Automated PR - 2026-06-17
This commit is contained in:
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"""Flexible training strategy for a unified conditioning framework.
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This strategy implements the Unified Conditioning Framework that supports:
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- Simple fine-tuning with text conditioning (text-to-video/audio)
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- Intrinsic conditioning (first_frame, prefix, suffix, spatial_crop, mask)
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- Extrinsic conditioning (concatenation-based, IC-LoRA style)
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The flexible strategy replaces TextToVideoStrategy and VideoToVideoStrategy by expressing
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all conditioning scenarios through configuration rather than code.
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"""
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Annotated, Any, Literal, Union
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import torch
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from pydantic import BaseModel, ConfigDict, Field, model_validator
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from torch import Tensor
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from ltx_core.model.transformer.modality import Modality
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from ltx_trainer.timestep_samplers import TimestepSampler
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from ltx_trainer.training_strategies.base_strategy import (
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DEFAULT_FPS,
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VIDEO_SCALE_FACTORS,
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ModelInputs,
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TrainingStrategy,
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TrainingStrategyConfigBase,
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)
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# =============================================================================
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# Configuration Classes
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# =============================================================================
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class IntrinsicConditionBase(BaseModel):
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"""Base for intrinsic conditioning — tokens get clean latents, timestep=0, no loss."""
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model_config = ConfigDict(extra="forbid")
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probability: float = Field(
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default=1.0,
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ge=0.0,
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le=1.0,
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description="Probability of applying this condition",
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)
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class FirstFrameConditionConfig(IntrinsicConditionBase):
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"""First frame conditioning — frame 0 is clean, excluded from loss."""
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type: Literal["first_frame"] = "first_frame"
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class PrefixConditionConfig(IntrinsicConditionBase):
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"""Prefix conditioning — first N temporal units are clean, excluded from loss."""
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type: Literal["prefix"] = "prefix"
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temporal_boundary: int = Field(
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...,
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ge=1,
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description="Number of temporal units for prefix region. "
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"For video: number of latent frames. For audio: number of audio latent timesteps.",
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)
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class SuffixConditionConfig(IntrinsicConditionBase):
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"""Suffix conditioning — last N temporal units are clean, excluded from loss."""
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type: Literal["suffix"] = "suffix"
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temporal_boundary: int = Field(
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...,
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ge=1,
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description="Number of temporal units for suffix region. "
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"For video: number of latent frames. For audio: number of audio latent timesteps.",
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)
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class SpatialCropConditionConfig(IntrinsicConditionBase):
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"""Spatial crop conditioning — rectangular pixel region is clean, excluded from loss."""
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type: Literal["spatial_crop"] = "spatial_crop"
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spatial_region: tuple[int, int, int, int] = Field(
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...,
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description="Spatial crop region as (y1, x1, y2, x2) in pixel coordinates",
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)
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class MaskConditionConfig(IntrinsicConditionBase):
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"""Mask conditioning — per-sample binary mask determines conditioning tokens."""
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type: Literal["mask"] = "mask"
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mask_dir: str = Field(
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...,
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description="Directory containing per-sample masks",
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)
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class ReferenceConditionConfig(BaseModel):
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"""Reference conditioning (IC-LoRA style concatenation).
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External reference latents are concatenated to the target sequence.
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Reference tokens are clean (timestep=0), excluded from loss, and
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participate in bidirectional self-attention.
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"""
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model_config = ConfigDict(extra="forbid")
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type: Literal["reference"] = "reference"
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latents_dir: str = Field(..., description="Directory for reference latents")
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probability: float = Field(default=1.0, ge=0.0, le=1.0, description="Probability of applying this condition")
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# Discriminated union for condition configs
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ConditionConfig = Annotated[
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Union[
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FirstFrameConditionConfig,
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PrefixConditionConfig,
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SuffixConditionConfig,
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SpatialCropConditionConfig,
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MaskConditionConfig,
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ReferenceConditionConfig,
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],
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Field(discriminator="type"),
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]
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class ModalityConfig(BaseModel):
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"""Configuration for a single modality (video or audio)."""
