diffusers/v0.4.0/en/index.html

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<p align="center"><br>
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<h1 class="relative group"><a id="diffusers" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#diffusers"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>🧨 Diffusers
	</span></h1>

<p>🤗 Diffusers provides pretrained vision diffusion models, and serves as a modular toolbox for inference and training.</p>
<p>More precisely, 🤗 Diffusers offers:</p>
<ul><li>State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see <a href="./using-diffusers/conditional_image_generation"><strong>Using Diffusers</strong></a>) or have a look at <a href="#pipelines"><strong>Pipelines</strong></a> to get an overview of all supported pipelines and their corresponding papers.</li>
<li>Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference. For more information see <a href="./api/schedulers"><strong>Schedulers</strong></a>.</li>
<li>Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system. See <a href="./api/models"><strong>Models</strong></a> for more details </li>
<li>Training examples to show how to train the most popular diffusion model tasks. For more information see <a href="./training/overview"><strong>Training</strong></a>.</li></ul>
<h2 class="relative group"><a id="diffusers-pipelines" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#diffusers-pipelines"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>🧨 Diffusers Pipelines
	</span></h2>

<p>The following table summarizes all officially supported pipelines, their corresponding paper, and if
available a colab notebook to directly try them out.</p>
<table><thead><tr><th>Pipeline</th>
<th>Paper</th>
<th align="center">Tasks</th>
<th align="center">Colab</th></tr></thead>
<tbody><tr><td><a href="./api/pipelines/ddpm">ddpm</a></td>
<td><a href="https://arxiv.org/abs/2006.11239" rel="nofollow"><strong>Denoising Diffusion Probabilistic Models</strong></a></td>
<td align="center">Unconditional Image Generation</td>
<td align="center"></td></tr>
<tr><td><a href="./api/pipelines/ddim">ddim</a></td>
<td><a href="https://arxiv.org/abs/2010.02502" rel="nofollow"><strong>Denoising Diffusion Implicit Models</strong></a></td>
<td align="center">Unconditional Image Generation</td>
<td align="center"></td></tr>
<tr><td><a href="./api/pipelines/latent_diffusion">latent_diffusion</a></td>
<td><a href="https://arxiv.org/abs/2112.10752" rel="nofollow"><strong>High-Resolution Image Synthesis with Latent Diffusion Models</strong></a></td>
<td align="center">Text-to-Image Generation</td>
<td align="center"></td></tr>
<tr><td><a href="./api/pipelines/latent_diffusion_uncond">latent_diffusion_uncond</a></td>
<td><a href="https://arxiv.org/abs/2112.10752" rel="nofollow"><strong>High-Resolution Image Synthesis with Latent Diffusion Models</strong></a></td>
<td align="center">Unconditional Image Generation</td>
<td align="center"></td></tr>
<tr><td><a href="./api/pipelines/pndm">pndm</a></td>
<td><a href="https://arxiv.org/abs/2202.09778" rel="nofollow"><strong>Pseudo Numerical Methods for Diffusion Models on Manifolds</strong></a></td>
<td align="center">Unconditional Image Generation</td>
<td align="center"></td></tr>
<tr><td><a href="./api/pipelines/score_sde_ve">score_sde_ve</a></td>
<td><a href="https://openreview.net/forum?id=PxTIG12RRHS" rel="nofollow"><strong>Score-Based Generative Modeling through Stochastic Differential Equations</strong></a></td>
<td align="center">Unconditional Image Generation</td>
<td align="center"></td></tr>
<tr><td><a href="./api/pipelines/score_sde_vp">score_sde_vp</a></td>
<td><a href="https://openreview.net/forum?id=PxTIG12RRHS" rel="nofollow"><strong>Score-Based Generative Modeling through Stochastic Differential Equations</strong></a></td>
<td align="center">Unconditional Image Generation</td>
<td align="center"></td></tr>
<tr><td><a href="./api/pipelines/stable_diffusion">stable_diffusion</a></td>
<td><a href="https://stability.ai/blog/stable-diffusion-public-release" rel="nofollow"><strong>Stable Diffusion</strong></a></td>
<td align="center">Text-to-Image Generation</td>
<td align="center"><a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr>
<tr><td><a href="./api/pipelines/stable_diffusion">stable_diffusion</a></td>
<td><a href="https://stability.ai/blog/stable-diffusion-public-release" rel="nofollow"><strong>Stable Diffusion</strong></a></td>
<td align="center">Image-to-Image Text-Guided Generation</td>
<td align="center"><a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr>
<tr><td><a href="./api/pipelines/stable_diffusion">stable_diffusion</a></td>
<td><a href="https://stability.ai/blog/stable-diffusion-public-release" rel="nofollow"><strong>Stable Diffusion</strong></a></td>
<td align="center">Text-Guided Image Inpainting</td>
<td align="center"><a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr>
<tr><td><a href="./api/pipelines/stochastic_karras_ve">stochastic_karras_ve</a></td>
<td><a href="https://arxiv.org/abs/2206.00364" rel="nofollow"><strong>Elucidating the Design Space of Diffusion-Based Generative Models</strong></a></td>
<td align="center">Unconditional Image Generation</td>
<td align="center"></td></tr></tbody></table>
<p><strong>Note</strong>: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. </p>


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