /dev/null +++ b/html/extra-networks-copy-path-button.html @@ -0,0 +1,5 @@ +<div class="copy-path-button card-button" + title="Copy path to clipboard" + onclick="extraNetworksCopyCardPath(event, '{filename}')" + data-clipboard-text="{filename}"> +</div>
\ No newline at end of file diff --git a/html/extra-networks-edit-item-button.html b/html/extra-networks-edit-item-button.html new file mode 100644 index 00000000..0fe43082 --- /dev/null +++ b/html/extra-networks-edit-item-button.html @@ -0,0 +1,4 @@ +<div class="edit-button card-button" + title="Edit metadata" + onclick="extraNetworksEditUserMetadata(event, '{tabname}', '{extra_networks_tabname}', '{name}')"> +</div>
\ No newline at end of file diff --git a/html/extra-networks-metadata-button.html b/html/extra-networks-metadata-button.html new file mode 100644 index 00000000..285b5b3b --- /dev/null +++ b/html/extra-networks-metadata-button.html @@ -0,0 +1,4 @@ +<div class="metadata-button card-button" + title="Show internal metadata" + onclick="extraNetworksRequestMetadata(event, '{extra_networks_tabname}', '{name}')"> +</div>
\ No newline at end of file diff --git a/html/extra-networks-pane.html b/html/extra-networks-pane.html new file mode 100644 index 00000000..0c763f71 --- /dev/null +++ b/html/extra-networks-pane.html @@ -0,0 +1,55 @@ +<div id='{tabname}_{extra_networks_tabname}_pane' class='extra-network-pane'> + <div class="extra-network-control" id="{tabname}_{extra_networks_tabname}_controls" style="display:none" > + <div class="extra-network-control--search"> + <input + id="{tabname}_{extra_networks_tabname}_extra_search" + class="extra-network-control--search-text" + type="search" + placeholder="Filter files" + > + </div> + <div + id="{tabname}_{extra_networks_tabname}_extra_sort" + class="extra-network-control--sort" + data-sortmode="{data_sortmode}" + data-sortkey="{data_sortkey}" + title="Sort by path" + onclick="extraNetworksControlSortOnClick(event, '{tabname}', '{extra_networks_tabname}');" + > + <i class="extra-network-control--sort-icon"></i> + </div> + <div + id="{tabname}_{extra_networks_tabname}_extra_sort_dir" + class="extra-network-control--sort-dir" + data-sortdir="{data_sortdir}" + title="Sort ascending" + onclick="extraNetworksControlSortDirOnClick(event, '{tabname}', '{extra_networks_tabname}');" + > + <i class="extra-network-control--sort-dir-icon"></i> + </div> + <div + id="{tabname}_{extra_networks_tabname}_extra_tree_view" + class="extra-network-control--tree-view {tree_view_btn_extra_class}" + title="Enable Tree View" + onclick="extraNetworksControlTreeViewOnClick(event, '{tabname}', '{extra_networks_tabname}');" + > + <i class="extra-network-control--tree-view-icon"></i> + </div> + <div + id="{tabname}_{extra_networks_tabname}_extra_refresh" + class="extra-network-control--refresh" + title="Refresh page" + onclick="extraNetworksControlRefreshOnClick(event, '{tabname}', '{extra_networks_tabname}');" + > + <i class="extra-network-control--refresh-icon"></i> + </div> + </div> + <div class="extra-network-pane-content"> + <div id='{tabname}_{extra_networks_tabname}_tree' class='extra-network-tree {tree_view_div_extra_class}'> + {tree_html} + </div> + <div id='{tabname}_{extra_networks_tabname}_cards' class='extra-network-cards'> + {items_html} + </div> + </div> +</div>
\ No newline at end of file diff --git a/html/extra-networks-tree-button.html b/html/extra-networks-tree-button.html new file mode 100644 index 00000000..9dc2e2a4 --- /dev/null +++ b/html/extra-networks-tree-button.html @@ -0,0 +1,23 @@ +<span data-filterable-item-text hidden>{search_terms}</span> +<div class="tree-list-content {subclass}" + type="button" + onclick="extraNetworksTreeOnClick(event, '{tabname}', '{extra_networks_tabname}');{onclick_extra}" + data-path="{data_path}" + data-hash="{data_hash}" +> + <span class='tree-list-item-action tree-list-item-action--leading'> + {action_list_item_action_leading} + </span> + <span class="tree-list-item-visual tree-list-item-visual--leading"> + {action_list_item_visual_leading} + </span> + <span class="tree-list-item-label tree-list-item-label--truncate"> + {action_list_item_label} + </span> + <span class="tree-list-item-visual tree-list-item-visual--trailing"> + {action_list_item_visual_trailing} + </span> + <span class="tree-list-item-action tree-list-item-action--trailing"> + {action_list_item_action_trailing} + </span> +</div>
\ No newline at end of file diff --git a/html/licenses.html b/html/licenses.html index ef6f2c0a..9f5d1e9d 100644 --- a/html/licenses.html +++ b/html/licenses.html @@ -4,107 +4,6 @@ #licenses pre { margin: 1em 0 2em 0;}
</style>
-<h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
-<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
-<pre>
-S-Lab License 1.0
-
-Copyright 2022 S-Lab
-
-Redistribution and use for non-commercial purpose in source and
-binary forms, with or without modification, are permitted provided
-that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright
- notice, this list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright
- notice, this list of conditions and the following disclaimer in
- the documentation and/or other materials provided with the
- distribution.
-
-3. Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived
- from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-In the event that redistribution and/or use for commercial purpose in
-source or binary forms, with or without modification is required,
-please contact the contributor(s) of the work.
-</pre>
-
-
-<h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
-<small>Code for architecture and reading models copied.</small>
-<pre>
-MIT License
-
-Copyright (c) 2021 victorca25
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-</pre>
-
-<h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
-<small>Some code is copied to support ESRGAN models.</small>
-<pre>
-BSD 3-Clause License
-
-Copyright (c) 2021, Xintao Wang
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
- list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from
- this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-</pre>
-
<h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
<small>Some code for compatibility with OSX is taken from lstein's repository.</small>
<pre>
@@ -183,213 +82,6 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
</pre>
-<h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
-<small>Code added by contributors, most likely copied from this repository.</small>
-
-<pre>
- Apache License
- Version 2.0, January 2004
- http://www.apache.org/licenses/
-
- TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
- 1. Definitions.
-
- "License" shall mean the terms and conditions for use, reproduction,
- and distribution as defined by Sections 1 through 9 of this document.
-
- "Licensor" shall mean the copyright owner or entity authorized by
- the copyright owner that is granting the License.
-
- "Legal Entity" shall mean the union of the acting entity and all
- other entities that control, are controlled by, or are under common
- control with that entity. For the purposes of this definition,
- "control" means (i) the power, direct or indirect, to cause the
- direction or management of such entity, whether by contract or
- otherwise, or (ii) ownership of fifty percent (50%) or more of the
- outstanding shares, or (iii) beneficial ownership of such entity.
-
- "You" (or "Your") shall mean an individual or Legal Entity
- exercising permissions granted by this License.
-
- "Source" form shall mean the preferred form for making modifications,
- including but not limited to software source code, documentation
- source, and configuration files.
-
- "Object" form shall mean any form resulting from mechanical
- transformation or translation of a Source form, including but
- not limited to compiled object code, generated documentation,
- and conversions to other media types.
-
- "Work" shall mean the work of authorship, whether in Source or
- Object form, made available under the License, as indicated by a
- copyright notice that is included in or attached to the work
- (an example is provided in the Appendix below).
-
- "Derivative Works" shall mean any work, whether in Source or Object
- form, that is based on (or derived from) the Work and for which the
- editorial revisions, annotations, elaborations, or other modifications
- represent, as a whole, an original work of authorship. For the purposes
- of this License, Derivative Works shall not include works that remain
- separable from, or merely link (or bind by name) to the interfaces of,
- the Work and Derivative Works thereof.
-
- "Contribution" shall mean any work of authorship, including
- the original version of the Work and any modifications or additions
- to that Work or Derivative Works thereof, that is intentionally
- submitted to Licensor for inclusion in the Work by the copyright owner
- or by an individual or Legal Entity authorized to submit on behalf of
- the copyright owner. For the purposes of this definition, "submitted"
- means any form of electronic, verbal, or written communication sent
- to the Licensor or its representatives, including but not limited to
- communication on electronic mailing lists, source code control systems,
- and issue tracking systems that are managed by, or on behalf of, the
- Licensor for the purpose of discussing and improving the Work, but
- excluding communication that is conspicuously marked or otherwise
- designated in writing by the copyright owner as "Not a Contribution."
-
- "Contributor" shall mean Licensor and any individual or Legal Entity
- on behalf of whom a Contribution has been received by Licensor and
- subsequently incorporated within the Work.
-
- 2. Grant of Copyright License. Subject to the terms and conditions of
- this License, each Contributor hereby grants to You a perpetual,
- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- copyright license to reproduce, prepare Derivative Works of,
- publicly display, publicly perform, sublicense, and distribute the
- Work and such Derivative Works in Source or Object form.
-
- 3. Grant of Patent License. Subject to the terms and conditions of
- this License, each Contributor hereby grants to You a perpetual,
- worldwide, non-exclusive, no-charge, royalty-free, irrevocable
- (except as stated in this section) patent license to make, have made,
- use, offer to sell, sell, import, and otherwise transfer the Work,
- where such license applies only to those patent claims licensable
- by such Contributor that are necessarily infringed by their
- Contribution(s) alone or by combination of their Contribution(s)
- with the Work to which such Contribution(s) was submitted. If You
- institute patent litigation against any entity (including a
- cross-claim or counterclaim in a lawsuit) alleging that the Work
- or a Contribution incorporated within the Work constitutes direct
- or contributory patent infringement, then any patent licenses
- granted to You under this License for that Work shall terminate
- as of the date such litigation is filed.
-
- 4. Redistribution. You may reproduce and distribute copies of the
- Work or Derivative Works thereof in any medium, with or without
- modifications, and in Source or Object form, provided that You
- meet the following conditions:
-
- (a) You must give any other recipients of the Work or
- Derivative Works a copy of this License; and
-
- (b) You must cause any modified files to carry prominent notices
- stating that You changed the files; and
-
- (c) You must retain, in the Source form of any Derivative Works
- that You distribute, all copyright, patent, trademark, and
- attribution notices from the Source form of the Work,
- excluding those notices that do not pertain to any part of
- the Derivative Works; and
-
- (d) If the Work includes a "NOTICE" text file as part of its
- distribution, then any Derivative Works that You distribute must
- include a readable copy of the attribution notices contained
- within such NOTICE file, excluding those notices that do not
- pertain to any part of the Derivative Works, in at least one
- of the following places: within a NOTICE text file distributed
- as part of the Derivative Works; within the Source form or
- documentation, if provided along with the Derivative Works; or,
- within a display generated by the Derivative Works, if and
- wherever such third-party notices normally appear. The contents
- of the NOTICE file are for informational purposes only and
- do not modify the License. You may add Your own attribution
- notices within Derivative Works that You distribute, alongside
- or as an addendum to the NOTICE text from the Work, provided
- that such additional attribution notices cannot be construed
- as modifying the License.
-
- You may add Your own copyright statement to Your modifications and
- may provide additional or different license terms and conditions
- for use, reproduction, or distribution of Your modifications, or
- for any such Derivative Works as a whole, provided Your use,
- reproduction, and distribution of the Work otherwise complies with
- the conditions stated in this License.
-
- 5. Submission of Contributions. Unless You explicitly state otherwise,
- any Contribution intentionally submitted for inclusion in the Work
- by You to the Licensor shall be under the terms and conditions of
- this License, without any additional terms or conditions.
- Notwithstanding the above, nothing herein shall supersede or modify
- the terms of any separate license agreement you may have executed
- with Licensor regarding such Contributions.
-
- 6. Trademarks. This License does not grant permission to use the trade
- names, trademarks, service marks, or product names of the Licensor,
- except as required for reasonable and customary use in describing the
- origin of the Work and reproducing the content of the NOTICE file.
-
- 7. Disclaimer of Warranty. Unless required by applicable law or
- agreed to in writing, Licensor provides the Work (and each
- Contributor provides its Contributions) on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
- implied, including, without limitation, any warranties or conditions
- of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
- PARTICULAR PURPOSE. You are solely responsible for determining the
- appropriateness of using or redistributing the Work and assume any
- risks associated with Your exercise of permissions under this License.
-
- 8. Limitation of Liability. In no event and under no legal theory,
- whether in tort (including negligence), contract, or otherwise,
- unless required by applicable law (such as deliberate and grossly
- negligent acts) or agreed to in writing, shall any Contributor be
- liable to You for damages, including any direct, indirect, special,
- incidental, or consequential damages of any character arising as a
- result of this License or out of the use or inability to use the
- Work (including but not limited to damages for loss of goodwill,
- work stoppage, computer failure or malfunction, or any and all
- other commercial damages or losses), even if such Contributor
- has been advised of the possibility of such damages.
-
- 9. Accepting Warranty or Additional Liability. While redistributing
- the Work or Derivative Works thereof, You may choose to offer,
- and charge a fee for, acceptance of support, warranty, indemnity,
- or other liability obligations and/or rights consistent with this
- License. However, in accepting such obligations, You may act only
- on Your own behalf and on Your sole responsibility, not on behalf
- of any other Contributor, and only if You agree to indemnify,
- defend, and hold each Contributor harmless for any liability
- incurred by, or claims asserted against, such Contributor by reason
- of your accepting any such warranty or additional liability.
-
- END OF TERMS AND CONDITIONS
-
- APPENDIX: How to apply the Apache License to your work.
-
- To apply the Apache License to your work, attach the following
- boilerplate notice, with the fields enclosed by brackets "[]"
- replaced with your own identifying information. (Don't include
- the brackets!) The text should be enclosed in the appropriate
- comment syntax for the file format. We also recommend that a
- file or class name and description of purpose be included on the
- same "printed page" as the copyright notice for easier
- identification within third-party archives.
-
- Copyright [2021] [SwinIR Authors]
-
- Licensed under the Apache License, Version 2.0 (the "License");
- you may not use this file except in compliance with the License.
- You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
- Unless required by applicable law or agreed to in writing, software
- distributed under the License is distributed on an "AS IS" BASIS,
- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- See the License for the specific language governing permissions and
- limitations under the License.
-</pre>
-
<h2><a href="https://github.com/AminRezaei0x443/memory-efficient-attention/blob/main/LICENSE">Memory Efficient Attention</a></h2>
<small>The sub-quadratic cross attention optimization uses modified code from the Memory Efficient Attention package that Alex Birch optimized for 3D tensors. This license is updated to reflect that.</small>
<pre>
@@ -687,4 +379,4 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
-</pre>
\ No newline at end of file +</pre>
diff --git a/javascript/extensions.js b/javascript/extensions.js index 312131b7..cc8ee220 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -2,8 +2,11 @@ function extensions_apply(_disabled_list, _update_list, disable_all) { var disable = []; var update = []; - - gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { + const extensions_input = gradioApp().querySelectorAll('#extensions input[type="checkbox"]'); + if (extensions_input.length == 0) { + throw Error("Extensions page not yet loaded."); + } + extensions_input.forEach(function(x) { if (x.name.startsWith("enable_") && !x.checked) { disable.push(x.name.substring(7)); } diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js index 98a7abb7..d5855fe9 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -16,99 +16,112 @@ function toggleCss(key, css, enable) { } function setupExtraNetworksForTab(tabname) { - gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks'); - - var tabs = gradioApp().querySelector('#' + tabname + '_extra_tabs > div'); - var searchDiv = gradioApp().getElementById(tabname + '_extra_search'); - var search = searchDiv.querySelector('textarea'); - var sort = gradioApp().getElementById(tabname + '_extra_sort'); - var sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder'); - var refresh = gradioApp().getElementById(tabname + '_extra_refresh'); - var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs'); - var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input'); - var promptContainer = gradioApp().querySelector('.prompt-container-compact#' + tabname + '_prompt_container'); - var negativePrompt = gradioApp().querySelector('#' + tabname + '_neg_prompt'); - - tabs.appendChild(searchDiv); - tabs.appendChild(sort); - tabs.appendChild(sortOrder); - tabs.appendChild(refresh); - tabs.appendChild(showDirsDiv); - - var applyFilter = function() { - var searchTerm = search.value.toLowerCase(); - - gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) { - var searchOnly = elem.querySelector('.search_only'); - var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase(); - - var visible = text.indexOf(searchTerm) != -1; - - if (searchOnly && searchTerm.length < 4) { - visible = false; - } + function registerPrompt(tabname, id) { + var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); + + if (!activePromptTextarea[tabname]) { + activePromptTextarea[tabname] = textarea; + } - elem.style.display = visible ? "" : "none"; + textarea.addEventListener("focus", function() { + activePromptTextarea[tabname] = textarea; }); + } - applySort(); - }; + var tabnav = gradioApp().querySelector('#' + tabname + '_extra_tabs > div.tab-nav'); + var controlsDiv = document.createElement('DIV'); + controlsDiv.classList.add('extra-networks-controls-div'); + tabnav.appendChild(controlsDiv); + tabnav.insertBefore(controlsDiv, null); + + var this_tab = gradioApp().querySelector('#' + tabname + '_extra_tabs'); + this_tab.querySelectorAll(":scope > [id^='" + tabname + "_']").forEach(function(elem) { + // tabname_full = {tabname}_{extra_networks_tabname} + var tabname_full = elem.id; + var search = gradioApp().querySelector("#" + tabname_full + "_extra_search"); + var sort_mode = gradioApp().querySelector("#" + tabname_full + "_extra_sort"); + var sort_dir = gradioApp().querySelector("#" + tabname_full + "_extra_sort_dir"); + var refresh = gradioApp().querySelector("#" + tabname_full + "_extra_refresh"); + + // If any of the buttons above don't exist, we want to skip this iteration of the loop. + if (!search || !sort_mode || !sort_dir || !refresh) { + return; // `return` is equivalent of `continue` but for forEach loops. + } - var applySort = function() { - var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card'); + var applyFilter = function(force) { + var searchTerm = search.value.toLowerCase(); + gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card').forEach(function(elem) { + var searchOnly = elem.querySelector('.search_only'); + var text = Array.prototype.map.call(elem.querySelectorAll('.search_terms'), function(t) { + return t.textContent.toLowerCase(); + }).join(" "); + + var visible = text.indexOf(searchTerm) != -1; + if (searchOnly && searchTerm.length < 4) { + visible = false; + } + if (visible) { + elem.classList.remove("hidden"); + } else { + elem.classList.add("hidden"); + } + }); - var reverse = sortOrder.classList.contains("sortReverse"); - var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name"; - sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1); - var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length; + applySort(force); + }; - if (sortKeyStore == sort.dataset.sortkey) { - return; - } - sort.dataset.sortkey = sortKeyStore; + var applySort = function(force) { + var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card'); + var reverse = sort_dir.dataset.sortdir == "Descending"; + var sortKey = sort_mode.dataset.sortmode.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name"; + sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1); + var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length; - cards.forEach(function(card) { - card.originalParentElement = card.parentElement; - }); - var sortedCards = Array.from(cards); - sortedCards.sort(function(cardA, cardB) { - var a = cardA.dataset[sortKey]; - var b = cardB.dataset[sortKey]; - if (!isNaN(a) && !isNaN(b)) { - return parseInt(a) - parseInt(b); + if (sortKeyStore == sort_mode.dataset.sortkey && !force) { + return; } + sort_mode.dataset.sortkey = sortKeyStore; + + cards.forEach(function(card) { + card.originalParentElement = card.parentElement; + }); + var sortedCards = Array.from(cards); + sortedCards.sort(function(cardA, cardB) { + var a = cardA.dataset[sortKey]; + var b = cardB.dataset[sortKey]; + if (!isNaN(a) && !isNaN(b)) { + return parseInt(a) - parseInt(b); + } - return (a < b ? -1 : (a > b ? 1 : 0)); - }); - if (reverse) { - sortedCards.reverse(); - } - cards.forEach(function(card) { - card.remove(); - }); - sortedCards.forEach(function(card) { - card.originalParentElement.appendChild(card); - }); - }; - - search.addEventListener("input", applyFilter); - sortOrder.addEventListener("click", function() { - sortOrder.classList.toggle("sortReverse"); + return (a < b ? -1 : (a > b ? 1 : 0)); + }); + if (reverse) { + sortedCards.reverse(); + } + cards.forEach(function(card) { + card.remove(); + }); + sortedCards.forEach(function(card) { + card.originalParentElement.appendChild(card); + }); + }; + + search.addEventListener("input", applyFilter); applySort(); - }); - applyFilter(); + applyFilter(); + extraNetworksApplySort[tabname_full] = applySort; + extraNetworksApplyFilter[tabname_full] = applyFilter; - extraNetworksApplySort[tabname] = applySort; - extraNetworksApplyFilter[tabname] = applyFilter; + var controls = gradioApp().querySelector("#" + tabname_full + "_controls"); + controlsDiv.insertBefore(controls, null); - var showDirsUpdate = function() { - var css = '#' + tabname + '_extra_tabs .extra-network-subdirs { display: none; }'; - toggleCss(tabname + '_extra_show_dirs_style', css, !showDirs.checked); - localSet('extra-networks-show-dirs', showDirs.checked ? 1 : 0); - }; - showDirs.checked = localGet('extra-networks-show-dirs', 1) == 1; - showDirs.addEventListener("change", showDirsUpdate); - showDirsUpdate(); + if (elem.style.display != "none") { + extraNetworksShowControlsForPage(tabname, tabname_full); + } + }); + + registerPrompt(tabname, tabname + "_prompt"); + registerPrompt(tabname, tabname + "_neg_prompt"); } function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) { @@ -137,21 +150,42 @@ function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePromp } -function extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate) +function extraNetworksShowControlsForPage(tabname, tabname_full) { + gradioApp().querySelectorAll('#' + tabname + '_extra_tabs .extra-networks-controls-div > div').forEach(function(elem) { + var targetId = tabname_full + "_controls"; + elem.style.display = elem.id == targetId ? "" : "none"; + }); +} + + +function extraNetworksUnrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate) extraNetworksMovePromptToTab(tabname, '', false, false); + + extraNetworksShowControlsForPage(tabname, null); } -function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab +function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt, tabname_full) { // called from python when user selects an extra networks tab extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt); + extraNetworksShowControlsForPage(tabname, tabname_full); } -function applyExtraNetworkFilter(tabname) { - setTimeout(extraNetworksApplyFilter[tabname], 1); +function applyExtraNetworkFilter(tabname_full) { + var doFilter = function() { + var applyFunction = extraNetworksApplyFilter[tabname_full]; + + if (applyFunction) { + applyFunction(true); + } + }; + setTimeout(doFilter, 1); } -function applyExtraNetworkSort(tabname) { - setTimeout(extraNetworksApplySort[tabname], 1); +function applyExtraNetworkSort(tabname_full) { + var doSort = function() { + extraNetworksApplySort[tabname_full](true); + }; + setTimeout(doSort, 1); } var extraNetworksApplyFilter = {}; @@ -161,41 +195,24 @@ var activePromptTextarea = {}; function setupExtraNetworks() { setupExtraNetworksForTab('txt2img'); setupExtraNetworksForTab('img2img'); - - function registerPrompt(tabname, id) { - var textarea = gradioApp().querySelector("#" + id + " > label > textarea"); - - if (!activePromptTextarea[tabname]) { - activePromptTextarea[tabname] = textarea; - } - - textarea.addEventListener("focus", function() { - activePromptTextarea[tabname] = textarea; - }); - } - - registerPrompt('txt2img', 'txt2img_prompt'); - registerPrompt('txt2img', 'txt2img_neg_prompt'); - registerPrompt('img2img', 'img2img_prompt'); - registerPrompt('img2img', 'img2img_neg_prompt'); } -onUiLoaded(setupExtraNetworks); - var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/; var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g; -function tryToRemoveExtraNetworkFromPrompt(textarea, text) { - var m = text.match(re_extranet); +var re_extranet_neg = /\(([^:^>]+:[\d.]+)\)/; +var re_extranet_g_neg = /\(([^:^>]+:[\d.]+)\)/g; +function tryToRemoveExtraNetworkFromPrompt(textarea, text, isNeg) { + var m = text.match(isNeg ? re_extranet_neg : re_extranet); var replaced = false; var newTextareaText; + var extraTextBeforeNet = opts.extra_networks_add_text_separator; if (m) { - var extraTextBeforeNet = opts.extra_networks_add_text_separator; var extraTextAfterNet = m[2]; var partToSearch = m[1]; var foundAtPosition = -1; - newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) { - m = found.match(re_extranet); + newTextareaText = textarea.value.replaceAll(isNeg ? re_extranet_g_neg : re_extranet_g, function(found, net, pos) { + m = found.match(isNeg ? re_extranet_neg : re_extranet); if (m[1] == partToSearch) { replaced = true; foundAtPosition = pos; @@ -203,9 +220,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) { } return found; }); - if (foundAtPosition >= 0) { - if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) { + if (extraTextAfterNet && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) { newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length); } if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) { @@ -213,13 +229,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) { } } } else { - newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) { - if (found == text) { - replaced = true; - return ""; - } - return found; - }); + newTextareaText = textarea.value.replaceAll(new RegExp(`((?:${extraTextBeforeNet})?${text})`, "g"), ""); + replaced = (newTextareaText != textarea.value); } if (replaced) { @@ -230,14 +241,22 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) { return false; } -function cardClicked(tabname, textToAdd, allowNegativePrompt) { - var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"); - - if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) { - textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd; +function updatePromptArea(text, textArea, isNeg) { + if (!tryToRemoveExtraNetworkFromPrompt(textArea, text, isNeg)) { + textArea.value = textArea.value + opts.extra_networks_add_text_separator + text; } - updateInput(textarea); + updateInput(textArea); +} + +function cardClicked(tabname, textToAdd, textToAddNegative, allowNegativePrompt) { + if (textToAddNegative.length > 0) { + updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")); + updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true); + } else { + var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"); + updatePromptArea(textToAdd, textarea); + } } function saveCardPreview(event, tabname, filename) { @@ -253,13 +272,200 @@ function saveCardPreview(event, tabname, filename) { event.preventDefault(); } -function extraNetworksSearchButton(tabs_id, event) { - var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > label > textarea'); - var button = event.target; - var text = button.classList.contains("search-all") ? "" : button.textContent.trim(); +function extraNetworksTreeProcessFileClick(event, btn, tabname, extra_networks_tabname) { + /** + * Processes `onclick` events when user clicks on files in tree. + * + * @param event The generated event. + * @param btn The clicked `tree-list-item` button. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + // NOTE: Currently unused. + return; +} + +function extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_networks_tabname) { + /** + * Processes `onclick` events when user clicks on directories in tree. + * + * Here is how the tree reacts to clicks for various states: + * unselected unopened directory: Diretory is selected and expanded. + * unselected opened directory: Directory is selected. + * selected opened directory: Directory is collapsed and deselected. + * chevron is clicked: Directory is expanded or collapsed. Selected state unchanged. + * + * @param event The generated event. + * @param btn The clicked `tree-list-item` button. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var ul = btn.nextElementSibling; + // This is the actual target that the user clicked on within the target button. + // We use this to detect if the chevron was clicked. + var true_targ = event.target; + + function _expand_or_collapse(_ul, _btn) { + // Expands <ul> if it is collapsed, collapses otherwise. Updates button attributes. + if (_ul.hasAttribute("hidden")) { + _ul.removeAttribute("hidden"); + _btn.dataset.expanded = ""; + } else { + _ul.setAttribute("hidden", ""); + delete _btn.dataset.expanded; + } + } + + function _remove_selected_from_all() { + // Removes the `selected` attribute from all buttons. + var sels = document.querySelectorAll("div.tree-list-content"); + [...sels].forEach(el => { + delete el.dataset.selected; + }); + } + + function _select_button(_btn) { + // Removes `data-selected` attribute from all buttons then adds to passed button. + _remove_selected_from_all(); + _btn.dataset.selected = ""; + } + + function _update_search(_tabname, _extra_networks_tabname, _search_text) { + // Update search input with select button's path. + var search_input_elem = gradioApp().querySelector("#" + tabname + "_" + extra_networks_tabname + "_extra_search"); + search_input_elem.value = _search_text; + updateInput(search_input_elem); + } + + + // If user clicks on the chevron, then we do not select the folder. + if (true_targ.matches(".tree-list-item-action--leading, .tree-list-item-action-chevron")) { + _expand_or_collapse(ul, btn); + } else { + // User clicked anywhere else on the button. + if ("selected" in btn.dataset && !(ul.hasAttribute("hidden"))) { + // If folder is select and open, collapse and deselect button. + _expand_or_collapse(ul, btn); + delete btn.dataset.selected; + _update_search(tabname, extra_networks_tabname, ""); + } else if (!(!("selected" in btn.dataset) && !(ul.hasAttribute("hidden")))) { + // If folder is open and not selected, then we don't collapse; just select. + // NOTE: Double inversion sucks but it is the clearest way to show the branching here. + _expand_or_collapse(ul, btn); + _select_button(btn, tabname, extra_networks_tabname); + _update_search(tabname, extra_networks_tabname, btn.dataset.path); + } else { + // All other cases, just select the button. + _select_button(btn, tabname, extra_networks_tabname); + _update_search(tabname, extra_networks_tabname, btn.dataset.path); + } + } +} + +function extraNetworksTreeOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for buttons within an `extra-network-tree .tree-list--tree`. + * + * Determines whether the clicked button in the tree is for a file entry or a directory + * then calls the appropriate function. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var btn = event.currentTarget; + var par = btn.parentElement; + if (par.dataset.treeEntryType === "file") { + extraNetworksTreeProcessFileClick(event, btn, tabname, extra_networks_tabname); + } else { + extraNetworksTreeProcessDirectoryClick(event, btn, tabname, extra_networks_tabname); + } +} + +function extraNetworksControlSortOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for the Sort Mode button. + * + * Modifies the data attributes of the Sort Mode button to cycle between + * various sorting modes. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var curr_mode = event.currentTarget.dataset.sortmode; + var el_sort_dir = gradioApp().querySelector("#" + tabname + "_" + extra_networks_tabname + "_extra_sort_dir"); + var sort_dir = el_sort_dir.dataset.sortdir; + if (curr_mode == "path") { + event.currentTarget.dataset.sortmode = "name"; + event.currentTarget.dataset.sortkey = "sortName-" + sort_dir + "-640"; + event.currentTarget.setAttribute("title", "Sort by filename"); + } else if (curr_mode == "name") { + event.currentTarget.dataset.sortmode = "date_created"; + event.currentTarget.dataset.sortkey = "sortDate_created-" + sort_dir + "-640"; + event.currentTarget.setAttribute("title", "Sort by date created"); + } else if (curr_mode == "date_created") { + event.currentTarget.dataset.sortmode = "date_modified"; + event.currentTarget.dataset.sortkey = "sortDate_modified-" + sort_dir + "-640"; + event.currentTarget.setAttribute("title", "Sort by date modified"); + } else { + event.currentTarget.dataset.sortmode = "path"; + event.currentTarget.dataset.sortkey = "sortPath-" + sort_dir + "-640"; + event.currentTarget.setAttribute("title", "Sort by path"); + } + applyExtraNetworkSort(tabname + "_" + extra_networks_tabname); +} + +function extraNetworksControlSortDirOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for the Sort Direction button. + * + * Modifies the data attributes of the Sort Direction button to cycle between + * ascending and descending sort directions. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + if (event.currentTarget.dataset.sortdir == "Ascending") { + event.currentTarget.dataset.sortdir = "Descending"; + event.currentTarget.setAttribute("title", "Sort descending"); + } else { + event.currentTarget.dataset.sortdir = "Ascending"; + event.currentTarget.setAttribute("title", "Sort ascending"); + } + applyExtraNetworkSort(tabname + "_" + extra_networks_tabname); +} - searchTextarea.value = text; - updateInput(searchTextarea); +function extraNetworksControlTreeViewOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for the Tree View button. + * + * Toggles the tree view in the extra networks pane. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_tree").classList.toggle("hidden"); + event.currentTarget.classList.toggle("extra-network-control--enabled"); +} + +function extraNetworksControlRefreshOnClick(event, tabname, extra_networks_tabname) { + /** + * Handles `onclick` events for the Refresh Page button. + * + * In order to actually call the python functions in `ui_extra_networks.py` + * to refresh the page, we created an empty gradio button in that file with an + * event handler that refreshes the page. So what this function here does + * is it manually raises a `click` event on that button. + * + * @param event The generated event. + * @param tabname The name of the active tab in the sd webui. Ex: txt2img, img2img, etc. + * @param extra_networks_tabname The id of the active extraNetworks tab. Ex: lora, checkpoints, etc. + */ + var btn_refresh_internal = gradioApp().getElementById(tabname + "_" + extra_networks_tabname + "_extra_refresh_internal"); + btn_refresh_internal.dispatchEvent(new Event("click")); } var globalPopup = null; @@ -337,6 +543,11 @@ function requestGet(url, data, handler, errorHandler) { xhr.send(js); } +function extraNetworksCopyCardPath(event, path) { + navigator.clipboard.writeText(path); + event.stopPropagation(); +} + function extraNetworksRequestMetadata(event, extraPage, cardName) { var showError = function() { extraNetworksShowMetadata("there was an error getting metadata"); @@ -398,3 +609,39 @@ window.addEventListener("keydown", function(event) { closePopup(); } }); + +/** + * Setup custom loading for this script. + * We need to wait for all of our HTML to be generated in the extra networks tabs + * before we can actually run the `setupExtraNetworks` function. + * The `onUiLoaded` function actually runs before all of our extra network tabs are + * finished generating. Thus we needed this new method. + * + */ + +var uiAfterScriptsCallbacks = []; +var uiAfterScriptsTimeout = null; +var executedAfterScripts = false; + +function scheduleAfterScriptsCallbacks() { + clearTimeout(uiAfterScriptsTimeout); + uiAfterScriptsTimeout = setTimeout(function() { + executeCallbacks(uiAfterScriptsCallbacks); + }, 200); +} + +onUiLoaded(function() { + var mutationObserver = new MutationObserver(function(m) { + let existingSearchfields = gradioApp().querySelectorAll("[id$='_extra_search']").length; + let neededSearchfields = gradioApp().querySelectorAll("[id$='_extra_tabs'] > .tab-nav > button").length - 2; + + if (!executedAfterScripts && existingSearchfields >= neededSearchfields) { + mutationObserver.disconnect(); + executedAfterScripts = true; + scheduleAfterScriptsCallbacks(); + } + }); + mutationObserver.observe(gradioApp(), {childList: true, subtree: true}); +}); + +uiAfterScriptsCallbacks.push(setupExtraNetworks); diff --git a/javascript/progressbar.js b/javascript/progressbar.js index 77761495..f068bac6 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -45,8 +45,15 @@ function formatTime(secs) { } } + +var originalAppTitle = undefined; + +onUiLoaded(function() { + originalAppTitle = document.title; +}); + function setTitle(progress) { - var title = 'Stable Diffusion'; + var title = originalAppTitle; if (opts.show_progress_in_title && progress) { title = '[' + progress.trim() + '] ' + title; diff --git a/javascript/resizeHandle.js b/javascript/resizeHandle.js index 8c5c5169..c4e9de58 100644 --- a/javascript/resizeHandle.js +++ b/javascript/resizeHandle.js @@ -1,8 +1,8 @@ (function() { const GRADIO_MIN_WIDTH = 320; - const GRID_TEMPLATE_COLUMNS = '1fr 16px 1fr'; const PAD = 16; const DEBOUNCE_TIME = 100; + const DOUBLE_TAP_DELAY = 200; //ms const R = { tracking: false, @@ -11,6 +11,7 @@ leftCol: null, leftColStartWidth: null, screenX: null, + lastTapTime: null, }; let resizeTimer; @@ -23,21 +24,17 @@ function displayResizeHandle(parent) { if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) { parent.style.display = 'flex'; - if (R.handle != null) { - R.handle.style.opacity = '0'; - } + parent.resizeHandle.style.display = "none"; return false; } else { parent.style.display = 'grid'; - if (R.handle != null) { - R.handle.style.opacity = '100'; - } + parent.resizeHandle.style.display = "block"; return true; } } function afterResize(parent) { - if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != GRID_TEMPLATE_COLUMNS) { + if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != parent.style.originalGridTemplateColumns) { const oldParentWidth = R.parentWidth; const newParentWidth = parent.offsetWidth; const widthL = parseInt(parent.style.gridTemplateColumns.split(' ')[0]); @@ -52,6 +49,14 @@ } function setup(parent) { + + function onDoubleClick(evt) { + evt.preventDefault(); + evt.stopPropagation(); + + parent.style.gridTemplateColumns = parent.style.originalGridTemplateColumns; + } + const leftCol = parent.firstElementChild; const rightCol = parent.lastElementChild; @@ -59,63 +64,97 @@ parent.style.display = 'grid'; parent.style.gap = '0'; - parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS; + const gridTemplateColumns = `${parent.children[0].style.flexGrow}fr ${PAD}px ${parent.children[1].style.flexGrow}fr`; + parent.style.gridTemplateColumns = gridTemplateColumns; + parent.style.originalGridTemplateColumns = gridTemplateColumns; const resizeHandle = document.createElement('div'); resizeHandle.classList.add('resize-handle'); parent.insertBefore(resizeHandle, rightCol); - - resizeHandle.addEventListener('mousedown', (evt) => { - if (evt.button !== 0) return; - - evt.preventDefault(); - evt.stopPropagation(); - - document.body.classList.add('resizing'); - - R.tracking = true; - R.parent = parent; - R.parentWidth = parent.offsetWidth; - R.handle = resizeHandle; - R.leftCol = leftCol; - R.leftColStartWidth = leftCol.offsetWidth; - R.screenX = evt.screenX; + parent.resizeHandle = resizeHandle; + + ['mousedown', 'touchstart'].forEach((eventType) => { + resizeHandle.addEventListener(eventType, (evt) => { + if (eventType.startsWith('mouse')) { + if (evt.button !== 0) return; + } else { + if (evt.changedTouches.length !== 1) return; + + const currentTime = new Date().getTime(); + if (R.lastTapTime && currentTime - R.lastTapTime <= DOUBLE_TAP_DELAY) { + onDoubleClick(evt); + return; + } + + R.lastTapTime = currentTime; + } + + evt.preventDefault(); + evt.stopPropagation(); + + document.body.classList.add('resizing'); + + R.tracking = true; + R.parent = parent; + R.parentWidth = parent.offsetWidth; + R.leftCol = leftCol; + R.leftColStartWidth = leftCol.offsetWidth; + if (eventType.startsWith('mouse')) { + R.screenX = evt.screenX; + } else { + R.screenX = evt.changedTouches[0].screenX; + } + }); }); - resizeHandle.addEventListener('dblclick', (evt) => { - evt.preventDefault(); - evt.stopPropagation(); - - parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS; - }); + resizeHandle.addEventListener('dblclick', onDoubleClick); afterResize(parent); } - window.addEventListener('mousemove', (evt) => { - if (evt.button !== 0) return; - - if (R.tracking) { - evt.preventDefault(); - evt.stopPropagation(); + ['mousemove', 'touchmove'].forEach((eventType) => { + window.addEventListener(eventType, (evt) => { + if (eventType.startsWith('mouse')) { + if (evt.button !== 0) return; + } else { + if (evt.changedTouches.length !== 1) return; + } - const delta = R.screenX - evt.screenX; - const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH); - setLeftColGridTemplate(R.parent, leftColWidth); - } + if (R.tracking) { + if (eventType.startsWith('mouse')) { + evt.preventDefault(); + } + evt.stopPropagation(); + + let delta = 0; + if (eventType.startsWith('mouse')) { + delta = R.screenX - evt.screenX; + } else { + delta = R.screenX - evt.changedTouches[0].screenX; + } + const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH); + setLeftColGridTemplate(R.parent, leftColWidth); + } + }); }); - window.addEventListener('mouseup', (evt) => { - if (evt.button !== 0) return; + ['mouseup', 'touchend'].forEach((eventType) => { + window.addEventListener(eventType, (evt) => { + if (eventType.startsWith('mouse')) { + if (evt.button !== 0) return; + } else { + if (evt.changedTouches.length !== 1) return; + } - if (R.tracking) { - evt.preventDefault(); - evt.stopPropagation(); + if (R.tracking) { + evt.preventDefault(); + evt.stopPropagation(); - R.tracking = false; + R.tracking = false; - document.body.classList.remove('resizing'); - } + document.body.classList.remove('resizing'); + } + }); }); diff --git a/javascript/settings.js b/javascript/settings.js index e6009290..b2d981c2 100644 --- a/javascript/settings.js +++ b/javascript/settings.js @@ -55,8 +55,8 @@ onOptionsChanged(function() { }); opts._categories.forEach(function(x) { - var section = x[0]; - var category = x[1]; + var section = localization[x[0]] ?? x[0]; + var category = localization[x[1]] ?? x[1]; var span = document.createElement('SPAN'); span.textContent = category; diff --git a/javascript/token-counters.js b/javascript/token-counters.js index 2ecc7d91..eeea7a5d 100644 --- a/javascript/token-counters.js +++ b/javascript/token-counters.js @@ -48,11 +48,6 @@ function setupTokenCounting(id, id_counter, id_button) { var counter = gradioApp().getElementById(id_counter); var textarea = gradioApp().querySelector(`#${id} > label > textarea`); - if (opts.disable_token_counters) { - counter.style.display = "none"; - return; - } - if (counter.parentElement == prompt.parentElement) { return; } @@ -61,15 +56,32 @@ function setupTokenCounting(id, id_counter, id_button) { prompt.parentElement.style.position = "relative"; var func = onEdit(id, textarea, 800, function() { - gradioApp().getElementById(id_button)?.click(); + if (counter.classList.contains("token-counter-visible")) { + gradioApp().getElementById(id_button)?.click(); + } }); promptTokenCountUpdateFunctions[id] = func; promptTokenCountUpdateFunctions[id_button] = func; } -function setupTokenCounters() { - setupTokenCounting('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button'); - setupTokenCounting('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button'); - setupTokenCounting('img2img_prompt', 'img2img_token_counter', 'img2img_token_button'); - setupTokenCounting('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button'); +function toggleTokenCountingVisibility(id, id_counter, id_button) { + var counter = gradioApp().getElementById(id_counter); + + counter.style.display = opts.disable_token_counters ? "none" : "block"; + counter.classList.toggle("token-counter-visible", !opts.disable_token_counters); } + +function runCodeForTokenCounters(fun) { + fun('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button'); + fun('txt2img_neg_prompt', 'txt2img_negative_token_counter', 'txt2img_negative_token_button'); + fun('img2img_prompt', 'img2img_token_counter', 'img2img_token_button'); + fun('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button'); +} + +onUiLoaded(function() { + runCodeForTokenCounters(setupTokenCounting); +}); + +onOptionsChanged(function() { + runCodeForTokenCounters(toggleTokenCountingVisibility); +}); diff --git a/javascript/ui.js b/javascript/ui.js index 18c9f891..3d079b3d 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -119,9 +119,18 @@ function create_submit_args(args) { return res; } +function setSubmitButtonsVisibility(tabname, showInterrupt, showSkip, showInterrupting) { + gradioApp().getElementById(tabname + '_interrupt').style.display = showInterrupt ? "block" : "none"; + gradioApp().getElementById(tabname + '_skip').style.display = showSkip ? "block" : "none"; + gradioApp().getElementById(tabname + '_interrupting').style.display = showInterrupting ? "block" : "none"; +} + function showSubmitButtons(tabname, show) { - gradioApp().getElementById(tabname + '_interrupt').style.display = show ? "none" : "block"; - gradioApp().getElementById(tabname + '_skip').style.display = show ? "none" : "block"; + setSubmitButtonsVisibility(tabname, !show, !show, false); +} + +function showSubmitInterruptingPlaceholder(tabname) { + setSubmitButtonsVisibility(tabname, false, true, true); } function showRestoreProgressButton(tabname, show) { @@ -150,6 +159,14 @@ function submit() { return res; } +function submit_txt2img_upscale() { + var res = submit(...arguments); + + res[2] = selected_gallery_index(); + + return res; +} + function submit_img2img() { showSubmitButtons('img2img', false); @@ -302,8 +319,6 @@ onAfterUiUpdate(function() { }); json_elem.parentElement.style.display = "none"; - - setupTokenCounters(); }); onOptionsChanged(function() { diff --git a/modules/api/api.py b/modules/api/api.py index b3d74e51..4e656082 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -17,7 +17,7 @@ from fastapi.encoders import jsonable_encoder from secrets import compare_digest import modules.shared as shared -from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models +from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models from modules.api import models from modules.shared import opts from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images @@ -31,7 +31,7 @@ from typing import Any import piexif import piexif.helper from contextlib import closing - +from modules.progress import create_task_id, add_task_to_queue, start_task, finish_task, current_task def script_name_to_index(name, scripts): try: @@ -230,6 +230,7 @@ class Api: self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem]) self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem]) self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse) + self.add_api_route("/sdapi/v1/refresh-embeddings", self.refresh_embeddings, methods=["POST"]) self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"]) self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=models.CreateResponse) @@ -251,6 +252,24 @@ class Api: self.default_script_arg_txt2img = [] self.default_script_arg_img2img = [] + txt2img_script_runner = scripts.scripts_txt2img + img2img_script_runner = scripts.scripts_img2img + + if not txt2img_script_runner.scripts or not img2img_script_runner.scripts: + ui.create_ui() + + if not txt2img_script_runner.scripts: + txt2img_script_runner.initialize_scripts(False) + if not self.default_script_arg_txt2img: + self.default_script_arg_txt2img = self.init_default_script_args(txt2img_script_runner) + + if not img2img_script_runner.scripts: + img2img_script_runner.initialize_scripts(True) + if not self.default_script_arg_img2img: + self.default_script_arg_img2img = self.init_default_script_args(img2img_script_runner) + + + def add_api_route(self, path: str, endpoint, **kwargs): if shared.cmd_opts.api_auth: return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) @@ -312,8 +331,13 @@ class Api: script_args[script.args_from:script.args_to] = ui_default_values return script_args - def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner): + def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner, *, input_script_args=None): script_args = default_script_args.copy() + + if input_script_args is not None: + for index, value in input_script_args.items(): + script_args[index] = value + # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run() if selectable_scripts: script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args @@ -335,13 +359,83 @@ class Api: script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx] return script_args + def apply_infotext(self, request, tabname, *, script_runner=None, mentioned_script_args=None): + """Processes `infotext` field from the `request`, and sets other fields of the `request` accoring to what's in infotext. + + If request already has a field set, and that field is encountered in infotext too, the value from infotext is ignored. + + Additionally, fills `mentioned_script_args` dict with index: value pairs for script arguments read from infotext. + """ + + if not request.infotext: + return {} + + possible_fields = infotext_utils.paste_fields[tabname]["fields"] + set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have differenrt names for this + params = infotext_utils.parse_generation_parameters(request.infotext) + + def get_field_value(field, params): + value = field.function(params) if field.function else params.get(field.label) + if value is None: + return None + + if field.api in request.