306 lines
14 KiB
Markdown
306 lines
14 KiB
Markdown
# Stable Diffusion web UI
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A browser interface based on Gradio library for Stable Diffusion.
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## Feature showcase
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[Detailed feature showcase with images, art by Greg Rutkowski](https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase)
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- Original txt2img and img2img modes
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- One click install and run script (but you still must install python and git)
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- Outpainting
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- Inpainting
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- Prompt matrix
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- Stable Diffusion upscale
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- Attention
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- Loopback
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- X/Y plot
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- Textual Inversion
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- Extras tab with:
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- GFPGAN, neural network that fixes faces
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- RealESRGAN, neural network upscaler
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- ESRGAN, neural network with a lot of third party models
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- Resizing aspect ratio options
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- Sampling method selection
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- Interrupt processing at any time
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- 4GB videocard support
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- Correct seeds for batches
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- Prompt length validation
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- Generation parameters added as text to PNG
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- Tab to view an existing picture's generation parameters
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- Settings page
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- Running custom code from UI
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- Mouseover hints fo 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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- Random artist button
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- Tiling support: UI checkbox to create images that can be tiled like textures
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- Progress bar and live image generation preview
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- Negative prompt
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- Styles
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- Variations
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- Seed resizing
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- CLIP interrogator
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## Installing and running
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You need [python](https://www.python.org/downloads/windows/) and [git](https://git-scm.com/download/win)
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installed to run this, and an NVidia videocard.
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You need `model.ckpt`, Stable Diffusion model checkpoint, a big file containing the neural network weights. You
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can obtain it from the following places:
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- [official download](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original)
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- [file storage](https://drive.yerf.org/wl/?id=EBfTrmcCCUAGaQBXVIj5lJmEhjoP1tgl)
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- magnet:?xt=urn:btih:3a4a612d75ed088ea542acac52f9f45987488d1c&dn=sd-v1-4.ckpt&tr=udp%3a%2f%2ftracker.openbittorrent.com%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337
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You optionally can use GFPGAN to improve faces, then you'll need to download the model from [here](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth).
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To use ESRGAN models, put them into ESRGAN directory in the same location as webui.py. A file will be loaded
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as model if it has .pth extension. Grab models from the [Model Database](https://upscale.wiki/wiki/Model_Database).
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> Note: RealESRGAN models are not ESRGAN models, they are not compatible. Do not download RealESRGAN models. Do not place
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RealESRGAN into the directory with ESRGAN models. Thank you.
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### Automatic installation/launch
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- install [Python 3.10.6](https://www.python.org/downloads/windows/) and check "Add Python to PATH" during installation. You must install this exact version.
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- install [git](https://git-scm.com/download/win)
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- place `model.ckpt` into webui directory, next to `webui.bat`.
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- _*(optional)*_ place `GFPGANv1.3.pth` into webui directory, next to `webui.bat`.
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- run `webui-user.bat` from Windows Explorer. Run it as normal user, ***not*** as administrator.
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#### Troubleshooting
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- if your version of Python is not in PATH (or if another version is), edit `webui-user.bat`, and modify the
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line `set PYTHON=python` to say the full path to your python executable, for example: `set PYTHON=B:\soft\Python310\python.exe`.
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You can do this for python, but not for git.
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- if you get out of memory errors and your video-card has a low amount of VRAM (4GB), use custom parameter `set COMMANDLINE_ARGS` (see section below)
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to enable appropriate optimization according to low VRAM guide below (for example, `set COMMANDLINE_ARGS=--medvram --opt-split-attention`).
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- to prevent the creation of virtual environment and use your system python, use custom parameter replacing `set VENV_DIR=-` (see below).
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- webui.bat installs requirements from files `requirements_versions.txt`, which lists versions for modules specifically compatible with
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Python 3.10.6. If you choose to install for a different version of python, using custom parameter `set REQS_FILE=requirements.txt`
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may help (but I still recommend you to just use the recommended version of python).
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- if you feel you broke something and want to reinstall from scratch, delete directories: `venv`, `repositories`.
