import{S as eo,i as to,s as oo,e as n,k as l,w as _,t as a,M as no,c as s,d as o,m as c,a as r,x as g,h as d,b as i,G as e,g as m,y as v,L as so,q as b,o as w,B as D,v as ro}from"../../../chunks/vendor-hf-doc-builder.js";import{D as Z}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as Mt}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function io(xt){let y,ge,k,A,ie,I,Le,ae,Ve,ve,$,Fe,O,Re,Ke,be,ee,Xe,we,te,S,We,z,He,Ge,De,N,T,de,B,Je,le,je,ye,u,L,Qe,ce,Ye,Ze,V,et,oe,tt,ot,nt,M,F,st,R,rt,K,it,at,dt,x,X,lt,W,ct,ne,ut,ft,pt,h,H,mt,ue,ht,_t,fe,gt,vt,P,G,bt,pe,wt,Dt,E,J,yt,me,kt,ke,U,q,he,j,Nt,_e,Ut,Ne,C,Q,Ct,Y,At,se,$t,Tt,Ue;return I=new Mt({}),B=new Mt({}),L=new Z({props:{name:"class diffusers.UNet3DConditionModel",anchor:"diffusers.UNet3DConditionModel",parameters:[{name:"sample_size",val:": typing.Optional[int] = None"},{name:"in_channels",val:": int = 4"},{name:"out_channels",val:": int = 4"},{name:"down_block_types",val:": typing.Tuple[str] = ('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')"},{name:"up_block_types",val:": typing.Tuple[str] = ('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')"},{name:"block_out_channels",val:": typing.Tuple[int] = (320, 640, 1280, 1280)"},{name:"layers_per_block",val:": int = 2"},{name:"downsample_padding",val:": int = 1"},{name:"mid_block_scale_factor",val:": float = 1"},{name:"act_fn",val:": str = 'silu'"},{name:"norm_num_groups",val:": typing.Optional[int] = 32"},{name:"norm_eps",val:": float = 1e-05"},{name:"cross_attention_dim",val:": int = 1024"},{name:"attention_head_dim",val:": typing.Union[int, typing.Tuple[int]] = 64"},{name:"num_attention_heads",val:": typing.Union[int, typing.Tuple[int], NoneType] = None"}],parametersDescription:[{anchor:"diffusers.UNet3DConditionModel.sample_size",description:`<strong>sample_size</strong> (<code>int</code> or <code>Tuple[int, int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
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The tuple of downsample blocks to use.`,name:"down_block_types"},{anchor:"diffusers.UNet3DConditionModel.up_block_types",description:`<strong>up_block_types</strong> (<code>Tuple[str]</code>, <em>optional</em>, defaults to <code>(&quot;UpBlock2D&quot;, &quot;CrossAttnUpBlock2D&quot;, &quot;CrossAttnUpBlock2D&quot;, &quot;CrossAttnUpBlock2D&quot;)</code>) &#x2014;
The tuple of upsample blocks to use.`,name:"up_block_types"},{anchor:"diffusers.UNet3DConditionModel.block_out_channels",description:`<strong>block_out_channels</strong> (<code>Tuple[int]</code>, <em>optional</em>, defaults to <code>(320, 640, 1280, 1280)</code>) &#x2014;
The tuple of output channels for each block.`,name:"block_out_channels"},{anchor:"diffusers.UNet3DConditionModel.layers_per_block",description:"<strong>layers_per_block</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014; The number of layers per block.",name:"layers_per_block"},{anchor:"diffusers.UNet3DConditionModel.downsample_padding",description:"<strong>downsample_padding</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014; The padding to use for the downsampling convolution.",name:"downsample_padding"},{anchor:"diffusers.UNet3DConditionModel.mid_block_scale_factor",description:"<strong>mid_block_scale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014; The scale factor to use for the mid block.",name:"mid_block_scale_factor"},{anchor:"diffusers.UNet3DConditionModel.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.UNet3DConditionModel.norm_num_groups",description:`<strong>norm_num_groups</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014; The number of groups to use for the normalization.
