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import torch
import numpy as np
from einops import rearrange
from typing import Optional, Tuple, Union
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from .models.cogvideox.custom_cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .models.cogvideox.enhance_a_video.globals import set_num_frames
def poly1d(coefficients, x):
result = torch.zeros_like(x)
for i, coeff in enumerate(coefficients):
result += coeff * (x ** (len(coefficients) - 1 - i))
return result.abs()
def fft(tensor):
tensor_fft = torch.fft.fft2(tensor)
tensor_fft_shifted = torch.fft.fftshift(tensor_fft)
B, C, H, W = tensor.size()
radius = min(H, W) // 5
Y, X = torch.meshgrid(torch.arange(H), torch.arange(W))
center_x, center_y = W // 2, H // 2
mask = (X - center_x) ** 2 + (Y - center_y) ** 2 <= radius ** 2
low_freq_mask = mask.unsqueeze(0).unsqueeze(0).to(tensor.device)
high_freq_mask = ~low_freq_mask
low_freq_fft = tensor_fft_shifted * low_freq_mask
high_freq_fft = tensor_fft_shifted * high_freq_mask
return low_freq_fft, high_freq_fft
def teacache_cogvideox_forward(
self,
hidden_states: torch.Tensor,
encoder_hidden_states: torch.Tensor,
timestep: Union[int, float, torch.LongTensor],
timestep_cond: Optional[torch.Tensor] = None,
ofs: Optional[Union[int, float, torch.LongTensor]] = None,
image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
controlnet_states: torch.Tensor = None,
controlnet_weights: Optional[Union[float, int, list, np.ndarray, torch.FloatTensor]] = 1.0,
video_flow_features: Optional[torch.Tensor] = None,
return_dict: bool = True,
):
batch_size, num_frames, channels, height, width = hidden_states.shape
set_num_frames(num_frames) # enhance a video global
# 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)
if self.ofs_embedding is not None: #1.5 I2V
ofs_emb = self.ofs_proj(ofs)
ofs_emb = ofs_emb.to(dtype=hidden_states.dtype)
ofs_emb = self.ofs_embedding(ofs_emb)
emb = emb + ofs_emb
# 2. Patch embedding
p = self.config.patch_size
p_t = self.config.patch_size_t
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:]
# enable teacache
if not hasattr(self, 'accumulated_rel_l1_distance'):
should_calc = True
self.accumulated_rel_l1_distance = 0
else:
try:
if not self.config.use_rotary_positional_embeddings:
# CogVideoX-2B
coefficients = [-3.10658903e+01, 2.54732368e+01, -5.92380459e+00, 1.75769064e+00, -3.61568434e-03]
else:
# CogVideoX-5B
coefficients = [-1.53880483e+03, 8.43202495e+02, -1.34363087e+02, 7.97131516e+00, -5.23162339e-02]
self.accumulated_rel_l1_distance += poly1d(coefficients, ((emb-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()))
if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
should_calc = False
else:
should_calc = True
self.accumulated_rel_l1_distance = 0
except:
should_calc = True
self.accumulated_rel_l1_distance = 0
self.previous_modulated_input = emb
if self.use_fastercache:
self.fastercache_counter += 1
if self.fastercache_counter >= self.fastercache_start_step + 3 and self.fastercache_counter % 5 != 0:
if not should_calc:
hidden_states += self.previous_residual
encoder_hidden_states += self.previous_residual_encoder
else:
ori_hidden_states = hidden_states.clone()
ori_encoder_hidden_states = encoder_hidden_states.clone()
# 3. Transformer blocks
for i, block in enumerate(self.transformer_blocks):
hidden_states, encoder_hidden_states = block(
hidden_states=hidden_states[:1],
encoder_hidden_states=encoder_hidden_states[:1],
temb=emb[:1],
image_rotary_emb=image_rotary_emb,
video_flow_feature=video_flow_features[i][:1] if video_flow_features is not None else None,
fuser = self.fuser_list[i] if self.fuser_list is not None else None,
block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
fastercache_counter = self.fastercache_counter,
fastercache_start_step = self.fastercache_start_step,
fastercache_device = self.fastercache_device
)
if (controlnet_states is not None) and (i < len(controlnet_states)):
controlnet_states_block = controlnet_states[i]
controlnet_block_weight = 1.0
if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
controlnet_block_weight = controlnet_weights[i]
elif isinstance(controlnet_weights, (float, int)):
controlnet_block_weight = controlnet_weights
hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
self.previous_residual = hidden_states - ori_hidden_states
self.previous_residual_encoder = encoder_hidden_states - ori_encoder_hidden_states
if not self.config.use_rotary_positional_embeddings:
# CogVideoX-2B
hidden_states = self.norm_final(hidden_states)
else:
# CogVideoX-5B
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
hidden_states = self.norm_final(hidden_states)
hidden_states = hidden_states[:, text_seq_length:]
# 4. Final block
hidden_states = self.norm_out(hidden_states, temb=emb[:1])
hidden_states = self.proj_out(hidden_states)
# 5. Unpatchify
# Note: we use `-1` instead of `channels`:
# - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
# - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
if p_t is None:
output = hidden_states.reshape(1, num_frames, height // p, width // p, -1, p, p)
output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
