import{S as br,i as Pr,s as vr,e as t,k as d,w as L,t as f,M as Tr,c as s,d as o,m,a as n,x as E,h as c,b as i,G as r,g as p,y as I,L as yr,q as N,o as O,B as C,v as wr}from"../../../chunks/vendor-hf-doc-builder.js";import{D as se}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as Ye}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function $r(Ze){let h,ne,_,v,B,j,$e,G,Ae,ie,T,xe,k,Le,Ee,ae,H,Ie,de,M,J,Ne,me,g,y,Q,D,Oe,Y,Ce,fe,l,S,je,Z,ke,De,w,F,Se,V,Fe,W,Ve,ze,Re,$,z,Ke,ee,Xe,qe,A,R,He,re,Me,ce,b,x,oe,K,We,te,Ue,pe,P,X,Be,q,Ge,U,Je,Qe,le;return j=new Ye({}),D=new Ye({}),S=new se({props:{name:"class diffusers.PriorTransformer",anchor:"diffusers.PriorTransformer",parameters:[{name:"num_attention_heads",val:": int = 32"},{name:"attention_head_dim",val:": int = 64"},{name:"num_layers",val:": int = 20"},{name:"embedding_dim",val:": int = 768"},{name:"num_embeddings",val:" = 77"},{name:"additional_embeddings",val:" = 4"},{name:"dropout",val:": float = 0.0"},{name:"time_embed_act_fn",val:": str = 'silu'"},{name:"norm_in_type",val:": typing.Optional[str] = None"},{name:"embedding_proj_norm_type",val:": typing.Optional[str] = None"},{name:"encoder_hid_proj_type",val:": typing.Optional[str] = 'linear'"},{name:"added_emb_type",val:": typing.Optional[str] = 'prd'"},{name:"time_embed_dim",val:": typing.Optional[int] = None"},{name:"embedding_proj_dim",val:": typing.Optional[int] = None"},{name:"clip_embed_dim",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"diffusers.PriorTransformer.num_attention_heads",description:"<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014; The number of heads to use for multi-head attention.",name:"num_attention_heads"},{anchor:"diffusers.PriorTransformer.attention_head_dim",description:"<strong>attention_head_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014; The number of channels in each head.",name:"attention_head_dim"},{anchor:"diffusers.PriorTransformer.num_layers",description:"<strong>num_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 20) &#x2014; The number of layers of Transformer blocks to use.",name:"num_layers"},{anchor:"diffusers.PriorTransformer.embedding_dim",description:"<strong>embedding_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014; The dimension of the model input <code>hidden_states</code>",name:"embedding_dim"},{anchor:"diffusers.PriorTransformer.num_embeddings",description:`<strong>num_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 77) &#x2014;
The number of embeddings of the model input <code>hidden_states</code>`,name:"num_embeddings"},{anchor:"diffusers.PriorTransformer.additional_embeddings",description:`<strong>additional_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014; The number of additional tokens appended to the
projected <code>hidden_states</code>. The actual length of the used <code>hidden_states</code> is <code>num_embeddings + additional_embeddings</code>.`,name:"additional_embeddings"},{anchor:"diffusers.PriorTransformer.dropout",description:"<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014; The dropout probability to use.",name:"dropout"},{anchor:"diffusers.PriorTransformer.time_embed_act_fn",description:`<strong>time_embed_act_fn</strong> (<code>str</code>, <em>optional</em>, defaults to &#x2018;silu&#x2019;) &#x2014;
The activation function to use to create timestep embeddings.`,name:"time_embed_act_fn"},{anchor:"diffusers.PriorTransformer.norm_in_type",description:`<strong>norm_in_type</strong> (<code>str</code>, <em>optional</em>, defaults to None) &#x2014; The normalization layer to apply on hidden states before
passing to Transformer blocks. Set it to <code>None</code> if normalization is not needed.`,name:"norm_in_type"},{anchor:"diffusers.PriorTransformer.embedding_proj_norm_type",description:`<strong>embedding_proj_norm_type</strong> (<code>str</code>, <em>optional</em>, defaults to None) &#x2014;
The normalization layer to apply on the input <code>proj_embedding</code>. Set it to <code>None</code> if normalization is not
needed.`,name:"embedding_proj_norm_type"},{anchor:"diffusers.PriorTransformer.encoder_hid_proj_type",description:`<strong>encoder_hid_proj_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>linear</code>) &#x2014;
The projection layer to apply on the input <code>encoder_hidden_states</code>. Set it to <code>None</code> if
<code>encoder_hidden_states</code> is <code>None</code>.`,name:"encoder_hid_proj_type"},{anchor:"diffusers.PriorTransformer.added_emb_type",description:`<strong>added_emb_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>prd</code>) &#x2014; Additional embeddings to condition the model.
