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Tokenizer of class
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<p><a
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the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,name:"text_encoder"},{anchor:"diffusers.StableDiffusionInpaintPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.StableDiffusionInpaintPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/pr_430/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.StableDiffusionInpaintPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/pr_430/en/api/schedulers#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latens. Can be one of
<a href="/docs/diffusers/pr_430/en/api/schedulers#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/pr_430/en/api/schedulers#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/pr_430/en/api/schedulers#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.StableDiffusionInpaintPipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>StableDiffusionSafetyChecker</code>) &#x2014;
Classification module that estimates whether generated images could be considered offsensive or harmful.
Please, refer to the <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" rel="nofollow">model card</a> for details.`,name:"safety_checker"},{anchor:"diffusers.StableDiffusionInpaintPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPFeatureExtractor</code>) &#x2014;
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<code>Image</code>, or tensor representing an image batch, that will be used as the starting point for the
process. This is the image whose masked region will be inpainted.`,name:"init_image"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.mask_image",description:`<strong>mask_image</strong> (<code>torch.FloatTensor</code> or <code>PIL.Image.Image</code>) &#x2014;
<code>Image</code>, or tensor representing an image batch, to mask <code>init_image</code>. White pixels in the mask will be
replaced by noise and therefore repainted, while black pixels will be preserved. The mask image will be
converted to a single channel (luminance) before use.`,name:"mask_image"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.strength",description:`<strong>strength</strong> (<code>float</code>, <em>optional</em>, defaults to 0.8) &#x2014;
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When <code>strength</code>
is 1, the denoising process will be run on the masked area for the full number of iterations specified
in <code>num_inference_steps</code>. <code>init_image</code> will be used as a reference for the masked area, adding more
noise to that region the larger the <code>strength</code>. If <code>strength</code> is 0, no inpainting will occur.`,name:"strength"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
the expense of slower inference. This parameter will be modulated by <code>strength</code>, as explained above.`,name:"num_inference_steps"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#x2014;
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<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/pr_430/en/api/schedulers#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
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deterministic.`,name:"generator"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>nd.array</code>.`,name:"output_type"},{anchor:"diffusers.StableDiffusionInpaintPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/pr_430/en/api/pipelines/stable_diffusion#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/vr_430/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L123",returnDescription:`
<p><a
  href="/docs/diffusers/pr_430/en/api/pipelines/stable_diffusion#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> if <code>return_dict</code> is True, otherwise a <code>tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of </code>bool<code>s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the </code>safety_checker\`.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/pr_430/en/api/pipelines/stable_diffusion#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),Ge=new S({props:{name:"enable_attention_slicing",anchor:"diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing",parameters:[{name:"slice_size",val:": typing.Union[str, int, NoneType] = 'auto'"}],parametersDescription:[{anchor:"diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing.slice_size",description:`<strong>slice_size</strong> (<code>str</code> or <code>int</code>, <em>optional</em>, defaults to <code>&quot;auto&quot;</code>) &#x2014;
When <code>&quot;auto&quot;</code>, halves the input to the attention heads, so attention will be computed in two steps. If
a number is provided, uses as many slices as <code>attention_head_dim // slice_size</code>. In this case,
<code>attention_head_dim</code> must be a multiple of <code>slice_size</code>.`,name:"slice_size"}],source:"https://github.com/huggingface/diffusers/blob/vr_430/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L96"}}),qe=new S({props:{name:"disable_attention_slicing",anchor:"diffusers.StableDiffusionInpaintPipeline.disable_attention_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/vr_430/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py#L115"}}),{c(){A=n("meta"),zt=f(),z=n("h1"),V=n("a"),it=n("span"),m(Z.$$.fragment),bi=f(),nt=n("span"),vi=a("Stable diffusion pipelines"),Nt=f(),y=n("p"),Di=a("Stable Diffusion is a text-to-image "),ot=n("em"),Ii=a("latent diffusion"),Pi=a(" model created by the researchers and engineers from "),ee=n("a"),wi=a("CompVis"),yi=a(", "),te=n("a"),Si=a("Stability AI"),xi=a(" and "),ie=n("a"),$i=a("LAION"),ki=a(". It\u2019s trained on 512x512 images from a subset of the "),ne=n("a"),Ei=a("LAION-5B"),Li=a(" dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs."),Gt=f(),L=n("p"),Ci=a("Latent diffusion is the research on top of which Stable Diffusion was built. It was proposed in "),oe=n("a"),Ti=a("High-Resolution Image Synthesis with Latent Diffusion Models"),Mi=a(" by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Bj\xF6rn Ommer. You can learn more details about it in the "),Fe=n("a"),Oi=a("specific pipeline for latent diffusion"),Ai=a(" that is part of \u{1F917} Diffusers."),qt=f(),C=n("p"),zi=a("For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, please refer to the official "),se=n("a"),Ni=a("launch announcement post"),Gi=a(" and "),ae=n("a"),qi=a("this section of our own blog post"),Ui=a("."),Ut=f(),re=n("p"),st=n("em"),Fi=a("Tips"),Vi=a(":"),Ft=f(),Ve=n("ul"),We=n("li"),Wi=a("To tweak your prompts on a specific result you liked, you can generate your own latents, as demonstrated in the following notebook: "),le=n("a"),Be=n("img"),Vt=f(),p=n("p"),at=n("em"),Bi=a("Overview"),Hi=a(`:
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