merge cleanup first step

2026-09-19 09:14:35 +02:00 · 2023-03-11 08:19:39 -05:00
parent 86e89e5446
commit b459794737
3 changed files with 140 additions and 232 deletions
@@ -1,162 +1,151 @@
-# Stable Diffusion web UI
-A browser interface based on Gradio library for Stable Diffusion.
+# Stable Diffusion - Automatic

-![](screenshot.png)
+*Heavily opinionated custom fork of* <https://github.com/AUTOMATIC1111/stable-diffusion-webui>  

-## 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/Z plot, a way to draw a 3 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
- 
+![](ui-screenshot.jpg)

-## 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.
+<br>

-Alternatively, use online services (like Google Colab):
+### Notes

- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
+Fork is as close as up-to-date with origin as time allows  
+All code changes are merged upstream whenever possible  

-### 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. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
+Fork adds extra functionality:
+- New skin and UI layout  
+- 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 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)
-```
-3. Run `webui.sh`.
-### Installation on Apple Silicon
+### Integrated Extensions:

-Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
+- [System Info](https://github.com/vladmandic/sd-extension-system-info)
+- [ControlNet](https://github.com/Mikubill/sd-webui-controlnet)
+- [Image Browser](https://github.com/AlUlkesh/stable-diffusion-webui-images-browser)
+- [LORA](https://github.com/kohya-ss/sd-scripts) (both training and inference)
+- [LoCon](https://github.com/KohakuBlueleaf/LoCon) (both training and inference)
+- [Model Converter](https://github.com/Akegarasu/sd-webui-model-converter)
+- [CLiP Interrogator](https://github.com/pharmapsychotic/clip-interrogator-ext)
+- [Dynamic Thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding)
+- [Steps Animation](https://github.com/vladmandic/sd-extension-steps-animation)
+- [Seed Travel](https://github.com/yownas/seed_travel)

-## Contributing
-Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
+*Note*: Extensions are automatically updated to latest version on `install`

-## Documentation
-The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
+### Start Script

-## Credits
-Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
+Simplified start script: `automatic.sh`  
+*Existing `webui.sh`/`webui.bat` scripts still exist for backward compatibility*  

- 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
- 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)
+> ./automatic.sh  
+
+Start in default mode with optimizations enabled  
+
+    SD server: optimized
+    Version: 56f779a9 Sat Feb 25 14:04:19 2023 -0500
+    Repository: https://github.com/vladmandic/automatic
+    Last Merge: Sun Feb 19 10:11:25 2023 -0500 Merge pull request #37 from AUTOMATIC1111/master
+    System
+    - Platform: Ubuntu 22.04.1 LTS 5.15.90.1-microsoft-standard-WSL2 x86_64
+    - nVIDIA: NVIDIA GeForce RTX 3060, 528.49
+    - Python: 3.10.6 Torch: 2.0.0.dev20230224+cu118 CUDA: 11.8 cuDNN: 8700 GPU: NVIDIA GeForce RTX 3060 Arch: (8, 6)
+    Launching Web UI
+
+> ./automatic.sh clean  
+
+Start with all optimizations disabled  
+Use this for troubleshooting  
+
+> ./automatic.sh install
+
+Installs and updates to latest supported version:
+- Dependencies
+- Fixed sub-repositories
+- Extensions
+- Sub-modules
+
+Does not update main repository
+
+> ./automatic.sh update
+
+Updates the main repository to the latest version  
+Recommended to run `install` after `update` to update dependencies as they may have changed  
+
+> ./automatic.sh help
+
+Print all available options
+
+> ./automatic.sh public  
+
+Start with listen on public IP with authentication enabled  
+
+<br>  
+
+## Install
+
+1. Install `Python`, `Git`  
+2. Install `PyTorch` and `Xformers`  
+   See [Wiki](wiki/Torch%20Optimizations.md) for details
+   If you don't want to use `xformers`, edit `automatic.sh` as they are enabled by default
+3. Clone and initialize repository  
+
+> git clone https://github.com/vladmandic/automatic  
+> cd automatic  
+> ./automatic.sh install  
+
+      SD server: install
+      Version: 56f779a9 Sat Feb 25 14:04:19 2023 -0500
+      Repository: https://github.com/vladmandic/automatic
+      Last Merge: Sun Feb 19 10:11:25 2023 -0500 Merge pull request #37 from AUTOMATIC1111/master
+      Installing general requirements
+      Installing versioned requirements
+      Installing requirements for Web UI
+      Updating submodules
+      Updating extensions
+      Updating wiki
+      Detached repos
+      Local changes
+ 
+<br>
+
+## Differences
+
+Fork does differ in few things:
+- Drops compatibility with `python` **3.7** and requires **3.9**  
+  Recommended is **Python 3.10**  
+  Note that **Python 3.11** or **3.12** are NOT supported  
+- 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  
+- Built-in `LoRA`, `LyCORIS`, `Custom Diffusion`, `Dreambooth` training  
+
+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>
+
+## Scripts
+
+This repository comes with a large collection of scripts that can be used to process inputs, train, generate, and benchmark models  
+
+As well as number of auxiliary scripts that do not rely on **WebUI**, but can be used for end-to-end solutions such as extract frames from videos, etc.  
+
+For full details see [Docs](cli/README.md)
+
+<br>
+
+## Docs
+
+- Scripts are in [Scripts](cli/README.md)  
+- Everything else is in [Wiki](https://github.com/vladmandic/automatic/wiki)  
+- Except my current [TODO](TODO.md)  
@@ -139,6 +139,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre

    var divProgress = document.createElement('div')
    divProgress.className='progressDiv'
+    divProgress.id = 'progressbar'
    divProgress.style.display = opts.show_progressbar ? "block" : "none"
    var divInner = document.createElement('div')
    divInner.className='progress'
@@ -1,82 +0,0 @@
-import unittest
-import requests
-
-
-class TestTxt2ImgWorking(unittest.TestCase):
-    def setUp(self):
-        self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
-        self.simple_txt2img = {
-            "enable_hr": False,
-            "denoising_strength": 0,
-            "firstphase_width": 0,
-            "firstphase_height": 0,
-            "prompt": "example prompt",
-            "styles": [],
-            "seed": -1,
-            "subseed": -1,
-            "subseed_strength": 0,
-            "seed_resize_from_h": -1,
-            "seed_resize_from_w": -1,
-            "batch_size": 1,
-            "n_iter": 1,
-            "steps": 3,
-            "cfg_scale": 7,
-            "width": 64,
-            "height": 64,
-            "restore_faces": False,
-            "tiling": False,
-            "negative_prompt": "",
-            "eta": 0,
-            "s_churn": 0,
-            "s_tmax": 0,
-            "s_tmin": 0,
-            "s_noise": 1,
-            "sampler_index": "Euler a"
-        }
-
-    def test_txt2img_simple_performed(self):
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_negative_prompt_performed(self):
-        self.simple_txt2img["negative_prompt"] = "example negative prompt"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_complex_prompt_performed(self):
-        self.simple_txt2img["prompt"] = "((emphasis)), (emphasis1:1.1), [to:1], [from::2], [from:to:0.3], [alt|alt1]"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_not_square_image_performed(self):
-        self.simple_txt2img["height"] = 128
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_hrfix_performed(self):
-        self.simple_txt2img["enable_hr"] = True
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_tiling_performed(self):
-        self.simple_txt2img["tiling"] = True
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_restore_faces_performed(self):
-        self.simple_txt2img["restore_faces"] = True
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_with_vanilla_sampler_performed(self):
-        self.simple_txt2img["sampler_index"] = "PLMS"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-        self.simple_txt2img["sampler_index"] = "DDIM"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-        self.simple_txt2img["sampler_index"] = "UniPC"
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_multiple_batches_performed(self):
-        self.simple_txt2img["n_iter"] = 2
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-    def test_txt2img_batch_performed(self):
-        self.simple_txt2img["batch_size"] = 2
-        self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
-
-
-if __name__ == "__main__":
-    unittest.main()