# Copyright 2025 The HuggingFace Team and SANA-Video Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from typing import Any, Dict, Optional, Tuple, Union import torch import torch.nn.functional as F from torch import nn from ...configuration_utils import ConfigMixin, register_to_config from ...loaders import FromOriginalModelMixin, PeftAdapterMixin from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers from ..attention import AttentionMixin from ..attention_dispatch import dispatch_attention_fn from ..attention_processor import Attention from ..embeddings import PixArtAlphaTextProjection, TimestepEmbedding, Timesteps, get_1d_rotary_pos_embed from ..modeling_outputs import Transformer2DModelOutput from ..modeling_utils import ModelMixin from ..normalization import AdaLayerNormSingle, RMSNorm logger = logging.get_logger(__name__) # pylint: disable=invalid-name class GLUMBTempConv(nn.Module): def __init__( self, in_channels: int, out_channels: int, expand_ratio: float = 4, norm_type: Optional[str] = None, residual_connection: bool = True, ) -> None: super().__init__() hidden_channels = int(expand_ratio * in_channels) self.norm_type = norm_type self.residual_connection = residual_connection self.nonlinearity = nn.SiLU() self.conv_inverted = nn.Conv2d(in_channels, hidden_channels * 2, 1, 1, 0) self.conv_depth = nn.Conv2d(hidden_channels * 2, hidden_channels * 2, 3, 1, 1, groups=hidden_channels * 2) self.conv_point = nn.Conv2d(hidden_channels, out_channels, 1, 1, 0, bias=False) self.norm = None if norm_type == "rms_norm": self.norm = RMSNorm(out_channels, eps=1e-5, elementwise_affine=True, bias=True) self.conv_temp = nn.Conv2d( out_channels, out_channels, kernel_size=(3, 1), stride=1, padding=(1, 0), bias=False ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: if self.residual_connection: residual = hidden_states batch_size, num_frames, height, width, num_channels = hidden_states.shape hidden_states = hidden_states.view(batch_size * num_frames, height, width, num_channels).permute(0, 3, 1, 2) hidden_states = self.conv_inverted(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv_depth(hidden_states) hidden_states, gate = torch.chunk(hidden_states, 2, dim=1) hidden_states = hidden_states * self.nonlinearity(gate) hidden_states = self.conv_point(hidden_states) # Temporal aggregation hidden_states_temporal = hidden_states.view(batch_size, num_frames, num_channels, height * width).permute( 0, 2, 1, 3 ) hidden_states = hidden_states_temporal + self.conv_temp(hidden_states_temporal) hidden_states = hidden_states.permute(0, 2, 3, 1).view(batch_size, num_frames, height, width, num_channels) if self.norm_type == "rms_norm": # move channel to the last dimension so we apply RMSnorm across channel dimension hidden_states = self.norm(hidden_states.movedim(1, -1)).movedim(-1, 1) if self.residual_connection: hidden_states = hidden_states + residual return hidden_states class SanaLinearAttnProcessor3_0: r""" Processor for implementing scaled dot-product linear attention. """ def __call__( self, attn: Attention, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, rotary_emb: Optional[torch.Tensor] = None, ) -> torch.Tensor: original_dtype = hidden_states.dtype if encoder_hidden_states is None: encoder_hidden_states = hidden_states query = attn.to_q(hidden_states) key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) if attn.norm_q is not None: query = attn.norm_q(query) if attn.norm_k is not None: key = attn.norm_k(key) query = query.unflatten(2, (attn.heads, -1)) key = key.unflatten(2, (attn.heads, -1)) value = value.unflatten(2, (attn.heads, -1)) # B,N,H,C query = F.relu(query) key = F.relu(key) if rotary_emb is not None: def apply_rotary_emb( hidden_states: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor, ): x1, x2 = hidden_states.unflatten(-1, (-1, 2)).unbind(-1) cos = freqs_cos[..., 0::2] sin = freqs_sin[..., 1::2] out = torch.empty_like(hidden_states) out[..., 0::2] = x1 * cos - x2 * sin out[..., 1::2] = x1 * sin + x2 * cos return