__fields__: + target_type = request.__fields__[field.api].type_ + else: + target_type = type(field.component.value) + + if target_type == type(None): + return None + + if isinstance(value, dict) and value.get('__type__') == 'generic_update': # this is a gradio.update rather than a value + value = value.get('value') + + if value is not None and not isinstance(value, target_type): + value = target_type(value) + + return value + + for field in possible_fields: + if not field.api: + continue + + if field.api in set_fields: + continue + + value = get_field_value(field, params) + if value is not None: + setattr(request, field.api, value) + + if request.override_settings is None: + request.override_settings = {} + + overriden_settings = infotext_utils.get_override_settings(params) + for _, setting_name, value in overriden_settings: + if setting_name not in request.override_settings: + request.override_settings[setting_name] = value + + if script_runner is not None and mentioned_script_args is not None: + indexes = {v: i for i, v in enumerate(script_runner.inputs)} + script_fields = ((field, indexes[field.component]) for field in possible_fields if field.component in indexes) + + for field, index in script_fields: + value = get_field_value(field, params) + + if value is None: + continue + + mentioned_script_args[index] = value + + return params + def text2imgapi(self, txt2imgreq: models.StableDiffusionTxt2ImgProcessingAPI): + task_id = txt2imgreq.force_task_id or create_task_id("txt2img") + script_runner = scripts.scripts_txt2img - if not script_runner.scripts: - script_runner.initialize_scripts(False) - ui.create_ui() - if not self.default_script_arg_txt2img: - self.default_script_arg_txt2img = self.init_default_script_args(script_runner) + + infotext_script_args = {} + self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args) + selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner) populate = txt2imgreq.copy(update={ # Override __init__ params @@ -356,12 +450,15 @@ class Api: args.pop('script_name', None) args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) + args.pop('infotext', None) - script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner) + script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args) send_images = args.pop('send_images', True) args.pop('save_images', None) + add_task_to_queue(task_id) + with self.queue_lock: with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p: p.is_api = True @@ -371,12 +468,14 @@ class Api: try: shared.state.begin(job="scripts_txt2img") + start_task(task_id) if selectable_scripts is not None: p.script_args = script_args processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here else: p.script_args = tuple(script_args) # Need to pass args as tuple here processed = process_images(p) + finish_task(task_id) finally: shared.state.end() shared.total_tqdm.clear() @@ -386,6 +485,8 @@ class Api: return models.TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) def img2imgapi(self, img2imgreq: models.StableDiffusionImg2ImgProcessingAPI): + task_id = img2imgreq.force_task_id or create_task_id("img2img") + init_images = img2imgreq.init_images if init_images is None: raise HTTPException(status_code=404, detail="Init image not found") @@ -395,11 +496,10 @@ class Api: mask = decode_base64_to_image(mask) script_runner = scripts.scripts_img2img - if not script_runner.scripts: - script_runner.initialize_scripts(True) - ui.create_ui() - if not self.default_script_arg_img2img: - self.default_script_arg_img2img = self.init_default_script_args(script_runner) + + infotext_script_args = {} + self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args) + selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner) populate = img2imgreq.copy(update={ # Override __init__ params @@ -416,12 +516,15 @@ class Api: args.pop('script_name', None) args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) + args.pop('infotext', None) - script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner) + script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner, input_script_args=infotext_script_args) send_images = args.pop('send_images', True) args.pop('save_images', None) + add_task_to_queue(task_id) + with self.queue_lock: with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p: p.init_images = [decode_base64_to_image(x) for x in init_images] @@ -432,12 +535,14 @@ class Api: try: shared.state.begin(job="scripts_img2img") + start_task(task_id) if selectable_scripts is not None: p.script_args = script_args processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here else: p.script_args = tuple(script_args) # Need to pass args as tuple here processed = process_images(p) + finish_task(task_id) finally: shared.state.end() shared.total_tqdm.clear() @@ -480,7 +585,7 @@ class Api: if geninfo is None: geninfo = "" - params = generation_parameters_copypaste.parse_generation_parameters(geninfo) + params = infotext_utils.parse_generation_parameters(geninfo) script_callbacks.infotext_pasted_callback(geninfo, params) return models.PNGInfoResponse(info=geninfo, items=items, parameters=params) @@ -511,7 +616,7 @@ class Api: if shared.state.current_image and not req.skip_current_image: current_image = encode_pil_to_base64(shared.state.current_image) - return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo) + return models.ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo, current_task=current_task) def interrogateapi(self, interrogatereq: models.InterrogateRequest): image_b64 = interrogatereq.image @@ -643,6 +748,10 @@ class Api: "skipped": convert_embeddings(db.skipped_embeddings), } + def refresh_embeddings(self): + with self.queue_lock: + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) + def refresh_checkpoints(self): with self.queue_lock: shared.refresh_checkpoints() @@ -775,7 +884,15 @@ class Api: def launch(self, server_name, port, root_path): self.app.include_router(self.router) - uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path) + uvicorn.run( + self.app, + host=server_name, + port=port, + timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, + root_path=root_path, + ssl_keyfile=shared.cmd_opts.tls_keyfile, + ssl_certfile=shared.cmd_opts.tls_certfile + ) def kill_webui(self): restart.stop_program() diff --git a/modules/api/models.py b/modules/api/models.py index 33894b3e..16edf11c 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -107,6 +107,8 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( {"key": "send_images", "type": bool, "default": True}, {"key": "save_images", "type": bool, "default": False}, {"key": "alwayson_scripts", "type": dict, "default": {}}, + {"key": "force_task_id", "type": str, "default": None}, + {"key": "infotext", "type": str, "default": None}, ] ).generate_model() @@ -124,6 +126,8 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( {"key": "send_images", "type": bool, "default": True}, {"key": "save_images", "type": bool, "default": False}, {"key": "alwayson_scripts", "type": dict, "default": {}}, + {"key": "force_task_id", "type": str, "default": None}, + {"key": "infotext", "type": str, "default": None}, ] ).generate_model() diff --git a/modules/cache.py b/modules/cache.py index 2d37e7b9..a9822a0e 100644 --- a/modules/cache.py +++ b/modules/cache.py @@ -62,16 +62,15 @@ def cache(subsection): if cache_data is None:
with cache_lock:
if cache_data is None:
- if not os.path.isfile(cache_filename):
+ try:
+ with open(cache_filename, "r", encoding="utf8") as file:
+ cache_data = json.load(file)
+ except FileNotFoundError:
+ cache_data = {}
+ except Exception:
+ os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
+ print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
cache_data = {}
- else:
- try:
- with open(cache_filename, "r", encoding="utf8") as file:
- cache_data = json.load(file)
- except Exception:
- os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
- print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
- cache_data = {}
s = cache_data.get(subsection, {})
cache_data[subsection] = s
diff --git a/modules/call_queue.py b/modules/call_queue.py index ddf0d573..bcd7c546 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -78,6 +78,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): shared.state.skipped = False
shared.state.interrupted = False
+ shared.state.stopping_generation = False
shared.state.job_count = 0
if not add_stats:
diff --git a/modules/cmd_args.py b/modules/cmd_args.py index da93eb26..213cba98 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -1,7 +1,7 @@ import argparse
import json
import os
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
+from modules.paths_internal import normalized_filepath, models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file # noqa: F401
parser = argparse.ArgumentParser()
@@ -19,21 +19,21 @@ parser.add_argument("--skip-install", action='store_true', help="launch.py argum parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit")
parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
-parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
-parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
-parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
-parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
+parser.add_argument("--data-dir", type=normalized_filepath, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
+parser.add_argument("--config", type=normalized_filepath, default=sd_default_config, help="path to config which constructs model",)
+parser.add_argument("--ckpt", type=normalized_filepath, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
+parser.add_argument("--ckpt-dir", type=normalized_filepath, default=None, help="Path to directory with stable diffusion checkpoints")
+parser.add_argument("--vae-dir", type=normalized_filepath, default=None, help="Path to directory with VAE files")
+parser.add_argument("--gfpgan-dir", type=normalized_filepath, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
+parser.add_argument("--gfpgan-model", type=normalized_filepath, help="GFPGAN model file name", default=None)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
-parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
-parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
+parser.add_argument("--embeddings-dir", type=normalized_filepath, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--textual-inversion-templates-dir", type=normalized_filepath, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
+parser.add_argument("--hypernetwork-dir", type=normalized_filepath, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=normalized_filepath, default=os.path.join(script_path, 'localizations'), help="localizations directory")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
parser.add_argument("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models")
@@ -48,12 +48,13 @@ parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to g parser.add_argument("--ngrok-region", type=str, help="does not do anything.", default="")
parser.add_argument("--ngrok-options", type=json.loads, help='The options to pass to ngrok in JSON format, e.g.: \'{"authtoken_from_env":true, "basic_auth":"user:password", "oauth_provider":"google", "oauth_allow_emails":"user@asdf.com"}\'', default=dict())
parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
+parser.add_argument("--codeformer-models-path", type=normalized_filepath, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=normalized_filepath, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=normalized_filepath, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=normalized_filepath, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=normalized_filepath, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--dat-models-path", type=normalized_filepath, help="Path to directory with DAT model file(s).", default=os.path.join(models_path, 'DAT'))
+parser.add_argument("--clip-models-path", type=normalized_filepath, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
@@ -77,22 +78,24 @@ parser.add_argument("--port", type=int, help="launch gradio with given server po parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
-parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing of all settings globally", default=False)
+parser.add_argument("--freeze-settings-in-sections", type=str, help='disable editing settings in specific sections of the settings page by specifying a comma-delimited list such like "saving-images,upscaling". The list of setting names can be found in the modules/shared_options.py file', default=None)
+parser.add_argument("--freeze-specific-settings", type=str, help='disable editing of individual settings by specifying a comma-delimited list like "samples_save,samples_format". The list of setting names can be found in the config.json file', default=None)
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
+parser.add_argument("--gradio-auth-path", type=normalized_filepath, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path])
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
-parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
+parser.add_argument("--styles-file", type=str, action='append', help="path or wildcard path of styles files, allow multiple entries.", default=[])
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="does not do anything", default=False) # Legacy compatibility, use as default value shared.opts.enable_console_prompts
-parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
+parser.add_argument('--vae-path', type=normalized_filepath, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py deleted file mode 100644 index 12db6814..00000000 --- a/modules/codeformer/codeformer_arch.py +++ /dev/null @@ -1,276 +0,0 @@ -# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py - -import math -import torch -from torch import nn, Tensor -import torch.nn.functional as F -from typing import Optional - -from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock -from basicsr.utils.registry import ARCH_REGISTRY - -def calc_mean_std(feat, eps=1e-5): - """Calculate mean and std for adaptive_instance_normalization. - - Args: - feat (Tensor): 4D tensor. - eps (float): A small value added to the variance to avoid - divide-by-zero. Default: 1e-5. - """ - size = feat.size() - assert len(size) == 4, 'The input feature should be 4D tensor.' - b, c = size[:2] - feat_var = feat.view(b, c, -1).var(dim=2) + eps - feat_std = feat_var.sqrt().view(b, c, 1, 1) - feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) - return feat_mean, feat_std - - -def adaptive_instance_normalization(content_feat, style_feat): - """Adaptive instance normalization. - - Adjust the reference features to have the similar color and illuminations - as those in the degradate features. - - Args: - content_feat (Tensor): The reference feature. - style_feat (Tensor): The degradate features. - """ - size = content_feat.size() - style_mean, style_std = calc_mean_std(style_feat) - content_mean, content_std = calc_mean_std(content_feat) - normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) - return normalized_feat * style_std.expand(size) + style_mean.expand(size) - - -class PositionEmbeddingSine(nn.Module): - """ - This is a more standard version of the position embedding, very similar to the one - used by the Attention is all you need paper, generalized to work on images. - """ - - def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): - super().__init__() - self.num_pos_feats = num_pos_feats - self.temperature = temperature - self.normalize = normalize - if scale is not None and normalize is False: - raise ValueError("normalize should be True if scale is passed") - if scale is None: - scale = 2 * math.pi - self.scale = scale - - def forward(self, x, mask=None): - if mask is None: - mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) - not_mask = ~mask - y_embed = not_mask.cumsum(1, dtype=torch.float32) - x_embed = not_mask.cumsum(2, dtype=torch.float32) - if self.normalize: - eps = 1e-6 - y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale - x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale - - dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) - dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) - - pos_x = x_embed[:, :, :, None] / dim_t - pos_y = y_embed[:, :, :, None] / dim_t - pos_x = torch.stack( - (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos_y = torch.stack( - (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 - ).flatten(3) - pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - return pos - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") - - -class TransformerSALayer(nn.Module): - def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"): - super().__init__() - self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) - # Implementation of Feedforward model - MLP - self.linear1 = nn.Linear(embed_dim, dim_mlp) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_mlp, embed_dim) - - self.norm1 = nn.LayerNorm(embed_dim) - self.norm2 = nn.LayerNorm(embed_dim) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - - def with_pos_embed(self, tensor, pos: Optional[Tensor]): - return tensor if pos is None else tensor + pos - - def forward(self, tgt, - tgt_mask: Optional[Tensor] = None, - tgt_key_padding_mask: Optional[Tensor] = None, - query_pos: Optional[Tensor] = None): - - # self attention - tgt2 = self.norm1(tgt) - q = k = self.with_pos_embed(tgt2, query_pos) - tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, - key_padding_mask=tgt_key_padding_mask)[0] - tgt = tgt + self.dropout1(tgt2) - - # ffn - tgt2 = self.norm2(tgt) - tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) - tgt = tgt + self.dropout2(tgt2) - return tgt - -class Fuse_sft_block(nn.Module): - def __init__(self, in_ch, out_ch): - super().__init__() - self.encode_enc = ResBlock(2*in_ch, out_ch) - - self.scale = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) - - self.shift = nn.Sequential( - nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), - nn.LeakyReLU(0.2, True), - nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) - - def forward(self, enc_feat, dec_feat, w=1): - enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) - scale = self.scale(enc_feat) - shift = self.shift(enc_feat) - residual = w * (dec_feat * scale + shift) - out = dec_feat + residual - return out - - -@ARCH_REGISTRY.register() -class CodeFormer(VQAutoEncoder): - def __init__(self, dim_embd=512, n_head=8, n_layers=9, - codebook_size=1024, latent_size=256, - connect_list=('32', '64', '128', '256'), - fix_modules=('quantize', 'generator')): - super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size) - - if fix_modules is not None: - for module in fix_modules: - for param in getattr(self, module).parameters(): - param.requires_grad = False - - self.connect_list = connect_list - self.n_layers = n_layers - self.dim_embd = dim_embd - self.dim_mlp = dim_embd*2 - - self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) - self.feat_emb = nn.Linear(256, self.dim_embd) - - # transformer - self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) - for _ in range(self.n_layers)]) - - # logits_predict head - self.idx_pred_layer = nn.Sequential( - nn.LayerNorm(dim_embd), - nn.Linear(dim_embd, codebook_size, bias=False)) - - self.channels = { - '16': 512, - '32': 256, - '64': 256, - '128': 128, - '256': 128, - '512': 64, - } - - # after second residual block for > 16, before attn layer for ==16 - self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18} - # after first residual block for > 16, before attn layer for ==16 - self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21} - - # fuse_convs_dict - self.fuse_convs_dict = nn.ModuleDict() - for f_size in self.connect_list: - in_ch = self.channels[f_size] - self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) - - def _init_weights(self, module): - if isinstance(module, (nn.Linear, nn.Embedding)): - module.weight.data.normal_(mean=0.0, std=0.02) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - - def forward(self, x, w=0, detach_16=True, code_only=False, adain=False): - # ################### Encoder ##################### - enc_feat_dict = {} - out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] - for i, block in enumerate(self.encoder.blocks): - x = block(x) - if i in out_list: - enc_feat_dict[str(x.shape[-1])] = x.clone() - - lq_feat = x - # ################# Transformer ################### - # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) - pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1) - # BCHW -> BC(HW) -> (HW)BC - feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1)) - query_emb = feat_emb - # Transformer encoder - for layer in self.ft_layers: - query_emb = layer(query_emb, query_pos=pos_emb) - - # output logits - logits = self.idx_pred_layer(query_emb) # (hw)bn - logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n - - if code_only: # for training stage II - # logits doesn't need softmax before cross_entropy loss - return logits, lq_feat - - # ################# Quantization ################### - # if self.training: - # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) - # # b(hw)c -> bc(hw) -> bchw - # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) - # ------------ - soft_one_hot = F.softmax(logits, dim=2) - _, top_idx = torch.topk(soft_one_hot, 1, dim=2) - quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256]) - # preserve gradients - # quant_feat = lq_feat + (quant_feat - lq_feat).detach() - - if detach_16: - quant_feat = quant_feat.detach() # for training stage III - if adain: - quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) - - # ################## Generator #################### - x = quant_feat - fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] - - for i, block in enumerate(self.generator.blocks): - x = block(x) - if i in fuse_list: # fuse after i-th block - f_size = str(x.shape[-1]) - if w>0: - x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w) - out = x - # logits doesn't need softmax before cross_entropy loss - return out, logits, lq_feat diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py deleted file mode 100644 index 09ee6660..00000000 --- a/modules/codeformer/vqgan_arch.py +++ /dev/null @@ -1,435 +0,0 @@ -# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py - -''' -VQGAN code, adapted from the original created by the Unleashing Transformers authors: -https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py - -''' -import torch -import torch.nn as nn -import torch.nn.functional as F -from basicsr.utils import get_root_logger -from basicsr.utils.registry import ARCH_REGISTRY - -def normalize(in_channels): - return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) - - -@torch.jit.script -def swish(x): - return x*torch.sigmoid(x) - - -# Define VQVAE classes -class VectorQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, beta): - super(VectorQuantizer, self).__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 - self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) - self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) - - def forward(self, z): - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.emb_dim) - - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) - - mean_distance = torch.mean(d) - # find closest encodings - # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) - min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) - # [0-1], higher score, higher confidence - min_encoding_scores = torch.exp(-min_encoding_scores/10) - - min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) - min_encodings.scatter_(1, min_encoding_indices, 1) - - # get quantized latent vectors - z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) - # compute loss for embedding - loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2) - # preserve gradients - z_q = z + (z_q - z).detach() - - # perplexity - e_mean = torch.mean(min_encodings, dim=0) - perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q, loss, { - "perplexity": perplexity, - "min_encodings": min_encodings, - "min_encoding_indices": min_encoding_indices, - "min_encoding_scores": min_encoding_scores, - "mean_distance": mean_distance - } - - def get_codebook_feat(self, indices, shape): - # input indices: batch*token_num -> (batch*token_num)*1 - # shape: batch, height, width, channel - indices = indices.view(-1,1) - min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) - min_encodings.scatter_(1, indices, 1) - # get quantized latent vectors - z_q = torch.matmul(min_encodings.float(), self.embedding.weight) - - if shape is not None: # reshape back to match original input shape - z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() - - return z_q - - -class GumbelQuantizer(nn.Module): - def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): - super().__init__() - self.codebook_size = codebook_size # number of embeddings - self.emb_dim = emb_dim # dimension of embedding - self.straight_through = straight_through - self.temperature = temp_init - self.kl_weight = kl_weight - self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits - self.embed = nn.Embedding(codebook_size, emb_dim) - - def forward(self, z): - hard = self.straight_through if self.training else True - - logits = self.proj(z) - - soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) - - z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) - - # + kl divergence to the prior loss - qy = F.softmax(logits, dim=1) - diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() - min_encoding_indices = soft_one_hot.argmax(dim=1) - - return z_q, diff, { - "min_encoding_indices": min_encoding_indices - } - - -class Downsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) - - def forward(self, x): - pad = (0, 1, 0, 1) - x = torch.nn.functional.pad(x, pad, mode="constant", value=0) - x = self.conv(x) - return x - - -class Upsample(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) - - def forward(self, x): - x = F.interpolate(x, scale_factor=2.0, mode="nearest") - x = self.conv(x) - - return x - - -class ResBlock(nn.Module): - def __init__(self, in_channels, out_channels=None): - super(ResBlock, self).__init__() - self.in_channels = in_channels - self.out_channels = in_channels if out_channels is None else out_channels - self.norm1 = normalize(in_channels) - self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) - self.norm2 = normalize(out_channels) - self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) - if self.in_channels != self.out_channels: - self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) - - def forward(self, x_in): - x = x_in - x = self.norm1(x) - x = swish(x) - x = self.conv1(x) - x = self.norm2(x) - x = swish(x) - x = self.conv2(x) - if self.in_channels != self.out_channels: - x_in = self.conv_out(x_in) - - return x + x_in - - -class AttnBlock(nn.Module): - def __init__(self, in_channels): - super().__init__() - self.in_channels = in_channels - - self.norm = normalize(in_channels) - self.q = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.k = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.v = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - self.proj_out = torch.nn.Conv2d( - in_channels, - in_channels, - kernel_size=1, - stride=1, - padding=0 - ) - - def forward(self, x): - h_ = x - h_ = self.norm(h_) - q = self.q(h_) - k = self.k(h_) - v = self.v(h_) - - # compute attention - b, c, h, w = q.shape - q = q.reshape(b, c, h*w) - q = q.permute(0, 2, 1) - k = k.reshape(b, c, h*w) - w_ = torch.bmm(q, k) - w_ = w_ * (int(c)**(-0.5)) - w_ = F.softmax(w_, dim=2) - - # attend to values - v = v.reshape(b, c, h*w) - w_ = w_.permute(0, 2, 1) - h_ = torch.bmm(v, w_) - h_ = h_.reshape(b, c, h, w) - - h_ = self.proj_out(h_) - - return x+h_ - - -class Encoder(nn.Module): - def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions): - super().__init__() - self.nf = nf - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.attn_resolutions = attn_resolutions - - curr_res = self.resolution - in_ch_mult = (1,)+tuple(ch_mult) - - blocks = [] - # initial convultion - blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) - - # residual and downsampling blocks, with attention on smaller res (16x16) - for i in range(self.num_resolutions): - block_in_ch = nf * in_ch_mult[i] - block_out_ch = nf * ch_mult[i] - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - if curr_res in attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != self.num_resolutions - 1: - blocks.append(Downsample(block_in_ch)) - curr_res = curr_res // 2 - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - # normalise and convert to latent size - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)) - self.blocks = nn.ModuleList(blocks) - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -class Generator(nn.Module): - def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): - super().__init__() - self.nf = nf - self.ch_mult = ch_mult - self.num_resolutions = len(self.ch_mult) - self.num_res_blocks = res_blocks - self.resolution = img_size - self.attn_resolutions = attn_resolutions - self.in_channels = emb_dim - self.out_channels = 3 - block_in_ch = self.nf * self.ch_mult[-1] - curr_res = self.resolution // 2 ** (self.num_resolutions-1) - - blocks = [] - # initial conv - blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) - - # non-local attention block - blocks.append(ResBlock(block_in_ch, block_in_ch)) - blocks.append(AttnBlock(block_in_ch)) - blocks.append(ResBlock(block_in_ch, block_in_ch)) - - for i in reversed(range(self.num_resolutions)): - block_out_ch = self.nf * self.ch_mult[i] - - for _ in range(self.num_res_blocks): - blocks.append(ResBlock(block_in_ch, block_out_ch)) - block_in_ch = block_out_ch - - if curr_res in self.attn_resolutions: - blocks.append(AttnBlock(block_in_ch)) - - if i != 0: - blocks.append(Upsample(block_in_ch)) - curr_res = curr_res * 2 - - blocks.append(normalize(block_in_ch)) - blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) - - self.blocks = nn.ModuleList(blocks) - - - def forward(self, x): - for block in self.blocks: - x = block(x) - - return x - - -@ARCH_REGISTRY.register() -class VQAutoEncoder(nn.Module): - def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256, - beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): - super().__init__() - logger = get_root_logger() - self.in_channels = 3 - self.nf = nf - self.n_blocks = res_blocks - self.codebook_size = codebook_size - self.embed_dim = emb_dim - self.ch_mult = ch_mult - self.resolution = img_size - self.attn_resolutions = attn_resolutions or [16] - self.quantizer_type = quantizer - self.encoder = Encoder( - self.in_channels, - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - if self.quantizer_type == "nearest": - self.beta = beta #0.25 - self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) - elif self.quantizer_type == "gumbel": - self.gumbel_num_hiddens = emb_dim - self.straight_through = gumbel_straight_through - self.kl_weight = gumbel_kl_weight - self.quantize = GumbelQuantizer( - self.codebook_size, - self.embed_dim, - self.gumbel_num_hiddens, - self.straight_through, - self.kl_weight - ) - self.generator = Generator( - self.nf, - self.embed_dim, - self.ch_mult, - self.n_blocks, - self.resolution, - self.attn_resolutions - ) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_ema' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) - logger.info(f'vqgan is loaded from: {model_path} [params_ema]') - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - logger.info(f'vqgan is loaded from: {model_path} [params]') - else: - raise ValueError('Wrong params!') - - - def forward(self, x): - x = self.encoder(x) - quant, codebook_loss, quant_stats = self.quantize(x) - x = self.generator(quant) - return x, codebook_loss, quant_stats - - - -# patch based discriminator -@ARCH_REGISTRY.register() -class VQGANDiscriminator(nn.Module): - def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None): - super().__init__() - - layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] - ndf_mult = 1 - ndf_mult_prev = 1 - for n in range(1, n_layers): # gradually increase the number of filters - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n, 8) - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - ndf_mult_prev = ndf_mult - ndf_mult = min(2 ** n_layers, 8) - - layers += [ - nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False), - nn.BatchNorm2d(ndf * ndf_mult), - nn.LeakyReLU(0.2, True) - ] - - layers += [ - nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map - self.main = nn.Sequential(*layers) - - if model_path is not None: - chkpt = torch.load(model_path, map_location='cpu') - if 'params_d' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d']) - elif 'params' in chkpt: - self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) - else: - raise ValueError('Wrong params!') - - def forward(self, x): - return self.main(x) diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index da42b5e9..44b84618 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -1,132 +1,64 @@ -import os
+from __future__ import annotations
-import cv2
-import torch
-
-import modules.face_restoration
-import modules.shared
-from modules import shared, devices, modelloader, errors
-from modules.paths import models_path
-
-# codeformer people made a choice to include modified basicsr library to their project which makes
-# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
-# I am making a choice to include some files from codeformer to work around this issue.
-model_dir = "Codeformer"
-model_path = os.path.join(models_path, model_dir)
-model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
-
-codeformer = None
-
-
-def setup_model(dirname):
- os.makedirs(model_path, exist_ok=True)
-
- path = modules.paths.paths.get("CodeFormer", None)
- if path is None:
- return
-
- try:
- from torchvision.transforms.functional import normalize
- from modules.codeformer.codeformer_arch import CodeFormer
- from basicsr.utils import img2tensor, tensor2img
- from facelib.utils.face_restoration_helper import FaceRestoreHelper
- from facelib.detection.retinaface import retinaface
-
- net_class = CodeFormer
-
- class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
- def name(self):
- return "CodeFormer"
-
- def __init__(self, dirname):
- self.net = None
- self.face_helper = None
- self.cmd_dir = dirname
+import logging
- def create_models(self):
-
- if self.net is not None and self.face_helper is not None:
- self.net.to(devices.device_codeformer)
- return self.net, self.face_helper
- model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
- if len(model_paths) != 0:
- ckpt_path = model_paths[0]
- else:
- print("Unable to load codeformer model.")
- return None, None
- net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
- checkpoint = torch.load(ckpt_path)['params_ema']
- net.load_state_dict(checkpoint)
- net.eval()
-
- if hasattr(retinaface, 'device'):
- retinaface.device = devices.device_codeformer
- face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
-
- self.net = net
- self.face_helper = face_helper
-
- return net, face_helper
-
- def send_model_to(self, device):
- self.net.to(device)
- self.face_helper.face_det.to(device)
- self.face_helper.face_parse.to(device)
-
- def restore(self, np_image, w=None):
- np_image = np_image[:, :, ::-1]
-
- original_resolution = np_image.shape[0:2]
+import torch
- self.create_models()
- if self.net is None or self.face_helper is None:
- return np_image
+from modules import (
+ devices,
+ errors,
+ face_restoration,
+ face_restoration_utils,
+ modelloader,
+ shared,
+)
- self.send_model_to(devices.device_codeformer)
+logger = logging.getLogger(__name__)
- self.face_helper.clean_all()
- self.face_helper.read_image(np_image)
- self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
- self.face_helper.align_warp_face()
+model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
+model_download_name = 'codeformer-v0.1.0.pth'
- for cropped_face in self.face_helper.cropped_faces:
- cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
- normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
- cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
+# used by e.g. postprocessing_codeformer.py
+codeformer: face_restoration.FaceRestoration | None = None
- try:
- with torch.no_grad():
- output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
- restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
- del output
- devices.torch_gc()
- except Exception:
- errors.report('Failed inference for CodeFormer', exc_info=True)
- restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
- restored_face = restored_face.astype('uint8')
- self.face_helper.add_restored_face(restored_face)
+class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration):
+ def name(self):
+ return "CodeFormer"
- self.face_helper.get_inverse_affine(None)
+ def load_net(self) -> torch.Module:
+ for model_path in modelloader.load_models(
+ model_path=self.model_path,
+ model_url=model_url,
+ command_path=self.model_path,
+ download_name=model_download_name,
+ ext_filter=['.pth'],
+ ):
+ return modelloader.load_spandrel_model(
+ model_path,
+ device=devices.device_codeformer,
+ expected_architecture='CodeFormer',
+ ).model
+ raise ValueError("No codeformer model found")
- restored_img = self.face_helper.paste_faces_to_input_image()
- restored_img = restored_img[:, :, ::-1]
+ def get_device(self):
+ return devices.device_codeformer
- if original_resolution != restored_img.shape[0:2]:
- restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
+ def restore(self, np_image, w: float | None = None):
+ if w is None:
+ w = getattr(shared.opts, "code_former_weight", 0.5)
- self.face_helper.clean_all()
+ def restore_face(cropped_face_t):
+ assert self.net is not None
+ return self.net(cropped_face_t, w=w, adain=True)[0]
- if shared.opts.face_restoration_unload:
- self.send_model_to(devices.cpu)
+ return self.restore_with_helper(np_image, restore_face)
- return restored_img
- global codeformer
+def setup_model(dirname: str) -> None:
+ global codeformer
+ try:
codeformer = FaceRestorerCodeFormer(dirname)
shared.face_restorers.append(codeformer)
-
except Exception:
errors.report("Error setting up CodeFormer", exc_info=True)
-
- # sys.path = stored_sys_path
diff --git a/modules/dat_model.py b/modules/dat_model.py new file mode 100644 index 00000000..495d5f49 --- /dev/null +++ b/modules/dat_model.py @@ -0,0 +1,79 @@ +import os + +from modules import modelloader, errors +from modules.shared import cmd_opts, opts +from modules.upscaler import Upscaler, UpscalerData +from modules.upscaler_utils import upscale_with_model + + +class UpscalerDAT(Upscaler): + def __init__(self, user_path): + self.name = "DAT" + self.user_path = user_path + self.scalers = [] + super().__init__() + + for file in self.find_models(ext_filter=[".pt", ".pth"]): + name = modelloader.friendly_name(file) + scaler_data = UpscalerData(name, file, upscaler=self, scale=None) + self.scalers.append(scaler_data) + + for model in get_dat_models(self): + if model.name in opts.dat_enabled_models: + self.scalers.append(model) + + def do_upscale(self, img, path): + try: + info = self.load_model(path) + except Exception: + errors.report(f"Unable to load DAT model {path}", exc_info=True) + return img + + model_descriptor = modelloader.load_spandrel_model( + info.local_data_path, + device=self.device, + prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling), + expected_architecture="DAT", + ) + return upscale_with_model( + model_descriptor, + img, + tile_size=opts.DAT_tile, + tile_overlap=opts.DAT_tile_overlap, + ) + + def load_model(self, path): + for scaler in self.scalers: + if scaler.data_path == path: + if scaler.local_data_path.startswith("http"): + scaler.local_data_path = modelloader.load_file_from_url( + scaler.data_path, + model_dir=self.model_download_path, + ) + if not os.path.exists(scaler.local_data_path): + raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}") + return scaler + raise ValueError(f"Unable to find model info: {path}") + + +def get_dat_models(scaler): + return [ + UpscalerData( + name="DAT x2", + path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth", + scale=2, + upscaler=scaler, + ), + UpscalerData( + name="DAT x3", + path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth", + scale=3, + upscaler=scaler, + ), + UpscalerData( + name="DAT x4", + path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth", + scale=4, + upscaler=scaler, + ), + ] diff --git a/modules/devices.py b/modules/devices.py index ea1f712f..28c0c54d 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -3,7 +3,7 @@ import contextlib from functools import lru_cache import torch -from modules import errors, shared +from modules import errors, shared, npu_specific if sys.platform == "darwin": from modules import mac_specific @@ -23,6 +23,23 @@ def has_mps() -> bool: return mac_specific.has_mps +def cuda_no_autocast(device_id=None) -> bool: + if device_id is None: + device_id = get_cuda_device_id() + return ( + torch.cuda.get_device_capability(device_id) == (7, 5) + and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16") + ) + + +def get_cuda_device_id(): + return ( + int(shared.cmd_opts.device_id) + if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() + else 0 + ) or torch.cuda.current_device() + + def get_cuda_device_string(): if shared.cmd_opts.device_id is not None: return f"cuda:{shared.cmd_opts.device_id}" @@ -40,6 +57,9 @@ def get_optimal_device_name(): if has_xpu(): return xpu_specific.get_xpu_device_string() + if npu_specific.has_npu: + return npu_specific.get_npu_device_string() + return "cpu" @@ -67,14 +87,23 @@ def torch_gc(): if has_xpu(): xpu_specific.torch_xpu_gc() + if npu_specific.has_npu: + torch_npu_set_device() + npu_specific.torch_npu_gc() + + +def torch_npu_set_device(): + # Work around due to bug in torch_npu, revert me after fixed, @see https://gitee.com/ascend/pytorch/issues/I8KECW?from=project-issue + if npu_specific.has_npu: + torch.npu.set_device(0) + def enable_tf32(): if torch.cuda.is_available(): # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 - device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device() - if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"): + if cuda_no_autocast(): torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True @@ -84,6 +113,7 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") cpu: torch.device = torch.device("cpu") +fp8: bool = False device: torch.device = None device_interrogate: torch.device = None device_gfpgan: torch.device = None @@ -92,6 +122,7 @@ device_codeformer: torch.device = None dtype: torch.dtype = torch.float16 dtype_vae: torch.dtype = torch.float16 dtype_unet: torch.dtype = torch.float16 +dtype_inference: torch.dtype = torch.float16 unet_needs_upcast = False @@ -104,15 +135,89 @@ def cond_cast_float(input): nv_rng = None +patch_module_list = [ + torch.nn.Linear, + torch.nn.Conv2d, + torch.nn.MultiheadAttention, + torch.nn.GroupNorm, + torch.nn.LayerNorm, +] + + +def manual_cast_forward(target_dtype): + def forward_wrapper(self, *args, **kwargs): + if any( + isinstance(arg, torch.Tensor) and arg.dtype != target_dtype + for arg in args + ): + args = [arg.to(target_dtype) if isinstance(arg, torch.Tensor) else arg for arg in args] + kwargs = {k: v.to(target_dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()} + + org_dtype = target_dtype + for param in self.parameters(): + if param.dtype != target_dtype: + org_dtype = param.dtype + break + + if org_dtype != target_dtype: + self.to(target_dtype) + result = self.org_forward(*args, **kwargs) + if org_dtype != target_dtype: + self.to(org_dtype) + + if target_dtype != dtype_inference: + if isinstance(result, tuple): + result = tuple( + i.to(dtype_inference) + if isinstance(i, torch.Tensor) + else i + for i in result + ) + elif isinstance(result, torch.Tensor): + result = result.to(dtype_inference) + return result + return forward_wrapper + + +@contextlib.contextmanager +def manual_cast(target_dtype): + applied = False + for module_type in patch_module_list: + if hasattr(module_type, "org_forward"): + continue + applied = True + org_forward = module_type.forward + if module_type == torch.nn.MultiheadAttention: + module_type.forward = manual_cast_forward(torch.float32) + else: + module_type.forward = manual_cast_forward(target_dtype) + module_type.org_forward = org_forward + try: + yield None + finally: + if applied: + for module_type in patch_module_list: + if hasattr(module_type, "org_forward"): + module_type.forward = module_type.org_forward + delattr(module_type, "org_forward") def autocast(disable=False): if disable: return contextlib.nullcontext() - if dtype == torch.float32 or shared.cmd_opts.precision == "full": + if fp8 and device==cpu: + return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True) + + if fp8 and dtype_inference == torch.float32: + return manual_cast(dtype) + + if dtype == torch.float32 or dtype_inference == torch.float32: return contextlib.nullcontext() + if has_xpu() or has_mps() or cuda_no_autocast(): + return manual_cast(dtype) + return torch.autocast("cuda") @@ -164,4 +269,3 @@ def first_time_calculation(): x = torch.zeros((1, 1, 3, 3)).to(device, dtype) conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype) conv2d(x) - diff --git a/modules/errors.py b/modules/errors.py index eb234a83..48aa13a1 100644 --- a/modules/errors.py +++ b/modules/errors.py @@ -107,8 +107,8 @@ def check_versions(): import torch
import gradio
- expected_torch_version = "2.0.0"
- expected_xformers_version = "0.0.20"
+ expected_torch_version = "2.1.2"
+ expected_xformers_version = "0.0.23.post1"
expected_gradio_version = "3.41.2"
if version.parse(torch.__version__) < version.parse(expected_torch_version):
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index 02a1727d..70041ab0 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -1,121 +1,7 @@ -import sys
-
-import numpy as np
-import torch
-from PIL import Image
-
-import modules.esrgan_model_arch as arch
-from modules import modelloader, images, devices
+from modules import modelloader, devices, errors
from modules.shared import opts
from modules.upscaler import Upscaler, UpscalerData
-
-
-def mod2normal(state_dict):
- # this code is copied from https://github.com/victorca25/iNNfer
- if 'conv_first.weight' in state_dict:
- crt_net = {}
- items = list(state_dict)
-
- crt_net['model.0.weight'] = state_dict['conv_first.weight']
- crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
- for k in items.copy():
- if 'RDB' in k:
- ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
- if '.weight' in k:
- ori_k = ori_k.replace('.weight', '.0.weight')
- elif '.bias' in k:
- ori_k = ori_k.replace('.bias', '.0.bias')
- crt_net[ori_k] = state_dict[k]
- items.remove(k)
-
- crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
- crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
- crt_net['model.3.weight'] = state_dict['upconv1.weight']
- crt_net['model.3.bias'] = state_dict['upconv1.bias']
- crt_net['model.6.weight'] = state_dict['upconv2.weight']
- crt_net['model.6.bias'] = state_dict['upconv2.bias']
- crt_net['model.8.weight'] = state_dict['HRconv.weight']
- crt_net['model.8.bias'] = state_dict['HRconv.bias']
- crt_net['model.10.weight'] = state_dict['conv_last.weight']
- crt_net['model.10.bias'] = state_dict['conv_last.bias']
- state_dict = crt_net
- return state_dict
-
-
-def resrgan2normal(state_dict, nb=23):
- # this code is copied from https://github.com/victorca25/iNNfer
- if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
- re8x = 0
- crt_net = {}
- items = list(state_dict)
-
- crt_net['model.0.weight'] = state_dict['conv_first.weight']
- crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
- for k in items.copy():
- if "rdb" in k:
- ori_k = k.replace('body.', 'model.1.sub.')
- ori_k = ori_k.replace('.rdb', '.RDB')
- if '.weight' in k:
- ori_k = ori_k.replace('.weight', '.0.weight')
- elif '.bias' in k:
- ori_k = ori_k.replace('.bias', '.0.bias')
- crt_net[ori_k] = state_dict[k]
- items.remove(k)
-
- crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
- crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
- crt_net['model.3.weight'] = state_dict['conv_up1.weight']
- crt_net['model.3.bias'] = state_dict['conv_up1.bias']
- crt_net['model.6.weight'] = state_dict['conv_up2.weight']
- crt_net['model.6.bias'] = state_dict['conv_up2.bias']
-
- if 'conv_up3.weight' in state_dict:
- # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
- re8x = 3
- crt_net['model.9.weight'] = state_dict['conv_up3.weight']
- crt_net['model.9.bias'] = state_dict['conv_up3.bias']
-
- crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
- crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
- crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
- crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
-
- state_dict = crt_net
- return state_dict
-
-
-def infer_params(state_dict):
- # this code is copied from https://github.com/victorca25/iNNfer
- scale2x = 0
- scalemin = 6
- n_uplayer = 0
- plus = False
-
- for block in list(state_dict):
- parts = block.split(".")
- n_parts = len(parts)
- if n_parts == 5 and parts[2] == "sub":
- nb = int(parts[3])
- elif n_parts == 3:
- part_num = int(parts[1])
- if (part_num > scalemin
- and parts[0] == "model"
- and parts[2] == "weight"):
- scale2x += 1
- if part_num > n_uplayer:
- n_uplayer = part_num
- out_nc = state_dict[block].shape[0]
- if not plus and "conv1x1" in block:
- plus = True
-
- nf = state_dict["model.0.weight"].shape[0]
- in_nc = state_dict["model.0.weight"].shape[1]
- out_nc = out_nc
- scale = 2 ** scale2x
-
- return in_nc, out_nc, nf, nb, plus, scale
+from modules.upscaler_utils import upscale_with_model
class UpscalerESRGAN(Upscaler):
@@ -143,12 +29,11 @@ class UpscalerESRGAN(Upscaler): def do_upscale(self, img, selected_model):
try:
model = self.load_model(selected_model)
- except Exception as e:
- print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
+ except Exception:
+ errors.report(f"Unable to load ESRGAN model {selected_model}", exc_info=True)
return img
model.to(devices.device_esrgan)
- img = esrgan_upscale(model, img)
- return img
+ return esrgan_upscale(model, img)
def load_model(self, path: str):
if path.startswith("http"):
@@ -161,69 +46,17 @@ class UpscalerESRGAN(Upscaler): else:
filename = path
- state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
-
- if "params_ema" in state_dict:
- state_dict = state_dict["params_ema"]
- elif "params" in state_dict:
- state_dict = state_dict["params"]
- num_conv = 16 if "realesr-animevideov3" in filename else 32
- model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
- model.load_state_dict(state_dict)
- model.eval()
- return model
-
- if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
- nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
- state_dict = resrgan2normal(state_dict, nb)
- elif "conv_first.weight" in state_dict:
- state_dict = mod2normal(state_dict)
- elif "model.0.weight" not in state_dict:
- raise Exception("The file is not a recognized ESRGAN model.")
