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Vladimir Mandic edited this page Jul 8, 2023 · 33 revisions

SD.Next includes experimental support for additional model pipelines
This includes support for additional models such as:

  • Stable Diffusion XL
  • Kandinsky
  • Deep Floyd IF

And soon:

  • Shap-E, UniDiffuser, Consistency Models, Diffedit Zero-Shot
  • Text2Video, Video2Video, etc...

Note that support is experimental, do not open GitHub issues for those models
and instead reach-out on Discord using dedicated #diffusers channels

This has been made possible by integration of huggingface diffusers library with the help of huggingface team!

Install

Initial support merged into dev branch

How to

  • Install SD.Next
  • Start with
    webui --backend diffusers
  • To go back to standard execution pipeline, start with
    webui --backend original

Integration

Standard workflows

  • txt2txt
  • img2img
  • process

Model Access

  • For standard SD 1.5 and SD 2.1 models, you can use either
    standard safetensor models (single file) or diffusers models (folder structure)
  • For additional models, you can use diffusers models only
  • You can download diffuser models directly from Huggingface hub
    or use built-in model search & download in SD.Next: UI -> Models -> Huggingface
  • Note that access to some models is gated
    In which case, you need to accept model EULA and provide your huggingface token
  • When loading safetensors models, you must specify model pipeline type in:
    UI -> Settings -> Diffusers -> Pipeline
    When loading huggingface models, pipeline type is automatically detected

Extra Networks

  • Lora networks
  • Textual inversions (embeddings)

Note that Lora and TI need are still model-specific, so you cannot use Lora trained on SD 1.5 on SD-XL
(just like you couldn't do it on SD 2.1 model) - it needs to be trained for a specific model

Support for SD-XL training is expected shortly

Diffuser Settings

  • UI -> Settings -> Diffuser Settings
    contains additional tunable parameters

Samplers

  • Samplers (schedulers) are pipeline specific, so when running with diffuser backend, you'll see a different list of samplers
  • UI -> Settings -> Sampler Settings shows different configurable parameters depending on backend
  • Recommended sampler for diffusers is DEIS

Other

  • Updated System Info tab with additional information
  • Support for lowvram and medvram modes
    Additional tunables are available in UI -> Settings -> Diffuser Settings
  • Support for both default SDP and xFormers cross-optimizations
    Other cross-optimization methods are not available
  • Extra Networks UI will show available diffusers models
  • CUDA model compile
    UI Settings -> Compute settings
    Requires GPU with high VRAM
    Diffusers recommend reduce overhead compile mode, but other methods are available as well
    Fullgraph compile is possible (with sufficient vram) when using diffusers

SD-XL Notes

  • SD-XL Technical Report
  • SD-XL model is designed as two-stage model
    You can run SD-XL pipeline using just base model or load both base and refiner models
    • base: Trained on images with variety of aspect ratios and uses OpenCLIP-ViT/G and CLIP-ViT/L for text encoding
    • refiner: Trained to denoise small noise levels of high quality data and uses the OpenCLIP model
    • Having both base model and refiner model loaded can require significant VRAM
    • If you want to use refiner model, it is advised to add sd_model_refiner to quicksettings
      in UI Settings -> User Interface
  • SD-XL model was trained on 1024px images
    You can use it with smaller sizes, but you will likely get better results with SD 1.5 models
  • SD-XL model NSFW filter has been turned off

Download

  1. Go to UI -> Models -> Huggingface
  2. Enter stabilityai/stable-diffusion-xl-base-0.9 in Select Model and press Download
  3. Enter stabilityai/stable-diffusion-xl-refiner-0.9 in Select Model and press Download

Limitations

  • Skip/Stop operations are not possible while running a diffusers model
  • Any extension that requires access to model internals will likely not work when using diffusers backend
    This for example includes standard extensions such as ControlNet, MultiDiffusion, LyCORIS
    Note: application will auto-disable incompatible built-in extensions when running in diffusers mode
    If you go back to original mode, you will need to re-enable extensions
  • Explict vae usage is not yet implemented
  • Explicit refiner as postprocessing is not yet implemented
  • Second-pass workflows such as hires fix are not yet implemented
  • Hypernetworks
  • Limited callbacks support for scripts/extensions: additional callbacks will be added as needed

Performance

Comparison of original stable diffusion pipeline and diffusers pipeline when using standard SD 1.5 model
Performance is measured for batch-size 1, 2, 4, 8 16

pipeline performance it/s memory cpu/gpu
original 7.99 / 7.93 / 8.83 / 9.14 / 9.2 6.7 / 7.2
original medvram 6.23 / 7.16 / 8.41 / 9.24 / 9.68 8.4 / 6.8
original lowvram 1.05 / 1.94 / 3.2 / 4.81 / 6.46 8.8 / 5.2
diffusers 9 / 7.4 / 8.2 / 8.4 / 7.0 4.3 / 9.0
diffusers medvram 7.5 / 6.7 / 7.5 / 7.8 / 7.2 6.6 / 8.2
diffusers lowvram 7.0 / 7.0 / 7.4 / 7.7 / 7.8 4.3 / 7.2
diffusers with safetensors 8.9 / 7.3 / 8.1 / 8.4 / 7.1 5.9 / 9.0

Notes:

  • Test environment: nVidia RTX 3060 GPU, Torch 2.1-nightly with CUDA 12.1, Cross-optimization: SDP
  • All being equal, diffussers seem to:
    • Use slightly less RAM and more VRAM
    • Have highly efficient medvram/lowvram equivalents which don't lose a lot of performance
    • Faster on smaller batch sizes, slower on larger batch sizes

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