Automated PR - 2026-05-11
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github-actions[bot]
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"""Two-stage lip-dubbing pipeline with IC-LoRA and appended audio reference conditioning."""
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from __future__ import annotations
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import logging
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from collections.abc import Iterator
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import torch
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from ltx_core.components.noisers import GaussianNoiser
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from ltx_core.components.patchifiers import AudioPatchifier
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from ltx_core.conditioning import AudioConditionByReferenceLatent
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from ltx_core.loader import LoraPathStrengthAndSDOps
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from ltx_core.loader.registry import Registry
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from ltx_core.model.audio_vae import encode_audio as vae_encode_audio
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from ltx_core.model.video_vae import TilingConfig, VideoEncoder, get_video_chunks_number
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from ltx_core.quantization import QuantizationPolicy
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from ltx_core.types import Audio, AudioLatentShape, SpatioTemporalScaleFactors, VideoPixelShape
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from ltx_pipelines.iclora_utils import (
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append_ic_lora_reference_video_conditionings,
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read_lora_reference_downscale_factor,
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)
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from ltx_pipelines.utils.args import (
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ImageConditioningInput,
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detect_checkpoint_path,
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lipdub_arg_parser,
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)
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from ltx_pipelines.utils.blocks import (
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AudioConditioner,
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AudioDecoder,
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DiffusionStage,
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ImageConditioner,
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PromptEncoder,
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VideoDecoder,
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VideoUpsampler,
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)
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from ltx_pipelines.utils.constants import DISTILLED_SIGMAS, STAGE_2_DISTILLED_SIGMAS, detect_params
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from ltx_pipelines.utils.denoisers import SimpleDenoiser
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from ltx_pipelines.utils.helpers import assert_resolution, combined_image_conditionings, get_device
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from ltx_pipelines.utils.media_io import decode_audio_from_file, encode_video, get_videostream_metadata
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from ltx_pipelines.utils.types import ModalitySpec, OffloadMode
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def _snap_frames_to_8k1(frames: int) -> int:
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"""Round ``frames`` down to the nearest ``8k+1`` (the model's required frame count)."""
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time_scale = SpatioTemporalScaleFactors.default().time
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return ((frames - 1) // time_scale) * time_scale + 1
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class LipDubPipeline:
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"""Two-stage lip-dubbing with IC-LoRA video reference and appended audio reference tokens."""
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def __init__(
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self,
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distilled_checkpoint_path: str,
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spatial_upsampler_path: str,
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gemma_root: str,
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ic_lora: LoraPathStrengthAndSDOps,
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device: torch.device | None = None,
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quantization: QuantizationPolicy | None = None,
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registry: Registry | None = None,
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torch_compile: bool = False,
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offload_mode: OffloadMode = OffloadMode.NONE,
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) -> None:
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self.device = device or get_device()
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self.dtype = torch.bfloat16
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self.ic_lora = ic_lora
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loras = (ic_lora,)
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self.prompt_encoder = PromptEncoder(
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distilled_checkpoint_path,
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gemma_root,
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self.dtype,
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self.device,
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registry=registry,
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offload_mode=offload_mode,
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)
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self.image_conditioner = ImageConditioner(distilled_checkpoint_path, self.dtype, self.device, registry=registry)
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self.audio_conditioner = AudioConditioner(
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distilled_checkpoint_path,
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self.dtype,
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self.device,
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registry=registry,
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)
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self.stage = DiffusionStage(
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distilled_checkpoint_path,
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self.dtype,
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self.device,
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loras=loras,
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quantization=quantization,
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registry=registry,
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torch_compile=torch_compile,
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offload_mode=offload_mode,
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)
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self.upsampler = VideoUpsampler(
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distilled_checkpoint_path, spatial_upsampler_path, self.dtype, self.device, registry=registry
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)
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self.video_decoder = VideoDecoder(distilled_checkpoint_path, self.dtype, self.device, registry=registry)
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self.audio_decoder = AudioDecoder(distilled_checkpoint_path, self.dtype, self.device, registry=registry)
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self.reference_downscale_factor = read_lora_reference_downscale_factor(ic_lora.path)
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def _create_stage_conditionings(
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self,
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images: list[ImageConditioningInput],
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reference_video_path: str,
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reference_strength: float,
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height: int,
