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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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Merge branch 'master' into img2img-enhance
This commit is contained in:
space-nuko
GitHub
commit
4414d36bf6
17
README.md
17
README.md
@ -13,9 +13,9 @@ A browser interface based on Gradio library for Stable Diffusion.
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- Prompt Matrix
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- Stable Diffusion Upscale
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- Attention, specify parts of text that the model should pay more attention to
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- a man in a ((tuxedo)) - will pay more attention to tuxedo
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- a man in a (tuxedo:1.21) - alternative syntax
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- select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
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- a man in a `((tuxedo))` - will pay more attention to tuxedo
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- a man in a `(tuxedo:1.21)` - alternative syntax
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- select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
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- Loopback, run img2img processing multiple times
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- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
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- Textual Inversion
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@ -46,7 +46,7 @@ A browser interface based on Gradio library for Stable Diffusion.
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- drag and drop an image/text-parameters to promptbox
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- Read Generation Parameters Button, loads parameters in promptbox to UI
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- Settings page
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- Running arbitrary python code from UI (must run with --allow-code to enable)
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- Running arbitrary python code from UI (must run with `--allow-code` to enable)
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- Mouseover hints for most UI elements
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- Possible to change defaults/mix/max/step values for UI elements via text config
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- Tiling support, a checkbox to create images that can be tiled like textures
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@ -69,7 +69,7 @@ A browser interface based on Gradio library for Stable Diffusion.
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- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
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- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
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- DeepDanbooru integration, creates danbooru style tags for anime prompts
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- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
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- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
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- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
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- Generate forever option
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- Training tab
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@ -78,11 +78,11 @@ A browser interface based on Gradio library for Stable Diffusion.
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- Clip skip
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- Hypernetworks
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- Loras (same as Hypernetworks but more pretty)
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- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt.
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- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
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- Can select to load a different VAE from settings screen
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- Estimated completion time in progress bar
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- API
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- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
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- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
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- 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))
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- [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
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- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
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@ -91,7 +91,6 @@ A browser interface based on Gradio library for Stable Diffusion.
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- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
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- Now with a license!
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- Reorder elements in the UI from settings screen
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-
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## Installation and Running
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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.
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@ -101,7 +100,7 @@ Alternatively, use online services (like Google Colab):
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- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
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### Automatic Installation on Windows
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1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
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1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH".
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2. Install [git](https://git-scm.com/download/win).
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3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
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4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
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@ -2,20 +2,34 @@ import glob
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import os
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import re
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import torch
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from typing import Union
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from modules import shared, devices, sd_models, errors
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metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
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re_digits = re.compile(r"\d+")
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re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
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re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
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re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
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re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
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re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
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re_compiled = {}
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suffix_conversion = {
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"attentions": {},
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"resnets": {
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"conv1": "in_layers_2",
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"conv2": "out_layers_3",
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"time_emb_proj": "emb_layers_1",