-
- in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
-
- model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
- model.load_state_dict(state_dict)
- model.eval()
-
- return model
-
-
-def upscale_without_tiling(model, img):
- img = np.array(img)
- img = img[:, :, ::-1]
- img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
- img = torch.from_numpy(img).float()
- img = img.unsqueeze(0).to(devices.device_esrgan)
- with torch.no_grad():
- output = model(img)
- output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
- output = 255. * np.moveaxis(output, 0, 2)
- output = output.astype(np.uint8)
- output = output[:, :, ::-1]
- return Image.fromarray(output, 'RGB')
+ return modelloader.load_spandrel_model(
+ filename,
+ device=('cpu' if devices.device_esrgan.type == 'mps' else None),
+ expected_architecture='ESRGAN',
+ )
def esrgan_upscale(model, img):
- if opts.ESRGAN_tile == 0:
- return upscale_without_tiling(model, img)
-
- grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
- newtiles = []
- scale_factor = 1
-
- for y, h, row in grid.tiles:
- newrow = []
- for tiledata in row:
- x, w, tile = tiledata
-
- output = upscale_without_tiling(model, tile)
- scale_factor = output.width // tile.width
-
- newrow.append([x * scale_factor, w * scale_factor, output])
- newtiles.append([y * scale_factor, h * scale_factor, newrow])
-
- newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
- output = images.combine_grid(newgrid)
- return output
+ return upscale_with_model(
+ model,
+ img,
+ tile_size=opts.ESRGAN_tile,
+ tile_overlap=opts.ESRGAN_tile_overlap,
+ )
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py deleted file mode 100644 index 2b9888ba..00000000 --- a/modules/esrgan_model_arch.py +++ /dev/null @@ -1,465 +0,0 @@ -# this file is adapted from https://github.com/victorca25/iNNfer
-
-from collections import OrderedDict
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-####################
-# RRDBNet Generator
-####################
-
-class RRDBNet(nn.Module):
- def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
- act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
- finalact=None, gaussian_noise=False, plus=False):
- super(RRDBNet, self).__init__()
- n_upscale = int(math.log(upscale, 2))
- if upscale == 3:
- n_upscale = 1
-
- self.resrgan_scale = 0
- if in_nc % 16 == 0:
- self.resrgan_scale = 1
- elif in_nc != 4 and in_nc % 4 == 0:
- self.resrgan_scale = 2
-
- fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
- rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
- norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
- gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
- LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
-
- if upsample_mode == 'upconv':
- upsample_block = upconv_block
- elif upsample_mode == 'pixelshuffle':
- upsample_block = pixelshuffle_block
- else:
- raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
- if upscale == 3:
- upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
- else:
- upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
- HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
- HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
-
- outact = act(finalact) if finalact else None
-
- self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
- *upsampler, HR_conv0, HR_conv1, outact)
-
- def forward(self, x, outm=None):
- if self.resrgan_scale == 1:
- feat = pixel_unshuffle(x, scale=4)
- elif self.resrgan_scale == 2:
- feat = pixel_unshuffle(x, scale=2)
- else:
- feat = x
-
- return self.model(feat)
-
-
-class RRDB(nn.Module):
- """
- Residual in Residual Dense Block
- (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
- """
-
- def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
- norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
- spectral_norm=False, gaussian_noise=False, plus=False):
- super(RRDB, self).__init__()
- # This is for backwards compatibility with existing models
- if nr == 3:
- self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus)
- self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus)
- self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus)
- else:
- RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
- norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
- gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
- self.RDBs = nn.Sequential(*RDB_list)
-
- def forward(self, x):
- if hasattr(self, 'RDB1'):
- out = self.RDB1(x)
- out = self.RDB2(out)
- out = self.RDB3(out)
- else:
- out = self.RDBs(x)
- return out * 0.2 + x
-
-
-class ResidualDenseBlock_5C(nn.Module):
- """
- Residual Dense Block
- The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
- Modified options that can be used:
- - "Partial Convolution based Padding" arXiv:1811.11718
- - "Spectral normalization" arXiv:1802.05957
- - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
- {Rakotonirina} and A. {Rasoanaivo}
- """
-
- def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
- norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
- spectral_norm=False, gaussian_noise=False, plus=False):
- super(ResidualDenseBlock_5C, self).__init__()
-
- self.noise = GaussianNoise() if gaussian_noise else None
- self.conv1x1 = conv1x1(nf, gc) if plus else None
-
- self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
- if mode == 'CNA':
- last_act = None
- else:
- last_act = act_type
- self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
- norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
- spectral_norm=spectral_norm)
-
- def forward(self, x):
- x1 = self.conv1(x)
- x2 = self.conv2(torch.cat((x, x1), 1))
- if self.conv1x1:
- x2 = x2 + self.conv1x1(x)
- x3 = self.conv3(torch.cat((x, x1, x2), 1))
- x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
- if self.conv1x1:
- x4 = x4 + x2
- x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
- if self.noise:
- return self.noise(x5.mul(0.2) + x)
- else:
- return x5 * 0.2 + x
-
-
-####################
-# ESRGANplus
-####################
-
-class GaussianNoise(nn.Module):
- def __init__(self, sigma=0.1, is_relative_detach=False):
- super().__init__()
- self.sigma = sigma
- self.is_relative_detach = is_relative_detach
- self.noise = torch.tensor(0, dtype=torch.float)
-
- def forward(self, x):
- if self.training and self.sigma != 0:
- self.noise = self.noise.to(x.device)
- scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
- sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
- x = x + sampled_noise
- return x
-
-def conv1x1(in_planes, out_planes, stride=1):
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-####################
-# SRVGGNetCompact
-####################
-
-class SRVGGNetCompact(nn.Module):
- """A compact VGG-style network structure for super-resolution.
- This class is copied from https://github.com/xinntao/Real-ESRGAN
- """
-
- def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
- super(SRVGGNetCompact, self).__init__()
- self.num_in_ch = num_in_ch
- self.num_out_ch = num_out_ch
- self.num_feat = num_feat
- self.num_conv = num_conv
- self.upscale = upscale
- self.act_type = act_type
-
- self.body = nn.ModuleList()
- # the first conv
- self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
- # the first activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the body structure
- for _ in range(num_conv):
- self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
- # activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the last conv
- self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
- # upsample
- self.upsampler = nn.PixelShuffle(upscale)
-
- def forward(self, x):
- out = x
- for i in range(0, len(self.body)):
- out = self.body[i](out)
-
- out = self.upsampler(out)
- # add the nearest upsampled image, so that the network learns the residual
- base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
- out += base
- return out
-
-
-####################
-# Upsampler
-####################
-
-class Upsample(nn.Module):
- r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
- The input data is assumed to be of the form
- `minibatch x channels x [optional depth] x [optional height] x width`.
- """
-
- def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
- super(Upsample, self).__init__()
- if isinstance(scale_factor, tuple):
- self.scale_factor = tuple(float(factor) for factor in scale_factor)
- else:
- self.scale_factor = float(scale_factor) if scale_factor else None
- self.mode = mode
- self.size = size
- self.align_corners = align_corners
-
- def forward(self, x):
- return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
-
- def extra_repr(self):
- if self.scale_factor is not None:
- info = f'scale_factor={self.scale_factor}'
- else:
- info = f'size={self.size}'
- info += f', mode={self.mode}'
- return info
-
-
-def pixel_unshuffle(x, scale):
- """ Pixel unshuffle.
- Args:
- x (Tensor): Input feature with shape (b, c, hh, hw).
- scale (int): Downsample ratio.
- Returns:
- Tensor: the pixel unshuffled feature.
- """
- b, c, hh, hw = x.size()
- out_channel = c * (scale**2)
- assert hh % scale == 0 and hw % scale == 0
- h = hh // scale
- w = hw // scale
- x_view = x.view(b, c, h, scale, w, scale)
- return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-
-def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
- pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
- """
- Pixel shuffle layer
- (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
- Neural Network, CVPR17)
- """
- conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
- pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
- pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
- n = norm(norm_type, out_nc) if norm_type else None
- a = act(act_type) if act_type else None
- return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
- pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
- """ Upconv layer """
- upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
- upsample = Upsample(scale_factor=upscale_factor, mode=mode)
- conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
- pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
- return sequential(upsample, conv)
-
-
-
-
-
-
-
-
-####################
-# Basic blocks
-####################
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
- """Make layers by stacking the same blocks.
- Args:
- basic_block (nn.module): nn.module class for basic block. (block)
- num_basic_block (int): number of blocks. (n_layers)
- Returns:
- nn.Sequential: Stacked blocks in nn.Sequential.
- """
- layers = []
- for _ in range(num_basic_block):
- layers.append(basic_block(**kwarg))
- return nn.Sequential(*layers)
-
-
-def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
- """ activation helper """
- act_type = act_type.lower()
- if act_type == 'relu':
- layer = nn.ReLU(inplace)
- elif act_type in ('leakyrelu', 'lrelu'):
- layer = nn.LeakyReLU(neg_slope, inplace)
- elif act_type == 'prelu':
- layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
- elif act_type == 'tanh': # [-1, 1] range output
- layer = nn.Tanh()
- elif act_type == 'sigmoid': # [0, 1] range output
- layer = nn.Sigmoid()
- else:
- raise NotImplementedError(f'activation layer [{act_type}] is not found')
- return layer
-
-
-class Identity(nn.Module):
- def __init__(self, *kwargs):
- super(Identity, self).__init__()
-
- def forward(self, x, *kwargs):
- return x
-
-
-def norm(norm_type, nc):
- """ Return a normalization layer """
- norm_type = norm_type.lower()
- if norm_type == 'batch':
- layer = nn.BatchNorm2d(nc, affine=True)
- elif norm_type == 'instance':
- layer = nn.InstanceNorm2d(nc, affine=False)
- elif norm_type == 'none':
- def norm_layer(x): return Identity()
- else:
- raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
- return layer
-
-
-def pad(pad_type, padding):
- """ padding layer helper """
- pad_type = pad_type.lower()
- if padding == 0:
- return None
- if pad_type == 'reflect':
- layer = nn.ReflectionPad2d(padding)
- elif pad_type == 'replicate':
- layer = nn.ReplicationPad2d(padding)
- elif pad_type == 'zero':
- layer = nn.ZeroPad2d(padding)
- else:
- raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
- return layer
-
-
-def get_valid_padding(kernel_size, dilation):
- kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
- padding = (kernel_size - 1) // 2
- return padding
-
-
-class ShortcutBlock(nn.Module):
- """ Elementwise sum the output of a submodule to its input """
- def __init__(self, submodule):
- super(ShortcutBlock, self).__init__()
- self.sub = submodule
-
- def forward(self, x):
- output = x + self.sub(x)
- return output
-
- def __repr__(self):
- return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
-
-
-def sequential(*args):
- """ Flatten Sequential. It unwraps nn.Sequential. """
- if len(args) == 1:
- if isinstance(args[0], OrderedDict):
- raise NotImplementedError('sequential does not support OrderedDict input.')
- return args[0] # No sequential is needed.
- modules = []
- for module in args:
- if isinstance(module, nn.Sequential):
- for submodule in module.children():
- modules.append(submodule)
- elif isinstance(module, nn.Module):
- modules.append(module)
- return nn.Sequential(*modules)
-
-
-def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
- pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
- spectral_norm=False):
- """ Conv layer with padding, normalization, activation """
- assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
- padding = get_valid_padding(kernel_size, dilation)
- p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
- padding = padding if pad_type == 'zero' else 0
-
- if convtype=='PartialConv2D':
- from torchvision.ops import PartialConv2d # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
- c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
- elif convtype=='DeformConv2D':
- from torchvision.ops import DeformConv2d # not tested
- c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
- elif convtype=='Conv3D':
- c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
- else:
- c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
- dilation=dilation, bias=bias, groups=groups)
-
- if spectral_norm:
- c = nn.utils.spectral_norm(c)
-
- a = act(act_type) if act_type else None
- if 'CNA' in mode:
- n = norm(norm_type, out_nc) if norm_type else None
- return sequential(p, c, n, a)
- elif mode == 'NAC':
- if norm_type is None and act_type is not None:
- a = act(act_type, inplace=False)
- n = norm(norm_type, in_nc) if norm_type else None
- return sequential(n, a, p, c)
diff --git a/modules/extensions.py b/modules/extensions.py index 1899cd52..04bda297 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -32,11 +32,12 @@ class ExtensionMetadata: self.config = configparser.ConfigParser()
filepath = os.path.join(path, self.filename)
- if os.path.isfile(filepath):
- try:
- self.config.read(filepath)
- except Exception:
- errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True)
+ # `self.config.read()` will quietly swallow OSErrors (which FileNotFoundError is),
+ # so no need to check whether the file exists beforehand.
+ try:
+ self.config.read(filepath)
+ except Exception:
+ errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True)
self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name)
self.canonical_name = canonical_name.lower().strip()
@@ -223,13 +224,16 @@ def list_extensions(): # check for requirements
for extension in extensions:
+ if not extension.enabled:
+ continue
+
for req in extension.metadata.requires:
required_extension = loaded_extensions.get(req)
if required_extension is None:
errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False)
continue
- if not extension.enabled:
+ if not required_extension.enabled:
errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False)
continue
diff --git a/modules/extra_networks.py b/modules/extra_networks.py index b9533677..04249dff 100644 --- a/modules/extra_networks.py +++ b/modules/extra_networks.py @@ -206,7 +206,7 @@ def parse_prompts(prompts): return res, extra_data
-def get_user_metadata(filename):
+def get_user_metadata(filename, lister=None):
if filename is None:
return {}
@@ -215,7 +215,8 @@ def get_user_metadata(filename): metadata = {}
try:
- if os.path.isfile(metadata_filename):
+ exists = lister.exists(metadata_filename) if lister else os.path.exists(metadata_filename)
+ if exists:
with open(metadata_filename, "r", encoding="utf8") as file:
metadata = json.load(file)
except Exception as e:
diff --git a/modules/face_restoration_utils.py b/modules/face_restoration_utils.py new file mode 100644 index 00000000..1cbac236 --- /dev/null +++ b/modules/face_restoration_utils.py @@ -0,0 +1,180 @@ +from __future__ import annotations + +import logging +import os +from functools import cached_property +from typing import TYPE_CHECKING, Callable + +import cv2 +import numpy as np +import torch + +from modules import devices, errors, face_restoration, shared + +if TYPE_CHECKING: + from facexlib.utils.face_restoration_helper import FaceRestoreHelper + +logger = logging.getLogger(__name__) + + +def bgr_image_to_rgb_tensor(img: np.ndarray) -> torch.Tensor: + """Convert a BGR NumPy image in [0..1] range to a PyTorch RGB float32 tensor.""" + assert img.shape[2] == 3, "image must be RGB" + if img.dtype == "float64": + img = img.astype("float32") + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + return torch.from_numpy(img.transpose(2, 0, 1)).float() + + +def rgb_tensor_to_bgr_image(tensor: torch.Tensor, *, min_max=(0.0, 1.0)) -> np.ndarray: + """ + Convert a PyTorch RGB tensor in range `min_max` to a BGR NumPy image in [0..1] range. + """ + tensor = tensor.squeeze(0).float().detach().cpu().clamp_(*min_max) + tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) + assert tensor.dim() == 3, "tensor must be RGB" + img_np = tensor.numpy().transpose(1, 2, 0) + if img_np.shape[2] == 1: # gray image, no RGB/BGR required + return np.squeeze(img_np, axis=2) + return cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB) + + +def create_face_helper(device) -> FaceRestoreHelper: + from facexlib.detection import retinaface + from facexlib.utils.face_restoration_helper import FaceRestoreHelper + if hasattr(retinaface, 'device'): + retinaface.device = device + return FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + use_parse=True, + device=device, + ) + + +def restore_with_face_helper( + np_image: np.ndarray, + face_helper: FaceRestoreHelper, + restore_face: Callable[[torch.Tensor], torch.Tensor], +) -> np.ndarray: + """ + Find faces in the image using face_helper, restore them using restore_face, and paste them back into the image. + + `restore_face` should take a cropped face image and return a restored face image. + """ + from torchvision.transforms.functional import normalize + np_image = np_image[:, :, ::-1] + original_resolution = np_image.shape[0:2] + + try: + logger.debug("Detecting faces...") + face_helper.clean_all() + face_helper.read_image(np_image) + face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) + face_helper.align_warp_face() + logger.debug("Found %d faces, restoring", len(face_helper.cropped_faces)) + for cropped_face in face_helper.cropped_faces: + cropped_face_t = bgr_image_to_rgb_tensor(cropped_face / 255.0) + normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer) + + try: + with torch.no_grad(): + cropped_face_t = restore_face(cropped_face_t) + devices.torch_gc() + except Exception: + errors.report('Failed face-restoration inference', exc_info=True) + + restored_face = rgb_tensor_to_bgr_image(cropped_face_t, min_max=(-1, 1)) + restored_face = (restored_face * 255.0).astype('uint8') + face_helper.add_restored_face(restored_face) + + logger.debug("Merging restored faces into image") + face_helper.get_inverse_affine(None) + img = face_helper.paste_faces_to_input_image() + img = img[:, :, ::-1] + if original_resolution != img.shape[0:2]: + img = cv2.resize( + img, + (0, 0), + fx=original_resolution[1] / img.shape[1], + fy=original_resolution[0] / img.shape[0], + interpolation=cv2.INTER_LINEAR, + ) + logger.debug("Face restoration complete") + finally: + face_helper.clean_all() + return img + + +class CommonFaceRestoration(face_restoration.FaceRestoration): + net: torch.Module | None + model_url: str + model_download_name: str + + def __init__(self, model_path: str): + super().__init__() + self.net = None + self.model_path = model_path + os.makedirs(model_path, exist_ok=True) + + @cached_property + def face_helper(self) -> FaceRestoreHelper: + return create_face_helper(self.get_device()) + + def send_model_to(self, device): + if self.net: + logger.debug("Sending %s to %s", self.net, device) + self.net.to(device) + if self.face_helper: + logger.debug("Sending face helper to %s", device) + self.face_helper.face_det.to(device) + self.face_helper.face_parse.to(device) + + def get_device(self): + raise NotImplementedError("get_device must be implemented by subclasses") + + def load_net(self) -> torch.Module: + raise NotImplementedError("load_net must be implemented by subclasses") + + def restore_with_helper( + self, + np_image: np.ndarray, + restore_face: Callable[[torch.Tensor], torch.Tensor], + ) -> np.ndarray: + try: + if self.net is None: + self.net = self.load_net() + except Exception: + logger.warning("Unable to load face-restoration model", exc_info=True) + return np_image + + try: + self.send_model_to(self.get_device()) + return restore_with_face_helper(np_image, self.face_helper, restore_face) + finally: + if shared.opts.face_restoration_unload: + self.send_model_to(devices.cpu) + + +def patch_facexlib(dirname: str) -> None: + import facexlib.detection + import facexlib.parsing + + det_facex_load_file_from_url = facexlib.detection.load_file_from_url + par_facex_load_file_from_url = facexlib.parsing.load_file_from_url + + def update_kwargs(kwargs): + return dict(kwargs, save_dir=dirname, model_dir=None) + + def facex_load_file_from_url(**kwargs): + return det_facex_load_file_from_url(**update_kwargs(kwargs)) + + def facex_load_file_from_url2(**kwargs): + return par_facex_load_file_from_url(**update_kwargs(kwargs)) + + facexlib.detection.load_file_from_url = facex_load_file_from_url + facexlib.parsing.load_file_from_url = facex_load_file_from_url2 diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py index 01d668ec..445b0409 100644 --- a/modules/gfpgan_model.py +++ b/modules/gfpgan_model.py @@ -1,125 +1,71 @@ +from __future__ import annotations
+
+import logging
import os
-import facexlib
-import gfpgan
+import torch
-import modules.face_restoration
-from modules import paths, shared, devices, modelloader, errors
+from modules import (
+ devices,
+ errors,
+ face_restoration,
+ face_restoration_utils,
+ modelloader,
+ shared,
+)
-model_dir = "GFPGAN"
-user_path = None
-model_path = os.path.join(paths.models_path, model_dir)
-model_file_path = None
+logger = logging.getLogger(__name__)
model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
-have_gfpgan = False
-loaded_gfpgan_model = None
-
-
-def gfpgann():
- global loaded_gfpgan_model
- global model_path
- global model_file_path
- if loaded_gfpgan_model is not None:
- loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
- return loaded_gfpgan_model
-
- if gfpgan_constructor is None:
- return None
-
- models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth'])
-
- if len(models) == 1 and models[0].startswith("http"):
- model_file = models[0]
- elif len(models) != 0:
- gfp_models = []
- for item in models:
- if 'GFPGAN' in os.path.basename(item):
- gfp_models.append(item)
- latest_file = max(gfp_models, key=os.path.getctime)
- model_file = latest_file
- else:
- print("Unable to load gfpgan model!")
- return None
-
- if hasattr(facexlib.detection.retinaface, 'device'):
- facexlib.detection.retinaface.device = devices.device_gfpgan
- model_file_path = model_file
- model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
- loaded_gfpgan_model = model
-
- return model
-
-
-def send_model_to(model, device):
- model.gfpgan.to(device)
- model.face_helper.face_det.to(device)
- model.face_helper.face_parse.to(device)
+model_download_name = "GFPGANv1.4.pth"
+gfpgan_face_restorer: face_restoration.FaceRestoration | None = None
+
+
+class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
+ def name(self):
+ return "GFPGAN"
+
+ def get_device(self):
+ return devices.device_gfpgan
+
+ def load_net(self) -> torch.Module:
+ for model_path in modelloader.load_models(
+ model_path=self.model_path,
+ model_url=model_url,
+ command_path=self.model_path,
+ download_name=model_download_name,
+ ext_filter=['.pth'],
+ ):
+ if 'GFPGAN' in os.path.basename(model_path):
+ model = modelloader.load_spandrel_model(
+ model_path,
+ device=self.get_device(),
+ expected_architecture='GFPGAN',
+ ).model
+ model.different_w = True # see https://github.com/chaiNNer-org/spandrel/pull/81
+ return model
+ raise ValueError("No GFPGAN model found")
+
+ def restore(self, np_image):
+ def restore_face(cropped_face_t):
+ assert self.net is not None
+ return self.net(cropped_face_t, return_rgb=False)[0]
+
+ return self.restore_with_helper(np_image, restore_face)
def gfpgan_fix_faces(np_image):
- model = gfpgann()
- if model is None:
- return np_image
-
- send_model_to(model, devices.device_gfpgan)
-
- np_image_bgr = np_image[:, :, ::-1]
- cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
- np_image = gfpgan_output_bgr[:, :, ::-1]
-
- model.face_helper.clean_all()
-
- if shared.opts.face_restoration_unload:
- send_model_to(model, devices.cpu)
-
+ if gfpgan_face_restorer:
+ return gfpgan_face_restorer.restore(np_image)
+ logger.warning("GFPGAN face restorer not set up")
return np_image
-gfpgan_constructor = None
+def setup_model(dirname: str) -> None:
+ global gfpgan_face_restorer
-
-def setup_model(dirname):
try:
- os.makedirs(model_path, exist_ok=True)
- from gfpgan import GFPGANer
- from facexlib import detection, parsing # noqa: F401
- global user_path
- global have_gfpgan
- global gfpgan_constructor
- global model_file_path
-
- facexlib_path = model_path
-
- if dirname is not None:
- facexlib_path = dirname
-
- load_file_from_url_orig = gfpgan.utils.load_file_from_url
- facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
- facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
-
- def my_load_file_from_url(**kwargs):
- return load_file_from_url_orig(**dict(kwargs, model_dir=model_file_path))
-
- def facex_load_file_from_url(**kwargs):
- return facex_load_file_from_url_orig(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
-
- def facex_load_file_from_url2(**kwargs):
- return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
-
- gfpgan.utils.load_file_from_url = my_load_file_from_url
- facexlib.detection.load_file_from_url = facex_load_file_from_url
- facexlib.parsing.load_file_from_url = facex_load_file_from_url2
- user_path = dirname
- have_gfpgan = True
- gfpgan_constructor = GFPGANer
-
- class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
- def name(self):
- return "GFPGAN"
-
- def restore(self, np_image):
- return gfpgan_fix_faces(np_image)
-
- shared.face_restorers.append(FaceRestorerGFPGAN())
+ face_restoration_utils.patch_facexlib(dirname)
+ gfpgan_face_restorer = FaceRestorerGFPGAN(model_path=dirname)
+ shared.face_restorers.append(gfpgan_face_restorer)
except Exception:
errors.report("Error setting up GFPGAN", exc_info=True)
diff --git a/modules/hashes.py b/modules/hashes.py index b7a33b42..d22e5fad 100644 --- a/modules/hashes.py +++ b/modules/hashes.py @@ -21,7 +21,10 @@ def calculate_sha256(filename): def sha256_from_cache(filename, title, use_addnet_hash=False):
hashes = cache("hashes-addnet") if use_addnet_hash else cache("hashes")
- ondisk_mtime = os.path.getmtime(filename)
+ try:
+ ondisk_mtime = os.path.getmtime(filename)
+ except FileNotFoundError:
+ return None
if title not in hashes:
return None
diff --git a/modules/hat_model.py b/modules/hat_model.py new file mode 100644 index 00000000..7f2abb41 --- /dev/null +++ b/modules/hat_model.py @@ -0,0 +1,43 @@ +import os
+import sys
+
+from modules import modelloader, devices
+from modules.shared import opts
+from modules.upscaler import Upscaler, UpscalerData
+from modules.upscaler_utils import upscale_with_model
+
+
+class UpscalerHAT(Upscaler):
+ def __init__(self, dirname):
+ self.name = "HAT"
+ self.scalers = []
+ self.user_path = dirname
+ super().__init__()
+ for file in self.find_models(ext_filter=[".pt", ".pth"]):
+ name = modelloader.friendly_name(file)
+ scale = 4 # TODO: scale might not be 4, but we can't know without loading the model
+ scaler_data = UpscalerData(name, file, upscaler=self, scale=scale)
+ self.scalers.append(scaler_data)
+
+ def do_upscale(self, img, selected_model):
+ try:
+ model = self.load_model(selected_model)
+ except Exception as e:
+ print(f"Unable to load HAT model {selected_model}: {e}", file=sys.stderr)
+ return img
+ model.to(devices.device_esrgan) # TODO: should probably be device_hat
+ return upscale_with_model(
+ model,
+ img,
+ tile_size=opts.ESRGAN_tile, # TODO: should probably be HAT_tile
+ tile_overlap=opts.ESRGAN_tile_overlap, # TODO: should probably be HAT_tile_overlap
+ )
+
+ def load_model(self, path: str):
+ if not os.path.isfile(path):
+ raise FileNotFoundError(f"Model file {path} not found")
+ return modelloader.load_spandrel_model(
+ path,
+ device=devices.device_esrgan, # TODO: should probably be device_hat
+ expected_architecture='HAT',
+ )
diff --git a/modules/images.py b/modules/images.py index daf4eebe..b6f2358c 100644 --- a/modules/images.py +++ b/modules/images.py @@ -61,12 +61,17 @@ def image_grid(imgs, batch_size=1, rows=None): return grid
-Grid = namedtuple("Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])
+class Grid(namedtuple("_Grid", ["tiles", "tile_w", "tile_h", "image_w", "image_h", "overlap"])):
+ @property
+ def tile_count(self) -> int:
+ """
+ The total number of tiles in the grid.
+ """
+ return sum(len(row[2]) for row in self.tiles)
-def split_grid(image, tile_w=512, tile_h=512, overlap=64):
- w = image.width
- h = image.height
+def split_grid(image: Image.Image, tile_w: int = 512, tile_h: int = 512, overlap: int = 64) -> Grid:
+ w, h = image.size
non_overlap_width = tile_w - overlap
non_overlap_height = tile_h - overlap
@@ -316,7 +321,7 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None): return res
-invalid_filename_chars = '<>:"/\\|?*\n\r\t'
+invalid_filename_chars = '#<>:"/\\|?*\n\r\t'
invalid_filename_prefix = ' '
invalid_filename_postfix = ' .'
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
@@ -791,3 +796,4 @@ def flatten(img, bgcolor): img = background
return img.convert('RGB')
+
diff --git a/modules/img2img.py b/modules/img2img.py index c583290a..f81405df 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -7,7 +7,7 @@ from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageErr import gradio as gr
from modules import images as imgutil
-from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
+from modules.infotext_utils import create_override_settings_dict, parse_generation_parameters
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, state
from modules.sd_models import get_closet_checkpoint_match
@@ -51,7 +51,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal if state.skipped:
state.skipped = False
- if state.interrupted:
+ if state.interrupted or state.stopping_generation:
break
try:
@@ -222,9 +222,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s if shared.opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
- if mask:
- p.extra_generation_params["Mask blur"] = mask_blur
-
with closing(p):
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
diff --git a/modules/generation_parameters_copypaste.py b/modules/infotext_utils.py index 4efe53e0..a938aa2a 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/infotext_utils.py @@ -4,12 +4,15 @@ import io import json
import os
import re
+import sys
import gradio as gr
from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, processing
+from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions
from PIL import Image
+sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__] # alias for old name
+
re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
re_param = re.compile(re_param_code)
re_imagesize = re.compile(r"^(\d+)x(\d+)$")
@@ -28,6 +31,19 @@ class ParamBinding: self.paste_field_names = paste_field_names or []
+class PasteField(tuple):
+ def __new__(cls, component, target, *, api=None):
+ return super().__new__(cls, (component, target))
+
+ def __init__(self, component, target, *, api=None):
+ super().__init__()
+
+ self.api = api
+ self.component = component
+ self.label = target if isinstance(target, str) else None
+ self.function = target if callable(target) else None
+
+
paste_fields: dict[str, dict] = {}
registered_param_bindings: list[ParamBinding] = []
@@ -84,6 +100,12 @@ def image_from_url_text(filedata): def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
+
+ if fields:
+ for i in range(len(fields)):
+ if not isinstance(fields[i], PasteField):
+ fields[i] = PasteField(*fields[i])
+
paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
# backwards compatibility for existing extensions
@@ -208,7 +230,7 @@ def restore_old_hires_fix_params(res): res['Hires resize-2'] = height
-def parse_generation_parameters(x: str):
+def parse_generation_parameters(x: str, skip_fields: list[str] | None = None):
"""parses generation parameters string, the one you see in text field under the picture in UI:
```
girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate
@@ -218,6 +240,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model returns a dict with field values
"""
+ if skip_fields is None:
+ skip_fields = shared.opts.infotext_skip_pasting
res = {}
@@ -290,6 +314,18 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "Hires negative prompt" not in res:
res["Hires negative prompt"] = ""
+ if "Mask mode" not in res:
+ res["Mask mode"] = "Inpaint masked"
+
+ if "Masked content" not in res:
+ res["Masked content"] = 'original'
+
+ if "Inpaint area" not in res:
+ res["Inpaint area"] = "Whole picture"
+
+ if "Masked area padding" not in res:
+ res["Masked area padding"] = 32
+
restore_old_hires_fix_params(res)
# Missing RNG means the default was set, which is GPU RNG
@@ -314,8 +350,19 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "VAE Decoder" not in res:
res["VAE Decoder"] = "Full"
- skip = set(shared.opts.infotext_skip_pasting)
- res = {k: v for k, v in res.items() if k not in skip}
+ if "FP8 weight" not in res:
+ res["FP8 weight"] = "Disable"
+
+ if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
+ res["Cache FP16 weight for LoRA"] = False
+
+ if "Emphasis" not in res:
+ res["Emphasis"] = "Original"
+
+ infotext_versions.backcompat(res)
+
+ for key in skip_fields:
+ res.pop(key, None)
return res
@@ -365,13 +412,57 @@ def create_override_settings_dict(text_pairs): return res
+def get_override_settings(params, *, skip_fields=None):
+ """Returns a list of settings overrides from the infotext parameters dictionary.
+
+ This function checks the `params` dictionary for any keys that correspond to settings in `shared.opts` and returns
+ a list of tuples containing the parameter name, setting name, and new value cast to correct type.
+
+ It checks for conditions before adding an override:
+ - ignores settings that match the current value
+ - ignores parameter keys present in skip_fields argument.
+
+ Example input:
+ {"Clip skip": "2"}
+
+ Example output:
+ [("Clip skip", "CLIP_stop_at_last_layers", 2)]
+ """
+
+ res = []
+
+ mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+ for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
+ if param_name in (skip_fields or {}):
+ continue
+
+ v = params.get(param_name, None)
+ if v is None:
+ continue
+
+ if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
+ continue
+
+ v = shared.opts.cast_value(setting_name, v)
+ current_value = getattr(shared.opts, setting_name, None)
+
+ if v == current_value:
+ continue
+
+ res.append((param_name, setting_name, v))
+
+ return res
+
+
def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
def paste_func(prompt):
if not prompt and not shared.cmd_opts.hide_ui_dir_config:
filename = os.path.join(data_path, "params.txt")
- if os.path.exists(filename):
+ try:
with open(filename, "r", encoding="utf8") as file:
prompt = file.read()
+ except OSError:
+ pass
params = parse_generation_parameters(prompt)
script_callbacks.infotext_pasted_callback(prompt, params)
@@ -393,6 +484,8 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, if valtype == bool and v == "False":
val = False
+ elif valtype == int:
+ val = float(v)
else:
val = valtype(v)
@@ -406,29 +499,9 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, already_handled_fields = {key: 1 for _, key in paste_fields}
def paste_settings(params):
- vals = {}
-
- mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
- for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
- if param_name in already_handled_fields:
- continue
-
- v = params.get(param_name, None)
- if v is None:
- continue
-
- if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
- continue
-
- v = shared.opts.cast_value(setting_name, v)
- current_value = getattr(shared.opts, setting_name, None)
-
- if v == current_value:
- continue
-
- vals[param_name] = v
+ vals = get_override_settings(params, skip_fields=already_handled_fields)
- vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
+ vals_pairs = [f"{infotext_text}: {value}" for infotext_text, setting_name, value in vals]
return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
diff --git a/modules/infotext_versions.py b/modules/infotext_versions.py new file mode 100644 index 00000000..23b45c3f --- /dev/null +++ b/modules/infotext_versions.py @@ -0,0 +1,42 @@ +from modules import shared
+from packaging import version
+import re
+
+
+v160 = version.parse("1.6.0")
+v170_tsnr = version.parse("v1.7.0-225")
+
+
+def parse_version(text):
+ if text is None:
+ return None
+
+ m = re.match(r'([^-]+-[^-]+)-.*', text)
+ if m:
+ text = m.group(1)
+
+ try:
+ return version.parse(text)
+ except Exception:
+ return None
+
+
+def backcompat(d):
+ """Checks infotext Version field, and enables backwards compatibility options according to it."""
+
+ if not shared.opts.auto_backcompat:
+ return
+
+ ver = parse_version(d.get("Version"))
+ if ver is None:
+ return
+
+ if ver < v160 and '[' in d.get('Prompt', ''):
+ d["Old prompt editing timelines"] = True
+
+ if ver < v160 and d.get('Sampler', '') in ('DDIM', 'PLMS'):
+ d["Pad conds v0"] = True
+
+ if ver < v170_tsnr:
+ d["Downcast alphas_cumprod"] = True
+
diff --git a/modules/initialize.py b/modules/initialize.py index ac95fc6f..f7313ff4 100644 --- a/modules/initialize.py +++ b/modules/initialize.py @@ -1,5 +1,6 @@ import importlib
import logging
+import os
import sys
import warnings
from threading import Thread
@@ -18,6 +19,7 @@ def imports(): warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+ os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
import gradio # noqa: F401
startup_timer.record("import gradio")
@@ -54,9 +56,6 @@ def initialize(): initialize_util.configure_sigint_handler()
initialize_util.configure_opts_onchange()
- from modules import modelloader
- modelloader.cleanup_models()
-
from modules import sd_models
sd_models.setup_model()
startup_timer.record("setup SD model")
@@ -143,13 +142,14 @@ def initialize_rest(*, reload_script_modules=False): its optimization may be None because the list of optimizaers has neet been filled
by that time, so we apply optimization again.
"""
+ from modules import devices
+ devices.torch_npu_set_device()
shared.sd_model # noqa: B018
if sd_hijack.current_optimizer is None:
sd_hijack.apply_optimizations()
- from modules import devices
devices.first_time_calculation()
if not shared.cmd_opts.skip_load_model_at_start:
Thread(target=load_model).start()
diff --git a/modules/initialize_util.py b/modules/initialize_util.py index 2e9b6d89..b6767138 100644 --- a/modules/initialize_util.py +++ b/modules/initialize_util.py @@ -177,6 +177,8 @@ def configure_opts_onchange(): shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)
+ shared.opts.onchange("fp8_storage", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
+ shared.opts.onchange("cache_fp16_weight", wrap_queued_call(lambda: sd_models.reload_model_weights(forced_reload=True)), call=False)
startup_timer.record("opts onchange")
diff --git a/modules/interrogate.py b/modules/interrogate.py index 3045560d..c93e7aa8 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -10,14 +10,14 @@ import torch.hub from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
-from modules import devices, paths, shared, lowvram, modelloader, errors
+from modules import devices, paths, shared, lowvram, modelloader, errors, torch_utils
blip_image_eval_size = 384
clip_model_name = 'ViT-L/14'
Category = namedtuple("Category", ["name", "topn", "items"])
-re_topn = re.compile(r"\.top(\d+)\.")
+re_topn = re.compile(r"\.top(\d+)$")
def category_types():
return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
@@ -131,7 +131,7 @@ class InterrogateModels: self.clip_model = self.clip_model.to(devices.device_interrogate)
- self.dtype = next(self.clip_model.parameters()).dtype
+ self.dtype = torch_utils.get_param(self.clip_model).dtype
def send_clip_to_ram(self):
if not shared.opts.interrogate_keep_models_in_memory:
diff --git a/modules/launch_utils.py b/modules/launch_utils.py index 29506f24..ad04eb36 100644 --- a/modules/launch_utils.py +++ b/modules/launch_utils.py @@ -27,8 +27,7 @@ dir_repos = "repositories" # Whether to default to printing command output
default_command_live = (os.environ.get('WEBUI_LAUNCH_LIVE_OUTPUT') == "1")
-if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
- os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False')
def check_python_version():
@@ -189,7 +188,7 @@ def git_clone(url, dir, name, commithash=None): return
try:
- run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
+ run(f'"{git}" clone --config core.filemode=false "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
except RuntimeError:
shutil.rmtree(dir, ignore_errors=True)
raise
@@ -245,11 +244,13 @@ def list_extensions(settings_file): settings = {}
try:
- if os.path.isfile(settings_file):
- with open(settings_file, "r", encoding="utf8") as file:
- settings = json.load(file)
+ with open(settings_file, "r", encoding="utf8") as file:
+ settings = json.load(file)
+ except FileNotFoundError:
+ pass
except Exception:
- errors.report("Could not load settings", exc_info=True)
+ errors.report(f'\nCould not load settings\nThe config file "{settings_file}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
+ os.replace(settings_file, os.path.join(script_path, "tmp", "config.json"))
disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
@@ -314,8 +315,8 @@ def requirements_met(requirements_file): def prepare_environment():
- torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu118")
- torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.1 torchvision==0.15.2 --extra-index-url {torch_index_url}")
+ torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121")
+ torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.1.2 torchvision==0.16.2 --extra-index-url {torch_index_url}")
if args.use_ipex:
if platform.system() == "Windows":
# The "Nuullll/intel-extension-for-pytorch" wheels were built from IPEX source for Intel Arc GPU: https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main
@@ -337,21 +338,22 @@ def prepare_environment(): torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://pytorch-extension.intel.com/release-whl/stable/xpu/us/")
torch_command = os.environ.get('TORCH_COMMAND', f"pip install torch==2.0.0a0 intel-extension-for-pytorch==2.0.110+gitba7f6c1 --extra-index-url {torch_index_url}")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
+ requirements_file_for_npu = os.environ.get('REQS_FILE_FOR_NPU', "requirements_npu.txt")
- xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+ xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.23.post1')
clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
+ assets_repo = os.environ.get('ASSETS_REPO', "https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets.git")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
- codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
+ assets_commit_hash = os.environ.get('ASSETS_COMMIT_HASH', "6f7db241d2f8ba7457bac5ca9753331f0c266917")
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
- codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
try:
@@ -405,18 +407,14 @@ def prepare_environment(): os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
+ git_clone(assets_repo, repo_dir('stable-diffusion-webui-assets'), "assets", assets_commit_hash)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
- git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
startup_timer.record("clone repositores")
- if not is_installed("lpips"):
- run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
- startup_timer.record("install CodeFormer requirements")
-
if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
@@ -424,6 +422,13 @@ def prepare_environment(): run_pip(f"install -r \"{requirements_file}\"", "requirements")
startup_timer.record("install requirements")
+ if not os.path.isfile(requirements_file_for_npu):
+ requirements_file_for_npu = os.path.join(script_path, requirements_file_for_npu)
+
+ if "torch_npu" in torch_command and not requirements_met(requirements_file_for_npu):
+ run_pip(f"install -r \"{requirements_file_for_npu}\"", "requirements_for_npu")
+ startup_timer.record("install requirements_for_npu")
+
if not args.skip_install:
run_extensions_installers(settings_file=args.ui_settings_file)
diff --git a/modules/logging_config.py b/modules/logging_config.py index 79269875..8e31d8c9 100644 --- a/modules/logging_config.py +++ b/modules/logging_config.py @@ -1,41 +1,58 @@ -import os
import logging
+import os
try:
- from tqdm.auto import tqdm
+ from tqdm import tqdm
+
class TqdmLoggingHandler(logging.Handler):
- def __init__(self, level=logging.INFO):
- super().__init__(level)
+ def __init__(self, fallback_handler: logging.Handler):
+ super().__init__()
+ self.fallback_handler = fallback_handler
def emit(self, record):
try:
- msg = self.format(record)
- tqdm.write(msg)
- self.flush()
+ # If there are active tqdm progress bars,
+ # attempt to not interfere with them.
+ if tqdm._instances:
+ tqdm.write(self.format(record))
+ else:
+ self.fallback_handler.emit(record)
except Exception:
- self.handleError(record)
+ self.fallback_handler.emit(record)
- TQDM_IMPORTED = True
except ImportError:
- # tqdm does not exist before first launch
- # I will import once the UI finishes seting up the enviroment and reloads.
- TQDM_IMPORTED = False
+ TqdmLoggingHandler = None
+
def setup_logging(loglevel):
if loglevel is None:
loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
- loghandlers = []
+ if not loglevel:
+ return
+
+ if logging.root.handlers:
+ # Already configured, do not interfere
+ return
+
+ formatter = logging.Formatter(
+ '%(asctime)s %(levelname)s [%(name)s] %(message)s',
+ '%Y-%m-%d %H:%M:%S',
+ )
+
+ if os.environ.get("SD_WEBUI_RICH_LOG"):
+ from rich.logging import RichHandler
+ handler = RichHandler()
+ else:
+ handler = logging.StreamHandler()
+ handler.setFormatter(formatter)
+
+ if TqdmLoggingHandler:
+ handler = TqdmLoggingHandler(handler)
- if TQDM_IMPORTED:
- loghandlers.append(TqdmLoggingHandler())
+ handler.setFormatter(formatter)
- if loglevel:
- log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
- logging.basicConfig(
- level=log_level,
- format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
- datefmt='%Y-%m-%d %H:%M:%S',
- handlers=loghandlers
- )
+ log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
+ logging.root.setLevel(log_level)
+ logging.root.addHandler(handler)
diff --git a/modules/masking.py b/modules/masking.py index be9f84c7..29a39452 100644 --- a/modules/masking.py +++ b/modules/masking.py @@ -3,40 +3,15 @@ from PIL import Image, ImageFilter, ImageOps def get_crop_region(mask, pad=0):
"""finds a rectangular region that contains all masked ares in an image. Returns (x1, y1, x2, y2) coordinates of the rectangle.
