Merge branch 'AUTOMATIC1111:master' into master

name: Run basic features tests on CPU with empty SD model

on:

  - push

  - pull_request

jobs:

  test:

    runs-on: ubuntu-latest

    steps:

      - name: Checkout Code

        uses: actions/checkout@v3

      - name: Set up Python 3.10

        uses: actions/setup-python@v4

        with:

          python-version: 3.10.6

      - uses: actions/cache@v3

        with:

          path: ~/.cache/pip

          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}

          restore-keys: ${{ runner.os }}-pip-

      - name: Run tests

        run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test

      - name: Upload main app stdout-stderr

        uses: actions/upload-artifact@v3

        if: always()

        with:

          name: stdout-stderr

          path: |

            test/stdout.txt

            test/stderr.txt

__pycache__

*.ckpt

*.safetensors

*.pth

/ESRGAN/*

/SwinIR/*

     - 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 (add --deepdanbooru to commandline args)

- 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

- 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 embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))

## Where are Aesthetic Gradients?!?!

Aesthetic Gradients are now an extension. You can install it using git:

```commandline

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients

```

After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart

the UI. The interface for Aesthetic Gradients should appear exactly the same as it was.

## Where is History/Image browser?!?!

Image browser is now an extension. You can install it using git:

```commandline

git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser extensions/images-browser

```

After running this command, make sure that you have `images-browser` dir in webui's `extensions` directory and restart

the UI. The interface for Image browser should appear exactly the same as it was.

- [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

## 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.

- 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)

        pl_sd = torch.load(self.modelPath, map_location="cpu")

        sd = pl_sd["state_dict"]

        config = OmegaConf.load(self.yamlPath)

        config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"

        model = instantiate_from_config(config.model)

        model.load_state_dict(sd, strict=False)

        model.cuda()

import os

from modules import paths

def preload(parser):

    parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))