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...

*This has been made possible by integration of [huggingface diffusers](https://huggingface.co/docs/diffusers/index) library with the help of huggingface team!*

## How to

Moved to [Installation](https://github.com/vladmandic/automatic/wiki/Installation) and [SDXL](https://github.com/vladmandic/automatic/wiki/SDXL)

## Integration

### Standard workflows  

- **txt2img**
- **img2img**
- **inpaint**
- **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](https://huggingface.co/)  
  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  
- If you get this `Diffuser model downloaded error: model=stabilityai/stable-diffusion-etc [Errno 2] No such file or directory:`  
  you need to go to the HuggingFace page and accept the EULA for that model.

### 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 - Both work extremely well  
  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  
- Note that some CUDA compile modes only work on Linux

## SD-XL Notes

- [SD-XL Technical Report](https://github.com/Stability-AI/generative-models/blob/main/assets/sdxl_report.pdf)
- 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 SD-XL 1.0

1. Enter `stabilityai/stable-diffusion-xl-base-1.0` in *Select Model* and press *Download*
2. Enter `stabilityai/stable-diffusion-xl-refiner-1.0` in *Select Model* and press *Download*

## Limitations

- 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`,  
  *Note: application will auto-disable incompatible built-in extensions when running in diffusers mode*  
- Explicit `refiner` as postprocessing is 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, diffusers 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