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model_config = ConfigDict(extra="forbid")
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is_generated: bool = Field(
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...,
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description="True = generated modality (denoised, contributes to loss), False = conditioning-only modality",
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)
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latents_dir: str = Field(
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...,
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description="Directory for latents",
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)
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conditions: list[ConditionConfig] = Field(
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default_factory=list,
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description="List of conditions (e.g. first_frame, prefix, reference). Text conditioning is always applied.",
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)
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class FlexibleStrategyConfig(TrainingStrategyConfigBase):
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"""Configuration for the flexible training strategy.
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This strategy supports all conditioning scenarios through configuration:
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- Text-to-video/audio with simple fine-tuning
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- Intrinsic conditioning like first-frame, extension, outpainting
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- Reference conditioning like IC-LoRA (concatenation-based reference)
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"""
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name: Literal["flexible"] = "flexible"
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video: ModalityConfig | None = Field(
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default=None,
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description="Video modality configuration",
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)
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audio: ModalityConfig | None = Field(
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default=None,
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description="Audio modality configuration",
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)
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@model_validator(mode="after")
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def validate_at_least_one_generated(self) -> "FlexibleStrategyConfig":
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"""Ensure at least one modality has is_generated=true."""
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has_video_target = self.video is not None and self.video.is_generated
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has_audio_target = self.audio is not None and self.audio.is_generated
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if not has_video_target and not has_audio_target:
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raise ValueError("At least one modality must have is_generated=true")
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return self
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@model_validator(mode="after")
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def validate_audio_intrinsic_regions(self) -> "FlexibleStrategyConfig":
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"""Reject video-only intrinsic regions on the audio modality."""
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if self.audio is None:
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return self
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for cond in self.audio.conditions:
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if isinstance(cond, (FirstFrameConditionConfig, SpatialCropConditionConfig)):
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raise ValueError(
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f"Intrinsic condition '{cond.type}' is not supported for audio. "
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f"Audio supports: prefix, suffix, mask."
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)
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return self
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def get_data_sources(self) -> dict[str, str]:
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"""Dynamically determine required data sources from config.
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Returns a mapping of directory name (under ``preprocessed_data_root``) to
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the dataset output key.
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"""
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sources: dict[str, str] = {"conditions": "conditions"}
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if self.video is not None:
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sources[self.video.latents_dir] = "video_latents"
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if self.audio is not None:
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sources[self.audio.latents_dir] = "audio_latents"
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for modality_config in (self.video, self.audio):
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if modality_config is None:
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continue
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for cond in modality_config.conditions:
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if isinstance(cond, ReferenceConditionConfig):
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sources[cond.latents_dir] = cond.latents_dir
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elif isinstance(cond, MaskConditionConfig):
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sources[cond.mask_dir] = cond.mask_dir
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return sources
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# =============================================================================
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# Helper Data Structures
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# =============================================================================
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@dataclass
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class ModalityProcessingResult:
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"""Result of processing a single modality."""
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modality: Modality
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targets: Tensor | None
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loss_mask: Tensor | None
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@dataclass
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class LatentData:
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"""Loaded and patchified latents with metadata."""
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latents: Tensor # [B, seq_len, C]
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num_frames: int
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height: int
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width: int
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fps: float
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# =============================================================================
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# FlexibleStrategy Implementation
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# =============================================================================
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class FlexibleStrategy(TrainingStrategy):
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"""Unified training strategy supporting all conditioning scenarios.
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This strategy implements the Unified Conditioning Framework, allowing
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any training scenario to be expressed through configuration.
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"""
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config: FlexibleStrategyConfig
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def __init__(self, config: FlexibleStrategyConfig):
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"""Initialize strategy with configuration.
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Args:
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config: Flexible strategy configuration
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"""
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super().__init__(config)
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self.config = config
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self.reference_spatial_scale_factor, self.reference_temporal_scale_factor = (
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self._infer_reference_scale_factors_from_config()
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)
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def prepare_training_inputs(
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self,
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batch: dict[str, Any],
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timestep_sampler: TimestepSampler,
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) -> ModelInputs:
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"""Prepare training inputs by processing video and audio modalities."""
