import{S as go,i as yo,s as vo,e as n,k as c,w as f,t as m,M as bo,c as a,d as t,m as d,a as s,x as h,h as u,b as r,G as o,g as l,y as g,L as _o,q as y,o as v,B as b,v as wo}from"../../../chunks/vendor-hf-doc-builder.js";import{D as He}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Ao}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as le}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function ko(Zt){let _,Te,w,J,ce,V,et,de,tt,Me,E,ot,K,nt,at,Ue,oe,st,xe,ne,ae,rt,j,it,Je,se,lt,Ee,A,L,me,Q,ct,ue,dt,Le,N,pe,B,mt,ut,fe,I,pt,Ne,k,G,he,z,ft,ge,ht,Ge,Y,Se,$,S,ye,C,gt,ve,yt,We,p,F,vt,R,bt,O,_t,wt,At,q,kt,re,$t,Tt,Mt,ie,X,Ze,T,W,be,P,Ut,_e,xt,De,M,H,Jt,we,Et,Ve,U,Z,Ae,ee,Lt,ke,Nt,Ke,x,te,Gt,$e,St,je;return V=new le({}),Q=new le({}),z=new le({}),Y=new Ao({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AsymmetricAutoencoderKL, StableDiffusionInpaintPipeline


<span class="hljs-keyword">def</span> <span class="hljs-title function_">download_image</span>(<span class="hljs-params">url: <span class="hljs-built_in">str</span></span>) -&gt; Image.Image:
    response = requests.get(url)
    <span class="hljs-keyword">return</span> Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)


prompt = <span class="hljs-string">&quot;a photo of a person&quot;</span>
img_url = <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png&quot;</span>
mask_url = <span class="hljs-string">&quot;https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png&quot;</span>

image = download_image(img_url).resize((<span class="hljs-number">256</span>, <span class="hljs-number">256</span>))
mask_image = download_image(mask_url).resize((<span class="hljs-number">256</span>, <span class="hljs-number">256</span>))

pipe = StableDiffusionInpaintPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-inpainting&quot;</span>)
pipe.vae = AsymmetricAutoencoderKL.from_pretrained(<span class="hljs-string">&quot;cross-attention/asymmetric-autoencoder-kl-x-1-5&quot;</span>)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

image = pipe(prompt=prompt, image=image, mask_image=mask_image).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;image.jpeg&quot;</span>)`}}),C=new le({}),F=new He({props:{name:"class diffusers.AsymmetricAutoencoderKL",anchor:"diffusers.AsymmetricAutoencoderKL",parameters:[{name:"in_channels",val:": int = 3"},{name:"out_channels",val:": int = 3"},{name:"down_block_types",val:": typing.Tuple[str] = ('DownEncoderBlock2D',)"},{name:"down_block_out_channels",val:": typing.Tuple[int] = (64,)"},{name:"layers_per_down_block",val:": int = 1"},{name:"up_block_types",val:": typing.Tuple[str] = ('UpDecoderBlock2D',)"},{name:"up_block_out_channels",val:": typing.Tuple[int] = (64,)"},{name:"layers_per_up_block",val:": int = 1"},{name:"act_fn",val:": str = 'silu'"},{name:"latent_channels",val:": int = 4"},{name:"norm_num_groups",val:": int = 32"},{name:"sample_size",val:": int = 32"},{name:"scaling_factor",val:": float = 0.18215"}],parametersDescription:[{anchor:"diffusers.AsymmetricAutoencoderKL.in_channels",description:"<strong>in_channels</strong> (int, <em>optional</em>, defaults to 3) &#x2014; Number of channels in the input image.",name:"in_channels"},{anchor:"diffusers.AsymmetricAutoencoderKL.out_channels",description:"<strong>out_channels</strong> (int,  <em>optional</em>, defaults to 3) &#x2014; Number of channels in the output.",name:"out_channels"},{anchor:"diffusers.AsymmetricAutoencoderKL.down_block_types",description:`<strong>down_block_types</strong> (<code>Tuple[str]</code>, <em>optional</em>, defaults to <code>(&quot;DownEncoderBlock2D&quot;,)</code>) &#x2014;
Tuple of downsample block types.`,name:"down_block_types"},{anchor:"diffusers.AsymmetricAutoencoderKL.down_block_out_channels",description:`<strong>down_block_out_channels</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>(64,)</code>) &#x2014;
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Tuple of upsample block types.`,name:"up_block_types"},{anchor:"diffusers.AsymmetricAutoencoderKL.up_block_out_channels",description:`<strong>up_block_out_channels</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>(64,)</code>) &#x2014;
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Number of groups to use for the first normalization layer in ResNet blocks.`,name:"norm_num_groups"},{anchor:"diffusers.AsymmetricAutoencoderKL.scaling_factor",description:`<strong>scaling_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 0.18215) &#x2014;
The component-wise standard deviation of the trained latent space computed using the first batch of the
training set. This is used to scale the latent space to have unit variance when training the diffusion
model. The latents are scaled with the formula <code>z = z * scaling_factor</code> before being passed to the
diffusion model. When decoding, the latents are scaled back to the original scale with the formula: <code>z = 1 / scaling_factor * z</code>. For more details, refer to sections 4.3.2 and D.1 of the <a href="https://arxiv.org/abs/2112.10752" rel="nofollow">High-Resolution Image
Synthesis with Latent Diffusion Models</a> paper.`,name:"scaling_factor"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoder_asym_kl.py#L26"}}),X=new He({props:{name:"forward",anchor:"diffusers.AsymmetricAutoencoderKL.forward",parameters:[{name:"sample",val:": FloatTensor"},{name:"mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"sample_posterior",val:": bool = False"},{name:"return_dict",val:": bool = True"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"}],parametersDescription:[{anchor:"diffusers.AsymmetricAutoencoderKL.forward.sample",description:"<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014; Input sample.",name:"sample"},{anchor:"diffusers.AsymmetricAutoencoderKL.forward.mask",description:"<strong>mask</strong> (<code>torch.FloatTensor</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014; Optional inpainting mask.",name:"mask"},{anchor:"diffusers.AsymmetricAutoencoderKL.forward.sample_posterior",description:`<strong>sample_posterior</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Encoded outputs of <code>Encoder</code> represented as the mean and logvar of <code>DiagonalGaussianDistribution</code>.
<code>DiagonalGaussianDistribution</code> allows for sampling latents from the distribution.`,name:"latent_dist"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoder_kl.py#L29"}}),ee=new le({}),te=new He({props:{name:"class diffusers.models.vae.DecoderOutput",anchor:"diffusers.models.vae.DecoderOutput",parameters:[{name:"sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.models.vae.DecoderOutput.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
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