import{S as zn,i as Sn,s as Vn,e as n,k as d,w as h,t as l,M as Bn,c as s,d as t,m as c,a as r,x as _,h as i,b as a,G as e,g as p,y as g,L as Rn,q as b,o as v,B as N,v as Wn}from"../../../chunks/vendor-hf-doc-builder.js";import{D as k}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as eo}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Kn(Ht){let T,oo,A,O,De,X,Fo,Ee,Po,to,I,qo,J,Oo,Io,no,ve,Lo,so,Ne,Fe,jo,ro,M,L,Pe,H,Uo,qe,zo,ao,u,Z,So,Oe,Vo,Bo,j,G,Ro,Q,Wo,xe,Ko,Xo,Jo,U,Y,Ho,D,Zo,ye,Go,Qo,we,Yo,et,ot,$,ee,tt,Ie,nt,st,Le,rt,at,z,oe,lt,je,it,dt,S,te,ct,Ue,ft,lo,E,V,ze,ne,pt,Se,ut,io,F,se,mt,re,ht,Ce,_t,gt,co,P,B,Ve,ae,bt,Be,vt,fo,m,le,Nt,Re,xt,yt,ie,wt,$e,Ct,$t,kt,de,Tt,ce,We,At,Mt,Dt,Ke,Et,Ft,w,Xe,fe,Pt,qt,Je,pe,Ot,It,He,ue,Lt,jt,Ze,me,Ut,po,q,R,Ge,he,zt,Qe,St,uo,C,_e,Vt,ge,Bt,ke,Rt,Wt,Kt,W,be,Xt,Ye,Jt,mo;return X=new eo({}),H=new eo({}),Z=new k({props:{name:"class diffusers.ControlNetModel",anchor:"diffusers.ControlNetModel",parameters:[{name:"in_channels",val:": int = 4"},{name:"conditioning_channels",val:": int = 3"},{name:"flip_sin_to_cos",val:": bool = True"},{name:"freq_shift",val:": int = 0"},{name:"down_block_types",val:": typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D')"},{name:"only_cross_attention",val:": typing.Union[bool, typing.Tuple[bool]] = False"},{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 = 1280"},{name:"attention_head_dim",val:": typing.Union[int, typing.Tuple[int]] = 8"},{name:"num_attention_heads",val:": typing.Union[int, typing.Tuple[int], NoneType] = None"},{name:"use_linear_projection",val:": bool = False"},{name:"class_embed_type",val:": typing.Optional[str] = None"},{name:"num_class_embeds",val:": typing.Optional[int] = None"},{name:"upcast_attention",val:": bool = False"},{name:"resnet_time_scale_shift",val:": str = 'default'"},{name:"projection_class_embeddings_input_dim",val:": typing.Optional[int] = None"},{name:"controlnet_conditioning_channel_order",val:": str = 'rgb'"},{name:"conditioning_embedding_out_channels",val:": typing.Optional[typing.Tuple[int]] = (16, 32, 96, 256)"},{name:"global_pool_conditions",val:": bool = False"}],parametersDescription:[{anchor:"diffusers.ControlNetModel.in_channels",description:`<strong>in_channels</strong> (<code>int</code>, defaults to 4) &#x2014;
The number of channels in the input sample.`,name:"in_channels"},{anchor:"diffusers.ControlNetModel.flip_sin_to_cos",description:`<strong>flip_sin_to_cos</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether to flip the sin to cos in the time embedding.`,name:"flip_sin_to_cos"},{anchor:"diffusers.ControlNetModel.freq_shift",description:`<strong>freq_shift</strong> (<code>int</code>, defaults to 0) &#x2014;
The frequency shift to apply to the time embedding.`,name:"freq_shift"},{anchor:"diffusers.ControlNetModel.down_block_types",description:`<strong>down_block_types</strong> (<code>tuple[str]</code>, defaults to <code>(&quot;CrossAttnDownBlock2D&quot;, &quot;CrossAttnDownBlock2D&quot;, &quot;CrossAttnDownBlock2D&quot;, &quot;DownBlock2D&quot;)</code>) &#x2014;
The tuple of downsample blocks to use.`,name:"down_block_types"},{anchor:"diffusers.ControlNetModel.only_cross_attention",description:"<strong>only_cross_attention</strong> (<code>Union[bool, Tuple[bool]]</code>, defaults to <code>False</code>) &#x2014;",name:"only_cross_attention"},{anchor:"diffusers.ControlNetModel.block_out_channels",description:`<strong>block_out_channels</strong> (<code>tuple[int]</code>, defaults to <code>(320, 640, 1280, 1280)</code>) &#x2014;
