diff options
72 files changed, 1978 insertions, 1351 deletions
diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml index be7ffa23..9a0b8d22 100644 --- a/.github/workflows/run_tests.yaml +++ b/.github/workflows/run_tests.yaml @@ -18,7 +18,7 @@ jobs: cache-dependency-path: | **/requirements*txt - name: Run tests - run: python launch.py --tests --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test + run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test - name: Upload main app stdout-stderr uses: actions/upload-artifact@v3 if: always() @@ -32,4 +32,4 @@ notification.mp3 /extensions /test/stdout.txt /test/stderr.txt -/cache.json +/cache.json* @@ -13,9 +13,9 @@ A browser interface based on Gradio library for Stable Diffusion. - Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- - a man in a ((tuxedo)) - will pay more attention to tuxedo
- - a man in a (tuxedo:1.21) - alternative syntax
- - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
+ - a man in a `((tuxedo))` - will pay more attention to tuxedo
+ - a man in a `(tuxedo:1.21)` - alternative syntax
+ - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
- Textual Inversion
@@ -28,7 +28,7 @@ A browser interface based on Gradio library for Stable Diffusion. - CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network upscaler with a lot of third party models
- - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+ - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
@@ -46,7 +46,7 @@ A browser interface based on Gradio library for Stable Diffusion. - drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
-- Running arbitrary python code from UI (must run with --allow-code to enable)
+- Running arbitrary python code from UI (must run with `--allow-code` to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Tiling support, a checkbox to create images that can be tiled like textures
@@ -69,7 +69,7 @@ A browser interface based on Gradio library for Stable Diffusion. - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
-- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
@@ -78,11 +78,11 @@ A browser interface based on Gradio library for Stable Diffusion. - Clip skip
- Hypernetworks
- Loras (same as Hypernetworks but more pretty)
-- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt.
+- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
- Can select to load a different VAE from settings screen
- Estimated completion time in progress bar
- API
-- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
@@ -91,7 +91,6 @@ A browser interface based on Gradio library for Stable Diffusion. - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
- Now with a license!
- Reorder elements in the UI from settings screen
--
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -101,7 +100,7 @@ Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
-1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
+1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
@@ -121,6 +120,7 @@ sudo pacman -S wget git python3 bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
3. Run `webui.sh`.
+4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
@@ -159,4 +159,4 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al - Security advice - RyotaK
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
-- (You)
+- (You)
\ No newline at end of file diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml index f8872750..0c4ae680 100644 --- a/environment-wsl2.yaml +++ b/environment-wsl2.yaml @@ -4,8 +4,8 @@ channels: - defaults dependencies: - python=3.10 - - pip=22.2.2 - - cudatoolkit=11.3 - - pytorch=1.12.1 - - torchvision=0.13.1 - - numpy=1.23.1
\ No newline at end of file + - pip=23.0 + - cudatoolkit=11.8 + - pytorch=2.0 + - torchvision=0.15 + - numpy=1.23 diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py index 6be6ef73..45f899fc 100644 --- a/extensions-builtin/Lora/extra_networks_lora.py +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): def activate(self, p, params_list):
additional = shared.opts.sd_lora
- if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+ if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 8937b585..d3eb0d3b 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -2,20 +2,34 @@ import glob import os
import re
import torch
+from typing import Union
from modules import shared, devices, sd_models, errors
metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
re_digits = re.compile(r"\d+")
-re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
-re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+ "attentions": {},
+ "resnets": {
+ "conv1": "in_layers_2",
+ "conv2": "out_layers_3",
+ "time_emb_proj": "emb_layers_1",
+ "conv_shortcut": "skip_connection",
+ }
+}
+
+
+def convert_diffusers_name_to_compvis(key, is_sd2):
+ def match(match_list, regex_text):
+ regex = re_compiled.get(regex_text)
+ if regex is None:
+ regex = re.compile(regex_text)
+ re_compiled[regex_text] = regex
-
-def convert_diffusers_name_to_compvis(key):
- def match(match_list, regex):
r = re.match(regex, key)
if not r:
return False
@@ -26,16 +40,33 @@ def convert_diffusers_name_to_compvis(key): m = []
- if match(m, re_unet_down_blocks):
- return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
+ if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
- if match(m, re_unet_mid_blocks):
- return f"diffusion_model_middle_block_1_{m[1]}"
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
- if match(m, re_unet_up_blocks):
- return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+ if is_sd2:
+ if 'mlp_fc1' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+ elif 'mlp_fc2' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+ else:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
- if match(m, re_text_block):
return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
return key
@@ -101,15 +132,22 @@ def load_lora(name, filename): sd = sd_models.read_state_dict(filename)
- keys_failed_to_match = []
+ keys_failed_to_match = {}
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
for key_diffusers, weight in sd.items():
- fullkey = convert_diffusers_name_to_compvis(key_diffusers)
- key, lora_key = fullkey.split(".", 1)
+ key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
+ key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
+
if sd_module is None:
- keys_failed_to_match.append(key_diffusers)
+ m = re_x_proj.match(key)
+ if m:
+ sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
+
+ if sd_module is None:
+ keys_failed_to_match[key_diffusers] = key
continue
lora_module = lora.modules.get(key, None)
@@ -123,15 +161,21 @@ def load_lora(name, filename): if type(sd_module) == torch.nn.Linear:
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+ elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+ elif type(sd_module) == torch.nn.MultiheadAttention:
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
elif type(sd_module) == torch.nn.Conv2d:
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
else:
+ print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
+ continue
assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
with torch.no_grad():
module.weight.copy_(weight)
- module.to(device=devices.device, dtype=devices.dtype)
+ module.to(device=devices.cpu, dtype=devices.dtype)
if lora_key == "lora_up.weight":
lora_module.up = module
@@ -177,28 +221,120 @@ def load_loras(names, multipliers=None): loaded_loras.append(lora)
-def lora_forward(module, input, res):
- if len(loaded_loras) == 0:
- return res
+def lora_calc_updown(lora, module, target):
+ with torch.no_grad():
+ up = module.up.weight.to(target.device, dtype=target.dtype)
+ down = module.down.weight.to(target.device, dtype=target.dtype)
- lora_layer_name = getattr(module, 'lora_layer_name', None)
- for lora in loaded_loras:
- module = lora.modules.get(lora_layer_name, None)
- if module is not None:
- if shared.opts.lora_apply_to_outputs and res.shape == input.shape:
- res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+ if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
+ updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+ else:
+ updown = up @ down
+
+ updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+ return updown
+
+
+def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+ """
+ Applies the currently selected set of Loras to the weights of torch layer self.
+ If weights already have this particular set of loras applied, does nothing.
+ If not, restores orginal weights from backup and alters weights according to loras.
+ """
+
+ lora_layer_name = getattr(self, 'lora_layer_name', None)
+ if lora_layer_name is None:
+ return
+
+ current_names = getattr(self, "lora_current_names", ())
+ wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
+
+ weights_backup = getattr(self, "lora_weights_backup", None)
+ if weights_backup is None:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
+ else:
+ weights_backup = self.weight.to(devices.cpu, copy=True)
+
+ self.lora_weights_backup = weights_backup
+
+ if current_names != wanted_names:
+ if weights_backup is not None:
+ if isinstance(self, torch.nn.MultiheadAttention):
+ self.in_proj_weight.copy_(weights_backup[0])
+ self.out_proj.weight.copy_(weights_backup[1])
else:
- res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+ self.weight.copy_(weights_backup)
- return res
+ for lora in loaded_loras:
+ module = lora.modules.get(lora_layer_name, None)
+ if module is not None and hasattr(self, 'weight'):
+ self.weight += lora_calc_updown(lora, module, self.weight)
+ continue
+
+ module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
+ module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
+ module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
+ module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
+
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
+ updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
+ updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
+ updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+
+ self.in_proj_weight += updown_qkv
+ self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
+ continue
+
+ if module is None:
+ continue
+
+ print(f'failed to calculate lora weights for layer {lora_layer_name}')
+
+ setattr(self, "lora_current_names", wanted_names)
+
+
+def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+ setattr(self, "lora_current_names", ())
+ setattr(self, "lora_weights_backup", None)
def lora_Linear_forward(self, input):
- return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Linear_forward_before_lora(self, input)
+
+
+def lora_Linear_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
def lora_Conv2d_forward(self, input):
- return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Conv2d_forward_before_lora(self, input)
+
+
+def lora_Conv2d_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_forward(self, *args, **kwargs):
+ lora_apply_weights(self)
+
+ return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
def list_available_loras():
@@ -211,7 +347,7 @@ def list_available_loras(): glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
- for filename in sorted(candidates):
+ for filename in sorted(candidates, key=str.lower):
if os.path.isdir(filename):
continue
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 2e860160..3fc38ab9 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -9,7 +9,11 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared def unload():
torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
+ torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+ torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
+ torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
+ torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
def before_ui():
@@ -20,11 +24,27 @@ def before_ui(): if not hasattr(torch.nn, 'Linear_forward_before_lora'):
torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
+ torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+
if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
+ torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
+ torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
+ torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+
torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
+torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
@@ -32,7 +52,5 @@ script_callbacks.on_before_ui(before_ui) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
- "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
- "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
-
+ "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
}))
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index e0fbf3a3..c7fd5739 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -5,11 +5,15 @@ import traceback import PIL.Image import numpy as np import torch +from tqdm import tqdm + from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from scunet_model_arch import SCUNet as net +from modules.shared import opts +from modules import images class UpscalerScuNET(modules.upscaler.Upscaler): @@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scalers.append(scaler_data2) self.scalers = scalers - def do_upscale(self, img: PIL.Image, selected_file): + @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): + torch.cuda.empty_cache() model = self.load_model(selected_file) if model is None: + print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr) return img device = devices.get_device_for('scunet') - 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) - - 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] + 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 torch.cuda.empty_cache() - return PIL.Image.fromarray(output, 'RGB') + + 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)) def load_model(self, path: str): device = devices.get_device_for('scunet') @@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler): model = model.to(device) return model - diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js index 4a85c8eb..5c7a836a 100644 --- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js +++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js @@ -1,110 +1,42 @@ // Stable Diffusion WebUI - Bracket checker -// Version 1.0 -// By Hingashi no Florin/Bwin4L +// By Hingashi no Florin/Bwin4L & @akx // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs. // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong. -function checkBrackets(evt, textArea, counterElt) { - errorStringParen = '(...) - Different number of opening and closing parentheses detected.\n'; - errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n'; - errorStringCurly = '{...} - Different number of opening and closing curly brackets detected.\n'; - - openBracketRegExp = /\(/g; - closeBracketRegExp = /\)/g; - - openSquareBracketRegExp = /\[/g; - closeSquareBracketRegExp = /\]/g; - - openCurlyBracketRegExp = /\{/g; - closeCurlyBracketRegExp = /\}/g; - - totalOpenBracketMatches = 0; - totalCloseBracketMatches = 0; - totalOpenSquareBracketMatches = 0; - totalCloseSquareBracketMatches = 0; - totalOpenCurlyBracketMatches = 0; - totalCloseCurlyBracketMatches = 0; - - openBracketMatches = textArea.value.match(openBracketRegExp); - if(openBracketMatches) { - totalOpenBracketMatches = openBracketMatches.length; - } - - closeBracketMatches = textArea.value.match(closeBracketRegExp); - if(closeBracketMatches) { - totalCloseBracketMatches = closeBracketMatches.length; - } - - openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp); - if(openSquareBracketMatches) { - totalOpenSquareBracketMatches = openSquareBracketMatches.length; - } - - closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp); - if(closeSquareBracketMatches) { - totalCloseSquareBracketMatches = closeSquareBracketMatches.length; - } - - openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp); - if(openCurlyBracketMatches) { - totalOpenCurlyBracketMatches = openCurlyBracketMatches.length; - } - - closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp); - if(closeCurlyBracketMatches) { - totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length; - } - - if(totalOpenBracketMatches != totalCloseBracketMatches) { - if(!counterElt.title.includes(errorStringParen)) { - counterElt.title += errorStringParen; +function checkBrackets(textArea, counterElt) { + var counts = {}; + (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => { + counts[bracket] = (counts[bracket] || 0) + 1; + }); + var errors = []; + + function checkPair(open, close, kind) { + if (counts[open] !== counts[close]) { + errors.push( + `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.` + ); } - } else { - counterElt.title = counterElt.title.replace(errorStringParen, ''); } - if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) { - if(!counterElt.title.includes(errorStringSquare)) { - counterElt.title += errorStringSquare; - } - } else { - counterElt.title = counterElt.title.replace(errorStringSquare, ''); - } + checkPair('(', ')', 'round brackets'); + checkPair('[', ']', 'square brackets'); + checkPair('{', '}', 'curly brackets'); + counterElt.title = errors.join('\n'); + counterElt.classList.toggle('error', errors.length !== 0); +} - if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) { - if(!counterElt.title.includes(errorStringCurly)) { - counterElt.title += errorStringCurly; - } - } else { - counterElt.title = counterElt.title.replace(errorStringCurly, ''); - } +function setupBracketChecking(id_prompt, id_counter) { + var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); + var counter = gradioApp().getElementById(id_counter) - if(counterElt.title != '') { - counterElt.classList.add('error'); - } else { - counterElt.classList.remove('error'); + if (textarea && counter) { + textarea.addEventListener("input", () => checkBrackets(textarea, counter)); } } -function setupBracketChecking(id_prompt, id_counter){ - var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); - var counter = gradioApp().getElementById(id_counter) - textarea.addEventListener("input", function(evt){ - checkBrackets(evt, textarea, counter) - }); -} - -var shadowRootLoaded = setInterval(function() { - var shadowRoot = document.querySelector('gradio-app').shadowRoot; - if(! shadowRoot) return false; - - var shadowTextArea = shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea'); - if(shadowTextArea.length < 1) return false; - - clearInterval(shadowRootLoaded); - - setupBracketChecking('txt2img_prompt', 'txt2img_token_counter') - setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter') - setupBracketChecking('img2img_prompt', 'imgimg_token_counter') - setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter') -}, 1000); +onUiLoaded(function () { + setupBracketChecking('txt2img_prompt', 'txt2img_token_counter'); + setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter'); + setupBracketChecking('img2img_prompt', 'img2img_token_counter'); + setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter'); +}); diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html index 1bf3fc30..ef4b613a 100644 --- a/html/extra-networks-card.html +++ b/html/extra-networks-card.html @@ -1,4 +1,4 @@ -<div class='card' {preview_html} onclick={card_clicked}> +<div class='card' style={style} onclick={card_clicked}> {metadata_button} <div class='actions'> diff --git a/html/licenses.html b/html/licenses.html index bddbf466..bc995aa0 100644 --- a/html/licenses.html +++ b/html/licenses.html @@ -635,4 +635,30 @@ SOFTWARE. WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
+</pre>
+
+<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
+<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
+<pre>
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
</pre>
\ No newline at end of file diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js index 0f164b82..a8278cca 100644 --- a/javascript/aspectRatioOverlay.js +++ b/javascript/aspectRatioOverlay.js @@ -12,7 +12,7 @@ function dimensionChange(e, is_width, is_height){ currentHeight = e.target.value*1.0
}
- var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
+ var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
if(!inImg2img){
return;
@@ -22,7 +22,7 @@ function dimensionChange(e, is_width, is_height){ var tabIndex = get_tab_index('mode_img2img')
if(tabIndex == 0){ // img2img
- targetElement = gradioApp().querySelector('div[data-testid=image] img');
+ targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
} else if(tabIndex == 1){ //Sketch
targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
} else if(tabIndex == 2){ // Inpaint
@@ -30,7 +30,7 @@ function dimensionChange(e, is_width, is_height){ } else if(tabIndex == 3){ // Inpaint sketch
targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
}
-
+
if(targetElement){
@@ -38,7 +38,7 @@ function dimensionChange(e, is_width, is_height){ if(!arPreviewRect){
arPreviewRect = document.createElement('div')
arPreviewRect.id = "imageARPreview";
- gradioApp().getRootNode().appendChild(arPreviewRect)
+ gradioApp().appendChild(arPreviewRect)
}
@@ -91,23 +91,26 @@ onUiUpdate(function(){ if(arPreviewRect){
arPreviewRect.style.display = 'none';
}
- var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
- if(inImg2img){
- let inputs = gradioApp().querySelectorAll('input');
- inputs.forEach(function(e){
- var is_width = e.parentElement.id == "img2img_width"
- var is_height = e.parentElement.id == "img2img_height"
-
- if((is_width || is_height) && !e.classList.contains('scrollwatch')){
- e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
- e.classList.add('scrollwatch')
- }
- if(is_width){
- currentWidth = e.value*1.0
- }
- if(is_height){
- currentHeight = e.value*1.0
- }
- })
- }
+ var tabImg2img = gradioApp().querySelector("#tab_img2img");
+ if (tabImg2img) {
+ var inImg2img = tabImg2img.style.display == "block";
+ if(inImg2img){
+ let inputs = gradioApp().querySelectorAll('input');
+ inputs.forEach(function(e){
+ var is_width = e.parentElement.id == "img2img_width"
+ var is_height = e.parentElement.id == "img2img_height"
+
+ if((is_width || is_height) && !e.classList.contains('scrollwatch')){
+ e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
