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* Setup LEdits++ file structure * Fix import * LEditsPP Stable Diffusion pipeline * Include variable image aspect ratios * Implement LEDITS++ for SDXL * clean up LEditsPPPipelineStableDiffusion * Adjust inversion output * Added docu, more cleanup for LEditsPPPipelineStableDiffusion * clean up LEditsPPPipelineStableDiffusionXL * Update documentation * Fix documentation import * Add skeleton IF implementation * Fix documentation typo * Add LEDTIS docu to toctree * Add missing title * Finalize SD documentation * Finalize SD-XL documentation * Fix code style and quality * Fix typo * Fix return types * added LEditsPPPipelineIF; minor changes for LEditsPPPipelineStableDiffusion and LEditsPPPipelineStableDiffusionXL * Fix copy reference * add documentation for IF * Add first tests * Fix batching for SD-XL * Fix text encoding and perfect reconstruction for SD-XL * Add tests for SD-XL, minor changes * move user_mask to correct device, use cross_attention_kwargs also for inversion * Example docstring * Fix attention resolution for non-square images * Refactoring for PR review * Safely remove ledits_utils.py * Style fixes * Replace assertions with ValueError * Remove LEditsPPPipelineIF * Remove unecessary input checks * Refactoring of CrossAttnProcessor * Revert unecessary changes to scheduler * Remove first progress-bar in inversion * Refactor scheduler usage and reset * Use imageprocessor instead of custom logic * Fix scheduler init warning * Fix error when running the pipeline in fp16 * Update documentation wrt perfect inversion * Update tests * Fix code quality and copy consistency * Update LEditsPP import * Remove enable/disable methods that are now in StableDiffusionMixin * Change import in docs * Revert import structure change * Fix ledits imports --------- Co-authored-by: Katharina Kornmeier <katharina.kornmeier@stud.tu-darmstadt.de>
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Pipelines
Pipelines provide a simple way to run state-of-the-art diffusion models in inference by bundling all of the necessary components (multiple independently-trained models, schedulers, and processors) into a single end-to-end class. Pipelines are flexible and they can be adapted to use different schedulers or even model components.
All pipelines are built from the base [DiffusionPipeline] class which provides basic functionality for loading, downloading, and saving all the components. Specific pipeline types (for example [StableDiffusionPipeline]) loaded with [~DiffusionPipeline.from_pretrained] are automatically detected and the pipeline components are loaded and passed to the __init__ function of the pipeline.
You shouldn't use the [DiffusionPipeline] class for training. Individual components (for example, [UNet2DModel] and [UNet2DConditionModel]) of diffusion pipelines are usually trained individually, so we suggest directly working with them instead.
Pipelines do not offer any training functionality. You'll notice PyTorch's autograd is disabled by decorating the [~DiffusionPipeline.__call__] method with a torch.no_grad decorator because pipelines should not be used for training. If you're interested in training, please take a look at the Training guides instead!
The table below lists all the pipelines currently available in 🤗 Diffusers and the tasks they support. Click on a pipeline to view its abstract and published paper.
| Pipeline | Tasks |
|---|---|
| AltDiffusion | image2image |
| AnimateDiff | text2video |
| Attend-and-Excite | text2image |
| Audio Diffusion | image2audio |
| AudioLDM | text2audio |
| AudioLDM2 | text2audio |
| BLIP Diffusion | text2image |
| Consistency Models | unconditional image generation |
| ControlNet | text2image, image2image, inpainting |
| ControlNet with Stable Diffusion XL | text2image |
| ControlNet-XS | text2image |
| ControlNet-XS with Stable Diffusion XL | text2image |
| Cycle Diffusion | image2image |
| Dance Diffusion | unconditional audio generation |
| DDIM | unconditional image generation |
| DDPM | unconditional image generation |
| DeepFloyd IF | text2image, image2image, inpainting, super-resolution |
| DiffEdit | inpainting |
| DiT | text2image |
| GLIGEN | text2image |
| InstructPix2Pix | image editing |
| Kandinsky 2.1 | text2image, image2image, inpainting, interpolation |
| Kandinsky 2.2 | text2image, image2image, inpainting |
| Kandinsky 3 | text2image, image2image |
| Latent Consistency Models | text2image |
| Latent Diffusion | text2image, super-resolution |
| LDM3D | text2image, text-to-3D, text-to-pano, upscaling |
| LEDITS++ | image editing |
| MultiDiffusion | text2image |
| MusicLDM | text2audio |
| Paint by Example | inpainting |
| ParaDiGMS | text2image |
| Pix2Pix Zero | image editing |
| PixArt-α | text2image |
| PNDM | unconditional image generation |
| RePaint | inpainting |
| Score SDE VE | unconditional image generation |
| Self-Attention Guidance | text2image |
| Semantic Guidance | text2image |
| Shap-E | text-to-3D, image-to-3D |
| Spectrogram Diffusion | |
| Stable Diffusion | text2image, image2image, depth2image, inpainting, image variation, latent upscaler, super-resolution |
| Stable Diffusion Model Editing | model editing |
| Stable Diffusion XL | text2image, image2image, inpainting |
| Stable Diffusion XL Turbo | text2image, image2image, inpainting |
| Stable unCLIP | text2image, image variation |
| Stochastic Karras VE | unconditional image generation |
| T2I-Adapter | text2image |
| Text2Video | text2video, video2video |
| Text2Video-Zero | text2video |
| unCLIP | text2image, image variation |
| Unconditional Latent Diffusion | unconditional image generation |
| UniDiffuser | text2image, image2text, image variation, text variation, unconditional image generation, unconditional audio generation |
| Value-guided planning | value guided sampling |
| Versatile Diffusion | text2image, image variation |
| VQ Diffusion | text2image |
| Wuerstchen | text2image |
DiffusionPipeline
autodoc DiffusionPipeline - all - call - device - to - components
FlaxDiffusionPipeline
autodoc pipelines.pipeline_flax_utils.FlaxDiffusionPipeline
PushToHubMixin
autodoc utils.PushToHubMixin