The tuple of output channels for each block.`,name:"block_out_channels"},{anchor:"diffusers.ControlNetModel.layers_per_block",description:`<strong>layers_per_block</strong> (<code>int</code>, defaults to 2) &#x2014;
The number of layers per block.`,name:"layers_per_block"},{anchor:"diffusers.ControlNetModel.downsample_padding",description:`<strong>downsample_padding</strong> (<code>int</code>, defaults to 1) &#x2014;
The padding to use for the downsampling convolution.`,name:"downsample_padding"},{anchor:"diffusers.ControlNetModel.mid_block_scale_factor",description:`<strong>mid_block_scale_factor</strong> (<code>float</code>, defaults to 1) &#x2014;
The scale factor to use for the mid block.`,name:"mid_block_scale_factor"},{anchor:"diffusers.ControlNetModel.act_fn",description:`<strong>act_fn</strong> (<code>str</code>, defaults to &#x201C;silu&#x201D;) &#x2014;
The activation function to use.`,name:"act_fn"},{anchor:"diffusers.ControlNetModel.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. If None, normalization and activation layers is skipped
in post-processing.`,name:"norm_num_groups"},{anchor:"diffusers.ControlNetModel.norm_eps",description:`<strong>norm_eps</strong> (<code>float</code>, defaults to 1e-5) &#x2014;
The epsilon to use for the normalization.`,name:"norm_eps"},{anchor:"diffusers.ControlNetModel.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, defaults to 1280) &#x2014;
The dimension of the cross attention features.`,name:"cross_attention_dim"},{anchor:"diffusers.ControlNetModel.attention_head_dim",description:`<strong>attention_head_dim</strong> (<code>Union[int, Tuple[int]]</code>, defaults to 8) &#x2014;
The dimension of the attention heads.`,name:"attention_head_dim"},{anchor:"diffusers.ControlNetModel.use_linear_projection",description:"<strong>use_linear_projection</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;",name:"use_linear_projection"},{anchor:"diffusers.ControlNetModel.class_embed_type",description:`<strong>class_embed_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None,
<code>&quot;timestep&quot;</code>, <code>&quot;identity&quot;</code>, <code>&quot;projection&quot;</code>, or <code>&quot;simple_projection&quot;</code>.`,name:"class_embed_type"},{anchor:"diffusers.ControlNetModel.num_class_embeds",description:`<strong>num_class_embeds</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Input dimension of the learnable embedding matrix to be projected to <code>time_embed_dim</code>, when performing
class conditioning with <code>class_embed_type</code> equal to <code>None</code>.`,name:"num_class_embeds"},{anchor:"diffusers.ControlNetModel.upcast_attention",description:"<strong>upcast_attention</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;",name:"upcast_attention"},{anchor:"diffusers.ControlNetModel.resnet_time_scale_shift",description:`<strong>resnet_time_scale_shift</strong> (<code>str</code>, defaults to <code>&quot;default&quot;</code>) &#x2014;
Time scale shift config for ResNet blocks (see <code>ResnetBlock2D</code>). Choose from <code>default</code> or <code>scale_shift</code>.`,name:"resnet_time_scale_shift"},{anchor:"diffusers.ControlNetModel.projection_class_embeddings_input_dim",description:`<strong>projection_class_embeddings_input_dim</strong> (<code>int</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
The dimension of the <code>class_labels</code> input when <code>class_embed_type=&quot;projection&quot;</code>. Required when