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width: int,
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num_frames: int,
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video_encoder: VideoEncoder,
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encode_tiling: TilingConfig | None,
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) -> list:
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conditionings = combined_image_conditionings(
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images=images,
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height=height,
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width=width,
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video_encoder=video_encoder,
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dtype=self.dtype,
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device=self.device,
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)
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append_ic_lora_reference_video_conditionings(
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conditionings,
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[(reference_video_path, reference_strength)],
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height=height,
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width=width,
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num_frames=num_frames,
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video_encoder=video_encoder,
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dtype=self.dtype,
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device=self.device,
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reference_downscale_factor=self.reference_downscale_factor,
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conditioning_attention_strength=1.0,
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conditioning_attention_mask=None,
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tiling_config=encode_tiling,
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)
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return conditionings
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def _encode_reference_audio_vae_latent(self, video_path: str) -> torch.Tensor:
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audio = decode_audio_from_file(video_path, self.device)
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if audio is None:
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msg = f"No audio stream found in {video_path}"
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raise ValueError(msg)
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return self.audio_conditioner(lambda enc: vae_encode_audio(audio, enc, None))
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@torch.inference_mode()
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def __call__( # noqa: PLR0913
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self,
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prompt: str,
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seed: int,
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height: int,
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width: int,
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images: list[ImageConditioningInput],
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reference_video_path: str,
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reference_strength: float = 1.0,
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enhance_prompt: bool = False,
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tiling_config: TilingConfig | None = None,
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stage_1_sigmas: torch.Tensor = DISTILLED_SIGMAS,
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stage_2_sigmas: torch.Tensor = STAGE_2_DISTILLED_SIGMAS,
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) -> tuple[Iterator[torch.Tensor], Audio]:
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assert_resolution(height=height, width=width, is_two_stage=True)
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meta = get_videostream_metadata(reference_video_path)
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num_frames = _snap_frames_to_8k1(meta.frames)
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frame_rate = float(meta.fps)
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generator = torch.Generator(device=self.device).manual_seed(seed)
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noiser = GaussianNoiser(generator=generator)
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(ctx_p,) = self.prompt_encoder(
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[prompt],
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enhance_first_prompt=enhance_prompt,
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enhance_prompt_image=images[0][0] if len(images) > 0 else None,
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enhance_prompt_seed=seed,
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)
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video_context, audio_context = ctx_p.video_encoding, ctx_p.audio_encoding
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stage_1_output_shape = VideoPixelShape(
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batch=1,
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frames=num_frames,
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width=width // 2,
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height=height // 2,
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fps=frame_rate,
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)
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encode_tiling = TilingConfig.default()
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def build_image_conditionings(output_shape: VideoPixelShape) -> list:
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return self.image_conditioner(
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lambda enc: self._create_stage_conditionings(
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images=images,
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reference_video_path=reference_video_path,
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reference_strength=reference_strength,
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height=output_shape.height,
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width=output_shape.width,
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num_frames=num_frames,
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video_encoder=enc,
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encode_tiling=encode_tiling,
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)
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)
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def build_audio_ref_conditioning(audio_latent: torch.Tensor) -> AudioConditionByReferenceLatent:
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ref_patch, ref_pos = patchify_lipdub_audio_reference_latent(
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audio_latent,
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negative_positions=True,
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device=self.device,
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)
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return AudioConditionByReferenceLatent(ref_patch, ref_pos, strength=1.0)
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stage_1_conditionings = build_image_conditionings(stage_1_output_shape)
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ref_vae = self._encode_reference_audio_vae_latent(reference_video_path)
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audio_conditionings = [build_audio_ref_conditioning(ref_vae)]
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stage_1_sigmas_tensor = stage_1_sigmas.to(dtype=torch.float32, device=self.device)
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video_state, audio_state = self.stage(
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denoiser=SimpleDenoiser(video_context, audio_context),
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sigmas=stage_1_sigmas_tensor,
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noiser=noiser,
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width=stage_1_output_shape.width,
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height=stage_1_output_shape.height,
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frames=num_frames,