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The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=<code>dim</code>.`,name:"chunk_size"},{anchor:"diffusers.UNet3DConditionModel.enable_forward_chunking.dim",description:`<strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to <code>0</code>) &#x2014;
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).`,name:"dim"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/models/unet_3d_condition.py#L397"}}),X=new Z({props:{name:"forward",anchor:"diffusers.UNet3DConditionModel.forward",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Union[torch.Tensor, float, int]"},{name:"encoder_hidden_states",val:": Tensor"},{name:"class_labels",val:": typing.Optional[torch.Tensor] = None"},{name:"timestep_cond",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"down_block_additional_residuals",val:": typing.Optional[typing.Tuple[torch.Tensor]] = None"},{name:"mid_block_additional_residual",val:": typing.Optional[torch.Tensor] = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.UNet3DConditionModel.forward.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
The noisy input tensor with the following shape <code>(batch, num_frames, channel, height, width</code>.`,name:"sample"},{anchor:"diffusers.UNet3DConditionModel.forward.timestep",description:"<strong>timestep</strong> (<code>torch.FloatTensor</code> or <code>float</code> or <code>int</code>) &#x2014; The number of timesteps to denoise an input.",name:"timestep"},{anchor:"diffusers.UNet3DConditionModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.FloatTensor</code>) &#x2014;
The encoder hidden states with shape <code>(batch, sequence_length, feature_dim)</code>.`,name:"encoder_hidden_states"},{anchor:"diffusers.UNet3DConditionModel.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/v0.19.2/en/api/models/unet3d-cond#diffusers.models.unet_3d_condition.UNet3DConditionOutput">UNet3DConditionOutput</a> instead of a plain
tuple.`,name:"return_dict"},{anchor:"diffusers.UNet3DConditionModel.forward.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttnProcessor</code>.`,name:"cross_attention_kwargs"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/models/unet_3d_condition.py#L448",returnDescription:`
<p>If <code>return_dict</code> is True, an <a
  href="/docs/diffusers/v0.19.2/en/api/models/unet3d-cond#diffusers.models.unet_3d_condition.UNet3DConditionOutput"
>UNet3DConditionOutput</a> is returned, otherwise
a <code>tuple</code> is returned where the first element is the sample tensor.</p>
`,returnType:`
<p><a
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>UNet3DConditionOutput</a> or <code>tuple</code></p>
`}}),H=new Z({props:{name:"set_attention_slice",anchor:"diffusers.UNet3DConditionModel.set_attention_slice",parameters:[{name:"slice_size",val:""}],parametersDescription:[{anchor:"diffusers.UNet3DConditionModel.set_attention_slice.slice_size",description:`<strong>slice_size</strong> (<code>str</code> or <code>int</code> or <code>list(int)</code>, <em>optional</em>, defaults to <code>&quot;auto&quot;</code>) &#x2014;
When <code>&quot;auto&quot;</code>, input to the attention heads is halved, so attention is computed in two steps. If
<code>&quot;max&quot;</code>, maximum amount of memory is saved by running only one slice at a time. 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/v0.19.2/src/diffusers/models/unet_3d_condition.py#L297"}}),G=new Z({props:{name:"set_attn_processor",anchor:"diffusers.UNet3DConditionModel.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.UNet3DConditionModel.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/v0.19.2/src/diffusers/models/unet_3d_condition.py#L363"}}),J=new Z({props:{name:"set_default_attn_processor",anchor:"diffusers.UNet3DConditionModel.set_default_attn_processor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.19.2/src/diffusers/models/unet_3d_condition.py#L438"}}),j=new Mt({}),Q=new Z({props:{name:"class diffusers.models.unet_3d_condition.UNet3DConditionOutput",anchor:"diffusers.models.unet_3d_condition.UNet3DConditionOutput",parameters:[{name:"sample",val:": FloatTensor"}],parametersDescription:[{anchor:"diffusers.models.unet_3d_condition.UNet3DConditionOutput.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_frames, num_channels, height, width)</code>) &#x2014;
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