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Vladimir Mandic
2023-01-28 10:35:26 -05:00
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README.md
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@@ -1,80 +1,162 @@
# Stable Diffusion - Automatic
# Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
*Heavily opinionated custom fork of* <https://github.com/AUTOMATIC1111/stable-diffusion-webui>
![](screenshot.png)
![](ui-screenshot.jpg)
## Features
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
- Original txt2img and img2img modes
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Color Sketch
- 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)
- Loopback, run img2img processing multiple times
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- train embeddings on 8GB (also reports of 6GB working)
- Extras tab with:
- GFPGAN, neural network that fixes faces
- 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
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
- Adjust sampler eta values (noise multiplier)
- More advanced noise setting options
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Live prompt token length validation
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- 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)
- 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
- Progress bar and live image generation preview
- Can use a separate neural network to produce previews with almost none VRAM or compute requirement
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
- Seed resizing, a way to generate same image but at slightly different resolution
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative, reverse Euler method of cross attention control
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
- 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)
- 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
- hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- 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.
- 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.
- 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
- Now without any bad letters!
- Load checkpoints in safetensors format
- 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
-
<br>
## 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.
## Notes
Alternatively, use online services (like Google Colab):
Fork is as close as up-to-date with origin as time allows
All code changes are merged upstream whenever possible
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
Fork adds extra functionality:
- Ships with additional **extensions**
e.g. `System Info`, `Steps Animation`, etc.
- Ships with set of **CLI** tools that rely on *SD API* for execution:
e.g. `generate`, `train`, `bench`, etc.
[Full list](<cli/>)
### Automatic Installation on Windows
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. Place stable diffusion checkpoint (`model.ckpt`) in the `models/Stable-diffusion` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
5. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
Simplified start script: `automatic.sh`
*Existing `webui.sh` still exists for backward compatibility, fresh installs to auto-install dependencies, etc.*
### Automatic Installation on Linux
1. Install the dependencies:
```bash
# Debian-based:
sudo apt install wget git python3 python3-venv
# Red Hat-based:
sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
> ./automatic.sh
### Installation on Apple Silicon
- Start in default mode with optimizations enabled
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
> ./automatic.sh env
## Contributing
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
- Print env info and exit
Example:
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
Version: c07487a Tue Jan 24 08:04:31 2023 -0500
Platform: Ubuntu 22.04.1 LTS 5.15.79.1-microsoft-standard-WSL2 x86_64
Python 3.10.6
Torch: 2.0.0.dev20230118+cu118 CUDA: 11.8 cuDNN: 8700 GPU: NVIDIA GeForce RTX 3060 Arch: (8, 6)
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
> ./automatic.sh install
- Install requirements and exit
> ./automatic.sh public
- Start with listen on public IP with authentication enabled
> ./automatic.sh clean
- Start with all optimizations disabled
Use this for troubleshooting
<br>
## Differences
Fork does differ in few things:
- Drops compatibility with `python` **3.7** and requires **3.9**
- Updated **Python** libraries to latest known compatible versions
e.g. `accelerate`, `transformers`, `numpy`, etc.
- Includes opinionated **System** and **Options** configuration
e.g. `samplers`, `upscalers`, etc.
- Includes reskinned **UI**
Black and orange dark theme with fixed width options panels and larger previews
- Includes **SD2** configuration files
- Uses simplified folder structure
e.g. `/train`, `/outputs/*`
- Modified training templates
Only Python library which is not auto-updated is `PyTorch` itself as that is very system specific
For some Torch optimizations notes, see Wiki
Fork is compatible with regular **PyTorch 1.13** as well as pre-release of **PyTorch 2.0**
See [Wiki](https://github.com/vladmandic/automatic/wiki) for **Torch** optimization notes
<br>
## Docs
Everything is in [Wiki](https://github.com/vladmandic/automatic/wiki)
Except my current [TODO](TODO.md)
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
cli/generate.py
+5 -3
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@@ -119,13 +119,14 @@ def sampler(params, options): # find sampler
return sd.generate.sampler_name
async def generate(prompt = None, options = None): # pylint: disable=redefined-outer-name
async def generate(prompt = None, options = None, quiet = False): # pylint: disable=redefined-outer-name
global sd
if options:
sd = Map(options)
if prompt is not None:
sd.generate.prompt = prompt
log.info({ 'generate': sd.generate })
if not quiet:
log.info({ 'generate': sd.generate })
names = []
b64s = []
images = []
@@ -145,7 +146,8 @@ async def generate(prompt = None, options = None): # pylint: disable=redefined-o
name = safestring(name) + '.jpg'
f = os.path.join(sd.paths.root, sd.paths.generate, name)
names.append(f)
log.info({ 'image': { 'name': f, 'size': images[i].size } })
if not quiet:
log.info({ 'image': { 'name': f, 'size': images[i].size } })
images[i].save(f, 'JPEG', exif = exif(info, i), optimize = True, quality = 70)
return Map({ 'name': names, 'image': images, 'b64': b64s, 'info': info })
cli/models.py
+32 -12
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@@ -2,18 +2,19 @@
import os
import sys
import json
import time
import asyncio