<code>class_embed_type=&quot;projection&quot;</code>.`,name:"projection_class_embeddings_input_dim"},{anchor:"diffusers.ControlNetModel.controlnet_conditioning_channel_order",description:`<strong>controlnet_conditioning_channel_order</strong> (<code>str</code>, defaults to <code>&quot;rgb&quot;</code>) &#x2014;
The channel order of conditional image. Will convert to <code>rgb</code> if it&#x2019;s <code>bgr</code>.`,name:"controlnet_conditioning_channel_order"},{anchor:"diffusers.ControlNetModel.conditioning_embedding_out_channels",description:`<strong>conditioning_embedding_out_channels</strong> (<code>tuple[int]</code>, <em>optional</em>, defaults to <code>(16, 32, 96, 256)</code>) &#x2014;
The tuple of output channel for each block in the <code>conditioning_embedding</code> layer.`,name:"conditioning_embedding_out_channels"},{anchor:"diffusers.ControlNetModel.global_pool_conditions",description:"<strong>global_pool_conditions</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;",name:"global_pool_conditions"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/models/controlnet.py#L103"}}),G=new k({props:{name:"forward",anchor:"diffusers.ControlNetModel.forward",parameters:[{name:"sample",val:": FloatTensor"},{name:"timestep",val:": typing.Union[torch.Tensor, float, int]"},{name:"encoder_hidden_states",val:": Tensor"},{name:"controlnet_cond",val:": FloatTensor"},{name:"conditioning_scale",val:": float = 1.0"},{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:"guess_mode",val:": bool = False"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.ControlNetModel.forward.sample",description:`<strong>sample</strong> (<code>torch.FloatTensor</code>) &#x2014;
The noisy input tensor.`,name:"sample"},{anchor:"diffusers.ControlNetModel.forward.timestep",description:`<strong>timestep</strong> (<code>Union[torch.Tensor, float, int]</code>) &#x2014;
The number of timesteps to denoise an input.`,name:"timestep"},{anchor:"diffusers.ControlNetModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong> (<code>torch.Tensor</code>) &#x2014;
The encoder hidden states.`,name:"encoder_hidden_states"},{anchor:"diffusers.ControlNetModel.forward.controlnet_cond",description:`<strong>controlnet_cond</strong> (<code>torch.FloatTensor</code>) &#x2014;
The conditional input tensor of shape <code>(batch_size, sequence_length, hidden_size)</code>.`,name:"controlnet_cond"},{anchor:"diffusers.ControlNetModel.forward.conditioning_scale",description:`<strong>conditioning_scale</strong> (<code>float</code>, defaults to <code>1.0</code>) &#x2014;
The scale factor for ControlNet outputs.`,name:"conditioning_scale"},{anchor:"diffusers.ControlNetModel.forward.class_labels",description:`<strong>class_labels</strong> (<code>torch.Tensor</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.`,name:"class_labels"},{anchor:"diffusers.ControlNetModel.forward.timestep_cond",description:"<strong>timestep_cond</strong> (<code>torch.Tensor</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;",name:"timestep_cond"},{anchor:"diffusers.ControlNetModel.forward.attention_mask",description:"<strong>attention_mask</strong> (<code>torch.Tensor</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;",name:"attention_mask"},{anchor:"diffusers.ControlNetModel.forward.cross_attention_kwargs(dict[str],",description:`<strong>cross_attention_kwargs(<code>dict[str]</code>,</strong> <em>optional</em>, defaults to <code>None</code>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttnProcessor</code>.`,name:"cross_attention_kwargs(dict[str],"},{anchor:"diffusers.ControlNetModel.forward.guess_mode",description:`<strong>guess_mode</strong> (<code>bool</code>, defaults to <code>False</code>) &#x2014;