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"conv_shortcut": "skip_connection",
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}
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}
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def convert_diffusers_name_to_compvis(key):
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def match(match_list, regex):
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def convert_diffusers_name_to_compvis(key, is_sd2):
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def match(match_list, regex_text):
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regex = re_compiled.get(regex_text)
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if regex is None:
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regex = re.compile(regex_text)
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re_compiled[regex_text] = regex
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r = re.match(regex, key)
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if not r:
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return False
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@ -26,16 +40,33 @@ def convert_diffusers_name_to_compvis(key):
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m = []
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if match(m, re_unet_down_blocks):
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return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
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if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
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return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
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if match(m, re_unet_mid_blocks):
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return f"diffusion_model_middle_block_1_{m[1]}"
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if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
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return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
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if match(m, re_unet_up_blocks):
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return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
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if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
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suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
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return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
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if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
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return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
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if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
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return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
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if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
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if is_sd2:
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if 'mlp_fc1' in m[1]:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
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elif 'mlp_fc2' in m[1]:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
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else:
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return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
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if match(m, re_text_block):
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return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
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return key
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@ -101,15 +132,22 @@ def load_lora(name, filename):
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sd = sd_models.read_state_dict(filename)
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keys_failed_to_match = []
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keys_failed_to_match = {}
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is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
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for key_diffusers, weight in sd.items():
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fullkey = convert_diffusers_name_to_compvis(key_diffusers)
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key, lora_key = fullkey.split(".", 1)
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key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
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key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
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sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
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if sd_module is None:
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keys_failed_to_match.append(key_diffusers)
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m = re_x_proj.match(key)
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if m:
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sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
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if sd_module is None:
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keys_failed_to_match[key_diffusers] = key
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continue
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lora_module = lora.modules.get(key, None)
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@ -123,15 +161,21 @@ def load_lora(name, filename):
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if type(sd_module) == torch.nn.Linear:
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.MultiheadAttention:
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.Conv2d:
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
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else:
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print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
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continue
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assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
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with torch.no_grad():
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module.weight.copy_(weight)
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module.to(device=devices.device, dtype=devices.dtype)
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module.to(device=devices.cpu, dtype=devices.dtype)
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if lora_key == "lora_up.weight":
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lora_module.up = module
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@ -177,29 +221,120 @@ def load_loras(names, multipliers=None):
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loaded_loras.append(lora)
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def lora_forward(module, input, res):
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input = devices.cond_cast_unet(input)
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if len(loaded_loras) == 0:
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return res
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def lora_calc_updown(lora, module, target):
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with torch.no_grad():
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up = module.up.weight.to(target.device, dtype=target.dtype)
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down = module.down.weight.to(target.device, dtype=target.dtype)
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if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
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updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
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else:
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updown = up @ down