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- if you get a green or black screen instead of generated pictures, you have a card that doesn't support half precision
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floating point numbers (Known issue with 16xx cards). You must use `--precision full --no-half` in addition to command line
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arguments (set them using `set COMMANDLINE_ARGS`, see below), and the model will take much more space in VRAM (you will likely
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have to also use at least `--medvram`).
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- installer creates python virtual environment, so none of installed modules will affect your system installation of python if
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you had one prior to installing this.
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- About _"You must install this exact version"_ from the instructions above: you can use any version of python you like,
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and it will likely work, but if you want to seek help about things not working, I will not offer help unless you this
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exact version for my sanity.
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#### How to run with custom parameters
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It's possible to edit `set COMMANDLINE_ARGS=` line in `webui.bat` to run the program with different command line arguments, but that may lead
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to inconveniences when the file is updated in the repository.
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The recommndended way is to use another .bat file named anything you like, set the parameters you want in it, and run webui.bat from it.
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A `webui-user.bat` file included into the repository does exactly this.
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Here is an example that runs the prgoram with `--opt-split-attention` argument:
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```commandline
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@echo off
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set COMMANDLINE_ARGS=--opt-split-attention
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call webui.bat
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```
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Another example, this file will run the program with custom python path, a different model named `a.ckpt` and without virtual environment:
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```commandline
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@echo off
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set PYTHON=b:/soft/Python310/Python.exe
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set VENV_DIR=-
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set COMMANDLINE_ARGS=--ckpt a.ckpt
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call webui.bat
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```
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### What options to use for low VRAM video-cards?
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You can, through command line arguments, enable the various optimizations which sacrifice some/a lot of speed in favor of
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using less VRAM. Those arguments are added to the `COMMANDLINE_ARGS` parameter, see section above.
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Here's a list of optimization arguments:
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- If you have 4GB VRAM and want to make 512x512 (or maybe up to 640x640) images, use `--medvram`.
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- If you have 4GB VRAM and want to make 512x512 images, but you get an out of memory error with `--medvram`, use `--medvram --opt-split-attention` instead.
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- If you have 4GB VRAM and want to make 512x512 images, and you still get an out of memory error, use `--lowvram --always-batch-cond-uncond --opt-split-attention` instead.
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- If you have 4GB VRAM and want to make images larger than you can with `--medvram`, use `--lowvram --opt-split-attention`.
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- If you have more VRAM and want to make larger images than you can usually make (for example 1024x1024 instead of 512x512), use `--medvram --opt-split-attention`. You can use `--lowvram`
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also but the effect will likely be barely noticeable.
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- Otherwise, do not use any of those.
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### Running online
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Use `--share` option to run online. You will get a xxx.app.gradio link. This is the intended way to use the
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program in collabs.
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Use `--listen` to make the server listen to network connections. This will allow computers on local newtork
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to access the UI, and if you configure port forwarding, also computers on the internet.
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Use `--port xxxx` to make the server listen on a specific port, xxxx being the wanted port. Remember that
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all ports below 1024 needs root/admin rights, for this reason it is advised to use a port above 1024.
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Defaults to port 7860 if available.
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### Google collab
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If you don't want or can't run locally, here is google collab that allows you to run the webui:
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https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh
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### Textual Inversion
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To make use of pretrained embeddings, create `embeddings` directory (in the same palce as `webui.py`)
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and put your embeddings into it. They must be .pt files, each with only one trained embedding,
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and the filename (without .pt) will be the term you'd use in prompt to get that embedding.
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As an example, I trained one for about 5000 steps: https://files.catbox.moe/e2ui6r.pt; it does not produce
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very good results, but it does work. Download and rename it to Usada Pekora.pt, and put it into embeddings dir
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and use Usada Pekora in prompt.
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### How to change UI defaults?
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After running once, a `ui-config.json` file appears in webui directory:
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```json
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{
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"txt2img/Sampling Steps/value": 20,
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"txt2img/Sampling Steps/minimum": 1,
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"txt2img/Sampling Steps/maximum": 150,
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"txt2img/Sampling Steps/step": 1,
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"txt2img/Batch count/value": 1,
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"txt2img/Batch count/minimum": 1,
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"txt2img/Batch count/maximum": 32,
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"txt2img/Batch count/step": 1,
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"txt2img/Batch size/value": 1,
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"txt2img/Batch size/minimum": 1,
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```
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Edit values to your liking and the next time you launch the program they will be applied.