- For example, if a user has painted the top-right part of a 512x512 image", the result may be (256, 0, 512, 256)"""
-
- h, w = mask.shape
-
- crop_left = 0
- for i in range(w):
- if not (mask[:, i] == 0).all():
- break
- crop_left += 1
-
- crop_right = 0
- for i in reversed(range(w)):
- if not (mask[:, i] == 0).all():
- break
- crop_right += 1
-
- crop_top = 0
- for i in range(h):
- if not (mask[i] == 0).all():
- break
- crop_top += 1
-
- crop_bottom = 0
- for i in reversed(range(h)):
- if not (mask[i] == 0).all():
- break
- crop_bottom += 1
-
- return (
- int(max(crop_left-pad, 0)),
- int(max(crop_top-pad, 0)),
- int(min(w - crop_right + pad, w)),
- int(min(h - crop_bottom + pad, h))
- )
+ For example, if a user has painted the top-right part of a 512x512 image, the result may be (256, 0, 512, 256)"""
+ mask_img = mask if isinstance(mask, Image.Image) else Image.fromarray(mask)
+ box = mask_img.getbbox()
+ if box:
+ x1, y1, x2, y2 = box
+ else: # when no box is found
+ x1, y1 = mask_img.size
+ x2 = y2 = 0
+ return max(x1 - pad, 0), max(y1 - pad, 0), min(x2 + pad, mask_img.size[0]), min(y2 + pad, mask_img.size[1])
def expand_crop_region(crop_region, processing_width, processing_height, image_width, image_height):
diff --git a/modules/modelloader.py b/modules/modelloader.py index 098bcb79..e100bb24 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -1,13 +1,20 @@ from __future__ import annotations -import os -import shutil import importlib +import logging +import os +from typing import TYPE_CHECKING from urllib.parse import urlparse +import torch + from modules import shared from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone -from modules.paths import script_path, models_path + +if TYPE_CHECKING: + import spandrel + +logger = logging.getLogger(__name__) def load_file_from_url( @@ -90,54 +97,6 @@ def friendly_name(file: str): return model_name -def cleanup_models(): - # This code could probably be more efficient if we used a tuple list or something to store the src/destinations - # and then enumerate that, but this works for now. In the future, it'd be nice to just have every "model" scaler - # somehow auto-register and just do these things... - root_path = script_path - src_path = models_path - dest_path = os.path.join(models_path, "Stable-diffusion") - move_files(src_path, dest_path, ".ckpt") - move_files(src_path, dest_path, ".safetensors") - src_path = os.path.join(root_path, "ESRGAN") - dest_path = os.path.join(models_path, "ESRGAN") - move_files(src_path, dest_path) - src_path = os.path.join(models_path, "BSRGAN") - dest_path = os.path.join(models_path, "ESRGAN") - move_files(src_path, dest_path, ".pth") - src_path = os.path.join(root_path, "gfpgan") - dest_path = os.path.join(models_path, "GFPGAN") - move_files(src_path, dest_path) - src_path = os.path.join(root_path, "SwinIR") - dest_path = os.path.join(models_path, "SwinIR") - move_files(src_path, dest_path) - src_path = os.path.join(root_path, "repositories/latent-diffusion/experiments/pretrained_models/") - dest_path = os.path.join(models_path, "LDSR") - move_files(src_path, dest_path) - - -def move_files(src_path: str, dest_path: str, ext_filter: str = None): - try: - os.makedirs(dest_path, exist_ok=True) - if os.path.exists(src_path): - for file in os.listdir(src_path): - fullpath = os.path.join(src_path, file) - if os.path.isfile(fullpath): - if ext_filter is not None: - if ext_filter not in file: - continue - print(f"Moving {file} from {src_path} to {dest_path}.") - try: - shutil.move(fullpath, dest_path) - except Exception: - pass - if len(os.listdir(src_path)) == 0: - print(f"Removing empty folder: {src_path}") - shutil.rmtree(src_path, True) - except Exception: - pass - - def load_upscalers(): # We can only do this 'magic' method to dynamically load upscalers if they are referenced, # so we'll try to import any _model.py files before looking in __subclasses__ @@ -177,3 +136,34 @@ def load_upscalers(): # Special case for UpscalerNone keeps it at the beginning of the list. key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else "" ) + + +def load_spandrel_model( + path: str | os.PathLike, + *, + device: str | torch.device | None, + prefer_half: bool = False, + dtype: str | torch.dtype | None = None, + expected_architecture: str | None = None, +) -> spandrel.ModelDescriptor: + import spandrel + model_descriptor = spandrel.ModelLoader(device=device).load_from_file(str(path)) + if expected_architecture and model_descriptor.architecture != expected_architecture: + logger.warning( + f"Model {path!r} is not a {expected_architecture!r} model (got {model_descriptor.architecture!r})", + ) + half = False + if prefer_half: + if model_descriptor.supports_half: + model_descriptor.model.half() + half = True + else: + logger.info("Model %s does not support half precision, ignoring --half", path) + if dtype: + model_descriptor.model.to(dtype=dtype) + model_descriptor.model.eval() + logger.debug( + "Loaded %s from %s (device=%s, half=%s, dtype=%s)", + model_descriptor, path, device, half, dtype, + ) + return model_descriptor diff --git a/modules/npu_specific.py b/modules/npu_specific.py new file mode 100644 index 00000000..94100691 --- /dev/null +++ b/modules/npu_specific.py @@ -0,0 +1,31 @@ +import importlib +import torch + +from modules import shared + + +def check_for_npu(): + if importlib.util.find_spec("torch_npu") is None: + return False + import torch_npu + + try: + # Will raise a RuntimeError if no NPU is found + _ = torch_npu.npu.device_count() + return torch.npu.is_available() + except RuntimeError: + return False + + +def get_npu_device_string(): + if shared.cmd_opts.device_id is not None: + return f"npu:{shared.cmd_opts.device_id}" + return "npu:0" + + +def torch_npu_gc(): + with torch.npu.device(get_npu_device_string()): + torch.npu.empty_cache() + + +has_npu = check_for_npu() diff --git a/modules/options.py b/modules/options.py index 4fead690..35ccade2 100644 --- a/modules/options.py +++ b/modules/options.py @@ -1,3 +1,4 @@ +import os
import json
import sys
from dataclasses import dataclass
@@ -6,6 +7,7 @@ import gradio as gr from modules import errors
from modules.shared_cmd_options import cmd_opts
+from modules.paths_internal import script_path
class OptionInfo:
@@ -91,18 +93,35 @@ class Options: if self.data is not None:
if key in self.data or key in self.data_labels:
+
+ # Check that settings aren't globally frozen
assert not cmd_opts.freeze_settings, "changing settings is disabled"
+ # Get the info related to the setting being changed
info = self.data_labels.get(key, None)
if info.do_not_save:
return
+ # Restrict component arguments
comp_args = info.component_args if info else None
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
- raise RuntimeError(f"not possible to set {key} because it is restricted")
+ raise RuntimeError(f"not possible to set '{key}' because it is restricted")
+
+ # Check that this section isn't frozen
+ if cmd_opts.freeze_settings_in_sections is not None:
+ frozen_sections = list(map(str.strip, cmd_opts.freeze_settings_in_sections.split(','))) # Trim whitespace from section names
+ section_key = info.section[0]
+ section_name = info.section[1]
+ assert section_key not in frozen_sections, f"not possible to set '{key}' because settings in section '{section_name}' ({section_key}) are frozen with --freeze-settings-in-sections"
+
+ # Check that this section of the settings isn't frozen
+ if cmd_opts.freeze_specific_settings is not None:
+ frozen_keys = list(map(str.strip, cmd_opts.freeze_specific_settings.split(','))) # Trim whitespace from setting keys
+ assert key not in frozen_keys, f"not possible to set '{key}' because this setting is frozen with --freeze-specific-settings"
+ # Check shorthand option which disables editing options in "saving-paths"
if cmd_opts.hide_ui_dir_config and key in self.restricted_opts:
- raise RuntimeError(f"not possible to set {key} because it is restricted")
+ raise RuntimeError(f"not possible to set '{key}' because it is restricted with --hide_ui_dir_config")
self.data[key] = value
return
@@ -176,9 +195,15 @@ class Options: return type_x == type_y
def load(self, filename):
- with open(filename, "r", encoding="utf8") as file:
- self.data = json.load(file)
-
+ try:
+ with open(filename, "r", encoding="utf8") as file:
+ self.data = json.load(file)
+ except FileNotFoundError:
+ self.data = {}
+ except Exception:
+ errors.report(f'\nCould not load settings\nThe config file "{filename}" is likely corrupted\nIt has been moved to the "tmp/config.json"\nReverting config to default\n\n''', exc_info=True)
+ os.replace(filename, os.path.join(script_path, "tmp", "config.json"))
+ self.data = {}
# 1.6.0 VAE defaults
if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')
diff --git a/modules/paths.py b/modules/paths.py index 187b9496..03064651 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -38,7 +38,6 @@ mute_sdxl_imports() path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion', []),
(os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
- (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
]
diff --git a/modules/paths_internal.py b/modules/paths_internal.py index 89131a54..6058b0cd 100644 --- a/modules/paths_internal.py +++ b/modules/paths_internal.py @@ -4,6 +4,10 @@ import argparse import os
import sys
import shlex
+from pathlib import Path
+
+
+normalized_filepath = lambda filepath: str(Path(filepath).absolute())
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
sys.argv += shlex.split(commandline_args)
@@ -28,5 +32,6 @@ models_path = os.path.join(data_path, "models") extensions_dir = os.path.join(data_path, "extensions")
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
config_states_dir = os.path.join(script_path, "config_states")
+default_output_dir = os.path.join(data_path, "output")
roboto_ttf_file = os.path.join(modules_path, 'Roboto-Regular.ttf')
diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 0c59fad4..f1488232 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -2,7 +2,7 @@ import os from PIL import Image
-from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
+from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext_utils
from modules.shared import opts
@@ -62,8 +62,6 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, else:
image_data = image_placeholder
- shared.state.assign_current_image(image_data)
-
parameters, existing_pnginfo = images.read_info_from_image(image_data)
if parameters:
existing_pnginfo["parameters"] = parameters
@@ -86,22 +84,25 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, basename = ''
forced_filename = None
- infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
+ infotext = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in pp.info.items() if v is not None])
if opts.enable_pnginfo:
pp.image.info = existing_pnginfo
pp.image.info["postprocessing"] = infotext
+ shared.state.assign_current_image(pp.image)
+
if save_output:
fullfn, _ = images.save_image(pp.image, path=outpath, basename=basename, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=forced_filename, suffix=suffix)
if pp.caption:
caption_filename = os.path.splitext(fullfn)[0] + ".txt"
- if os.path.isfile(caption_filename):
+ existing_caption = ""
+ try:
with open(caption_filename, encoding="utf8") as file:
existing_caption = file.read().strip()
- else:
- existing_caption = ""
+ except FileNotFoundError:
+ pass
action = shared.opts.postprocessing_existing_caption_action
if action == 'Prepend' and existing_caption:
diff --git a/modules/processing.py b/modules/processing.py index 6f01c95f..411c7c3f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -16,7 +16,7 @@ from skimage import exposure from typing import Any
import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
from modules.rng import slerp # noqa: F401
from modules.sd_hijack import model_hijack
from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@@ -62,28 +62,37 @@ def apply_color_correction(correction, original_image): return image.convert('RGB')
-def apply_overlay(image, paste_loc, index, overlays):
- if overlays is None or index >= len(overlays):
- return image
+def uncrop(image, dest_size, paste_loc):
+ x, y, w, h = paste_loc
+ base_image = Image.new('RGBA', dest_size)
+ image = images.resize_image(1, image, w, h)
+ base_image.paste(image, (x, y))
+ image = base_image
+
+ return image
- overlay = overlays[index]
+
+def apply_overlay(image, paste_loc, overlay):
+ if overlay is None:
+ return image, image.copy()
if paste_loc is not None:
- x, y, w, h = paste_loc
- base_image = Image.new('RGBA', (overlay.width, overlay.height))
- image = images.resize_image(1, image, w, h)
- base_image.paste(image, (x, y))
- image = base_image
+ image = uncrop(image, (overlay.width, overlay.height), paste_loc)
+
+ original_denoised_image = image.copy()
image = image.convert('RGBA')
image.alpha_composite(overlay)
image = image.convert('RGB')
- return image
+ return image, original_denoised_image
-def create_binary_mask(image):
+def create_binary_mask(image, round=True):
if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
- image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+ if round:
+ image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+ else:
+ image = image.split()[-1].convert("L")
else:
image = image.convert('L')
return image
@@ -106,6 +115,21 @@ def txt2img_image_conditioning(sd_model, x, width, height): return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
else:
+ sd = sd_model.model.state_dict()
+ diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+ if diffusion_model_input is not None:
+ if diffusion_model_input.shape[1] == 9:
+ # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
+ image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
+ image_conditioning = images_tensor_to_samples(image_conditioning,
+ approximation_indexes.get(opts.sd_vae_encode_method))
+
+ # Add the fake full 1s mask to the first dimension.
+ image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+ image_conditioning = image_conditioning.to(x.dtype)
+
+ return image_conditioning
+
# Dummy zero conditioning if we're not using inpainting or unclip models.
# Still takes up a bit of memory, but no encoder call.
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
@@ -157,6 +181,7 @@ class StableDiffusionProcessing: token_merging_ratio = 0
token_merging_ratio_hr = 0
disable_extra_networks: bool = False
+ firstpass_image: Image = None
scripts_value: scripts.ScriptRunner = field(default=None, init=False)
script_args_value: list = field(default=None, init=False)
@@ -308,7 +333,7 @@ class StableDiffusionProcessing: c_adm = torch.cat((c_adm, noise_level_emb), 1)
return c_adm
- def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
+ def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
self.is_using_inpainting_conditioning = True
# Handle the different mask inputs
@@ -320,8 +345,10 @@ class StableDiffusionProcessing: conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
- # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
- conditioning_mask = torch.round(conditioning_mask)
+ if round_image_mask:
+ # Caller is requesting a discretized mask as input, so we round to either 1.0 or 0.0
+ conditioning_mask = torch.round(conditioning_mask)
+
else:
conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
@@ -345,7 +372,7 @@ class StableDiffusionProcessing: return image_conditioning
- def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+ def img2img_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
source_image = devices.cond_cast_float(source_image)
# HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
@@ -357,11 +384,17 @@ class StableDiffusionProcessing: return self.edit_image_conditioning(source_image)
if self.sampler.conditioning_key in {'hybrid', 'concat'}:
- return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+ return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask, round_image_mask=round_image_mask)
if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image)
+ sd = self.sampler.model_wrap.inner_model.model.state_dict()
+ diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+ if diffusion_model_input is not None:
+ if diffusion_model_input.shape[1] == 9:
+ return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
@@ -422,6 +455,9 @@ class StableDiffusionProcessing: opts.sdxl_crop_top,
self.width,
self.height,
+ opts.fp8_storage,
+ opts.cache_fp16_weight,
+ opts.emphasis,
)
def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
@@ -596,20 +632,33 @@ def decode_latent_batch(model, batch, target_device=None, check_for_nans=False): sample = decode_first_stage(model, batch[i:i + 1])[0]
if check_for_nans:
+
try:
devices.test_for_nans(sample, "vae")
except devices.NansException as e:
- if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+ if shared.opts.auto_vae_precision_bfloat16:
+ autofix_dtype = torch.bfloat16
+ autofix_dtype_text = "bfloat16"
+ autofix_dtype_setting = "Automatically convert VAE to bfloat16"
+ autofix_dtype_comment = ""
+ elif shared.opts.auto_vae_precision:
+ autofix_dtype = torch.float32
+ autofix_dtype_text = "32-bit float"
+ autofix_dtype_setting = "Automatically revert VAE to 32-bit floats"
+ autofix_dtype_comment = "\nTo always start with 32-bit VAE, use --no-half-vae commandline flag."
+ else:
+ raise e
+
+ if devices.dtype_vae == autofix_dtype:
raise e
errors.print_error_explanation(
"A tensor with all NaNs was produced in VAE.\n"
- "Web UI will now convert VAE into 32-bit float and retry.\n"
- "To disable this behavior, disable the 'Automatically revert VAE to 32-bit floats' setting.\n"
- "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+ f"Web UI will now convert VAE into {autofix_dtype_text} and retry.\n"
+ f"To disable this behavior, disable the '{autofix_dtype_setting}' setting.{autofix_dtype_comment}"
)
- devices.dtype_vae = torch.float32
+ devices.dtype_vae = autofix_dtype
model.first_stage_model.to(devices.dtype_vae)
batch = batch.to(devices.dtype_vae)
@@ -679,12 +728,14 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Size": f"{p.width}x{p.height}",
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
"Model": p.sd_model_name if opts.add_model_name_to_info else None,
+ "FP8 weight": opts.fp8_storage if devices.fp8 else None,
+ "Cache FP16 weight for LoRA": opts.cache_fp16_weight if devices.fp8 else None,
"VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
- "Denoising strength": getattr(p, 'denoising_strength', None),
+ "Denoising strength": p.extra_generation_params.get("Denoising strength"),
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
"Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
@@ -699,7 +750,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "User": p.user if opts.add_user_name_to_info else None,
}
- generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
+ generation_params_text = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in generation_params.items() if v is not None])
prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
@@ -818,7 +869,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if state.skipped:
state.skipped = False
- if state.interrupted:
+ if state.interrupted or state.stopping_generation:
break
sd_models.reload_model_weights() # model can be changed for example by refiner
@@ -864,9 +915,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+ sd_models.apply_alpha_schedule_override(p.sd_model, p)
+
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
+ if p.scripts is not None:
+ ps = scripts.PostSampleArgs(samples_ddim)
+ p.scripts.post_sample(p, ps)
+ samples_ddim = ps.samples
+
if getattr(samples_ddim, 'already_decoded', False):
x_samples_ddim = samples_ddim
else:
@@ -922,13 +980,37 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: pp = scripts.PostprocessImageArgs(image)
p.scripts.postprocess_image(p, pp)
image = pp.image
+
+ mask_for_overlay = getattr(p, "mask_for_overlay", None)
+
+ if not shared.opts.overlay_inpaint:
+ overlay_image = None
+ elif getattr(p, "overlay_images", None) is not None and i < len(p.overlay_images):
+ overlay_image = p.overlay_images[i]
+ else:
+ overlay_image = None
+
+ if p.scripts is not None:
+ ppmo = scripts.PostProcessMaskOverlayArgs(i, mask_for_overlay, overlay_image)
+ p.scripts.postprocess_maskoverlay(p, ppmo)
+ mask_for_overlay, overlay_image = ppmo.mask_for_overlay, ppmo.overlay_image
+
if p.color_corrections is not None and i < len(p.color_corrections):
if save_samples and opts.save_images_before_color_correction:
- image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
+ image_without_cc, _ = apply_overlay(image, p.paste_to, overlay_image)
images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
image = apply_color_correction(p.color_corrections[i], image)
- image = apply_overlay(image, p.paste_to, i, p.overlay_images)
+ # If the intention is to show the output from the model
+ # that is being composited over the original image,
+ # we need to keep the original image around
+ # and use it in the composite step.
+ image, original_denoised_image = apply_overlay(image, p.paste_to, overlay_image)
+
+ if p.scripts is not None:
+ pp = scripts.PostprocessImageArgs(image)
+ p.scripts.postprocess_image_after_composite(p, pp)
+ image = pp.image
if save_samples:
images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
@@ -938,16 +1020,17 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if opts.enable_pnginfo:
image.info["parameters"] = text
output_images.append(image)
- if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+
+ if mask_for_overlay is not None:
if opts.return_mask or opts.save_mask:
- image_mask = p.mask_for_overlay.convert('RGB')
+ image_mask = mask_for_overlay.convert('RGB')
if save_samples and opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.return_mask:
output_images.append(image_mask)
if opts.return_mask_composite or opts.save_mask_composite:
- image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
+ image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if save_samples and opts.save_mask_composite:
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
if opts.return_mask_composite:
@@ -1025,6 +1108,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): hr_sampler_name: str = None
hr_prompt: str = ''
hr_negative_prompt: str = ''
+ force_task_id: str = None
cached_hr_uc = [None, None]
cached_hr_c = [None, None]
@@ -1097,7 +1181,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def init(self, all_prompts, all_seeds, all_subseeds):
if self.enable_hr:
- if self.hr_checkpoint_name:
+ self.extra_generation_params["Denoising strength"] = self.denoising_strength
+
+ if self.hr_checkpoint_name and self.hr_checkpoint_name != 'Use same checkpoint':
self.hr_checkpoint_info = sd_models.get_closet_checkpoint_match(self.hr_checkpoint_name)
if self.hr_checkpoint_info is None:
@@ -1124,8 +1210,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not state.processing_has_refined_job_count:
if state.job_count == -1:
state.job_count = self.n_iter
-
- shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
+ if getattr(self, 'txt2img_upscale', False):
+ total_steps = (self.hr_second_pass_steps or self.steps) * state.job_count
+ else:
+ total_steps = (self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count
+ shared.total_tqdm.updateTotal(total_steps)
state.job_count = state.job_count * 2
state.processing_has_refined_job_count = True
@@ -1138,18 +1227,45 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
- x = self.rng.next()
- samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
- del x
+ if self.firstpass_image is not None and self.enable_hr:
+ # here we don't need to generate image, we just take self.firstpass_image and prepare it for hires fix
- if not self.enable_hr:
- return samples
- devices.torch_gc()
+ if self.latent_scale_mode is None:
+ image = np.array(self.firstpass_image).astype(np.float32) / 255.0 * 2.0 - 1.0
+ image = np.moveaxis(image, 2, 0)
+
+ samples = None
+ decoded_samples = torch.asarray(np.expand_dims(image, 0))
+
+ else:
+ image = np.array(self.firstpass_image).astype(np.float32) / 255.0
+ image = np.moveaxis(image, 2, 0)
+ image = torch.from_numpy(np.expand_dims(image, axis=0))
+ image = image.to(shared.device, dtype=devices.dtype_vae)
+
+ if opts.sd_vae_encode_method != 'Full':
+ self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
+
+ samples = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
+ decoded_samples = None
+ devices.torch_gc()
- if self.latent_scale_mode is None:
- decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
else:
- decoded_samples = None
+ # here we generate an image normally
+
+ x = self.rng.next()
+ samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+ del x
+
+ if not self.enable_hr:
+ return samples
+
+ devices.torch_gc()
+
+ if self.latent_scale_mode is None:
+ decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
+ else:
+ decoded_samples = None
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info)
@@ -1351,12 +1467,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): mask_blur_x: int = 4
mask_blur_y: int = 4
mask_blur: int = None
+ mask_round: bool = True
inpainting_fill: int = 0
inpaint_full_res: bool = True
inpaint_full_res_padding: int = 0
inpainting_mask_invert: int = 0
initial_noise_multiplier: float = None
latent_mask: Image = None
+ force_task_id: str = None
image_mask: Any = field(default=None, init=False)
@@ -1386,6 +1504,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask_blur_y = value
def init(self, all_prompts, all_seeds, all_subseeds):
+ self.extra_generation_params["Denoising strength"] = self.denoising_strength
+
self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
@@ -1396,10 +1516,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if image_mask is not None:
# image_mask is passed in as RGBA by Gradio to support alpha masks,
# but we still want to support binary masks.
- image_mask = create_binary_mask(image_mask)
+ image_mask = create_binary_mask(image_mask, round=self.mask_round)
if self.inpainting_mask_invert:
image_mask = ImageOps.invert(image_mask)
+ self.extra_generation_params["Mask mode"] = "Inpaint not masked"
if self.mask_blur_x > 0:
np_mask = np.array(image_mask)
@@ -1413,16 +1534,22 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
image_mask = Image.fromarray(np_mask)
+ if self.mask_blur_x > 0 or self.mask_blur_y > 0:
+ self.extra_generation_params["Mask blur"] = self.mask_blur
+
if self.inpaint_full_res:
self.mask_for_overlay = image_mask
mask = image_mask.convert('L')
- crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
+ crop_region = masking.get_crop_region(mask, self.inpaint_full_res_padding)
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
x1, y1, x2, y2 = crop_region
mask = mask.crop(crop_region)
image_mask = images.resize_image(2, mask, self.width, self.height)
self.paste_to = (x1, y1, x2-x1, y2-y1)
+
+ self.extra_generation_params["Inpaint area"] = "Only masked"
+ self.extra_generation_params["Masked area padding"] = self.inpaint_full_res_padding
else:
image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
np_mask = np.array(image_mask)
@@ -1442,7 +1569,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): # Save init image
if opts.save_init_img:
self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
- images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+ images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False, existing_info=img.info)
image = images.flatten(img, opts.img2img_background_color)
@@ -1464,6 +1591,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.inpainting_fill != 1:
image = masking.fill(image, latent_mask)
+ if self.inpainting_fill == 0:
+ self.extra_generation_params["Masked content"] = 'fill'
+
if add_color_corrections:
self.color_corrections.append(setup_color_correction(image))
@@ -1503,7 +1633,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
latmask = latmask[0]
- latmask = np.around(latmask)
+ if self.mask_round:
+ latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
@@ -1512,10 +1643,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): # this needs to be fixed to be done in sample() using actual seeds for batches
if self.inpainting_fill == 2:
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
+ self.extra_generation_params["Masked content"] = 'latent noise'
+
elif self.inpainting_fill == 3:
self.init_latent = self.init_latent * self.mask
+ self.extra_generation_params["Masked content"] = 'latent nothing'
- self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
+ self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask, self.mask_round)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
x = self.rng.next()
@@ -1527,7 +1661,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
if self.mask is not None:
- samples = samples * self.nmask + self.init_latent * self.mask
+ blended_samples = samples * self.nmask + self.init_latent * self.mask
+
+ if self.scripts is not None:
+ mba = scripts.MaskBlendArgs(samples, self.nmask, self.init_latent, self.mask, blended_samples)
+ self.scripts.on_mask_blend(self, mba)
+ blended_samples = mba.blended_latent
+
+ samples = blended_samples
del x
devices.torch_gc()
diff --git a/modules/processing_scripts/comments.py b/modules/processing_scripts/comments.py new file mode 100644 index 00000000..638e39f2 --- /dev/null +++ b/modules/processing_scripts/comments.py @@ -0,0 +1,42 @@ +from modules import scripts, shared, script_callbacks
+import re
+
+
+def strip_comments(text):
+ text = re.sub('(^|\n)#[^\n]*(\n|$)', '\n', text) # while line comment
+ text = re.sub('#[^\n]*(\n|$)', '\n', text) # in the middle of the line comment
+
+ return text
+
+
+class ScriptStripComments(scripts.Script):
+ def title(self):
+ return "Comments"
+
+ def show(self, is_img2img):
+ return scripts.AlwaysVisible
+
+ def process(self, p, *args):
+ if not shared.opts.enable_prompt_comments:
+ return
+
+ p.all_prompts = [strip_comments(x) for x in p.all_prompts]
+ p.all_negative_prompts = [strip_comments(x) for x in p.all_negative_prompts]
+
+ p.main_prompt = strip_comments(p.main_prompt)
+ p.main_negative_prompt = strip_comments(p.main_negative_prompt)
+
+
+def before_token_counter(params: script_callbacks.BeforeTokenCounterParams):
+ if not shared.opts.enable_prompt_comments:
+ return
+
+ params.prompt = strip_comments(params.prompt)
+
+
+script_callbacks.on_before_token_counter(before_token_counter)
+
+
+shared.options_templates.update(shared.options_section(('sd', "Stable Diffusion", "sd"), {
+ "enable_prompt_comments": shared.OptionInfo(True, "Enable comments").info("Use # anywhere in the prompt to hide the text between # and the end of the line from the generation."),
+}))
diff --git a/modules/processing_scripts/refiner.py b/modules/processing_scripts/refiner.py index 29ccb78f..ba33d8a4 100644 --- a/modules/processing_scripts/refiner.py +++ b/modules/processing_scripts/refiner.py @@ -1,6 +1,7 @@ import gradio as gr
from modules import scripts, sd_models
+from modules.infotext_utils import PasteField
from modules.ui_common import create_refresh_button
from modules.ui_components import InputAccordion
@@ -31,9 +32,9 @@ class ScriptRefiner(scripts.ScriptBuiltinUI): return None if info is None else info.title
self.infotext_fields = [
- (enable_refiner, lambda d: 'Refiner' in d),
- (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
- (refiner_switch_at, 'Refiner switch at'),
+ PasteField(enable_refiner, lambda d: 'Refiner' in d),
+ PasteField(refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner')), api="refiner_checkpoint"),
+ PasteField(refiner_switch_at, 'Refiner switch at', api="refiner_switch_at"),
]
return enable_refiner, refiner_checkpoint, refiner_switch_at
diff --git a/modules/processing_scripts/seed.py b/modules/processing_scripts/seed.py index dc9c2da5..7a4c0159 100644 --- a/modules/processing_scripts/seed.py +++ b/modules/processing_scripts/seed.py @@ -3,8 +3,10 @@ import json import gradio as gr
from modules import scripts, ui, errors
+from modules.infotext_utils import PasteField
from modules.shared import cmd_opts
from modules.ui_components import ToolButton
+from modules import infotext_utils
class ScriptSeed(scripts.ScriptBuiltinUI):
@@ -51,12 +53,12 @@ class ScriptSeed(scripts.ScriptBuiltinUI): seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
self.infotext_fields = [
- (self.seed, "Seed"),
- (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
- (subseed, "Variation seed"),
- (subseed_strength, "Variation seed strength"),
- (seed_resize_from_w, "Seed resize from-1"),
- (seed_resize_from_h, "Seed resize from-2"),
+ PasteField(self.seed, "Seed", api="seed"),
+ PasteField(seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
+ PasteField(subseed, "Variation seed", api="subseed"),
+ PasteField(subseed_strength, "Variation seed strength", api="subseed_strength"),
+ PasteField(seed_resize_from_w, "Seed resize from-1", api="seed_resize_from_h"),
+ PasteField(seed_resize_from_h, "Seed resize from-2", api="seed_resize_from_w"),
]
self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
@@ -76,7 +78,6 @@ class ScriptSeed(scripts.ScriptBuiltinUI): p.seed_resize_from_h = seed_resize_from_h
-
def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
""" Connects a 'reuse (sub)seed' button's click event so that it copies last used
(sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
@@ -84,21 +85,14 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: def copy_seed(gen_info_string: str, index):
res = -1
-
try:
gen_info = json.loads(gen_info_string)
- index -= gen_info.get('index_of_first_image', 0)
-
- if is_subseed and gen_info.get('subseed_strength', 0) > 0:
- all_subseeds = gen_info.get('all_subseeds', [-1])
- res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
- else:
- all_seeds = gen_info.get('all_seeds', [-1])
- res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
- except json.decoder.JSONDecodeError:
+ infotext = gen_info.get('infotexts')[index]
+ gen_parameters = infotext_utils.parse_generation_parameters(infotext, [])
+ res = int(gen_parameters.get('Variation seed' if is_subseed else 'Seed', -1))
+ except Exception:
if gen_info_string:
- errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+ errors.report(f"Error retrieving seed from generation info: {gen_info_string}", exc_info=True)
return [res, gr.update()]
diff --git a/modules/progress.py b/modules/progress.py index 69921de7..85255e82 100644 --- a/modules/progress.py +++ b/modules/progress.py @@ -8,10 +8,13 @@ from pydantic import BaseModel, Field from modules.shared import opts
import modules.shared as shared
-
+from collections import OrderedDict
+import string
+import random
+from typing import List
current_task = None
-pending_tasks = {}
+pending_tasks = OrderedDict()
finished_tasks = []
recorded_results = []
recorded_results_limit = 2
@@ -34,6 +37,11 @@ def finish_task(id_task): if len(finished_tasks) > 16:
finished_tasks.pop(0)
+def create_task_id(task_type):
+ N = 7
+ res = ''.join(random.choices(string.ascii_uppercase +
+ string.digits, k=N))
+ return f"task({task_type}-{res})"
def record_results(id_task, res):
recorded_results.append((id_task, res))
@@ -44,6 +52,9 @@ def record_results(id_task, res): def add_task_to_queue(id_job):
pending_tasks[id_job] = time.time()
+class PendingTasksResponse(BaseModel):
+ size: int = Field(title="Pending task size")
+ tasks: List[str] = Field(title="Pending task ids")
class ProgressRequest(BaseModel):
id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
@@ -63,9 +74,16 @@ class ProgressResponse(BaseModel): def setup_progress_api(app):
+ app.add_api_route("/internal/pending-tasks", get_pending_tasks, methods=["GET"])
return app.add_api_route("/internal/progress", progressapi, methods=["POST"], response_model=ProgressResponse)
+def get_pending_tasks():
+ pending_tasks_ids = list(pending_tasks)
+ pending_len = len(pending_tasks_ids)
+ return PendingTasksResponse(size=pending_len, tasks=pending_tasks_ids)
+
+
def progressapi(req: ProgressRequest):
active = req.id_task == current_task
queued = req.id_task in pending_tasks
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index 02841c30..ff9d8ac0 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -1,12 +1,9 @@ import os
-import numpy as np
-from PIL import Image
-from realesrgan import RealESRGANer
-
-from modules.upscaler import Upscaler, UpscalerData
-from modules.shared import cmd_opts, opts
from modules import modelloader, errors
+from modules.shared import cmd_opts, opts
+from modules.upscaler import Upscaler, UpscalerData
+from modules.upscaler_utils import upscale_with_model
class UpscalerRealESRGAN(Upscaler):
@@ -14,29 +11,20 @@ class UpscalerRealESRGAN(Upscaler): self.name = "RealESRGAN"
self.user_path = path
super().__init__()
- try:
- from basicsr.archs.rrdbnet_arch import RRDBNet # noqa: F401
- from realesrgan import RealESRGANer # noqa: F401
- from realesrgan.archs.srvgg_arch import SRVGGNetCompact # noqa: F401
- self.enable = True
- self.scalers = []
- scalers = self.load_models(path)
+ self.enable = True
+ self.scalers = []
+ scalers = get_realesrgan_models(self)
- local_model_paths = self.find_models(ext_filter=[".pth"])
- for scaler in scalers:
- if scaler.local_data_path.startswith("http"):
- filename = modelloader.friendly_name(scaler.local_data_path)
- local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
- if local_model_candidates:
- scaler.local_data_path = local_model_candidates[0]
+ local_model_paths = self.find_models(ext_filter=[".pth"])
+ for scaler in scalers:
+ if scaler.local_data_path.startswith("http"):
+ filename = modelloader.friendly_name(scaler.local_data_path)
+ local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
+ if local_model_candidates:
+ scaler.local_data_path = local_model_candidates[0]
- if scaler.name in opts.realesrgan_enabled_models:
- self.scalers.append(scaler)
-
- except Exception:
- errors.report("Error importing Real-ESRGAN", exc_info=True)
- self.enable = False
- self.scalers = []
+ if scaler.name in opts.realesrgan_enabled_models:
+ self.scalers.append(scaler)
def do_upscale(self, img, path):
if not self.enable:
@@ -48,20 +36,19 @@ class UpscalerRealESRGAN(Upscaler): errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
return img
- upsampler = RealESRGANer(
- scale=info.scale,
- model_path=info.local_data_path,
- model=info.model(),
- half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
- tile=opts.ESRGAN_tile,
- tile_pad=opts.ESRGAN_tile_overlap,
+ model_descriptor = modelloader.load_spandrel_model(
+ info.local_data_path,
device=self.device,
+ prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
+ expected_architecture="ESRGAN", # "RealESRGAN" isn't a specific thing for Spandrel
+ )
+ return upscale_with_model(
+ model_descriptor,
+ img,
+ tile_size=opts.ESRGAN_tile,
+ tile_overlap=opts.ESRGAN_tile_overlap,
+ # TODO: `outscale`?
)
-
- upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
-
- image = Image.fromarray(upsampled)
- return image
def load_model(self, path):
for scaler in self.scalers:
@@ -76,58 +63,43 @@ class UpscalerRealESRGAN(Upscaler): return scaler
raise ValueError(f"Unable to find model info: {path}")
- def load_models(self, _):
- return get_realesrgan_models(self)
-
-def get_realesrgan_models(scaler):
- try:
- from basicsr.archs.rrdbnet_arch import RRDBNet
- from realesrgan.archs.srvgg_arch import SRVGGNetCompact
- models = [
- UpscalerData(
- name="R-ESRGAN General 4xV3",
- path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
- scale=4,
- upscaler=scaler,
- model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
- ),
- UpscalerData(
- name="R-ESRGAN General WDN 4xV3",
- path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
- scale=4,
- upscaler=scaler,
- model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
- ),
- UpscalerData(
- name="R-ESRGAN AnimeVideo",
- path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
- scale=4,
- upscaler=scaler,
- model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
- ),
- UpscalerData(
- name="R-ESRGAN 4x+",
- path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
- scale=4,
- upscaler=scaler,
- model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- ),
- UpscalerData(
- name="R-ESRGAN 4x+ Anime6B",
- path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
- scale=4,
- upscaler=scaler,
- model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
- ),
- UpscalerData(
- name="R-ESRGAN 2x+",
- path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
- scale=2,
- upscaler=scaler,
- model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
- ),
- ]
- return models
- except Exception:
- errors.report("Error making Real-ESRGAN models list", exc_info=True)
+def get_realesrgan_models(scaler: UpscalerRealESRGAN):
+ return [
+ UpscalerData(
+ name="R-ESRGAN General 4xV3",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+ scale=4,
+ upscaler=scaler,
+ ),
+ UpscalerData(
+ name="R-ESRGAN General WDN 4xV3",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+ scale=4,
+ upscaler=scaler,
+ ),
+ UpscalerData(
+ name="R-ESRGAN AnimeVideo",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+ scale=4,
+ upscaler=scaler,
+ ),
+ UpscalerData(
+ name="R-ESRGAN 4x+",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+ scale=4,
+ upscaler=scaler,
+ ),
+ UpscalerData(
+ name="R-ESRGAN 4x+ Anime6B",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+ scale=4,
+ upscaler=scaler,
+ ),
+ UpscalerData(
+ name="R-ESRGAN 2x+",
+ path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+ scale=2,
+ upscaler=scaler,
+ ),
+ ]
diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 9ed7ad21..08bc5256 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -1,3 +1,4 @@ +import dataclasses
import inspect
import os
from collections import namedtuple
@@ -41,7 +42,7 @@ class ExtraNoiseParams: class CFGDenoiserParams:
- def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
+ def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond, denoiser=None):
self.x = x
"""Latent image representation in the process of being denoised"""
@@ -63,6 +64,9 @@ class CFGDenoiserParams: self.text_uncond = text_uncond
""" Encoder hidden states of text conditioning from negative prompt"""
+ self.denoiser = denoiser
+ """Current CFGDenoiser object with processing parameters"""
+
class CFGDenoisedParams:
def __init__(self, x, sampling_step, total_sampling_steps, inner_model):
@@ -103,6 +107,15 @@ class ImageGridLoopParams: self.rows = rows
+@dataclasses.dataclass
+class BeforeTokenCounterParams:
+ prompt: str
+ steps: int
+ styles: list
+
+ is_positive: bool = True
+
+
ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
callback_map = dict(
callbacks_app_started=[],
@@ -125,6 +138,7 @@ callback_map = dict( callbacks_on_reload=[],
callbacks_list_optimizers=[],
callbacks_list_unets=[],
+ callbacks_before_token_counter=[],
)
@@ -306,6 +320,14 @@ def list_unets_callback(): return res
+def before_token_counter_callback(params: BeforeTokenCounterParams):
+ for c in callback_map['callbacks_before_token_counter']:
+ try:
+ c.callback(params)
+ except Exception:
+ report_exception(c, 'before_token_counter')
+
+
def add_callback(callbacks, fun):
stack = [x for x in inspect.stack() if x.filename != __file__]
filename = stack[0].filename if stack else 'unknown file'
@@ -480,3 +502,10 @@ def on_list_unets(callback): The function will be called with one argument, a list, and shall add objects of type modules.sd_unet.SdUnetOption to it."""
add_callback(callback_map['callbacks_list_unets'], callback)
+
+
+def on_before_token_counter(callback):
+ """register a function to be called when UI is counting tokens for a prompt.
+ The function will be called with one argument of type BeforeTokenCounterParams, and should modify its fields if necessary."""
+
+ add_callback(callback_map['callbacks_before_token_counter'], callback)
diff --git a/modules/scripts.py b/modules/scripts.py index 7f9454eb..94690a22 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -11,11 +11,31 @@ from modules import shared, paths, script_callbacks, extensions, script_loading, AlwaysVisible = object()
+class MaskBlendArgs:
+ def __init__(self, current_latent, nmask, init_latent, mask, blended_latent, denoiser=None, sigma=None):
+ self.current_latent = current_latent
+ self.nmask = nmask
+ self.init_latent = init_latent
+ self.mask = mask
+ self.blended_latent = blended_latent
+
+ self.denoiser = denoiser
+ self.is_final_blend = denoiser is None
+ self.sigma = sigma
+
+class PostSampleArgs:
+ def __init__(self, samples):
+ self.samples = samples
class PostprocessImageArgs:
def __init__(self, image):
self.image = image
+class PostProcessMaskOverlayArgs:
+ def __init__(self, index, mask_for_overlay, overlay_image):
+ self.index = index
+ self.mask_for_overlay = mask_for_overlay
+ self.overlay_image = overlay_image
class PostprocessBatchListArgs:
def __init__(self, images):
@@ -71,6 +91,9 @@ class Script: setup_for_ui_only = False
"""If true, the script setup will only be run in Gradio UI, not in API"""
+ controls = None
+ """A list of controls retured by the ui()."""
+
def title(self):
"""this function should return the title of the script. This is what will be displayed in the dropdown menu."""
@@ -206,6 +229,25 @@ class Script: pass
+ def on_mask_blend(self, p, mba: MaskBlendArgs, *args):
+ """
+ Called in inpainting mode when the original content is blended with the inpainted content.
+ This is called at every step in the denoising process and once at the end.
+ If is_final_blend is true, this is called for the final blending stage.
+ Otherwise, denoiser and sigma are defined and may be used to inform the procedure.
+ """
+
+ pass
+
+ def post_sample(self, p, ps: PostSampleArgs, *args):
+ """
+ Called after the samples have been generated,
+ but before they have been decoded by the VAE, if applicable.
+ Check getattr(samples, 'already_decoded', False) to test if the images are decoded.
+ """
+
+ pass
+
def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
"""
Called for every image after it has been generated.
@@ -213,6 +255,22 @@ class Script: pass
+ def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs, *args):
+ """
+ Called for every image after it has been generated.
+ """
+
+ pass
+
+ def postprocess_image_after_composite(self, p, pp: PostprocessImageArgs, *args):
+ """
+ Called for every image after it has been generated.
+ Same as postprocess_image but after inpaint_full_res composite
+ So that it operates on the full image instead of the inpaint_full_res crop region.
+ """
+
+ pass
+
def postprocess(self, p, processed, *args):
"""
This function is called after processing ends for AlwaysVisible scripts.
@@ -520,7 +578,12 @@ class ScriptRunner: auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
for script_data in auto_processing_scripts + scripts_data:
- script = script_data.script_class()
+ try:
+ script = script_data.script_class()
+ except Exception:
+ errors.report(f"Error # failed to initialize Script {script_data.module}: ", exc_info=True)
+ continue
+
script.filename = script_data.path
script.is_txt2img = not is_img2img
script.is_img2img = is_img2img
@@ -573,6 +636,7 @@ class ScriptRunner: import modules.api.models as api_models
controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)
+ script.controls = controls
if controls is None:
return
@@ -645,6 +709,8 @@ class ScriptRunner: self.setup_ui_for_section(None, self.selectable_scripts)
def select_script(script_index):
+ if script_index is None:
+ script_index = 0
selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
@@ -688,7 +754,7 @@ class ScriptRunner: def run(self, p, *args):
script_index = args[0]
- if script_index == 0:
+ if script_index == 0 or script_index is None:
return None
script = self.selectable_scripts[script_index-1]
@@ -767,6 +833,22 @@ class ScriptRunner: except Exception:
errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
+ def post_sample(self, p, ps: PostSampleArgs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.post_sample(p, ps, *script_args)
+ except Exception:
+ errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
+
+ def on_mask_blend(self, p, mba: MaskBlendArgs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.on_mask_blend(p, mba, *script_args)
+ except Exception:
+ errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
+
def postprocess_image(self, p, pp: PostprocessImageArgs):
for script in self.alwayson_scripts:
try:
@@ -775,6 +857,22 @@ class ScriptRunner: except Exception:
errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
+ def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess_maskoverlay(p, ppmo, *script_args)
+ except Exception:
+ errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
+
+ def postprocess_image_after_composite(self, p, pp: PostprocessImageArgs):
+ for script in self.alwayson_scripts:
+ try:
+ script_args = p.script_args[script.args_from:script.args_to]
+ script.postprocess_image_after_composite(p, pp, *script_args)
+ except Exception:
+ errors.report(f"Error running postprocess_image_after_composite: {script.filename}", exc_info=True)
+
def before_component(self, component, **kwargs):
for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
try:
@@ -841,6 +939,35 @@ class ScriptRunner: except Exception:
errors.report(f"Error running setup: {script.filename}", exc_info=True)
+ def set_named_arg(self, args, script_name, arg_elem_id, value, fuzzy=False):
+ """Locate an arg of a specific script in script_args and set its value
+ Args:
+ args: all script args of process p, p.script_args
+ script_name: the name target script name to
+ arg_elem_id: the elem_id of the target arg
+ value: the value to set
+ fuzzy: if True, arg_elem_id can be a substring of the control.elem_id else exact match
+ Returns:
+ Updated script args
+ when script_name in not found or arg_elem_id is not found in script controls, raise RuntimeError
+ """
+ script = next((x for x in self.scripts if x.name == script_name), None)
+ if script is None:
+ raise RuntimeError(f"script {script_name} not found")
+
+ for i, control in enumerate(script.controls):
+ if arg_elem_id in control.elem_id if fuzzy else arg_elem_id == control.elem_id:
+ index = script.args_from + i
+
+ if isinstance(args, tuple):
+ return args[:index] + (value,) + args[index + 1:]
+ elif isinstance(args, list):
+ args[index] = value
+ return args
+ else:
+ raise RuntimeError(f"args is not a list or tuple, but {type(args)}")
+ raise RuntimeError(f"arg_elem_id {arg_elem_id} not found in script {script_name}")
+
scripts_txt2img: ScriptRunner = None
scripts_img2img: ScriptRunner = None
diff --git a/modules/sd_emphasis.py b/modules/sd_emphasis.py new file mode 100644 index 00000000..654817b6 --- /dev/null +++ b/modules/sd_emphasis.py @@ -0,0 +1,70 @@ +from __future__ import annotations
+import torch
+
+
+class Emphasis:
+ """Emphasis class decides how to death with (emphasized:1.1) text in prompts"""
+
+ name: str = "Base"
+ description: str = ""
+
+ tokens: list[list[int]]
+ """tokens from the chunk of the prompt"""
+
+ multipliers: torch.Tensor
+ """tensor with multipliers, once for each token"""
+
+ z: torch.Tensor
+ """output of cond transformers network (CLIP)"""
+
+ def after_transformers(self):
+ """Called after cond transformers network has processed the chunk of the prompt; this function should modify self.z to apply the emphasis"""
+
+ pass
+
+
+class EmphasisNone(Emphasis):
+ name = "None"
+ description = "disable the mechanism entirely and treat (:.1.1) as literal characters"
+
+
+class EmphasisIgnore(Emphasis):
+ name = "Ignore"
+ description = "treat all empasised words as if they have no emphasis"
+
+
+class EmphasisOriginal(Emphasis):
+ name = "Original"
+ description = "the orginal emphasis implementation"
+
+ def after_transformers(self):
+ original_mean = self.z.mean()
+ self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
+
+ # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
+ new_mean = self.z.mean()
+ self.z = self.z * (original_mean / new_mean)
+
+
+class EmphasisOriginalNoNorm(EmphasisOriginal):
+ name = "No norm"
+ description = "same as orginal, but without normalization (seems to work better for SDXL)"
+
+ def after_transformers(self):
+ self.z = self.z * self.multipliers.reshape(self.multipliers.shape + (1,)).expand(self.z.shape)
+
+
+def get_current_option(emphasis_option_name):
+ return next(iter([x for x in options if x.name == emphasis_option_name]), EmphasisOriginal)
+
+
+def get_options_descriptions():
+ return ", ".join(f"{x.name}: {x.description}" for x in options)
+
+
+options = [
+ EmphasisNone,
+ EmphasisIgnore,
+ EmphasisOriginal,
+ EmphasisOriginalNoNorm,
+]
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index 8f29057a..98350ac4 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -3,7 +3,7 @@ from collections import namedtuple import torch
-from modules import prompt_parser, devices, sd_hijack
+from modules import prompt_parser, devices, sd_hijack, sd_emphasis
from modules.shared import opts
@@ -88,7 +88,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): Returns the list and the total number of tokens in the prompt.