else:
output = hidden_states.reshape(
1, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
)
output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
(bb, tt, cc, hh, ww) = output.shape
cond = rearrange(output, "B T C H W -> (B T) C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
lf_c, hf_c = fft(cond.float())
if self.fastercache_counter <= self.fastercache_lf_step:
self.delta_lf = self.delta_lf * 1.1
if self.fastercache_counter >= self.fastercache_hf_step:
self.delta_hf = self.delta_hf * 1.1
new_hf_uc = self.delta_hf + hf_c
new_lf_uc = self.delta_lf + lf_c
combine_uc = new_lf_uc + new_hf_uc
combined_fft = torch.fft.ifftshift(combine_uc)
recovered_uncond = torch.fft.ifft2(combined_fft).real
recovered_uncond = rearrange(recovered_uncond.to(output.dtype), "(B T) C H W -> B T C H W", B=bb, C=cc, T=tt, H=hh, W=ww)
output = torch.cat([output, recovered_uncond])
else:
if not should_calc:
hidden_states += self.previous_residual
encoder_hidden_states += self.previous_residual_encoder
else:
ori_hidden_states = hidden_states.clone()
ori_encoder_hidden_states = encoder_hidden_states.clone()
for i, block in enumerate(self.transformer_blocks):
hidden_states, encoder_hidden_states = block(
hidden_states=hidden_states,
encoder_hidden_states=encoder_hidden_states,
temb=emb,
image_rotary_emb=image_rotary_emb,
video_flow_feature=video_flow_features[i] if video_flow_features is not None else None,
fuser = self.fuser_list[i] if self.fuser_list is not None else None,
block_use_fastercache = i <= self.fastercache_num_blocks_to_cache,
fastercache_counter = self.fastercache_counter,
fastercache_start_step = self.fastercache_start_step,
fastercache_device = self.fastercache_device
)
# controlnet
if (controlnet_states is not None) and (i < len(controlnet_states)):
controlnet_states_block = controlnet_states[i]
controlnet_block_weight = 1.0
if isinstance(controlnet_weights, (list, np.ndarray)) or torch.is_tensor(controlnet_weights):
controlnet_block_weight = controlnet_weights[i]
print(controlnet_block_weight)
elif isinstance(controlnet_weights, (float, int)):
controlnet_block_weight = controlnet_weights
hidden_states = hidden_states + controlnet_states_block * controlnet_block_weight
self.previous_residual = hidden_states - ori_hidden_states
self.previous_residual_encoder = encoder_hidden_states - ori_encoder_hidden_states
if not self.config.use_rotary_positional_embeddings:
# CogVideoX-2B
hidden_states = self.norm_final(hidden_states)
else:
# CogVideoX-5B
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
hidden_states = self.norm_final(hidden_states)
hidden_states = hidden_states[:, text_seq_length:]
# 4. Final block
hidden_states = self.norm_out(hidden_states, temb=emb)
hidden_states = self.proj_out(hidden_states)
# 5. Unpatchify
# Note: we use `-1` instead of `channels`:
# - It is okay to `channels` use for CogVideoX-2b and CogVideoX-5b (number of input channels is equal to output channels)
# - However, for CogVideoX-5b-I2V also takes concatenated input image latents (number of input channels is twice the output channels)
if p_t is None:
output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p)
output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4)
else:
output = hidden_states.reshape(
batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p
)
output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2)
if self.fastercache_counter >= self.fastercache_start_step + 1:
(bb, tt, cc, hh, ww) = output.shape
cond = rearrange(output[0:1].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
uncond = rearrange(output[1:2].float(), "B T C H W -> (B T) C H W", B=bb//2, C=cc, T=tt, H=hh, W=ww)
lf_c, hf_c = fft(cond)
lf_uc, hf_uc = fft(uncond)
self.delta_lf = lf_uc - lf_c
self.delta_hf = hf_uc - hf_c
if not return_dict:
return (output,)
return Transformer2DModelOutput(sample=output)
class TeaCacheForCogVideoX:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("COGVIDEOMODEL", {"tooltip": "The CogVideoX model the TeaCache will be applied to."}),
"enable_teacache": ("BOOLEAN", {"default": True, "tooltip": "Enable teacache will speed up inference but may lose visual quality."}),
"rel_l1_thresh": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 10.0, "step": 0.01, "tooltip": "How strongly to cache the output of diffusion model. This value must be non-negative."})
}
}
RETURN_TYPES = ("COGVIDEOMODEL",)
RETURN_NAMES = ("model",)
FUNCTION = "apply_teacache"
CATEGORY = "TeaCache"
TITLE = "TeaCache For CogVideoX"
def apply_teacache(self, model, enable_teacache: bool, rel_l1_thresh: float):
if enable_teacache:
transformer = model["pipe"].transformer
transformer.__class__.rel_l1_thresh = rel_l1_thresh
transformer.forward = teacache_cogvideox_forward.__get__(
transformer,
transformer.__class__
)
else:
transformer = model["pipe"].transformer
transformer.forward = CogVideoXTransformer3DModel.forward.__get__(
transformer,
transformer.__class__
)
return (model,)
NODE_CLASS_MAPPINGS = {
"TeaCacheForCogVideoX": TeaCacheForCogVideoX
}
NODE_DISPLAY_NAME_MAPPINGS = {k: v.TITLE for k, v in NODE_CLASS_MAPPINGS.items()}
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