+ e.classList.add('scrollwatch')
+ }
+ if(is_width){
+ currentWidth = e.value*1.0
+ }
+ if(is_height){
+ currentHeight = e.value*1.0
+ }
+ })
+ }
+ }
});
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js index 11bcce1b..9468c107 100644 --- a/javascript/contextMenus.js +++ b/javascript/contextMenus.js @@ -43,7 +43,7 @@ contextMenuInit = function(){ })
- gradioApp().getRootNode().appendChild(contextMenu)
+ gradioApp().appendChild(contextMenu)
let menuWidth = contextMenu.offsetWidth + 4;
let menuHeight = contextMenu.offsetHeight + 4;
@@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2]; appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
- appendContextMenuOption('#roll','Roll three',
- function(){
- let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
- setTimeout(function(){rollbutton.click()},100)
- setTimeout(function(){rollbutton.click()},200)
- setTimeout(function(){rollbutton.click()},300)
- }
- )
})();
//End example Context Menu Items
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index 619bb1fa..588c7b77 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -1,6 +1,6 @@ function keyupEditAttention(event){
let target = event.originalTarget || event.composedPath()[0];
- if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
+ if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
if (! (event.metaKey || event.ctrlKey)) return;
let isPlus = event.key == "ArrowUp"
@@ -17,7 +17,7 @@ function keyupEditAttention(event){ // Find opening parenthesis around current cursor
const before = text.substring(0, selectionStart);
let beforeParen = before.lastIndexOf(OPEN);
- if (beforeParen == -1) return false;
+ if (beforeParen == -1) return false;
let beforeParenClose = before.lastIndexOf(CLOSE);
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
@@ -27,7 +27,7 @@ function keyupEditAttention(event){ // Find closing parenthesis around current cursor
const after = text.substring(selectionStart);
let afterParen = after.indexOf(CLOSE);
- if (afterParen == -1) return false;
+ if (afterParen == -1) return false;
let afterParenOpen = after.indexOf(OPEN);
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
afterParen = after.indexOf(CLOSE, afterParen + 1);
@@ -43,10 +43,28 @@ function keyupEditAttention(event){ target.setSelectionRange(selectionStart, selectionEnd);
return true;
}
+
+ function selectCurrentWord(){
+ if (selectionStart !== selectionEnd) return false;
+ const delimiters = opts.keyedit_delimiters + " \r\n\t";
+
+ // seek backward until to find beggining
+ while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+ selectionStart--;
+ }
+
+ // seek forward to find end
+ while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+ selectionEnd++;
+ }
- // If the user hasn't selected anything, let's select their current parenthesis block
- if(! selectCurrentParenthesisBlock('<', '>')){
- selectCurrentParenthesisBlock('(', ')')
+ target.setSelectionRange(selectionStart, selectionEnd);
+ return true;
+ }
+
+ // If the user hasn't selected anything, let's select their current parenthesis block or word
+ if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+ selectCurrentWord();
}
event.preventDefault();
@@ -81,7 +99,13 @@ function keyupEditAttention(event){ weight = parseFloat(weight.toPrecision(12));
if(String(weight).length == 1) weight += ".0"
- text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+ if (closeCharacter == ')' && weight == 1) {
+ text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
+ selectionStart--;
+ selectionEnd--;
+ } else {
+ text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+ }
target.focus();
target.value = text;
@@ -93,4 +117,4 @@ function keyupEditAttention(event){ addEventListener('keydown', (event) => {
keyupEditAttention(event);
-});
\ No newline at end of file +});
diff --git a/javascript/extensions.js b/javascript/extensions.js index c593cd2e..72924a28 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -1,5 +1,5 @@ -function extensions_apply(_, _){
+function extensions_apply(_, _, disable_all){
var disable = []
var update = []
@@ -13,10 +13,10 @@ function extensions_apply(_, _){ restart_reload()
- return [JSON.stringify(disable), JSON.stringify(update)]
+ return [JSON.stringify(disable), JSON.stringify(update), disable_all]
}
-function extensions_check(){
+function extensions_check(_, _){
var disable = []
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js index 2fb87cd5..25322138 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -139,3 +139,41 @@ function extraNetworksShowMetadata(text){ popup(elem);
}
+
+function requestGet(url, data, handler, errorHandler){
+ var xhr = new XMLHttpRequest();
+ var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
+ xhr.open("GET", url + "?" + args, true);
+
+ xhr.onreadystatechange = function () {
+ if (xhr.readyState === 4) {
+ if (xhr.status === 200) {
+ try {
+ var js = JSON.parse(xhr.responseText);
+ handler(js)
+ } catch (error) {
+ console.error(error);
+ errorHandler()
+ }
+ } else{
+ errorHandler()
+ }
+ }
+ };
+ var js = JSON.stringify(data);
+ xhr.send(js);
+}
+
+function extraNetworksRequestMetadata(event, extraPage, cardName){
+ showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+
+ requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
+ if(data && data.metadata){
+ extraNetworksShowMetadata(data.metadata)
+ } else{
+ showError()
+ }
+ }, showError)
+
+ event.stopPropagation()
+}
diff --git a/javascript/generationParams.js b/javascript/generationParams.js index 95f05093..06a771bc 100644 --- a/javascript/generationParams.js +++ b/javascript/generationParams.js @@ -16,7 +16,7 @@ onUiUpdate(function(){ let modalObserver = new MutationObserver(function(mutations) { mutations.forEach(function(mutationRecord) { - let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText + let selectedTab = gradioApp().querySelector('#tabs div button')?.innerText if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img') gradioApp().getElementById(selectedTab+"_generation_info_button").click() }); diff --git a/javascript/hints.js b/javascript/hints.js index 730ce7bd..c6bae360 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -18,11 +18,10 @@ titles = { "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", "\u{1f4c2}": "Open images output directory", "\u{1f4be}": "Save style", - "\u{1f5d1}": "Clear prompt", + "\u{1f5d1}\ufe0f": "Clear prompt", "\u{1f4cb}": "Apply selected styles to current prompt", "\u{1f4d2}": "Paste available values into the field", - "\u{1f3b4}": "Show extra networks", - + "\u{1f3b4}": "Show/hide extra networks", "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", @@ -40,8 +39,7 @@ titles = { "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", - "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.", - + "Skip": "Stop processing current image and continue processing.", "Interrupt": "Stop processing images and return any results accumulated so far.", "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.", @@ -71,8 +69,10 @@ titles = { "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.", "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle", - "Loopback": "Process an image, use it as an input, repeat.", - "Loops": "How many times to repeat processing an image and using it as input for the next iteration", + "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.", + "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.", + "Final denoising strength": "The denoising strength for the final loop of each image in the batch.", + "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.", "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both", "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both", diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index 28e748b7..3deffa9b 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -32,13 +32,7 @@ function negmod(n, m) { function updateOnBackgroundChange() { const modalImage = gradioApp().getElementById("modalImage") if (modalImage && modalImage.offsetParent) { - let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") - let currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) + let currentButton = selected_gallery_button(); if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { modalImage.src = currentButton.children[0].src; @@ -50,22 +44,10 @@ function updateOnBackgroundChange() { } function modalImageSwitch(offset) { - var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all") - var galleryButtons = [] - allgalleryButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - galleryButtons.push(elem); - } - }) + var galleryButtons = all_gallery_buttons(); if (galleryButtons.length > 1) { - var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") - var currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) + var currentButton = selected_gallery_button(); var result = -1 galleryButtons.forEach(function(v, i) { @@ -136,37 +118,29 @@ function modalKeyHandler(event) { } } -function showGalleryImage() { - setTimeout(function() { - fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain') - - if (fullImg_preview != null) { - fullImg_preview.forEach(function function_name(e) { - if (e.dataset.modded) - return; - e.dataset.modded = true; - if(e && e.parentElement.tagName == 'DIV'){ - e.style.cursor='pointer' - e.style.userSelect='none' - - var isFirefox = isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1 - - // For Firefox, listening on click first switched to next image then shows the lightbox. - // If you know how to fix this without switching to mousedown event, please. - // For other browsers the event is click to make it possiblr to drag picture. - var event = isFirefox ? 'mousedown' : 'click' - - e.addEventListener(event, function (evt) { - if(!opts.js_modal_lightbox || evt.button != 0) return; - modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) - evt.preventDefault() - showModal(evt) - }, true); - } - }); - } +function setupImageForLightbox(e) { + if (e.dataset.modded) + return; + + e.dataset.modded = true; + e.style.cursor='pointer' + e.style.userSelect='none' + + var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1 + + // For Firefox, listening on click first switched to next image then shows the lightbox. + // If you know how to fix this without switching to mousedown event, please. + // For other browsers the event is click to make it possiblr to drag picture. + var event = isFirefox ? 'mousedown' : 'click' + + e.addEventListener(event, function (evt) { + if(!opts.js_modal_lightbox || evt.button != 0) return; + + modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) + evt.preventDefault() + showModal(evt) + }, true); - }, 100); } function modalZoomSet(modalImage, enable) { @@ -199,21 +173,21 @@ function modalTileImageToggle(event) { } function galleryImageHandler(e) { - if (e && e.parentElement.tagName == 'BUTTON') { + //if (e && e.parentElement.tagName == 'BUTTON') { e.onclick = showGalleryImage; - } + //} } onUiUpdate(function() { - fullImg_preview = gradioApp().querySelectorAll('img.w-full') + fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img') if (fullImg_preview != null) { - fullImg_preview.forEach(galleryImageHandler); + fullImg_preview.forEach(setupImageForLightbox); } updateOnBackgroundChange(); }) document.addEventListener("DOMContentLoaded", function() { - const modalFragment = document.createDocumentFragment(); + //const modalFragment = document.createDocumentFragment(); const modal = document.createElement('div') modal.onclick = closeModal; modal.id = "lightboxModal"; @@ -277,9 +251,12 @@ document.addEventListener("DOMContentLoaded", function() { modal.appendChild(modalNext) + try { + gradioApp().appendChild(modal); + } catch (e) { + gradioApp().body.appendChild(modal); + } - gradioApp().getRootNode().appendChild(modal) - - document.body.appendChild(modalFragment); + document.body.appendChild(modal); }); diff --git a/javascript/notification.js b/javascript/notification.js index 5ae6df24..8ddd4c5d 100644 --- a/javascript/notification.js +++ b/javascript/notification.js @@ -15,7 +15,7 @@ onUiUpdate(function(){ } } - const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] img.h-full.w-full.overflow-hidden'); + const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] .thumbnail-item > img'); if (galleryPreviews == null) return; diff --git a/javascript/progressbar.js b/javascript/progressbar.js index 9ccc9da4..8df3f569 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -1,78 +1,13 @@ // code related to showing and updating progressbar shown as the image is being made - -galleries = {} -storedGallerySelections = {} -galleryObservers = {} - function rememberGallerySelection(id_gallery){ - storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery) -} -function getGallerySelectedIndex(id_gallery){ - let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') - let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') - - let currentlySelectedIndex = -1 - galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } }) - - return currentlySelectedIndex } -// this is a workaround for https://github.com/gradio-app/gradio/issues/2984 -function check_gallery(id_gallery){ - let gallery = gradioApp().getElementById(id_gallery) - // if gallery has no change, no need to setting up observer again. - if (gallery && galleries[id_gallery] !== gallery){ - galleries[id_gallery] = gallery; - if(galleryObservers[id_gallery]){ - galleryObservers[id_gallery].disconnect(); - } +function getGallerySelectedIndex(id_gallery){ - storedGallerySelections[id_gallery] = -1 - - galleryObservers[id_gallery] = new MutationObserver(function (){ - let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') - let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') - let currentlySelectedIndex = getGallerySelectedIndex(id_gallery) - prevSelectedIndex = storedGallerySelections[id_gallery] - storedGallerySelections[id_gallery] = -1 - - if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) { - // automatically re-open previously selected index (if exists) - activeElement = gradioApp().activeElement; - let scrollX = window.scrollX; - let scrollY = window.scrollY; - - galleryButtons[prevSelectedIndex].click(); - showGalleryImage(); - - // When the gallery button is clicked, it gains focus and scrolls itself into view - // We need to scroll back to the previous position - setTimeout(function (){ - window.scrollTo(scrollX, scrollY); - }, 50); - - if(activeElement){ - // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it - // if someone has a better solution please by all means - setTimeout(function (){ - activeElement.focus({ - preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it - }) - }, 1); - } - } - }) - galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false }) - } } -onUiUpdate(function(){ - check_gallery('txt2img_gallery') - check_gallery('img2img_gallery') -}) - function request(url, data, handler, errorHandler){ var xhr = new XMLHttpRequest(); var url = url; @@ -203,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre return } - if(elapsedFromStart > 5 && !res.queued && !res.active){ + if(elapsedFromStart > 40 && !res.queued && !res.active){ removeProgressBar() return } diff --git a/javascript/ui.js b/javascript/ui.js index b7a8268a..4a440193 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -7,9 +7,31 @@ function set_theme(theme){ } } +function all_gallery_buttons() { + var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small'); + var visibleGalleryButtons = []; + allGalleryButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleGalleryButtons.push(elem); + } + }) + return visibleGalleryButtons; +} + +function selected_gallery_button() { + var allCurrentButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnail-item.thumbnail-small.selected'); + var visibleCurrentButton = null; + allCurrentButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleCurrentButton = elem; + } + }) + return visibleCurrentButton; +} + function selected_gallery_index(){ - var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item') - var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2') + var buttons = all_gallery_buttons(); + var button = selected_gallery_button(); var result = -1 buttons.forEach(function(v, i){ if(v==button) { result = i } }) @@ -18,14 +40,18 @@ function selected_gallery_index(){ } function extract_image_from_gallery(gallery){ - if(gallery.length == 1){ - return [gallery[0]] + if (gallery.length == 0){ + return [null]; + } + if (gallery.length == 1){ + return [gallery[0]]; } index = selected_gallery_index() if (index < 0 || index >= gallery.length){ - return [null] + // Use the first image in the gallery as the default + index = 0; } return [gallery[index]]; @@ -86,7 +112,7 @@ function get_tab_index(tabId){ var res = 0 gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){ - if(button.className.indexOf('bg-white') != -1) + if(button.className.indexOf('selected') != -1) res = i }) @@ -255,7 +281,6 @@ onUiUpdate(function(){ } prompt.parentElement.insertBefore(counter, prompt) - counter.classList.add("token-counter") prompt.parentElement.style.position = "relative" promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); } @@ -5,24 +5,25 @@ import sys import importlib.util
import shlex
import platform
-import argparse
import json
-parser = argparse.ArgumentParser(add_help=False)
-parser.add_argument("--ui-settings-file", type=str, default='config.json')
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.realpath(__file__)))
-args, _ = parser.parse_known_args(sys.argv)
+from modules import cmd_args
+from modules.paths_internal import script_path, extensions_dir
-script_path = os.path.dirname(__file__)
-data_path = os.getcwd()
+commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+sys.argv += shlex.split(commandline_args)
+
+args, _ = cmd_args.parser.parse_known_args()
-dir_repos = "repositories"
-dir_extensions = "extensions"
python = sys.executable
git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL', "")
stored_commit_hash = None
skip_install = False
+dir_repos = "repositories"
+
+if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
+ os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
def check_python_version():
@@ -70,23 +71,6 @@ def commit_hash(): return stored_commit_hash
-def extract_arg(args, name):
- return [x for x in args if x != name], name in args
-
-
-def extract_opt(args, name):
- opt = None
- is_present = False
- if name in args:
- is_present = True
- idx = args.index(name)
- del args[idx]
- if idx < len(args) and args[idx][0] != "-":
- opt = args[idx]
- del args[idx]
- return args, is_present, opt
-
-
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
if desc is not None:
print(desc)
@@ -137,12 +121,12 @@ def run_python(code, desc=None, errdesc=None): return run(f'"{python}" -c "{code}"', desc, errdesc)
-def run_pip(args, desc=None):
+def run_pip(args, desc=None, live=False):
if skip_install:
return
index_url_line = f' --index-url {index_url}' if index_url != '' else ''
- return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+ return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
def check_run_python(code):
@@ -222,26 +206,29 @@ def list_extensions(settings_file): print(e, file=sys.stderr)
disabled_extensions = set(settings.get('disabled_extensions', []))
+ disable_all_extensions = settings.get('disable_all_extensions', 'none')
+
+ if disable_all_extensions != 'none':
+ return []
- return [x for x in os.listdir(os.path.join(data_path, dir_extensions)) if x not in disabled_extensions]
+ return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
def run_extensions_installers(settings_file):
- if not os.path.isdir(dir_extensions):
+ if not os.path.isdir(extensions_dir):
return
for dirname_extension in list_extensions(settings_file):
- run_extension_installer(os.path.join(dir_extensions, dirname_extension))
+ run_extension_installer(os.path.join(extensions_dir, dirname_extension))
def prepare_environment():
global skip_install
- torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
+ torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
- commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
- xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
+ xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
@@ -252,27 +239,13 @@ def prepare_environment(): 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')
- stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
+ stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
- sys.argv += shlex.split(commandline_args)
-
- sys.argv, _ = extract_arg(sys.argv, '-f')
- sys.argv, update_all_extensions = extract_arg(sys.argv, '--update-all-extensions')
- sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
- sys.argv, skip_python_version_check = extract_arg(sys.argv, '--skip-python-version-check')
- sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
- sys.argv, reinstall_torch = extract_arg(sys.argv, '--reinstall-torch')
- sys.argv, update_check = extract_arg(sys.argv, '--update-check')
- sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
- sys.argv, skip_install = extract_arg(sys.argv, '--skip-install')
- xformers = '--xformers' in sys.argv
- ngrok = '--ngrok' in sys.argv
-
- if not skip_python_version_check:
+ if not args.skip_python_version_check:
check_python_version()
commit = commit_hash()
@@ -280,10 +253,10 @@ def prepare_environment(): print(f"Python {sys.version}")
print(f"Commit hash: {commit}")
- if reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
+ if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)
- if not skip_torch_cuda_test:
+ if not args.skip_torch_cuda_test:
run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")
if not is_installed("gfpgan"):
@@ -295,10 +268,10 @@ def prepare_environment(): if not is_installed("open_clip"):
run_pip(f"install {openclip_package}", "open_clip")
- if (not is_installed("xformers") or reinstall_xformers) and xformers:
+ if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
- run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+ run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
else:
print("Installation of xformers is not supported in this version of Python.")
