import{S as tn,i as on,s as rn,e as r,k as i,w as h,t as d,M as sn,c as s,d as o,m as l,a as n,x as g,h as c,b as a,G as e,g as f,y as _,L as nn,q as v,o as b,B as A,v as an}from"../../../chunks/vendor-hf-doc-builder.js";import{D as w}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as Se}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function ln(Zr){let k,Kt,K,N,Ue,oe,_o,Ge,vo,Tt,M,bo,re,Ao,$o,Ft,Ne,wo,Pt,Me,Xe,xo,Ot,T,j,qe,se,yo,Je,Lo,Vt,p,ne,Eo,Ye,Do,ko,ae,Ko,je,To,Fo,Po,R,ie,Oo,le,Vo,Qe,zo,Io,So,W,de,No,ce,Mo,Ze,jo,Ro,Wo,C,ue,Co,et,Bo,Ho,B,fe,Uo,tt,Go,Xo,Re,pe,qo,H,me,Jo,ot,Yo,Qo,U,he,Zo,rt,er,tr,G,ge,or,st,rr,sr,D,_e,nr,nt,ar,ir,at,lr,zt,F,X,it,ve,dr,lt,cr,It,P,be,ur,dt,fr,St,O,q,ct,Ae,pr,ut,mr,Nt,V,$e,hr,ft,gr,Mt,z,J,pt,we,_r,mt,vr,jt,$,xe,br,ht,Ar,$r,ye,wr,We,xr,yr,Lr,Le,Er,Ee,Dr,kr,Kr,gt,Tr,Fr,y,_t,De,Pr,Or,vt,ke,Vr,zr,bt,Ke,Ir,Sr,At,Te,Nr,Rt,I,Y,$t,Fe,Mr,wt,jr,Wt,L,Pe,Rr,xt,Wr,Cr,Q,Oe,Br,yt,Hr,Ct,S,Z,Lt,Ve,Ur,Et,Gr,Bt,E,ze,Xr,Dt,qr,Jr,ee,Ie,Yr,kt,Qr,Ht;return oe=new Se({}),se=new Se({}),ne=new w({props:{name:"class diffusers.AutoencoderKL",anchor:"diffusers.AutoencoderKL",parameters:[{name:"in_channels",val:": int = 3"},{name:"out_channels",val:": int = 3"},{name:"down_block_types",val:": typing.Tuple[str] = ('DownEncoderBlock2D',)"},{name:"up_block_types",val:": typing.Tuple[str] = ('UpDecoderBlock2D',)"},{name:"block_out_channels",val:": typing.Tuple[int] = (64,)"},{name:"layers_per_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.AutoencoderKL.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.AutoencoderKL.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.AutoencoderKL.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.AutoencoderKL.up_block_types",description:`<strong>up_block_types</strong> (<code>Tuple[str]</code>, <em>optional</em>, defaults to <code>(&quot;UpDecoderBlock2D&quot;,)</code>) &#x2014;
Tuple of upsample block types.`,name:"up_block_types"},{anchor:"diffusers.AutoencoderKL.block_out_channels",description:`<strong>block_out_channels</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>(64,)</code>) &#x2014;
Tuple of block output channels.`,name:"block_out_channels"},{anchor:"diffusers.AutoencoderKL.act_fn",description:"<strong>act_fn</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;silu&quot;</code>) &#x2014; The activation function to use.",name:"act_fn"},{anchor:"diffusers.AutoencoderKL.latent_channels",description:"<strong>latent_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014; Number of channels in the latent space.",name:"latent_channels"},{anchor:"diffusers.AutoencoderKL.sample_size",description:"<strong>sample_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>32</code>) &#x2014; Sample input size.",name:"sample_size"},{anchor:"diffusers.AutoencoderKL.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/v0.18.2/src/diffusers/models/autoencoder_kl.py#L41"}}),ie=new w({props:{name:"disable_slicing",anchor:"diffusers.AutoencoderKL.disable_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L153"}}),de=new w({props:{name:"disable_tiling",anchor:"diffusers.AutoencoderKL.disable_tiling",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L139"}}),ue=new w({props:{name:"enable_slicing",anchor:"diffusers.AutoencoderKL.enable_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L146"}}),fe=new w({props:{name:"enable_tiling",anchor:"diffusers.AutoencoderKL.enable_tiling",parameters:[{name:"use_tiling",val:": bool = True"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L131"}}),pe=new w({props:{name:"forward",anchor:"diffusers.AutoencoderKL.forward",parameters:[{name:"sample",val:": FloatTensor"},{name:"sample_posterior",val:": bool = False"},{name:"return_dict",val:": bool = True"},{name:"generator",val:": typing.Optional[torch._C.Generator] = None"}],parametersDescription:[{anchor:"diffusers.AutoencoderKL.forward.sample",description:"<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014; Input sample.",name:"sample"},{anchor:"diffusers.AutoencoderKL.forward.sample_posterior",description:`<strong>sample_posterior</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to sample from the posterior.