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video_result = self._process_modality(self.config.video, batch, "video", timestep_sampler)
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audio_result = self._process_modality(self.config.audio, batch, "audio", timestep_sampler)
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return ModelInputs(
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video=video_result.modality if video_result else None,
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audio=audio_result.modality if audio_result else None,
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video_targets=video_result.targets if video_result else None,
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audio_targets=audio_result.targets if audio_result else None,
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video_loss_mask=video_result.loss_mask if video_result else None,
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audio_loss_mask=audio_result.loss_mask if audio_result else None,
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)
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def compute_loss(
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self,
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video_pred: Tensor | None,
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audio_pred: Tensor | None,
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inputs: ModelInputs,
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) -> Tensor:
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"""Compute masked MSE loss for video and audio predictions. Returns [B,]."""
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total_loss = None
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if video_pred is not None and inputs.video_targets is not None:
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video_loss = self._compute_modality_loss(
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pred=video_pred,
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targets=inputs.video_targets,
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loss_mask=inputs.video_loss_mask,
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)
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total_loss = video_loss
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if audio_pred is not None and inputs.audio_targets is not None:
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audio_loss = self._compute_modality_loss(
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pred=audio_pred,
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targets=inputs.audio_targets,
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loss_mask=inputs.audio_loss_mask,
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)
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total_loss = audio_loss if total_loss is None else total_loss + audio_loss
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if total_loss is None:
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raise ValueError("No valid predictions and targets provided for loss computation")
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return total_loss
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def get_checkpoint_metadata(self) -> dict[str, Any]:
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"""Include reference scale factors in checkpoint metadata for inference pipelines."""
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metadata: dict[str, Any] = {}
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spatial = self.reference_spatial_scale_factor
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temporal = self.reference_temporal_scale_factor
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if spatial is not None and spatial != 1:
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metadata["reference_spatial_scale_factor"] = spatial
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metadata["reference_downscale_factor"] = spatial # backward compat
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if temporal is not None and temporal != 1:
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metadata["reference_temporal_scale_factor"] = temporal
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return metadata
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def _infer_reference_scale_factors_from_config(self) -> tuple[int | None, int | None]:
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"""Infer spatial and temporal scale factors by peeking at one sample pair."""
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if self.config.video is None:
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return None, None
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for cond in self.config.video.conditions:
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if not isinstance(cond, ReferenceConditionConfig):
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continue
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target_dir = Path(self.config.video.latents_dir)
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ref_dir = Path(cond.latents_dir)
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for sample_file in target_dir.rglob("*.pt"):
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ref_file = ref_dir / sample_file.relative_to(target_dir)
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if not ref_file.exists():
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continue
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target_data = torch.load(sample_file, map_location="cpu", weights_only=True)
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ref_data = torch.load(ref_file, map_location="cpu", weights_only=True)
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if "height" not in ref_data or "height" not in target_data:
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continue
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spatial = self._infer_scale_factor(
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ref_data["height"],
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ref_data["width"],
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target_data["height"],
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target_data["width"],
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)
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temporal = self._infer_temporal_scale_factor(
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ref_data["num_frames"],
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target_data["num_frames"],
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)
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return spatial, temporal
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return None, None
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def _process_modality(
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self,
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modality_config: ModalityConfig | None,
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batch: dict[str, Any],
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modality_key: str,
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timestep_sampler: TimestepSampler,
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) -> ModalityProcessingResult | None:
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"""Process a single modality: load latents, add noise, apply conditions, build Modality."""