In this mode, the ControlNet encoder tries its best to recognize the input content of the input even if
you remove all prompts. A <code>guidance_scale</code> between 3.0 and 5.0 is recommended.`,name:"guess_mode"},{anchor:"diffusers.ControlNetModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.18.0/en/api/models/controlnet#diffusers.models.controlnet.ControlNetOutput">ControlNetOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/models/controlnet.py#L535",returnDescription:`
<p>If <code>return_dict</code> is <code>True</code>, a <a
  href="/docs/diffusers/v0.18.0/en/api/models/controlnet#diffusers.models.controlnet.ControlNetOutput"
>ControlNetOutput</a> is returned, otherwise a tuple is
returned where the first element is the sample tensor.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.18.0/en/api/models/controlnet#diffusers.models.controlnet.ControlNetOutput"
>ControlNetOutput</a> <strong>or</strong> <code>tuple</code></p>
`}}),Y=new k({props:{name:"from_unet",anchor:"diffusers.ControlNetModel.from_unet",parameters:[{name:"unet",val:": UNet2DConditionModel"},{name:"controlnet_conditioning_channel_order",val:": str = 'rgb'"},{name:"conditioning_embedding_out_channels",val:": typing.Optional[typing.Tuple[int]] = (16, 32, 96, 256)"},{name:"load_weights_from_unet",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.ControlNetModel.from_unet.unet",description:`<strong>unet</strong> (<code>UNet2DConditionModel</code>) &#x2014;
The UNet model weights to copy to the <a href="/docs/diffusers/v0.18.0/en/api/models/controlnet#diffusers.ControlNetModel">ControlNetModel</a>. All configuration options are also copied
where applicable.`,name:"unet"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/models/controlnet.py#L343"}}),ee=new k({props:{name:"set_attention_slice",anchor:"diffusers.ControlNetModel.set_attention_slice",parameters:[{name:"slice_size",val:""}],parametersDescription:[{anchor:"diffusers.ControlNetModel.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/v0.18.0/src/diffusers/models/controlnet.py#L466"}}),oe=new k({props:{name:"set_attn_processor",anchor:"diffusers.ControlNetModel.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.ControlNetModel.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/v0.18.0/src/diffusers/models/controlnet.py#L424"}}),te=new k({props:{name:"set_default_attn_processor",anchor:"diffusers.ControlNetModel.set_default_attn_processor",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/models/controlnet.py#L459"}}),ne=new eo({}),se=new k({props:{name:"class diffusers.models.controlnet.ControlNetOutput",anchor:"diffusers.models.controlnet.ControlNetOutput",parameters:[{name:"down_block_res_samples",val:": typing.Tuple[torch.Tensor]"},{name:"mid_block_res_sample",val:": Tensor"}],parametersDescription:[{anchor:"diffusers.models.controlnet.ControlNetOutput.down_block_res_samples",description:`<strong>down_block_res_samples</strong> (<code>tuple[torch.Tensor]</code>) &#x2014;
A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should
be of shape <code>(batch_size, channel * resolution, height //resolution, width // resolution)</code>. Output can be
used to condition the original UNet&#x2019;s downsampling activations.`,name:"down_block_res_samples"},{anchor:"diffusers.models.controlnet.ControlNetOutput.mid_down_block_re_sample",description:`<strong>mid_down_block_re_sample</strong> (<code>torch.Tensor</code>) &#x2014;
The activation of the midde block (the lowest sample resolution). Each tensor should be of shape
<code>(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)</code>.