"""
- if opts.enable_emphasis:
+ if opts.emphasis != "None":
parsed = prompt_parser.parse_prompt_attention(line)
else:
parsed = [[line, 1.0]]
@@ -249,6 +249,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
+ if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
+ self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
+
if getattr(self.wrapped, 'return_pooled', False):
return torch.hstack(zs), zs[0].pooled
else:
@@ -274,12 +277,14 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module): pooled = getattr(z, 'pooled', None)
- # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
- batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
- original_mean = z.mean()
- z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
- new_mean = z.mean()
- z = z * (original_mean / new_mean)
+ emphasis = sd_emphasis.get_current_option(opts.emphasis)()
+ emphasis.tokens = remade_batch_tokens
+ emphasis.multipliers = torch.asarray(batch_multipliers).to(devices.device)
+ emphasis.z = z
+
+ emphasis.after_transformers()
+
+ z = emphasis.z
if pooled is not None:
z.pooled = pooled
diff --git a/modules/sd_hijack_clip_old.py b/modules/sd_hijack_clip_old.py index c5c6270b..43e9b952 100644 --- a/modules/sd_hijack_clip_old.py +++ b/modules/sd_hijack_clip_old.py @@ -32,7 +32,7 @@ def process_text_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
- mult_change = self.token_mults.get(token) if shared.opts.enable_emphasis else None
+ mult_change = self.token_mults.get(token) if shared.opts.emphasis != "None" else None
if mult_change is not None:
mult *= mult_change
i += 1
diff --git a/modules/sd_hijack_utils.py b/modules/sd_hijack_utils.py index f8684475..79bf6e46 100644 --- a/modules/sd_hijack_utils.py +++ b/modules/sd_hijack_utils.py @@ -11,10 +11,14 @@ class CondFunc: break
except ImportError:
pass
- for attr_name in func_path[i:-1]:
- resolved_obj = getattr(resolved_obj, attr_name)
- orig_func = getattr(resolved_obj, func_path[-1])
- setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
+ try:
+ for attr_name in func_path[i:-1]:
+ resolved_obj = getattr(resolved_obj, attr_name)
+ orig_func = getattr(resolved_obj, func_path[-1])
+ setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
+ except AttributeError:
+ print(f"Warning: Failed to resolve {orig_func} for CondFunc hijack")
+ pass
self.__init__(orig_func, sub_func, cond_func)
return lambda *args, **kwargs: self(*args, **kwargs) def __init__(self, orig_func, sub_func, cond_func): diff --git a/modules/sd_models.py b/modules/sd_models.py index 9355f1e1..747fc39e 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -15,6 +15,7 @@ from ldm.util import instantiate_from_config from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
from modules.timer import Timer
+from modules.shared import opts
import tomesd
import numpy as np
@@ -348,10 +349,28 @@ class SkipWritingToConfig: SkipWritingToConfig.skip = self.previous
+def check_fp8(model):
+ if model is None:
+ return None
+ if devices.get_optimal_device_name() == "mps":
+ enable_fp8 = False
+ elif shared.opts.fp8_storage == "Enable":
+ enable_fp8 = True
+ elif getattr(model, "is_sdxl", False) and shared.opts.fp8_storage == "Enable for SDXL":
+ enable_fp8 = True
+ else:
+ enable_fp8 = False
+ return enable_fp8
+
+
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
+ if devices.fp8:
+ # prevent model to load state dict in fp8
+ model.half()
+
if not SkipWritingToConfig.skip:
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
@@ -383,6 +402,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer if shared.cmd_opts.no_half:
model.float()
+ model.alphas_cumprod_original = model.alphas_cumprod
devices.dtype_unet = torch.float32
timer.record("apply float()")
else:
@@ -396,7 +416,11 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer if shared.cmd_opts.upcast_sampling and depth_model:
model.depth_model = None
+ alphas_cumprod = model.alphas_cumprod
+ model.alphas_cumprod = None
model.half()
+ model.alphas_cumprod = alphas_cumprod
+ model.alphas_cumprod_original = alphas_cumprod
model.first_stage_model = vae
if depth_model:
model.depth_model = depth_model
@@ -404,6 +428,30 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer devices.dtype_unet = torch.float16
timer.record("apply half()")
+ apply_alpha_schedule_override(model)
+
+ for module in model.modules():
+ if hasattr(module, 'fp16_weight'):
+ del module.fp16_weight
+ if hasattr(module, 'fp16_bias'):
+ del module.fp16_bias
+
+ if check_fp8(model):
+ devices.fp8 = True
+ first_stage = model.first_stage_model
+ model.first_stage_model = None
+ for module in model.modules():
+ if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
+ if shared.opts.cache_fp16_weight:
+ module.fp16_weight = module.weight.data.clone().cpu().half()
+ if module.bias is not None:
+ module.fp16_bias = module.bias.data.clone().cpu().half()
+ module.to(torch.float8_e4m3fn)
+ model.first_stage_model = first_stage
+ timer.record("apply fp8")
+ else:
+ devices.fp8 = False
+
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
model.first_stage_model.to(devices.dtype_vae)
@@ -505,6 +553,48 @@ def repair_config(sd_config): sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
+def rescale_zero_terminal_snr_abar(alphas_cumprod):
+ alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+ # Store old values.
+ alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+ alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+ # Shift so the last timestep is zero.
+ alphas_bar_sqrt -= (alphas_bar_sqrt_T)
+
+ # Scale so the first timestep is back to the old value.
+ alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+ # Convert alphas_bar_sqrt to betas
+ alphas_bar = alphas_bar_sqrt ** 2 # Revert sqrt
+ alphas_bar[-1] = 4.8973451890853435e-08
+ return alphas_bar
+
+
+def apply_alpha_schedule_override(sd_model, p=None):
+ """
+ Applies an override to the alpha schedule of the model according to settings.
+ - downcasts the alpha schedule to half precision
+ - rescales the alpha schedule to have zero terminal SNR
+ """
+
+ if not hasattr(sd_model, 'alphas_cumprod') or not hasattr(sd_model, 'alphas_cumprod_original'):
+ return
+
+ sd_model.alphas_cumprod = sd_model.alphas_cumprod_original.to(shared.device)
+
+ if opts.use_downcasted_alpha_bar:
+ if p is not None:
+ p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
+ sd_model.alphas_cumprod = sd_model.alphas_cumprod.half().to(shared.device)
+
+ if opts.sd_noise_schedule == "Zero Terminal SNR":
+ if p is not None:
+ p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
+ sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(sd_model.alphas_cumprod).to(shared.device)
+
+
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
sdxl_clip_weight = 'conditioner.embedders.1.model.ln_final.weight'
@@ -651,6 +741,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None): else:
weight_dtype_conversion = {
'first_stage_model': None,
+ 'alphas_cumprod': None,
'': torch.float16,
}
@@ -746,7 +837,7 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer): return None
-def reload_model_weights(sd_model=None, info=None):
+def reload_model_weights(sd_model=None, info=None, forced_reload=False):
checkpoint_info = info or select_checkpoint()
timer = Timer()
@@ -758,11 +849,14 @@ def reload_model_weights(sd_model=None, info=None): current_checkpoint_info = None
else:
current_checkpoint_info = sd_model.sd_checkpoint_info
- if sd_model.sd_model_checkpoint == checkpoint_info.filename:
+ if check_fp8(sd_model) != devices.fp8:
+ # load from state dict again to prevent extra numerical errors
+ forced_reload = True
+ elif sd_model.sd_model_checkpoint == checkpoint_info.filename and not forced_reload:
return sd_model
sd_model = reuse_model_from_already_loaded(sd_model, checkpoint_info, timer)
- if sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
+ if not forced_reload and sd_model is not None and sd_model.sd_checkpoint_info.filename == checkpoint_info.filename:
return sd_model
if sd_model is not None:
@@ -793,13 +887,13 @@ def reload_model_weights(sd_model=None, info=None): sd_hijack.model_hijack.hijack(sd_model)
timer.record("hijack")
- script_callbacks.model_loaded_callback(sd_model)
- timer.record("script callbacks")
-
if not sd_model.lowvram:
sd_model.to(devices.device)
timer.record("move model to device")
+ script_callbacks.model_loaded_callback(sd_model)
+ timer.record("script callbacks")
+
print(f"Weights loaded in {timer.summary()}.")
model_data.set_sd_model(sd_model)
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index deab2f6e..b38137eb 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -15,6 +15,7 @@ config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml") config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
config_sdxl = os.path.join(sd_xl_repo_configs_path, "sd_xl_base.yaml")
config_sdxl_refiner = os.path.join(sd_xl_repo_configs_path, "sd_xl_refiner.yaml")
+config_sdxl_inpainting = os.path.join(sd_configs_path, "sd_xl_inpaint.yaml")
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
@@ -71,7 +72,10 @@ def guess_model_config_from_state_dict(sd, filename): sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
- return config_sdxl
+ if diffusion_model_input.shape[1] == 9:
+ return config_sdxl_inpainting
+ else:
+ return config_sdxl
if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
return config_sdxl_refiner
elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py index 01123321..0de17af3 100644 --- a/modules/sd_models_xl.py +++ b/modules/sd_models_xl.py @@ -6,6 +6,7 @@ import sgm.models.diffusion import sgm.modules.diffusionmodules.denoiser_scaling
import sgm.modules.diffusionmodules.discretizer
from modules import devices, shared, prompt_parser
+from modules import torch_utils
def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
@@ -34,6 +35,12 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
+ sd = self.model.state_dict()
+ diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
+ if diffusion_model_input is not None:
+ if diffusion_model_input.shape[1] == 9:
+ x = torch.cat([x] + cond['c_concat'], dim=1)
+
return self.model(x, t, cond)
@@ -84,7 +91,7 @@ sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt def extend_sdxl(model):
"""this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
- dtype = next(model.model.diffusion_model.parameters()).dtype
+ dtype = torch_utils.get_param(model.model.diffusion_model).dtype
model.model.diffusion_model.dtype = dtype
model.model.conditioning_key = 'crossattn'
model.cond_stage_key = 'txt'
@@ -93,7 +100,7 @@ def extend_sdxl(model): model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
- model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
+ model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=torch.float32)
model.conditioner.wrapped = torch.nn.Module()
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 45faae62..a58528a0 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -1,4 +1,4 @@ -from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared
+from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared
# imports for functions that previously were here and are used by other modules
from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # noqa: F401
@@ -6,6 +6,7 @@ from modules.sd_samplers_common import samples_to_image_grid, sample_to_image # all_samplers = [
*sd_samplers_kdiffusion.samplers_data_k_diffusion,
*sd_samplers_timesteps.samplers_data_timesteps,
+ *sd_samplers_lcm.samplers_data_lcm,
]
all_samplers_map = {x.name: x for x in all_samplers}
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py index b8101d38..a73d3b03 100644 --- a/modules/sd_samplers_cfg_denoiser.py +++ b/modules/sd_samplers_cfg_denoiser.py @@ -53,9 +53,13 @@ class CFGDenoiser(torch.nn.Module): self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
+ self.padded_cond_uncond_v0 = False
self.sampler = sampler
self.model_wrap = None
self.p = None
+
+ # NOTE: masking before denoising can cause the original latents to be oversmoothed
+ # as the original latents do not have noise
self.mask_before_denoising = False
@property
@@ -88,6 +92,62 @@ class CFGDenoiser(torch.nn.Module): self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
+ def pad_cond_uncond(self, cond, uncond):
+ empty = shared.sd_model.cond_stage_model_empty_prompt
+ num_repeats = (cond.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+ if num_repeats < 0:
+ cond = pad_cond(cond, -num_repeats, empty)
+ self.padded_cond_uncond = True
+ elif num_repeats > 0:
+ uncond = pad_cond(uncond, num_repeats, empty)
+ self.padded_cond_uncond = True
+
+ return cond, uncond
+
+ def pad_cond_uncond_v0(self, cond, uncond):
+ """
+ Pads the 'uncond' tensor to match the shape of the 'cond' tensor.
+
+ If 'uncond' is a dictionary, it is assumed that the 'crossattn' key holds the tensor to be padded.
+ If 'uncond' is a tensor, it is padded directly.
+
+ If the number of columns in 'uncond' is less than the number of columns in 'cond', the last column of 'uncond'
+ is repeated to match the number of columns in 'cond'.
+
+ If the number of columns in 'uncond' is greater than the number of columns in 'cond', 'uncond' is truncated
+ to match the number of columns in 'cond'.
+
+ Args:
+ cond (torch.Tensor or DictWithShape): The condition tensor to match the shape of 'uncond'.
+ uncond (torch.Tensor or DictWithShape): The tensor to be padded, or a dictionary containing the tensor to be padded.
+
+ Returns:
+ tuple: A tuple containing the 'cond' tensor and the padded 'uncond' tensor.
+
+ Note:
+ This is the padding that was always used in DDIM before version 1.6.0
+ """
+
+ is_dict_cond = isinstance(uncond, dict)
+ uncond_vec = uncond['crossattn'] if is_dict_cond else uncond
+
+ if uncond_vec.shape[1] < cond.shape[1]:
+ last_vector = uncond_vec[:, -1:]
+ last_vector_repeated = last_vector.repeat([1, cond.shape[1] - uncond_vec.shape[1], 1])
+ uncond_vec = torch.hstack([uncond_vec, last_vector_repeated])
+ self.padded_cond_uncond_v0 = True
+ elif uncond_vec.shape[1] > cond.shape[1]:
+ uncond_vec = uncond_vec[:, :cond.shape[1]]
+ self.padded_cond_uncond_v0 = True
+
+ if is_dict_cond:
+ uncond['crossattn'] = uncond_vec
+ else:
+ uncond = uncond_vec
+
+ return cond, uncond
+
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@@ -105,8 +165,21 @@ class CFGDenoiser(torch.nn.Module): assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+ # If we use masks, blending between the denoised and original latent images occurs here.
+ def apply_blend(current_latent):
+ blended_latent = current_latent * self.nmask + self.init_latent * self.mask
+
+ if self.p.scripts is not None:
+ from modules import scripts
+ mba = scripts.MaskBlendArgs(current_latent, self.nmask, self.init_latent, self.mask, blended_latent, denoiser=self, sigma=sigma)
+ self.p.scripts.on_mask_blend(self.p, mba)
+ blended_latent = mba.blended_latent
+
+ return blended_latent
+
+ # Blend in the original latents (before)
if self.mask_before_denoising and self.mask is not None:
- x = self.init_latent * self.mask + self.nmask * x
+ x = apply_blend(x)
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
@@ -130,7 +203,7 @@ class CFGDenoiser(torch.nn.Module): sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
- denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+ denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond, self)
cfg_denoiser_callback(denoiser_params)
x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond
@@ -146,16 +219,11 @@ class CFGDenoiser(torch.nn.Module): sigma_in = sigma_in[:-batch_size]
self.padded_cond_uncond = False
- if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
- empty = shared.sd_model.cond_stage_model_empty_prompt
- num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
- if num_repeats < 0:
- tensor = pad_cond(tensor, -num_repeats, empty)
- self.padded_cond_uncond = True
- elif num_repeats > 0:
- uncond = pad_cond(uncond, num_repeats, empty)
- self.padded_cond_uncond = True
+ self.padded_cond_uncond_v0 = False
+ if shared.opts.pad_cond_uncond_v0 and tensor.shape[1] != uncond.shape[1]:
+ tensor, uncond = self.pad_cond_uncond_v0(tensor, uncond)
+ elif shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+ tensor, uncond = self.pad_cond_uncond(tensor, uncond)
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
@@ -207,8 +275,9 @@ class CFGDenoiser(torch.nn.Module): else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+ # Blend in the original latents (after)
if not self.mask_before_denoising and self.mask is not None:
- denoised = self.init_latent * self.mask + self.nmask * denoised
+ denoised = apply_blend(denoised)
self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index 58efcad2..6bd38e12 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -335,3 +335,10 @@ class Sampler: def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
raise NotImplementedError()
+
+ def add_infotext(self, p):
+ if self.model_wrap_cfg.padded_cond_uncond:
+ p.extra_generation_params["Pad conds"] = True
+
+ if self.model_wrap_cfg.padded_cond_uncond_v0:
+ p.extra_generation_params["Pad conds v0"] = True
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 8a8c87e0..337106c0 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -187,8 +187,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler): samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
@@ -234,8 +233,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler): samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
diff --git a/modules/sd_samplers_lcm.py b/modules/sd_samplers_lcm.py new file mode 100644 index 00000000..59839b72 --- /dev/null +++ b/modules/sd_samplers_lcm.py @@ -0,0 +1,104 @@ +import torch + +from k_diffusion import utils, sampling +from k_diffusion.external import DiscreteEpsDDPMDenoiser +from k_diffusion.sampling import default_noise_sampler, trange + +from modules import shared, sd_samplers_cfg_denoiser, sd_samplers_kdiffusion, sd_samplers_common + + +class LCMCompVisDenoiser(DiscreteEpsDDPMDenoiser): + def __init__(self, model): + timesteps = 1000 + original_timesteps = 50 # LCM Original Timesteps (default=50, for current version of LCM) + self.skip_steps = timesteps // original_timesteps + + alphas_cumprod_valid = torch.zeros((original_timesteps), dtype=torch.float32, device=model.device) + for x in range(original_timesteps): + alphas_cumprod_valid[original_timesteps - 1 - x] = model.alphas_cumprod[timesteps - 1 - x * self.skip_steps] + + super().__init__(model, alphas_cumprod_valid, quantize=None) + + + def get_sigmas(self, n=None,): + if n is None: + return sampling.append_zero(self.sigmas.flip(0)) + + start = self.sigma_to_t(self.sigma_max) + end = self.sigma_to_t(self.sigma_min) + + t = torch.linspace(start, end, n, device=shared.sd_model.device) + + return sampling.append_zero(self.t_to_sigma(t)) + + + def sigma_to_t(self, sigma, quantize=None): + log_sigma = sigma.log() + dists = log_sigma - self.log_sigmas[:, None] + return dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1) + + + def t_to_sigma(self, timestep): + t = torch.clamp(((timestep - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) + return super().t_to_sigma(t) + + + def get_eps(self, *args, **kwargs): + return self.inner_model.apply_model(*args, **kwargs) + + + def get_scaled_out(self, sigma, output, input): + sigma_data = 0.5 + scaled_timestep = utils.append_dims(self.sigma_to_t(sigma), output.ndim) * 10.0 + + c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) + c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 + + return c_out * output + c_skip * input + + + def forward(self, input, sigma, **kwargs): + c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)] + eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs) + return self.get_scaled_out(sigma, input + eps * c_out, input) + + +def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + x = denoised + if sigmas[i + 1] > 0: + x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1]) + return x + + +class CFGDenoiserLCM(sd_samplers_cfg_denoiser.CFGDenoiser): + @property + def inner_model(self): + if self.model_wrap is None: + denoiser = LCMCompVisDenoiser + self.model_wrap = denoiser(shared.sd_model) + + return self.model_wrap + + +class LCMSampler(sd_samplers_kdiffusion.KDiffusionSampler): + def __init__(self, funcname, sd_model, options=None): + super().__init__(funcname, sd_model, options) + self.model_wrap_cfg = CFGDenoiserLCM(self) + self.model_wrap = self.model_wrap_cfg.inner_model + + +samplers_lcm = [('LCM', sample_lcm, ['k_lcm'], {})] +samplers_data_lcm = [ + sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: LCMSampler(funcname, model), aliases, options) + for label, funcname, aliases, options in samplers_lcm +] diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py index b17a8f93..8cc7d384 100644 --- a/modules/sd_samplers_timesteps.py +++ b/modules/sd_samplers_timesteps.py @@ -36,7 +36,7 @@ class CompVisTimestepsVDenoiser(torch.nn.Module): self.inner_model = model
def predict_eps_from_z_and_v(self, x_t, t, v):
- return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
+ return torch.sqrt(self.inner_model.alphas_cumprod)[t.to(torch.int), None, None, None] * v + torch.sqrt(1 - self.inner_model.alphas_cumprod)[t.to(torch.int), None, None, None] * x_t
def forward(self, input, timesteps, **kwargs):
model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
@@ -80,6 +80,7 @@ class CompVisSampler(sd_samplers_common.Sampler): self.eta_default = 0.0
self.model_wrap_cfg = CFGDenoiserTimesteps(self)
+ self.model_wrap = self.model_wrap_cfg.inner_model
def get_timesteps(self, p, steps):
discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
@@ -132,8 +133,7 @@ class CompVisSampler(sd_samplers_common.Sampler): samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
@@ -157,8 +157,7 @@ class CompVisSampler(sd_samplers_common.Sampler): }
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
+ self.add_infotext(p)
return samples
diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 31306d8b..43687e48 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -273,10 +273,11 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified): load_vae(sd_model, vae_file, vae_source) sd_hijack.model_hijack.hijack(sd_model) - script_callbacks.model_loaded_callback(sd_model) if not sd_model.lowvram: sd_model.to(devices.device) + script_callbacks.model_loaded_callback(sd_model) + print("VAE weights loaded.") return sd_model diff --git a/modules/shared.py b/modules/shared.py index 63661939..ccdca4e7 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -1,3 +1,4 @@ +import os
import sys
import gradio as gr
@@ -11,7 +12,7 @@ parser = shared_cmd_options.parser batch_cond_uncond = True # old field, unused now in favor of shared.opts.batch_cond_uncond
parallel_processing_allowed = True
-styles_filename = cmd_opts.styles_file
+styles_filename = cmd_opts.styles_file = cmd_opts.styles_file if len(cmd_opts.styles_file) > 0 else [os.path.join(data_path, 'styles.csv')]
config_filename = cmd_opts.ui_settings_file
hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
diff --git a/modules/shared_gradio_themes.py b/modules/shared_gradio_themes.py index 822db0a9..b6dc3145 100644 --- a/modules/shared_gradio_themes.py +++ b/modules/shared_gradio_themes.py @@ -65,3 +65,7 @@ def reload_gradio_theme(theme_name=None): except Exception as e:
errors.display(e, "changing gradio theme")
shared.gradio_theme = gr.themes.Default(**default_theme_args)
+
+ # append additional values gradio_theme
+ shared.gradio_theme.sd_webui_modal_lightbox_toolbar_opacity = shared.opts.sd_webui_modal_lightbox_toolbar_opacity
+ shared.gradio_theme.sd_webui_modal_lightbox_icon_opacity = shared.opts.sd_webui_modal_lightbox_icon_opacity
diff --git a/modules/shared_init.py b/modules/shared_init.py index d3fb687e..935e3a21 100644 --- a/modules/shared_init.py +++ b/modules/shared_init.py @@ -18,8 +18,10 @@ def initialize(): shared.options_templates = shared_options.options_templates
shared.opts = options.Options(shared_options.options_templates, shared_options.restricted_opts)
shared.restricted_opts = shared_options.restricted_opts
- if os.path.exists(shared.config_filename):
+ try:
shared.opts.load(shared.config_filename)
+ except FileNotFoundError:
+ pass
from modules import devices
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -27,6 +29,7 @@ def initialize(): devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
+ devices.dtype_inference = torch.float32 if cmd_opts.precision == 'full' else devices.dtype
shared.device = devices.device
shared.weight_load_location = None if cmd_opts.lowram else "cpu"
diff --git a/modules/shared_items.py b/modules/shared_items.py index 991971ad..88f63645 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -8,6 +8,11 @@ def realesrgan_models_names(): return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
+def dat_models_names():
+ import modules.dat_model
+ return [x.name for x in modules.dat_model.get_dat_models(None)]
+
+
def postprocessing_scripts():
import modules.scripts
@@ -67,14 +72,14 @@ def reload_hypernetworks(): def get_infotext_names():
- from modules import generation_parameters_copypaste, shared
+ from modules import infotext_utils, shared
res = {}
for info in shared.opts.data_labels.values():
if info.infotext:
res[info.infotext] = 1
- for tab_data in generation_parameters_copypaste.paste_fields.values():
+ for tab_data in infotext_utils.paste_fields.values():
for _, name in tab_data.get("fields") or []:
if isinstance(name, str):
res[name] = 1
diff --git a/modules/shared_options.py b/modules/shared_options.py index d2e86ff1..64f8f196 100644 --- a/modules/shared_options.py +++ b/modules/shared_options.py @@ -1,7 +1,8 @@ +import os
import gradio as gr
-from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes
-from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
+from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes, util, sd_emphasis
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir, default_output_dir # noqa: F401
from modules.shared_cmd_options import cmd_opts
from modules.options import options_section, OptionInfo, OptionHTML, categories
@@ -74,14 +75,14 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" options_templates.update(options_section(('saving-paths', "Paths for saving", "saving"), {
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
- "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
- "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
- "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
+ "outdir_txt2img_samples": OptionInfo(util.truncate_path(os.path.join(default_output_dir, 'txt2img-images')), 'Output directory for txt2img images', component_args=hide_dirs),
+ "outdir_img2img_samples": OptionInfo(util.truncate_path(os.path.join(default_output_dir, 'img2img-images')), 'Output directory for img2img images', component_args=hide_dirs),
+ "outdir_extras_samples": OptionInfo(util.truncate_path(os.path.join(default_output_dir, 'extras-images')), 'Output directory for images from extras tab', component_args=hide_dirs),
"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
- "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
- "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
- "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
- "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
+ "outdir_txt2img_grids": OptionInfo(util.truncate_path(os.path.join(default_output_dir, 'txt2img-grids')), 'Output directory for txt2img grids', component_args=hide_dirs),
+ "outdir_img2img_grids": OptionInfo(util.truncate_path(os.path.join(default_output_dir, 'img2img-grids')), 'Output directory for img2img grids', component_args=hide_dirs),
+ "outdir_save": OptionInfo(util.truncate_path(os.path.join(data_path, 'log', 'images')), "Directory for saving images using the Save button", component_args=hide_dirs),
+ "outdir_init_images": OptionInfo(util.truncate_path(os.path.join(default_output_dir, 'init-images')), "Directory for saving init images when using img2img", component_args=hide_dirs),
}))
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory", "saving"), {
@@ -96,6 +97,9 @@ options_templates.update(options_section(('upscaling', "Upscaling", "postprocess "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
+ "dat_enabled_models": OptionInfo(["DAT x2", "DAT x3", "DAT x4"], "Select which DAT models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.dat_models_names()}),
+ "DAT_tile": OptionInfo(192, "Tile size for DAT upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
+ "DAT_tile_overlap": OptionInfo(8, "Tile overlap for DAT upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
}))
@@ -114,6 +118,7 @@ options_templates.update(options_section(('system', "System", "system"), { "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
+ "enable_upscale_progressbar": OptionInfo(True, "Show a progress bar in the console for tiled upscaling."),
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
@@ -149,7 +154,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), { "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
"sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
- "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+ "emphasis": OptionInfo("Original", "Emphasis mode", gr.Radio, lambda: {"choices": [x.name for x in sd_emphasis.options]}, infotext="Emphasis").info("makes it possible to make model to pay (more:1.1) or (less:0.9) attention to text when you use the syntax in prompt; " + sd_emphasis.get_options_descriptions()),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}, infotext="Clip skip").link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
@@ -176,6 +181,7 @@ For img2img, VAE is used to process user's input image before the sampling, and "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list, infotext='VAE').info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),
+ "auto_vae_precision_bfloat16": OptionInfo(False, "Automatically convert VAE to bfloat16").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image; if enabled, overrides the option below"),
"auto_vae_precision": OptionInfo(True, "Automatically revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
"sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Encoder').info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}, infotext='VAE Decoder').info("method to decode latent to image"),
@@ -195,6 +201,7 @@ options_templates.update(options_section(('img2img', "img2img", "sd"), { "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
"img2img_batch_show_results_limit": OptionInfo(32, "Show the first N batch img2img results in UI", gr.Slider, {"minimum": -1, "maximum": 1000, "step": 1}).info('0: disable, -1: show all images. Too many images can cause lag'),
+ "overlay_inpaint": OptionInfo(True, "Overlay original for inpaint").info("when inpainting, overlay the original image over the areas that weren't inpainted."),
}))
options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
@@ -203,12 +210,16 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd" "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
- "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+ "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt", infotext='Pad conds').info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+ "pad_cond_uncond_v0": OptionInfo(False, "Pad prompt/negative prompt (v0)", infotext='Pad conds v0').info("alternative implementation for the above; used prior to 1.6.0 for DDIM sampler; overrides the above if set; WARNING: truncates negative prompt if it's too long; changes seeds"),
"persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("do not recalculate conds from prompts if prompts have not changed since previous calculation"),
"batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
+ "fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Radio, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."),
+ "cache_fp16_weight": OptionInfo(False, "Cache FP16 weight for LoRA").info("Cache fp16 weight when enabling FP8, will increase the quality of LoRA. Use more system ram."),
}))
options_templates.update(options_section(('compatibility', "Compatibility", "sd"), {
+ "auto_backcompat": OptionInfo(True, "Automatic backward compatibility").info("automatically enable options for backwards compatibility when importing generation parameters from infotext that has program version."),
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
@@ -216,6 +227,7 @@ options_templates.update(options_section(('compatibility', "Compatibility", "sd" "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
"use_old_scheduling": OptionInfo(False, "Use old prompt editing timelines.", infotext="Old prompt editing timelines").info("For [red:green:N]; old: If N < 1, it's a fraction of steps (and hires fix uses range from 0 to 1), if N >= 1, it's an absolute number of steps; new: If N has a decimal point in it, it's a fraction of steps (and hires fix uses range from 1 to 2), othewrwise it's an absolute number of steps"),
+ "use_downcasted_alpha_bar": OptionInfo(False, "Downcast model alphas_cumprod to fp16 before sampling. For reproducing old seeds.", infotext="Downcast alphas_cumprod")
}))
options_templates.update(options_section(('interrogate', "Interrogate"), {
@@ -242,8 +254,10 @@ options_templates.update(options_section(('extra_networks', "Extra Networks", "s "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
"extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
"extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
+ "extra_networks_card_description_is_html": OptionInfo(False, "Treat card description as HTML"),
"extra_networks_card_order_field": OptionInfo("Path", "Default order field for Extra Networks cards", gr.Dropdown, {"choices": ['Path', 'Name', 'Date Created', 'Date Modified']}).needs_reload_ui(),
"extra_networks_card_order": OptionInfo("Ascending", "Default order for Extra Networks cards", gr.Dropdown, {"choices": ['Ascending', 'Descending']}).needs_reload_ui(),
+ "extra_networks_tree_view_default_enabled": OptionInfo(False, "Enables the Extra Networks directory tree view by default").needs_reload_ui(),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
"textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
@@ -257,7 +271,8 @@ options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Word delimiters when editing the prompt with Ctrl+up/down"),
"keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
- "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
+ "disable_token_counters": OptionInfo(False, "Disable prompt token counters"),
+ "include_styles_into_token_counters": OptionInfo(True, "Count tokens of enabled styles").info("When calculating how many tokens the prompt has, also consider tokens added by enabled styles."),
}))
options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), {
@@ -267,7 +282,10 @@ options_templates.update(options_section(('ui_gallery', "Gallery", "ui"), { "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Full page image viewer: show images zoomed in by default"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Full page image viewer: navigate with gamepad"),
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Full page image viewer: gamepad repeat period").info("in milliseconds"),
+ "sd_webui_modal_lightbox_icon_opacity": OptionInfo(1, "Full page image viewer: control icon unfocused opacity", gr.Slider, {"minimum": 0.0, "maximum": 1, "step": 0.01}, onchange=shared.reload_gradio_theme).info('for mouse only').needs_reload_ui(),
+ "sd_webui_modal_lightbox_toolbar_opacity": OptionInfo(0.9, "Full page image viewer: tool bar opacity", gr.Slider, {"minimum": 0.0, "maximum": 1, "step": 0.01}, onchange=shared.reload_gradio_theme).info('for mouse only').needs_reload_ui(),
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox).info("can be any valid CSS value, for example 768px or 20em").needs_reload_ui(),
+ "open_dir_button_choice": OptionInfo("Subdirectory", "What directory the [📂] button opens", gr.Radio, {"choices": ["Output Root", "Subdirectory", "Subdirectory (even temp dir)"]}),
}))
options_templates.update(options_section(('ui_alternatives', "UI alternatives", "ui"), {
@@ -279,6 +297,7 @@ options_templates.update(options_section(('ui_alternatives', "UI alternatives", "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
"txt2img_settings_accordion": OptionInfo(False, "Settings in txt2img hidden under Accordion").needs_reload_ui(),
"img2img_settings_accordion": OptionInfo(False, "Settings in img2img hidden under Accordion").needs_reload_ui(),
+ "interrupt_after_current": OptionInfo(True, "Don't Interrupt in the middle").info("when using Interrupt button, if generating more than one image, stop after the generation of an image has finished, instead of immediately"),
}))
options_templates.update(options_section(('ui', "User interface", "ui"), {
@@ -354,6 +373,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, infotext='UniPC skip type'),
'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
+ 'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models")
}))
options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {
diff --git a/modules/shared_state.py b/modules/shared_state.py index a68789cc..33996691 100644 --- a/modules/shared_state.py +++ b/modules/shared_state.py @@ -12,6 +12,7 @@ log = logging.getLogger(__name__) class State:
skipped = False
interrupted = False
+ stopping_generation = False
job = ""
job_no = 0
job_count = 0
@@ -79,6 +80,10 @@ class State: self.interrupted = True
log.info("Received interrupt request")
+ def stop_generating(self):
+ self.stopping_generation = True
+ log.info("Received stop generating request")
+
def nextjob(self):
if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps == -1:
self.do_set_current_image()
@@ -91,6 +96,7 @@ class State: obj = {
"skipped": self.skipped,
"interrupted": self.interrupted,
+ "stopping_generation": self.stopping_generation,
"job": self.job,
"job_count": self.job_count,
"job_timestamp": self.job_timestamp,
@@ -114,6 +120,7 @@ class State: self.id_live_preview = 0
self.skipped = False
self.interrupted = False
+ self.stopping_generation = False
self.textinfo = None
self.job = job
devices.torch_gc()
diff --git a/modules/styles.py b/modules/styles.py index 81d9800d..60bd8a7f 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -1,16 +1,16 @@ +from pathlib import Path
+from modules import errors
import csv
-import fnmatch
import os
-import os.path
import typing
import shutil
class PromptStyle(typing.NamedTuple):
name: str
- prompt: str
- negative_prompt: str
- path: str = None
+ prompt: str | None
+ negative_prompt: str | None
+ path: str | None = None
def merge_prompts(style_prompt: str, prompt: str) -> str:
@@ -30,38 +30,29 @@ def apply_styles_to_prompt(prompt, styles): return prompt
-def unwrap_style_text_from_prompt(style_text, prompt):
- """
- Checks the prompt to see if the style text is wrapped around it. If so,
- returns True plus the prompt text without the style text. Otherwise, returns
- False with the original prompt.
+def extract_style_text_from_prompt(style_text, prompt):
+ """This function extracts the text from a given prompt based on a provided style text. It checks if the style text contains the placeholder {prompt} or if it appears at the end of the prompt. If a match is found, it returns True along with the extracted text. Otherwise, it returns False and the original prompt.
- Note that the "cleaned" version of the style text is only used for matching
- purposes here. It isn't returned; the original style text is not modified.
+ extract_style_text_from_prompt("masterpiece", "1girl, art by greg, masterpiece") outputs (True, "1girl, art by greg")
+ extract_style_text_from_prompt("masterpiece, {prompt}", "masterpiece, 1girl, art by greg") outputs (True, "1girl, art by greg")
+ extract_style_text_from_prompt("masterpiece, {prompt}", "exquisite, 1girl, art by greg") outputs (False, "exquisite, 1girl, art by greg")
"""
- stripped_prompt = prompt
- stripped_style_text = style_text
+
+ stripped_prompt = prompt.strip()
+ stripped_style_text = style_text.strip()
+
if "{prompt}" in stripped_style_text:
- # Work out whether the prompt is wrapped in the style text. If so, we
- # return True and the "inner" prompt text that isn't part of the style.
- try:
- left, right = stripped_style_text.split("{prompt}", 2)
- except ValueError as e:
- # If the style text has multple "{prompt}"s, we can't split it into
- # two parts. This is an error, but we can't do anything about it.
- print(f"Unable to compare style text to prompt:\n{style_text}")
- print(f"Error: {e}")
- return False, prompt
+ left, right = stripped_style_text.split("{prompt}", 2)
if stripped_prompt.startswith(left) and stripped_prompt.endswith(right):
- prompt = stripped_prompt[len(left) : len(stripped_prompt) - len(right)]
+ prompt = stripped_prompt[len(left):len(stripped_prompt)-len(right)]
return True, prompt
else:
- # Work out whether the given prompt ends with the style text. If so, we
- # return True and the prompt text up to where the style text starts.
if stripped_prompt.endswith(stripped_style_text):
- prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)]
- if prompt.endswith(", "):
+ prompt = stripped_prompt[:len(stripped_prompt)-len(stripped_style_text)]
+
+ if prompt.endswith(', '):
prompt = prompt[:-2]
+
return True, prompt
return False, prompt
@@ -76,15 +67,11 @@ def extract_original_prompts(style: PromptStyle, prompt, negative_prompt): if not style.prompt and not style.negative_prompt:
return False, prompt, negative_prompt
- match_positive, extracted_positive = unwrap_style_text_from_prompt(
- style.prompt, prompt
- )
+ match_positive, extracted_positive = extract_style_text_from_prompt(style.prompt, prompt)
if not match_positive:
return False, prompt, negative_prompt
- match_negative, extracted_negative = unwrap_style_text_from_prompt(
- style.negative_prompt, negative_prompt
- )
+ match_negative, extracted_negative = extract_style_text_from_prompt(style.negative_prompt, negative_prompt)
if not match_negative:
return False, prompt, negative_prompt
@@ -92,14 +79,19 @@ def extract_original_prompts(style: PromptStyle, prompt, negative_prompt): class StyleDatabase:
- def __init__(self, path: str):
+ def __init__(self, paths: list[str | Path]):
self.no_style = PromptStyle("None", "", "", None)
self.styles = {}
- self.path = path
-
- folder, file = os.path.split(self.path)
- filename, _, ext = file.partition('*')
- self.default_path = os.path.join(folder, filename + ext)
+ self.paths = paths
+ self.all_styles_files: list[Path] = []
+
+ folder, file = os.path.split(self.paths[0])
+ if '*' in file or '?' in file:
+ # if the first path is a wildcard pattern, find the first match else use "folder/styles.csv" as the default path
+ self.default_path = next(Path(folder).glob(file), Path(os.path.join(folder, 'styles.csv')))
+ self.paths.insert(0, self.default_path)
+ else:
+ self.default_path = Path(self.paths[0])
self.prompt_fields = [field for field in PromptStyle._fields if field != "path"]
@@ -112,57 +104,58 @@ class StyleDatabase: """
self.styles.clear()
- path, filename = os.path.split(self.path)
-
- if "*" in filename:
- fileglob = filename.split("*")[0] + "*.csv"
- filelist = []
- for file in os.listdir(path):
- if fnmatch.fnmatch(file, fileglob):
- filelist.append(file)
- # Add a visible divider to the style list
- half_len = round(len(file) / 2)
- divider = f"{'-' * (20 - half_len)} {file.upper()}"
- divider = f"{divider} {'-' * (40 - len(divider))}"
- self.styles[divider] = PromptStyle(
- f"{divider}", None, None, "do_not_save"
+ # scans for all styles files
+ all_styles_files = []
+ for pattern in self.paths:
+ folder, file = os.path.split(pattern)
+ if '*' in file or '?' in file:
+ found_files = Path(folder).glob(file)
+ [all_styles_files.append(file) for file in found_files]
+ else:
+ # if os.path.exists(pattern):
+ all_styles_files.append(Path(pattern))
+
+ # Remove any duplicate entries
+ seen = set()
+ self.all_styles_files = [s for s in all_styles_files if not (s in seen or seen.add(s))]
+
+ for styles_file in self.all_styles_files:
+ if len(all_styles_files) > 1:
+ # add divider when more than styles file
+ # '---------------- STYLES ----------------'
+ divider = f' {styles_file.stem.upper()} '.center(40, '-')
+ self.styles[divider] = PromptStyle(f"{divider}", None, None, "do_not_save")
+ if styles_file.is_file():
+ self.load_from_csv(styles_file)
+
+ def load_from_csv(self, path: str | Path):
+ try:
+ with open(path, "r", encoding="utf-8-sig", newline="") as file:
+ reader = csv.DictReader(file, skipinitialspace=True)
+ for row in reader:
+ # Ignore empty rows or rows starting with a comment
+ if not row or row["name"].startswith("#"):
+ continue
+ # Support loading old CSV format with "name, text"-columns
+ prompt = row["prompt"] if "prompt" in row else row["text"]
+ negative_prompt = row.get("negative_prompt", "")
+ # Add style to database
+ self.styles[row["name"]] = PromptStyle(
+ row["name"], prompt, negative_prompt, str(path)
)
- # Add styles from this CSV file
- self.load_from_csv(os.path.join(path, file))
- if len(filelist) == 0:
- print(f"No styles found in {path} matching {fileglob}")
- return
- elif not os.path.exists(self.path):
- print(f"Style database not found: {self.path}")
- return
- else:
- self.load_from_csv(self.path)
-
- def load_from_csv(self, path: str):
- with open(path, "r", encoding="utf-8-sig", newline="") as file:
- reader = csv.DictReader(file, skipinitialspace=True)
- for row in reader:
- # Ignore empty rows or rows starting with a comment
- if not row or row["name"].startswith("#"):
- continue
- # Support loading old CSV format with "name, text"-columns
- prompt = row["prompt"] if "prompt" in row else row["text"]
- negative_prompt = row.get("negative_prompt", "")
- # Add style to database
- self.styles[row["name"]] = PromptStyle(
- row["name"], prompt, negative_prompt, path
- )
+ except Exception:
+ errors.report(f'Error loading styles from {path}: ', exc_info=True)
def get_style_paths(self) -> set:
"""Returns a set of all distinct paths of files that styles are loaded from."""
