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| import diffusers | |
| from diffusers.models.transformer_temporal import TransformerTemporalModel, TransformerTemporalModelOutput | |
| import torch.nn as nn | |
| from einops import rearrange | |
| from diffusers.models.attention_processor import Attention | |
| # from t2v_enhanced.model.diffusers_conditional.models.controlnet.attention_processor import Attention | |
| from t2v_enhanced.model.diffusers_conditional.models.controlnet.transformer_temporal_crossattention import TransformerTemporalModel as TransformerTemporalModelCrossAttn | |
| import torch | |
| class CrossAttention(nn.Module): | |
| def __init__(self, input_channels, attention_head_dim, norm_num_groups=32): | |
| super().__init__() | |
| self.attention = Attention( | |
| query_dim=input_channels, cross_attention_dim=input_channels, heads=input_channels//attention_head_dim, dim_head=attention_head_dim, bias=False, upcast_attention=False) | |
| self.norm = torch.nn.GroupNorm( | |
| num_groups=norm_num_groups, num_channels=input_channels, eps=1e-6, affine=True) | |
| self.proj_in = nn.Linear(input_channels, input_channels) | |
| self.proj_out = nn.Linear(input_channels, input_channels) | |
| def forward(self, hidden_state, encoder_hidden_states, num_frames): | |
| h, w = hidden_state.shape[2], hidden_state.shape[3] | |
| hidden_state_norm = rearrange( | |
| hidden_state, "(B F) C H W -> B C F H W", F=num_frames) | |
| hidden_state_norm = self.norm(hidden_state_norm) | |
| hidden_state_norm = rearrange( | |
| hidden_state_norm, "B C F H W -> (B H W) F C") | |
| hidden_state_norm = self.proj_in(hidden_state_norm) | |
| attn = self.attention(hidden_state_norm, | |
| encoder_hidden_states=encoder_hidden_states, | |
| attention_mask=None, | |
| ) | |
| # proj_out | |
| residual = self.proj_out(attn) | |
| residual = rearrange( | |
| residual, "(B H W) F C -> (B F) C H W", H=h, W=w) | |
| output = hidden_state + residual | |
| return TransformerTemporalModelOutput(sample=output) | |
| class ConditionalModel(nn.Module): | |
| def __init__(self, input_channels, conditional_model: str, attention_head_dim=64): | |
| super().__init__() | |
| num_layers = 1 | |
| if "_layers_" in conditional_model: | |
| config = conditional_model.split("_layers_") | |
| conditional_model = config[0] | |
| num_layers = int(config[1]) | |
| if conditional_model == "self_cross_transformer": | |
| self.temporal_transformer = TransformerTemporalModel(num_attention_heads=input_channels//attention_head_dim, attention_head_dim=attention_head_dim, in_channels=input_channels, | |
| double_self_attention=False, cross_attention_dim=input_channels) | |
| elif conditional_model == "cross_transformer": | |
| self.temporal_transformer = TransformerTemporalModelCrossAttn(num_attention_heads=input_channels//attention_head_dim, attention_head_dim=attention_head_dim, in_channels=input_channels, | |
| double_self_attention=False, cross_attention_dim=input_channels, num_layers=num_layers) | |
| elif conditional_model == "cross_attention": | |
| self.temporal_transformer = CrossAttention( | |
| input_channels=input_channels, attention_head_dim=attention_head_dim) | |
| elif conditional_model == "test_conv": | |
| self.temporal_transformer = nn.Conv2d( | |
| input_channels, input_channels, kernel_size=1) | |
| else: | |
| raise NotImplementedError( | |
| f"mode {conditional_model} not implemented") | |
| if conditional_model != "test_conv": | |
| nn.init.zeros_(self.temporal_transformer.proj_out.weight) | |
| nn.init.zeros_(self.temporal_transformer.proj_out.bias) | |
| else: | |
| nn.init.zeros_(self.temporal_transformer.weight) | |
| nn.init.zeros_(self.temporal_transformer.bias) | |
| self.conditional_model = conditional_model | |
| def forward(self, sample, conditioning, num_frames=None): | |
| assert conditioning.ndim == 5 | |
| assert sample.ndim == 5 | |
| if self.conditional_model != "test_conv": | |
| conditioning = rearrange(conditioning, "B F C H W -> (B H W) F C") | |
| num_frames = sample.shape[1] | |
| sample = rearrange(sample, "B F C H W -> (B F) C H W") | |
| sample = self.temporal_transformer( | |
| sample, encoder_hidden_states=conditioning, num_frames=num_frames).sample | |
| sample = rearrange( | |
| sample, "(B F) C H W -> B F C H W", F=num_frames) | |
| else: | |
| conditioning = rearrange(conditioning, "B F C H W -> (B F) C H W") | |
| f = sample.shape[1] | |
| sample = rearrange(sample, "B F C H W -> (B F) C H W") | |
| sample = sample + self.temporal_transformer(conditioning) | |
| sample = rearrange(sample, "(B F) C H W -> B F C H W", F=f) | |
| return sample | |