diff options
| author | Sj-Si <sjw.jetty@gmail.com> | 2024-01-11 16:37:35 -0500 |
|---|---|---|
| committer | Sj-Si <sjw.jetty@gmail.com> | 2024-01-11 16:37:35 -0500 |
| commit | 036500223de0a3caaa86360a8ad3ed301e4367b0 (patch) | |
| tree | f05f0d5fc503d9c35d57bad077a5dab1dfd6569e | |
| parent | 0726a6e12e85a37d1e514f5603acf9f058c11783 (diff) | |
| parent | cb5b335acddd126d4f6c990982816c06beb0d6ae (diff) | |
Merge changes from dev
117 files changed, 3371 insertions, 4920 deletions
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 @@ -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..342fcd0d 100644 --- a/extensions-builtin/Lora/network_oft.py +++ b/extensions-builtin/Lora/network_oft.py @@ -56,7 +56,7 @@ class NetworkModuleOFT(network.NetworkModule): self.block_size, self.num_blocks = factorization(self.out_dim, self.dim) def calc_updown(self, orig_weight): - oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) + oft_blocks = self.oft_blocks.to(orig_weight.device) eye = torch.eye(self.block_size, device=self.oft_blocks.device) if self.is_kohya: @@ -66,7 +66,7 @@ class NetworkModuleOFT(network.NetworkModule): 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) + R = oft_blocks.to(orig_weight.device) # 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) @@ -77,6 +77,6 @@ class NetworkModuleOFT(network.NetworkModule): ) merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...') - updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_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..32e10b62 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
@@ -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/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 db612fa2..66d15dd0 100644 --- a/extensions-builtin/Lora/ui_extra_networks_lora.py +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -48,6 +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/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/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
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-Copyright 2022 S-Lab
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-<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
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-Permission is hereby granted, free of charge, to any person obtaining a copy
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-<small>Some code is copied to support ESRGAN models.</small>
-<pre>
-BSD 3-Clause License
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<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.
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- See the License for the specific language governing permissions and
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-</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/extraNetworks.js b/javascript/extraNetworks.js index 4cc67fd1..3e3b03f3 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -183,8 +183,10 @@ 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; if (m) { @@ -192,8 +194,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) { 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; @@ -203,7 +205,7 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) { }); 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) { @@ -228,14 +230,23 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) { return false; } -function cardClicked(tabname, textToAdd, allowNegativePrompt) { - var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"); +function updatePromptArea(text, textArea, isNeg) { - if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) { - textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd; + 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) { diff --git a/javascript/ui.js b/javascript/ui.js index 18c9f891..3430b3fe 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -150,6 +150,14 @@ 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); diff --git a/modules/api/api.py b/modules/api/api.py index b3d74e51..59e46335 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: @@ -251,6 +251,24 @@ 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 +330,13 @@ 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 +358,83 @@ 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 +449,15 @@ 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 +467,14 @@ 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 +484,8 @@ 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 +495,10 @@ 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 +515,15 @@ 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 +534,14 @@ 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 +584,7 @@ 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 +615,7 @@ 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 @@ -775,7 +879,15 @@ 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..e58059a1 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -77,7 +77,9 @@ 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)
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/devices.py b/modules/devices.py index ea1f712f..0321d12c 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -4,6 +4,7 @@ from functools import lru_cache import torch from modules import errors, shared +from modules import torch_utils if sys.platform == "darwin": from modules import mac_specific @@ -23,6 +24,23 @@ 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}" @@ -73,8 +91,7 @@ def enable_tf32(): # 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 +101,7 @@ 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 +110,7 @@ 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 +123,77 @@ 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 = torch_utils.get_param(self).dtype + 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): + for module_type in patch_module_list: + org_forward = module_type.forward + if module_type == torch.nn.MultiheadAttention and has_xpu(): + 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: + for module_type in patch_module_list: + module_type.forward = 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") 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..99e7ee60 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()
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/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..87a7bf22 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
@@ -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..9a02cdf2 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
@@ -290,6 +312,18 @@ 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
@@ -314,6 +348,14 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model if "VAE Decoder" not in res:
res["VAE Decoder"] = "Full"