# Update any styles without a path to the default path
for style in list(self.styles.values()):
if not style.path:
- self.styles[style.name] = style._replace(path=self.default_path)
+ self.styles[style.name] = style._replace(path=str(self.default_path))
# Create a list of all distinct paths, including the default path
style_paths = set()
- style_paths.add(self.default_path)
+ style_paths.add(str(self.default_path))
for _, style in self.styles.items():
if style.path:
style_paths.add(style.path)
@@ -190,7 +183,6 @@ class StyleDatabase: def save_styles(self, path: str = None) -> None:
# The path argument is deprecated, but kept for backwards compatibility
- _ = path
style_paths = self.get_style_paths()
diff --git a/modules/sysinfo.py b/modules/sysinfo.py index b669edd0..f336251e 100644 --- a/modules/sysinfo.py +++ b/modules/sysinfo.py @@ -24,13 +24,13 @@ environment_whitelist = { "XFORMERS_PACKAGE",
"CLIP_PACKAGE",
"OPENCLIP_PACKAGE",
+ "ASSETS_REPO",
"STABLE_DIFFUSION_REPO",
"K_DIFFUSION_REPO",
- "CODEFORMER_REPO",
"BLIP_REPO",
+ "ASSETS_COMMIT_HASH",
"STABLE_DIFFUSION_COMMIT_HASH",
"K_DIFFUSION_COMMIT_HASH",
- "CODEFORMER_COMMIT_HASH",
"BLIP_COMMIT_HASH",
"COMMANDLINE_ARGS",
"IGNORE_CMD_ARGS_ERRORS",
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 04dda585..6d815c0b 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -11,7 +11,6 @@ import safetensors.torch import numpy as np
from PIL import Image, PngImagePlugin
-from torch.utils.tensorboard import SummaryWriter
from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
import modules.textual_inversion.dataset
@@ -151,6 +150,7 @@ class EmbeddingDatabase: return embedding
def get_expected_shape(self):
+ devices.torch_npu_set_device()
vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
return vec.shape[1]
@@ -344,6 +344,7 @@ def write_loss(log_directory, filename, step, epoch_len, values): })
def tensorboard_setup(log_directory):
+ from torch.utils.tensorboard import SummaryWriter
os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
return SummaryWriter(
log_dir=os.path.join(log_directory, "tensorboard"),
@@ -448,8 +449,12 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
old_parallel_processing_allowed = shared.parallel_processing_allowed
+ tensorboard_writer = None
if shared.opts.training_enable_tensorboard:
- tensorboard_writer = tensorboard_setup(log_directory)
+ try:
+ tensorboard_writer = tensorboard_setup(log_directory)
+ except ImportError:
+ errors.report("Error initializing tensorboard", exc_info=True)
pin_memory = shared.opts.pin_memory
@@ -622,7 +627,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
last_saved_image += f", prompt: {preview_text}"
- if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+ if tensorboard_writer and shared.opts.training_tensorboard_save_images:
tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)
if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
diff --git a/modules/torch_utils.py b/modules/torch_utils.py new file mode 100644 index 00000000..e5b52393 --- /dev/null +++ b/modules/torch_utils.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +import torch.nn + + +def get_param(model) -> torch.nn.Parameter: + """ + Find the first parameter in a model or module. + """ + if hasattr(model, "model") and hasattr(model.model, "parameters"): + # Unpeel a model descriptor to get at the actual Torch module. + model = model.model + + for param in model.parameters(): + return param + + raise ValueError(f"No parameters found in model {model!r}") diff --git a/modules/txt2img.py b/modules/txt2img.py index e4e18ceb..fc56b8a8 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,17 +1,22 @@ +import json
from contextlib import closing
import modules.scripts
-from modules import processing
-from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules import processing, infotext_utils
+from modules.infotext_utils import create_override_settings_dict, parse_generation_parameters
from modules.shared import opts
import modules.shared as shared
from modules.ui import plaintext_to_html
+from PIL import Image
import gradio as gr
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
+def txt2img_create_processing(id_task: str, request: gr.Request, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, *args, force_enable_hr=False):
override_settings = create_override_settings_dict(override_settings_texts)
+ if force_enable_hr:
+ enable_hr = True
+
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -27,7 +32,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step width=width,
height=height,
enable_hr=enable_hr,
- denoising_strength=denoising_strength if enable_hr else None,
+ denoising_strength=denoising_strength,
hr_scale=hr_scale,
hr_upscaler=hr_upscaler,
hr_second_pass_steps=hr_second_pass_steps,
@@ -48,8 +53,58 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step if shared.opts.enable_console_prompts:
print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)
+ return p
+
+
+def txt2img_upscale(id_task: str, request: gr.Request, gallery, gallery_index, generation_info, *args):
+ assert len(gallery) > 0, 'No image to upscale'
+ assert 0 <= gallery_index < len(gallery), f'Bad image index: {gallery_index}'
+
+ p = txt2img_create_processing(id_task, request, *args, force_enable_hr=True)
+ p.batch_size = 1
+ p.n_iter = 1
+ # txt2img_upscale attribute that signifies this is called by txt2img_upscale
+ p.txt2img_upscale = True
+
+ geninfo = json.loads(generation_info)
+
+ image_info = gallery[gallery_index] if 0 <= gallery_index < len(gallery) else gallery[0]
+ p.firstpass_image = infotext_utils.image_from_url_text(image_info)
+
+ parameters = parse_generation_parameters(geninfo.get('infotexts')[gallery_index], [])
+ p.seed = parameters.get('Seed', -1)
+ p.subseed = parameters.get('Variation seed', -1)
+
+ p.override_settings['save_images_before_highres_fix'] = False
+
+ with closing(p):
+ processed = modules.scripts.scripts_txt2img.run(p, *p.script_args)
+
+ if processed is None:
+ processed = processing.process_images(p)
+
+ shared.total_tqdm.clear()
+
+ new_gallery = []
+ for i, image in enumerate(gallery):
+ if i == gallery_index:
+ geninfo["infotexts"][gallery_index: gallery_index+1] = processed.infotexts
+ new_gallery.extend(processed.images)
+ else:
+ fake_image = Image.new(mode="RGB", size=(1, 1))
+ fake_image.already_saved_as = image["name"].rsplit('?', 1)[0]
+ new_gallery.append(fake_image)
+
+ geninfo["infotexts"][gallery_index] = processed.info
+
+ return new_gallery, json.dumps(geninfo), plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")
+
+
+def txt2img(id_task: str, request: gr.Request, *args):
+ p = txt2img_create_processing(id_task, request, *args)
+
with closing(p):
- processed = modules.scripts.scripts_txt2img.run(p, *args)
+ processed = modules.scripts.scripts_txt2img.run(p, *p.script_args)
if processed is None:
processed = processing.process_images(p)
diff --git a/modules/ui.py b/modules/ui.py index d80486dd..dcba8e88 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -13,7 +13,7 @@ from PIL import Image, PngImagePlugin # noqa: F401 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import gradio_extensons # noqa: F401
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
from modules.paths import script_path
from modules.ui_common import create_refresh_button
@@ -21,14 +21,14 @@ from modules.ui_gradio_extensions import reload_javascript from modules.shared import opts, cmd_opts
-import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.infotext_utils as parameters_copypaste
import modules.hypernetworks.ui as hypernetworks_ui
import modules.textual_inversion.ui as textual_inversion_ui
import modules.textual_inversion.textual_inversion as textual_inversion
import modules.shared as shared
from modules import prompt_parser
from modules.sd_hijack import model_hijack
-from modules.generation_parameters_copypaste import image_from_url_text
+from modules.infotext_utils import image_from_url_text, PasteField
create_setting_component = ui_settings.create_setting_component
@@ -151,7 +151,18 @@ def connect_clear_prompt(button): )
-def update_token_counter(text, steps, *, is_positive=True):
+def update_token_counter(text, steps, styles, *, is_positive=True):
+ params = script_callbacks.BeforeTokenCounterParams(text, steps, styles, is_positive=is_positive)
+ script_callbacks.before_token_counter_callback(params)
+ text = params.prompt
+ steps = params.steps
+ styles = params.styles
+ is_positive = params.is_positive
+
+ if shared.opts.include_styles_into_token_counters:
+ apply_styles = shared.prompt_styles.apply_styles_to_prompt if is_positive else shared.prompt_styles.apply_negative_styles_to_prompt
+ text = apply_styles(text, styles)
+
try:
text, _ = extra_networks.parse_prompt(text)
@@ -173,9 +184,8 @@ def update_token_counter(text, steps, *, is_positive=True): return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
-def update_negative_prompt_token_counter(text, steps):
- return update_token_counter(text, steps, is_positive=False)
-
+def update_negative_prompt_token_counter(*args):
+ return update_token_counter(*args, is_positive=False)
def setup_progressbar(*args, **kwargs):
@@ -267,7 +277,7 @@ def create_ui(): dummy_component = gr.Label(visible=False)
- extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
+ extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs", elem_classes=["extra-networks"])
extra_tabs.__enter__()
with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
@@ -376,50 +386,60 @@ def create_ui(): show_progress=False,
)
- txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
+ output_panel = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
+
+ txt2img_inputs = [
+ dummy_component,
+ toprow.prompt,
+ toprow.negative_prompt,
+ toprow.ui_styles.dropdown,
+ steps,
+ sampler_name,
+ batch_count,
+ batch_size,
+ cfg_scale,
+ height,
+ width,
+ enable_hr,
+ denoising_strength,
+ hr_scale,
+ hr_upscaler,
+ hr_second_pass_steps,
+ hr_resize_x,
+ hr_resize_y,
+ hr_checkpoint_name,
+ hr_sampler_name,
+ hr_prompt,
+ hr_negative_prompt,
+ override_settings,
+ ] + custom_inputs
+
+ txt2img_outputs = [
+ output_panel.gallery,
+ output_panel.generation_info,
+ output_panel.infotext,
+ output_panel.html_log,
+ ]
txt2img_args = dict(
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
_js="submit",
- inputs=[
- dummy_component,
- toprow.prompt,
- toprow.negative_prompt,
- toprow.ui_styles.dropdown,
- steps,
- sampler_name,
- batch_count,
- batch_size,
- cfg_scale,
- height,
- width,
- enable_hr,
- denoising_strength,
- hr_scale,
- hr_upscaler,
- hr_second_pass_steps,
- hr_resize_x,
- hr_resize_y,
- hr_checkpoint_name,
- hr_sampler_name,
- hr_prompt,
- hr_negative_prompt,
- override_settings,
-
- ] + custom_inputs,
-
- outputs=[
- txt2img_gallery,
- generation_info,
- html_info,
- html_log,
- ],
+ inputs=txt2img_inputs,
+ outputs=txt2img_outputs,
show_progress=False,
)
toprow.prompt.submit(**txt2img_args)
toprow.submit.click(**txt2img_args)
+ output_panel.button_upscale.click(
+ fn=wrap_gradio_gpu_call(modules.txt2img.txt2img_upscale, extra_outputs=[None, '', '']),
+ _js="submit_txt2img_upscale",
+ inputs=txt2img_inputs[0:1] + [output_panel.gallery, dummy_component, output_panel.generation_info] + txt2img_inputs[1:],
+ outputs=txt2img_outputs,
+ show_progress=False,
+ )
+
res_switch_btn.click(fn=None, _js="function(){switchWidthHeight('txt2img')}", inputs=None, outputs=None, show_progress=False)
toprow.restore_progress_button.click(
@@ -427,37 +447,37 @@ def create_ui(): _js="restoreProgressTxt2img",
inputs=[dummy_component],
outputs=[
- txt2img_gallery,
- generation_info,
- html_info,
- html_log,
+ output_panel.gallery,
+ output_panel.generation_info,
+ output_panel.infotext,
+ output_panel.html_log,
],
show_progress=False,
)
txt2img_paste_fields = [
- (toprow.prompt, "Prompt"),
- (toprow.negative_prompt, "Negative prompt"),
- (steps, "Steps"),
- (sampler_name, "Sampler"),
- (cfg_scale, "CFG scale"),
- (width, "Size-1"),
- (height, "Size-2"),
- (batch_size, "Batch size"),
- (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
- (denoising_strength, "Denoising strength"),
- (enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
- (hr_scale, "Hires upscale"),
- (hr_upscaler, "Hires upscaler"),
- (hr_second_pass_steps, "Hires steps"),
- (hr_resize_x, "Hires resize-1"),
- (hr_resize_y, "Hires resize-2"),
- (hr_checkpoint_name, "Hires checkpoint"),
- (hr_sampler_name, "Hires sampler"),
- (hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()),
- (hr_prompt, "Hires prompt"),
- (hr_negative_prompt, "Hires negative prompt"),
- (hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
+ PasteField(toprow.prompt, "Prompt", api="prompt"),
+ PasteField(toprow.negative_prompt, "Negative prompt", api="negative_prompt"),
+ PasteField(steps, "Steps", api="steps"),
+ PasteField(sampler_name, "Sampler", api="sampler_name"),
+ PasteField(cfg_scale, "CFG scale", api="cfg_scale"),
+ PasteField(width, "Size-1", api="width"),
+ PasteField(height, "Size-2", api="height"),
+ PasteField(batch_size, "Batch size", api="batch_size"),
+ PasteField(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update(), api="styles"),
+ PasteField(denoising_strength, "Denoising strength", api="denoising_strength"),
+ PasteField(enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d), api="enable_hr"),
+ PasteField(hr_scale, "Hires upscale", api="hr_scale"),
+ PasteField(hr_upscaler, "Hires upscaler", api="hr_upscaler"),
+ PasteField(hr_second_pass_steps, "Hires steps", api="hr_second_pass_steps"),
+ PasteField(hr_resize_x, "Hires resize-1", api="hr_resize_x"),
+ PasteField(hr_resize_y, "Hires resize-2", api="hr_resize_y"),
+ PasteField(hr_checkpoint_name, "Hires checkpoint", api="hr_checkpoint_name"),
+ PasteField(hr_sampler_name, "Hires sampler", api="hr_sampler_name"),
+ PasteField(hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" else gr.update()),
+ PasteField(hr_prompt, "Hires prompt", api="hr_prompt"),
+ PasteField(hr_negative_prompt, "Hires negative prompt", api="hr_negative_prompt"),
+ PasteField(hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
*scripts.scripts_txt2img.infotext_fields
]
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
@@ -476,11 +496,13 @@ def create_ui(): height,
]
- toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
- toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
+ toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
+ toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
+ toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
- ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
+ ui_extra_networks.setup_ui(extra_networks_ui, output_panel.gallery)
extra_tabs.__exit__()
@@ -490,7 +512,7 @@ def create_ui(): with gr.Blocks(analytics_enabled=False) as img2img_interface:
toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box)
- extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs")
+ extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs", elem_classes=["extra-networks"])
extra_tabs.__enter__()
with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
@@ -523,7 +545,7 @@ def create_ui(): if category == "image":
with gr.Tabs(elem_id="mode_img2img"):
- img2img_selected_tab = gr.State(0)
+ img2img_selected_tab = gr.Number(value=0, visible=False)
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA", height=opts.img2img_editor_height)
@@ -604,7 +626,7 @@ def create_ui(): elif category == "dimensions":
with FormRow():
with gr.Column(elem_id="img2img_column_size", scale=4):
- selected_scale_tab = gr.State(value=0)
+ selected_scale_tab = gr.Number(value=0, visible=False)
with gr.Tabs():
with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
@@ -711,7 +733,7 @@ def create_ui(): outputs=[inpaint_controls, mask_alpha],
)
- img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
+ output_panel = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
img2img_args = dict(
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
@@ -756,10 +778,10 @@ def create_ui(): img2img_batch_png_info_dir,
] + custom_inputs,
outputs=[
- img2img_gallery,
- generation_info,
- html_info,
- html_log,
+ output_panel.gallery,
+ output_panel.generation_info,
+ output_panel.infotext,
+ output_panel.html_log,
],
show_progress=False,
)
@@ -797,10 +819,10 @@ def create_ui(): _js="restoreProgressImg2img",
inputs=[dummy_component],
outputs=[
- img2img_gallery,
- generation_info,
- html_info,
- html_log,
+ output_panel.gallery,
+ output_panel.generation_info,
+ output_panel.infotext,
+ output_panel.html_log,
],
show_progress=False,
)
@@ -815,8 +837,10 @@ def create_ui(): **interrogate_args,
)
- toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
- toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
+ toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
+ toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
+ toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
+ toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
img2img_paste_fields = [
(toprow.prompt, "Prompt"),
@@ -831,6 +855,10 @@ def create_ui(): (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
(denoising_strength, "Denoising strength"),
(mask_blur, "Mask blur"),
+ (inpainting_mask_invert, 'Mask mode'),
+ (inpainting_fill, 'Masked content'),
+ (inpaint_full_res, 'Inpaint area'),
+ (inpaint_full_res_padding, 'Masked area padding'),
*scripts.scripts_img2img.infotext_fields
]
parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
@@ -840,7 +868,7 @@ def create_ui(): ))
extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
- ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)
+ ui_extra_networks.setup_ui(extra_networks_ui_img2img, output_panel.gallery)
extra_tabs.__exit__()
@@ -850,7 +878,7 @@ def create_ui(): ui_postprocessing.create_ui()
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
- with gr.Row(equal_height=False):
+ with ResizeHandleRow(equal_height=False):
with gr.Column(variant='panel'):
image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
@@ -878,7 +906,7 @@ def create_ui(): with gr.Row(equal_height=False):
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
- with gr.Row(variant="compact", equal_height=False):
+ with ResizeHandleRow(variant="compact", equal_height=False):
with gr.Tabs(elem_id="train_tabs"):
with gr.Tab(label="Create embedding", id="create_embedding"):
@@ -1086,6 +1114,7 @@ def create_ui(): )
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
+ ui_settings_from_file = loadsave.ui_settings.copy()
settings = ui_settings.UiSettings()
settings.create_ui(loadsave, dummy_component)
@@ -1146,7 +1175,8 @@ def create_ui(): modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=settings.component_dict['sd_model_checkpoint'])
- loadsave.dump_defaults()
+ if ui_settings_from_file != loadsave.ui_settings:
+ loadsave.dump_defaults()
demo.ui_loadsave = loadsave
return demo
@@ -1208,3 +1238,5 @@ def setup_ui_api(app): app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
+ import fastapi.staticfiles
+ app.mount("/webui-assets", fastapi.staticfiles.StaticFiles(directory=launch_utils.repo_dir('stable-diffusion-webui-assets')), name="webui-assets")
diff --git a/modules/ui_common.py b/modules/ui_common.py index 032ec4af..cf1b8b32 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -1,3 +1,5 @@ +import csv
+import dataclasses
import json
import html
import os
@@ -7,11 +9,11 @@ import sys import gradio as gr
import subprocess as sp
-from modules import call_queue, shared
-from modules.generation_parameters_copypaste import image_from_url_text
+from modules import call_queue, shared, ui_tempdir
+from modules.infotext_utils import image_from_url_text
import modules.images
from modules.ui_components import ToolButton
-import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.infotext_utils as parameters_copypaste
folder_symbol = '\U0001f4c2' # 📂
refresh_symbol = '\U0001f504' # 🔄
@@ -35,12 +37,38 @@ def plaintext_to_html(text, classname=None): return f"<p class='{classname}'>{content}</p>" if classname else f"<p>{content}</p>"
+def update_logfile(logfile_path, fields):
+ """Update a logfile from old format to new format to maintain CSV integrity."""
+ with open(logfile_path, "r", encoding="utf8", newline="") as file:
+ reader = csv.reader(file)
+ rows = list(reader)
+
+ # blank file: leave it as is
+ if not rows:
+ return
+
+ # file is already synced, do nothing
+ if len(rows[0]) == len(fields):
+ return
+
+ rows[0] = fields
+
+ # append new fields to each row as empty values
+ for row in rows[1:]:
+ while len(row) < len(fields):
+ row.append("")
+
+ with open(logfile_path, "w", encoding="utf8", newline="") as file:
+ writer = csv.writer(file)
+ writer.writerows(rows)
+
+
def save_files(js_data, images, do_make_zip, index):
- import csv
filenames = []
fullfns = []
+ parsed_infotexts = []
- #quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
+ # quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
class MyObject:
def __init__(self, d=None):
if d is not None:
@@ -48,35 +76,55 @@ def save_files(js_data, images, do_make_zip, index): setattr(self, key, value)
data = json.loads(js_data)
-
p = MyObject(data)
+
path = shared.opts.outdir_save
save_to_dirs = shared.opts.use_save_to_dirs_for_ui
extension: str = shared.opts.samples_format
start_index = 0
- only_one = False
if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
- only_one = True
images = [images[index]]
start_index = index
os.makedirs(shared.opts.outdir_save, exist_ok=True)
- with open(os.path.join(shared.opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
+ fields = [
+ "prompt",
+ "seed",
+ "width",
+ "height",
+ "sampler",
+ "cfgs",
+ "steps",
+ "filename",
+ "negative_prompt",
+ "sd_model_name",
+ "sd_model_hash",
+ ]
+ logfile_path = os.path.join(shared.opts.outdir_save, "log.csv")
+
+ # NOTE: ensure csv integrity when fields are added by
+ # updating headers and padding with delimeters where needed
+ if os.path.exists(logfile_path):
+ update_logfile(logfile_path, fields)
+
+ with open(logfile_path, "a", encoding="utf8", newline='') as file:
at_start = file.tell() == 0
writer = csv.writer(file)
if at_start:
- writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
+ writer.writerow(fields)
for image_index, filedata in enumerate(images, start_index):
image = image_from_url_text(filedata)
is_grid = image_index < p.index_of_first_image
- i = 0 if is_grid else (image_index - p.index_of_first_image)
p.batch_index = image_index-1
- fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
+
+ parameters = parameters_copypaste.parse_generation_parameters(data["infotexts"][image_index], [])
+ parsed_infotexts.append(parameters)
+ fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=parameters['Seed'], prompt=parameters['Prompt'], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
filename = os.path.relpath(fullfn, path)
filenames.append(filename)
@@ -85,12 +133,12 @@ def save_files(js_data, images, do_make_zip, index): filenames.append(os.path.basename(txt_fullfn))
fullfns.append(txt_fullfn)
- writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+ writer.writerow([parsed_infotexts[0]['Prompt'], parsed_infotexts[0]['Seed'], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], parsed_infotexts[0]['Negative prompt'], data["sd_model_name"], data["sd_model_hash"]])
# Make Zip
if do_make_zip:
- zip_fileseed = p.all_seeds[index-1] if only_one else p.all_seeds[0]
- namegen = modules.images.FilenameGenerator(p, zip_fileseed, p.all_prompts[0], image, True)
+ p.all_seeds = [parameters['Seed'] for parameters in parsed_infotexts]
+ namegen = modules.images.FilenameGenerator(p, parsed_infotexts[0]['Seed'], parsed_infotexts[0]['Prompt'], image, True)
zip_filename = namegen.apply(shared.opts.grid_zip_filename_pattern or "[datetime]_[[model_name]]_[seed]-[seed_last]")
zip_filepath = os.path.join(path, f"{zip_filename}.zip")
@@ -104,31 +152,55 @@ def save_files(js_data, images, do_make_zip, index): return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
+@dataclasses.dataclass
+class OutputPanel:
+ gallery = None
+ generation_info = None
+ infotext = None
+ html_log = None
+ button_upscale = None
+
+
def create_output_panel(tabname, outdir, toprow=None):
+ res = OutputPanel()
+
+ def open_folder(f, images=None, index=None):
+ if shared.cmd_opts.hide_ui_dir_config:
+ return
+
+ try:
+ if 'Sub' in shared.opts.open_dir_button_choice:
+ image_dir = os.path.split(images[index]["name"].rsplit('?', 1)[0])[0]
+ if 'temp' in shared.opts.open_dir_button_choice or not ui_tempdir.is_gradio_temp_path(image_dir):
+ f = image_dir
+ except Exception:
+ pass
- def open_folder(f):
if not os.path.exists(f):
- print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+ msg = f'Folder "{f}" does not exist. After you create an image, the folder will be created.'
+ print(msg)
+ gr.Info(msg)
return
elif not os.path.isdir(f):
- print(f"""
+ msg = f"""
WARNING
An open_folder request was made with an argument that is not a folder.
This could be an error or a malicious attempt to run code on your computer.
Requested path was: {f}
-""", file=sys.stderr)
+"""
+ print(msg, file=sys.stderr)
+ gr.Warning(msg)
return
- if not shared.cmd_opts.hide_ui_dir_config:
- path = os.path.normpath(f)
- if platform.system() == "Windows":
- os.startfile(path)
- elif platform.system() == "Darwin":
- sp.Popen(["open", path])
- elif "microsoft-standard-WSL2" in platform.uname().release:
- sp.Popen(["wsl-open", path])
- else:
- sp.Popen(["xdg-open", path])
+ path = os.path.normpath(f)
+ if platform.system() == "Windows":
+ os.startfile(path)
+ elif platform.system() == "Darwin":
+ sp.Popen(["open", path])
+ elif "microsoft-standard-WSL2" in platform.uname().release:
+ sp.Popen(["wsl-open", path])
+ else:
+ sp.Popen(["xdg-open", path])
with gr.Column(elem_id=f"{tabname}_results"):
if toprow:
@@ -136,9 +208,8 @@ Requested path was: {f} with gr.Column(variant='panel', elem_id=f"{tabname}_results_panel"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
- result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
+ res.gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery", columns=4, preview=True, height=shared.opts.gallery_height or None)
- generation_info = None
with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
open_folder_button = ToolButton(folder_symbol, elem_id=f'{tabname}_open_folder', visible=not shared.cmd_opts.hide_ui_dir_config, tooltip="Open images output directory.")
@@ -152,9 +223,16 @@ Requested path was: {f} 'extras': ToolButton('📐', elem_id=f'{tabname}_send_to_extras', tooltip="Send image and generation parameters to extras tab.")
}
+ if tabname == 'txt2img':
+ res.button_upscale = ToolButton('✨', elem_id=f'{tabname}_upscale', tooltip="Create an upscaled version of the current image using hires fix settings.")
+
open_folder_button.click(
- fn=lambda: open_folder(shared.opts.outdir_samples or outdir),
- inputs=[],
+ fn=lambda images, index: open_folder(shared.opts.outdir_samples or outdir, images, index),
+ _js="(y, w) => [y, selected_gallery_index()]",
+ inputs=[
+ res.gallery,
+ open_folder_button, # placeholder for index
+ ],
outputs=[],
)
@@ -162,17 +240,17 @@ Requested path was: {f} download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
with gr.Group():
- html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
- html_log = gr.HTML(elem_id=f'html_log_{tabname}', elem_classes="html-log")
+ res.infotext = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
+ res.html_log = gr.HTML(elem_id=f'html_log_{tabname}', elem_classes="html-log")
- generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
+ res.generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
if tabname == 'txt2img' or tabname == 'img2img':
generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
generation_info_button.click(
fn=update_generation_info,
_js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
- inputs=[generation_info, html_info, html_info],
- outputs=[html_info, html_info],
+ inputs=[res.generation_info, res.infotext, res.infotext],
+ outputs=[res.infotext, res.infotext],
show_progress=False,
)
@@ -180,14 +258,14 @@ Requested path was: {f} fn=call_queue.wrap_gradio_call(save_files),
_js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
inputs=[
- generation_info,
- result_gallery,
- html_info,
- html_info,
+ res.generation_info,
+ res.gallery,
+ res.infotext,
+ res.infotext,
],
outputs=[
download_files,
- html_log,
+ res.html_log,
],
show_progress=False,
)
@@ -196,21 +274,21 @@ Requested path was: {f} fn=call_queue.wrap_gradio_call(save_files),
_js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
inputs=[
- generation_info,
- result_gallery,
- html_info,
- html_info,
+ res.generation_info,
+ res.gallery,
+ res.infotext,
+ res.infotext,
],
outputs=[
download_files,
- html_log,
+ res.html_log,
]
)
else:
- html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
- html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
- html_log = gr.HTML(elem_id=f'html_log_{tabname}')
+ res.generation_info = gr.HTML(elem_id=f'html_info_x_{tabname}')
+ res.infotext = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
+ res.html_log = gr.HTML(elem_id=f'html_log_{tabname}')
paste_field_names = []
if tabname == "txt2img":
@@ -220,11 +298,11 @@ Requested path was: {f} for paste_tabname, paste_button in buttons.items():
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
- paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery,
+ paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=res.gallery,
paste_field_names=paste_field_names
))
- return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
+ return res
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index dc1e34c8..a24ea32e 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -548,6 +548,7 @@ def create_ui(): extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False, container=False)
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False, container=False)
+ refresh = gr.Button(value='Refresh', variant="compact")
html = ""
@@ -566,7 +567,8 @@ def create_ui(): with gr.Row(elem_classes="progress-container"):
extensions_table = gr.HTML('Loading...', elem_id="extensions_installed_html")
- ui.load(fn=extension_table, inputs=[], outputs=[extensions_table])
+ ui.load(fn=extension_table, inputs=[], outputs=[extensions_table], show_progress=False)
+ refresh.click(fn=extension_table, inputs=[], outputs=[extensions_table], show_progress=False)
apply.click(
fn=apply_and_restart,
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index fe5d3ba3..34c46ed4 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -2,23 +2,22 @@ import functools import os.path
import urllib.parse
from pathlib import Path
+from typing import Optional, Union
+from dataclasses import dataclass
-from modules import shared, ui_extra_networks_user_metadata, errors, extra_networks
+from modules import shared, ui_extra_networks_user_metadata, errors, extra_networks, util
from modules.images import read_info_from_image, save_image_with_geninfo
import gradio as gr
import json
import html
from fastapi.exceptions import HTTPException
-from modules.generation_parameters_copypaste import image_from_url_text
-from modules.ui_components import ToolButton
+from modules.infotext_utils import image_from_url_text
extra_pages = []
allowed_dirs = set()
-
default_allowed_preview_extensions = ["png", "jpg", "jpeg", "webp", "gif"]
-
@functools.cache
def allowed_preview_extensions_with_extra(extra_extensions=None):
return set(default_allowed_preview_extensions) | set(extra_extensions or [])
@@ -28,6 +27,62 @@ def allowed_preview_extensions(): return allowed_preview_extensions_with_extra((shared.opts.samples_format, ))
+@dataclass
+class ExtraNetworksItem:
+ """Wrapper for dictionaries representing ExtraNetworks items."""
+ item: dict
+
+
+def get_tree(paths: Union[str, list[str]], items: dict[str, ExtraNetworksItem]) -> dict:
+ """Recursively builds a directory tree.
+
+ Args:
+ paths: Path or list of paths to directories. These paths are treated as roots from which
+ the tree will be built.
+ items: A dictionary associating filepaths to an ExtraNetworksItem instance.
+
+ Returns:
+ The result directory tree.
+ """
+ if isinstance(paths, (str,)):
+ paths = [paths]
+
+ def _get_tree(_paths: list[str], _root: str):
+ _res = {}
+ for path in _paths:
+ relpath = os.path.relpath(path, _root)
+ if os.path.isdir(path):
+ dir_items = os.listdir(path)
+ # Ignore empty directories.
+ if not dir_items:
+ continue
+ dir_tree = _get_tree([os.path.join(path, x) for x in dir_items], _root)
+ # We only want to store non-empty folders in the tree.
+ if dir_tree:
+ _res[relpath] = dir_tree
+ else:
+ if path not in items:
+ continue
+ # Add the ExtraNetworksItem to the result.
+ _res[relpath] = items[path]
+ return _res
+
+ res = {}
+ # Handle each root directory separately.
+ # Each root WILL have a key/value at the root of the result dict though
+ # the value can be an empty dict if the directory is empty. We want these
+ # placeholders for empty dirs so we can inform the user later.
+ for path in paths:
+ root = os.path.dirname(path)
+ relpath = os.path.relpath(path, root)
+ # Wrap the path in a list since that is what the `_get_tree` expects.
+ res[relpath] = _get_tree([path], root)
+ if res[relpath]:
+ # We need to pull the inner path out one for these root dirs.
+ res[relpath] = res[relpath][relpath]
+
+ return res
+
def register_page(page):
"""registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""
@@ -79,8 +134,8 @@ def get_single_card(page: str = "", tabname: str = "", name: str = ""): errors.display(e, "creating item for extra network")
item = page.items.get(name)
- page.read_user_metadata(item)
- item_html = page.create_html_for_item(item, tabname)
+ page.read_user_metadata(item, use_cache=False)
+ item_html = page.create_item_html(tabname, item, shared.html("extra-networks-card.html"))
return JSONResponse({"html": item_html})
@@ -96,24 +151,31 @@ def quote_js(s): s = s.replace('"', '\\"')
return f'"{s}"'
-
class ExtraNetworksPage:
def __init__(self, title):
self.title = title
self.name = title.lower()
- self.id_page = self.name.replace(" ", "_")
- self.card_page = shared.html("extra-networks-card.html")
+ # This is the actual name of the extra networks tab (not txt2img/img2img).
+ self.extra_networks_tabname = self.name.replace(" ", "_")
self.allow_prompt = True
self.allow_negative_prompt = False
self.metadata = {}
self.items = {}
+ self.lister = util.MassFileLister()
+ # HTML Templates
+ self.pane_tpl = shared.html("extra-networks-pane.html")
+ self.card_tpl = shared.html("extra-networks-card.html")
+ self.btn_tree_tpl = shared.html("extra-networks-tree-button.html")
+ self.btn_copy_path_tpl = shared.html("extra-networks-copy-path-button.html")
+ self.btn_metadata_tpl = shared.html("extra-networks-metadata-button.html")
+ self.btn_edit_item_tpl = shared.html("extra-networks-edit-item-button.html")
def refresh(self):
pass
- def read_user_metadata(self, item):
+ def read_user_metadata(self, item, use_cache=True):
filename = item.get("filename", None)
- metadata = extra_networks.get_user_metadata(filename)
+ metadata = extra_networks.get_user_metadata(filename, lister=self.lister if use_cache else None)
desc = metadata.get("description", None)
if desc is not None:
@@ -123,117 +185,74 @@ class ExtraNetworksPage: def link_preview(self, filename):
quoted_filename = urllib.parse.quote(filename.replace('\\', '/'))
- mtime = os.path.getmtime(filename)
+ mtime, _ = self.lister.mctime(filename)
return f"./sd_extra_networks/thumb?filename={quoted_filename}&mtime={mtime}"
def search_terms_from_path(self, filename, possible_directories=None):
abspath = os.path.abspath(filename)
-
for parentdir in (possible_directories if possible_directories is not None else self.allowed_directories_for_previews()):
- parentdir = os.path.abspath(parentdir)
+ parentdir = os.path.dirname(os.path.abspath(parentdir))
if abspath.startswith(parentdir):
- return abspath[len(parentdir):].replace('\\', '/')
+ return os.path.relpath(abspath, parentdir)
return ""
- def create_html(self, tabname):
- items_html = ''
-
- self.metadata = {}
-
- subdirs = {}
- for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
- for root, dirs, _ in sorted(os.walk(parentdir, followlinks=True), key=lambda x: shared.natural_sort_key(x[0])):
- for dirname in sorted(dirs, key=shared.natural_sort_key):
- x = os.path.join(root, dirname)
-
- if not os.path.isdir(x):
- continue
-
- subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
-
- if shared.opts.extra_networks_dir_button_function:
- if not subdir.startswith("/"):
- subdir = "/" + subdir
- else:
- while subdir.startswith("/"):
- subdir = subdir[1:]
-
- is_empty = len(os.listdir(x)) == 0
- if not is_empty and not subdir.endswith("/"):
- subdir = subdir + "/"
-
- if ("/." in subdir or subdir.startswith(".")) and not shared.opts.extra_networks_show_hidden_directories:
- continue
-
- subdirs[subdir] = 1
-
- if subdirs:
- subdirs = {"": 1, **subdirs}
-
- subdirs_html = "".join([f"""
-<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_search", event)'>
-{html.escape(subdir if subdir!="" else "all")}
-</button>
-""" for subdir in subdirs])
-
- self.items = {x["name"]: x for x in self.list_items()}
- for item in self.items.values():
- metadata = item.get("metadata")
- if metadata:
- self.metadata[item["name"]] = metadata
-
- if "user_metadata" not in item:
- self.read_user_metadata(item)
-
- items_html += self.create_html_for_item(item, tabname)
-
- if items_html == '':
- dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
- items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
-
- self_name_id = self.name.replace(" ", "_")
-
- res = f"""
-<div id='{tabname}_{self_name_id}_subdirs' class='extra-network-subdirs extra-network-subdirs-cards'>
-{subdirs_html}
-</div>
-<div id='{tabname}_{self_name_id}_cards' class='extra-network-cards'>
-{items_html}
-</div>
-"""
-
- return res
-
- def create_item(self, name, index=None):
- raise NotImplementedError()
-
- def list_items(self):
- raise NotImplementedError()
-
- def allowed_directories_for_previews(self):
- return []
-
- def create_html_for_item(self, item, tabname):
- """
- Create HTML for card item in tab tabname; can return empty string if the item is not meant to be shown.
+ def create_item_html(
+ self,
+ tabname: str,
+ item: dict,
+ template: Optional[str] = None,
+ ) -> Union[str, dict]:
+ """Generates HTML for a single ExtraNetworks Item.
+
+ Args:
+ tabname: The name of the active tab.
+ item: Dictionary containing item information.
+ template: Optional template string to use.
+
+ Returns:
+ If a template is passed: HTML string generated for this item.
+ Can be empty if the item is not meant to be shown.
+ If no template is passed: A dictionary containing the generated item's attributes.
"""
-
preview = item.get("preview", None)
+ style_height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
+ style_width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
+ style_font_size = f"font-size: {shared.opts.extra_networks_card_text_scale*100}%;"
+ card_style = style_height + style_width + style_font_size
+ background_image = f'<img src="{html.escape(preview)}" class="preview" loading="lazy">' if preview else ''
onclick = item.get("onclick", None)
if onclick is None:
- onclick = '"' + html.escape(f"""return cardClicked({quote_js(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
-
- height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
- width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
- background_image = f'<img src="{html.escape(preview)}" class="preview" loading="lazy">' if preview else ''
- metadata_button = ""
+ # Don't quote prompt/neg_prompt since they are stored as js strings already.
+ onclick_js_tpl = "cardClicked('{tabname}', {prompt}, {neg_prompt}, {allow_neg});"
+ onclick = onclick_js_tpl.format(
+ **{
+ "tabname": tabname,
+ "prompt": item["prompt"],
+ "neg_prompt": item.get("negative_prompt", "''"),
+ "allow_neg": str(self.allow_negative_prompt).lower(),
+ }
+ )
+ onclick = html.escape(onclick)
+
+ btn_copy_path = self.btn_copy_path_tpl.format(**{"filename": item["filename"]})
+ btn_metadata = ""
metadata = item.get("metadata")
if metadata:
- metadata_button = f"<div class='metadata-button card-button' title='Show internal metadata' onclick='extraNetworksRequestMetadata(event, {quote_js(self.name)}, {quote_js(html.escape(item['name']))})'></div>"
-
- edit_button = f"<div class='edit-button card-button' title='Edit metadata' onclick='extraNetworksEditUserMetadata(event, {quote_js(tabname)}, {quote_js(self.id_page)}, {quote_js(html.escape(item['name']))})'></div>"
+ btn_metadata = self.btn_metadata_tpl.format(
+ **{
+ "extra_networks_tabname": self.extra_networks_tabname,
+ "name": html.escape(item["name"]),
+ }
+ )
+ btn_edit_item = self.btn_edit_item_tpl.format(
+ **{
+ "tabname": tabname,
+ "extra_networks_tabname": self.extra_networks_tabname,
+ "name": html.escape(item["name"]),
+ }
+ )
local_path = ""
filename = item.get("filename", "")
@@ -253,38 +272,303 @@ class ExtraNetworksPage: if search_only and shared.opts.extra_networks_hidden_models == "Never":
return ""
- sort_keys = " ".join([f'data-sort-{k}="{html.escape(str(v))}"' for k, v in item.get("sort_keys", {}).items()]).strip()
-
+ sort_keys = " ".join(
+ [
+ f'data-sort-{k}="{html.escape(str(v))}"'
+ for k, v in item.get("sort_keys", {}).items()
+ ]
+ ).strip()
+
+ search_terms_html = ""
+ search_term_template = "<span class='hidden {class}'>{search_term}</span>"
+ for search_term in item.get("search_terms", []):
+ search_terms_html += search_term_template.format(
+ **{
+ "class": f"search_terms{' search_only' if search_only else ''}",
+ "search_term": search_term,
+ }
+ )
+
+ description = (item.get("description", "") or "" if shared.opts.extra_networks_card_show_desc else "")
+ if not shared.opts.extra_networks_card_description_is_html:
+ description = html.escape(description)
+
+ # Some items here might not be used depending on HTML template used.
args = {
"background_image": background_image,
- "style": f"'display: none; {height}{width}; font-size: {shared.opts.extra_networks_card_text_scale*100}%'",
- "prompt": item.get("prompt", None),
- "tabname": quote_js(tabname),
+ "card_clicked": onclick,
+ "copy_path_button": btn_copy_path,
+ "description": description,
+ "edit_button": btn_edit_item,
"local_preview": quote_js(item["local_preview"]),
+ "metadata_button": btn_metadata,
"name": html.escape(item["name"]),
- "description": (item.get("description") or "" if shared.opts.extra_networks_card_show_desc else ""),
- "card_clicked": onclick,
- "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {quote_js(tabname)}, {quote_js(item["local_preview"])})""") + '"',
- "search_term": item.get("search_term", ""),
- "metadata_button": metadata_button,
- "edit_button": edit_button,
+ "prompt": item.get("prompt", None),
+ "save_card_preview": html.escape(f"return saveCardPreview(event, '{tabname}', '{item['local_preview']}');"),
"search_only": " search_only" if search_only else "",
+ "search_terms": search_terms_html,
"sort_keys": sort_keys,
+ "style": card_style,
+ "tabname": tabname,
+ "extra_networks_tabname": self.extra_networks_tabname,
}
- return self.card_page.format(**args)
+ if template:
+ return template.format(**args)
+ else:
+ return args
+
+ def create_tree_dir_item_html(
+ self,
+ tabname: str,
+ dir_path: str,
+ content: Optional[str] = None,
+ ) -> Optional[str]:
+ """Generates HTML for a directory item in the tree.
+
+ The generated HTML is of the format:
+ ```html
+ <li class="tree-list-item tree-list-item--has-subitem">
+ <div class="tree-list-content tree-list-content-dir"></div>
+ <ul class="tree-list tree-list--subgroup">
+ {content}
+ </ul>
+ </li>
+ ```
+
+ Args:
+ tabname: The name of the active tab.
+ dir_path: Path to the directory for this item.
+ content: Optional HTML string that will be wrapped by this <ul>.
+
+ Returns:
+ HTML formatted string.
+ """
+ if not content:
+ return None
+
+ btn = self.btn_tree_tpl.format(
+ **{
+ "search_terms": "",
+ "subclass": "tree-list-content-dir",
+ "tabname": tabname,
+ "extra_networks_tabname": self.extra_networks_tabname,
+ "onclick_extra": "",
+ "data_path": dir_path,
+ "data_hash": "",
+ "action_list_item_action_leading": "<i class='tree-list-item-action-chevron'></i>",
+ "action_list_item_visual_leading": "🗀",
+ "action_list_item_label": os.path.basename(dir_path),
+ "action_list_item_visual_trailing": "",
+ "action_list_item_action_trailing": "",
+ }
+ )
+ ul = f"<ul class='tree-list tree-list--subgroup' hidden>{content}</ul>"
+ return (
+ "<li class='tree-list-item tree-list-item--has-subitem' data-tree-entry-type='dir'>"
+ f"{btn}{ul}"
+ "</li>"
+ )
+
+ def create_tree_file_item_html(self, tabname: str, file_path: str, item: dict) -> str:
+ """Generates HTML for a file item in the tree.
+
+ The generated HTML is of the format:
+ ```html
+ <li class="tree-list-item tree-list-item--subitem">
+ <span data-filterable-item-text hidden></span>
+ <div class="tree-list-content tree-list-content-file"></div>
+ </li>
+ ```
+
+ Args:
+ tabname: The name of the active tab.
+ file_path: The path to the file for this item.
+ item: Dictionary containing the item information.
+
+ Returns:
+ HTML formatted string.
+ """
+ item_html_args = self.create_item_html(tabname, item)
+ action_buttons = "".join(
+ [
+ item_html_args["copy_path_button"],
+ item_html_args["metadata_button"],
+ item_html_args["edit_button"],
+ ]
+ )
+ action_buttons = f"<div class=\"button-row\">{action_buttons}</div>"
+ btn = self.btn_tree_tpl.format(
+ **{
+ "search_terms": "",
+ "subclass": "tree-list-content-file",
+ "tabname": tabname,
+ "extra_networks_tabname": self.extra_networks_tabname,
+ "onclick_extra": item_html_args["card_clicked"],
+ "data_path": file_path,
+ "data_hash": item["shorthash"],
+ "action_list_item_action_leading": "<i class='tree-list-item-action-chevron'></i>",
+ "action_list_item_visual_leading": "🗎",
+ "action_list_item_label": item["name"],
+ "action_list_item_visual_trailing": "",
+ "action_list_item_action_trailing": action_buttons,
+ }
+ )
+ return (
+ "<li class='tree-list-item tree-list-item--subitem' data-tree-entry-type='file'>"
+ f"{btn}"
+ "</li>"
+ )
+
+ def create_tree_view_html(self, tabname: str) -> str:
+ """Generates HTML for displaying folders in a tree view.
