File size: 11,930 Bytes
a69cd16 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | """Video-to-video training strategy for IC-LoRA.
This strategy implements training with reference video conditioning where:
- Reference latents (clean) are concatenated with target latents (noised)
- Video coordinates handle both reference and target sequences
- Loss is computed only on the target portion
"""
from typing import Any, Literal
import torch
from pydantic import Field
from torch import Tensor
from ltx_core.model.transformer.modality import Modality
from ltx_trainer import logger
from ltx_trainer.timestep_samplers import TimestepSampler
from ltx_trainer.training_strategies.base_strategy import (
DEFAULT_FPS,
ModelInputs,
TrainingStrategy,
TrainingStrategyConfigBase,
)
class VideoToVideoConfig(TrainingStrategyConfigBase):
"""Configuration for video-to-video (IC-LoRA) training strategy."""
name: Literal["video_to_video"] = "video_to_video"
first_frame_conditioning_p: float = Field(
default=0.1,
description="Probability of conditioning on the first frame during training",
ge=0.0,
le=1.0,
)
reference_latents_dir: str = Field(
default="reference_latents",
description="Directory name for latents of reference videos",
)
class VideoToVideoStrategy(TrainingStrategy):
"""Video-to-video training strategy for IC-LoRA.
This strategy implements training with reference video conditioning where:
- Reference latents (clean) are concatenated with target latents (noised)
- Video coordinates handle both reference and target sequences
- Loss is computed only on the target portion
Attributes:
reference_downscale_factor: The inferred downscale factor of reference videos.
This is computed from the first batch and cached for metadata export.
"""
config: VideoToVideoConfig
reference_downscale_factor: int | None
def __init__(self, config: VideoToVideoConfig):
"""Initialize strategy with configuration.
Args:
config: Video-to-video configuration
"""
super().__init__(config)
self.reference_downscale_factor = None # Will be inferred from first batch
def get_data_sources(self) -> dict[str, str]:
"""IC-LoRA training requires latents, conditions, and reference latents."""
return {
"latents": "latents",
"conditions": "conditions",
self.config.reference_latents_dir: "ref_latents",
}
def prepare_training_inputs( # noqa: PLR0915
self,
batch: dict[str, Any],
timestep_sampler: TimestepSampler,
) -> ModelInputs:
"""Prepare inputs for IC-LoRA training with reference videos."""
# Get pre-encoded latents - dataset provides uniform non-patchified format [B, C, F, H, W]
latents = batch["latents"]
target_latents = latents["latents"]
ref_latents = batch["ref_latents"]["latents"]
# Get dimensions
num_frames = latents["num_frames"][0].item()
height = latents["height"][0].item()
width = latents["width"][0].item()
ref_latents_info = batch["ref_latents"]
ref_frames = ref_latents_info["num_frames"][0].item()
ref_height = ref_latents_info["height"][0].item()
ref_width = ref_latents_info["width"][0].item()
# Infer reference downscale factor from dimension ratios
# This allows training with downscaled reference videos for efficiency
reference_downscale_factor = self._infer_reference_downscale_factor(
target_height=height,
target_width=width,
ref_height=ref_height,
ref_width=ref_width,
)
# Cache the scale factor for metadata export (only on first batch)
if self.reference_downscale_factor is None:
self.reference_downscale_factor = reference_downscale_factor
elif self.reference_downscale_factor != reference_downscale_factor:
raise ValueError(
f"Inconsistent reference downscale factor across batches. "
f"First batch had factor={self.reference_downscale_factor}, "
f"but current batch has factor={reference_downscale_factor}. "
f"All training samples must use the same reference/target resolution ratio."