+ 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
+
+ infotext_versions.backcompat(res)
+
skip = set(shared.opts.infotext_skip_pasting)
res = {k: v for k, v in res.items() if k not in skip}
@@ -365,13 +407,57 @@ 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 +479,8 @@ 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 +494,9 @@ 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..a5afeebf --- /dev/null +++ b/modules/infotext_versions.py @@ -0,0 +1,39 @@ +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:
+ d["Old prompt editing timelines"] = True
+
+ if ver < v170_tsnr:
+ d["Downcast alphas_cumprod"] = True
+
diff --git a/modules/initialize.py b/modules/initialize.py index ac95fc6f..7c1ac99e 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")
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..35a627ca 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -10,7 +10,7 @@ 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'
@@ -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..8e58d714 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():
@@ -245,11 +244,14 @@ 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"))
+ settings = {}
disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
@@ -314,8 +316,8 @@ 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
@@ -338,20 +340,20 @@ def prepare_environment(): 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")
- 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)
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/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/options.py b/modules/options.py index 4fead690..503b40e9 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 +195,13 @@ 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 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..b86ecd7f 100644 --- a/modules/paths_internal.py +++ b/modules/paths_internal.py @@ -28,5 +28,6 @@ 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..7449b0dc 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
@@ -86,7 +86,7 @@ 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
@@ -97,11 +97,12 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, 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..dcc807fe 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
@@ -62,18 +62,22 @@ 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
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)
image = image.convert('RGBA')
image.alpha_composite(overlay)
@@ -81,9 +85,12 @@ def apply_overlay(image, paste_loc, index, overlays): return 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 +113,21 @@ 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 +179,7 @@ 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 +331,7 @@ 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 +343,10 @@ 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 +370,7 @@ 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 +382,17 @@ 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 +453,8 @@ class StableDiffusionProcessing: opts.sdxl_crop_top,
self.width,
self.height,
+ opts.fp8_storage,
+ opts.cache_fp16_weight,
)
def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
@@ -596,20 +629,33 @@ 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 +725,14 @@ 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 +747,7 @@ 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 +866,7 @@ 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 +912,42 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
+ 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
+
+ if hasattr(p.sd_model, 'alphas_cumprod') and hasattr(p.sd_model, 'alphas_cumprod_original'):
+ p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod_original.to(shared.device)
+
+ if opts.use_downcasted_alpha_bar:
+ p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
+ p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod.half().to(shared.device)
+ if opts.sd_noise_schedule == "Zero Terminal SNR":
+ p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
+ p.sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(p.sd_model.alphas_cumprod).to(shared.device)
+
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 +1003,31 @@ 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)
+ overlay_image = p.overlay_images[i] if getattr(p, "overlay_images", None) is not None and i < len(p.overlay_images) else 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.
+ original_denoised_image = image.copy()
+
+ if p.paste_to is not None:
+ original_denoised_image = uncrop(original_denoised_image, (overlay_image.width, overlay_image.height), p.paste_to)
+
+ image = apply_overlay(image, p.paste_to, overlay_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 +1037,17 @@ 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 +1125,7 @@ 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 +1198,9 @@ 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:
@@ -1138,18 +1241,45 @@ 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 +1481,14 @@ 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 +1518,8 @@ 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 +1530,11 @@ 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)
@@ -1413,6 +1548,9 @@ 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')
@@ -1423,6 +1561,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): 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 +1583,7 @@ 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 +1605,9 @@ 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 +1647,8 @@ 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 +1657,13 @@ 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 +1675,14 @@ 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/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..2d3cbb97 100644 --- a/modules/processing_scripts/seed.py +++ b/modules/processing_scripts/seed.py @@ -3,6 +3,7 @@ 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
@@ -51,12 +52,12 @@ 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}')
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..a54cb3eb 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -41,7 +41,7 @@ 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 +63,9 @@ 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):
diff --git a/modules/scripts.py b/modules/scripts.py index 7f9454eb..cf938ebb 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 +255,13 @@ class Script: pass
+ def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs, *args):
+ """
+ Called for every image after it has been generated.
+ """
+
+ pass
+
def postprocess(self, p, processed, *args):
"""
This function is called after processing ends for AlwaysVisible scripts.