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if modality_config is None:
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return None
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# Step 1: Load and patchify latents
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data = self._patchify_latent_data(batch[f"{modality_key}_latents"], modality_key)
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latents = data.latents
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batch_size, seq_len, _ = latents.shape
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device = latents.device
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dtype = latents.dtype
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# Step 2: Get text embeddings
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conditions = batch["conditions"]
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prompt_embeds = conditions[f"{modality_key}_prompt_embeds"]
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prompt_attention_mask = conditions["prompt_attention_mask"]
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# Step 3: Initialize noise, timesteps, and loss mask based on is_generated flag
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if modality_config.is_generated:
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noisy_latents, targets, timesteps, loss_mask, sigmas = self._initialize_noisy_target(
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latents, timestep_sampler
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)
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else:
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# Conditioning modality: keep clean (sigma=0), no loss
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noisy_latents = latents
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targets = None
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timesteps = torch.zeros(batch_size, seq_len, device=device, dtype=dtype)
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loss_mask = None
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sigmas = torch.zeros(batch_size, device=device, dtype=dtype)
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# Step 4: Generate positions
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if modality_key == "video":
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positions = self._get_video_positions(
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num_frames=data.num_frames,
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height=data.height,
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width=data.width,
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batch_size=batch_size,
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fps=data.fps,
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device=device,
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)
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else:
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positions = self._get_audio_positions(
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num_time_steps=seq_len,
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batch_size=batch_size,
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device=device,
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)
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# Step 5: Apply conditions (intrinsic first, then extrinsic)
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for cond in modality_config.conditions:
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if isinstance(cond, IntrinsicConditionBase) and modality_config.is_generated:
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noisy_latents, timesteps, loss_mask = self._apply_intrinsic_condition(
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noisy_latents=noisy_latents,
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clean_latents=latents,
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timesteps=timesteps,
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loss_mask=loss_mask,
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config=cond,
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height=data.height,
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width=data.width,
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batch=batch,
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)
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for cond in modality_config.conditions:
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if isinstance(cond, ReferenceConditionConfig):
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noisy_latents, positions, timesteps, loss_mask, targets = self._apply_reference_condition(
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noisy_latents=noisy_latents,
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positions=positions,
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timesteps=timesteps,
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loss_mask=loss_mask,
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targets=targets,
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batch=batch,
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config=cond,
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modality_key=modality_key,
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)
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# Step 6: Build Modality
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modality = Modality(
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enabled=True,
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latent=noisy_latents,
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sigma=sigmas,
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timesteps=timesteps,
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positions=positions,
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context=prompt_embeds,
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context_mask=prompt_attention_mask,
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)
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return ModalityProcessingResult(
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modality=modality,
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targets=targets,
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loss_mask=loss_mask,
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)
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@staticmethod
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def _initialize_noisy_target(
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latents: Tensor,
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timestep_sampler: TimestepSampler,
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) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
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"""Add noise to latents and create training targets. Returns (noisy, targets, timesteps, mask, sigmas)."""
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batch_size, seq_len, _ = latents.shape
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sigmas = timestep_sampler.sample_for(latents)
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noise = torch.randn_like(latents)
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sigmas_expanded = sigmas.view(-1, 1, 1)
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noisy_latents = (1 - sigmas_expanded) * latents + sigmas_expanded * noise
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targets = noise - latents # velocity prediction
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timesteps = sigmas.view(-1, 1).expand(batch_size, seq_len).clone()
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loss_mask = torch.ones(batch_size, seq_len, dtype=torch.bool, device=latents.device)
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return noisy_latents, targets, timesteps, loss_mask, sigmas
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|
||||
def _apply_intrinsic_condition(
|
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self,
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noisy_latents: Tensor,
|
||||
clean_latents: Tensor,
|
||||
timesteps: Tensor,
|
||||
loss_mask: Tensor,
|
||||
config: IntrinsicConditionBase,
|
||||
height: int,
|
||||
width: int,
|
||||
batch: dict[str, Any],
|
||||
) -> tuple[Tensor, Tensor, Tensor]:
|
||||
"""Apply intrinsic conditioning using a binary mask.
|
||||
For each token, the mask value determines conditioning strength:
|
||||
- mask=1: conditioned (clean latent, timestep=0, excluded from loss)
|
||||
- mask=0: generated (noisy latent, original timestep, contributes to loss)
|
||||
The conditioning decision is drawn independently per batch element so the training
|
||||
signal across samples in a batch is i.i.d. -- a single batch-wide draw would
|
||||
correlate gradient updates across the batch.