import argparse
sys.path.append(os.path.join(os.path.dirname(__file__), 'modules'))
from generate import sd, generate
from modules.util import Map, log
from modules.sdapi import get, post, close
from generate import sd, generate
from modules.grid import grid
embeddings = ['blonde', 'bruntette', 'sexy', 'mia', 'lin', 'kelly', 'hanna', 'rreid-random-v0']
exclude = ['sd-v20', 'sd-v21', 'inpainting']
embeddings = ['blonde', 'bruntette', 'sexy', 'naked', 'mia', 'lin', 'kelly', 'hanna', 'rreid-random-v0']
exclude = ['sd-v20', 'sd-v21', 'inpainting', 'pix2pix']
prompt = "photo of beautiful woman <embedding>, photograph, posing, pose, high detailed, intricate, elegant, sharp focus, skin texture, looking forward, facing camera, 135mm, shot on dslr, canon 5d, 4k, modelshoot style, cinematic lighting"
options = Map({
'generate': {
@@ -48,47 +49,66 @@ async def models(params):
all = [m['title'] for m in data]
models = []
excluded = []
for m in all:
for m in all: # loop through all registered models
ok = True
for e in exclude:
for e in exclude: # check if model is excluded
if e in m:
excluded.append(m)
ok = False
break
if len(params.input) > 0: # check if model is included in cmd line
found = m if m in params.input else None
if found is None:
found = [i for i in params.input if m.startswith(i)]
if len(found) == 0:
ok = False
break
if ok:
models.append(m)
short = m.split(' [')[0]
short = short.replace('.ckpt', '').replace('.safetensors', '')
models.append(short)
log.info({ 'models preview' })
log.info({ 'models': len(models), 'excluded': len(excluded) })
log.info({ 'embeddings': len(embeddings) })
log.info({ 'batch size': options.generate.batch_size })
log.info({ 'total jobs': len(models) * len(embeddings) * options.generate.batch_size })
log.info({ 'total jobs': len(models) * len(embeddings) * options.generate.batch_size, 'per-model': len(embeddings) * options.generate.batch_size })
log.info(json.dumps(options, indent=2))
models = ['sd-v15-runwayml.ckpt [cc6cb27103]']
# models = ['sd-v15-runwayml.ckpt [cc6cb27103]']
for model in models:
fn = os.path.join(params.output, model + '.jpg')
if os.path.exists(fn):
log.info({ 'model': model, 'model preview exists': fn })
continue
opt = await get('/sdapi/v1/options')
opt['sd_model_checkpoint'] = model
await post('/sdapi/v1/options', opt)
images = []
labels = []
t0 = time.time()
for embedding in embeddings:
options.generate.prompt = prompt.replace('<embedding>', f'\"{embedding}\"')
log.info({ 'embedding': embedding, 'prompt': options.generate.prompt })
data = await generate(options = options)
log.info({ 'model': model, 'embedding': embedding, 'prompt': options.generate.prompt })
data = await generate(options = options, quiet=True)
if 'image' in data:
for img in data['image']:
images.append(img)
labels.append(embedding)
else:
log.error({ 'model': model, 'embedding': embedding, 'error': data })
t1 = time.time()
image = grid(images = images, labels = labels, border = 8)
fn = os.path.join(params.output, model + '.jpg')
image.save(fn)
log.info({ 'file': fn, 'model': model, 'images': len(images), 'grid': [image.width, image.height] })
t = t1 - t0
its = 1.0 * options.generate.batch_size * len(images) / t
log.info({ 'model': model, 'created preview': fn, 'images': len(images), 'grid': [image.width, image.height], 'time': round(t, 2), 'its': round(its, 2) })
await close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description = 'generate model previews')
parser.add_argument('--output', type = str, default = '', required = False, help = 'output directory')
parser.add_argument('input', type = str, nargs = '*')
params = parser.parse_args()
print('INPUT', params.input)
exit()
asyncio.run(models(params))
config.json
+2 -2
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@@ -60,7 +60,7 @@
"training_enable_tensorboard": false,
"training_tensorboard_save_images": false,
"training_tensorboard_flush_every": 120,
"sd_model_checkpoint": "sd-v15-runwayml.ckpt [cc6cb27103]",
"sd_model_checkpoint": "zeipher-f222",
"sd_checkpoint_cache": 0,
"sd_vae_checkpoint_cache": 0,
"sd_vae": "vae-ft-mse-840000-ema-pruned.ckpt",
@@ -144,7 +144,7 @@
"sdweb-merge-board",
"ScuNET"
],
"sd_checkpoint_hash": "cc6cb27103417325ff94f52b7a5d2dde45a7515b25c255d8e396c90014281516",
"sd_checkpoint_hash": "22fdccddef1a2466198c02851f26d76cd5a08430d231083ee4958de662c1a8e0",
"ldsr_steps": 100,
"ldsr_cached": false,
"SWIN_tile": 192,
modules/shared.py
+13 -10
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@@ -13,14 +13,13 @@ 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
from modules import localization, sd_vae, extensions, script_loading, errors, ui_components
from modules.paths import models_path, script_path, sd_path
demo = None
sd_configs_path = os.path.join(script_path, "configs")
sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
sd_default_config = os.path.join(script_path, "configs/v1-inference.yaml")
sd_model_file = os.path.join(script_path, 'model.ckpt')
default_sd_model_file = sd_model_file
@@ -36,7 +35,7 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
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(script_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, 'train/templates'), help="directory with textual inversion templates")
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")
@@ -104,7 +103,6 @@ parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requ
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("--compile", type=str, help="Use Torch Dynamo compile with specified backend", default=None)
script_loading.preload_extensions(extensions.extensions_dir, parser)
@@ -266,6 +264,12 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
face_restorers = []
def realesrgan_models_names():
import modules.realesrgan_model
return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
self.default = default
@@ -356,7 +360,7 @@ options_templates.update(options_section(('saving-to-dirs', "Saving to a directo
options_templates.update(options_section(('upscaling', "Upscaling"), {
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
"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()}),
"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": realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
}))
@@ -393,7 +397,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
@@ -479,8 +483,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
}))
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'postprocessing_scipts_order': OptionInfo("upscale, gfpgan, codeformer", "Postprocessing operation order"),
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
}))