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### Manual installation
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Alternatively, if you don't want to run webui.bat, here are instructions for installing
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everything by hand. This can run on both Windows and Linux (if you're on linux, use `ls`
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instead of `dir`).
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```bash
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# install torch with CUDA support. See https://pytorch.org/get-started/locally/ for more instructions if this fails.
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pip install torch --extra-index-url https://download.pytorch.org/whl/cu113
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# check if torch supports GPU; this must output "True". You need CUDA 11. installed for this. You might be able to use
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# a different version, but this is what I tested.
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python -c "import torch; print(torch.cuda.is_available())"
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# clone web ui and go into its directory
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git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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cd stable-diffusion-webui
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# clone repositories for Stable Diffusion and (optionally) CodeFormer
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mkdir repositories
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git clone https://github.com/CompVis/stable-diffusion.git repositories/stable-diffusion
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git clone https://github.com/CompVis/taming-transformers.git repositories/taming-transformers
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git clone https://github.com/sczhou/CodeFormer.git repositories/CodeFormer
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git clone https://github.com/salesforce/BLIP.git repositories/BLIP
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# install requirements of Stable Diffusion
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pip install transformers==4.19.2 diffusers invisible-watermark --prefer-binary
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# install k-diffusion
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pip install git+https://github.com/crowsonkb/k-diffusion.git --prefer-binary
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# (optional) install GFPGAN (face resoration)
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pip install git+https://github.com/TencentARC/GFPGAN.git --prefer-binary
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# (optional) install requirements for CodeFormer (face resoration)
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pip install -r repositories/CodeFormer/requirements.txt --prefer-binary
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# install requirements of web ui
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pip install -r requirements.txt --prefer-binary
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# update numpy to latest version
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pip install -U numpy --prefer-binary
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# (outside of command line) put stable diffusion model into web ui directory
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# the command below must output something like: 1 File(s) 4,265,380,512 bytes
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dir model.ckpt
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# (outside of command line) put the GFPGAN model into web ui directory
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# the command below must output something like: 1 File(s) 348,632,874 bytes
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dir GFPGANv1.3.pth
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```
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> Note: the directory structure for manual instruction has been changed on 2022-09-09 to match automatic installation: previosuly
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> webui was in a subdirectory of stable diffusion, now it's the reverse. If you followed manual installation before the
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> chage, you can still use the program with you existing directory sctructure.
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After that the installation is finished.
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Run the command to start web ui:
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```
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python webui.py
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```
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If you have a 4GB video card, run the command with either `--lowvram` or `--medvram` argument:
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```
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python webui.py --medvram
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```
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After a while, you will get a message like this:
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```
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Running on local URL: http://127.0.0.1:7860/
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```
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Open the URL in browser, and you are good to go.
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### Windows 11 WSL2 instructions
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Alternatively, here are instructions for installing under Windows 11 WSL2 Linux distro, everything by hand:
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```bash
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# install conda (if not already done)
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wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh
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chmod +x Anaconda3-2022.05-Linux-x86_64.sh
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./Anaconda3-2022.05-Linux-x86_64.sh
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# Clone webui repo
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git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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cd stable-diffusion-webui
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# Create and activate conda env
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conda env create -f environment-wsl2.yaml
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conda activate automatic
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# (optional) install requirements for GFPGAN (upscaling)
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wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth
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```
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After that follow the instructions in the `Manual instructions` section starting at step `:: clone repositories for Stable Diffusion and (optionally) CodeFormer`.
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### img2img alterantive test
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- find it in scripts section
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- put description of input image into the Original prompt field
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- use Euler only
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- recommended: 50 steps, low cfg scale between 1 and 2
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- denoising and seed don't matter
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- decode cfg scale between 0 and 1
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- decode steps 50
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- original blue haired woman close nearly reproduces with cfg scale=1.8
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## Credits
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- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
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- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
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- GFPGAN - https://github.com/TencentARC/GFPGAN.git
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- ESRGAN - https://github.com/xinntao/ESRGAN
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- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
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- Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion
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- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
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- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
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- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
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- (You)
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