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
@@ -307,7 +280,7 @@ def prepare_environment(): elif platform.system() == "Linux":
run_pip(f"install {xformers_package}", "xformers")
- if not is_installed("pyngrok") and ngrok:
+ if not is_installed("pyngrok") and args.ngrok:
run_pip("install pyngrok", "ngrok")
os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
@@ -323,22 +296,22 @@ def prepare_environment(): if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
- run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
+ run_pip(f"install -r \"{requirements_file}\"", "requirements")
run_extensions_installers(settings_file=args.ui_settings_file)
- if update_check:
+ if args.update_check:
version_check(commit)
- if update_all_extensions:
- git_pull_recursive(os.path.join(data_path, dir_extensions))
+ if args.update_all_extensions:
+ git_pull_recursive(extensions_dir)
if "--exit" in sys.argv:
print("Exiting because of --exit argument")
exit(0)
- if run_tests:
- exitcode = tests(test_dir)
+ if args.tests and not args.no_tests:
+ exitcode = tests(args.tests)
exit(exitcode)
@@ -352,6 +325,8 @@ def tests(test_dir): sys.argv.append("--skip-torch-cuda-test")
if "--disable-nan-check" not in sys.argv:
sys.argv.append("--disable-nan-check")
+ if "--no-tests" not in sys.argv:
+ sys.argv.append("--no-tests")
print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")
diff --git a/models/karlo/ViT-L-14_stats.th b/models/karlo/ViT-L-14_stats.th Binary files differnew file mode 100644 index 00000000..a6a06e94 --- /dev/null +++ b/models/karlo/ViT-L-14_stats.th diff --git a/modules/api/api.py b/modules/api/api.py index 35e17afc..9ffcbd5f 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -3,11 +3,14 @@ import io import time import datetime import uvicorn +import gradio as gr from threading import Lock from io import BytesIO -from gradio.processing_utils import decode_base64_to_file -from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response +from fastapi import APIRouter, Depends, FastAPI, Request, Response from fastapi.security import HTTPBasic, HTTPBasicCredentials +from fastapi.exceptions import HTTPException +from fastapi.responses import JSONResponse +from fastapi.encoders import jsonable_encoder from secrets import compare_digest import modules.shared as shared @@ -18,7 +21,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_ from modules.textual_inversion.preprocess import preprocess from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork from PIL import PngImagePlugin,Image -from modules.sd_models import checkpoints_list +from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights from modules.sd_models_config import find_checkpoint_config_near_filename from modules.realesrgan_model import get_realesrgan_models from modules import devices @@ -90,6 +93,16 @@ def encode_pil_to_base64(image): return base64.b64encode(bytes_data) def api_middleware(app: FastAPI): + rich_available = True + try: + import anyio # importing just so it can be placed on silent list + import starlette # importing just so it can be placed on silent list + from rich.console import Console + console = Console() + except: + import traceback + rich_available = False + @app.middleware("http") async def log_and_time(req: Request, call_next): ts = time.time() @@ -110,6 +123,36 @@ def api_middleware(app: FastAPI): )) return res + def handle_exception(request: Request, e: Exception): + err = { + "error": type(e).__name__, + "detail": vars(e).get('detail', ''), + "body": vars(e).get('body', ''), + "errors": str(e), + } + print(f"API error: {request.method}: {request.url} {err}") + if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions + if rich_available: + console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200])) + else: + traceback.print_exc() + return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err)) + + @app.middleware("http") + async def exception_handling(request: Request, call_next): + try: + return await call_next(request) + except Exception as e: + return handle_exception(request, e) + + @app.exception_handler(Exception) + async def fastapi_exception_handler(request: Request, e: Exception): + return handle_exception(request, e) + + @app.exception_handler(HTTPException) + async def http_exception_handler(request: Request, e: HTTPException): + return handle_exception(request, e) + class Api: def __init__(self, app: FastAPI, queue_lock: Lock): @@ -150,8 +193,13 @@ class Api: self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse) self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse) self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse) + self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList) + self.default_script_arg_txt2img = [] + self.default_script_arg_img2img = [] + 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) @@ -185,7 +233,7 @@ class Api: script_idx = script_name_to_index(script_name, script_runner.scripts) return script_runner.scripts[script_idx] - def init_script_args(self, request, selectable_scripts, selectable_idx, script_runner): + def init_default_script_args(self, script_runner): #find max idx from the scripts in runner and generate a none array to init script_args last_arg_index = 1 for script in script_runner.scripts: @@ -193,13 +241,24 @@ class Api: last_arg_index = script.args_to # None everywhere except position 0 to initialize script args script_args = [None]*last_arg_index + script_args[0] = 0 + + # get default values + with gr.Blocks(): # will throw errors calling ui function without this + for script in script_runner.scripts: + if script.ui(script.is_img2img): + ui_default_values = [] + for elem in script.ui(script.is_img2img): + ui_default_values.append(elem.value) + 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): + script_args = default_script_args.copy() # 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 script_args[0] = selectable_idx + 1 - else: - # when [0] = 0 no selectable script to run - script_args[0] = 0 # Now check for always on scripts if request.alwayson_scripts and (len(request.alwayson_scripts) > 0): @@ -212,7 +271,9 @@ class Api: raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params") # always on script with no arg should always run so you don't really need to add them to the requests if "args" in request.alwayson_scripts[alwayson_script_name]: - script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"] + # min between arg length in scriptrunner and arg length in the request + for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))): + script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx] return script_args def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): @@ -220,6 +281,8 @@ class Api: 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) selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner) populate = txt2imgreq.copy(update={ # Override __init__ params @@ -235,7 +298,7 @@ class Api: args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) - script_args = self.init_script_args(txt2imgreq, 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) send_images = args.pop('send_images', True) args.pop('save_images', None) @@ -272,6 +335,8 @@ class Api: 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) selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner) populate = img2imgreq.copy(update={ # Override __init__ params @@ -289,7 +354,7 @@ class Api: args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) - script_args = self.init_script_args(img2imgreq, 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) send_images = args.pop('send_images', True) args.pop('save_images', None) @@ -331,16 +396,11 @@ class Api: def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): reqDict = setUpscalers(req) - def prepareFiles(file): - file = decode_base64_to_file(file.data, file_path=file.name) - file.orig_name = file.name - return file - - reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList'])) - reqDict.pop('imageList') + image_list = reqDict.pop('imageList', []) + image_folder = [decode_base64_to_image(x.data) for x in image_list] with self.queue_lock: - result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict) + result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict) return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) @@ -412,6 +472,16 @@ class Api: return {} + def unloadapi(self): + unload_model_weights() + + return {} + + def reloadapi(self): + reload_model_weights() + + return {} + def skip(self): shared.state.skip() diff --git a/modules/cmd_args.py b/modules/cmd_args.py new file mode 100644 index 00000000..81c0b82a --- /dev/null +++ b/modules/cmd_args.py @@ -0,0 +1,103 @@ +import argparse
+import os
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui
+parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program")
+parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version")
+parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
+parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
+parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
+parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
+parser.add_argument("--tests", type=str, default=None, help="launch.py argument: run tests in the specified directory")
+parser.add_argument("--no-tests", action='store_true', help="launch.py argument: do not run tests even if --tests option is specified")
+parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
+parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
+parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
+parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
+parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
+parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
+parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
+parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
+parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
+parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
+parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
+parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
+parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
+parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
+parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
+parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
+parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
+parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
+parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
+parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
+parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
+parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
+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("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
+parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
+parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
+parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
+parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
+parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
+parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
+parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
+parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
+parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
+parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
+parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
+parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
+parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
+parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
+parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
diff --git a/modules/devices.py b/modules/devices.py index 52c3e7cd..c705a3cb 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -92,14 +92,18 @@ def cond_cast_float(input): def randn(seed, shape): + from modules.shared import opts + torch.manual_seed(seed) - if device.type == 'mps': + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_seed(shape): - if device.type == 'mps': + from modules.shared import opts + + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) diff --git a/modules/extensions.py b/modules/extensions.py index ed4b58fe..3a7a0372 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -5,17 +5,22 @@ import traceback import time
import git
-from modules import paths, shared
+from modules import shared
+from modules.paths_internal import extensions_dir, extensions_builtin_dir
extensions = []
-extensions_dir = os.path.join(paths.data_path, "extensions")
-extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
if not os.path.exists(extensions_dir):
os.makedirs(extensions_dir)
+
def active():
- return [x for x in extensions if x.enabled]
+ if shared.opts.disable_all_extensions == "all":
+ return []
+ elif shared.opts.disable_all_extensions == "extra":
+ return [x for x in extensions if x.enabled and x.is_builtin]
+ else:
+ return [x for x in extensions if x.enabled]
class Extension:
@@ -27,21 +32,29 @@ class Extension: self.can_update = False
self.is_builtin = is_builtin
self.version = ''
+ self.remote = None
+ self.have_info_from_repo = False
+
+ def read_info_from_repo(self):
+ if self.have_info_from_repo:
+ return
+
+ self.have_info_from_repo = True
repo = None
try:
- if os.path.exists(os.path.join(path, ".git")):
- repo = git.Repo(path)
+ if os.path.exists(os.path.join(self.path, ".git")):
+ repo = git.Repo(self.path)
except Exception:
- print(f"Error reading github repository info from {path}:", file=sys.stderr)
+ print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
if repo is None or repo.bare:
self.remote = None
else:
try:
- self.remote = next(repo.remote().urls, None)
self.status = 'unknown'
+ self.remote = next(repo.remote().urls, None)
head = repo.head.commit
ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
self.version = f'{head.hexsha[:8]} ({ts})'
@@ -89,7 +102,12 @@ def list_extensions(): if not os.path.isdir(extensions_dir):
return
- paths = []
+ if shared.opts.disable_all_extensions == "all":
+ print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+ elif shared.opts.disable_all_extensions == "extra":
+ print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
+
+ extension_paths = []
for dirname in [extensions_dir, extensions_builtin_dir]:
if not os.path.isdir(dirname):
return
@@ -99,9 +117,8 @@ def list_extensions(): if not os.path.isdir(path):
continue
- paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+ extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
- for dirname, path, is_builtin in paths:
+ for dirname, path, is_builtin in extension_paths:
extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
extensions.append(extension)
-
diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index d3a4d7ad..33d100dd 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): def activate(self, p, params_list):
additional = shared.opts.sd_hypernetwork
- if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+ if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 7c0b5b4e..e7269363 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model restore_old_hires_fix_params(res)
+ # Missing RNG means the default was set, which is GPU RNG
+ if "RNG" not in res:
+ res["RNG"] = "GPU"
+
return res
@@ -304,6 +308,7 @@ infotext_to_setting_name_mapping = [ ('UniPC skip type', 'uni_pc_skip_type'),
('UniPC order', 'uni_pc_order'),
('UniPC lower order final', 'uni_pc_lower_order_final'),
+ ('RNG', 'randn_source'),
]
@@ -401,9 +406,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component, button.click(
fn=paste_func,
- _js=f"recalculate_prompts_{tabname}",
inputs=[input_comp],
outputs=[x[0] for x in paste_fields],
)
+ button.click(
+ fn=None,
+ _js=f"recalculate_prompts_{tabname}",
+ inputs=[],
+ outputs=[],
+ )
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index f6ef42d5..1fc49537 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -312,7 +312,7 @@ class Hypernetwork: def list_hypernetworks(path):
res = {}
- for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
+ for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
diff --git a/modules/images.py b/modules/images.py index cdbb77e1..559de810 100644 --- a/modules/images.py +++ b/modules/images.py @@ -261,9 +261,12 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None): if scale > 1.0:
upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
- assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
+ if len(upscalers) == 0:
+ upscaler = shared.sd_upscalers[0]
+ print(f"could not find upscaler named {upscaler_name or '<empty string>'}, using {upscaler.name} as a fallback")
+ else:
+ upscaler = upscalers[0]
- upscaler = upscalers[0]
im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
if im.width != w or im.height != h:
@@ -350,6 +353,7 @@ class FilenameGenerator: 'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
'prompt_words': lambda self: self.prompt_words(),
'hasprompt': lambda self, *args: self.hasprompt(*args), #accept formats:[hasprompt<prompt1|default><prompt2>..]
+ 'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
}
default_time_format = '%Y%m%d%H%M%S'
@@ -662,6 +666,8 @@ Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]} def image_data(data):
+ import gradio as gr
+
try:
image = Image.open(io.BytesIO(data))
textinfo, _ = read_info_from_image(image)
@@ -677,7 +683,7 @@ def image_data(data): except Exception:
pass
- return '', None
+ return gr.update(), None
def flatten(img, bgcolor):
diff --git a/modules/img2img.py b/modules/img2img.py index c973b770..d22d9a49 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -151,13 +151,14 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s override_settings=override_settings,
)
- p.scripts = modules.scripts.scripts_txt2img
+ p.scripts = modules.scripts.scripts_img2img
p.script_args = args
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
- p.extra_generation_params["Mask blur"] = mask_blur
+ if mask:
+ p.extra_generation_params["Mask blur"] = mask_blur
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/interrogate.py b/modules/interrogate.py index cbb80683..e1665708 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir): category_types = ["artists", "flavors", "mediums", "movements"]
try:
- os.makedirs(tmpdir)
+ os.makedirs(tmpdir, exist_ok=True)
for category_type in category_types:
torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
os.rename(tmpdir, content_dir)
@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir): errors.display(e, "downloading default CLIP interrogate categories")
finally:
if os.path.exists(tmpdir):
- os.remove(tmpdir)
+ os.removedirs(tmpdir)
class InterrogateModels:
diff --git a/modules/lowvram.py b/modules/lowvram.py index 042a0254..e254cc13 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram): if hasattr(sd_model.cond_stage_model, 'model'):
sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
- # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
+ # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
- stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
+ stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
sd_model.to(devices.device)
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
# register hooks for those the first three models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
@@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram): sd_model.first_stage_model.decode = first_stage_model_decode_wrap
if sd_model.depth_model:
sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
+ if sd_model.embedder:
+ sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if hasattr(sd_model.cond_stage_model, 'model'):
diff --git a/modules/mac_specific.py b/modules/mac_specific.py index 18e6ff72..6fe8dea0 100644 --- a/modules/mac_specific.py +++ b/modules/mac_specific.py @@ -1,4 +1,5 @@ import torch +import platform from modules import paths from modules.sd_hijack_utils import CondFunc from packaging import version @@ -32,6 +33,10 @@ if has_mps: # MPS fix for randn in torchsde CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps') + if platform.mac_ver()[0].startswith("13.2."): + # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124) + CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760) + if version.parse(torch.__version__) < version.parse("1.13"): # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working @@ -49,4 +54,6 @@ if has_mps: CondFunc('torch.cumsum', cumsum_fix_func, None) CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None) CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None) - + if version.parse(torch.__version__) == version.parse("2.0"): + # MPS workaround for https://github.com/pytorch/pytorch/issues/96113 + CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6) diff --git a/modules/modelloader.py b/modules/modelloader.py index e351d808..522affc6 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -4,7 +4,6 @@ import shutil import importlib from urllib.parse import urlparse -from basicsr.utils.download_util import load_file_from_url from modules import shared from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone from modules.paths import script_path, models_path @@ -59,6 +58,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None if model_url is not None and len(output) == 0: if download_name is not None: + from basicsr.utils.download_util import load_file_from_url dl = load_file_from_url(model_url, model_path, True, download_name) output.append(dl) else: diff --git a/modules/paths.py b/modules/paths.py index d991cc71..0e1e00e7 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -1,16 +1,9 @@ -import argparse
import os
import sys
-import modules.safe
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
-script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+import modules.safe
-# Parse the --data-dir flag first so we can use it as a base for our other argument default values
-parser = argparse.ArgumentParser(add_help=False)
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
-cmd_opts_pre = parser.parse_known_args()[0]
-data_path = cmd_opts_pre.data_dir
-models_path = os.path.join(data_path, "models")
# data_path = cmd_opts_pre.data
sys.path.insert(0, script_path)
diff --git a/modules/paths_internal.py b/modules/paths_internal.py new file mode 100644 index 00000000..926ec3bb --- /dev/null +++ b/modules/paths_internal.py @@ -0,0 +1,22 @@ +"""this module defines internal paths used by program and is safe to import before dependencies are installed in launch.py"""
+
+import argparse
+import os
+
+script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
+sd_configs_path = os.path.join(script_path, "configs")
+sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
+sd_model_file = os.path.join(script_path, 'model.ckpt')
+default_sd_model_file = sd_model_file
+
+# Parse the --data-dir flag first so we can use it as a base for our other argument default values
+parser_pre = argparse.ArgumentParser(add_help=False)
+parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+cmd_opts_pre = parser_pre.parse_known_args()[0]
+
+data_path = cmd_opts_pre.data_dir
+
+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")
diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 09d8e605..4dc1a2ab 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -18,9 +18,15 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, if extras_mode == 1:
for img in image_folder:
- image = Image.open(img)
+ if isinstance(img, Image.Image):
+ image = img
+ fn = ''
+ else:
+ image = Image.open(os.path.abspath(img.name))
+ fn = os.path.splitext(img.orig_name)[0]
+
image_data.append(image)
- image_names.append(os.path.splitext(img.orig_name)[0])
+ image_names.append(fn)
elif extras_mode == 2:
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
assert input_dir, 'input directory not selected'
diff --git a/modules/processing.py b/modules/processing.py index 59717b4c..98557fb1 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -3,6 +3,7 @@ import math import os
import sys
import warnings
+import hashlib
import torch
import numpy as np
@@ -78,21 +79,27 @@ def apply_overlay(image, paste_loc, index, overlays): def txt2img_image_conditioning(sd_model, x, width, height):
- if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
- # Dummy zero conditioning if we're not using inpainting model.