@@ -520,7 +569,12 @@ 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 +627,7 @@ 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 +700,8 @@ 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 +745,7 @@ 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 +824,22 @@ 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 +848,14 @@ 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 before_component(self, component, **kwargs):
for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
try:
@@ -841,6 +922,23 @@ class ScriptRunner: except Exception:
errors.report(f"Error running setup: {script.filename}", exc_info=True)
+ def set_named_arg(self, args, script_type, arg_elem_id, value):
+ script = next((x for x in self.scripts if type(x).__name__ == script_type), None)
+ if script is None:
+ return
+
+ for i, control in enumerate(script.controls):
+ if arg_elem_id in control.elem_id:
+ index = script.args_from + i
+
+ if isinstance(args, list):
+ args[index] = value
+ return args
+ elif isinstance(args, tuple):
+ return args[:index] + (value,) + args[index+1:]
+ else:
+ return None
+
scripts_txt2img: ScriptRunner = None
scripts_img2img: ScriptRunner = None
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..2c045771 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -348,10 +348,28 @@ 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 +401,7 @@ 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 +415,11 @@ 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 +427,28 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer devices.dtype_unet = torch.float16
timer.record("apply half()")
+ 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)
@@ -651,6 +696,7 @@ 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 +792,7 @@ 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 +804,14 @@ 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 +842,13 @@ 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..6d76aa96 100644 --- a/modules/sd_samplers_cfg_denoiser.py +++ b/modules/sd_samplers_cfg_denoiser.py @@ -56,6 +56,9 @@ class CFGDenoiser(torch.nn.Module): 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
@@ -105,8 +108,21 @@ 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 +146,7 @@ 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
@@ -207,8 +223,9 @@ 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_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..777dd8d0 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)
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_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..13fb2814 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -67,14 +67,14 @@ 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..63488f4e 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
+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"), {
@@ -114,6 +115,7 @@ 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"),
@@ -176,6 +178,7 @@ 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"),
@@ -206,9 +209,12 @@ options_templates.update(options_section(('optimizations', "Optimizations", "sd" "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"),
"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 +222,7 @@ 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"), {
@@ -267,6 +274,8 @@ 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(),
}))
@@ -279,6 +288,7 @@ 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 +364,7 @@ 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..026c4300 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -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
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..c6bcab15 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
@@ -344,6 +343,7 @@ 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 +448,12 @@ 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 +626,7 @@ 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..d22a1f31 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
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 +53,64 @@ 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)
+ p.enable_hr = True
+ p.batch_size = 1
+ p.n_iter = 1
+
+ geninfo = json.loads(generation_info)
+ all_seeds = geninfo["all_seeds"]
+ all_subseeds = geninfo["all_subseeds"]
+
+ 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)
+
+ gallery_index_from_end = len(gallery) - gallery_index
+ seed = all_seeds[-gallery_index_from_end if gallery_index_from_end < len(all_seeds) + 1 else 0]
+ subseed = all_subseeds[-gallery_index_from_end if gallery_index_from_end < len(all_seeds) + 1 else 0]
+ p.seed = seed
+ p.subseed = subseed
+
+ 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):
+ fake_image = Image.new(mode="RGB", size=(1, 1))
+
+ if i == gallery_index:
+ already_saved_as = getattr(processed.images[0], 'already_saved_as', None)
+ if already_saved_as is not None:
+ fake_image.already_saved_as = already_saved_as
+ else:
+ fake_image = processed.images[0]
+ else:
+ fake_image.already_saved_as = image["name"]
+
+ 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..a716a040 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
@@ -177,7 +177,6 @@ def update_negative_prompt_token_counter(text, steps): return update_token_counter(text, steps, is_positive=False)
-
def setup_progressbar(*args, **kwargs):
pass
@@ -376,50 +375,60 @@ 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 +436,37 @@ 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)
@@ -480,7 +489,7 @@ def create_ui(): toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps], 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__()
@@ -711,7 +720,7 @@ 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 +765,10 @@ 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 +806,10 @@ 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,
)
@@ -831,6 +840,10 @@ 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 +853,7 @@ 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__()
@@ -1086,6 +1099,7 @@ 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 +1160,8 @@ 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 +1223,5 @@ 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..f17259c2 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -1,3 +1,4 @@ +import dataclasses
import json
import html
import os
@@ -8,10 +9,10 @@ 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.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' # 🔄
@@ -104,7 +105,17 @@ 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):
if not os.path.exists(f):
@@ -136,9 +147,8 @@ 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.")
