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# 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) |