- # 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.
- return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+ if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+
+ # The "masked-image" in this case will just be all zeros since the entire image is masked.
+ image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+ image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+ # 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)
- # The "masked-image" in this case will just be all zeros since the entire image is masked.
- image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
- image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+ return image_conditioning
- # 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)
+ elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
- return image_conditioning
+ return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+
+ else:
+ # 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.
+ return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
class StableDiffusionProcessing:
@@ -190,6 +197,14 @@ class StableDiffusionProcessing: return conditioning_image
+ def unclip_image_conditioning(self, source_image):
+ c_adm = self.sd_model.embedder(source_image)
+ if self.sd_model.noise_augmentor is not None:
+ noise_level = 0 # TODO: Allow other noise levels?
+ c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+ 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):
self.is_using_inpainting_conditioning = True
@@ -241,6 +256,9 @@ class StableDiffusionProcessing: if self.sampler.conditioning_key in {'hybrid', 'concat'}:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+ if self.sampler.conditioning_key == "crossattn-adm":
+ return self.unclip_image_conditioning(source_image)
+
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
@@ -459,6 +477,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "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": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+ "Init image hash": getattr(p, 'init_img_hash', None),
+ "RNG": (opts.randn_source if opts.randn_source != "GPU" else None)
}
generation_params.update(p.extra_generation_params)
@@ -622,8 +642,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0))
- uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
- c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+ step_multiplier = 1
+ if not shared.opts.dont_fix_second_order_samplers_schedule:
+ try:
+ step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
+ except:
+ pass
+ uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
+ c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -689,6 +715,22 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: image.info["parameters"] = text
output_images.append(image)
+ if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+ image_mask = p.mask_for_overlay.convert('RGB')
+ image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
+
+ if opts.save_mask:
+ images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+
+ if opts.save_mask_composite:
+ images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+
+ if opts.return_mask:
+ output_images.append(image_mask)
+
+ if opts.return_mask_composite:
+ output_images.append(image_mask_composite)
+
del x_samples_ddim
devices.torch_gc()
@@ -974,6 +1016,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.color_corrections = []
imgs = []
for img in self.init_images:
+
+ # 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)
+
image = images.flatten(img, opts.img2img_background_color)
if crop_region is None and self.resize_mode != 3:
diff --git a/modules/safe.py b/modules/safe.py index 82d44be3..dadf319c 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -1,6 +1,5 @@ # this code is adapted from the script contributed by anon from /h/
-import io
import pickle
import collections
import sys
@@ -12,11 +11,9 @@ import _codecs import zipfile
import re
-
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
-
def encode(*args):
out = _codecs.encode(*args)
return out
@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler): def persistent_load(self, saved_id):
assert saved_id[0] == 'storage'
- return TypedStorage()
+ return TypedStorage(_internal=True)
def find_class(self, module, name):
if self.extra_handler is not None:
diff --git a/modules/scripts.py b/modules/scripts.py index 8de19884..4d0bbd66 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -239,7 +239,15 @@ def load_scripts(): elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
- for scriptfile in sorted(scripts_list):
+ def orderby(basedir):
+ # 1st webui, 2nd extensions-builtin, 3rd extensions
+ priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+ for key in priority:
+ if basedir.startswith(key):
+ return priority[key]
+ return 9999
+
+ for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
try:
if scriptfile.basedir != paths.script_path:
sys.path = [scriptfile.basedir] + sys.path
@@ -513,6 +521,18 @@ def reload_scripts(): scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
+def add_classes_to_gradio_component(comp):
+ """
+ this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+ """
+
+ comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+
+ if getattr(comp, 'multiselect', False):
+ comp.elem_classes.append('multiselect')
+
+
+
def IOComponent_init(self, *args, **kwargs):
if scripts_current is not None:
scripts_current.before_component(self, **kwargs)
@@ -521,6 +541,8 @@ def IOComponent_init(self, *args, **kwargs): res = original_IOComponent_init(self, *args, **kwargs)
+ add_classes_to_gradio_component(self)
+
script_callbacks.after_component_callback(self, **kwargs)
if scripts_current is not None:
@@ -531,3 +553,15 @@ def IOComponent_init(self, *args, **kwargs): original_IOComponent_init = gr.components.IOComponent.__init__
gr.components.IOComponent.__init__ = IOComponent_init
+
+
+def BlockContext_init(self, *args, **kwargs):
+ res = original_BlockContext_init(self, *args, **kwargs)
+
+ add_classes_to_gradio_component(self)
+
+ return res
+
+
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+gr.blocks.BlockContext.__init__ = BlockContext_init
diff --git a/modules/scripts_postprocessing.py b/modules/scripts_postprocessing.py index ce0ebb61..b11568c0 100644 --- a/modules/scripts_postprocessing.py +++ b/modules/scripts_postprocessing.py @@ -109,7 +109,7 @@ class ScriptPostprocessingRunner: inputs = []
for script in self.scripts_in_preferred_order():
- with gr.Box() as group:
+ with gr.Row() as group:
self.create_script_ui(script, inputs)
script.group = group
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index 2e307b5d..372555ff 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -337,7 +337,7 @@ def xformers_attention_forward(self, x, context=None, mask=None): dtype = q.dtype
if shared.opts.upcast_attn:
- q, k = q.float(), k.float()
+ q, k, v = q.float(), k.float(), v.float()
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
@@ -372,7 +372,7 @@ def scaled_dot_product_attention_forward(self, x, context=None, mask=None): dtype = q.dtype
if shared.opts.upcast_attn:
- q, k = q.float(), k.float()
+ q, k, v = q.float(), k.float(), v.float()
# the output of sdp = (batch, num_heads, seq_len, head_dim)
hidden_states = torch.nn.functional.scaled_dot_product_attention(
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py index 843ab66c..15858263 100644 --- a/modules/sd_hijack_unet.py +++ b/modules/sd_hijack_unet.py @@ -67,7 +67,7 @@ def hijack_ddpm_edit(): unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
-if version.parse(torch.__version__) <= version.parse("1.13.1"):
+if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
diff --git a/modules/sd_models.py b/modules/sd_models.py index f0cb1240..6ea874df 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -122,7 +122,7 @@ def list_models(): elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
- for filename in model_list:
+ for filename in sorted(model_list, key=str.lower):
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
@@ -178,7 +178,7 @@ def select_checkpoint(): return checkpoint_info
-chckpoint_dict_replacements = {
+checkpoint_dict_replacements = {
'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
@@ -186,7 +186,7 @@ chckpoint_dict_replacements = { def transform_checkpoint_dict_key(k):
- for text, replacement in chckpoint_dict_replacements.items():
+ for text, replacement in checkpoint_dict_replacements.items():
if k.startswith(text):
k = replacement + k[len(text):]
@@ -383,6 +383,14 @@ def repair_config(sd_config): elif shared.cmd_opts.upcast_sampling:
sd_config.model.params.unet_config.params.use_fp16 = True
+ if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
+ sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
+
+ # For UnCLIP-L, override the hardcoded karlo directory
+ if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
+ karlo_path = os.path.join(paths.models_path, 'karlo')
+ sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
+
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
@@ -494,7 +502,7 @@ def reload_model_weights(sd_model=None, info=None): if sd_model is None or checkpoint_config != sd_model.used_config:
del sd_model
checkpoints_loaded.clear()
- load_model(checkpoint_info, already_loaded_state_dict=state_dict, time_taken_to_load_state_dict=timer.records["load weights from disk"])
+ load_model(checkpoint_info, already_loaded_state_dict=state_dict)
return shared.sd_model
try:
@@ -517,3 +525,23 @@ def reload_model_weights(sd_model=None, info=None): print(f"Weights loaded in {timer.summary()}.")
return sd_model
+
+def unload_model_weights(sd_model=None, info=None):
+ from modules import lowvram, devices, sd_hijack
+ timer = Timer()
+
+ if shared.sd_model:
+
+ # shared.sd_model.cond_stage_model.to(devices.cpu)
+ # shared.sd_model.first_stage_model.to(devices.cpu)
+ shared.sd_model.to(devices.cpu)
+ sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+ shared.sd_model = None
+ sd_model = None
+ gc.collect()
+ devices.torch_gc()
+ torch.cuda.empty_cache()
+
+ print(f"Unloaded weights {timer.summary()}.")
+
+ return sd_model
\ No newline at end of file diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 91c21700..9398f528 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -14,6 +14,8 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml") 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_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")
config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
@@ -65,9 +67,14 @@ def is_using_v_parameterization_for_sd2(state_dict): def guess_model_config_from_state_dict(sd, filename):
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+ sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
return config_depth_model
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+ return config_unclip
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+ return config_unopenclip
if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
if diffusion_model_input.shape[1] == 9:
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index a1aac7cf..bc074238 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -60,3 +60,13 @@ def store_latent(decoded): class InterruptedException(BaseException):
pass
+
+
+if opts.randn_source == "CPU":
+ import torchsde._brownian.brownian_interval
+
+ def torchsde_randn(size, dtype, device, seed):
+ generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+
+ torchsde._brownian.brownian_interval._randn = torchsde_randn
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index 083da18c..bfcc5574 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -70,8 +70,13 @@ class VanillaStableDiffusionSampler: # Have to unwrap the inpainting conditioning here to perform pre-processing
image_conditioning = None
+ uc_image_conditioning = None
if isinstance(cond, dict):
- image_conditioning = cond["c_concat"][0]
+ if self.conditioning_key == "crossattn-adm":
+ image_conditioning = cond["c_adm"]
+ uc_image_conditioning = unconditional_conditioning["c_adm"]
+ else:
+ image_conditioning = cond["c_concat"][0]
cond = cond["c_crossattn"][0]
unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
@@ -98,8 +103,12 @@ class VanillaStableDiffusionSampler: # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
# Note that they need to be lists because it just concatenates them later.
if image_conditioning is not None:
- cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
+ else:
+ cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
return x, ts, cond, unconditional_conditioning
@@ -176,8 +185,12 @@ class VanillaStableDiffusionSampler: # Wrap the conditioning models with additional image conditioning for inpainting model
if image_conditioning is not None:
- conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
+ else:
+ conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@@ -195,8 +208,12 @@ class VanillaStableDiffusionSampler: # Wrap the conditioning models with additional image conditioning for inpainting model
# dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
if image_conditioning is not None:
- conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
- unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
+ else:
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 93f0e55a..a547d1b5 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module): batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
+ if shared.sd_model.model.conditioning_key == "crossattn-adm":
+ image_uncond = torch.zeros_like(image_cond)
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
+ else:
+ image_uncond = image_cond
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
+
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
else:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
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_cond] + [torch.zeros_like(self.init_latent)])
+ 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)
cfg_denoiser_callback(denoiser_params)
@@ -116,13 +123,13 @@ class CFGDenoiser(torch.nn.Module): cond_in = torch.cat([tensor, uncond, uncond])
if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
else:
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, x_out.shape[0], batch_size):
a = batch_offset
b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
else:
x_out = torch.zeros_like(x_in)
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
@@ -135,9 +142,9 @@ class CFGDenoiser(torch.nn.Module): else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
cfg_denoised_callback(denoised_params)
@@ -183,7 +190,7 @@ class TorchHijack: if noise.shape == x.shape:
return noise
- if x.device.type == 'mps':
+ if opts.randn_source == "CPU" or x.device.type == 'mps':
return torch.randn_like(x, device=devices.cpu).to(x.device)
else:
return torch.randn_like(x)
diff --git a/modules/shared.py b/modules/shared.py index f28a12cc..26bd56eb 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -4,6 +4,7 @@ import json import os
import sys
import time
+import requests
from PIL import Image
import gradio as gr
@@ -13,114 +14,22 @@ import modules.interrogate import modules.memmon
import modules.styles
import modules.devices as devices
-from modules import localization, extensions, script_loading, errors, ui_components, shared_items
-from modules.paths import models_path, script_path, data_path
-
+from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
+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
demo = None
-sd_configs_path = os.path.join(script_path, "configs")
-sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
-sd_model_file = os.path.join(script_path, 'model.ckpt')
-default_sd_model_file = sd_model_file
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
-parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
-parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
-parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
-parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
-parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
-parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
-parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
-parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
-parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
-parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
-parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
-parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
-parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
-parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
-parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
-parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
-parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
-parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
-parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
-parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
-parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
-parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
-parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
-parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
-parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
-parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
-parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
-parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
-parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
-parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
-parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
-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("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
-parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
-parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
-parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
-parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
-parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
-parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
-parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
-parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
-parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
-parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
-parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
-parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
-parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
-parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
-parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
-parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
-parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
-parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
-parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
-parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
-parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
-parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
-
-
-script_loading.preload_extensions(extensions.extensions_dir, parser)
-script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
+parser = cmd_args.parser
+
+script_loading.preload_extensions(extensions_dir, parser)
+script_loading.preload_extensions(extensions_builtin_dir, parser)
if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
cmd_opts = parser.parse_args()
else:
cmd_opts, _ = parser.parse_known_args()
+
restricted_opts = {
"samples_filename_pattern",
"directories_filename_pattern",
@@ -131,6 +40,7 @@ restricted_opts = { "outdir_grids",
"outdir_txt2img_grids",
"outdir_save",
+ "outdir_init_images"
}
ui_reorder_categories = [
@@ -146,6 +56,21 @@ ui_reorder_categories = [ "scripts",
]
+# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
+gradio_hf_hub_themes = [
+ "gradio/glass",
+ "gradio/monochrome",
+ "gradio/seafoam",
+ "gradio/soft",
+ "freddyaboulton/dracula_revamped",
+ "gradio/dracula_test",
+ "abidlabs/dracula_test",
+ "abidlabs/pakistan",
+ "dawood/microsoft_windows",
+ "ysharma/steampunk"
+]
+
+
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -332,6 +257,8 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
+ "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
+ "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
"export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
@@ -343,6 +270,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+ "save_init_img": OptionInfo(False, "Save init images when using img2img"),
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
@@ -358,6 +286,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), { "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),
}))
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
@@ -373,6 +302,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), { "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+ "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+ "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
}))
options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -421,6 +352,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+ "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -428,6 +360,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), { "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."),
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
+ "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
}))
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -448,12 +381,16 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
+ "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
- "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+ "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
"return_grid": OptionInfo(True, "Show grid in results for web"),
+ "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
+ "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
@@ -468,11 +405,13 @@ options_templates.update(options_section(('ui', "User interface"), { "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+ "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
"hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
+ "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
}))
options_templates.update(options_section(('ui', "Live previews"), {
@@ -508,7 +447,8 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), { }))
options_templates.update(options_section((None, "Hidden options"), {
- "disabled_extensions": OptionInfo([], "Disable those extensions"),
+ "disabled_extensions": OptionInfo([], "Disable these extensions"),
+ "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))
@@ -685,6 +625,24 @@ clip_model = None progress_print_out = sys.stdout
+gradio_theme = gr.themes.Base()
+
+
+def reload_gradio_theme(theme_name=None):
+ global gradio_theme
+ if not theme_name:
+ theme_name = opts.gradio_theme
+
+ if theme_name == "Default":
+ gradio_theme = gr.themes.Default()
+ else:
+ try:
+ gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+ except requests.exceptions.ConnectionError:
+ print("Can't access HuggingFace Hub, falling back to default Gradio theme")
+ gradio_theme = gr.themes.Default()
+
+
class TotalTQDM:
def __init__(self):
@@ -726,7 +684,7 @@ mem_mon.start() def listfiles(dirname):
- filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+ filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
return [file for file in filenames if os.path.isfile(file)]
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index c63c7d1d..d2e62e58 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -152,7 +152,11 @@ class EmbeddingDatabase: name = data.get('name', name)
else:
data = extract_image_data_embed(embed_image)
- name = data.get('name', name)
+ if data:
+ name = data.get('name', name)
+ else:
+ # if data is None, means this is not an embeding, just a preview image
+ return
elif ext in ['.BIN', '.PT']:
data = torch.load(path, map_location="cpu")
elif ext in ['.SAFETENSORS']:
diff --git a/modules/ui.py b/modules/ui.py index 7e603332..dab94b93 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -20,7 +20,7 @@ from PIL import Image, PngImagePlugin from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
+from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
from modules.paths import script_path, data_path
from modules.shared import opts, cmd_opts, restricted_opts
@@ -70,17 +70,6 @@ def gr_show(visible=True): sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.wrap.cover-bg .z-20::before { content:"" }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
