added detailed installation instructions
fixed bug with missing same dir for a new install added ctrl+c hander to immediately stop the program instead of waiting
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AUTOMATIC
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3 changed files with 114 additions and 47 deletions
83
README.md
83
README.md
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@ -6,50 +6,77 @@ Original script with Gradio UI was written by a kind anonymous user. This is a m
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## Installing and running
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### Stable Diffusion
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You need python and git installed to run this. I tested the installation to work with Python 3.8.10,
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you may be able to run this on different versions.
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This script assumes that you already have main Stable Diffusion sutff installed, assumed to be in directory `/sd`.
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If you don't have it installed, follow the guide:
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You need 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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- [torrent](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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- https://rentry.org/kretard
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You optionally can use GPFGAN 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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This repository's `webgui.py` is a replacement for `kdiff.py` from the guide.
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Instructions:
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Particularly, following files must exist:
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```commandline
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:: crate a directory somewhere for stable diffusion and open cmd in it; below the directorty is assumed to be b:\src\sd
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:: make sure you are in the right directory; the command must output b:\src\sd1
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echo %cd%
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- `/sd/configs/stable-diffusion/v1-inference.yaml`
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- `/sd/models/ldm/stable-diffusion-v1/model.ckpt`
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- `/sd/ldm/util.py`
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- `/sd/k_diffusion/__init__.py`
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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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### GFPGAN
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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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If you want to use GFPGAN to improve generated faces, you need to install it separately.
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Follow instructions from https://github.com/TencentARC/GFPGAN, but when cloning it, do so into Stable Diffusion main directory, `/sd`.
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After that download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and put it
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into the `/sd/GFPGAN/experiments/pretrained_models` directory. If you're getting troubles with GFPGAN support, follow instructions
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from the GFPGAN's repository until `inference_gfpgan.py` script works.
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:: clone Stable Diffusion repositories
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git clone https://github.com/CompVis/stable-diffusion.git
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git clone https://github.com/CompVis/taming-transformers
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The following files must exist:
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:: install requirements of Stable Diffusion
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pip install transformers==4.19.2 diffusers invisible-watermark
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- `/sd/GFPGAN/inference_gfpgan.py`
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- `/sd/GFPGAN/experiments/pretrained_models/GFPGANv1.3.pth`
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:: install k-diffusion
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pip install git+https://github.com/crowsonkb/k-diffusion.git
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If the GFPGAN directory does not exist, you will not get the option to use GFPGAN in the UI. If it does exist, you will either be able
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to use it, or there will be a message in console with an error related to GFPGAN.
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:: (optional) install GFPGAN to fix faces
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pip install git+https://github.com/TencentARC/GFPGAN.git
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### Web UI
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:: go into stable diffusion's repo directory
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cd stable-diffusion
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Run the script as:
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:: clone web ui
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git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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`python webui.py`
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:: install requirements of web ui
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pip install -r stable-diffusion-webui/requirements.txt
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When running the script, you must be in the main Stable Diffusion directory, `/sd`. If you cloned this repository into a subdirectory
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of `/sd`, say, the `stable-diffusion-webui` directory, you will run it as:
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:: (outside of command line) put stable diffusion model into models/ldm/stable-diffusion-v1/model.ckpt; you'll have
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:: to create one missing 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 models\ldm\stable-diffusion-v1\model.ckpt
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`python stable-diffusion-webui/webui.py`
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:: (outside of command line) put the GFPGAN model into same directory as webui script
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:: the command below must output something like: 1 File(s) 348,632,874 bytes
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dir stable-diffusion-webui\GFPGANv1.3.pth
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```
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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 stable-diffusion-webui/webui.py
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```
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If you have a 4GB video card, run the command with `--lowvram` argument:
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```
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python stable-diffusion-webui/webui.py --lowvram
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```
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When launching, you may get a very long warning message related to some weights not being used. You may freely ignore it.
