# https://github.com/TheDenk/cogvideox-controlnet/blob/main/cogvideo_controlnet.py from typing import Any, Dict, Optional, Tuple, Union import torch from torch import nn from einops import rearrange import torch.nn.functional as F from .custom_cogvideox_transformer_3d import Transformer2DModelOutput, CogVideoXBlock from diffusers.utils import is_torch_version from diffusers.loaders import PeftAdapterMixin from diffusers.models.embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from diffusers.configuration_utils import ConfigMixin, register_to_config class CogVideoXControlnet(ModelMixin, ConfigMixin, PeftAdapterMixin): _supports_gradient_checkpointing = True @register_to_config def __init__( self, num_attention_heads: int = 30, attention_head_dim: int = 64, vae_channels: int = 16, in_channels: int = 3, downscale_coef: int = 8, flip_sin_to_cos: bool = True, freq_shift: int = 0, time_embed_dim: int = 512, num_layers: int = 8, 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, use_rotary_positional_embeddings: bool = False, use_learned_positional_embeddings: bool = False, out_proj_dim = None, ): super().__init__() inner_dim = num_attention_heads * attention_head_dim if not use_rotary_positional_embeddings and use_learned_positional_embeddings: raise ValueError( "There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional " "embeddings. If you're using a custom model and/or believe this should be supported, please open an " "issue at https://github.com/huggingface/diffusers/issues." ) start_channels = in_channels * (downscale_coef ** 2) input_channels = [start_channels, start_channels // 2, start_channels // 4] self.unshuffle = nn.PixelUnshuffle(downscale_coef) self.controlnet_encode_first = nn.Sequential( nn.Conv2d(input_channels[0], input_channels[1], kernel_size=1, stride=1, padding=0), nn.GroupNorm(2, input_channels[1]), nn.ReLU(), ) self.controlnet_encode_second = nn.Sequential( nn.Conv2d(input_channels[1], input_channels[2], kernel_size=1, stride=1, padding=0), nn.GroupNorm(2, input_channels[2]), nn.ReLU(), ) # 1. Patch embedding self.patch_embed = CogVideoXPatchEmbed( patch_size=patch_size, in_channels=vae_channels + input_channels[2], embed_dim=inner_dim, bias=True, sample_width=sample_width, sample_height=sample_height, sample_frames=sample_frames, temporal_compression_ratio=temporal_compression_ratio, spatial_interpolation_scale=spatial_interpolation_scale, temporal_interpolation_scale=temporal_interpolation_scale, use_positional_embeddings=not use_rotary_positional_embeddings, use_learned_positional_embeddings=use_learned_positional_embeddings, ) self.embedding_dropout = nn.Dropout(dropout) # 2. 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) # 3. 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.out_projectors = None if out_proj_dim is not None: self.out_projectors = nn.ModuleList( [nn.Linear(inner_dim, out_proj_dim) for _ in range(num_layers)] ) self.gradient_checkpointing = False def _set_gradient_checkpointing(self, module, value=False): self.gradient_checkpointing = value def compress_time(self, x, num_frames): x = rearrange(x, '(b f) c h w -> b f c h w', f=num_frames) batch_size, frames, channels, height, width = x.shape x = rearrange(x, 'b f c h w -> (b h w) c f') if x.shape[-1] % 2 == 1: x_first, x_rest = x[..., 0], x[..., 1:] if x_rest.shape[-1] > 0: x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2) x = torch.cat([x_first[..., None], x_rest], dim=-1) else: x = F.avg_pool1d(x, kernel_size=2, stride=2) x = rearrange(x, '(b h w) c f -> (b f) c h w', b=batch_size, h=height, w=width) return x def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, controlnet_states: torch.Tensor, timestep: Union[int, float, torch.LongTensor], image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, timestep_cond: Optional[torch.Tensor] = None, return_dict: bool = True, ): batch_size, num_frames, channels, height, width = controlnet_states.shape # 0. Controlnet encoder controlnet_states = rearrange(controlnet_states, 'b f c h w -> (b f) c h w') controlnet_states = self.unshuffle(controlnet_states) controlnet_states = self.controlnet_encode_first(controlnet_states) controlnet_states = self.compress_time(controlnet_states, num_frames=num_frames) num_frames = controlnet_states.shape[0] // batch_size controlnet_states = self.controlnet_encode_second(controlnet_states) controlnet_states = self.compress_time(controlnet_states, num_frames=num_frames) controlnet_states = rearrange(controlnet_states, '(b f) c h w -> b f c h w', b=batch_size) hidden_states = torch.cat([hidden_states, controlnet_states], dim=2) # controlnet_states = self.controlnext_encoder(controlnet_states, timestep=timestep) # 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) hidden_states = self.patch_embed(encoder_hidden_states, hidden_states) hidden_states = self.embedding_dropout(hidden_states) text_seq_length = encoder_hidden_states.shape[1] encoder_hidden_states = hidden_states[:, :text_seq_length] hidden_states = hidden_states[:, text_seq_length:] controlnet_hidden_states = () # 3. 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, image_rotary_emb, **ckpt_kwargs, ) else: hidden_states, encoder_hidden_states = block( hidden_states=hidden_states, encoder_hidden_states=encoder_hidden_states, temb=emb, image_rotary_emb=image_rotary_emb, ) if self.out_projectors is not None: controlnet_hidden_states += (self.out_projectors[i](hidden_states),) else: controlnet_hidden_states += (hidden_states,) if not return_dict: return (controlnet_hidden_states,) return Transformer2DModelOutput(sample=controlnet_hidden_states)