`,name:"sample_posterior"},{anchor:"diffusers.AutoencoderKL.forward.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 <code>DecoderOutput</code> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L385"}}),me=new w({props:{name:"set_attn_processor",anchor:"diffusers.AutoencoderKL.set_attn_processor",parameters:[{name:"processor",val:": typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, typing.Dict[str, typing.Union[diffusers.models.attention_processor.AttnProcessor, diffusers.models.attention_processor.AttnProcessor2_0, diffusers.models.attention_processor.XFormersAttnProcessor, diffusers.models.attention_processor.SlicedAttnProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor, diffusers.models.attention_processor.SlicedAttnAddedKVProcessor, diffusers.models.attention_processor.AttnAddedKVProcessor2_0, diffusers.models.attention_processor.XFormersAttnAddedKVProcessor, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor, diffusers.models.attention_processor.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor]]]"}],parametersDescription:[{anchor:"diffusers.AutoencoderKL.set_attn_processor.processor",description:`<strong>processor</strong> (<code>dict</code> of <code>AttentionProcessor</code> or only <code>AttentionProcessor</code>) &#x2014;
The instantiated processor class or a dictionary of processor classes that will be set as the processor
for <strong>all</strong> <code>Attention</code> layers.</p>
<p>If <code>processor</code> is a dict, the key needs to define the path to the corresponding cross attention
processor. This is strongly recommended when setting trainable attention processors.`,name:"processor"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L186"}}),he=new w({props:{name:"set_default_attn_processor",anchor:"diffusers.AutoencoderKL.set_default_attn_processor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L221"}}),ge=new w({props:{name:"tiled_decode",anchor:"diffusers.AutoencoderKL.tiled_decode",parameters:[{name:"z",val:": FloatTensor"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.AutoencoderKL.tiled_decode.z",description:"<strong>z</strong> (<code>torch.FloatTensor</code>) &#x2014; Input batch of latent vectors.",name:"z"},{anchor:"diffusers.AutoencoderKL.tiled_decode.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/v0.18.2/en/api/models/autoencoderkl#diffusers.models.vae.DecoderOutput">DecoderOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L337",returnDescription:`
<p>If return_dict is True, a <a
  href="/docs/diffusers/v0.18.2/en/api/models/autoencoderkl#diffusers.models.vae.DecoderOutput"
>DecoderOutput</a> is returned, otherwise a plain <code>tuple</code> is
returned.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.18.2/en/api/models/autoencoderkl#diffusers.models.vae.DecoderOutput"
>DecoderOutput</a> or <code>tuple</code></p>
`}}),_e=new w({props:{name:"tiled_encode",anchor:"diffusers.AutoencoderKL.tiled_encode",parameters:[{name:"x",val:": FloatTensor"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.AutoencoderKL.tiled_encode.x",description:"<strong>x</strong> (<code>torch.FloatTensor</code>) &#x2014; Input batch of images.",name:"x"},{anchor:"diffusers.AutoencoderKL.tiled_encode.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/v0.18.2/en/api/models/autoencoderkl#diffusers.models.autoencoder_kl.AutoencoderKLOutput">AutoencoderKLOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/autoencoder_kl.py#L283",returnDescription:`