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updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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return updown
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def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
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"""
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Applies the currently selected set of Loras to the weights of torch layer self.
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If weights already have this particular set of loras applied, does nothing.
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If not, restores orginal weights from backup and alters weights according to loras.
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"""
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lora_layer_name = getattr(self, 'lora_layer_name', None)
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if lora_layer_name is None:
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return
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current_names = getattr(self, "lora_current_names", ())
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wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
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weights_backup = getattr(self, "lora_weights_backup", None)
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if weights_backup is None:
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if isinstance(self, torch.nn.MultiheadAttention):
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weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
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else:
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weights_backup = self.weight.to(devices.cpu, copy=True)
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self.lora_weights_backup = weights_backup
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if current_names != wanted_names:
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if weights_backup is not None:
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if isinstance(self, torch.nn.MultiheadAttention):
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self.in_proj_weight.copy_(weights_backup[0])
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self.out_proj.weight.copy_(weights_backup[1])
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else:
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self.weight.copy_(weights_backup)
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lora_layer_name = getattr(module, 'lora_layer_name', None)
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for lora in loaded_loras:
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module = lora.modules.get(lora_layer_name, None)
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if module is not None:
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if shared.opts.lora_apply_to_outputs and res.shape == input.shape:
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res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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else:
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res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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if module is not None and hasattr(self, 'weight'):
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self.weight += lora_calc_updown(lora, module, self.weight)
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continue
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return res
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module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
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module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
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module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
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module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
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if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
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updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
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updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
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updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
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updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
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self.in_proj_weight += updown_qkv
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self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
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continue
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if module is None:
|
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continue
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print(f'failed to calculate lora weights for layer {lora_layer_name}')
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setattr(self, "lora_current_names", wanted_names)
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def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
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setattr(self, "lora_current_names", ())
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setattr(self, "lora_weights_backup", None)
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def lora_Linear_forward(self, input):
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return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
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lora_apply_weights(self)
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return torch.nn.Linear_forward_before_lora(self, input)
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def lora_Linear_load_state_dict(self, *args, **kwargs):
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lora_reset_cached_weight(self)
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return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
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def lora_Conv2d_forward(self, input):
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return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
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lora_apply_weights(self)
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return torch.nn.Conv2d_forward_before_lora(self, input)
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def lora_Conv2d_load_state_dict(self, *args, **kwargs):
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lora_reset_cached_weight(self)
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return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
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def lora_MultiheadAttention_forward(self, *args, **kwargs):
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lora_apply_weights(self)
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return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
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def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
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lora_reset_cached_weight(self)
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return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
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||||
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def list_available_loras():
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@ -212,7 +347,7 @@ def list_available_loras():
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
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for filename in sorted(candidates):
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||||
for filename in sorted(candidates, key=str.lower):
|
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if os.path.isdir(filename):
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continue
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||||