+
+ Args:
+ tabname: The name of the active tab.
+
+ Returns:
+ HTML string generated for this tree view.
+ """
+ res = ""
+
+ # Setup the tree dictionary.
+ roots = self.allowed_directories_for_previews()
+ tree_items = {v["filename"]: ExtraNetworksItem(v) for v in self.items.values()}
+ tree = get_tree([os.path.abspath(x) for x in roots], items=tree_items)
+
+ if not tree:
+ return res
+
+ def _build_tree(data: Optional[dict[str, ExtraNetworksItem]] = None) -> Optional[str]:
+ """Recursively builds HTML for a tree.
+
+ Args:
+ data: Dictionary representing a directory tree. Can be NoneType.
+ Data keys should be absolute paths from the root and values
+ should be subdirectory trees or an ExtraNetworksItem.
+
+ Returns:
+ If data is not None: HTML string
+ Else: None
+ """
+ if not data:
+ return None
+
+ # Lists for storing <li> items html for directories and files separately.
+ _dir_li = []
+ _file_li = []
+
+ for k, v in sorted(data.items(), key=lambda x: shared.natural_sort_key(x[0])):
+ if isinstance(v, (ExtraNetworksItem,)):
+ _file_li.append(self.create_tree_file_item_html(tabname, k, v.item))
+ else:
+ _dir_li.append(self.create_tree_dir_item_html(tabname, k, _build_tree(v)))
+
+ # Directories should always be displayed before files so we order them here.
+ return "".join(_dir_li) + "".join(_file_li)
+
+ # Add each root directory to the tree.
+ for k, v in sorted(tree.items(), key=lambda x: shared.natural_sort_key(x[0])):
+ item_html = self.create_tree_dir_item_html(tabname, k, _build_tree(v))
+ # Only add non-empty entries to the tree.
+ if item_html is not None:
+ res += item_html
+
+ return f"<ul class='tree-list tree-list--tree'>{res}</ul>"
+
+ def create_card_view_html(self, tabname: str, *, none_message) -> str:
+ """Generates HTML for the network Card View section for a tab.
+
+ This HTML goes into the `extra-networks-pane.html` <div> with
+ `id='{tabname}_{extra_networks_tabname}_cards`.
+
+ Args:
+ tabname: The name of the active tab.
+ none_message: HTML text to show when there are no cards.
+
+ Returns:
+ HTML formatted string.
+ """
+ res = ""
+ for item in self.items.values():
+ res += self.create_item_html(tabname, item, self.card_tpl)
+
+ if res == "":
+ dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
+ res = none_message or shared.html("extra-networks-no-cards.html").format(dirs=dirs)
+
+ return res
+
+ def create_html(self, tabname, *, empty=False):
+ """Generates an HTML string for the current pane.
+
+ The generated HTML uses `extra-networks-pane.html` as a template.
+
+ Args:
+ tabname: The name of the active tab.
+ empty: create an empty HTML page with no items
+
+ Returns:
+ HTML formatted string.
+ """
+ self.lister.reset()
+ self.metadata = {}
+
+ items_list = [] if empty else self.list_items()
+ self.items = {x["name"]: x for x in items_list}
+
+ # Populate the instance metadata for each item.
+ for item in self.items.values():
+ metadata = item.get("metadata")
+ if metadata:
+ self.metadata[item["name"]] = metadata
+
+ if "user_metadata" not in item:
+ self.read_user_metadata(item)
+
+ data_sortdir = shared.opts.extra_networks_card_order
+ data_sortmode = shared.opts.extra_networks_card_order_field.lower().replace("sort", "").replace(" ", "_").rstrip("_").strip()
+ data_sortkey = f"{data_sortmode}-{data_sortdir}-{len(self.items)}"
+ tree_view_btn_extra_class = ""
+ tree_view_div_extra_class = "hidden"
+ if shared.opts.extra_networks_tree_view_default_enabled:
+ tree_view_btn_extra_class = "extra-network-control--enabled"
+ tree_view_div_extra_class = ""
+
+ return self.pane_tpl.format(
+ **{
+ "tabname": tabname,
+ "extra_networks_tabname": self.extra_networks_tabname,
+ "data_sortmode": data_sortmode,
+ "data_sortkey": data_sortkey,
+ "data_sortdir": data_sortdir,
+ "tree_view_btn_extra_class": tree_view_btn_extra_class,
+ "tree_view_div_extra_class": tree_view_div_extra_class,
+ "tree_html": self.create_tree_view_html(tabname),
+ "items_html": self.create_card_view_html(tabname, none_message="Loading..." if empty else None),
+ }
+ )
+
+ def create_item(self, name, index=None):
+ raise NotImplementedError()
+
+ def list_items(self):
+ raise NotImplementedError()
+
+ def allowed_directories_for_previews(self):
+ return []
def get_sort_keys(self, path):
"""
List of default keys used for sorting in the UI.
"""
pth = Path(path)
- stat = pth.stat()
+ mtime, ctime = self.lister.mctime(path)
return {
- "date_created": int(stat.st_ctime or 0),
- "date_modified": int(stat.st_mtime or 0),
+ "date_created": int(mtime),
+ "date_modified": int(ctime),
"name": pth.name.lower(),
- "path": str(pth.parent).lower(),
+ "path": str(pth).lower(),
}
def find_preview(self, path):
@@ -292,10 +576,10 @@ class ExtraNetworksPage: Find a preview PNG for a given path (without extension) and call link_preview on it.
"""
- potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in allowed_preview_extensions()], [])
+ potential_files = sum([[f"{path}.{ext}", f"{path}.preview.{ext}"] for ext in allowed_preview_extensions()], [])
for file in potential_files:
- if os.path.isfile(file):
+ if self.lister.exists(file):
return self.link_preview(file)
return None
@@ -305,6 +589,9 @@ class ExtraNetworksPage: Find and read a description file for a given path (without extension).
"""
for file in [f"{path}.txt", f"{path}.description.txt"]:
+ if not self.lister.exists(file):
+ continue
+
try:
with open(file, "r", encoding="utf-8", errors="replace") as f:
return f.read()
@@ -362,8 +649,6 @@ def pages_in_preferred_order(pages): def create_ui(interface: gr.Blocks, unrelated_tabs, tabname):
- from modules.ui import switch_values_symbol
-
ui = ExtraNetworksUi()
ui.pages = []
ui.pages_contents = []
@@ -374,62 +659,51 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname): related_tabs = []
for page in ui.stored_extra_pages:
- with gr.Tab(page.title, elem_id=f"{tabname}_{page.id_page}", elem_classes=["extra-page"]) as tab:
- with gr.Column(elem_id=f"{tabname}_{page.id_page}_prompts", elem_classes=["extra-page-prompts"]):
+ with gr.Tab(page.title, elem_id=f"{tabname}_{page.extra_networks_tabname}", elem_classes=["extra-page"]) as tab:
+ with gr.Column(elem_id=f"{tabname}_{page.extra_networks_tabname}_prompts", elem_classes=["extra-page-prompts"]):
pass
- elem_id = f"{tabname}_{page.id_page}_cards_html"
- page_elem = gr.HTML('Loading...', elem_id=elem_id)
+ elem_id = f"{tabname}_{page.extra_networks_tabname}_cards_html"
+ page_elem = gr.HTML(page.create_html(tabname, empty=True), elem_id=elem_id)
ui.pages.append(page_elem)
-
- page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + quote_js(tabname) + '); return []}', inputs=[], outputs=[])
-
editor = page.create_user_metadata_editor(ui, tabname)
editor.create_ui()
ui.user_metadata_editors.append(editor)
-
related_tabs.append(tab)
- edit_search = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", elem_classes="search", placeholder="Search...", visible=False, interactive=True)
- dropdown_sort = gr.Dropdown(choices=['Path', 'Name', 'Date Created', 'Date Modified', ], value=shared.opts.extra_networks_card_order_field, elem_id=tabname+"_extra_sort", elem_classes="sort", multiselect=False, visible=False, show_label=False, interactive=True, label=tabname+"_extra_sort_order")
- button_sortorder = ToolButton(switch_values_symbol, elem_id=tabname+"_extra_sortorder", elem_classes=["sortorder"] + ([] if shared.opts.extra_networks_card_order == "Ascending" else ["sortReverse"]), visible=False, tooltip="Invert sort order")
- button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh", visible=False)
- checkbox_show_dirs = gr.Checkbox(True, label='Show dirs', elem_id=tabname+"_extra_show_dirs", elem_classes="show-dirs", visible=False)
-
- ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
- ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)
-
- tab_controls = [edit_search, dropdown_sort, button_sortorder, button_refresh, checkbox_show_dirs]
+ ui.button_save_preview = gr.Button('Save preview', elem_id=f"{tabname}_save_preview", visible=False)
+ ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=f"{tabname}_preview_filename", visible=False)
for tab in unrelated_tabs:
- tab.select(fn=lambda: [gr.update(visible=False) for _ in tab_controls], _js='function(){ extraNetworksUrelatedTabSelected("' + tabname + '"); }', inputs=[], outputs=tab_controls, show_progress=False)
+ tab.select(fn=None, _js=f"function(){{extraNetworksUnrelatedTabSelected('{tabname}');}}", inputs=[], outputs=[], show_progress=False)
for page, tab in zip(ui.stored_extra_pages, related_tabs):
- allow_prompt = "true" if page.allow_prompt else "false"
- allow_negative_prompt = "true" if page.allow_negative_prompt else "false"
-
- jscode = 'extraNetworksTabSelected("' + tabname + '", "' + f"{tabname}_{page.id_page}_prompts" + '", ' + allow_prompt + ', ' + allow_negative_prompt + ');'
-
- tab.select(fn=lambda: [gr.update(visible=True) for _ in tab_controls], _js='function(){ ' + jscode + ' }', inputs=[], outputs=tab_controls, show_progress=False)
-
- dropdown_sort.change(fn=lambda: None, _js="function(){ applyExtraNetworkSort('" + tabname + "'); }")
+ jscode = (
+ "function(){{"
+ f"extraNetworksTabSelected('{tabname}', '{tabname}_{page.extra_networks_tabname}_prompts', {str(page.allow_prompt).lower()}, {str(page.allow_negative_prompt).lower()}, '{tabname}_{page.extra_networks_tabname}');"
+ f"applyExtraNetworkFilter('{tabname}_{page.extra_networks_tabname}');"
+ "}}"
+ )
+ tab.select(fn=None, _js=jscode, inputs=[], outputs=[], show_progress=False)
+
+ def refresh():
+ for pg in ui.stored_extra_pages:
+ pg.refresh()
+ create_html()
+ return ui.pages_contents
+
+ button_refresh = gr.Button("Refresh", elem_id=f"{tabname}_{page.extra_networks_tabname}_extra_refresh_internal", visible=False)
+ button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages).then(fn=lambda: None, _js="function(){ " + f"applyExtraNetworkFilter('{tabname}_{page.extra_networks_tabname}');" + " }")
+
+ def create_html():
+ ui.pages_contents = [pg.create_html(ui.tabname) for pg in ui.stored_extra_pages]
def pages_html():
if not ui.pages_contents:
- return refresh()
-
+ create_html()
return ui.pages_contents
- def refresh():
- for pg in ui.stored_extra_pages:
- pg.refresh()
-
- ui.pages_contents = [pg.create_html(ui.tabname) for pg in ui.stored_extra_pages]
-
- return ui.pages_contents
-
- interface.load(fn=pages_html, inputs=[], outputs=[*ui.pages])
- button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
+ interface.load(fn=pages_html, inputs=[], outputs=ui.pages)
return ui
@@ -478,5 +752,3 @@ def setup_ui(ui, gallery): for editor in ui.user_metadata_editors:
editor.setup_ui(gallery)
-
-
diff --git a/modules/ui_extra_networks_checkpoints.py b/modules/ui_extra_networks_checkpoints.py index 1693e71f..d69d144d 100644 --- a/modules/ui_extra_networks_checkpoints.py +++ b/modules/ui_extra_networks_checkpoints.py @@ -2,7 +2,6 @@ import html import os
from modules import shared, ui_extra_networks, sd_models
-from modules.ui_extra_networks import quote_js
from modules.ui_extra_networks_checkpoints_user_metadata import CheckpointUserMetadataEditor
@@ -21,14 +20,17 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): return
path, ext = os.path.splitext(checkpoint.filename)
+ search_terms = [self.search_terms_from_path(checkpoint.filename)]
+ if checkpoint.sha256:
+ search_terms.append(checkpoint.sha256)
return {
"name": checkpoint.name_for_extra,
"filename": checkpoint.filename,
"shorthash": checkpoint.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
- "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),
- "onclick": '"' + html.escape(f"""return selectCheckpoint({quote_js(name)})""") + '"',
+ "search_terms": search_terms,
+ "onclick": html.escape(f"return selectCheckpoint({ui_extra_networks.quote_js(name)})"),
"local_preview": f"{path}.{shared.opts.samples_format}",
"metadata": checkpoint.metadata,
"sort_keys": {'default': index, **self.get_sort_keys(checkpoint.filename)},
diff --git a/modules/ui_extra_networks_hypernets.py b/modules/ui_extra_networks_hypernets.py index c96c4fa3..2fb4bd19 100644 --- a/modules/ui_extra_networks_hypernets.py +++ b/modules/ui_extra_networks_hypernets.py @@ -20,14 +20,16 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): path, ext = os.path.splitext(full_path)
sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
shorthash = sha256[0:10] if sha256 else None
-
+ search_terms = [self.search_terms_from_path(path)]
+ if sha256:
+ search_terms.append(sha256)
return {
"name": name,
"filename": full_path,
"shorthash": shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
- "search_term": self.search_terms_from_path(path) + " " + (sha256 or ""),
+ "search_terms": search_terms,
"prompt": quote_js(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + quote_js(">"),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
"sort_keys": {'default': index, **self.get_sort_keys(path + ext)},
diff --git a/modules/ui_extra_networks_textual_inversion.py b/modules/ui_extra_networks_textual_inversion.py index 1b334fda..deb7cb87 100644 --- a/modules/ui_extra_networks_textual_inversion.py +++ b/modules/ui_extra_networks_textual_inversion.py @@ -18,13 +18,16 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): return
path, ext = os.path.splitext(embedding.filename)
+ search_terms = [self.search_terms_from_path(embedding.filename)]
+ if embedding.hash:
+ search_terms.append(embedding.hash)
return {
"name": name,
"filename": embedding.filename,
"shorthash": embedding.shorthash,
"preview": self.find_preview(path),
"description": self.find_description(path),
- "search_term": self.search_terms_from_path(embedding.filename) + " " + (embedding.hash or ""),
+ "search_terms": search_terms,
"prompt": quote_js(embedding.name),
"local_preview": f"{path}.preview.{shared.opts.samples_format}",
"sort_keys": {'default': index, **self.get_sort_keys(embedding.filename)},
diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py index 36a807fc..2ca937fd 100644 --- a/modules/ui_extra_networks_user_metadata.py +++ b/modules/ui_extra_networks_user_metadata.py @@ -5,7 +5,7 @@ import os.path import gradio as gr
-from modules import generation_parameters_copypaste, images, sysinfo, errors, ui_extra_networks
+from modules import infotext_utils, images, sysinfo, errors, ui_extra_networks
class UserMetadataEditor:
@@ -14,7 +14,7 @@ class UserMetadataEditor: self.ui = ui
self.tabname = tabname
self.page = page
- self.id_part = f"{self.tabname}_{self.page.id_page}_edit_user_metadata"
+ self.id_part = f"{self.tabname}_{self.page.extra_networks_tabname}_edit_user_metadata"
self.box = None
@@ -181,7 +181,7 @@ class UserMetadataEditor: index = len(gallery) - 1 if index >= len(gallery) else index
img_info = gallery[index if index >= 0 else 0]
- image = generation_parameters_copypaste.image_from_url_text(img_info)
+ image = infotext_utils.image_from_url_text(img_info)
geninfo, items = images.read_info_from_image(image)
images.save_image_with_geninfo(image, geninfo, item["local_preview"])
diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py index 0d368f8b..f5278d22 100644 --- a/modules/ui_gradio_extensions.py +++ b/modules/ui_gradio_extensions.py @@ -1,17 +1,12 @@ import os
import gradio as gr
-from modules import localization, shared, scripts
-from modules.paths import script_path, data_path, cwd
+from modules import localization, shared, scripts, util
+from modules.paths import script_path, data_path
def webpath(fn):
- if fn.startswith(cwd):
- web_path = os.path.relpath(fn, cwd)
- else:
- web_path = os.path.abspath(fn)
-
- return f'file={web_path}?{os.path.getmtime(fn)}'
+ return f'file={util.truncate_path(fn)}?{os.path.getmtime(fn)}'
def javascript_html():
@@ -40,13 +35,11 @@ def css_html(): return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
for cssfile in scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
head += stylesheet(cssfile)
- if os.path.exists(os.path.join(data_path, "user.css")):
- head += stylesheet(os.path.join(data_path, "user.css"))
+ user_css = os.path.join(data_path, "user.css")
+ if os.path.exists(user_css):
+ head += stylesheet(user_css)
return head
diff --git a/modules/ui_loadsave.py b/modules/ui_loadsave.py index 7826786c..2555cdb6 100644 --- a/modules/ui_loadsave.py +++ b/modules/ui_loadsave.py @@ -26,8 +26,9 @@ class UiLoadsave: self.ui_defaults_review = None
try:
- if os.path.exists(self.filename):
- self.ui_settings = self.read_from_file()
+ self.ui_settings = self.read_from_file()
+ except FileNotFoundError:
+ pass
except Exception as e:
self.error_loading = True
errors.display(e, "loading settings")
@@ -144,7 +145,7 @@ class UiLoadsave: json.dump(current_ui_settings, file, indent=4, ensure_ascii=False)
def dump_defaults(self):
- """saves default values to a file unless tjhe file is present and there was an error loading default values at start"""
+ """saves default values to a file unless the file is present and there was an error loading default values at start"""
if self.error_loading and os.path.exists(self.filename):
return
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index 13d888e4..7261c2df 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -1,13 +1,14 @@ import gradio as gr
from modules import scripts, shared, ui_common, postprocessing, call_queue, ui_toprow
-import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.infotext_utils as parameters_copypaste
+from modules.ui_components import ResizeHandleRow
def create_ui():
dummy_component = gr.Label(visible=False)
- tab_index = gr.State(value=0)
+ tab_index = gr.Number(value=0, visible=False)
- with gr.Row(equal_height=False, variant='compact'):
+ with ResizeHandleRow(equal_height=False, variant='compact'):
with gr.Column(variant='compact'):
with gr.Tabs(elem_id="mode_extras"):
with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:
@@ -28,7 +29,7 @@ def create_ui(): toprow.create_inline_toprow_image()
submit = toprow.submit
- result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
+ output_panel = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
tab_batch.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
@@ -48,9 +49,9 @@ def create_ui(): *script_inputs
],
outputs=[
- result_images,
- html_info_x,
- html_log,
+ output_panel.gallery,
+ output_panel.generation_info,
+ output_panel.html_log,
],
show_progress=False,
)
diff --git a/modules/ui_prompt_styles.py b/modules/ui_prompt_styles.py index 0d74c23f..d67e3f17 100644 --- a/modules/ui_prompt_styles.py +++ b/modules/ui_prompt_styles.py @@ -22,9 +22,12 @@ def save_style(name, prompt, negative_prompt): if not name:
return gr.update(visible=False)
- style = styles.PromptStyle(name, prompt, negative_prompt)
+ existing_style = shared.prompt_styles.styles.get(name)
+ path = existing_style.path if existing_style is not None else None
+
+ style = styles.PromptStyle(name, prompt, negative_prompt, path)
shared.prompt_styles.styles[style.name] = style
- shared.prompt_styles.save_styles(shared.styles_filename)
+ shared.prompt_styles.save_styles()
return gr.update(visible=True)
@@ -34,7 +37,7 @@ def delete_style(name): return
shared.prompt_styles.styles.pop(name, None)
- shared.prompt_styles.save_styles(shared.styles_filename)
+ shared.prompt_styles.save_styles()
return '', '', ''
diff --git a/modules/ui_tempdir.py b/modules/ui_tempdir.py index 85015db5..ecd6bdec 100644 --- a/modules/ui_tempdir.py +++ b/modules/ui_tempdir.py @@ -35,12 +35,9 @@ def save_pil_to_file(self, pil_image, dir=None, format="png"): already_saved_as = getattr(pil_image, 'already_saved_as', None)
if already_saved_as and os.path.isfile(already_saved_as):
register_tmp_file(shared.demo, already_saved_as)
- filename = already_saved_as
-
- if not shared.opts.save_images_add_number:
- filename += f'?{os.path.getmtime(already_saved_as)}'
-
- return filename
+ filename_with_mtime = f'{already_saved_as}?{os.path.getmtime(already_saved_as)}'
+ register_tmp_file(shared.demo, filename_with_mtime)
+ return filename_with_mtime
if shared.opts.temp_dir != "":
dir = shared.opts.temp_dir
@@ -86,3 +83,18 @@ def cleanup_tmpdr(): filename = os.path.join(root, name)
os.remove(filename)
+
+
+def is_gradio_temp_path(path):
+ """
+ Check if the path is a temp dir used by gradio
+ """
+ path = Path(path)
+ if shared.opts.temp_dir and path.is_relative_to(shared.opts.temp_dir):
+ return True
+ if gradio_temp_dir := os.environ.get("GRADIO_TEMP_DIR"):
+ if path.is_relative_to(gradio_temp_dir):
+ return True
+ if path.is_relative_to(Path(tempfile.gettempdir()) / "gradio"):
+ return True
+ return False
diff --git a/modules/ui_toprow.py b/modules/ui_toprow.py index 88838f97..dc3c3aa3 100644 --- a/modules/ui_toprow.py +++ b/modules/ui_toprow.py @@ -17,6 +17,7 @@ class Toprow: button_deepbooru = None
interrupt = None
+ interrupting = None
skip = None
submit = None
@@ -79,11 +80,11 @@ class Toprow: def create_prompts(self):
with gr.Column(elem_id=f"{self.id_part}_prompt_container", elem_classes=["prompt-container-compact"] if self.is_compact else [], scale=6):
with gr.Row(elem_id=f"{self.id_part}_prompt_row", elem_classes=["prompt-row"]):
- self.prompt = gr.Textbox(label="Prompt", elem_id=f"{self.id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+ self.prompt = gr.Textbox(label="Prompt", elem_id=f"{self.id_part}_prompt", show_label=False, lines=3, placeholder="Prompt\n(Press Ctrl+Enter to generate, Alt+Enter to skip, Esc to interrupt)", elem_classes=["prompt"])
self.prompt_img = gr.File(label="", elem_id=f"{self.id_part}_prompt_image", file_count="single", type="binary", visible=False)
with gr.Row(elem_id=f"{self.id_part}_neg_prompt_row", elem_classes=["prompt-row"]):
- self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{self.id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+ self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{self.id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt\n(Press Ctrl+Enter to generate, Alt+Enter to skip, Esc to interrupt)", elem_classes=["prompt"])
self.prompt_img.change(
fn=modules.images.image_data,
@@ -96,21 +97,21 @@ class Toprow: with gr.Row(elem_id=f"{self.id_part}_generate_box", elem_classes=["generate-box"] + (["generate-box-compact"] if self.is_compact else []), render=not self.is_compact) as submit_box:
self.submit_box = submit_box
- self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt")
- self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip")
- self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary')
+ self.interrupt = gr.Button('Interrupt', elem_id=f"{self.id_part}_interrupt", elem_classes="generate-box-interrupt", tooltip="End generation immediately or after completing current batch")
+ self.skip = gr.Button('Skip', elem_id=f"{self.id_part}_skip", elem_classes="generate-box-skip", tooltip="Stop generation of current batch and continues onto next batch")
+ self.interrupting = gr.Button('Interrupting...', elem_id=f"{self.id_part}_interrupting", elem_classes="generate-box-interrupting", tooltip="Interrupting generation...")
+ self.submit = gr.Button('Generate', elem_id=f"{self.id_part}_generate", variant='primary', tooltip="Right click generate forever menu")
- self.skip.click(
- fn=lambda: shared.state.skip(),
- inputs=[],
- outputs=[],
- )
+ def interrupt_function():
+ if not shared.state.stopping_generation and shared.state.job_count > 1 and shared.opts.interrupt_after_current:
+ shared.state.stop_generating()
+ gr.Info("Generation will stop after finishing this image, click again to stop immediately.")
+ else:
+ shared.state.interrupt()
- self.interrupt.click(
- fn=lambda: shared.state.interrupt(),
- inputs=[],
- outputs=[],
- )
+ self.skip.click(fn=shared.state.skip)
+ self.interrupt.click(fn=interrupt_function, _js='function(){ showSubmitInterruptingPlaceholder("' + self.id_part + '"); }')
+ self.interrupting.click(fn=interrupt_function)
def create_tools_row(self):
with gr.Row(elem_id=f"{self.id_part}_tools"):
@@ -126,9 +127,9 @@ class Toprow: self.restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{self.id_part}_restore_progress", visible=False, tooltip="Restore progress")
- self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"])
+ self.token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_token_counter", elem_classes=["token-counter"], visible=False)
self.token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_token_button")
- self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_negative_token_counter", elem_classes=["token-counter"])
+ self.negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{self.id_part}_negative_token_counter", elem_classes=["token-counter"], visible=False)
self.negative_token_button = gr.Button(visible=False, elem_id=f"{self.id_part}_negative_token_button")
self.clear_prompt_button.click(
diff --git a/modules/upscaler.py b/modules/upscaler.py index b256e085..3aee69db 100644 --- a/modules/upscaler.py +++ b/modules/upscaler.py @@ -98,6 +98,9 @@ class UpscalerData: self.scale = scale self.model = model + def __repr__(self): + return f"<UpscalerData name={self.name} path={self.data_path} scale={self.scale}>" + class UpscalerNone(Upscaler): name = "None" diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py new file mode 100644 index 00000000..b5e5a80c --- /dev/null +++ b/modules/upscaler_utils.py @@ -0,0 +1,190 @@ +import logging +from typing import Callable + +import numpy as np +import torch +import tqdm +from PIL import Image + +from modules import devices, images, shared, torch_utils + +logger = logging.getLogger(__name__) + + +def pil_image_to_torch_bgr(img: Image.Image) -> torch.Tensor: + img = np.array(img.convert("RGB")) + img = img[:, :, ::-1] # flip RGB to BGR + img = np.transpose(img, (2, 0, 1)) # HWC to CHW + img = np.ascontiguousarray(img) / 255 # Rescale to [0, 1] + return torch.from_numpy(img) + + +def torch_bgr_to_pil_image(tensor: torch.Tensor) -> Image.Image: + if tensor.ndim == 4: + # If we're given a tensor with a batch dimension, squeeze it out + # (but only if it's a batch of size 1). + if tensor.shape[0] != 1: + raise ValueError(f"{tensor.shape} does not describe a BCHW tensor") + tensor = tensor.squeeze(0) + assert tensor.ndim == 3, f"{tensor.shape} does not describe a CHW tensor" + # TODO: is `tensor.float().cpu()...numpy()` the most efficient idiom? + arr = tensor.float().cpu().clamp_(0, 1).numpy() # clamp + arr = 255.0 * np.moveaxis(arr, 0, 2) # CHW to HWC, rescale + arr = arr.round().astype(np.uint8) + arr = arr[:, :, ::-1] # flip BGR to RGB + return Image.fromarray(arr, "RGB") + + +def upscale_pil_patch(model, img: Image.Image) -> Image.Image: + """ + Upscale a given PIL image using the given model. + """ + param = torch_utils.get_param(model) + + with torch.no_grad(): + tensor = pil_image_to_torch_bgr(img).unsqueeze(0) # add batch dimension + tensor = tensor.to(device=param.device, dtype=param.dtype) + with devices.without_autocast(): + return torch_bgr_to_pil_image(model(tensor)) + + +def upscale_with_model( + model: Callable[[torch.Tensor], torch.Tensor], + img: Image.Image, + *, + tile_size: int, + tile_overlap: int = 0, + desc="tiled upscale", +) -> Image.Image: + if tile_size <= 0: + logger.debug("Upscaling %s without tiling", img) + output = upscale_pil_patch(model, img) + logger.debug("=> %s", output) + return output + + grid = images.split_grid(img, tile_size, tile_size, tile_overlap) + newtiles = [] + + with tqdm.tqdm(total=grid.tile_count, desc=desc, disable=not shared.opts.enable_upscale_progressbar) as p: + for y, h, row in grid.tiles: + newrow = [] + for x, w, tile in row: + logger.debug("Tile (%d, %d) %s...", x, y, tile) + output = upscale_pil_patch(model, tile) + scale_factor = output.width // tile.width + logger.debug("=> %s (scale factor %s)", output, scale_factor) + newrow.append([x * scale_factor, w * scale_factor, output]) + p.update(1) + newtiles.append([y * scale_factor, h * scale_factor, newrow]) + + newgrid = images.Grid( + newtiles, + tile_w=grid.tile_w * scale_factor, + tile_h=grid.tile_h * scale_factor, + image_w=grid.image_w * scale_factor, + image_h=grid.image_h * scale_factor, + overlap=grid.overlap * scale_factor, + ) + return images.combine_grid(newgrid) + + +def tiled_upscale_2( + img: torch.Tensor, + model, + *, + tile_size: int, + tile_overlap: int, + scale: int, + device: torch.device, + desc="Tiled upscale", +): + # Alternative implementation of `upscale_with_model` originally used by + # SwinIR and ScuNET. It differs from `upscale_with_model` in that tiling and + # weighting is done in PyTorch space, as opposed to `images.Grid` doing it in + # Pillow space without weighting. + + b, c, h, w = img.size() + tile_size = min(tile_size, h, w) + + if tile_size <= 0: + logger.debug("Upscaling %s without tiling", img.shape) + return model(img) + + stride = tile_size - tile_overlap + h_idx_list = list(range(0, h - tile_size, stride)) + [h - tile_size] + w_idx_list = list(range(0, w - tile_size, stride)) + [w - tile_size] + result = torch.zeros( + b, + c, + h * scale, + w * scale, + device=device, + dtype=img.dtype, + ) + weights = torch.zeros_like(result) + logger.debug("Upscaling %s to %s with tiles", img.shape, result.shape) + with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc, disable=not shared.opts.enable_upscale_progressbar) as pbar: + for h_idx in h_idx_list: + if shared.state.interrupted or shared.state.skipped: + break + + for w_idx in w_idx_list: + if shared.state.interrupted or shared.state.skipped: + break + + # Only move this patch to the device if it's not already there. + in_patch = img[ + ..., + h_idx : h_idx + tile_size, + w_idx : w_idx + tile_size, + ].to(device=device) + + out_patch = model(in_patch) + + result[ + ..., + h_idx * scale : (h_idx + tile_size) * scale, + w_idx * scale : (w_idx + tile_size) * scale, + ].add_(out_patch) + + out_patch_mask = torch.ones_like(out_patch) + + weights[ + ..., + h_idx * scale : (h_idx + tile_size) * scale, + w_idx * scale : (w_idx + tile_size) * scale, + ].add_(out_patch_mask) + + pbar.update(1) + + output = result.div_(weights) + + return output + + +def upscale_2( + img: Image.Image, + model, + *, + tile_size: int, + tile_overlap: int, + scale: int, + desc: str, +): + """ + Convenience wrapper around `tiled_upscale_2` that handles PIL images. + """ + param = torch_utils.get_param(model) + tensor = pil_image_to_torch_bgr(img).to(dtype=param.dtype).unsqueeze(0) # add batch dimension + + with torch.no_grad(): + output = tiled_upscale_2( + tensor, + model, + tile_size=tile_size, + tile_overlap=tile_overlap, + scale=scale, + desc=desc, + device=param.device, + ) + return torch_bgr_to_pil_image(output) diff --git a/modules/util.py b/modules/util.py index 60afc067..8d1aea44 100644 --- a/modules/util.py +++ b/modules/util.py @@ -2,7 +2,7 @@ import os import re
from modules import shared
-from modules.paths_internal import script_path
+from modules.paths_internal import script_path, cwd
def natural_sort_key(s, regex=re.compile('([0-9]+)')):
@@ -21,11 +21,11 @@ def html_path(filename): def html(filename):
path = html_path(filename)
- if os.path.exists(path):
+ try:
with open(path, encoding="utf8") as file:
return file.read()
-
- return ""
+ except OSError:
+ return ""
def walk_files(path, allowed_extensions=None):
@@ -42,7 +42,7 @@ def walk_files(path, allowed_extensions=None): for filename in sorted(files, key=natural_sort_key):
if allowed_extensions is not None:
_, ext = os.path.splitext(filename)
- if ext not in allowed_extensions:
+ if ext.lower() not in allowed_extensions:
continue
if not shared.opts.list_hidden_files and ("/." in root or "\\." in root):
@@ -56,3 +56,83 @@ def ldm_print(*args, **kwargs): return
print(*args, **kwargs)
+
+
+def truncate_path(target_path, base_path=cwd):
+ abs_target, abs_base = os.path.abspath(target_path), os.path.abspath(base_path)
+ try:
+ if os.path.commonpath([abs_target, abs_base]) == abs_base:
+ return os.path.relpath(abs_target, abs_base)
+ except ValueError:
+ pass
+ return abs_target
+
+
+class MassFileListerCachedDir:
+ """A class that caches file metadata for a specific directory."""
+
+ def __init__(self, dirname):
+ self.files = None
+ self.files_cased = None
+ self.dirname = dirname
+
+ stats = ((x.name, x.stat(follow_symlinks=False)) for x in os.scandir(self.dirname))
+ files = [(n, s.st_mtime, s.st_ctime) for n, s in stats]
+ self.files = {x[0].lower(): x for x in files}
+ self.files_cased = {x[0]: x for x in files}
+
+
+class MassFileLister:
+ """A class that provides a way to check for the existence and mtime/ctile of files without doing more than one stat call per file."""
+
+ def __init__(self):
+ self.cached_dirs = {}
+
+ def find(self, path):
+ """
+ Find the metadata for a file at the given path.
+
+ Returns:
+ tuple or None: A tuple of (name, mtime, ctime) if the file exists, or None if it does not.
+ """
+
+ dirname, filename = os.path.split(path)
+
+ cached_dir = self.cached_dirs.get(dirname)
+ if cached_dir is None:
+ cached_dir = MassFileListerCachedDir(dirname)
+ self.cached_dirs[dirname] = cached_dir
+
+ stats = cached_dir.files_cased.get(filename)
+ if stats is not None:
+ return stats
+
+ stats = cached_dir.files.get(filename.lower())
+ if stats is None:
+ return None
+
+ try:
+ os_stats = os.stat(path, follow_symlinks=False)
+ return filename, os_stats.st_mtime, os_stats.st_ctime
+ except Exception:
+ return None
+
+ def exists(self, path):
+ """Check if a file exists at the given path."""
+
+ return self.find(path) is not None
+
+ def mctime(self, path):
+ """
+ Get the modification and creation times for a file at the given path.
+
+ Returns:
+ tuple: A tuple of (mtime, ctime) if the file exists, or (0, 0) if it does not.
+ """
+
+ stats = self.find(path)
+ return (0, 0) if stats is None else stats[1:3]
+
+ def reset(self):
+ """Clear the cache of all directories."""
+ self.cached_dirs.clear()
diff --git a/modules/xlmr.py b/modules/xlmr.py index a407a3ca..319771b7 100644 --- a/modules/xlmr.py +++ b/modules/xlmr.py @@ -5,6 +5,9 @@ from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRoberta from transformers import XLMRobertaModel,XLMRobertaTokenizer from typing import Optional +from modules import torch_utils + + class BertSeriesConfig(BertConfig): def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs): @@ -62,7 +65,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): self.post_init() def encode(self,c): - device = next(self.parameters()).device + device = torch_utils.get_param(self).device text = self.tokenizer(c, truncation=True, max_length=77, diff --git a/modules/xlmr_m18.py b/modules/xlmr_m18.py index a727e865..f6055504 100644 --- a/modules/xlmr_m18.py +++ b/modules/xlmr_m18.py @@ -4,6 +4,8 @@ import torch from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig from transformers import XLMRobertaModel,XLMRobertaTokenizer from typing import Optional +from modules import torch_utils + class BertSeriesConfig(BertConfig): def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs): @@ -68,7 +70,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel): self.post_init() def encode(self,c): - device = next(self.parameters()).device + device = torch_utils.get_param(self).device text = self.tokenizer(c, truncation=True, max_length=77, diff --git a/modules/xpu_specific.py b/modules/xpu_specific.py index d8da94a0..2971dbc3 100644 --- a/modules/xpu_specific.py +++ b/modules/xpu_specific.py @@ -27,11 +27,90 @@ def torch_xpu_gc(): has_xpu = check_for_xpu() + +# Arc GPU cannot allocate a single block larger than 4GB: https://github.com/intel/compute-runtime/issues/627 +# Here we implement a slicing algorithm to split large batch size into smaller chunks, +# so that SDPA of each chunk wouldn't require any allocation larger than ARC_SINGLE_ALLOCATION_LIMIT. +# The heuristic limit (TOTAL_VRAM // 8) is tuned for Intel Arc A770 16G and Arc A750 8G, +# which is the best trade-off between VRAM usage and performance. +ARC_SINGLE_ALLOCATION_LIMIT = {} +orig_sdp_attn_func = torch.nn.functional.scaled_dot_product_attention +def torch_xpu_scaled_dot_product_attention( + query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, *args, **kwargs +): + # cast to same dtype first + key = key.to(query.dtype) + value = value.to(query.dtype) + if attn_mask is not None and attn_mask.dtype != torch.bool: + attn_mask = attn_mask.to(query.dtype) + + N = query.shape[:-2] # Batch size + L = query.size(-2) # Target sequence length + E = query.size(-1) # Embedding dimension of the query and key + S = key.size(-2) # Source sequence length + Ev = value.size(-1) # Embedding dimension of the value + + total_batch_size = torch.numel(torch.empty(N)) + device_id = query.device.index + if device_id not in ARC_SINGLE_ALLOCATION_LIMIT: + ARC_SINGLE_ALLOCATION_LIMIT[device_id] = min(torch.xpu.get_device_properties(device_id).total_memory // 8, 4 * 1024 * 1024 * 1024) + batch_size_limit = max(1, ARC_SINGLE_ALLOCATION_LIMIT[device_id] // (L * S * query.element_size())) + + if total_batch_size <= batch_size_limit: + return orig_sdp_attn_func( + query, + key, + value, + attn_mask, + dropout_p, + is_causal, + *args, **kwargs + ) + + query = torch.reshape(query, (-1, L, E)) + key = torch.reshape(key, (-1, S, E)) + value = torch.reshape(value, (-1, S, Ev)) + if attn_mask is not None: + attn_mask = attn_mask.view(-1, L, S) + chunk_count = (total_batch_size + batch_size_limit - 1) // batch_size_limit + outputs = [] + for i in range(chunk_count): + attn_mask_chunk = ( + None + if attn_mask is None + else attn_mask[i * batch_size_limit : (i + 1) * batch_size_limit, :, :] + ) + chunk_output = orig_sdp_attn_func( + query[i * batch_size_limit : (i + 1) * batch_size_limit, :, :], + key[i * batch_size_limit : (i + 1) * batch_size_limit, :, :], + value[i * batch_size_limit : (i + 1) * batch_size_limit, :, :], + attn_mask_chunk, + dropout_p, + is_causal, + *args, **kwargs + ) + outputs.append(chunk_output) + result = torch.cat(outputs, dim=0) + return torch.reshape(result, (*N, L, Ev)) + + +def is_xpu_device(device: str | torch.device = None): + if device is None: + return False + if isinstance(device, str): + return device.startswith("xpu") + return device.type == "xpu" + + if has_xpu: - # W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device - CondFunc('torch.Generator', - lambda orig_func, device=None: torch.xpu.Generator(device), - lambda orig_func, device=None: device is not None and device.type == "xpu") + try: + # torch.Generator supports "xpu" device since 2.1 + torch.Generator("xpu") + except RuntimeError: + # W/A for https://github.com/intel/intel-extension-for-pytorch/issues/452: torch.Generator API doesn't support XPU device (for torch < 2.1) + CondFunc('torch.Generator', + lambda orig_func, device=None: torch.xpu.Generator(device), + lambda orig_func, device=None: is_xpu_device(device)) # W/A for some OPs that could not handle different input dtypes CondFunc('torch.nn.functional.layer_norm', @@ -55,5 +134,5 @@ if has_xpu: lambda orig_func, tensors, dim=0, out=None: orig_func([t.to(tensors[0].dtype) for t in tensors], dim=dim, out=out), lambda orig_func, tensors, dim=0, out=None: not all(t.dtype == tensors[0].dtype for t in tensors)) CondFunc('torch.nn.functional.scaled_dot_product_attention', - lambda orig_func, query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False: orig_func(query, key.to(query.dtype), value.to(query.dtype), attn_mask, dropout_p, is_causal), - lambda orig_func, query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False: query.dtype != key.dtype or query.dtype != value.dtype) + lambda orig_func, *args, **kwargs: torch_xpu_scaled_dot_product_attention(*args, **kwargs), + lambda orig_func, query, *args, **kwargs: query.is_xpu) diff --git a/requirements.txt b/requirements.txt index 80b43845..731a1be7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,12 +2,11 @@ GitPython Pillow
accelerate
-basicsr
blendmodes
clean-fid
einops
+facexlib
fastapi>=0.90.1
-gfpgan
gradio==3.41.2
inflection
jsonmerge
@@ -20,13 +19,11 @@ open-clip-torch piexif
psutil
pytorch_lightning
-realesrgan
requests
resize-right
safetensors
scikit-image>=0.19
-timm
tomesd
torch
torchdiffeq
diff --git a/requirements_npu.txt b/requirements_npu.txt new file mode 100644 index 00000000..5e6a4364 --- /dev/null +++ b/requirements_npu.txt @@ -0,0 +1,4 @@ +cloudpickle +decorator +synr==0.5.0 +tornado diff --git a/requirements_versions.txt b/requirements_versions.txt index cb7403a9..5e30b5ea 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -1,29 +1,27 @@ GitPython==3.1.32
Pillow==9.5.0
accelerate==0.21.0
-basicsr==1.4.2
blendmodes==2022
clean-fid==0.1.35
einops==0.4.1
+facexlib==0.3.0
fastapi==0.94.0
-gfpgan==1.3.8
gradio==3.41.2
httpcore==0.15
inflection==0.5.1
jsonmerge==1.8.0
kornia==0.6.7
lark==1.1.2
-numpy==1.23.5
+numpy==1.26.2
omegaconf==2.2.3
open-clip-torch==2.20.0
piexif==1.1.3
psutil==5.9.5
pytorch_lightning==1.9.4
-realesrgan==0.3.0
resize-right==0.0.2
-safetensors==0.3.1
+safetensors==0.4.2
scikit-image==0.21.0
-timm==0.9.2
+spandrel==0.1.6
tomesd==0.1.3
torch
torchdiffeq==0.2.3
@@ -121,16 +121,22 @@ document.addEventListener("DOMContentLoaded", function() { }); /** - * Add a ctrl+enter as a shortcut to start a generation + * Add keyboard shortcuts: + * Ctrl+Enter to start/restart a generation + * Alt/Option+Enter to skip a generation + * Esc to interrupt a generation */ document.addEventListener('keydown', function(e) { const isEnter = e.key === 'Enter' || e.keyCode === 13; - const isModifierKey = e.metaKey || e.ctrlKey || e.altKey; + const isCtrlKey = e.metaKey || e.ctrlKey; + const isAltKey = e.altKey; + const isEsc = e.key === 'Escape'; - const interruptButton = get_uiCurrentTabContent().querySelector('button[id$=_interrupt]'); const generateButton = get_uiCurrentTabContent().querySelector('button[id$=_generate]'); + const interruptButton = get_uiCurrentTabContent().querySelector('button[id$=_interrupt]'); + const skipButton = get_uiCurrentTabContent().querySelector('button[id$=_skip]'); - if (isEnter && isModifierKey) { + if (isCtrlKey && isEnter) { if (interruptButton.style.display === 'block') { interruptButton.click(); const callback = (mutationList) => { @@ -150,6 +156,23 @@ document.addEventListener('keydown', function(e) { } e.preventDefault(); } + + if (isAltKey && isEnter) { + skipButton.click(); + e.preventDefault(); + } + + if (isEsc) { + const globalPopup = document.querySelector('.global-popup'); + const lightboxModal = document.querySelector('#lightboxModal'); + if (!globalPopup || globalPopup.style.display === 'none') { + if (document.activeElement === lightboxModal) return; + if (interruptButton.style.display === 'block') { + interruptButton.click(); + e.preventDefault(); + } + } + } }); /** diff --git a/scripts/loopback.py b/scripts/loopback.py index 2d5feaf9..800ee882 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -95,7 +95,7 @@ class Script(scripts.Script): processed = processing.process_images(p)
# Generation cancelled.
- if state.interrupted:
+ if state.interrupted or state.stopping_generation:
break
if initial_seed is None:
@@ -122,8 +122,8 @@ class Script(scripts.Script): p.inpainting_fill = original_inpainting_fill
- if state.interrupted:
- break
+ if state.interrupted or state.stopping_generation:
+ break
if len(history) > 1:
grid = images.image_grid(history, rows=1)
diff --git a/scripts/postprocessing_caption.py b/scripts/postprocessing_caption.py index 243e3ad9..5592a898 100644 --- a/scripts/postprocessing_caption.py +++ b/scripts/postprocessing_caption.py @@ -4,7 +4,7 @@ import gradio as gr class ScriptPostprocessingCeption(scripts_postprocessing.ScriptPostprocessing):
name = "Caption"
- order = 4000
+ order = 4040
def ui(self):
with ui_components.InputAccordion(False, label="Caption") as enable:
@@ -25,6 +25,6 @@ class ScriptPostprocessingCeption(scripts_postprocessing.ScriptPostprocessing): captions.append(deepbooru.model.tag(pp.image))
if "BLIP" in option:
- captions.append(shared.interrogator.generate_caption(pp.image))
+ captions.append(shared.interrogator.interrogate(pp.image.convert("RGB")))
pp.caption = ", ".join([x for x in captions if x])
diff --git a/scripts/postprocessing_create_flipped_copies.py b/scripts/postprocessing_create_flipped_copies.py index 3425571d..b673003b 100644 --- a/scripts/postprocessing_create_flipped_copies.py +++ b/scripts/postprocessing_create_flipped_copies.py @@ -6,7 +6,7 @@ import gradio as gr class ScriptPostprocessingCreateFlippedCopies(scripts_postprocessing.ScriptPostprocessing):
name = "Create flipped copies"
- order = 4000
+ order = 4030
def ui(self):
with ui_components.InputAccordion(False, label="Create flipped copies") as enable:
diff --git a/scripts/postprocessing_focal_crop.py b/scripts/postprocessing_focal_crop.py index d3baf298..cff1dbc5 100644 --- a/scripts/postprocessing_focal_crop.py +++ b/scripts/postprocessing_focal_crop.py @@ -7,7 +7,7 @@ from modules.textual_inversion import autocrop class ScriptPostprocessingFocalCrop(scripts_postprocessing.ScriptPostprocessing):
name = "Auto focal point crop"
- order = 4000
+ order = 4010
def ui(self):
with ui_components.InputAccordion(False, label="Auto focal point crop") as enable:
diff --git a/scripts/postprocessing_split_oversized.py b/scripts/postprocessing_split_oversized.py index c4a03160..133e199b 100644 --- a/scripts/postprocessing_split_oversized.py +++ b/scripts/postprocessing_split_oversized.py @@ -61,7 +61,7 @@ class ScriptPostprocessingSplitOversized(scripts_postprocessing.ScriptPostproces ratio = (pp.image.height * width) / (pp.image.width * height)
inverse_xy = True
- if ratio >= 1.0 and ratio > split_threshold:
+ if ratio >= 1.0 or ratio > split_threshold:
return
result, *others = split_pic(pp.image, inverse_xy, width, height, overlap_ratio)
diff --git a/scripts/postprocessing_upscale.py b/scripts/postprocessing_upscale.py index ed709688..e269682d 100644 --- a/scripts/postprocessing_upscale.py +++ b/scripts/postprocessing_upscale.py @@ -15,7 +15,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): order = 1000
def ui(self):
- selected_tab = gr.State(value=0)
+ selected_tab = gr.Number(value=0, visible=False)
with gr.Column():
with FormRow():
@@ -26,8 +26,8 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
with FormRow():
with gr.Column(elem_id="upscaling_column_size", scale=4):
- upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
- upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
+ upscaling_resize_w = gr.Slider(minimum=64, maximum=8192, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
+ upscaling_resize_h = gr.Slider(minimum=64, maximum=8192, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn", tooltip="Switch width/height")
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
diff --git a/scripts/processing_autosized_crop.py b/scripts/processing_autosized_crop.py index c0980226..7e674989 100644 --- a/scripts/processing_autosized_crop.py +++ b/scripts/processing_autosized_crop.py @@ -28,7 +28,7 @@ def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, thr class ScriptPostprocessingAutosizedCrop(scripts_postprocessing.ScriptPostprocessing):
name = "Auto-sized crop"
- order = 4000
+ order = 4020
def ui(self):
with ui_components.InputAccordion(False, label="Auto-sized crop") as enable:
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 0dc255bc..6d3e42c0 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -270,6 +270,7 @@ axis_options = [ AxisOption("Refiner checkpoint", str, apply_field('refiner_checkpoint'), format_value=format_remove_path, confirm=confirm_checkpoints_or_none, cost=1.0, choices=lambda: ['None'] + sorted(sd_models.checkpoints_list, key=str.casefold)),
AxisOption("Refiner switch at", float, apply_field('refiner_switch_at')),
AxisOption("RNG source", str, apply_override("randn_source"), choices=lambda: ["GPU", "CPU", "NV"]),
+ AxisOption("FP8 mode", str, apply_override("fp8_storage"), cost=0.9, choices=lambda: ["Disable", "Enable for SDXL", "Enable"]),
]
@@ -437,13 +438,16 @@ class Script(scripts.Script): with gr.Column():
draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
+ with gr.Row():
+ vary_seeds_x = gr.Checkbox(label='Vary seeds for X', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_x"), tooltip="Use different seeds for images along X axis.")
