# Adapted from CogVideo # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # References: # CogVideo: https://github.com/THUDM/CogVideo # diffusers: https://github.com/huggingface/diffusers # -------------------------------------------------------- from typing import Any, Dict, Optional, Union import torch from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.models.attention import Attention, FeedForward from diffusers.models.embeddings import TimestepEmbedding, Timesteps from diffusers.models.modeling_outputs import Transformer2DModelOutput from diffusers.models.modeling_utils import ModelMixin from diffusers.utils import is_torch_version, logging from diffusers.utils.torch_utils import maybe_allow_in_graph from torch import nn from .modules import AdaLayerNorm, CogVideoXLayerNormZero, CogVideoXPatchEmbed, get_3d_sincos_pos_embed logger = logging.get_logger(__name__) # pylint: disable=invalid-name @maybe_allow_in_graph class CogVideoXBlock(nn.Module): r""" Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads (`int`): The number of heads to use for multi-head attention. attention_head_dim (`int`): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. attention_bias (: obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter. qk_norm (`bool`, defaults to `True`): Whether or not to use normalization after query and key projections in Attention. norm_elementwise_affine (`bool`, defaults to `True`): Whether to use learnable elementwise affine parameters for normalization. norm_eps (`float`, defaults to `1e-5`): Epsilon value for normalization layers. final_dropout (`bool` defaults to `False`): Whether to apply a final dropout after the last feed-forward layer. ff_inner_dim (`int`, *optional*, defaults to `None`): Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used. ff_bias (`bool`, defaults to `True`): Whether or not to use bias in Feed-forward layer. attention_out_bias (`bool`, defaults to `True`): Whether or not to use bias in Attention output projection layer. """ def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, time_embed_dim: int, dropout: float = 0.0, activation_fn: str = "gelu-approximate", attention_bias: bool = False, qk_norm: bool = True, norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, final_dropout: bool = True, ff_inner_dim: Optional[int] = None, ff_bias: bool = True, attention_out_bias: bool = True, ): super().__init__() # 1. Self Attention self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True) self.attn1 = Attention( query_dim=dim, dim_head=attention_head_dim, heads=num_attention_heads, qk_norm="layer_norm" if qk_norm else None, eps=1e-6, bias=attention_bias, out_bias=attention_out_bias, ) # 2. Feed Forward self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True) self.ff = FeedForward( dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout, inner_dim=ff_inner_dim, bias=ff_bias, ) def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, temb: torch.Tensor, ) -> torch.Tensor: norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1( hidden_states, encoder_hidden_states, temb ) # attention text_length = norm_encoder_hidden_states.size(1) # CogVideoX uses concatenated text + video embeddings with self-attention instead of using # them in cross-attention individually norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1) attn_output = self.attn1( hidden_states=norm_hidden_states, encoder_hidden_states=None, ) hidden_states = hidden_states + gate_msa * attn_output[:, text_length:] encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_output[:, :text_length] # norm & modulate norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2( hidden_states, encoder_hidden_states, temb ) # feed-forward norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1) ff_output = self.ff(norm_hidden_states) hidden_states = hidden_states + gate_ff * ff_output[:, text_length:] encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_length] return hidden_states, encoder_hidden_states class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin): """ A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo). Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input. out_channels (`int`, *optional*): The number of channels in the output. num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. attention_bias (`bool`, *optional*): Configure if the `TransformerBlocks` attention should contain a bias parameter. sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). This is fixed during training since it is used to learn a number of position embeddings. patch_size (`int`, *optional*): The size of the patches to use in the patch embedding layer. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. num_embeds_ada_norm ( `int`, *optional*): The number of diffusion steps used during training. Pass if at least one of the norm_layers is `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. norm_type (`str`, *optional*, defaults to `"layer_norm"`): The type of normalization to use. Options are `"layer_norm"` or `"ada_layer_norm"`. norm_elementwise_affine (`bool`, *optional*, defaults to `True`): Whether or not to use elementwise affine in normalization layers. norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon value to use in normalization layers. caption_channels (`int`, *optional*): The number of channels in the caption embeddings. video_length (`int`, *optional*): The number of frames in the video-like data. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, num_attention_heads: int = 30, attention_head_dim: int = 64, in_channels: Optional[int] = 16, out_channels: Optional[int] = 16, flip_sin_to_cos: bool = True, freq_shift: int = 0, time_embed_dim: int = 512, text_embed_dim: int = 4096, num_layers: int = 30, dropout: float = 0.0, attention_bias: bool = True, sample_width: int = 90, sample_height: int = 60, sample_frames: int = 49, patch_size: int = 2, temporal_compression_ratio: int = 4, max_text_seq_length: int = 226, activation_fn: str = "gelu-approximate", timestep_activation_fn: str = "silu", norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, spatial_interpolation_scale: float = 1.875, temporal_interpolation_scale: float = 1.0, ): super().__init__() inner_dim = num_attention_heads * attention_head_dim post_patch_height = sample_height // patch_size post_patch_width = sample_width // patch_size post_time_compression_frames = (sample_frames - 1) // temporal_compression_ratio + 1 self.num_patches = post_patch_height * post_patch_width * post_time_compression_frames # 1. Patch embedding self.patch_embed = CogVideoXPatchEmbed(patch_size, in_channels, inner_dim, text_embed_dim, bias=True) self.embedding_dropout = nn.Dropout(dropout) # 2. 3D positional embeddings spatial_pos_embedding = get_3d_sincos_pos_embed( inner_dim, (post_patch_width, post_patch_height), post_time_compression_frames, spatial_interpolation_scale, temporal_interpolation_scale, ) spatial_pos_embedding = torch.from_numpy(spatial_pos_embedding).flatten(0, 1) pos_embedding = torch.zeros(1, max_text_seq_length + self.num_patches, inner_dim, requires_grad=False) pos_embedding.data[:, max_text_seq_length:].copy_(spatial_pos_embedding) self.register_buffer("pos_embedding", pos_embedding, persistent=False) # 3. Time embeddings self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift) self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn) # 4. Define spatio-temporal transformers blocks self.transformer_blocks = nn.ModuleList( [ CogVideoXBlock( dim=inner_dim, num_attention_heads=num_attention_heads, attention_head_dim=attention_head_dim, time_embed_dim=time_embed_dim, dropout=dropout, activation_fn=activation_fn, attention_bias=attention_bias, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, ) for _ in range(num_layers) ] ) self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine) # 5. Output blocks self.norm_out = AdaLayerNorm( embedding_dim=time_embed_dim, output_dim=2 * inner_dim, norm_elementwise_affine=norm_elementwise_affine, norm_eps=norm_eps, chunk_dim=1, ) self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels) self.gradient_checkpointing = False def _set_gradient_checkpointing(self, module, value=False): self.gradient_checkpointing = value def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, timestep: Union[int, float, torch.LongTensor], timestep_cond: Optional[torch.Tensor] = None, return_dict: bool = True, ): batch_size, num_frames, channels, height, width = hidden_states.shape # 1. Time embedding timesteps = timestep t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=hidden_states.dtype) emb = self.time_embedding(t_emb, timestep_cond) # 2. Patch embedding hidden_states = self.patch_embed(encoder_hidden_states, hidden_states) # 3. Position embedding seq_length = height * width * num_frames // (self.config.patch_size**2) pos_embeds = self.pos_embedding[:, : self.config.max_text_seq_length + seq_length] hidden_states = hidden_states + pos_embeds hidden_states = self.embedding_dropout(hidden_states) encoder_hidden_states = hidden_states[:, : self.config.max_text_seq_length] hidden_states = hidden_states[:, self.config.max_text_seq_length :] # 5. Transformer blocks for i, block in enumerate(self.transformer_blocks): if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states, encoder_hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, encoder_hidden_states, emb, **ckpt_kwargs, ) else: hidden_states, encoder_hidden_states = block( hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=emb, ) hidden_states = self.norm_final(hidden_states) # 6. Final block hidden_states = self.norm_out(hidden_states, temb=emb) hidden_states = self.proj_out(hidden_states) # 7. Unpatchify p = self.config.patch_size output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, channels, p, p) output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4) if not return_dict: return (output,) return Transformer2DModelOutput(sample=output)