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from typing import Any, Dict, List, Tuple
import torch
from diffusers import (
AutoencoderKLCogVideoX,
CogVideoXDPMScheduler,
CogVideoXPipeline,
CogVideoXTransformer3DModel,
)
from diffusers.models.embeddings import get_3d_rotary_pos_embed
from PIL import Image
from transformers import AutoTokenizer, T5EncoderModel
from typing_extensions import override
from finetune.schemas import Components
from finetune.trainer import Trainer
from finetune.utils import unwrap_model
from ..utils import register
class CogVideoXT2VLoraTrainer(Trainer):
UNLOAD_LIST = ["text_encoder", "vae"]
@override
def load_components(self) -> Components:
components = Components()
model_path = str(self.args.model_path)
components.pipeline_cls = CogVideoXPipeline
components.tokenizer = AutoTokenizer.from_pretrained(model_path, subfolder="tokenizer")
components.text_encoder = T5EncoderModel.from_pretrained(model_path, subfolder="text_encoder")
components.transformer = CogVideoXTransformer3DModel.from_pretrained(model_path, subfolder="transformer")
components.vae = AutoencoderKLCogVideoX.from_pretrained(model_path, subfolder="vae")
components.scheduler = CogVideoXDPMScheduler.from_pretrained(model_path, subfolder="scheduler")
return components
@override
def initialize_pipeline(self) -> CogVideoXPipeline:
pipe = CogVideoXPipeline(
tokenizer=self.components.tokenizer,
text_encoder=self.components.text_encoder,
vae=self.components.vae,
transformer=unwrap_model(self.accelerator, self.components.transformer),
scheduler=self.components.scheduler,
)
return pipe
@override
def encode_video(self, video: torch.Tensor) -> torch.Tensor:
# shape of input video: [B, C, F, H, W]
vae = self.components.vae
video = video.to(vae.device, dtype=vae.dtype)
latent_dist = vae.encode(video).latent_dist
latent = latent_dist.sample() * vae.config.scaling_factor
return latent
@override
def encode_text(self, prompt: str) -> torch.Tensor:
prompt_token_ids = self.components.tokenizer(
prompt,
padding="max_length",
max_length=self.state.transformer_config.max_text_seq_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
prompt_token_ids = prompt_token_ids.input_ids
prompt_embedding = self.components.text_encoder(prompt_token_ids.to(self.accelerator.device))[0]
return prompt_embedding
@override
def collate_fn(self, samples: List[Dict[str, Any]]) -> Dict[str, Any]:
ret = {"encoded_videos": [], "prompt_embedding": []}
for sample in samples:
encoded_video = sample["encoded_video"]
prompt_embedding = sample["prompt_embedding"]
ret["encoded_videos"].append(encoded_video)
ret["prompt_embedding"].append(prompt_embedding)
ret["encoded_videos"] = torch.stack(ret["encoded_videos"])
ret["prompt_embedding"] = torch.stack(ret["prompt_embedding"])
return ret
@override
def compute_loss(self, batch) -> torch.Tensor:
prompt_embedding = batch["prompt_embedding"]
latent = batch["encoded_videos"]
# Shape of prompt_embedding: [B, seq_len, hidden_size]
# Shape of latent: [B, C, F, H, W]
patch_size_t = self.state.transformer_config.patch_size_t
if patch_size_t is not None and latent.shape[2] % patch_size_t != 0:
raise ValueError(
"Number of frames in latent must be divisible by patch size, please check your args for training."
)
# Add 2 random noise frames at the beginning of frame dimension
noise_frames = torch.randn(
latent.shape[0],
latent.shape[1],
2,
latent.shape[3],
latent.shape[4],
device=latent.device,
dtype=latent.dtype,
)
latent = torch.cat([noise_frames, latent], dim=2)
batch_size, num_channels, num_frames, height, width = latent.shape
# Get prompt embeddings
_, seq_len, _ = prompt_embedding.shape
prompt_embedding = prompt_embedding.view(batch_size, seq_len, -1)
# Sample a random timestep for each sample
timesteps = torch.randint(
0, self.components.scheduler.config.num_train_timesteps, (batch_size,), device=self.accelerator.device
)
timesteps = timesteps.long()
# Add noise to latent
latent = latent.permute(0, 2, 1, 3, 4) # from [B, C, F, H, W] to [B, F, C, H, W]
noise = torch.randn_like(latent)
latent_added_noise = self.components.scheduler.add_noise(latent, noise, timesteps)
# Prepare rotary embeds
vae_scale_factor_spatial = 2 ** (len(self.components.vae.config.block_out_channels) - 1)
transformer_config = self.state.transformer_config
rotary_emb = (
self.prepare_rotary_positional_embeddings(
height=height * vae_scale_factor_spatial,
width=width * vae_scale_factor_spatial,
num_frames=num_frames,
transformer_config=transformer_config,
vae_scale_factor_spatial=vae_scale_factor_spatial,
device=self.accelerator.device,
)
if transformer_config.use_rotary_positional_embeddings
else None
)
# Predict noise
predicted_noise = self.components.transformer(
hidden_states=latent_added_noise,
encoder_hidden_states=prompt_embedding,
timestep=timesteps,
image_rotary_emb=rotary_emb,
return_dict=False,
)[0]
# Denoise
latent_pred = self.components.scheduler.get_velocity(predicted_noise, latent_added_noise, timesteps)
alphas_cumprod = self.components.scheduler.alphas_cumprod[timesteps]
weights = 1 / (1 - alphas_cumprod)
while len(weights.shape) < len(latent_pred.shape):
weights = weights.unsqueeze(-1)
loss = torch.mean((weights * (latent_pred - latent) ** 2).reshape(batch_size, -1), dim=1)
loss = loss.mean()
return loss
@override
def validation_step(
self, eval_data: Dict[str, Any], pipe: CogVideoXPipeline
) -> List[Tuple[str, Image.Image | List[Image.Image]]]:
"""
Return the data that needs to be saved. For videos, the data format is List[PIL],
and for images, the data format is PIL
"""
prompt, image, video = eval_data["prompt"], eval_data["image"], eval_data["video"]
video_generate = pipe(
num_frames=self.state.train_frames, # since we pad 2 frames in latent, we still use train_frames
height=self.state.train_height,
width=self.state.train_width,
prompt=prompt,
generator=self.state.generator,
).frames[0]
return [("video", video_generate)]
def prepare_rotary_positional_embeddings(
self,
height: int,
width: int,
num_frames: int,
transformer_config: Dict,
vae_scale_factor_spatial: int,
device: torch.device,
) -> Tuple[torch.Tensor, torch.Tensor]:
grid_height = height // (vae_scale_factor_spatial * transformer_config.patch_size)
grid_width = width // (vae_scale_factor_spatial * transformer_config.patch_size)
if transformer_config.patch_size_t is None:
base_num_frames = num_frames
else:
base_num_frames = (num_frames + transformer_config.patch_size_t - 1) // transformer_config.patch_size_t
freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
embed_dim=transformer_config.attention_head_dim,
crops_coords=None,
grid_size=(grid_height, grid_width),
temporal_size=base_num_frames,
grid_type="slice",
max_size=(grid_height, grid_width),
device=device,
)
return freqs_cos, freqs_sin
register("cogvideox-t2v", "lora", CogVideoXT2VLoraTrainer)
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