|
||||
"""
|
||||
batch_size, seq_len, _ = noisy_latents.shape
|
||||
device = noisy_latents.device
|
||||
|
||||
# Per-sample Bernoulli draw -- each element is independently conditioned.
|
||||
apply_per_sample = torch.rand(batch_size, device=device) < config.probability
|
||||
if not apply_per_sample.any():
|
||||
return noisy_latents, timesteps, loss_mask
|
||||
|
||||
if isinstance(config, FirstFrameConditionConfig):
|
||||
mask = self._compute_temporal_mask(batch_size, seq_len, height, width, 1, False, device)
|
||||
elif isinstance(config, PrefixConditionConfig):
|
||||
mask = self._compute_temporal_mask(
|
||||
batch_size, seq_len, height, width, config.temporal_boundary, False, device
|
||||
)
|
||||
elif isinstance(config, SuffixConditionConfig):
|
||||
mask = self._compute_temporal_mask(
|
||||
batch_size, seq_len, height, width, config.temporal_boundary, True, device
|
||||
)
|
||||
elif isinstance(config, SpatialCropConditionConfig):
|
||||
mask = self._compute_spatial_crop_mask(batch_size, seq_len, height, width, config.spatial_region, device)
|
||||
elif isinstance(config, MaskConditionConfig):
|
||||
# Binarize to match inference, which thresholds masks at load time
|
||||
# (validation_runner._load_and_downsample_mask / _load_audio_mask).
|
||||
mask = (batch[config.mask_dir]["mask"].reshape(batch_size, seq_len) > 0.5).float()
|
||||
else:
|
||||
raise ValueError(f"Unknown intrinsic condition type: {type(config).__name__}")
|
||||
|
||||
# Zero the mask for samples the per-sample draw did not select.
|
||||
mask = mask * apply_per_sample.view(-1, 1).to(mask.dtype)
|
||||
|
||||
# Apply binary mask: clean conditioned tokens, noisy generated tokens.
|
||||
m = mask.unsqueeze(-1)
|
||||
noisy_latents = m * clean_latents + (1 - m) * noisy_latents
|
||||
timesteps = (1 - mask) * timesteps
|
||||
loss_mask = loss_mask & (mask == 0)
|
||||
|
||||
return noisy_latents, timesteps, loss_mask
|
||||
|
||||
@staticmethod
|
||||
def _compute_temporal_mask(
|
||||
batch_size: int,
|
||||
seq_len: int,
|
||||
height: int,
|
||||
width: int,
|
||||
num_frames: int,
|
||||
from_end: bool,
|
||||
device: torch.device,
|
||||
) -> Tensor:
|
||||
"""Compute float mask for temporal region (prefix or suffix). Returns [B, seq_len] in {0, 1}."""
|
||||
tokens_per_frame = height * width
|
||||
num_tokens = num_frames * tokens_per_frame
|
||||
mask = torch.zeros(batch_size, seq_len, device=device)
|
||||
if from_end:
|
||||
mask[:, -num_tokens:] = 1.0
|
||||
else:
|
||||
mask[:, :num_tokens] = 1.0
|
||||
return mask
|
||||
|
||||
@staticmethod
|
||||
def _compute_spatial_crop_mask(
|
||||
batch_size: int,
|
||||
seq_len: int,
|
||||
height: int,
|
||||
width: int,
|
||||
region: tuple[int, int, int, int],
|
||||
device: torch.device,
|
||||
) -> Tensor:
|
||||
"""Compute float mask for spatial crop region (y1, x1, y2, x2) in pixel coords.
|
||||
Returns [B, seq_len] in {0, 1}.
|
||||
"""
|
||||
y1, x1, y2, x2 = region
|
||||
num_frames = seq_len // (height * width)
|
||||
|
||||
# Convert pixel to latent coordinates and clamp (per-axis VAE scale factor).
|
||||
def to_latent(v: int, scale: int, max_v: int) -> int:
|
||||
return max(0, min(v // scale, max_v))
|
||||
|
||||
ly1 = to_latent(y1, VIDEO_SCALE_FACTORS.height, height)
|
||||
ly2 = to_latent(y2, VIDEO_SCALE_FACTORS.height, height)
|
||||
lx1 = to_latent(x1, VIDEO_SCALE_FACTORS.width, width)
|
||||
lx2 = to_latent(x2, VIDEO_SCALE_FACTORS.width, width)
|
||||
|
||||
# Create spatial mask and tile across frames
|
||||
spatial_mask = torch.zeros(height, width, device=device)
|
||||
spatial_mask[ly1:ly2, lx1:lx2] = 1.0
|
||||
full_mask = spatial_mask.flatten().repeat(num_frames)
|
||||
|
||||
return full_mask.unsqueeze(0).expand(batch_size, -1)
|
||||
|
||||
def _patchify_latent_data(self, latent_data: dict[str, Any], modality_key: str) -> LatentData:
|
||||
"""Patchify latent data and extract metadata."""