<p>If return_dict is True, a <a
  href="/docs/diffusers/v0.18.2/en/api/models/autoencoderkl#diffusers.models.autoencoder_kl.AutoencoderKLOutput"
>AutoencoderKLOutput</a> is returned, otherwise a plain
<code>tuple</code> is returned.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.18.2/en/api/models/autoencoderkl#diffusers.models.autoencoder_kl.AutoencoderKLOutput"
>AutoencoderKLOutput</a> or <code>tuple</code></p>
`}}),ve=new Se({}),be=new w({props:{name:"class diffusers.models.autoencoder_kl.AutoencoderKLOutput",anchor:"diffusers.models.autoencoder_kl.AutoencoderKLOutput",parameters:[{name:"latent_dist",val:": DiagonalGaussianDistribution"}],parametersDescription:[{anchor:"diffusers.models.autoencoder_kl.AutoencoderKLOutput.latent_dist",description:`<strong>latent_dist</strong> (<code>DiagonalGaussianDistribution</code>) &#x2014;
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/v0.18.2/src/diffusers/models/autoencoder_kl.py#L28"}}),Ae=new Se({}),$e=new w({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;
The decoded output sample from the last layer of the model.`,name:"sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/vae.py#L27"}}),we=new Se({}),xe=new w({props:{name:"class diffusers.FlaxAutoencoderKL",anchor:"diffusers.FlaxAutoencoderKL",parameters:[{name:"in_channels",val:": int = 3"},{name:"out_channels",val:": int = 3"},{name:"down_block_types",val:": typing.Tuple[str] = ('DownEncoderBlock2D',)"},{name:"up_block_types",val:": typing.Tuple[str] = ('UpDecoderBlock2D',)"},{name:"block_out_channels",val:": typing.Tuple[int] = (64,)"},{name:"layers_per_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"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"parent",val:": typing.Union[typing.Type[flax.linen.module.Module], typing.Type[flax.core.scope.Scope], typing.Type[flax.linen.module._Sentinel], NoneType] = <flax.linen.module._Sentinel object at 0x7facb47fa310>"},{name:"name",val:": str = None"}],parametersDescription:[{anchor:"diffusers.FlaxAutoencoderKL.in_channels",description:`<strong>in_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
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Tuple of block output channels.`,name:"block_out_channels"},{anchor:"diffusers.FlaxAutoencoderKL.layers_per_block",description:`<strong>layers_per_block</strong> (<code>int</code>, <em>optional</em>, defaults to <code>2</code>) &#x2014;
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The number of groups for normalization.`,name:"norm_num_groups"},{anchor:"diffusers.FlaxAutoencoderKL.sample_size",description:`<strong>sample_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Sample input size.`,name:"sample_size"},{anchor:"diffusers.FlaxAutoencoderKL.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
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The <code>dtype</code> of the parameters.`,name:"dtype"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.2/src/diffusers/models/vae_flax.py#L721"}}),Fe=new Se({}),Pe=new w({props:{name:"class diffusers.models.vae_flax.FlaxAutoencoderKLOutput",anchor:"diffusers.models.vae_flax.FlaxAutoencoderKLOutput",parameters:[{name:"latent_dist",val:": FlaxDiagonalGaussianDistribution"}],parametersDescription:[{anchor:"diffusers.models.vae_flax.FlaxAutoencoderKLOutput.latent_dist",description:`<strong>latent_dist</strong> (<code>FlaxDiagonalGaussianDistribution</code>) &#x2014;
Encoded outputs of <code>Encoder</code> represented as the mean and logvar of <code>FlaxDiagonalGaussianDistribution</code>.
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