)
# Patchify latents: [B, C, F, H, W] -> [B, seq_len, C]
target_latents = self._video_patchifier.patchify(target_latents)
ref_latents = self._video_patchifier.patchify(ref_latents)
# Handle FPS
fps = latents.get("fps", None)
if fps is not None and not torch.all(fps == fps[0]):
logger.warning(
f"Different FPS values found in the batch. Found: {fps.tolist()}, using the first one: {fps[0].item()}"
)
fps = fps[0].item() if fps is not None else DEFAULT_FPS
# Get text embeddings (already processed by embedding connectors in trainer)
# Video-to-video uses only video embeddings
conditions = batch["conditions"]
prompt_embeds = conditions["video_prompt_embeds"]
prompt_attention_mask = conditions["prompt_attention_mask"]
batch_size = target_latents.shape[0]
ref_seq_len = ref_latents.shape[1]
target_seq_len = target_latents.shape[1]
device = target_latents.device
dtype = target_latents.dtype
# Create conditioning mask
# Reference tokens are always conditioning (timestep=0)
ref_conditioning_mask = torch.ones(batch_size, ref_seq_len, dtype=torch.bool, device=device)
# Target tokens: check for first frame conditioning
target_conditioning_mask = self._create_first_frame_conditioning_mask(
batch_size=batch_size,
sequence_length=target_seq_len,
height=height,
width=width,
device=device,
first_frame_conditioning_p=self.config.first_frame_conditioning_p,
)
# Combined conditioning mask
conditioning_mask = torch.cat([ref_conditioning_mask, target_conditioning_mask], dim=1)
# Sample noise and sigmas for target
sigmas = timestep_sampler.sample_for(target_latents)
noise = torch.randn_like(target_latents)
sigmas_expanded = sigmas.view(-1, 1, 1)
# Apply noise to target
noisy_target = (1 - sigmas_expanded) * target_latents + sigmas_expanded * noise
# For first frame conditioning in target, use clean latents
target_conditioning_mask_expanded = target_conditioning_mask.unsqueeze(-1)
noisy_target = torch.where(target_conditioning_mask_expanded, target_latents, noisy_target)
# Targets for loss computation
targets = noise - target_latents
# Concatenate reference (clean) and target (noisy)
combined_latents = torch.cat([ref_latents, noisy_target], dim=1)
# Create per-token timesteps
timesteps = self._create_per_token_timesteps(conditioning_mask, sigmas.squeeze())
# Generate positions for reference and target separately, then concatenate
ref_positions = self._get_video_positions(
num_frames=ref_frames,
height=ref_height,
width=ref_width,
batch_size=batch_size,
fps=fps,
device=device,
dtype=dtype,
)
# Scale reference positions to match target coordinate space
# This maps ref positions from (0, ref_H, ref_W) to (0, target_H, target_W)
# Position tensor shape: [B, 3, seq_len, 2] where dim 1 is (time, height, width)
if reference_downscale_factor != 1:
ref_positions = ref_positions.clone()
ref_positions[:, 1, ...] *= reference_downscale_factor # height axis
ref_positions[:, 2, ...] *= reference_downscale_factor # width axis
# Time axis (index 0) remains unchanged
target_positions = self._get_video_positions(
num_frames=num_frames,
height=height,
width=width,
batch_size=batch_size,
fps=fps,
device=device,
dtype=dtype,
)
# Concatenate positions along sequence dimension
positions = torch.cat([ref_positions, target_positions], dim=2)
# Create video Modality
video_modality = Modality(
enabled=True,
latent=combined_latents,
sigma=sigmas,
timesteps=timesteps,
positions=positions,
context=prompt_embeds,
context_mask=prompt_attention_mask,
)
# Loss mask: only compute loss on non-conditioning target tokens
# Reference tokens: all False (no loss)
# Target tokens: True where not conditioning
ref_loss_mask = torch.zeros(batch_size, ref_seq_len, dtype=torch.bool, device=device)
target_loss_mask = ~target_conditioning_mask
video_loss_mask = torch.cat([ref_loss_mask, target_loss_mask], dim=1)
return ModelInputs(
video=video_modality,
audio=None,
video_targets=targets,
audio_targets=None,
video_loss_mask=video_loss_mask,
audio_loss_mask=None,
ref_seq_len=ref_seq_len,
)
def compute_loss(
self,
video_pred: Tensor,
_audio_pred: Tensor | None,
inputs: ModelInputs,
) -> Tensor:
"""Compute masked loss only on target portion. Returns [B,]."""
# Extract target portion of prediction
ref_seq_len = inputs.ref_seq_len
target_pred = video_pred[:, ref_seq_len:, :]
# Get target portion of loss mask
target_loss_mask = inputs.video_loss_mask[:, ref_seq_len:]
# Compute per-element loss [B,]
loss = (target_pred - inputs.video_targets).pow(2)
loss_mask = target_loss_mask.unsqueeze(-1).float()
masked = loss.mul(loss_mask)
return masked.mean(dim=[-2, -1]) / loss_mask.mean(dim=[-2, -1]).clamp(min=1e-8)
def get_checkpoint_metadata(self) -> dict[str, Any]:
"""Get metadata for checkpoint files."""
metadata: dict[str, Any] = {}
# Always include reference_downscale_factor for IC-LoRAs so inference
# pipelines know the expected scale factor for reference videos.
if self.reference_downscale_factor is not None:
metadata["reference_downscale_factor"] = self.reference_downscale_factor
return metadata
@staticmethod
def _infer_reference_downscale_factor(
target_height: int,
target_width: int,
ref_height: int,
ref_width: int,
) -> int:
"""Infer the reference downscale factor from target and reference dimensions."""
# If dimensions match, no scaling needed
if target_height == ref_height and target_width == ref_width:
return 1
# Calculate scale factors for each dimension
if target_height % ref_height != 0 or target_width % ref_width != 0:
raise ValueError(
f"Target dimensions ({target_height}x{target_width}) must be exact multiples "
f"of reference dimensions ({ref_height}x{ref_width})"
)
scale_h = target_height // ref_height
scale_w = target_width // ref_width
if scale_h != scale_w:
raise ValueError(
f"Reference scale must be uniform. Got height scale {scale_h} and width scale {scale_w}. "
f"Target: {target_height}x{target_width}, Reference: {ref_height}x{ref_width}"
)
if scale_h < 1:
raise ValueError(
f"Reference dimensions ({ref_height}x{ref_width}) cannot be larger than "
f"target dimensions ({target_height}x{target_width})"
)
return scale_h
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