Spaces:
Paused
Paused
File size: 9,793 Bytes
59d751c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 |
from typing import Any, Dict, List, Tuple
import torch
from diffusers import (
AutoencoderKLCogVideoX,
CogVideoXDPMScheduler,
CogVideoXImageToVideoPipeline,
CogVideoXTransformer3DModel,
)
from diffusers.models.embeddings import get_3d_rotary_pos_embed
from PIL import Image
from numpy import dtype
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 CogVideoXI2VLoraTrainer(Trainer):
UNLOAD_LIST = ["text_encoder"]
@override
def load_components(self) -> Dict[str, Any]:
components = Components()
model_path = str(self.args.model_path)
components.pipeline_cls = CogVideoXImageToVideoPipeline
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) -> CogVideoXImageToVideoPipeline:
pipe = CogVideoXImageToVideoPipeline(
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": [], "images": []}
for sample in samples:
encoded_video = sample["encoded_video"]
prompt_embedding = sample["prompt_embedding"]
image = sample["image"]
ret["encoded_videos"].append(encoded_video)
ret["prompt_embedding"].append(prompt_embedding)
ret["images"].append(image)
ret["encoded_videos"] = torch.stack(ret["encoded_videos"])
ret["prompt_embedding"] = torch.stack(ret["prompt_embedding"])
ret["images"] = torch.stack(ret["images"])
return ret
@override
def compute_loss(self, batch) -> torch.Tensor:
prompt_embedding = batch["prompt_embedding"]
latent = batch["encoded_videos"]
images = batch["images"]
# Shape of prompt_embedding: [B, seq_len, hidden_size]
# Shape of latent: [B, C, F, H, W]
# Shape of images: [B, C, H, W]
patch_size_t = self.state.transformer_config.patch_size_t
if patch_size_t is not None:
ncopy = latent.shape[2] % patch_size_t
# Copy the first frame ncopy times to match patch_size_t
first_frame = latent[:, :, :1, :, :] # Get first frame [B, C, 1, H, W]
latent = torch.cat([first_frame.repeat(1, 1, ncopy, 1, 1), latent], dim=2)
assert latent.shape[2] % patch_size_t == 0
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).to(dtype=latent.dtype)
# Add frame dimension to images [B,C,H,W] -> [B,C,F,H,W]
images = images.unsqueeze(2)
# Add noise to images
image_noise_sigma = torch.normal(mean=-3.0, std=0.5, size=(1,), device=self.accelerator.device)
image_noise_sigma = torch.exp(image_noise_sigma).to(dtype=images.dtype)
noisy_images = images + torch.randn_like(images) * image_noise_sigma[:, None, None, None, None]
image_latent_dist = self.components.vae.encode(noisy_images.to(dtype=self.components.vae.dtype)).latent_dist
image_latents = image_latent_dist.sample() * self.components.vae.config.scaling_factor
# 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()
# from [B, C, F, H, W] to [B, F, C, H, W]
latent = latent.permute(0, 2, 1, 3, 4)
image_latents = image_latents.permute(0, 2, 1, 3, 4)
assert (latent.shape[0], *latent.shape[2:]) == (image_latents.shape[0], *image_latents.shape[2:])
# Padding image_latents to the same frame number as latent
padding_shape = (latent.shape[0], latent.shape[1] - 1, *latent.shape[2:])
latent_padding = image_latents.new_zeros(padding_shape)
image_latents = torch.cat([image_latents, latent_padding], dim=1)
# Add noise to latent
noise = torch.randn_like(latent)
latent_noisy = self.components.scheduler.add_noise(latent, noise, timesteps)
# Concatenate latent and image_latents in the channel dimension
latent_img_noisy = torch.cat([latent_noisy, image_latents], dim=2)
# 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, For CogVideoX1.5 Only.
ofs_emb = (
None if self.state.transformer_config.ofs_embed_dim is None else latent.new_full((1,), fill_value=2.0)
)
predicted_noise = self.components.transformer(
hidden_states=latent_img_noisy,
encoder_hidden_states=prompt_embedding,
timestep=timesteps,
ofs=ofs_emb,
image_rotary_emb=rotary_emb,
return_dict=False,
)[0]
# Denoise
latent_pred = self.components.scheduler.get_velocity(predicted_noise, latent_noisy, 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: CogVideoXImageToVideoPipeline
) -> 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,
height=self.state.train_height,
width=self.state.train_width,
prompt=prompt,
image=image,
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-i2v", "lora", CogVideoXI2VLoraTrainer)
|