Output can be used to condition the original UNet&#x2019;s middle block activation.`,name:"mid_down_block_re_sample"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/models/controlnet.py#L39"}}),ae=new eo({}),le=new k({props:{name:"class diffusers.FlaxControlNetModel",anchor:"diffusers.FlaxControlNetModel",parameters:[{name:"sample_size",val:": int = 32"},{name:"in_channels",val:": int = 4"},{name:"down_block_types",val:": typing.Tuple[str] = ('CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D', 'DownBlock2D')"},{name:"only_cross_attention",val:": typing.Union[bool, typing.Tuple[bool]] = False"},{name:"block_out_channels",val:": typing.Tuple[int] = (320, 640, 1280, 1280)"},{name:"layers_per_block",val:": int = 2"},{name:"attention_head_dim",val:": typing.Union[int, typing.Tuple[int]] = 8"},{name:"num_attention_heads",val:": typing.Union[int, typing.Tuple[int], NoneType] = None"},{name:"cross_attention_dim",val:": int = 1280"},{name:"dropout",val:": float = 0.0"},{name:"use_linear_projection",val:": bool = False"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"flip_sin_to_cos",val:": bool = True"},{name:"freq_shift",val:": int = 0"},{name:"controlnet_conditioning_channel_order",val:": str = 'rgb'"},{name:"conditioning_embedding_out_channels",val:": typing.Tuple[int] = (16, 32, 96, 256)"},{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 0x7fb818669a30>"},{name:"name",val:": str = None"}],parametersDescription:[{anchor:"diffusers.FlaxControlNetModel.sample_size",description:`<strong>sample_size</strong> (<code>int</code>, <em>optional</em>) &#x2014;
The size of the input sample.`,name:"sample_size"},{anchor:"diffusers.FlaxControlNetModel.in_channels",description:`<strong>in_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of channels in the input sample.`,name:"in_channels"},{anchor:"diffusers.FlaxControlNetModel.down_block_types",description:`<strong>down_block_types</strong> (<code>Tuple[str]</code>, <em>optional</em>, defaults to <code>(&quot;FlaxCrossAttnDownBlock2D&quot;, &quot;FlaxCrossAttnDownBlock2D&quot;, &quot;FlaxCrossAttnDownBlock2D&quot;, &quot;FlaxDownBlock2D&quot;)</code>) &#x2014;
The tuple of downsample blocks to use.`,name:"down_block_types"},{anchor:"diffusers.FlaxControlNetModel.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.FlaxControlNetModel.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.FlaxControlNetModel.attention_head_dim",description:`<strong>attention_head_dim</strong> (<code>int</code> or <code>Tuple[int]</code>, <em>optional</em>, defaults to 8) &#x2014;
The dimension of the attention heads.`,name:"attention_head_dim"},{anchor:"diffusers.FlaxControlNetModel.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code> or <code>Tuple[int]</code>, <em>optional</em>) &#x2014;
The number of attention heads.`,name:"num_attention_heads"},{anchor:"diffusers.FlaxControlNetModel.cross_attention_dim",description:`<strong>cross_attention_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
The dimension of the cross attention features.`,name:"cross_attention_dim"},{anchor:"diffusers.FlaxControlNetModel.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
Dropout probability for down, up and bottleneck blocks.`,name:"dropout"},{anchor:"diffusers.FlaxControlNetModel.flip_sin_to_cos",description:`<strong>flip_sin_to_cos</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to flip the sin to cos in the time embedding.`,name:"flip_sin_to_cos"},{anchor:"diffusers.FlaxControlNetModel.freq_shift",description:"<strong>freq_shift</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014; The frequency shift to apply to the time embedding.",name:"freq_shift"},{anchor:"diffusers.FlaxControlNetModel.controlnet_conditioning_channel_order",description:`<strong>controlnet_conditioning_channel_order</strong> (<code>str</code>, <em>optional</em>, defaults to <code>rgb</code>) &#x2014;
The channel order of conditional image. Will convert to <code>rgb</code> if it&#x2019;s <code>bgr</code>.`,name:"controlnet_conditioning_channel_order"},{anchor:"diffusers.FlaxControlNetModel.conditioning_embedding_out_channels",description:`<strong>conditioning_embedding_out_channels</strong> (<code>tuple</code>, <em>optional</em>, defaults to <code>(16, 32, 96, 256)</code>) &#x2014;
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