out.type_as(hidden_states) query_rotate = apply_rotary_emb(query, *rotary_emb) key_rotate = apply_rotary_emb(key, *rotary_emb) # B,H,C,N query = query.permute(0, 2, 3, 1) key = key.permute(0, 2, 3, 1) query_rotate = query_rotate.permute(0, 2, 3, 1) key_rotate = key_rotate.permute(0, 2, 3, 1) value = value.permute(0, 2, 3, 1) query_rotate, key_rotate, value = query_rotate.float(), key_rotate.float(), value.float() z = 1 / (key.sum(dim=-1, keepdim=True).transpose(-2, -1) @ query + 1e-15) scores = torch.matmul(value, key_rotate.transpose(-1, -2)) hidden_states = torch.matmul(scores, query_rotate) hidden_states = hidden_states * z # B,H,C,N hidden_states = hidden_states.flatten(1, 2).transpose(1, 2) hidden_states = hidden_states.to(original_dtype) hidden_states = attn.to_out[0](hidden_states) hidden_states = attn.to_out[1](hidden_states) return hidden_states class WanRotaryPosEmbed(nn.Module): def __init__( self, attention_head_dim: int, patch_size: Tuple[int, int, int], max_seq_len: int, theta: float = 10000.0, ): super().__init__() self.attention_head_dim = attention_head_dim self.patch_size = patch_size self.max_seq_len = max_seq_len h_dim = w_dim = 2 * (attention_head_dim // 6) t_dim = attention_head_dim - h_dim - w_dim self.t_dim = t_dim self.h_dim = h_dim self.w_dim = w_dim freqs_dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64 freqs_cos = [] freqs_sin = [] for dim in [t_dim, h_dim, w_dim]: freq_cos, freq_sin = get_1d_rotary_pos_embed( dim, max_seq_len, theta, use_real=True, repeat_interleave_real=True, freqs_dtype=freqs_dtype, ) freqs_cos.append(freq_cos) freqs_sin.append(freq_sin) self.register_buffer("freqs_cos", torch.cat(freqs_cos, dim=1), persistent=False) self.register_buffer("freqs_sin", torch.cat(freqs_sin, dim=1), persistent=False) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: batch_size, num_channels, num_frames, height, width = hidden_states.shape p_t, p_h, p_w = self.patch_size ppf, pph, ppw = num_frames // p_t, height // p_h, width // p_w split_sizes = [self.t_dim, self.h_dim, self.w_dim] freqs_cos = self.freqs_cos.split(split_sizes, dim=1) freqs_sin = self.freqs_sin.split(split_sizes, dim=1) freqs_cos_f = freqs_cos[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1) freqs_cos_h = freqs_cos[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1) freqs_cos_w = freqs_cos[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1) freqs_sin_f = freqs_sin[0][:ppf].view(ppf, 1, 1, -1).expand(ppf, pph, ppw, -1) freqs_sin_h = freqs_sin[1][:pph].view(1, pph, 1, -1).expand(ppf, pph, ppw, -1) freqs_sin_w = freqs_sin[2][:ppw].view(1, 1, ppw, -1).expand(ppf, pph, ppw, -1) freqs_cos = torch.cat([freqs_cos_f, freqs_cos_h, freqs_cos_w], dim=-1).reshape(1, ppf * pph * ppw, 1, -1) freqs_sin = torch.cat([freqs_sin_f, freqs_sin_h, freqs_sin_w], dim=-1).reshape(1, ppf * pph * ppw, 1, -1) return freqs_cos, freqs_sin class SanaModulatedNorm(nn.Module): def __init__(self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6): super().__init__() self.norm = nn.LayerNorm(dim, elementwise_affine=elementwise_affine, eps=eps) def forward( self, hidden_states: torch.Tensor, temb: torch.Tensor, scale_shift_table: torch.Tensor ) -> torch.Tensor: hidden_states = self.norm(hidden_states) shift, scale = (scale_shift_table[None, None] + temb[:, :, None].to(scale_shift_table.device)).unbind(dim=2) hidden_states = hidden_states * (1 + scale) + shift return hidden_states class SanaCombinedTimestepGuidanceEmbeddings(nn.Module): def __init__(self, embedding_dim): super().