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# Pipelines
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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 scheduler or even model components.
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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.
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All pipelines are built from the base [`DiffusionPipeline`] class which provides basic functionality for loading, downloading, and saving all the components.
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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.
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<Tipwarning={true}>
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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`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../traininig/overview) guides instead!
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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`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should not be used for training. If you're interested in training, please take a look at the [Training](../../training/overview) guides instead!
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</Tip>
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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.
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<p class="text-gray-700">Technical descriptions of how 🤗 Diffusers classes and methods work.</p>
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</a>
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</div>
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## Supported pipelines
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| Pipeline | Paper/Repository | Tasks |
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|---|---|:---:|
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|[alt_diffusion](./api/pipelines/alt_diffusion)|[AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679)| Image-to-Image Text-Guided Generation |
|[controlnet](./api/pipelines/controlnet)|[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543)| Image-to-Image Text-Guided Generation |
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|[cycle_diffusion](./api/pipelines/cycle_diffusion)|[Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance](https://arxiv.org/abs/2210.05559)| Image-to-Image Text-Guided Generation |
|[stochastic_karras_ve](./api/pipelines/stochastic_karras_ve)|[Elucidating the Design Space of Diffusion-Based Generative Models](https://arxiv.org/abs/2206.00364)| Unconditional Image Generation |
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|[text_to_video_sd](./api/pipelines/text_to_video)|[Modelscope's Text-to-video-synthesis Model in Open Domain](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)| Text-to-Video Generation |
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|[unclip](./api/pipelines/unclip)|[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125)(implementation by [kakaobrain](https://github.com/kakaobrain/karlo)) | Text-to-Image Generation |
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|[versatile_diffusion](./api/pipelines/versatile_diffusion)|[Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332)| Text-to-Image Generation |
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|[versatile_diffusion](./api/pipelines/versatile_diffusion)|[Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332)| Image Variations Generation |
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|[versatile_diffusion](./api/pipelines/versatile_diffusion)|[Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332)| Dual Image and Text Guided Generation |
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|[vq_diffusion](./api/pipelines/vq_diffusion)|[Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822)| Text-to-Image Generation |
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|[stable_diffusion_ldm3d](./api/pipelines/stable_diffusion/ldm3d_diffusion)|[LDM3D: Latent Diffusion Model for 3D](https://arxiv.org/abs/2305.10853)| Text to Image and Depth Generation |
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