Choose from <code>prd</code> or <code>None</code>. if choose <code>prd</code>, it will prepend a token indicating the (quantized) dot
product between the text embedding and image embedding as proposed in the unclip paper
<a href="https://arxiv.org/abs/2204.06125" rel="nofollow">https://arxiv.org/abs/2204.06125</a> If it is <code>None</code>, no additional embeddings will be prepended.`,name:"added_emb_type"},{anchor:"diffusers.PriorTransformer.time_embed_dim",description:"<strong>time_embed_dim</strong> (<code>int, *optional*, defaults to None) -- The dimension of timestep embeddings. If None, will be set to </code>num_attention_heads * attention_head_dim`",name:"time_embed_dim"},{anchor:"diffusers.PriorTransformer.embedding_proj_dim",description:`<strong>embedding_proj_dim</strong> (<code>int</code>, <em>optional</em>, default to None) &#x2014;
The dimension of <code>proj_embedding</code>. If None, will be set to <code>embedding_dim</code>.`,name:"embedding_proj_dim"},{anchor:"diffusers.PriorTransformer.clip_embed_dim",description:`<strong>clip_embed_dim</strong> (<code>int</code>, <em>optional</em>, default to None) &#x2014;
The dimension of the output. If None, will be set to <code>embedding_dim</code>.`,name:"clip_embed_dim"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/prior_transformer.py#L29"}}),F=new se({props:{name:"forward",anchor:"diffusers.PriorTransformer.forward",parameters:[{name:"hidden_states",val:""},{name:"timestep",val:": typing.Union[torch.Tensor, float, int]"},{name:"proj_embedding",val:": FloatTensor"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.BoolTensor] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.PriorTransformer.forward.hidden_states",description:`<strong>hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embedding_dim)</code>) &#x2014;
The currently predicted image embeddings.`,name:"hidden_states"},{anchor:"diffusers.PriorTransformer.forward.timestep",description:`<strong>timestep</strong> (<code>torch.LongTensor</code>) &#x2014;
Current denoising step.`,name:"timestep"},{anchor:"diffusers.PriorTransformer.forward.proj_embedding",description:`<strong>proj_embedding</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embedding_dim)</code>) &#x2014;
Projected embedding vector the denoising process is conditioned on.`,name:"proj_embedding"},{anchor:"diffusers.PriorTransformer.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_embeddings, embedding_dim)</code>) &#x2014;
Hidden states of the text embeddings the denoising process is conditioned on.`,name:"encoder_hidden_states"},{anchor:"diffusers.PriorTransformer.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, num_embeddings)</code>) &#x2014;
Text mask for the text embeddings.`,name:"attention_mask"},{anchor:"diffusers.PriorTransformer.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 <a href="/docs/diffusers/main/en/api/models/prior_transformer#diffusers.models.prior_transformer.PriorTransformerOutput">PriorTransformerOutput</a> instead of a plain
tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/prior_transformer.py#L229",returnDescription:`
<p>If return_dict is True, a <a
  href="/docs/diffusers/main/en/api/models/prior_transformer#diffusers.models.prior_transformer.PriorTransformerOutput"
>PriorTransformerOutput</a> is returned, otherwise a
tuple is returned where the first element is the sample tensor.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/main/en/api/models/prior_transformer#diffusers.models.prior_transformer.PriorTransformerOutput"
>PriorTransformerOutput</a> or <code>tuple</code></p>
`}}),z=new se({props:{name:"set_attn_processor",anchor:"diffusers.PriorTransformer.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.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor, 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.CustomDiffusionAttnProcessor, diffusers.models.attention_processor.CustomDiffusionXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor, diffusers.models.attention_processor.LoRAAttnProcessor2_0, diffusers.models.attention_processor.LoRAXFormersAttnProcessor, diffusers.models.attention_processor.LoRAAttnAddedKVProcessor]]]"}],parametersDescription:[{anchor:"diffusers.PriorTransformer.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/main/src/diffusers/models/prior_transformer.py#L188"}}),R=new se({props:{name:"set_default_attn_processor",anchor:"diffusers.PriorTransformer.set_default_attn_processor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/prior_transformer.py#L223"}}),K=new Ye({}),X=new se({props:{name:"class diffusers.models.prior_transformer.PriorTransformerOutput",anchor:"diffusers.models.prior_transformer.PriorTransformerOutput",parameters:[{name:"predicted_image_embedding",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.models.prior_transformer.PriorTransformerOutput.predicted_image_embedding",description:`<strong>predicted_image_embedding</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, embedding_dim)</code>) &#x2014;
The predicted CLIP image embedding conditioned on the CLIP text embedding input.`,name:"predicted_image_embedding"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/prior_transformer.py#L17"}}),{c(){h=t("meta"),ne=d(),_=t("h1"),v=t("a"),B=t("span"),L(j.$$.fragment),$e=d(),G=t("span"),Ae=f("Prior Transformer"),ie=d(),T=t("p"),xe=f("The Prior Transformer was originally introduced in "),k=t("a"),Le=f("Hierarchical Text-Conditional Image Generation with CLIP Latents"),Ee=f(" by Ramesh et al. It is used to predict CLIP image embeddings from CLIP text embeddings; image embeddings are predicted through a denoising diffusion process."),ae=d(),H=t("p"),Ie=f("The abstract from the paper is:"),de=d(),M=t("p"),J=t("em"),Ne=f("Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. 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