__init__() self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) self.guidance_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0) self.guidance_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim) self.silu = nn.SiLU() self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) def forward(self, timestep: torch.Tensor, guidance: torch.Tensor = None, hidden_dtype: torch.dtype = None): timesteps_proj = self.time_proj(timestep) timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D) guidance_proj = self.guidance_condition_proj(guidance) guidance_emb = self.guidance_embedder(guidance_proj.to(dtype=hidden_dtype)) conditioning = timesteps_emb + guidance_emb return self.linear(self.silu(conditioning)), conditioning class SanaAttnProcessor2_0: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). """ _attention_backend = None _parallel_config = None def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError("SanaAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") def __call__( self, attn: Attention, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: batch_size, sequence_length, _ = ( hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape ) if attention_mask is not None: attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) query = attn.to_q(hidden_states) if encoder_hidden_states is None: encoder_hidden_states = hidden_states key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) if attn.norm_q is not None: query = attn.norm_q(query) if attn.norm_k is not None: key = attn.norm_k(key) inner_dim = key.shape[-1] head_dim = inner_dim // attn.heads query = query.view(batch_size, -1, attn.heads, head_dim) key = key.view(batch_size, -1, attn.heads, head_dim) value = value.view(batch_size, -1, attn.heads, head_dim) # the output of sdp = (batch, num_heads, seq_len, head_dim) hidden_states = dispatch_attention_fn( query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False, backend=self._attention_backend, parallel_config=self._parallel_config, ) hidden_states = hidden_states.flatten(2, 3) hidden_states = hidden_states.type_as(query) # linear proj hidden_states = attn.to_out[0](hidden_states) # dropout hidden_states = attn.to_out[1](hidden_states) hidden_states = hidden_states / attn.rescale_output_factor return hidden_states class SanaVideoTransformerBlock(nn.Module): r""" Transformer block introduced in [Sana-Video](https://huggingface.co/papers/2509.24695). """ def __init__( self, dim: int = 2240, num_attention_heads: int = 20, attention_head_dim: int = 112, dropout: float = 0.0, num_cross_attention_heads: Optional[int] = 20, cross_attention_head_dim: Optional[int] = 112, cross_attention_dim: Optional[int] = 2240, attention_bias: bool = True, norm_elementwise_affine: bool = False, norm_eps: float = 1e-6, attention_out_bias: bool = True, mlp_ratio: float = 3.0, qk_norm: Optional[str] = "rms_norm_across_heads", rope_max_seq_len: int = 1024, ) -> None: super().__init__() # 1. Self Attention self.norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=norm_eps) self.attn1 = Attention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, kv_heads=num_attention_heads if qk_norm is not None else None, qk_norm=qk_norm, dropout=dropout, bias=attention_bias, cross_attention_dim=None, processor=SanaLinearAttnProcessor3_0(), ) # 2. Cross Attention if cross_attention_dim is not None: self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps) self.attn2 = Attention( query_dim=dim, qk_norm=qk_norm, kv_heads=num_cross_attention_heads if qk_norm is not None else None, cross_attention_dim=cross_attention_dim, heads=num_cross_attention_heads, dim_head=cross_attention_head_dim, dropout=dropout, bias=True, out_bias=attention_out_bias, processor=SanaAttnProcessor2_0(), ) # 3. Feed-forward self.ff = GLUMBTempConv(dim, dim, mlp_ratio, norm_type=None, residual_connection=False) self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, timestep: Optional[torch.LongTensor] = None, frames: int = None, height: int = None, width: int = None, rotary_emb: Optional[torch.Tensor] = None, ) -> torch.Tensor: batch_size = hidden_states.shape[0] # 1. Modulation shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None, None] + timestep.reshape(batch_size, timestep.shape[1], 6, -1) ).unbind(dim=2) # 2. Self Attention norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa norm_hidden_states = norm_hidden_states.to(hidden_states.dtype) attn_output = self.attn1(norm_hidden_states, rotary_emb=rotary_emb) hidden_states = hidden_states + gate_msa * attn_output # 3. Cross Attention if self.attn2 is not None: attn_output = self.attn2( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = attn_output + hidden_states # 4. Feed-forward norm_hidden_states = self.norm2(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp norm_hidden_states = norm_hidden_states.unflatten(1, (frames, height, width)) ff_output = self.ff(norm_hidden_states) ff_output = ff_output.flatten(1, 3) hidden_states = hidden_states + gate_mlp * ff_output return hidden_states class SanaVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, AttentionMixin): r""" A 3D Transformer model introduced in [Sana-Video](https://huggingface.co/papers/2509.24695) family of models. Args: in_channels (`int`, defaults to `16`): The number of channels in the input. out_channels (`int`, *optional*, defaults to `16`): The number of channels in the output. num_attention_heads (`int`, defaults to `20`): The number of heads to use for multi-head attention. attention_head_dim (`int`, defaults to `112`): The number of channels in each head. num_layers (`int`, defaults to `20`): The number of layers of Transformer blocks to use. num_cross_attention_heads (`int`, *optional*, defaults to `20`): The number of heads to use for cross-attention. cross_attention_head_dim (`int`, *optional*, defaults to `112`): The number of channels in each head for cross-attention. cross_attention_dim (`int`, *optional*, defaults to `2240`): The number of channels in the cross-attention output. caption_channels (`int`, defaults to `2304`): The number of channels in the caption embeddings. mlp_ratio (`float`, defaults to `2.5`): The expansion ratio to use in the GLUMBConv layer. dropout (`float`, defaults to `0.0`): The dropout probability. attention_bias (`bool`, defaults to `False`): Whether to use bias in the attention layer. sample_size (`int`, defaults to `32`): The base size of the input latent. patch_size (`int`, defaults to `1`): The size of the patches to use in the patch embedding layer. norm_elementwise_affine (`bool`, defaults to `False`): Whether to use elementwise affinity in the normalization layer. norm_eps (`float`, defaults to `1e-6`): The epsilon value for the normalization layer. qk_norm (`str`, *optional*, defaults to `None`): The normalization to use for the query and key. """ _supports_gradient_checkpointing = True _no_split_modules = ["SanaVideoTransformerBlock", "SanaModulatedNorm"] _skip_layerwise_casting_patterns = ["patch_embedding", "norm"] @register_to_config def __init__( self, in_channels: int = 16, out_channels: Optional[int] = 16, num_attention_heads: int = 20, attention_head_dim: int = 112, num_layers: int = 20, num_cross_attention_heads: Optional[int] = 20, cross_attention_head_dim: Optional[int] = 112, cross_attention_dim: Optional[int] = 2240, caption_channels: int = 2304, mlp_ratio: float = 2.5, dropout: float = 0.0, attention_bias: bool = False, sample_size: int = 30, patch_size: Tuple[int, int, int] = (1, 2, 2), norm_elementwise_affine: bool = False, norm_eps: float = 1e-6, interpolation_scale: Optional[int] = None, guidance_embeds: bool = False, guidance_embeds_scale: float = 0.1, qk_norm: Optional[str] = "rms_norm_across_heads", rope_max_seq_len: int = 1024, ) -> None: super().__init__() out_channels = out_channels or in_channels inner_dim = num_attention_heads * attention_head_dim # 1. Patch & position embedding self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len) self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size) # 2. Additional condition embeddings if guidance_embeds: self.time_embed = SanaCombinedTimestepGuidanceEmbeddings(inner_dim) else: self.time_embed = AdaLayerNormSingle(inner_dim) self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim) self.caption_norm = RMSNorm(inner_dim, eps=1e-5, elementwise_affine=True) # 3. Transformer blocks self.transformer_blocks = nn.ModuleList( [ SanaVideoTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, num_cross_attention_heads=num_cross_attention_heads, cross_attention_head_dim=cross_attention_head_dim, cross_attention_dim=cross_attention_dim, attention_bias=attention_bias, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, mlp_ratio=mlp_ratio, qk_norm=qk_norm, ) for _ in range(num_layers) ] ) # 4. Output blocks self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5) self.norm_out = SanaModulatedNorm(inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out = nn.Linear(inner_dim, math.prod(patch_size) * out_channels) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, timestep: torch.Tensor, guidance: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_block_samples: Optional[Tuple[torch.Tensor]] = None, return_dict: bool = True, ) -> Union[Tuple[torch.Tensor, ...], Transformer2DModelOutput]: if attention_kwargs is not None: attention_kwargs = attention_kwargs.copy() lora_scale = attention_kwargs.pop("scale", 1.0) else: lora_scale = 1.0 if USE_PEFT_BACKEND: # weight the lora layers by setting `lora_scale` for each PEFT layer scale_lora_layers(self, lora_scale) else: if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None: logger.warning( "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective." ) # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is not None and attention_mask.ndim == 2: # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # convert encoder_attention_mask to a bias the same way we do for attention_mask if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 encoder_attention_mask = encoder_attention_mask.unsqueeze(1) # 1. Input batch_size, num_channels, num_frames, height, width = hidden_states.shape p_t, p_h, p_w = self.config.patch_size post_patch_num_frames = num_frames // p_t post_patch_height = height // p_h post_patch_width = width // p_w rotary_emb = self.rope(hidden_states) hidden_states = self.patch_embedding(hidden_states) hidden_states = hidden_states.flatten(2).transpose(1, 2) if guidance is not None: timestep, embedded_timestep = self.time_embed( timestep.flatten(), guidance=guidance, hidden_dtype=hidden_states.dtype ) else: timestep, embedded_timestep = self.time_embed( timestep.flatten(), batch_size=batch_size, hidden_dtype=hidden_states.dtype ) timestep = timestep.view(batch_size, -1, timestep.size(-1)) embedded_timestep = embedded_timestep.view(batch_size, -1, embedded_timestep.size(-1)) encoder_hidden_states = self.caption_projection(encoder_hidden_states) encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) encoder_hidden_states = self.caption_norm(encoder_hidden_states) # 2. Transformer blocks if torch.is_grad_enabled() and self.gradient_checkpointing: for index_block, block in enumerate(self.transformer_blocks): hidden_states = self._gradient_checkpointing_func( block, hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, post_patch_num_frames, post_patch_height, post_patch_width, rotary_emb, ) if controlnet_block_samples is not None and 0 < index_block <= len(controlnet_block_samples): hidden_states = hidden_states + controlnet_block_samples[index_block - 1] else: for index_block, block in enumerate(self.transformer_blocks): hidden_states = block( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, post_patch_num_frames, post_patch_height, post_patch_width, rotary_emb, ) if controlnet_block_samples is not None and 0 < index_block <= len(controlnet_block_samples): hidden_states = hidden_states + controlnet_block_samples[index_block - 1] # 3. Normalization hidden_states = self.norm_out(hidden_states, embedded_timestep, self.scale_shift_table) hidden_states = self.proj_out(hidden_states) # 5. Unpatchify hidden_states = hidden_states.reshape( batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1 ) hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6) output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3) if USE_PEFT_BACKEND: # remove `lora_scale` from each PEFT layer unscale_lora_layers(self, lora_scale) if not return_dict: return (output,) return Transformer2DModelOutput(sample=output)