# Using constants for these since the variation selector isn't visible.
# Important that they exactly match script.js for tooltip to work.
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
@@ -89,7 +78,7 @@ paste_symbol = '\u2199\ufe0f' # ↙ refresh_symbol = '\U0001f504' # 🔄
save_style_symbol = '\U0001f4be' # 💾
apply_style_symbol = '\U0001f4cb' # 📋
-clear_prompt_symbol = '\U0001F5D1' # 🗑️
+clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️
extra_networks_symbol = '\U0001F3B4' # 🎴
switch_values_symbol = '\U000021C5' # ⇅
@@ -179,14 +168,13 @@ def interrogate_deepbooru(image): def create_seed_inputs(target_interface):
- with FormRow(elem_id=target_interface + '_seed_row'):
+ with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
seed.style(container=False)
- random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
- reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+ random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
+ reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')
- with gr.Group(elem_id=target_interface + '_subseed_show_box'):
- seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
+ seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
# Components to show/hide based on the 'Extra' checkbox
seed_extras = []
@@ -195,8 +183,8 @@ def create_seed_inputs(target_interface): seed_extras.append(seed_extra_row_1)
subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
subseed.style(container=False)
- random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
- reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+ random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
+ reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
with FormRow(visible=False) as seed_extra_row_2:
@@ -291,19 +279,19 @@ def create_toprow(is_img2img): with gr.Row():
with gr.Column(scale=80):
with gr.Row():
- negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
+ negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
button_interrogate = None
button_deepbooru = None
if is_img2img:
- with gr.Column(scale=1, elem_id="interrogate_col"):
+ with gr.Column(scale=1, elem_classes="interrogate-col"):
button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
- with gr.Row(elem_id=f"{id_part}_generate_box"):
- interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
- skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+ with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+ interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+ skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
skip.click(
@@ -325,9 +313,9 @@ def create_toprow(is_img2img): prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
- token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+ token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
- negative_token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_negative_token_counter")
+ negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")
clear_prompt_button.click(
@@ -479,7 +467,9 @@ def create_ui(): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
+ with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
+
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="txt2img_column_batch"):
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
@@ -492,7 +482,7 @@ def create_ui(): seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
elif category == "checkboxes":
- with FormRow(elem_id="txt2img_checkboxes", variant="compact"):
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
@@ -586,7 +576,7 @@ def create_ui(): txt2img_prompt.submit(**txt2img_args)
submit.click(**txt2img_args)
- res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height])
+ res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
txt_prompt_img.change(
fn=modules.images.image_data,
@@ -757,7 +747,9 @@ def create_ui(): width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+ with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="img2img_column_batch"):
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
@@ -774,7 +766,7 @@ def create_ui(): seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
elif category == "checkboxes":
- with FormRow(elem_id="img2img_checkboxes", variant="compact"):
+ with FormRow(elem_classes="checkboxes-row", variant="compact"):
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
@@ -904,7 +896,7 @@ def create_ui(): img2img_prompt.submit(**img2img_args)
submit.click(**img2img_args)
- res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height])
+ res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
img2img_interrogate.click(
fn=lambda *args: process_interrogate(interrogate, *args),
@@ -1212,7 +1204,7 @@ def create_ui(): with gr.Column(elem_id='ti_gallery_container'):
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
- ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+ ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
ti_progress = gr.HTML(elem_id="ti_progress", value="")
ti_outcome = gr.HTML(elem_id="ti_error", value="")
@@ -1491,11 +1483,33 @@ def create_ui(): request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+ with gr.Row():
+ unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+ reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
with gr.TabItem("Licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+
+
+ def unload_sd_weights():
+ modules.sd_models.unload_model_weights()
+
+ def reload_sd_weights():
+ modules.sd_models.reload_model_weights()
+
+ unload_sd_model.click(
+ fn=unload_sd_weights,
+ inputs=[],
+ outputs=[]
+ )
+
+ reload_sd_model.click(
+ fn=reload_sd_weights,
+ inputs=[],
+ outputs=[]
+ )
request_notifications.click(
fn=lambda: None,
@@ -1541,22 +1555,6 @@ def create_ui(): (train_interface, "Train", "ti"),
]
- css = ""
-
- for cssfile in modules.scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
- with open(cssfile, "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if os.path.exists(os.path.join(data_path, "user.css")):
- with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if not cmd_opts.no_progressbar_hiding:
- css += css_hide_progressbar
-
interfaces += script_callbacks.ui_tabs_callback()
interfaces += [(settings_interface, "Settings", "settings")]
@@ -1567,7 +1565,7 @@ def create_ui(): for _interface, label, _ifid in interfaces:
shared.tab_names.append(label)
- with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+ with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
with gr.Row(elem_id="quicksettings", variant="compact"):
for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
component = create_setting_component(k, is_quicksettings=True)
@@ -1598,11 +1596,13 @@ def create_ui(): for i, k, item in quicksettings_list:
component = component_dict[k]
+ info = opts.data_labels[k]
component.change(
fn=lambda value, k=k: run_settings_single(value, key=k),
inputs=[component],
outputs=[component, text_settings],
+ show_progress=info.refresh is not None,
)
text_settings.change(
@@ -1628,6 +1628,7 @@ def create_ui(): fn=get_settings_values,
inputs=[],
outputs=[component_dict[k] for k in component_keys],
+ queue=False,
)
def modelmerger(*args):
@@ -1704,7 +1705,7 @@ def create_ui(): if init_field is not None:
init_field(saved_value)
- if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
+ if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
apply_field(x, 'visible')
if type(x) == gr.Slider:
@@ -1750,25 +1751,60 @@ def create_ui(): return demo
-def reload_javascript():
+def webpath(fn):
+ if fn.startswith(script_path):
+ web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+ else:
+ web_path = os.path.abspath(fn)
+
+ return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
script_js = os.path.join(script_path, "script.js")
- head = f'<script type="text/javascript" src="file={os.path.abspath(script_js)}?{os.path.getmtime(script_js)}"></script>\n'
+ head = f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
inline = f"{localization.localization_js(shared.opts.localization)};"
if cmd_opts.theme is not None:
inline += f"set_theme('{cmd_opts.theme}');"
for script in modules.scripts.list_scripts("javascript", ".js"):
- head += f'<script type="text/javascript" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
+ head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
for script in modules.scripts.list_scripts("javascript", ".mjs"):
- head += f'<script type="module" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
+ head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
head += f'<script type="text/javascript">{inline}</script>\n'
+ return head
+
+
+def css_html():
+ head = ""
+
+ def stylesheet(fn):
+ return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
+
+ for cssfile in modules.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"))
+
+ return head
+
+
+def reload_javascript():
+ js = javascript_html()
+ css = css_html()
+
def template_response(*args, **kwargs):
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))
+ res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
+ res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
res.init_headers()
return res
diff --git a/modules/ui_common.py b/modules/ui_common.py index a12433d2..27ab3ebb 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -125,12 +125,12 @@ Requested path was: {f} with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
- result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+ result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
generation_info = None
with gr.Column():
- with gr.Row(elem_id=f"image_buttons_{tabname}"):
- open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
+ with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+ open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)
if tabname != "extras":
save = gr.Button('Save', elem_id=f'save_{tabname}')
@@ -145,11 +145,10 @@ Requested path was: {f} )
if tabname != "extras":
- with gr.Row():
- download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
+ 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}')
+ html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
@@ -160,6 +159,7 @@ Requested path was: {f} _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
inputs=[generation_info, html_info, html_info],
outputs=[html_info, html_info],
+ show_progress=False,
)
save.click(
@@ -195,7 +195,7 @@ Requested path was: {f} else:
html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
- html_info = gr.HTML(elem_id=f'html_info_{tabname}')
+ html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
paste_field_names = []
diff --git a/modules/ui_components.py b/modules/ui_components.py index 284ca0cf..64451df7 100644 --- a/modules/ui_components.py +++ b/modules/ui_components.py @@ -1,58 +1,74 @@ import gradio as gr
-class ToolButton(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, fits inside gradio forms"""
+class FormComponent:
+ def get_expected_parent(self):
+ return gr.components.Form
- def __init__(self, **kwargs):
- super().__init__(variant="tool", **kwargs)
- def get_block_name(self):
- return "button"
+gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
-class ToolButtonTop(gr.Button, gr.components.FormComponent):
- """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
+class ToolButton(FormComponent, gr.Button):
+ """Small button with single emoji as text, fits inside gradio forms"""
- def __init__(self, **kwargs):
- super().__init__(variant="tool-top", **kwargs)
+ def __init__(self, *args, **kwargs):
+ classes = kwargs.pop("elem_classes", [])
+ super().__init__(*args, elem_classes=["tool", *classes], **kwargs)
def get_block_name(self):
return "button"
-class FormRow(gr.Row, gr.components.FormComponent):
+class FormRow(FormComponent, gr.Row):
"""Same as gr.Row but fits inside gradio forms"""
def get_block_name(self):
return "row"
-class FormGroup(gr.Group, gr.components.FormComponent):
+class FormColumn(FormComponent, gr.Column):
+ """Same as gr.Column but fits inside gradio forms"""
+
+ def get_block_name(self):
+ return "column"
+
+
+class FormGroup(FormComponent, gr.Group):
"""Same as gr.Row but fits inside gradio forms"""
def get_block_name(self):
return "group"
-class FormHTML(gr.HTML, gr.components.FormComponent):
+class FormHTML(FormComponent, gr.HTML):
"""Same as gr.HTML but fits inside gradio forms"""
def get_block_name(self):
return "html"
-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
+class FormColorPicker(FormComponent, gr.ColorPicker):
"""Same as gr.ColorPicker but fits inside gradio forms"""
def get_block_name(self):
return "colorpicker"
-class DropdownMulti(gr.Dropdown):
+class DropdownMulti(FormComponent, gr.Dropdown):
"""Same as gr.Dropdown but always multiselect"""
def __init__(self, **kwargs):
super().__init__(multiselect=True, **kwargs)
def get_block_name(self):
return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+ """Same as gr.Dropdown but allows editing value"""
+ def __init__(self, **kwargs):
+ super().__init__(allow_custom_value=True, **kwargs)
+
+ def get_block_name(self):
+ return "dropdown"
+
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index df75a925..b402bc8b 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -1,6 +1,5 @@ import json
import os.path
-import shutil
import sys
import time
import traceback
@@ -22,7 +21,7 @@ def check_access(): assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
-def apply_and_restart(disable_list, update_list):
+def apply_and_restart(disable_list, update_list, disable_all):
check_access()
disabled = json.loads(disable_list)
@@ -44,6 +43,7 @@ def apply_and_restart(disable_list, update_list): print(traceback.format_exc(), file=sys.stderr)
shared.opts.disabled_extensions = disabled
+ shared.opts.disable_all_extensions = disable_all
shared.opts.save(shared.config_filename)
shared.state.interrupt()
@@ -64,6 +64,9 @@ def check_updates(id_task, disable_list): try:
ext.check_updates()
+ except FileNotFoundError as e:
+ if 'FETCH_HEAD' not in str(e):
+ raise
except Exception:
print(f"Error checking updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
@@ -88,6 +91,8 @@ def extension_table(): """
for ext in extensions.extensions:
+ ext.read_info_from_repo()
+
remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
if ext.can_update:
@@ -95,9 +100,13 @@ def extension_table(): else:
ext_status = ext.status
+ style = ""
+ if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
+ style = ' style="color: var(--primary-400)"'
+
code += f"""
<tr>
- <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+ <td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
<td>{remote}</td>
<td>{ext.version}</td>
<td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
@@ -120,7 +129,7 @@ def normalize_git_url(url): return url
-def install_extension_from_url(dirname, url):
+def install_extension_from_url(dirname, branch_name, url):
check_access()
assert url, 'No URL specified'
@@ -141,22 +150,27 @@ def install_extension_from_url(dirname, url): try:
shutil.rmtree(tmpdir, True)
-
- repo = git.Repo.clone_from(url, tmpdir)
- repo.remote().fetch()
-
+ if branch_name == '':
+ # if no branch is specified, use the default branch
+ with git.Repo.clone_from(url, tmpdir) as repo:
+ repo.remote().fetch()
+ for submodule in repo.submodules:
+ submodule.update()
+ else:
+ with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
+ repo.remote().fetch()
+ for submodule in repo.submodules:
+ submodule.update()
try:
os.rename(tmpdir, target_dir)
except OSError as err:
- # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
- # Shouldn't cause any new issues at least but we probably want to handle it there too.
if err.errno == errno.EXDEV:
# Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
# Since we can't use a rename, do the slower but more versitile shutil.move()
shutil.move(tmpdir, target_dir)
else:
# Something else, not enough free space, permissions, etc. rethrow it so that it gets handled.
- raise(err)
+ raise err
import launch
launch.run_extension_installer(target_dir)
@@ -167,12 +181,12 @@ def install_extension_from_url(dirname, url): shutil.rmtree(tmpdir, True)
-def install_extension_from_index(url, hide_tags, sort_column):
+def install_extension_from_index(url, hide_tags, sort_column, filter_text):
ext_table, message = install_extension_from_url(None, url)
- code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
+ code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
- return code, ext_table, message
+ return code, ext_table, message, ''
def refresh_available_extensions(url, hide_tags, sort_column):
@@ -186,11 +200,17 @@ def refresh_available_extensions(url, hide_tags, sort_column): code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)
- return url, code, gr.CheckboxGroup.update(choices=tags), ''
+ return url, code, gr.CheckboxGroup.update(choices=tags), '', ''
-def refresh_available_extensions_for_tags(hide_tags, sort_column):
- code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
+def refresh_available_extensions_for_tags(hide_tags, sort_column, filter_text):
+ code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
+
+ return code, ''
+
+
+def search_extensions(filter_text, hide_tags, sort_column):
+ code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
return code, ''
@@ -205,7 +225,7 @@ sort_ordering = [ ]
-def refresh_available_extensions_from_data(hide_tags, sort_column):
+def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
extlist = available_extensions["extensions"]
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
@@ -244,7 +264,12 @@ def refresh_available_extensions_from_data(hide_tags, sort_column): hidden += 1
continue
- install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+ if filter_text and filter_text.strip():
+ if filter_text.lower() not in html.escape(name).lower() and filter_text.lower() not in html.escape(description).lower():
+ hidden += 1
+ continue
+
+ install_code = f"""<button onclick="install_extension_from_index(this, '{html.escape(url)}')" {"disabled=disabled" if existing else ""} class="lg secondary gradio-button custom-button">{"Install" if not existing else "Installed"}</button>"""
tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
@@ -281,16 +306,24 @@ def create_ui(): with gr.Row(elem_id="extensions_installed_top"):
apply = gr.Button(value="Apply and restart UI", variant="primary")
check = gr.Button(value="Check for updates")
+ extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
- info = gr.HTML()
+ html = ""
+ if shared.opts.disable_all_extensions != "none":
+ html = """
+<span style="color: var(--primary-400);">
+ "Disable all extensions" was set, change it to "none" to load all extensions again
+</span>
+ """
+ info = gr.HTML(html)
extensions_table = gr.HTML(lambda: extension_table())
apply.click(
fn=apply_and_restart,
_js="extensions_apply",
- inputs=[extensions_disabled_list, extensions_update_list],
+ inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all],
outputs=[],
)
@@ -312,42 +345,52 @@ def create_ui(): hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")
+ with gr.Row():
+ search_extensions_text = gr.Text(label="Search").style(container=False)
+
install_result = gr.HTML()
available_extensions_table = gr.HTML()
refresh_available_extensions_button.click(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
inputs=[available_extensions_index, hide_tags, sort_column],
- outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
+ outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
)
install_extension_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
- inputs=[extension_to_install, hide_tags, sort_column],
+ inputs=[extension_to_install, hide_tags, sort_column, search_extensions_text],
outputs=[available_extensions_table, extensions_table, install_result],
)
+ search_extensions_text.change(
+ fn=modules.ui.wrap_gradio_call(search_extensions, extra_outputs=[gr.update()]),
+ inputs=[search_extensions_text, hide_tags, sort_column],
+ outputs=[available_extensions_table, install_result],
+ )
+
hide_tags.change(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
- inputs=[hide_tags, sort_column],
+ inputs=[hide_tags, sort_column, search_extensions_text],
outputs=[available_extensions_table, install_result]
)
sort_column.change(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
- inputs=[hide_tags, sort_column],
+ inputs=[hide_tags, sort_column, search_extensions_text],
outputs=[available_extensions_table, install_result]
)
with gr.TabItem("Install from URL"):
install_url = gr.Text(label="URL for extension's git repository")
+ install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch")
install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
install_button = gr.Button(value="Install", variant="primary")
install_result = gr.HTML(elem_id="extension_install_result")
install_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
- inputs=[install_dirname, install_url],
+ inputs=[install_dirname, install_branch, install_url],
outputs=[extensions_table, install_result],
)
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index cdfd6f2a..25eb464b 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -2,8 +2,10 @@ import glob import os.path
import urllib.parse
from pathlib import Path
+from PIL import PngImagePlugin
from modules import shared
+from modules.images import read_info_from_image
import gradio as gr
import json
import html
@@ -22,21 +24,37 @@ def register_page(page): allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
-def add_pages_to_demo(app):
- def fetch_file(filename: str = ""):
- from starlette.responses import FileResponse
+def fetch_file(filename: str = ""):
+ from starlette.responses import FileResponse
+
+ if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
+ raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+
+ ext = os.path.splitext(filename)[1].lower()
+ if ext not in (".png", ".jpg", ".webp"):
+ raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+
+ # would profit from returning 304
+ return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+
+
+def get_metadata(page: str = "", item: str = ""):
+ from starlette.responses import JSONResponse
- if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
- raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+ page = next(iter([x for x in extra_pages if x.name == page]), None)
+ if page is None:
+ return JSONResponse({})
- ext = os.path.splitext(filename)[1].lower()
- if ext not in (".png", ".jpg", ".webp"):
- raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+ metadata = page.metadata.get(item)
+ if metadata is None:
+ return JSONResponse({})
- # would profit from returning 304
- return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+ return JSONResponse({"metadata": metadata})
+
+def add_pages_to_demo(app):
app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
+ app.add_api_route("/sd_extra_networks/metadata", get_metadata, methods=["GET"])
class ExtraNetworksPage:
@@ -45,6 +63,7 @@ class ExtraNetworksPage: self.name = title.lower()
self.card_page = shared.html("extra-networks-card.html")
self.allow_negative_prompt = False
+ self.metadata = {}
def refresh(self):
pass
@@ -66,6 +85,8 @@ class ExtraNetworksPage: view = shared.opts.extra_networks_default_view
items_html = ''
+ self.metadata = {}
+
subdirs = {}
for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
@@ -86,12 +107,16 @@ class ExtraNetworksPage: subdirs = {"": 1, **subdirs}
subdirs_html = "".join([f"""
-<button class='gr-button gr-button-lg gr-button-secondary{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
+<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
{html.escape(subdir if subdir!="" else "all")}
</button>
""" for subdir in subdirs])
for item in self.list_items():
+ metadata = item.get("metadata")
+ if metadata:
+ self.metadata[item["name"]] = metadata
+
items_html += self.create_html_for_item(item, tabname)
if items_html == '':
@@ -124,14 +149,16 @@ class ExtraNetworksPage: if onclick is None:
onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
+ height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
+ width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
+ background_image = f"background-image: url(\"{html.escape(preview)}\");" if preview else ''
metadata_button = ""
metadata = item.get("metadata")
if metadata:
- metadata_onclick = '"' + html.escape(f"""extraNetworksShowMetadata({json.dumps(metadata)}); return false;""") + '"'
- metadata_button = f"<div class='metadata-button' title='Show metadata' onclick={metadata_onclick}></div>"
+ metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata(event, {json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
args = {
- "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
+ "style": f"'{height}{width}{background_image}'",
"prompt": item.get("prompt", None),
"tabname": json.dumps(tabname),
"local_preview": json.dumps(item["local_preview"]),
@@ -215,6 +242,7 @@ def create_ui(container, button, tabname): with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
for page in ui.stored_extra_pages:
with gr.Tab(page.title):
+
page_elem = gr.HTML(page.create_html(ui.tabname))
ui.pages.append(page_elem)
@@ -226,10 +254,10 @@ def create_ui(container, button, tabname): def toggle_visibility(is_visible):
is_visible = not is_visible
- return is_visible, gr.update(visible=is_visible)
+ return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
state_visible = gr.State(value=False)
- button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
+ button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])
def refresh():
res = []
@@ -264,6 +292,7 @@ def setup_ui(ui, gallery): img_info = images[index if index >= 0 else 0]
image = image_from_url_text(img_info)
+ geninfo, items = read_info_from_image(image)
is_allowed = False
for extra_page in ui.stored_extra_pages:
@@ -273,7 +302,12 @@ def setup_ui(ui, gallery): assert is_allowed, f'writing to {filename} is not allowed'
- image.save(filename)
+ if geninfo:
+ pnginfo_data = PngImagePlugin.PngInfo()
+ pnginfo_data.add_text('parameters', geninfo)
+ image.save(filename, pnginfo=pnginfo_data)
+ else:
+ image.save(filename)
return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index b418d955..d278e1b6 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -13,7 +13,7 @@ def create_ui(): extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
- image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
+ image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
diff --git a/requirements.txt b/requirements.txt index 6d53f089..9091f612 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,11 @@ +astunparse
blendmodes
accelerate
basicsr
fonts
font-roboto
gfpgan
-gradio==3.16.2
+gradio==3.27
invisible-watermark
numpy
omegaconf
@@ -30,3 +31,4 @@ GitPython torchsde
safetensors
psutil
+rich
diff --git a/requirements_versions.txt b/requirements_versions.txt index 0031c616..94d32d3d 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -1,15 +1,15 @@ blendmodes==2022
transformers==4.25.1
-accelerate==0.12.0
+accelerate==0.18.0
basicsr==1.4.2
gfpgan==1.3.8
-gradio==3.16.2
-numpy==1.23.3
+gradio==3.27
+numpy==1.23.5
Pillow==9.4.0
realesrgan==0.3.0
torch
omegaconf==2.2.3
-pytorch_lightning==1.7.6
+pytorch_lightning==1.9.4
scikit-image==0.19.2
fonts
font-roboto
@@ -25,6 +25,6 @@ lark==1.1.2 inflection==0.5.1
GitPython==3.1.30
torchsde==0.2.5
-safetensors==0.2.7
+safetensors==0.3.1
httpcore<=0.15
fastapi==0.94.0
@@ -1,7 +1,9 @@ function gradioApp() { const elems = document.getElementsByTagName('gradio-app') - const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot - return !!gradioShadowRoot ? gradioShadowRoot : document; + const elem = elems.length == 0 ? document : elems[0] + + if (elem !== document) elem.getElementById = function(id){ return document.getElementById(id) } + return elem.shadowRoot ? elem.shadowRoot : elem } function get_uiCurrentTab() { diff --git a/scripts/custom_code.py b/scripts/custom_code.py index d29113e6..4071d86d 100644 --- a/scripts/custom_code.py +++ b/scripts/custom_code.py @@ -1,9 +1,40 @@ import modules.scripts as scripts
import gradio as gr
+import ast
+import copy
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
+
+def convertExpr2Expression(expr):
+ expr.lineno = 0
+ expr.col_offset = 0
+ result = ast.Expression(expr.value, lineno=0, col_offset = 0)
+
+ return result
+
+
+def exec_with_return(code, module):
+ """
+ like exec() but can return values
+ https://stackoverflow.com/a/52361938/5862977
+ """
+ code_ast = ast.parse(code)
+
+ init_ast = copy.deepcopy(code_ast)
+ init_ast.body = code_ast.body[:-1]
+
+ last_ast = copy.deepcopy(code_ast)
+ last_ast.body = code_ast.body[-1:]
+
+ exec(compile(init_ast, "<ast>", "exec"), module.__dict__)
+ if type(last_ast.body[0]) == ast.Expr:
+ return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
+ else:
+ exec(compile(last_ast, "<ast>", "exec"), module.__dict__)
+
+
class Script(scripts.Script):
def title(self):
@@ -13,12 +44,23 @@ class Script(scripts.Script): return cmd_opts.allow_code
def ui(self, is_img2img):
- code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code"))
+ example = """from modules.processing import process_images
+
+p.width = 768
+p.height = 768
+p.batch_size = 2
+p.steps = 10
+
+return process_images(p)
+"""
+
- return [code]
+ code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code"))
+ indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level"))
+ return [code, indent_level]
- def run(self, p, code):
+ def run(self, p, code, indent_level):
assert cmd_opts.allow_code, '--allow-code option must be enabled'
display_result_data = [[], -1, ""]
@@ -29,13 +71,20 @@ class Script(scripts.Script): display_result_data[2] = i
from types import ModuleType
- compiled = compile(code, '', 'exec')
module = ModuleType("testmodule")
module.__dict__.update(globals())
module.p = p
module.display = display
- exec(compiled, module.__dict__)
+
+ indent = " " * indent_level
+ indented = code.replace('\n', '\n' + indent)
+ body = f"""def __webuitemp__():
+{indent}{indented}
+__webuitemp__()"""
+
+ result = exec_with_return(body, module)
+
+ if isinstance(result, Processed):
+ return result
return Processed(p, *display_result_data)
-
-
\ No newline at end of file diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 2572443f..bb00fb3f 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -6,23 +6,21 @@ from tqdm import trange import modules.scripts as scripts
import gradio as gr
-from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common
-from modules.processing import Processed
-from modules.shared import opts, cmd_opts, state
+from modules import processing, shared, sd_samplers, sd_samplers_common
import torch
import k_diffusion as K
-from PIL import Image
-from torch import autocast
-from einops import rearrange, repeat
-
-
def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
x = p.init_latent
s_in = x.new_ones([x.shape[0]])
- dnw = K.external.CompVisDenoiser(shared.sd_model)
+ if shared.sd_model.parameterization == "v":
+ dnw = K.external.CompVisVDenoiser(shared.sd_model)
+ skip = 1
+ else:
+ dnw = K.external.CompVisDenoiser(shared.sd_model)
+ skip = 0
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
@@ -37,7 +35,7 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps): image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
- c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+ c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
t = dnw.sigma_to_t(sigma_in)
eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
@@ -69,7 +67,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): x = p.init_latent
s_in = x.new_ones([x.shape[0]])
- dnw = K.external.CompVisDenoiser(shared.sd_model)
+ if shared.sd_model.parameterization == "v":
+ dnw = K.external.CompVisVDenoiser(shared.sd_model)
+ skip = 1
+ else:
+ dnw = K.external.CompVisDenoiser(shared.sd_model)
+ skip = 0
sigmas = dnw.get_sigmas(steps).flip(0)
shared.state.sampling_steps = steps
@@ -84,7 +87,7 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): image_conditioning = torch.cat([p.image_conditioning] * 2)
cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
- c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)]
+ c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
if i == 1:
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
@@ -125,7 +128,7 @@ class Script(scripts.Script): def show(self, is_img2img):
return is_img2img
- def ui(self, is_img2img):
+ def ui(self, is_img2img):
info = gr.Markdown('''
* `CFG Scale` should be 2 or lower.
''')
@@ -213,4 +216,3 @@ class Script(scripts.Script): processed = processing.process_images(p)
return processed
-
diff --git a/scripts/loopback.py b/scripts/loopback.py index ec1f85e5..d3065fe6 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -1,14 +1,10 @@ -import numpy as np
-from tqdm import trange
+import math
-import modules.scripts as scripts
import gradio as gr
-
-from modules import processing, shared, sd_samplers, images
+import modules.scripts as scripts
+from modules import deepbooru, images, processing, shared
from modules.processing import Processed
-from modules.sd_samplers import samplers
-from modules.shared import opts, cmd_opts, state
-from modules import deepbooru
+from modules.shared import opts, state
class Script(scripts.Script):
@@ -20,39 +16,65 @@ class Script(scripts.Script): def ui(self, is_img2img):
loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
- denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor"))
+ final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
+ denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
- return [loops, denoising_strength_change_factor, append_interrogation]
+ return [loops, final_denoising_strength, denoising_curve, append_interrogation]
- def run(self, p, loops, denoising_strength_change_factor, append_interrogation):
+ def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation):
processing.fix_seed(p)
batch_count = p.n_iter
p.extra_generation_params = {
- "Denoising strength change factor": denoising_strength_change_factor,
+ "Final denoising strength": final_denoising_strength,
+ "Denoising curve": denoising_curve
}
p.batch_size = 1
p.n_iter = 1
- output_images, info = None, None
+ info = None
initial_seed = None
initial_info = None
+ initial_denoising_strength = p.denoising_strength
grids = []
all_images = []
original_init_image = p.init_images
original_prompt = p.prompt
+ original_inpainting_fill = p.inpainting_fill
state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
- for n in range(batch_count):
- history = []
+ def calculate_denoising_strength(loop):
+ strength = initial_denoising_strength
+
+ if loops == 1:
+ return strength
+ progress = loop / (loops - 1)
+ if denoising_curve == "Aggressive":
+ strength = math.sin((progress) * math.pi * 0.5)
+ elif denoising_curve == "Lazy":
+ strength = 1 - math.cos((progress) * math.pi * 0.5)
+ else:
+ strength = progress
+
+ change = (final_denoising_strength - initial_denoising_strength) * strength
+ return initial_denoising_strength + change
+
+ history = []
+
+ for n in range(batch_count):
# Reset to original init image at the start of each batch
p.init_images = original_init_image
+ # Reset to original denoising strength
+ p.denoising_strength = initial_denoising_strength
+
+ last_image = None
+
for i in range(loops):
p.n_iter = 1
p.batch_size = 1
@@ -72,26 +94,46 @@ class Script(scripts.Script): processed = processing.process_images(p)
+ # Generation cancelled.
+ if state.interrupted:
+ break
+
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
- init_img = processed.images[0]
-
- p.init_images = [init_img]
p.seed = processed.seed + 1
- p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)
- history.append(processed.images[0])
+ p.denoising_strength = calculate_denoising_strength(i + 1)
+
+ if state.skipped:
+ break
+
+ last_image = processed.images[0]
+ p.init_images = [last_image]
+ p.inpainting_fill = 1 # Set "masked content" to "original" for next loop.