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After a while, you will get a message like this:
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```
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10
requirements.txt
Normal file
10
requirements.txt
Normal file
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@ -0,0 +1,10 @@
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basicsr
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gfpgan
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gradio
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numpy
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Pillow
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realesrgan
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torch
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transformers
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omegaconf
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pytorch_lightning
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68
webui.py
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webui.py
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@ -1,8 +1,18 @@
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import argparse
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import os
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import sys
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from collections import namedtuple
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from contextlib import nullcontext
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script_path = os.path.dirname(os.path.realpath(__file__))
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sd_path = os.path.dirname(script_path)
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# add parent directory to path; this is where Stable diffusion repo should be
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path_dirs = [(sd_path, 'ldm', 'Stable Diffusion'), ('../../taming-transformers', 'taming', 'Taming Transformers')]
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for d, must_exist, what in path_dirs:
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must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
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if not os.path.exists(must_exist_path):
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print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)
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else:
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sys.path.append(os.path.join(script_path, d))
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import torch
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import torch.nn as nn
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@ -19,6 +29,9 @@ import html
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import time
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import json
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import traceback
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from collections import namedtuple
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from contextlib import nullcontext
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import signal
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import k_diffusion.sampling
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from ldm.util import instantiate_from_config
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@ -33,7 +46,6 @@ gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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script_path = os.path.dirname(os.path.realpath(__file__))
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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@ -44,9 +56,10 @@ invalid_filename_chars = '<>:"/\\|?*\n'
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config_filename = "config.json"
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
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parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
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parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
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parser.add_argument("--ckpt", type=str, default=os.path.join(sd_path, "models/ldm/stable-diffusion-v1/model.ckpt"), help="path to checkpoint of model",)
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parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
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parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default='GFPGANv1.3.pth')
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parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
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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 accleration in browser)")
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parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
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@ -122,25 +135,34 @@ sd_upscalers = {
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}
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have_gfpgan = False
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if os.path.exists(cmd_opts.gfpgan_dir):
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try:
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sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
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from gfpgan import GFPGANer
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def gfpgan_model_path():
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places = [script_path, '.', os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models')]
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files = [cmd_opts.gfpgan_model] + [os.path.join(dirname, cmd_opts.gfpgan_model) for dirname in places]
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found = [x for x in files if os.path.exists(x)]
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have_gfpgan = True
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except:
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print("Error importing GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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if len(found) == 0:
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raise Exception("GFPGAN model not found in paths: " + ", ".join(files))
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return found[0]
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def gfpgan():
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model_name = 'GFPGANv1.3'
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model_path = os.path.join(cmd_opts.gfpgan_dir, 'experiments/pretrained_models', model_name + '.pth')
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if not os.path.isfile(model_path):
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raise Exception("GFPGAN model not found at path "+model_path)
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return GFPGANer(model_path=gfpgan_model_path(), upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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have_gfpgan = False
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try:
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model_path = gfpgan_model_path()
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if os.path.exists(cmd_opts.gfpgan_dir):
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sys.path.append(os.path.abspath(cmd_opts.gfpgan_dir))
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from gfpgan import GFPGANer
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have_gfpgan = True
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except Exception:
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print("Error setting up GFPGAN:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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return GFPGANer(model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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class Options:
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@ -865,6 +887,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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seed = int(random.randrange(4294967294) if p.seed == -1 else p.seed)
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sample_path = os.path.join(p.outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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base_count = len(os.listdir(sample_path))
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grid_count = len(os.listdir(p.outpath)) - 1
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@ -1669,5 +1692,12 @@ demo = gr.TabbedInterface(
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analytics_enabled=False,
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)
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# make the program just exit at ctrl+c without waiting for anything
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def sigint_handler(signal, frame):
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print('Interrupted')
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os._exit(0)
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signal.signal(signal.SIGINT, sigint_handler)
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demo.queue(concurrency_count=1)
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demo.launch()
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