+ vary_seeds_y = gr.Checkbox(label='Vary seeds for Y', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_y"), tooltip="Use different seeds for images along Y axis.")
+ vary_seeds_z = gr.Checkbox(label='Vary seeds for Z', value=False, min_width=80, elem_id=self.elem_id("vary_seeds_z"), tooltip="Use different seeds for images along Z axis.")
with gr.Column():
include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id("include_lone_images"))
include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
+ csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
with gr.Column():
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
- with gr.Column():
- csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
with gr.Row(variant="compact", elem_id="swap_axes"):
swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
@@ -475,6 +479,8 @@ class Script(scripts.Script): fill_z_button.click(fn=fill, inputs=[z_type, csv_mode], outputs=[z_values, z_values_dropdown])
def select_axis(axis_type, axis_values, axis_values_dropdown, csv_mode):
+ axis_type = axis_type or 0 # if axle type is None set to 0
+
choices = self.current_axis_options[axis_type].choices
has_choices = choices is not None
@@ -522,9 +528,11 @@ class Script(scripts.Script): (z_values_dropdown, lambda params: get_dropdown_update_from_params("Z", params)),
)
- return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode]
+ return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode]
+
+ def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode):
+ x_type, y_type, z_type = x_type or 0, y_type or 0, z_type or 0 # if axle type is None set to 0
- def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
@@ -546,6 +554,8 @@ class Script(scripts.Script): valslist_ext = []
for val in valslist:
+ if val.strip() == '':
+ continue
m = re_range.fullmatch(val)
mc = re_range_count.fullmatch(val)
if m is not None:
@@ -568,6 +578,8 @@ class Script(scripts.Script): valslist_ext = []
for val in valslist:
+ if val.strip() == '':
+ continue
m = re_range_float.fullmatch(val)
mc = re_range_count_float.fullmatch(val)
if m is not None:
@@ -688,7 +700,7 @@ class Script(scripts.Script): grid_infotext = [None] * (1 + len(zs))
def cell(x, y, z, ix, iy, iz):
- if shared.state.interrupted:
+ if shared.state.interrupted or state.stopping_generation:
return Processed(p, [], p.seed, "")
pc = copy(p)
@@ -697,6 +709,16 @@ class Script(scripts.Script): y_opt.apply(pc, y, ys)
z_opt.apply(pc, z, zs)
+ xdim = len(xs) if vary_seeds_x else 1
+ ydim = len(ys) if vary_seeds_y else 1
+
+ if vary_seeds_x:
+ pc.seed += ix
+ if vary_seeds_y:
+ pc.seed += iy * xdim
+ if vary_seeds_z:
+ pc.seed += iz * xdim * ydim
+
try:
res = process_images(pc)
except Exception as e:
@@ -1,6 +1,6 @@ /* temporary fix to load default gradio font in frontend instead of backend */
-@import url('https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap');
+@import url('webui-assets/css/sourcesanspro.css');
/* temporary fix to hide gradio crop tool until it's fixed https://github.com/gradio-app/gradio/issues/3810 */
@@ -28,7 +28,7 @@ div.gradio-container{ }
.hidden{
- display: none;
+ display: none !important;
}
.compact{
@@ -222,6 +222,10 @@ input[type="checkbox"].input-accordion-checkbox{ top: -0.75em;
}
+.block.token-counter-visible{
+ display: block !important;
+}
+
.block.token-counter span{
background: var(--input-background-fill) !important;
box-shadow: 0 0 0.0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075);
@@ -331,17 +335,17 @@ input[type="checkbox"].input-accordion-checkbox{ .generate-box{
position: relative;
}
-.gradio-button.generate-box-skip, .gradio-button.generate-box-interrupt{
+.gradio-button.generate-box-skip, .gradio-button.generate-box-interrupt, .gradio-button.generate-box-interrupting{
position: absolute;
width: 50%;
height: 100%;
display: none;
background: #b4c0cc;
}
-.gradio-button.generate-box-skip:hover, .gradio-button.generate-box-interrupt:hover{
+.gradio-button.generate-box-skip:hover, .gradio-button.generate-box-interrupt:hover, .gradio-button.generate-box-interrupting:hover{
background: #c2cfdb;
}
-.gradio-button.generate-box-interrupt{
+.gradio-button.generate-box-interrupt, .gradio-button.generate-box-interrupting{
left: 0;
border-radius: 0.5rem 0 0 0.5rem;
}
@@ -679,7 +683,7 @@ table.popup-table .link{ transition: 0.2s ease background-color;
}
.modalControls:hover {
- background-color:rgba(0,0,0,0.9);
+ background-color:rgba(0,0,0, var(--sd-webui-modal-lightbox-toolbar-opacity));
}
.modalClose {
margin-left: auto;
@@ -749,6 +753,22 @@ table.popup-table .link{ display: none;
}
+@media (pointer: fine) {
+ .modalPrev:hover,
+ .modalNext:hover,
+ .modalControls:hover ~ .modalPrev,
+ .modalControls:hover ~ .modalNext,
+ .modalControls:hover .cursor {
+ opacity: 1;
+ }
+
+ .modalPrev,
+ .modalNext,
+ .modalControls .cursor {
+ opacity: var(--sd-webui-modal-lightbox-icon-opacity);
+ }
+}
+
/* context menu (ie for the generate button) */
#context-menu{
@@ -830,6 +850,20 @@ table.popup-table .link{ display: inline-block;
}
+/* extensions tab table row hover highlight */
+
+#extensions tr:hover td,
+#config_state_extensions tr:hover td,
+#available_extensions tr:hover td {
+ background: rgba(0, 0, 0, 0.15);
+}
+
+.dark #extensions tr:hover td ,
+.dark #config_state_extensions tr:hover td ,
+.dark #available_extensions tr:hover td {
+ background: rgba(255, 255, 255, 0.15);
+}
+
/* replace original footer with ours */
footer {
@@ -863,31 +897,21 @@ footer { margin-bottom: 1em;
}
-.extra-network-cards{
- height: calc(100vh - 24rem);
- overflow: clip scroll;
- resize: vertical;
- min-height: 52rem;
+.extra-networks > div.tab-nav{
+ min-height: 2.7rem;
}
-.extra-networks > div.tab-nav{
- min-height: 3.4rem;
+.extra-networks-controls-div{
+ align-self: center;
+ margin-left: auto;
}
.extra-networks > div > [id *= '_extra_']{
margin: 0.3em;
}
-.extra-network-subdirs{
- padding: 0.2em 0.35em;
-}
-
-.extra-network-subdirs button{
- margin: 0 0.15em;
-}
.extra-networks .tab-nav .search,
-.extra-networks .tab-nav .sort,
-.extra-networks .tab-nav .show-dirs
+.extra-networks .tab-nav .sort
{
margin: 0.3em;
align-self: center;
@@ -908,53 +932,69 @@ footer { width: auto;
}
-.extra-network-cards .nocards{
+.extra-network-pane .nocards{
margin: 1.25em 0.5em 0.5em 0.5em;
}
-.extra-network-cards .nocards h1{
+.extra-network-pane .nocards h1{
font-size: 1.5em;
margin-bottom: 1em;
}
-.extra-network-cards .nocards li{
+.extra-network-pane .nocards li{
margin-left: 0.5em;
}
+.extra-network-pane .card .button-row{
+ display: inline-flex;
+ visibility: hidden;
+ color: white;
+}
-.extra-network-cards .card .button-row{
- display: none;
+.extra-network-pane .card .button-row {
position: absolute;
- color: white;
right: 0;
- z-index: 1
+ z-index: 1;
}
-.extra-network-cards .card:hover .button-row{
- display: flex;
+
+.extra-network-pane .card:hover .button-row{
+ visibility: visible;
}
-.extra-network-cards .card .card-button{
+.extra-network-pane .card-button{
color: white;
}
-.extra-network-cards .card .metadata-button:before{
+.extra-network-pane .copy-path-button::before {
+ content: "⎘";
+}
+
+.extra-network-pane .metadata-button::before{
content: "🛈";
}
-.extra-network-cards .card .edit-button:before{
+.extra-network-pane .edit-button::before{
content: "🛠";
}
-.extra-network-cards .card .card-button {
+.extra-network-pane .card-button {
+ width: 1.5em;
text-shadow: 2px 2px 3px black;
+ color: white;
padding: 0.25em 0.1em;
- font-size: 200%;
- width: 1.5em;
}
-.extra-network-cards .card .card-button:hover{
+
+.extra-network-pane .card-button:hover{
color: red;
}
+.extra-network-pane .card .card-button {
+ font-size: 2rem;
+}
+
+.extra-network-pane .card-minimal .card-button {
+ font-size: 1rem;
+}
.standalone-card-preview.card .preview{
position: absolute;
@@ -963,7 +1003,7 @@ footer { height:100%;
}
-.extra-network-cards .card, .standalone-card-preview.card{
+.extra-network-pane .card, .standalone-card-preview.card{
display: inline-block;
margin: 0.5rem;
width: 16rem;
@@ -980,15 +1020,15 @@ footer { background-image: url('./file=html/card-no-preview.png')
}
-.extra-network-cards .card:hover{
+.extra-network-pane .card:hover{
box-shadow: 0 0 2px 0.3em rgba(0, 128, 255, 0.35);
}
-.extra-network-cards .card .actions .additional{
+.extra-network-pane .card .actions .additional{
display: none;
}
-.extra-network-cards .card .actions{
+.extra-network-pane .card .actions{
position: absolute;
bottom: 0;
left: 0;
@@ -999,45 +1039,45 @@ footer { text-shadow: 0 0 0.2em black;
}
-.extra-network-cards .card .actions *{
+.extra-network-pane .card .actions *{
color: white;
}
-.extra-network-cards .card .actions .name{
+.extra-network-pane .card .actions .name{
font-size: 1.7em;
font-weight: bold;
line-break: anywhere;
}
-.extra-network-cards .card .actions .description {
+.extra-network-pane .card .actions .description {
display: block;
max-height: 3em;
white-space: pre-wrap;
line-height: 1.1;
}
-.extra-network-cards .card .actions .description:hover {
+.extra-network-pane .card .actions .description:hover {
max-height: none;
}
-.extra-network-cards .card .actions:hover .additional{
+.extra-network-pane .card .actions:hover .additional{
display: block;
}
-.extra-network-cards .card ul{
+.extra-network-pane .card ul{
margin: 0.25em 0 0.75em 0.25em;
cursor: unset;
}
-.extra-network-cards .card ul a{
+.extra-network-pane .card ul a{
cursor: pointer;
}
-.extra-network-cards .card ul a:hover{
+.extra-network-pane .card ul a:hover{
color: red;
}
-.extra-network-cards .card .preview{
+.extra-network-pane .card .preview{
position: absolute;
object-fit: cover;
width: 100%;
@@ -1080,9 +1120,6 @@ div.block.gradio-box.edit-user-metadata { margin-top: 1.5em;
}
-
-
-
div.block.gradio-box.popup-dialog, .popup-dialog {
width: 56em;
background: var(--body-background-fill);
@@ -1157,3 +1194,430 @@ body.resizing .resize-handle { left: 7.5px;
border-left: 1px dashed var(--border-color-primary);
}
+
+/* ========================= */
+.extra-network-pane {
+ display: flex;
+ height: calc(100vh - 24rem);
+ resize: vertical;
+ min-height: 52rem;
+ flex-direction: column;
+ overflow: hidden;
+}
+
+.extra-network-pane .extra-network-pane-content {
+ display: flex;
+ flex: 1;
+ overflow: hidden;
+}
+
+.extra-network-pane .extra-network-tree {
+ flex: 1;
+ font-size: 1rem;
+ border: 1px solid var(--block-border-color);
+ overflow: clip auto !important;
+}
+
+.extra-network-pane .extra-network-cards {
+ flex: 3;
+ overflow: clip auto !important;
+ border: 1px solid var(--block-border-color);
+}
+
+.extra-network-pane .extra-network-tree .tree-list {
+ flex: 1;
+ display: flex;
+ flex-direction: column;
+ padding: 0;
+ width: 100%;
+ overflow: hidden;
+}
+
+
+.extra-network-pane .extra-network-cards::-webkit-scrollbar,
+.extra-network-pane .extra-network-tree::-webkit-scrollbar {
+ background-color: transparent;
+ width: 16px;
+}
+
+.extra-network-pane .extra-network-cards::-webkit-scrollbar-track,
+.extra-network-pane .extra-network-tree::-webkit-scrollbar-track {
+ background-color: transparent;
+ background-clip: content-box;
+}
+
+.extra-network-pane .extra-network-cards::-webkit-scrollbar-thumb,
+.extra-network-pane .extra-network-tree::-webkit-scrollbar-thumb {
+ background-color: var(--border-color-primary);
+ border-radius: 16px;
+ border: 4px solid var(--background-fill-primary);
+}
+
+.extra-network-pane .extra-network-cards::-webkit-scrollbar-button,
+.extra-network-pane .extra-network-tree::-webkit-scrollbar-button {
+ display: none;
+}
+
+.extra-network-control {
+ position: relative;
+ display: grid;
+ width: 100%;
+ padding: 0 !important;
+ margin-top: 0 !important;
+ margin-bottom: 0 !important;
+ font-size: 1rem;
+ text-align: left;
+ user-select: none;
+ background-color: transparent;
+ border: none;
+ transition: background 33.333ms linear;
+ grid-template-rows: min-content;
+ grid-template-columns: minmax(0, auto) repeat(4, min-content);
+ grid-gap: 0.1rem;
+ align-items: start;
+}
+
+.extra-network-tree .tree-list--tree {}
+
+/* Remove auto indentation from tree. Will be overridden later. */
+.extra-network-tree .tree-list--subgroup {
+ margin: 0 !important;
+ padding: 0 !important;
+ box-shadow: 0.5rem 0 0 var(--body-background-fill) inset,
+ 0.7rem 0 0 var(--neutral-800) inset;
+}
+
+/* Set indentation for each depth of tree. */
+.extra-network-tree .tree-list--subgroup > .tree-list-item {
+ margin-left: 0.4rem !important;
+ padding-left: 0.4rem !important;
+}
+
+/* Styles for tree <li> elements. */
+.extra-network-tree .tree-list-item {
+ list-style: none;
+ position: relative;
+ background-color: transparent;
+}
+
+/* Directory <ul> visibility based on data-expanded attribute. */
+.extra-network-tree .tree-list-content+.tree-list--subgroup {
+ height: 0;
+ visibility: hidden;
+ opacity: 0;
+}
+
+.extra-network-tree .tree-list-content[data-expanded]+.tree-list--subgroup {
+ height: auto;
+ visibility: visible;
+ opacity: 1;
+}
+
+/* File <li> */
+.extra-network-tree .tree-list-item--subitem {
+ padding-top: 0 !important;
+ padding-bottom: 0 !important;
+ margin-top: 0 !important;
+ margin-bottom: 0 !important;
+}
+
+/* <li> containing <ul> */
+.extra-network-tree .tree-list-item--has-subitem {}
+
+/* BUTTON ELEMENTS */
+/* <button> */
+.extra-network-tree .tree-list-content {
+ position: relative;
+ display: grid;
+ width: 100%;
+ padding: 0 !important;
+ margin-top: 0 !important;
+ margin-bottom: 0 !important;
+ font-size: 1rem;
+ text-align: left;
+ user-select: none;
+ background-color: transparent;
+ border: none;
+ transition: background 33.333ms linear;
+ grid-template-rows: min-content;
+ grid-template-areas: "leading-action leading-visual label trailing-visual trailing-action";
+ grid-template-columns: min-content min-content minmax(0, auto) min-content min-content;
+ grid-gap: 0.1rem;
+ align-items: start;
+ flex-grow: 1;
+ flex-basis: 100%;
+}
+/* Buttons for directories. */
+.extra-network-tree .tree-list-content-dir {}
+
+/* Buttons for files. */
+.extra-network-tree .tree-list-item--has-subitem .tree-list--subgroup > li:first-child {
+ padding-top: 0.5rem !important;
+}
+
+.dark .extra-network-tree div.tree-list-content:hover {
+ -webkit-transition: all 0.05s ease-in-out;
+ transition: all 0.05s ease-in-out;
+ background-color: var(--neutral-800);
+}
+
+.dark .extra-network-tree div.tree-list-content[data-selected] {
+ background-color: var(--neutral-700);
+}
+
+.extra-network-tree div.tree-list-content[data-selected] {
+ background-color: var(--neutral-300);
+}
+
+.extra-network-tree div.tree-list-content:hover {
+ -webkit-transition: all 0.05s ease-in-out;
+ transition: all 0.05s ease-in-out;
+ background-color: var(--neutral-200);
+}
+
+/* ==== CHEVRON ICON ACTIONS ==== */
+/* Define the animation for the arrow when it is clicked. */
+.extra-network-tree .tree-list-content-dir .tree-list-item-action-chevron {
+ -ms-transform: rotate(135deg);
+ -webkit-transform: rotate(135deg);
+ transform: rotate(135deg);
+ transition: transform 0.2s;
+}
+
+.extra-network-tree .tree-list-content-dir[data-expanded] .tree-list-item-action-chevron {
+ -ms-transform: rotate(225deg);
+ -webkit-transform: rotate(225deg);
+ transform: rotate(225deg);
+ transition: transform 0.2s;
+}
+
+.tree-list-item-action-chevron {
+ display: inline-flex;
+ /* Uses box shadow to generate a pseudo chevron `>` icon. */
+ padding: 0.3rem;
+ box-shadow: 0.1rem 0.1rem 0 0 var(--neutral-200) inset;
+ transform: rotate(135deg);
+}
+
+/* ==== SEARCH INPUT ACTIONS ==== */
+/* Add icon to left side of <input> */
+.extra-network-control .extra-network-control--search::before {
+ content: "🔎︎";
+ position: absolute;
+ margin: 0.5rem;
+ font-size: 1rem;
+ color: var(--input-placeholder-color);
+}
+
+.extra-network-control .extra-network-control--search {
+ display: inline-flex;
+ position: relative;
+}
+
+.extra-network-control .extra-network-control--search .extra-network-control--search-text {
+ border: 1px solid var(--button-secondary-border-color);
+ border-radius: 0.5rem;
+ color: var(--button-secondary-text-color);
+ background-color: transparent;
+ width: 100%;
+ padding-left: 2rem;
+ line-height: 1rem;
+}
+
+/* <input> clear button (x on right side) styling */
+.extra-network-control .extra-network-control--search .extra-network-control--search-text::-webkit-search-cancel-button {
+ -webkit-appearance: none;
+ appearance: none;
+ cursor: pointer;
+ height: 1rem;
+ width: 1rem;
+ mask-image: url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="black" stroke-width="4" stroke-linecap="round" stroke-linejoin="round"><line x1="18" y1="6" x2="6" y2="18"></line><line x1="6" y1="6" x2="18" y2="18"></line></svg>');
+ mask-repeat: no-repeat;
+ mask-position: center center;
+ mask-size: 100%;
+ background-color: var(--input-placeholder-color);
+}
+
+/* ==== SORT ICON ACTIONS ==== */
+.extra-network-control .extra-network-control--sort {
+ padding: 0.25rem;
+ display: inline-flex;
+ cursor: pointer;
+ justify-self: center;
+ align-self: center;
+}
+
+.extra-network-control .extra-network-control--sort .extra-network-control--sort-icon {
+ height: 1.5rem;
+ width: 1.5rem;
+ mask-repeat: no-repeat;
+ mask-position: center center;
+ mask-size: 100%;
+ background-color: var(--input-placeholder-color);
+}
+
+.extra-network-control .extra-network-control--sort[data-sortmode="path"] .extra-network-control--sort-icon {
+ mask-image: url('data:image/svg+xml,<svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"><path fill-rule="evenodd" clip-rule="evenodd" d="M1 5C1 3.34315 2.34315 2 4 2H8.43845C9.81505 2 11.015 2.93689 11.3489 4.27239L11.7808 6H13.5H20C21.6569 6 23 7.34315 23 9V11C23 11.5523 22.5523 12 22 12C21.4477 12 21 11.5523 21 11V9C21 8.44772 20.5523 8 20 8H13.5H11.7808H4C3.44772 8 3 8.44772 3 9V10V19C3 19.5523 3.44772 20 4 20H9C9.55228 20 10 20.4477 10 21C10 21.5523 9.55228 22 9 22H4C2.34315 22 1 20.6569 1 19V10V9V5ZM3 6.17071C3.31278 6.06015 3.64936 6 4 6H9.71922L9.40859 4.75746C9.2973 4.3123 8.89732 4 8.43845 4H4C3.44772 4 3 4.44772 3 5V6.17071ZM20.1716 18.7574C20.6951 17.967 21 17.0191 21 16C21 13.2386 18.7614 11 16 11C13.2386 11 11 13.2386 11 16C11 18.7614 13.2386 21 16 21C17.0191 21 17.967 20.6951 18.7574 20.1716L21.2929 22.7071C21.6834 23.0976 22.3166 23.0976 22.7071 22.7071C23.0976 22.3166 23.0976 21.6834 22.7071 21.2929L20.1716 18.7574ZM13 16C13 14.3431 14.3431 13 16 13C17.6569 13 19 14.3431 19 16C19 17.6569 17.6569 19 16 19C14.3431 19 13 17.6569 13 16Z" fill="%23000000"></path></g></svg>');
+}
+
+.extra-network-control .extra-network-control--sort[data-sortmode="name"] .extra-network-control--sort-icon {
+ mask-image: url('data:image/svg+xml,<svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"><path fill-rule="evenodd" clip-rule="evenodd" d="M17.1841 6.69223C17.063 6.42309 16.7953 6.25 16.5002 6.25C16.2051 6.25 15.9374 6.42309 15.8162 6.69223L11.3162 16.6922C11.1463 17.07 11.3147 17.514 11.6924 17.6839C12.0701 17.8539 12.5141 17.6855 12.6841 17.3078L14.1215 14.1136H18.8789L20.3162 17.3078C20.4862 17.6855 20.9302 17.8539 21.308 17.6839C21.6857 17.514 21.8541 17.07 21.6841 16.6922L17.1841 6.69223ZM16.5002 8.82764L14.7965 12.6136H18.2039L16.5002 8.82764Z" fill="%231C274C"></path><path opacity="0.5" fill-rule="evenodd" clip-rule="evenodd" d="M2.25 7C2.25 6.58579 2.58579 6.25 3 6.25H13C13.4142 6.25 13.75 6.58579 13.75 7C13.75 7.41421 13.4142 7.75 13 7.75H3C2.58579 7.75 2.25 7.41421 2.25 7Z" fill="%231C274C"></path><path opacity="0.5" d="M2.25 12C2.25 11.5858 2.58579 11.25 3 11.25H10C10.4142 11.25 10.75 11.5858 10.75 12C10.75 12.4142 10.4142 12.75 10 12.75H3C2.58579 12.75 2.25 12.4142 2.25 12Z" fill="%231C274C"></path><path opacity="0.5" d="M2.25 17C2.25 16.5858 2.58579 16.25 3 16.25H8C8.41421 16.25 8.75 16.5858 8.75 17C8.75 17.4142 8.41421 17.75 8 17.75H3C2.58579 17.75 2.25 17.4142 2.25 17Z" fill="%231C274C"></path></g></svg>');
+}
+
+.extra-network-control .extra-network-control--sort[data-sortmode="date_created"] .extra-network-control--sort-icon {
+ mask-image: url('data:image/svg+xml,<svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"><path d="M17 11C14.2386 11 12 13.2386 12 16C12 18.7614 14.2386 21 17 21C19.7614 21 22 18.7614 22 16C22 13.2386 19.7614 11 17 11ZM17 11V9M2 9V15.8C2 16.9201 2 17.4802 2.21799 17.908C2.40973 18.2843 2.71569 18.5903 3.09202 18.782C3.51984 19 4.0799 19 5.2 19H13M2 9V8.2C2 7.0799 2 6.51984 2.21799 6.09202C2.40973 5.71569 2.71569 5.40973 3.09202 5.21799C3.51984 5 4.0799 5 5.2 5H13.8C14.9201 5 15.4802 5 15.908 5.21799C16.2843 5.40973 16.5903 5.71569 16.782 6.09202C17 6.51984 17 7.0799 17 8.2V9M2 9H17M5 3V5M14 3V5M15 16H17M17 16H19M17 16V14M17 16V18" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></g></svg>');
+}
+
+.extra-network-control .extra-network-control--sort[data-sortmode="date_modified"] .extra-network-control--sort-icon {
+ mask-image: url('data:image/svg+xml,<svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"><path d="M10 21H6.2C5.0799 21 4.51984 21 4.09202 20.782C3.71569 20.5903 3.40973 20.2843 3.21799 19.908C3 19.4802 3 18.9201 3 17.8V8.2C3 7.0799 3 6.51984 3.21799 6.09202C3.40973 5.71569 3.71569 5.40973 4.09202 5.21799C4.51984 5 5.0799 5 6.2 5H17.8C18.9201 5 19.4802 5 19.908 5.21799C20.2843 5.40973 20.5903 5.71569 20.782 6.09202C21 6.51984 21 7.0799 21 8.2V10M7 3V5M17 3V5M3 9H21M13.5 13.0001L7 13M10 17.0001L7 17M14 21L16.025 20.595C16.2015 20.5597 16.2898 20.542 16.3721 20.5097C16.4452 20.4811 16.5147 20.4439 16.579 20.399C16.6516 20.3484 16.7152 20.2848 16.8426 20.1574L21 16C21.5523 15.4477 21.5523 14.5523 21 14C20.4477 13.4477 19.5523 13.4477 19 14L14.8426 18.1574C14.7152 18.2848 14.6516 18.3484 14.601 18.421C14.5561 18.4853 14.5189 18.5548 14.4903 18.6279C14.458 18.7102 14.4403 18.7985 14.405 18.975L14 21Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></g></svg>');
+}
+
+/* ==== SORT DIRECTION ICON ACTIONS ==== */
+.extra-network-control .extra-network-control--sort-dir {
+ padding: 0.25rem;
+ display: inline-flex;
+ cursor: pointer;
+ justify-self: center;
+ align-self: center;
+}
+
+.extra-network-control .extra-network-control--sort-dir .extra-network-control--sort-dir-icon {
+ height: 1.5rem;
+ width: 1.5rem;
+ mask-repeat: no-repeat;
+ mask-position: center center;
+ mask-size: 100%;
+ background-color: var(--input-placeholder-color);
+}
+
+.extra-network-control .extra-network-control--sort-dir[data-sortdir="Ascending"] .extra-network-control--sort-dir-icon {
+ mask-image: url('data:image/svg+xml,<svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"><path d="M13 12H21M13 8H21M13 16H21M6 7V17M6 7L3 10M6 7L9 10" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></g></svg>');
+}
+
+.extra-network-control .extra-network-control--sort-dir[data-sortdir="Descending"] .extra-network-control--sort-dir-icon {
+ mask-image: url('data:image/svg+xml,<svg viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"><path d="M13 12H21M13 8H21M13 16H21M6 7V17M6 17L3 14M6 17L9 14" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></g></svg>');
+}
+
+/* ==== TREE VIEW ICON ACTIONS ==== */
+.extra-network-control .extra-network-control--tree-view {
+ padding: 0.25rem;
+ display: inline-flex;
+ cursor: pointer;
+ justify-self: center;
+ align-self: center;
+}
+
+.extra-network-control .extra-network-control--tree-view .extra-network-control--tree-view-icon {
+ height: 1.5rem;
+ width: 1.5rem;
+ mask-image: url('data:image/svg+xml,<svg viewBox="0 0 16 16" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" fill="black"><g id="SVGRepo_bgCarrier" stroke-width="0"></g><g id="SVGRepo_tracerCarrier" stroke-linecap="round" stroke-linejoin="round"></g><g id="SVGRepo_iconCarrier"><path fill="black" d="M16 10v-4h-11v1h-2v-3h9v-4h-12v4h2v10h3v2h11v-4h-11v1h-2v-5h2v2z"></path></g></svg>');
+ mask-repeat: no-repeat;
+ mask-position: center center;
+ mask-size: 100%;
+ background-color: var(--input-placeholder-color);
+}
+
+.extra-network-control .extra-network-control--enabled {
+ background-color: rgba(0, 0, 0, 0.15);
+}
+
+.dark .extra-network-control .extra-network-control--enabled {
+ background-color: rgba(255, 255, 255, 0.15);
+}
+
+/* ==== REFRESH ICON ACTIONS ==== */
+.extra-network-control .extra-network-control--refresh {
+ padding: 0.25rem;
+ display: inline-flex;
+ cursor: pointer;
+ justify-self: center;
+ align-self: center;
+}
+
+.extra-network-control .extra-network-control--refresh .extra-network-control--refresh-icon {
+ height: 1.5rem;
+ width: 1.5rem;
+ mask-image: url('data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="bevel"><path d="M21.5 2v6h-6M21.34 15.57a10 10 0 1 1-.57-8.38"/></svg>');
+ mask-repeat: no-repeat;
+ mask-position: center center;
+ mask-size: 100%;
+ background-color: var(--input-placeholder-color);
+}
+
+.extra-network-control .extra-network-control--refresh-icon:active {
+ -ms-transform: rotate(180deg);
+ -webkit-transform: rotate(180deg);
+ transform: rotate(180deg);
+ transition: transform 0.2s;
+}
+
+/* ==== TREE GRID CONFIG ==== */
+
+/* Text for button. */
+.extra-network-tree .tree-list-item-label {
+ position: relative;
+ line-height: 1.25rem;
+ color: var(--button-secondary-text-color);
+ grid-area: label;
+ padding-left: 0.5rem;
+}
+
+/* Text for button truncated. */
+.extra-network-tree .tree-list-item-label--truncate {
+ overflow: hidden;
+ text-overflow: ellipsis;
+ white-space: nowrap;
+}
+
+/* Icon for button. */
+.extra-network-tree .tree-list-item-visual {
+ min-height: 1rem;
+ color: var(--button-secondary-text-color);
+ pointer-events: none;
+ align-items: right;
+}
+
+
+/* Icon for button when it is before label. */
+.extra-network-tree .tree-list-item-visual--leading {
+ grid-area: leading-visual;
+ width: 1rem;
+ text-align: right;
+}
+
+/* Icon for button when it is after label. */
+.extra-network-tree .tree-list-item-visual--trailing {
+ grid-area: trailing-visual;
+ width: 1rem;
+ text-align: right;
+}
+
+/* Dropdown arrow for button. */
+.extra-network-tree .tree-list-item-action--leading {
+ margin-right: 0.5rem;
+ margin-left: 0.2rem;
+}
+
+.extra-network-tree .tree-list-content-file .tree-list-item-action--leading {
+ visibility: hidden;
+}
+
+.extra-network-tree .tree-list-item-action--leading {
+ grid-area: leading-action;
+}
+
+.extra-network-tree .tree-list-item-action--trailing {
+ grid-area: trailing-action;
+ display: inline-flex;
+}
+
+.extra-network-tree .tree-list-content .button-row {
+ display: inline-flex;
+ visibility: hidden;
+ color: var(--button-secondary-text-color);
+
+}
+
+.extra-network-tree .tree-list-content:hover .button-row {
+ visibility: visible;
+}
diff --git a/test/conftest.py b/test/conftest.py index 31a5d9ea..e4fc5678 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -1,10 +1,16 @@ +import base64 import os import pytest -import base64 - test_files_path = os.path.dirname(__file__) + "/test_files" +test_outputs_path = os.path.dirname(__file__) + "/test_outputs" + + +def pytest_configure(config): + # We don't want to fail on Py.test command line arguments being + # parsed by webui: + os.environ.setdefault("IGNORE_CMD_ARGS_ERRORS", "1") def file_to_base64(filename): @@ -23,3 +29,8 @@ def img2img_basic_image_base64() -> str: @pytest.fixture(scope="session") # session so we don't read this over and over def mask_basic_image_base64() -> str: return file_to_base64(os.path.join(test_files_path, "mask_basic.png")) + + +@pytest.fixture(scope="session") +def initialize() -> None: + import webui # noqa: F401 diff --git a/test/test_face_restorers.py b/test/test_face_restorers.py new file mode 100644 index 00000000..7760d51b --- /dev/null +++ b/test/test_face_restorers.py @@ -0,0 +1,29 @@ +import os +from test.conftest import test_files_path, test_outputs_path + +import numpy as np +import pytest +from PIL import Image + + +@pytest.mark.usefixtures("initialize") +@pytest.mark.parametrize("restorer_name", ["gfpgan", "codeformer"]) +def test_face_restorers(restorer_name): + from modules import shared + + if restorer_name == "gfpgan": + from modules import gfpgan_model + gfpgan_model.setup_model(shared.cmd_opts.gfpgan_models_path) + restorer = gfpgan_model.gfpgan_fix_faces + elif restorer_name == "codeformer": + from modules import codeformer_model + codeformer_model.setup_model(shared.cmd_opts.codeformer_models_path) + restorer = codeformer_model.codeformer.restore + else: + raise NotImplementedError("...") + img = Image.open(os.path.join(test_files_path, "two-faces.jpg")) + np_img = np.array(img, dtype=np.uint8) + fixed_image = restorer(np_img) + assert fixed_image.shape == np_img.shape + assert not np.allclose(fixed_image, np_img) # should have visibly changed + Image.fromarray(fixed_image).save(os.path.join(test_outputs_path, f"{restorer_name}.png")) diff --git a/test/test_files/two-faces.jpg b/test/test_files/two-faces.jpg Binary files differnew file mode 100644 index 00000000..c9d1b010 --- /dev/null +++ b/test/test_files/two-faces.jpg diff --git a/test/test_outputs/.gitkeep b/test/test_outputs/.gitkeep new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/test/test_outputs/.gitkeep diff --git a/test/test_torch_utils.py b/test/test_torch_utils.py new file mode 100644 index 00000000..23ccb93a --- /dev/null +++ b/test/test_torch_utils.py @@ -0,0 +1,19 @@ +import types + +import pytest +import torch + +from modules import torch_utils + + +@pytest.mark.parametrize("wrapped", [True, False]) +def test_get_param(wrapped): + mod = torch.nn.Linear(1, 1) + cpu = torch.device("cpu") + mod.to(dtype=torch.float16, device=cpu) + if wrapped: + # more or less how spandrel wraps a thing + mod = types.SimpleNamespace(model=mod) + p = torch_utils.get_param(mod) + assert p.dtype == torch.float16 + assert p.device == cpu diff --git a/webui-macos-env.sh b/webui-macos-env.sh index 24bc5c42..db7e8b1a 100644 --- a/webui-macos-env.sh +++ b/webui-macos-env.sh @@ -11,7 +11,7 @@ fi export install_dir="$HOME" export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate" -export TORCH_COMMAND="pip install torch==2.0.1 torchvision==0.15.2" +export TORCH_COMMAND="pip install torch==2.1.0 torchvision==0.16.0" export PYTORCH_ENABLE_MPS_FALLBACK=1 #################################################################### @@ -39,7 +39,7 @@ def api_only(): print(f"Startup time: {startup_timer.summary()}.")
api.launch(
- server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
+ server_name=initialize_util.gradio_server_name(),
port=cmd_opts.port if cmd_opts.port else 7861,
root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else ""
)
@@ -133,7 +133,7 @@ case "$gpu_info" in if [[ $(bc <<< "$pyv <= 3.10") -eq 1 ]] then # Navi users will still use torch 1.13 because 2.0 does not seem to work. - export TORCH_COMMAND="pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 --index-url https://download.pytorch.org/whl/rocm5.2" + export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.6" else printf "\e[1m\e[31mERROR: RX 5000 series GPUs must be using at max python 3.10, aborting...\e[0m" exit 1 @@ -143,8 +143,7 @@ case "$gpu_info" in *"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0 ;; *"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \ - export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/test/rocm5.6" - # Navi 3 needs at least 5.5 which is only on the torch 2.1.0 release candidates right now + export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.7" ;; *"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0 printf "\n%s\n" "${delimiter}" @@ -159,6 +158,10 @@ then if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]] then export TORCH_COMMAND="pip install torch==2.0.1+rocm5.4.2 torchvision==0.15.2+rocm5.4.2 --index-url https://download.pytorch.org/whl/rocm5.4.2" + elif echo "$gpu_info" | grep -q "Huawei" && [[ -z "${TORCH_COMMAND}" ]] + then + export TORCH_COMMAND="pip install torch==2.1.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu; pip install torch_npu" + fi fi @@ -223,12 +226,45 @@ fi # Try using TCMalloc on Linux prepare_tcmalloc() { if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then - TCMALLOC="$(PATH=/sbin:$PATH ldconfig -p | grep -Po "libtcmalloc(_minimal|)\.so\.\d" | head -n 1)" - if [[ ! -z "${TCMALLOC}" ]]; then - echo "Using TCMalloc: ${TCMALLOC}" - export LD_PRELOAD="${TCMALLOC}" - else - printf "\e[1m\e[31mCannot locate TCMalloc (improves CPU memory usage)\e[0m\n" + # check glibc version + LIBC_VER=$(echo $(ldd --version | awk 'NR==1 {print $NF}') | grep -oP '\d+\.\d+') + echo "glibc version is $LIBC_VER" + libc_vernum=$(expr $LIBC_VER) + # Since 2.34 libpthread is integrated into libc.so + libc_v234=2.34 + # Define Tcmalloc Libs arrays + TCMALLOC_LIBS=("libtcmalloc(_minimal|)\.so\.\d" "libtcmalloc\.so\.\d") + # Traversal array + for lib in "${TCMALLOC_LIBS[@]}" + do + # Determine which type of tcmalloc library the library supports + TCMALLOC="$(PATH=/usr/sbin:$PATH ldconfig -p | grep -P $lib | head -n 1)" + TC_INFO=(${TCMALLOC//=>/}) + if [[ ! -z "${TC_INFO}" ]]; then + echo "Check TCMalloc: ${TC_INFO}" + # Determine if the library is linked to libpthread and resolve undefined symbol: pthread_key_create + if [ $(echo "$libc_vernum < $libc_v234" | bc) -eq 1 ]; then + # glibc < 2.34 pthread_key_create into libpthread.so. check linking libpthread.so... + if ldd ${TC_INFO[2]} | grep -q 'libpthread'; then + echo "$TC_INFO is linked with libpthread,execute LD_PRELOAD=${TC_INFO[2]}" + # set fullpath LD_PRELOAD (To be on the safe side) + export LD_PRELOAD="${TC_INFO[2]}" + break + else + echo "$TC_INFO is not linked with libpthread will trigger undefined symbol: pthread_Key_create error" + fi + else + # Version 2.34 of libc.so (glibc) includes the pthread library IN GLIBC. (USE ubuntu 22.04 and modern linux system and WSL) + # libc.so(glibc) is linked with a library that works in ALMOST ALL Linux userlands. SO NO CHECK! + echo "$TC_INFO is linked with libc.so,execute LD_PRELOAD=${TC_INFO[2]}" + # set fullpath LD_PRELOAD (To be on the safe side) + export LD_PRELOAD="${TC_INFO[2]}" + break + fi + fi + done + if [[ -z "${LD_PRELOAD}" ]]; then + printf "\e[1m\e[31mCannot locate TCMalloc. Do you have tcmalloc or google-perftool installed on your system? (improves CPU memory usage)\e[0m\n" fi fi } |