+ if batch_count == 1:
+ history.append(last_image)
+ all_images.append(last_image)
+
+ if batch_count > 1 and not state.skipped and not state.interrupted:
+ history.append(last_image)
+ all_images.append(last_image)
+
+ p.inpainting_fill = original_inpainting_fill
+
+ if state.interrupted:
+ break
+
+ if len(history) > 1:
grid = images.image_grid(history, rows=1)
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
- grids.append(grid)
- all_images += history
-
- if opts.return_grid:
- all_images = grids + all_images
+ if opts.return_grid:
+ grids.append(grid)
+
+ all_images = grids + all_images
processed = Processed(p, all_images, initial_seed, initial_info)
diff --git a/scripts/postprocessing_upscale.py b/scripts/postprocessing_upscale.py index 8842bd91..ef1186ac 100644 --- a/scripts/postprocessing_upscale.py +++ b/scripts/postprocessing_upscale.py @@ -4,8 +4,8 @@ import numpy as np from modules import scripts_postprocessing, shared
import gradio as gr
-from modules.ui_components import FormRow
-
+from modules.ui_components import FormRow, ToolButton
+from modules.ui import switch_values_symbol
upscale_cache = {}
@@ -17,23 +17,29 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): def ui(self):
selected_tab = gr.State(value=0)
- with gr.Tabs(elem_id="extras_resize_mode"):
- with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
- upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
-
- with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
- with FormRow():
- upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
- upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
- upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
-
- with FormRow():
- extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
-
- with FormRow():
- extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
- extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
-
+ with gr.Column():
+ with FormRow():
+ with gr.Tabs(elem_id="extras_resize_mode"):
+ with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
+ upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
+
+ 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")
+ 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")
+ upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+
+ with FormRow():
+ extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+
+ with FormRow():
+ extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
+ extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+
+ upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index ce584981..52ae1c6e 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -247,7 +247,7 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend state.job = f"{index(ix, iy, iz) + 1} out of {list_size}"
- processed: Processed = cell(x, y, z)
+ processed: Processed = cell(x, y, z, ix, iy, iz)
if processed_result is None:
# Use our first processed result object as a template container to hold our full results
@@ -374,16 +374,19 @@ class Script(scripts.Script): with gr.Row():
x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
+ x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
with gr.Row():
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
+ y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
with gr.Row():
z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
+ z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
with gr.Row(variant="compact", elem_id="axis_options"):
@@ -401,54 +404,74 @@ class Script(scripts.Script): swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
- def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
- return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
+ def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
+ return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
- xy_swap_args = [x_type, x_values, y_type, y_values]
+ xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
- yz_swap_args = [y_type, y_values, z_type, z_values]
+ yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
- xz_swap_args = [x_type, x_values, z_type, z_values]
+ xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
def fill(x_type):
axis = self.current_axis_options[x_type]
- return ", ".join(axis.choices()) if axis.choices else gr.update()
-
- fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
- fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
- fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
-
- def select_axis(x_type):
- return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
-
- x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
- y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
- z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
+ return axis.choices() if axis.choices else gr.update()
+
+ fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
+ fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
+ fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
+
+ def select_axis(axis_type,axis_values_dropdown):
+ choices = self.current_axis_options[axis_type].choices
+ has_choices = choices is not None
+ current_values = axis_values_dropdown
+ if has_choices:
+ choices = choices()
+ if isinstance(current_values,str):
+ current_values = current_values.split(",")
+ current_values = list(filter(lambda x: x in choices, current_values))
+ return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
+
+ x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
+ y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
+ z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
+
+ def get_dropdown_update_from_params(axis,params):
+ val_key = axis + " Values"
+ vals = params.get(val_key,"")
+ valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+ return gr.update(value = valslist)
self.infotext_fields = (
(x_type, "X Type"),
(x_values, "X Values"),
+ (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
(y_type, "Y Type"),
(y_values, "Y Values"),
+ (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
(z_type, "Z Type"),
(z_values, "Z Values"),
+ (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
)
- return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
+ 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]
- def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
+ 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):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
if not opts.return_grid:
p.batch_size = 1
- def process_axis(opt, vals):
+ def process_axis(opt, vals, vals_dropdown):
if opt.label == 'Nothing':
return [0]
- valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+ if opt.choices is not None:
+ valslist = vals_dropdown
+ else:
+ valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
if opt.type == int:
valslist_ext = []
@@ -506,15 +529,22 @@ class Script(scripts.Script): return valslist
x_opt = self.current_axis_options[x_type]
- xs = process_axis(x_opt, x_values)
+ if x_opt.choices is not None:
+ x_values = ",".join(x_values_dropdown)
+ xs = process_axis(x_opt, x_values, x_values_dropdown)
y_opt = self.current_axis_options[y_type]
- ys = process_axis(y_opt, y_values)
+ if y_opt.choices is not None:
+ y_values = ",".join(y_values_dropdown)
+ ys = process_axis(y_opt, y_values, y_values_dropdown)
z_opt = self.current_axis_options[z_type]
- zs = process_axis(z_opt, z_values)
+ if z_opt.choices is not None:
+ z_values = ",".join(z_values_dropdown)
+ zs = process_axis(z_opt, z_values, z_values_dropdown)
# this could be moved to common code, but unlikely to be ever triggered anywhere else
+ Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'
@@ -558,8 +588,6 @@ class Script(scripts.Script): print(f"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})")
shared.total_tqdm.updateTotal(total_steps)
- grid_infotext = [None]
-
state.xyz_plot_x = AxisInfo(x_opt, xs)
state.xyz_plot_y = AxisInfo(y_opt, ys)
state.xyz_plot_z = AxisInfo(z_opt, zs)
@@ -588,7 +616,9 @@ class Script(scripts.Script): else:
second_axes_processed = 'y'
- def cell(x, y, z):
+ grid_infotext = [None] * (1 + len(zs))
+
+ def cell(x, y, z, ix, iy, iz):
if shared.state.interrupted:
return Processed(p, [], p.seed, "")
@@ -600,7 +630,9 @@ class Script(scripts.Script): res = process_images(pc)
- if grid_infotext[0] is None:
+ # Sets subgrid infotexts
+ subgrid_index = 1 + iz
+ if grid_infotext[subgrid_index] is None and ix == 0 and iy == 0:
pc.extra_generation_params = copy(pc.extra_generation_params)
pc.extra_generation_params['Script'] = self.title()
@@ -616,6 +648,12 @@ class Script(scripts.Script): if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
+ grid_infotext[subgrid_index] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
+
+ # Sets main grid infotext
+ if grid_infotext[0] is None and ix == 0 and iy == 0 and iz == 0:
+ pc.extra_generation_params = copy(pc.extra_generation_params)
+
if z_opt.label != 'Nothing':
pc.extra_generation_params["Z Type"] = z_opt.label
pc.extra_generation_params["Z Values"] = z_values
@@ -650,6 +688,9 @@ class Script(scripts.Script): z_count = len(zs)
+ # Set the grid infotexts to the real ones with extra_generation_params (1 main grid + z_count sub-grids)
+ processed.infotexts[:1+z_count] = grid_infotext[:1+z_count]
+
if not include_lone_images:
# Don't need sub-images anymore, drop from list:
processed.images = processed.images[:z_count+1]
@@ -1,270 +1,355 @@ -.container {
- max-width: 100%;
-}
-.token-counter{
- position: absolute;
- display: inline-block;
- right: 2em;
- min-width: 0 !important;
- width: auto;
- z-index: 100;
+/* general gradio fixes */
+
+:root, .dark{
+ --checkbox-label-gap: 0.25em 0.1em;
+ --section-header-text-size: 12pt;
+ --block-background-fill: transparent;
}
-.token-counter.error span{
- box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075);
- border: 2px solid rgba(255,0,0,0.4) !important;
+.block.padded:not(.gradio-accordion) {
+ padding: 0 !important;
}
-.token-counter div{
- display: inline;
+div.gradio-container{
+ max-width: unset !important;
}
-.token-counter span{
- padding: 0.1em 0.75em;
+.hidden{
+ display: none;
}
-#sh{
- min-width: 2em;
- min-height: 2em;
- max-width: 2em;
- max-height: 2em;
- flex-grow: 0;
- padding-left: 0.25em;
- padding-right: 0.25em;
- margin: 0.1em 0;
- opacity: 0%;
- cursor: default;
+.compact{
+ background: transparent !important;
+ padding: 0 !important;
}
-.output-html p {margin: 0 0.5em;}
+div.form{
+ border-width: 0;
+ box-shadow: none;
+ background: transparent;
+ overflow: visible;
+ gap: 0.5em;
+}
-.row > *,
-.row > .gr-form > * {
- min-width: min(120px, 100%);
- flex: 1 1 0%;
+.block.gradio-dropdown,
+.block.gradio-slider,
+.block.gradio-checkbox,
+.block.gradio-textbox,
+.block.gradio-radio,
+.block.gradio-checkboxgroup,
+.block.gradio-number,
+.block.gradio-colorpicker
+{
+ border-width: 0 !important;
+ box-shadow: none !important;
}
-.performance {
- font-size: 0.85em;
- color: #444;
+.gap.compact{
+ padding: 0;
+ gap: 0.2em 0;
}
-.performance p{
- display: inline-block;
+div.compact{
+ gap: 1em;
}
-.performance .time {
- margin-right: 0;
+.gradio-dropdown label span:not(.has-info),
+.gradio-textbox label span:not(.has-info),
+.gradio-number label span:not(.has-info)
+{
+ margin-bottom: 0;
}
-.performance .vram {
+.gradio-dropdown ul.options{
+ z-index: 3000;
+ min-width: fit-content;
+ max-width: inherit;
+ white-space: nowrap;
}
-#txt2img_generate, #img2img_generate {
- min-height: 4.5em;
+.gradio-dropdown ul.options li.item {
+ padding: 0.05em 0;
}
-@media screen and (min-width: 2500px) {
- #txt2img_gallery, #img2img_gallery {
- min-height: 768px;
- }
+.gradio-dropdown ul.options li.item.selected {
+ background-color: var(--neutral-100);
}
-#txt2img_gallery img, #img2img_gallery img{
- object-fit: scale-down;
+.dark .gradio-dropdown ul.options li.item.selected {
+ background-color: var(--neutral-900);
}
-#txt2img_actions_column, #img2img_actions_column {
- margin: 0.35rem 0.75rem 0.35rem 0;
+
+.gradio-dropdown div.wrap.wrap.wrap.wrap{
+ box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
}
-#script_list {
- padding: .625rem .75rem 0 .625rem;
+
+.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{
+ flex-wrap: unset;
}
-.justify-center.overflow-x-scroll {
- justify-content: left;
+
+.gradio-dropdown .single-select{
+ white-space: nowrap;
+ overflow: hidden;
}
-.justify-center.overflow-x-scroll button:first-of-type {
- margin-left: auto;
+.gradio-dropdown .token-remove.remove-all.remove-all{
+ display: none;
}
-.justify-center.overflow-x-scroll button:last-of-type {
- margin-right: auto;
+.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{
+ display: flex;
}
-[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{
- min-width: 2.3em;
- height: 2.5em;
- flex-grow: 0;
- padding-left: 0.25em;
- padding-right: 0.25em;
+.gradio-slider input[type="number"]{
+ width: 6em;
}
-#hidden_element{
- display: none;
+.block.gradio-checkbox {
+ margin: 0.75em 1.5em 0 0;
}
-[id$=_seed_row], [id$=_subseed_row]{
- gap: 0.5rem;
- padding: 0.6em;
+.gradio-html div.wrap{
+ height: 100%;
+}
+div.gradio-html.min{
+ min-height: 0;
}
-[id$=_subseed_show_box]{
- min-width: auto;
- flex-grow: 0;
+.block.gradio-gallery{
+ background: var(--input-background-fill);
}
-[id$=_subseed_show_box] > div{
- border: 0;
- height: 100%;
+.gradio-container .prose a, .gradio-container .prose a:visited{
+ color: unset;
+ text-decoration: none;
}
-[id$=_subseed_show]{
- min-width: auto;
- flex-grow: 0;
- padding: 0;
+
+
+/* general styled components */
+
+.gradio-button.tool{
+ max-width: 2.2em;
+ min-width: 2.2em !important;
+ height: 2.4em;
+ align-self: end;
+ line-height: 1em;
+ border-radius: 0.5em;
}
-[id$=_subseed_show] label{
- height: 100%;
+.gradio-button.secondary-down{
+ background: var(--button-secondary-background-fill);
+ color: var(--button-secondary-text-color);
+}
+.gradio-button.secondary-down, .gradio-button.secondary-down:hover{
+ box-shadow: 1px 1px 1px rgba(0,0,0,0.25) inset, 0px 0px 3px rgba(0,0,0,0.15) inset;
+}
+.gradio-button.secondary-down:hover{
+ background: var(--button-secondary-background-fill-hover);
+ color: var(--button-secondary-text-color-hover);
}
-#txt2img_actions_column, #img2img_actions_column{
- gap: 0;
- margin-right: .75rem;
+.checkboxes-row{
+ margin-bottom: 0.5em;
+ margin-left: 0em;
+}
+.checkboxes-row > div{
+ flex: 0;
+ white-space: nowrap;
+ min-width: auto;
}
-#txt2img_tools, #img2img_tools{
- gap: 0.4em;
+button.custom-button{
+ border-radius: var(--button-large-radius);
+ padding: var(--button-large-padding);
+ font-weight: var(--button-large-text-weight);
+ border: var(--button-border-width) solid var(--button-secondary-border-color);
+ background: var(--button-secondary-background-fill);
+ color: var(--button-secondary-text-color);
+ font-size: var(--button-large-text-size);
+ display: inline-flex;
+ justify-content: center;
+ align-items: center;
+ transition: var(--button-transition);
+ box-shadow: var(--button-shadow);
+ text-align: center;
}
-#interrogate_col{
+
+/* txt2img/img2img specific */
+
+.block.token-counter{
+ position: absolute;
+ display: inline-block;
+ right: 1em;
min-width: 0 !important;
- max-width: 8em !important;
- margin-right: 1em;
- gap: 0;
+ width: auto;
+ z-index: 100;
+ top: -0.75em;
}
-#interrogate, #deepbooru{
- margin: 0em 0.25em 0.5em 0.25em;
- min-width: 8em;
- max-width: 8em;
+
+.block.token-counter span{
+ background: var(--input-background-fill) !important;
+ box-shadow: 0 0 0.0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075);
+ border: 2px solid rgba(192,192,192,0.4) !important;
+ border-radius: 0.4em;
}
-#style_pos_col, #style_neg_col{
- min-width: 8em !important;
+.block.token-counter.error span{
+ box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075);
+ border: 2px solid rgba(255,0,0,0.4) !important;
}
-#txt2img_styles_row, #img2img_styles_row{
- gap: 0.25em;
- margin-top: 0.3em;
+.block.token-counter div{
+ display: inline;
}
-#txt2img_styles_row > button, #img2img_styles_row > button{
- margin: 0;
+.block.token-counter span{
+ padding: 0.1em 0.75em;
}
-#txt2img_styles, #img2img_styles{
- padding: 0;
+[id$=_subseed_show]{
+ min-width: auto !important;
+ flex-grow: 0 !important;
+ display: flex;
}
-#txt2img_styles > label > div, #img2img_styles > label > div{
- min-height: 3.2em;
+[id$=_subseed_show] label{
+ margin-bottom: 0.5em;
+ align-self: end;
}
-ul.list-none{
- max-height: 35em;
- z-index: 2000;
+.performance {
+ font-size: 0.85em;
+ color: #444;
}
-.gr-form{
- background: transparent;
+.performance p{
+ display: inline-block;
}
-.my-4{
- margin-top: 0;
- margin-bottom: 0;
+.performance .time {
+ margin-right: 0;
}
-#resize_mode{
- flex: 1.5;
+.performance .vram {
}
-button{
- align-self: stretch !important;
+#txt2img_generate, #img2img_generate {
+ min-height: 4.5em;
}
-.overflow-hidden, .gr-panel{
- overflow: visible !important;
+@media screen and (min-width: 2500px) {
+ #txt2img_gallery, #img2img_gallery {
+ min-height: 768px;
+ }
}
-#x_type, #y_type{
- max-width: 10em;
+#txt2img_gallery img, #img2img_gallery img{
+ object-fit: scale-down;
+}
+#txt2img_actions_column, #img2img_actions_column {
+ gap: 0.5em;
+}
+#txt2img_tools, #img2img_tools{
+ gap: 0.4em;
+}
+
+.interrogate-col{
+ min-width: 0 !important;
+ max-width: fit-content;
+ gap: 0.5em;
+}
+.interrogate-col > button{
+ flex: 1;
}
-#txt2img_preview, #img2img_preview, #ti_preview{
+.generate-box{
+ position: relative;
+}
+.gradio-button.generate-box-skip, .gradio-button.generate-box-interrupt{
position: absolute;
- width: 320px;
+ width: 50%;
+ height: 100%;
+ display: none;