|
||||
latents = latent_data["latents"]
|
||||
|
||||
if modality_key == "video":
|
||||
num_frames = latent_data["num_frames"][0].item()
|
||||
height = latent_data["height"][0].item()
|
||||
width = latent_data["width"][0].item()
|
||||
fps = latent_data.get("fps")
|
||||
fps = fps[0].item() if fps is not None else DEFAULT_FPS
|
||||
latents = self._video_patchifier.patchify(latents)
|
||||
else:
|
||||
num_frames = latent_data.get("num_frames", [latents.shape[2]])[0]
|
||||
if isinstance(num_frames, Tensor):
|
||||
num_frames = num_frames.item()
|
||||
height = 1
|
||||
width = 1
|
||||
fps = 1.0
|
||||
latents = self._audio_patchifier.patchify(latents)
|
||||
|
||||
return LatentData(latents=latents, num_frames=num_frames, height=height, width=width, fps=fps)
|
||||
|
||||
def _apply_reference_condition(
|
||||
self,
|
||||
noisy_latents: Tensor,
|
||||
positions: Tensor,
|
||||
timesteps: Tensor,
|
||||
loss_mask: Tensor | None,
|
||||
targets: Tensor | None,
|
||||
batch: dict[str, Any],
|
||||
config: ReferenceConditionConfig,
|
||||
modality_key: str,
|
||||
) -> tuple[Tensor, Tensor, Tensor, Tensor | None, Tensor | None]:
|
||||
"""Concatenate reference latents to target sequence for reference conditioning (IC-LoRA style).
|
||||
The apply/skip decision is batch-wide (reference conditioning changes the sequence
|
||||
length, so it cannot be applied to only part of a batch) but is drawn from the torch
|
||||
RNG so runs are reproducible under ``torch.manual_seed`` — mirroring the intrinsic
|
||||
per-sample draw rather than Python's unseeded ``random``.
|
||||
"""
|
||||
if torch.rand((), device=noisy_latents.device).item() >= config.probability:
|
||||
return noisy_latents, positions, timesteps, loss_mask, targets
|
||||
|
||||
# Load and patchify condition latents
|
||||
cond = self._patchify_latent_data(batch[config.latents_dir], modality_key)
|
||||
cond_latents = cond.latents
|
||||
|
||||
batch_size, cond_seq_len, _ = cond_latents.shape
|
||||
device = cond_latents.device
|
||||
dtype = cond_latents.dtype
|
||||
|
||||
# Generate condition positions
|
||||
if modality_key == "video":
|
||||
cond_positions = self._get_video_positions(
|
||||
num_frames=cond.num_frames,
|
||||
height=cond.height,
|
||||
width=cond.width,
|
||||
batch_size=batch_size,
|
||||
fps=cond.fps,
|
||||
device=device,
|
||||
)
|
||||
else:
|
||||
cond_positions = self._get_audio_positions(
|
||||
num_time_steps=cond_seq_len,
|
||||
batch_size=batch_size,
|
||||
device=device,
|
||||
)
|
||||
|
||||
# Translate / rescale ref positions into the target's frame (video only).
|
||||
if modality_key == "video":
|
||||
spatial_sf = self.reference_spatial_scale_factor or 1
|
||||
temporal_sf = self.reference_temporal_scale_factor or 1
|
||||
if spatial_sf != 1 or temporal_sf != 1:
|
||||
cond_positions = cond_positions.clone()
|
||||
if temporal_sf != 1:
|
||||
# Ref positions are already at the ref's effective fps (source_fps / S,
|
||||
# stored by process_videos.py). Shift by (S - 1) / target_fps so ref's
|
||||
# last patch aligns with target's last; clamp the causal patch at 0.
|
||||
t_target = positions[:, 0, 0:1, 1:2] # = 1 / target_fps
|
||||
cond_positions[:, 0, ...] = torch.clamp(
|
||||
cond_positions[:, 0, ...] - (temporal_sf - 1) * t_target, min=0
|
||||
)
|
||||
if spatial_sf != 1:
|
||||
cond_positions[:, 1, ...] *= spatial_sf
|
||||
cond_positions[:, 2, ...] *= spatial_sf
|
||||
|
||||
# Condition tokens: clean, timestep=0, no loss
|
||||
cond_timesteps = torch.zeros(batch_size, cond_seq_len, device=device, dtype=dtype)
|
||||
cond_loss_mask = torch.zeros(batch_size, cond_seq_len, dtype=torch.bool, device=device)
|
||||
|
||||
# Concatenate condition and target sequences (condition first, then target)
|
||||
combined_latents = torch.cat([cond_latents, noisy_latents], dim=1)
|
||||
combined_positions = torch.cat([cond_positions, positions], dim=2)
|
||||
combined_timesteps = torch.cat([cond_timesteps, timesteps], dim=1)
|
||||
|
||||
combined_loss_mask = torch.cat([cond_loss_mask, loss_mask], dim=1) if loss_mask is not None else None
|
||||
|
||||
# Targets remain unchanged (only for target portion, not condition portion)
|
||||
|
||||
return combined_latents, combined_positions, combined_timesteps, combined_loss_mask, targets
|
||||
|
||||
@staticmethod
|
||||
def _compute_modality_loss(pred: Tensor, targets: Tensor, loss_mask: Tensor) -> Tensor:
|
||||
"""Compute per-element MSE loss for a single modality. Returns [B,]."""
|
||||
# Slice prediction to match targets length (removes any prepended condition tokens)
|
||||
target_len = targets.shape[1]
|
||||
pred = pred[:, -target_len:, :]
|
||||
mask = loss_mask[:, -target_len:]
|
||||
|
||||
# Compute masked MSE loss, reduce per-element [B,] over (seq, channels)
|
||||
mask_expanded = mask.unsqueeze(-1).float()
|
||||
squared_error = (pred - targets).pow(2)
|
||||
masked_loss = squared_error * mask_expanded
|
||||
return masked_loss.mean(dim=[-2, -1]) / mask_expanded.mean(dim=[-2, -1]).clamp(min=1e-8)
|
||||
|
||||
@staticmethod
|
||||
def _infer_scale_factor(cond_height: int, cond_width: int, target_height: int, target_width: int) -> int:
|
||||
"""Infer spatial scale factor between condition and target resolutions."""
|
||||
if target_height == cond_height and target_width == cond_width:
|
||||
return 1
|
||||
scale_h = target_height // cond_height if cond_height > 0 else 1
|
||||
scale_w = target_width // cond_width if cond_width > 0 else 1
|
||||
if scale_h != scale_w:
|
||||
raise ValueError(
|
||||
f"Non-uniform scale factors between condition and target: height={scale_h}, width={scale_w}. "
|
||||
"Condition and target resolutions must scale uniformly."
|
||||
)
|
||||
return scale_h
|
||||
|
||||
@staticmethod
|
||||
def _infer_temporal_scale_factor(cond_num_frames: int, target_num_frames: int) -> int:
|
||||
"""Infer temporal scale factor between condition and target latent frame counts.
|
||||
The first latent frame encodes a single pixel frame (the VAE's causal structure),
|
||||
so the temporal groups count is (num_frames - 1). The scale factor is the ratio
|
||||
of target groups to condition groups.
|
||||
"""
|
||||
if target_num_frames == cond_num_frames:
|
||||
return 1
|
||||
target_groups = target_num_frames - 1
|
||||
cond_groups = cond_num_frames - 1
|
||||
if cond_groups <= 0 or target_groups <= 0:
|
||||
return 1
|
||||
if target_groups % cond_groups != 0:
|
||||
raise ValueError(
|
||||
f"Target temporal groups ({target_groups}) is not evenly divisible by "
|
||||
f"condition temporal groups ({cond_groups})."
|
||||
)
|
||||
return target_groups // cond_groups
|
||||
Reference in New Issue
Block a user