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@ -9,7 +9,11 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared
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|
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def unload():
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torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
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torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
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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)
|
||||
@ -33,6 +53,4 @@ 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)"),
|
||||
|
||||
}))
|
||||
|
||||
@ -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
|
||||
@ -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)
|
||||
}
|
||||
|
||||
|
||||
@ -91,7 +91,9 @@ onUiUpdate(function(){
|
||||
if(arPreviewRect){
|
||||
arPreviewRect.style.display = 'none';
|
||||
}
|
||||
var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
|
||||
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){
|
||||
@ -110,4 +112,5 @@ onUiUpdate(function(){
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
@ -21,8 +21,7 @@ titles = {
|
||||
"\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",
|
||||
|
||||
@ -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(".gradio-gallery .thumbnail-item")
|
||||
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(".gradio-gallery .thumbnail-item.selected")
|
||||
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) {
|
||||
|
||||
@ -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$="_results"] .thumbnail-item > img');
|
||||
|
||||
if (galleryPreviews == null) return;
|
||||
|
||||
|
||||
@ -7,9 +7,31 @@ function set_theme(theme){
|
||||
}
|
||||
}
|
||||
|
||||
function all_gallery_buttons() {
|
||||
var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_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] .thumbnails > .thumbnail-item')
|
||||
var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .thumbnails > .thumbnail-item.selected')
|
||||
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 == 0){
|
||||
return [null];
|
||||
}
|
||||
if (gallery.length == 1){
|
||||
return [gallery[0]]
|
||||
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]];
|
||||
|
||||
@ -3,6 +3,7 @@ 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
|
||||
@ -197,6 +198,9 @@ class Api:
|
||||
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)
|
||||
@ -230,7 +234,7 @@ 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:
|
||||
@ -238,13 +242,24 @@ 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):
|
||||
@ -265,6 +280,8 @@ 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
|
||||
@ -280,7 +297,7 @@ 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)
|
||||
@ -317,6 +334,8 @@ 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
|
||||
@ -334,7 +353,7 @@ 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)
|
||||
|
||||
@ -4,6 +4,7 @@ from modules.paths_internal import models_path, script_path, data_path, extensio
|
||||
|
||||
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")
|
||||
|
||||
@ -5,13 +5,14 @@ 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 = []
|
||||
|
||||
if not os.path.exists(paths.extensions_dir):
|
||||
os.makedirs(paths.extensions_dir)
|
||||
if not os.path.exists(extensions_dir):
|
||||
os.makedirs(extensions_dir)
|
||||
|
||||
|
||||
def active():
|
||||
return [x for x in extensions if x.enabled]
|
||||
@ -26,21 +27,29 @@ 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})'
|
||||
@ -85,11 +94,11 @@ class Extension:
|
||||
def list_extensions():
|
||||
extensions.clear()
|
||||
|
||||
if not os.path.isdir(paths.extensions_dir):
|
||||
if not os.path.isdir(extensions_dir):
|
||||
return
|
||||
|
||||
extension_paths = []
|
||||
for dirname in [paths.extensions_dir, paths.extensions_builtin_dir]:
|
||||
for dirname in [extensions_dir, extensions_builtin_dir]:
|
||||
if not os.path.isdir(dirname):
|
||||
return
|
||||
|
||||
@ -98,7 +107,7 @@ def list_extensions():
|
||||
if not os.path.isdir(path):
|
||||
continue
|
||||
|
||||
extension_paths.append((extension_dirname, path, dirname == paths.extensions_builtin_dir))
|
||||
extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
|
||||
|
||||
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)
|
||||
|
||||
@ -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]
|
||||
|
||||
im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
|
||||
|
||||
if im.width != w or im.height != h:
|
||||
|
||||
@ -553,3 +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
|
||||
|
||||
@ -640,7 +640,7 @@ 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)]
|
||||
|
||||
|
||||
|
||||
@ -145,7 +145,6 @@ 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}')
|
||||
|
||||
with gr.Group():
|
||||
|
||||
@ -63,6 +63,9 @@ 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)
|
||||
@ -87,6 +90,8 @@ 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:
|
||||
|
||||
@ -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
|
||||
@ -252,10 +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 = []
|
||||
@ -290,6 +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:
|
||||
@ -299,6 +302,11 @@ def setup_ui(ui, gallery):
|
||||
|
||||
assert is_allowed, f'writing to {filename} is not allowed'
|
||||
|
||||
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]
|
||||
|
||||
@ -54,14 +54,11 @@ class Script(scripts.Script):
|
||||
return strength
|
||||
|
||||
progress = loop / (loops - 1)
|
||||
match denoising_curve:
|
||||
case "Aggressive":
|
||||
if denoising_curve == "Aggressive":
|
||||
strength = math.sin((progress) * math.pi * 0.5)
|
||||
|
||||
case "Lazy":
|
||||
elif denoising_curve == "Lazy":
|
||||
strength = 1 - math.cos((progress) * math.pi * 0.5)
|
||||
|
||||
case _:
|
||||
else:
|
||||
strength = progress
|
||||
|
||||
change = (final_denoising_strength - initial_denoising_strength) * strength
|
||||
|
||||
50
style.css
50
style.css
@ -7,7 +7,7 @@
|
||||
--block-background-fill: transparent;
|
||||
}
|
||||
|
||||
.block.padded{
|
||||
.block.padded:not(.gradio-accordion) {
|
||||
padding: 0 !important;
|
||||
}
|
||||
|
||||
@ -54,10 +54,6 @@ div.compact{
|
||||
gap: 1em;
|
||||
}
|
||||
|
||||
.gradio-dropdown ul.options{
|
||||
z-index: 3000;
|
||||
}
|
||||
|
||||
.gradio-dropdown label span:not(.has-info),
|
||||
.gradio-textbox label span:not(.has-info),
|
||||
.gradio-number label span:not(.has-info)
|
||||
@ -65,11 +61,30 @@ div.compact{
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
.gradio-dropdown ul.options{
|
||||
z-index: 3000;
|
||||
min-width: fit-content;
|
||||
max-width: inherit;
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.gradio-dropdown ul.options li.item {
|
||||
padding: 0.05em 0;
|
||||
}
|
||||
|
||||
.gradio-dropdown ul.options li.item.selected {
|
||||
background-color: var(--neutral-100);
|
||||
}
|
||||
|
||||
.dark .gradio-dropdown ul.options li.item.selected {
|
||||
background-color: var(--neutral-900);
|
||||
}
|
||||
|
||||
.gradio-dropdown div.wrap.wrap.wrap.wrap{
|
||||
box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
|
||||
}
|
||||
|
||||
.gradio-dropdown .wrap-inner.wrap-inner.wrap-inner{
|
||||
.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{
|
||||
flex-wrap: unset;
|
||||
}
|
||||
|
||||
@ -123,6 +138,18 @@ div.gradio-html.min{
|
||||
border-radius: 0.5em;
|
||||
}
|
||||
|
||||
.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);
|
||||
}
|
||||
|
||||
.checkboxes-row{
|
||||
margin-bottom: 0.5em;
|
||||
margin-left: 0em;
|
||||
@ -507,6 +534,17 @@ div.dimensions-tools{
|
||||
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;
|
||||
}
|
||||
|
||||
/* context menu (ie for the generate button) */
|
||||
|
||||
#context-menu{
|
||||
|
||||
3
webui.py
3
webui.py
@ -265,9 +265,6 @@ def webui():
|
||||
inbrowser=cmd_opts.autolaunch,
|
||||
prevent_thread_lock=True
|
||||
)
|
||||
for dep in shared.demo.dependencies:
|
||||
dep['show_progress'] = False # disable gradio css animation on component update
|
||||
|
||||
# after initial launch, disable --autolaunch for subsequent restarts
|
||||
cmd_opts.autolaunch = False
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user