Unverified Commit eeb1de43 authored by AUTOMATIC1111's avatar AUTOMATIC1111 Committed by GitHub
Browse files

Merge branch 'master' into gradient-clipping

parents d85c2cb2 b7deea47

.github/workflows/run_tests.yaml

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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

.gitignore

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__pycache__

*.ckpt

*.safetensors

*.pth

/ESRGAN/*

/SwinIR/*

README.md

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@@ -70,7 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web 
     - 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
@@ -83,27 +83,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web 
- 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 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.

- 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



## 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.
@@ -146,6 +127,8 @@ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC 
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).



## Credits

Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.



- 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
@@ -154,6 +137,7 @@ The documentation was moved from this README over to the project's [wiki](https: 
- 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)

configs/alt-diffusion-inference.yaml

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model:

  base_learning_rate: 1.0e-04

  target: ldm.models.diffusion.ddpm.LatentDiffusion

  params:

    linear_start: 0.00085

    linear_end: 0.0120

    num_timesteps_cond: 1

    log_every_t: 200

    timesteps: 1000

    first_stage_key: "jpg"

    cond_stage_key: "txt"

    image_size: 64

    channels: 4

    cond_stage_trainable: false   # Note: different from the one we trained before

    conditioning_key: crossattn

    monitor: val/loss_simple_ema

    scale_factor: 0.18215

    use_ema: False



    scheduler_config: # 10000 warmup steps

      target: ldm.lr_scheduler.LambdaLinearScheduler

      params:

        warm_up_steps: [ 10000 ]

        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases

        f_start: [ 1.e-6 ]

        f_max: [ 1. ]

        f_min: [ 1. ]



    unet_config:

      target: ldm.modules.diffusionmodules.openaimodel.UNetModel

      params:

        image_size: 32 # unused

        in_channels: 4

        out_channels: 4

        model_channels: 320

        attention_resolutions: [ 4, 2, 1 ]

        num_res_blocks: 2

        channel_mult: [ 1, 2, 4, 4 ]

        num_heads: 8

        use_spatial_transformer: True

        transformer_depth: 1

        context_dim: 768

        use_checkpoint: True

        legacy: False



    first_stage_config:

      target: ldm.models.autoencoder.AutoencoderKL

      params:

        embed_dim: 4

        monitor: val/rec_loss

        ddconfig:

          double_z: true

          z_channels: 4

          resolution: 256

          in_channels: 3

          out_ch: 3

          ch: 128

          ch_mult:

          - 1

          - 2

          - 4

          - 4

          num_res_blocks: 2

          attn_resolutions: []

          dropout: 0.0

        lossconfig:

          target: torch.nn.Identity



    cond_stage_config:

      target: modules.xlmr.BertSeriesModelWithTransformation

      params:

        name: "XLMR-Large"
 
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configs/v1-inference.yaml

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model:

  base_learning_rate: 1.0e-04

  target: ldm.models.diffusion.ddpm.LatentDiffusion

  params:

    linear_start: 0.00085

    linear_end: 0.0120

    num_timesteps_cond: 1

    log_every_t: 200

    timesteps: 1000

    first_stage_key: "jpg"

    cond_stage_key: "txt"

    image_size: 64

    channels: 4

    cond_stage_trainable: false   # Note: different from the one we trained before

    conditioning_key: crossattn

    monitor: val/loss_simple_ema

    scale_factor: 0.18215

    use_ema: False



    scheduler_config: # 10000 warmup steps

      target: ldm.lr_scheduler.LambdaLinearScheduler

      params:

        warm_up_steps: [ 10000 ]

        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases

        f_start: [ 1.e-6 ]

        f_max: [ 1. ]

        f_min: [ 1. ]



    unet_config:

      target: ldm.modules.diffusionmodules.openaimodel.UNetModel

      params:

        image_size: 32 # unused

        in_channels: 4

        out_channels: 4

        model_channels: 320

        attention_resolutions: [ 4, 2, 1 ]

        num_res_blocks: 2

        channel_mult: [ 1, 2, 4, 4 ]

        num_heads: 8

        use_spatial_transformer: True

        transformer_depth: 1

        context_dim: 768

        use_checkpoint: True

        legacy: False



    first_stage_config:

      target: ldm.models.autoencoder.AutoencoderKL

      params:

        embed_dim: 4

        monitor: val/rec_loss

        ddconfig:

          double_z: true

          z_channels: 4

          resolution: 256

          in_channels: 3

          out_ch: 3

          ch: 128

          ch_mult:

          - 1

          - 2

          - 4

          - 4

          num_res_blocks: 2

          attn_resolutions: []

          dropout: 0.0

        lossconfig:

          target: torch.nn.Identity



    cond_stage_config:

      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

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