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fps=frame_rate,
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video=ModalitySpec(
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context=video_context,
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conditionings=stage_1_conditionings,
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),
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audio=ModalitySpec(
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context=audio_context,
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conditionings=audio_conditionings,
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),
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)
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s1_audio_latent = audio_state.latent.clone()
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upscaled_video_latent = self.upsampler(video_state.latent[:1])
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stage_2_sigmas_tensor = stage_2_sigmas.to(dtype=torch.float32, device=self.device)
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stage_2_output_shape = VideoPixelShape(batch=1, frames=num_frames, width=width, height=height, fps=frame_rate)
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stage_2_conditionings = build_image_conditionings(stage_2_output_shape)
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stage_2_audio_conditionings = [build_audio_ref_conditioning(s1_audio_latent)]
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video_state, _audio_unused = self.stage(
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denoiser=SimpleDenoiser(video_context, audio_context),
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sigmas=stage_2_sigmas_tensor,
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noiser=noiser,
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width=width,
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height=height,
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frames=num_frames,
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fps=frame_rate,
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video=ModalitySpec(
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context=video_context,
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conditionings=stage_2_conditionings,
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noise_scale=stage_2_sigmas_tensor[0].item(),
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initial_latent=upscaled_video_latent,
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),
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audio=ModalitySpec(
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context=audio_context,
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conditionings=stage_2_audio_conditionings,
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frozen=True,
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noise_scale=0.0,
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initial_latent=s1_audio_latent,
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),
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)
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decoded_video = self.video_decoder(video_state.latent, tiling_config, generator)
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decoded_audio = self.audio_decoder(s1_audio_latent)
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return decoded_video, decoded_audio
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def patchify_lipdub_audio_reference_latent(
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vae_latents: torch.Tensor,
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*,
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negative_positions: bool,
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device: torch.device,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""Patchify audio VAE latents and build RoPE positions (optional negative shift for reference)."""
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patchifier = AudioPatchifier(patch_size=1)
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patchified = patchifier.patchify(vae_latents)
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b, c, _t, mel_bins = vae_latents.shape
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seq_len = patchified.shape[1]
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latent_coords = patchifier.get_patch_grid_bounds(
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output_shape=AudioLatentShape(batch=b, channels=c, frames=seq_len, mel_bins=mel_bins),
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device=device,
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)
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positions = latent_coords.to(dtype=torch.float32)
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if negative_positions:
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aud_dur = positions[:, :, -1, 1].max().item()
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positions = positions - aud_dur - 0.04
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return patchified, positions
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@torch.inference_mode()
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def main() -> None:
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logging.getLogger().setLevel(logging.INFO)
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checkpoint_path = detect_checkpoint_path(distilled=True)
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params = detect_params(checkpoint_path)
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parser = lipdub_arg_parser(params=params)
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args = parser.parse_args()
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if not args.lora or len(args.lora) != 1:
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raise ValueError("LipDub requires exactly one --lora (the lip-dub IC-LoRA).")
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pipeline = LipDubPipeline(
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distilled_checkpoint_path=args.distilled_checkpoint_path,
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spatial_upsampler_path=args.spatial_upsampler_path,
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gemma_root=args.gemma_root,
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ic_lora=args.lora[0],
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quantization=args.quantization,
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torch_compile=args.compile,
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offload_mode=args.offload_mode,
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)
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tiling_config = TilingConfig.default()
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src = get_videostream_metadata(args.reference_video)
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video_chunks_number = get_video_chunks_number(_snap_frames_to_8k1(src.frames), tiling_config)
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video, audio = pipeline(
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prompt=args.prompt,
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seed=args.seed,
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height=args.height,
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width=args.width,
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images=[],
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reference_video_path=args.reference_video,
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reference_strength=args.reference_strength,
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tiling_config=tiling_config,
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enhance_prompt=args.enhance_prompt,
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)
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encode_video(
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video=video,
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fps=int(src.fps),
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audio=audio,
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output_path=args.output_path,
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video_chunks_number=video_chunks_number,
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)
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if __name__ == "__main__":
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main()
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