+ background: #b4c0cc;
+}
+.gradio-button.generate-box-skip:hover, .gradio-button.generate-box-interrupt:hover{
+ background: #c2cfdb;
+}
+.gradio-button.generate-box-interrupt{
left: 0;
+ border-radius: 0.5rem 0 0 0.5rem;
+}
+.gradio-button.generate-box-skip{
right: 0;
- margin-left: auto;
- margin-right: auto;
- margin-top: 34px;
- z-index: 100;
- border: none;
- border-top-left-radius: 0;
- border-top-right-radius: 0;
+ border-radius: 0 0.5rem 0.5rem 0;
}
-@media screen and (min-width: 768px) {
- #txt2img_preview, #img2img_preview, #ti_preview {
- position: absolute;
- }
+#txtimg_hr_finalres{
+ min-height: 0 !important;
+ padding: .625rem .75rem;
+ margin-left: -0.75em
}
-@media screen and (max-width: 767px) {
- #txt2img_preview, #img2img_preview, #ti_preview {
- position: relative;
- }
+#txtimg_hr_finalres .resolution{
+ font-weight: bold;
}
-#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{
- display: none;
+.inactive{
+ opacity: 0.5;
}
-fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{
- position: absolute;
- top: -0.7em;
- line-height: 1.2em;
- padding: 0;
- margin: 0 0.5em;
+[id$=_column_batch]{
+ min-width: min(13.5em, 100%) !important;
+}
- background-color: white;
- box-shadow: 6px 0 6px 0px white, -6px 0 6px 0px white;
+div.dimensions-tools{
+ min-width: 0 !important;
+ max-width: fit-content;
+ flex-direction: row;
+ align-content: center;
+}
- z-index: 300;
+div#extras_scale_to_tab div.form{
+ flex-direction: row;
}
-.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
- background-color: rgb(31, 41, 55);
- box-shadow: none;
- border: 1px solid rgba(128, 128, 128, 0.1);
- border-radius: 6px;
- padding: 0.1em 0.5em;
+#mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
+ height: 480px !important;
+ max-height: 480px !important;
+ min-height: 480px !important;
}
-#txt2img_column_batch, #img2img_column_batch{
- min-width: min(13.5em, 100%) !important;
+#img2img_sketch, #img2maskimg, #inpaint_sketch {
+ overflow: overlay !important;
+ resize: auto;
+ background: var(--panel-background-fill);
+ z-index: 5;
}
-#settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
- position: relative;
- border: none;
- margin-right: 8em;
+.image-buttons button{
+ min-width: auto;
}
-#settings .gr-panel div.flex-col div.justify-between div{
- position: relative;
- z-index: 200;
+.infotext {
+ overflow-wrap: break-word;
+}
+
+/* settings */
+#quicksettings {
+ width: fit-content;
+ align-items: end;
+}
+
+#quicksettings > div, #quicksettings > fieldset{
+ max-width: 24em;
+ min-width: 24em;
+ padding: 0;
+ border: none;
+ box-shadow: none;
+ background: none;
}
#settings{
@@ -276,17 +361,18 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s margin-left: 10em;
}
-#settings > div.flex-wrap{
+#settings > div.tab-nav{
float: left;
display: block;
margin-left: 0;
width: 10em;
}
-#settings > div.flex-wrap button{
+#settings > div.tab-nav button{
display: block;
border: none;
text-align: left;
+ white-space: initial;
}
#settings_result{
@@ -294,29 +380,8 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s margin: 0 1.2em;
}
-input[type="range"]{
- margin: 0.5em 0 -0.3em 0;
-}
-
-#mask_bug_info {
- text-align: center;
- display: block;
- margin-top: -0.75em;
- margin-bottom: -0.75em;
-}
-
-#txt2img_negative_prompt, #img2img_negative_prompt{
-}
-
-/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */
-.transition.opacity-20 {
- opacity: 1 !important;
-}
-
-/* more gradio's garbage cleanup */
-.min-h-\[4rem\] { min-height: unset !important; }
-.min-h-\[6rem\] { min-height: unset !important; }
+/* live preview */
.progressDiv{
position: relative;
height: 20px;
@@ -362,6 +427,8 @@ input[type="range"]{ height: 100%;
}
+/* fullscreen popup (ie in Lora's (i) button) */
+
.popup-metadata{
color: black;
background: white;
@@ -402,87 +469,54 @@ input[type="range"]{ padding: 2em;
}
+/* fullpage image viewer */
+
#lightboxModal{
- display: none;
- position: fixed;
- z-index: 1001;
- padding-top: 100px;
- left: 0;
- top: 0;
- width: 100%;
- height: 100%;
- overflow: auto;
- background-color: rgba(20, 20, 20, 0.95);
- user-select: none;
- -webkit-user-select: none;
+ display: none;
+ position: fixed;
+ z-index: 1001;
+ left: 0;
+ top: 0;
+ width: 100%;
+ height: 100%;
+ overflow: auto;
+ background-color: rgba(20, 20, 20, 0.95);
+ user-select: none;
+ -webkit-user-select: none;
+ flex-direction: column;
}
.modalControls {
- display: grid;
- grid-template-columns: 32px 32px 32px 1fr 32px;
- grid-template-areas: "zoom tile save space close";
- position: absolute;
- top: 0;
- left: 0;
- right: 0;
- padding: 16px;
- gap: 16px;
+ display: flex;
+ gap: 1em;
+ padding: 1em;
background-color: rgba(0,0,0,0.2);
}
-
.modalClose {
- grid-area: close;
-}
-
-.modalZoom {
- grid-area: zoom;
-}
-
-.modalSave {
- grid-area: save;
-}
-
-.modalTileImage {
- grid-area: tile;
-}
-
-.modalClose,
-.modalZoom,
-.modalTileImage {
- color: white;
- font-size: 35px;
- font-weight: bold;
- cursor: pointer;
+ margin-left: auto;
}
-
-.modalSave {
+.modalControls span{
color: white;
- font-size: 28px;
- margin-top: 8px;
+ font-size: 35px;
font-weight: bold;
cursor: pointer;
+ width: 1em;
}
-.modalClose:hover,
-.modalClose:focus,
-.modalSave:hover,
-.modalSave:focus,
-.modalZoom:hover,
-.modalZoom:focus {
- color: #999;
- text-decoration: none;
- cursor: pointer;
+.modalControls span:hover, .modalControls span:focus{
+ color: #999;
+ text-decoration: none;
}
-#modalImage {
+#lightboxModal > img {
display: block;
margin: auto;
width: auto;
}
-.modalImageFullscreen {
+#lightboxModal > img.modalImageFullscreen{
object-fit: contain;
- height: 90%;
+ height: 100%;
}
.modalPrev,
@@ -512,45 +546,18 @@ input[type="range"]{ background-color: rgba(0, 0, 0, 0.8);
}
-#imageARPreview{
- position:absolute;
- top:0px;
- left:0px;
- border:2px solid red;
- background:rgba(255, 0, 0, 0.3);
- z-index: 900;
- pointer-events:none;
- display:none
-}
-
-#txt2img_generate_box, #img2img_generate_box{
- position: relative;
-}
-
-#txt2img_interrupt, #img2img_interrupt, #txt2img_skip, #img2img_skip{
+#imageARPreview {
position: absolute;
- width: 50%;
- height: 100%;
- background: #b4c0cc;
+ top: 0px;
+ left: 0px;
+ border: 2px solid red;
+ background: rgba(255, 0, 0, 0.3);
+ z-index: 900;
+ pointer-events: none;
display: none;
}
-#txt2img_interrupt, #img2img_interrupt{
- left: 0;
- border-radius: 0.5rem 0 0 0.5rem;
-}
-#txt2img_skip, #img2img_skip{
- right: 0;
- border-radius: 0 0.5rem 0.5rem 0;
-}
-
-.red {
- color: red;
-}
-
-.gallery-item {
- --tw-bg-opacity: 0 !important;
-}
+/* context menu (ie for the generate button) */
#context-menu{
z-index:9999;
@@ -579,61 +586,8 @@ input[type="range"]{ background: #a55000;
}
-#quicksettings {
- width: fit-content;
-}
-
-#quicksettings > div, #quicksettings > fieldset{
- max-width: 24em;
- min-width: 24em;
- padding: 0;
- border: none;
- box-shadow: none;
- background: none;
- margin-right: 10px;
-}
-
-#quicksettings > div > div > div > label > span {
- position: relative;
- margin-right: 9em;
- margin-bottom: -1em;
-}
-
-canvas[key="mask"] {
- z-index: 12 !important;
- filter: invert();
- mix-blend-mode: multiply;
- pointer-events: none;
-}
-
-/* gradio 3.4.1 stuff for editable scrollbar values */
-.gr-box > div > div > input.gr-text-input{
- position: absolute;
- right: 0.5em;
- top: -0.6em;
- z-index: 400;
- width: 6em;
-}
-#quicksettings .gr-box > div > div > input.gr-text-input {
- top: -1.12em;
-}
-
-.row.gr-compact{
- overflow: visible;
-}
-
-#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
-#img2img_sketch, #img2img_sketch > .h-60, #img2img_sketch > .h-60 > div, #img2img_sketch > .h-60 > div > img,
-#img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img,
-#inpaint_sketch, #inpaint_sketch > .h-60, #inpaint_sketch > .h-60 > div, #inpaint_sketch > .h-60 > div > img
-{
- height: 480px !important;
- max-height: 480px !important;
- min-height: 480px !important;
-}
-
-/* Extensions */
+/* extensions */
#tab_extensions table{
border-collapse: collapse;
@@ -646,6 +600,7 @@ canvas[key="mask"] { #tab_extensions table input[type="checkbox"]{
margin-right: 0.5em;
+ appearance: checkbox;
}
#tab_extensions button{
@@ -670,74 +625,7 @@ canvas[key="mask"] { font-size: 90%;
}
-#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{
- min-width: auto;
- padding-left: 0.5em;
- padding-right: 0.5em;
-}
-
-.gr-form{
- background-color: white;
-}
-
-.dark .gr-form{
- background-color: rgb(31 41 55 / var(--tw-bg-opacity));
-}
-
-.gr-button-tool, .gr-button-tool-top{
- max-width: 2.5em;
- min-width: 2.5em !important;
- height: 2.4em;
-}
-
-.gr-button-tool{
- margin: 0.6em 0em 0.55em 0;
-}
-
-.gr-button-tool-top, #settings .gr-button-tool{
- margin: 1.6em 0.7em 0.55em 0;
-}
-
-
-#modelmerger_results_container{
- margin-top: 1em;
- overflow: visible;
-}
-
-#modelmerger_models{
- gap: 0;
-}
-
-
-#quicksettings .gr-button-tool{
- margin: 0;
- border-color: unset;
- background-color: unset;
-}
-
-#modelmerger_interp_description>p {
- margin: 0!important;
- text-align: center;
-}
-#modelmerger_interp_description {
- margin: 0.35rem 0.75rem 1.23rem;
-}
-#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form {
- padding-top: 0.9em;
- padding-bottom: 0.9em;
-}
-#txt2img_settings {
- padding-top: 1.16em;
- padding-bottom: 0.9em;
-}
-#img2img_settings {
- padding-bottom: 0.9em;
-}
-
-#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form, #train_tabs div.gr-form .gr-form{
- border: none;
- padding-bottom: 0.5em;
-}
+/* replace original footer with ours */
footer {
display: none !important;
@@ -756,90 +644,7 @@ footer { opacity: 0.85;
}
-#txtimg_hr_finalres{
- min-height: 0 !important;
- padding: .625rem .75rem;
- margin-left: -0.75em
-
-}
-
-#txtimg_hr_finalres .resolution{
- font-weight: bold;
-}
-
-#txt2img_checkboxes, #img2img_checkboxes{
- margin-bottom: 0.5em;
- margin-left: 0em;
-}
-#txt2img_checkboxes > div, #img2img_checkboxes > div{
- flex: 0;
- white-space: nowrap;
- min-width: auto;
-}
-
-#img2img_copy_to_img2img, #img2img_copy_to_sketch, #img2img_copy_to_inpaint, #img2img_copy_to_inpaint_sketch{
- margin-left: 0em;
-}
-
-#axis_options {
- margin-left: 0em;
-}
-
-.inactive{
- opacity: 0.5;
-}
-
-[id*='_prompt_container']{
- gap: 0;
-}
-
-[id*='_prompt_container'] > div{
- margin: -0.4em 0 0 0;
-}
-
-.gr-compact {
- border: none;
-}
-
-.dark .gr-compact{
- background-color: rgb(31 41 55 / var(--tw-bg-opacity));
- margin-left: 0;
-}
-
-.gr-compact{
- overflow: visible;
-}
-
-.gr-compact > *{
-}
-
-.gr-compact .gr-block, .gr-compact .gr-form{
- border: none;
- box-shadow: none;
-}
-
-.gr-compact .gr-box{
- border-radius: .5rem !important;
- border-width: 1px !important;
-}
-
-#mode_img2img > div > div{
- gap: 0 !important;
-}
-
-[id*='img2img_copy_to_'] {
- border: none;
-}
-
-[id*='img2img_copy_to_'] > button {
-}
-
-[id*='img2img_label_copy_to_'] {
- font-size: 1.0em;
- font-weight: bold;
- text-align: center;
- line-height: 2.4em;
-}
+/* extra networks UI */
.extra-networks > div > [id *= '_extra_']{
margin: 0.3em;
@@ -852,12 +657,12 @@ footer { .extra-network-subdirs button{
margin: 0 0.15em;
}
-
-#txt2img_extra_networks .search, #img2img_extra_networks .search{
+.extra-networks .tab-nav .search{
display: inline-block;
max-width: 16em;
margin: 0.3em;
align-self: center;
+ width: 16em;
}
#txt2img_extra_view, #img2img_extra_view {
@@ -889,6 +694,7 @@ footer { text-shadow: 2px 2px 3px black;
padding: 0.25em;
font-size: 22pt;
+ width: 1.5em;
}
.extra-network-cards .card:hover .metadata-button, .extra-network-thumbs .card:hover .metadata-button{
display: inline-block;
@@ -982,12 +788,15 @@ footer { left: 0;
right: 0;
padding: 0.5em;
- color: white;
background: rgba(0,0,0,0.5);
box-shadow: 0 0 0.25em 0.25em rgba(0,0,0,0.5);
text-shadow: 0 0 0.2em black;
}
+.extra-network-cards .card .actions *{
+ color: white;
+}
+
.extra-network-cards .card .actions:hover{
box-shadow: 0 0 0.75em 0.75em rgba(0,0,0,0.5) !important;
}
@@ -1025,7 +834,3 @@ footer { .extra-network-cards .card ul a:hover{
color: red;
}
-
-[id*='_prompt_container'] > div {
- margin: 0!important;
-}
diff --git a/webui-macos-env.sh b/webui-macos-env.sh index 37cac4fb..65d80413 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==1.12.1 torchvision==0.13.1" +export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118" export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git" export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71" export PYTORCH_ENABLE_MPS_FALLBACK=1 diff --git a/webui-user.sh b/webui-user.sh index bfa53cb7..49a426ff 100644 --- a/webui-user.sh +++ b/webui-user.sh @@ -43,4 +43,7 @@ # Uncomment to enable accelerated launch #export ACCELERATE="True" +# Uncomment to disable TCMalloc +#export NO_TCMALLOC="True" + ########################################### @@ -4,6 +4,7 @@ import time import importlib
import signal
import re
+import warnings
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
@@ -17,6 +18,11 @@ from modules import paths, timer, import_hook, errors startup_timer = timer.Timer()
import torch
+import pytorch_lightning # pytorch_lightning should be imported after torch, but it re-enables warnings on import so import once to disable them
+warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
+warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+
+
startup_timer.record("import torch")
import gradio
@@ -64,11 +70,51 @@ else: server_name = "0.0.0.0" if cmd_opts.listen else None
+def fix_asyncio_event_loop_policy():
+ """
+ The default `asyncio` event loop policy only automatically creates
+ event loops in the main threads. Other threads must create event
+ loops explicitly or `asyncio.get_event_loop` (and therefore
+ `.IOLoop.current`) will fail. Installing this policy allows event
+ loops to be created automatically on any thread, matching the
+ behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
+ """
+
+ import asyncio
+
+ if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
+ # "Any thread" and "selector" should be orthogonal, but there's not a clean
+ # interface for composing policies so pick the right base.
+ _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy # type: ignore
+ else:
+ _BasePolicy = asyncio.DefaultEventLoopPolicy
+
+ class AnyThreadEventLoopPolicy(_BasePolicy): # type: ignore
+ """Event loop policy that allows loop creation on any thread.
+ Usage::
+
+ asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+ """
+
+ def get_event_loop(self) -> asyncio.AbstractEventLoop:
+ try:
+ return super().get_event_loop()
+ except (RuntimeError, AssertionError):
+ # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
+ # and changed to a RuntimeError in 3.4.3.
+ # "There is no current event loop in thread %r"
+ loop = self.new_event_loop()
+ self.set_event_loop(loop)
+ return loop
+
+ asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+
+
def check_versions():
if shared.cmd_opts.skip_version_check:
return
- expected_torch_version = "1.13.1"
+ expected_torch_version = "2.0.0"
if version.parse(torch.__version__) < version.parse(expected_torch_version):
errors.print_error_explanation(f"""
@@ -81,7 +127,7 @@ there are reports of issues with training tab on the latest version. Use --skip-version-check commandline argument to disable this check.
""".strip())
- expected_xformers_version = "0.0.16rc425"
+ expected_xformers_version = "0.0.17"
if shared.xformers_available:
import xformers
@@ -96,6 +142,8 @@ Use --skip-version-check commandline argument to disable this check. def initialize():
+ fix_asyncio_event_loop_policy()
+
check_versions()
extensions.list_extensions()
@@ -123,9 +171,6 @@ def initialize(): modules.scripts.load_scripts()
startup_timer.record("load scripts")
- modelloader.load_upscalers()
- startup_timer.record("load upscalers")
-
modules.sd_vae.refresh_vae_list()
startup_timer.record("refresh VAE")
@@ -147,6 +192,7 @@ def initialize(): shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
+ shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
startup_timer.record("opts onchange")
shared.reload_hypernetworks()
@@ -240,7 +286,7 @@ def webui(): shared.demo = modules.ui.create_ui()
startup_timer.record("create ui")
- if cmd_opts.gradio_queue:
+ if not cmd_opts.no_gradio_queue:
shared.demo.queue(64)
gradio_auth_creds = []
@@ -23,7 +23,7 @@ fi # Install directory without trailing slash if [[ -z "${install_dir}" ]] then - install_dir="/home/$(whoami)" + install_dir="${HOME}" fi # Name of the subdirectory (defaults to stable-diffusion-webui) @@ -113,13 +113,13 @@ case "$gpu_info" in printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half" printf "\n%s\n" "${delimiter}" ;; - *) + *) ;; esac if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]] then export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2" -fi +fi for preq in "${GIT}" "${python_cmd}" do @@ -172,15 +172,30 @@ else exit 1 fi +# Try using TCMalloc on Linux +prepare_tcmalloc() { + if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then + TCMALLOC="$(ldconfig -p | grep -Po "libtcmalloc.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 + fi +} + if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ] then printf "\n%s\n" "${delimiter}" printf "Accelerating launch.py..." printf "\n%s\n" "${delimiter}" + prepare_tcmalloc exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@" else printf "\n%s\n" "${delimiter}" printf "Launching launch.py..." - printf "\n%s\n" "${delimiter}" + printf "\n%s\n" "${delimiter}" + prepare_tcmalloc exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@" fi |