@@ -152,6 +162,9 @@ 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=[],
@@ -162,17 +175,17 @@ 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 +193,14 @@ 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 +209,21 @@ 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 +233,11 @@ 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_extra_networks.py b/modules/ui_extra_networks.py index 2e226ba0..093ac7b4 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -5,14 +5,14 @@ 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.infotext_utils import image_from_url_text
from modules.ui_components import ToolButton
extra_pages = []
@@ -164,13 +164,14 @@ class ExtraNetworksPage: self.allow_negative_prompt = False
self.metadata = {}
self.items = {}
+ self.lister = util.MassFileLister()
def refresh(self):
pass
def read_user_metadata(self, item):
filename = item.get("filename", None)
- metadata = extra_networks.get_user_metadata(filename)
+ metadata = extra_networks.get_user_metadata(filename, lister=self.lister)
desc = metadata.get("description", None)
if desc is not None:
@@ -180,7 +181,7 @@ 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):
@@ -217,7 +218,10 @@ class ExtraNetworksPage: 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"})""") + '"'
+ if "negative_prompt" in item:
+ onclick = '"' + html.escape(f"""return cardClicked({quote_js(tabname)}, {item["prompt"]}, {item["negative_prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
+ else:
+ onclick = '"' + html.escape(f"""return cardClicked({quote_js(tabname)}, {item["prompt"]}, {'""'}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
copy_path_button = f"<div class='copy-path-button card-button' title='Copy path to clipboard' onclick='extraNetworksCopyCardPath(event, {quote_js(item['filename'])})' data-clipboard-text='{quote_js(item['filename'])}'></div>"
@@ -365,6 +369,7 @@ class ExtraNetworksPage: return res
def create_html(self, tabname):
+ self.lister.reset()
self.metadata = {}
self.items = {x["name"]: x for x in self.list_items()}
@@ -395,10 +400,10 @@ class ExtraNetworksPage: 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(),
}
@@ -411,7 +416,7 @@ class ExtraNetworksPage: potential_files = sum([[path + "." + ext, 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
@@ -421,6 +426,9 @@ 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()
@@ -556,22 +564,21 @@ def create_ui(interface: gr.Blocks, unrelated_tabs, tabname): dropdown_sort.change(fn=lambda: None, _js="function(){ applyExtraNetworkSort('" + 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]
-
+ create_html()
return ui.pages_contents
- interface.load(fn=pages_html, inputs=[], outputs=[*ui.pages])
+ interface.load(fn=pages_html, inputs=[], outputs=ui.pages)
button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
return ui
diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py index 36a807fc..989a649b 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:
@@ -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..7a132ac2 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -1,6 +1,6 @@ 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
def create_ui():
@@ -28,7 +28,7 @@ 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 +48,9 @@ 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_toprow.py b/modules/ui_toprow.py index 88838f97..1abc9117 100644 --- a/modules/ui_toprow.py +++ b/modules/ui_toprow.py @@ -79,11 +79,11 @@ 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,
@@ -106,8 +106,14 @@ class Toprow: outputs=[],
)
+ def interrupt_function():
+ if shared.state.job_count > 1 and shared.opts.interrupt_after_current:
+ shared.state.stop_generating()
+ else:
+ shared.state.interrupt()
+
self.interrupt.click(
- fn=lambda: shared.state.interrupt(),
+ fn=interrupt_function,
inputs=[],
outputs=[],
)
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..afed8b40 --- /dev/null +++ b/modules/upscaler_utils.py @@ -0,0 +1,189 @@ +import logging +from typing import Callable + +import numpy as np +import torch +import tqdm +from PIL import Image + +from modules import 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) + 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..ee373e92 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):
@@ -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_versions.txt b/requirements_versions.txt index cb7403a9..2a922f28 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
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 +156,21 @@ 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; + 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_upscale.py b/scripts/postprocessing_upscale.py index ed709688..a57f9d4a 100644 --- a/scripts/postprocessing_upscale.py +++ b/scripts/postprocessing_upscale.py @@ -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..2f385ebf 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)
@@ -688,7 +696,7 @@ 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 +705,16 @@ 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 */
@@ -679,7 +679,7 @@ 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 +749,22 @@ 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{
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" "${delimiter}" @@ -223,13 +222,30 @@ fi # Try using TCMalloc on Linux prepare_tcmalloc() { if [[ "${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" - fi + # 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 "Using TCMalloc: ${TC_INFO}" + #Determine if the library is linked to libptthread and resolve undefined symbol: ptthread_Key_Create + if ldd ${TC_INFO[2]} | grep -q 'libpthread'; then + echo "$TC_INFO is linked with libpthread,execute LD_PRELOAD=${TC_INFO}" + export LD_PRELOAD="${TC_INFO}" + break + else + echo "$TC_INFO is not linked with libpthreadand will trigger undefined symbol: ptthread_Key_Create error" + fi + else + printf "\e[1m\e[31mCannot locate TCMalloc (improves CPU memory usage)\e[0m\n" + fi + done + fi } |
