999 lines
44 KiB
Python
999 lines
44 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Fully independent LTX-2.3 pipeline for vLLM-Omni.
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This pipeline does NOT inherit from LTX2Pipeline because:
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- LTX-2.3 uses a different text encoding strategy (flatten ALL 49 hidden states
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vs. LTX-2's _pack_text_embeds with per-layer normalization and pooling)
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- LTX-2.3 connectors expect the padding_side API (not additive_mask)
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- LTX-2.3 uses a BWE vocoder outputting 48kHz audio (not 16kHz)
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- LTX-2.3 transformer requires the sigma parameter for prompt modulation
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- CPU offloading is required for the 22B transformer (~44GB VRAM)
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"""
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from __future__ import annotations
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import copy
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import json
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import os
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from collections.abc import Iterable
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from contextlib import nullcontext
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from typing import Any
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import numpy as np
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import torch
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from diffusers import AutoencoderKLLTX2Audio, AutoencoderKLLTX2Video, FlowMatchEulerDiscreteScheduler
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from diffusers.pipelines.ltx2 import LTX2TextConnectors
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from diffusers.pipelines.ltx2.vocoder import LTX2Vocoder
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import retrieve_timesteps
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from diffusers.utils.torch_utils import randn_tensor
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from diffusers.video_processor import VideoProcessor
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from huggingface_hub import hf_hub_download
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from torch import nn
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from transformers import AutoTokenizer, Gemma3ForConditionalGeneration
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from vllm.logger import init_logger
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from vllm.model_executor.models.utils import AutoWeightsLoader
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from vllm_omni.diffusion.data import DiffusionOutput, OmniDiffusionConfig
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from vllm_omni.diffusion.distributed.utils import get_local_device
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from vllm_omni.diffusion.model_loader.diffusers_loader import DiffusersPipelineLoader
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from vllm_omni.diffusion.models.progress_bar import ProgressBarMixin
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from .pipeline_ltx2 import (
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_get_prompt_field,
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calculate_shift,
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create_transformer_from_config,
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load_transformer_config,
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)
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logger = init_logger(__name__)
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# Try to import LTX2VocoderWithBWE (diffusers >= 0.38.0)
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try:
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from diffusers.pipelines.ltx2.vocoder import LTX2VocoderWithBWE
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except ImportError:
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LTX2VocoderWithBWE = None
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def _detect_vocoder_output_sample_rate(model: str) -> int | None:
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"""Detect the vocoder output sample rate from vocoder/config.json.
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This runs at factory time (engine process) so the rate is captured in
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the post-process closure and doesn't need cross-process communication.
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Returns:
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Output sample rate (e.g. 48000 for LTX-2.3 BWE vocoder) or None.
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"""
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vocoder_config_path = os.path.join(model, "vocoder", "config.json")
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if not os.path.exists(vocoder_config_path):
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try:
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vocoder_config_path = hf_hub_download(model, "vocoder/config.json")
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except Exception:
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return None
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try:
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with open(vocoder_config_path) as f:
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cfg = json.load(f)
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return cfg.get("output_sampling_rate")
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except Exception:
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return None
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def get_ltx2_post_process_func(od_config: OmniDiffusionConfig):
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"""Factory for the LTX-2.3 post-process function.
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Detects the vocoder output sample rate at factory time and captures it
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in the closure so that the audio_sample_rate flows through
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DiffusionEngine -> OmniRequestOutput -> serving_video.
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"""
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output_sr = _detect_vocoder_output_sample_rate(od_config.model)
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def post_process_func(output: tuple[torch.Tensor, torch.Tensor] | torch.Tensor):
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if isinstance(output, tuple) and len(output) == 2:
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video, audio = output
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if isinstance(audio, torch.Tensor):
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audio = audio.detach().cpu()
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result: dict[str, Any] = {"video": video, "audio": audio}
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if output_sr is not None:
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result["audio_sample_rate"] = output_sr
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return result
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return output
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return post_process_func
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class LTX23Pipeline(nn.Module, ProgressBarMixin):
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"""Fully independent LTX-2.3 pipeline.
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Key differences from LTX2Pipeline:
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- Text encoding: uses ALL 49 hidden states from Gemma-3-12B, flattened
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- Connectors: uses padding_side API (not additive_mask)
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- Vocoder: uses LTX2VocoderWithBWE (48kHz output)
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- Transformer: passes sigma for prompt_adaln
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- CPU offloading: text encoder, connectors, VAE, vocoder stay on CPU
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"""
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def __init__(
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self,
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*,
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od_config: OmniDiffusionConfig,
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prefix: str = "",
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):
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super().__init__()
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self.od_config = od_config
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self.device = get_local_device()
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dtype = getattr(od_config, "dtype", torch.bfloat16)
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model = od_config.model
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local_files_only = os.path.exists(model)
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# Weight sources for transformer (loaded via AutoWeightsLoader)
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self.weights_sources = [
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DiffusersPipelineLoader.ComponentSource(
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model_or_path=od_config.model,
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subfolder="transformer",
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revision=None,
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prefix="transformer.",
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fall_back_to_pt=True,
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),
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]
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# --- Tokenizer (lightweight, stays wherever) ---
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self.tokenizer = AutoTokenizer.from_pretrained(model, subfolder="tokenizer", local_files_only=local_files_only)
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# --- Text encoder: load on CPU, move to GPU only during encoding ---
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with torch.device("cpu"):
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self.text_encoder = Gemma3ForConditionalGeneration.from_pretrained(
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model, subfolder="text_encoder", torch_dtype=dtype, local_files_only=local_files_only
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)
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# --- Connectors: CPU (LTX-2.3 connectors include caption projection) ---
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self.connectors = LTX2TextConnectors.from_pretrained(
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model, subfolder="connectors", torch_dtype=dtype, local_files_only=local_files_only
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)
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# --- VAE, Audio VAE: CPU ---
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self.vae = AutoencoderKLLTX2Video.from_pretrained(
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model, subfolder="vae", torch_dtype=dtype, local_files_only=local_files_only
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)
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self.audio_vae = AutoencoderKLLTX2Audio.from_pretrained(
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model, subfolder="audio_vae", torch_dtype=dtype, local_files_only=local_files_only
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)
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# --- Vocoder: prefer BWE vocoder (48kHz) for LTX-2.3 ---
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vocoder_cls = LTX2VocoderWithBWE or LTX2Vocoder
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try:
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self.vocoder = vocoder_cls.from_pretrained(
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model, subfolder="vocoder", torch_dtype=dtype, local_files_only=local_files_only
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)
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except (TypeError, OSError, ValueError):
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self.vocoder = LTX2Vocoder.from_pretrained(
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model, subfolder="vocoder", torch_dtype=dtype, local_files_only=local_files_only
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)
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# --- Transformer: created empty, weights loaded via AutoWeightsLoader ---
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transformer_config = load_transformer_config(model, "transformer", local_files_only)
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self.transformer = create_transformer_from_config(transformer_config)
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# --- Scheduler ---
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self.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
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model, subfolder="scheduler", local_files_only=local_files_only
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)
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# --- Derived compression ratios ---
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self.vae_spatial_compression_ratio = self.vae.spatial_compression_ratio if self.vae is not None else 32
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self.vae_temporal_compression_ratio = self.vae.temporal_compression_ratio if self.vae is not None else 8
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self.audio_vae_mel_compression_ratio = self.audio_vae.mel_compression_ratio if self.audio_vae is not None else 4
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self.audio_vae_temporal_compression_ratio = (
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self.audio_vae.temporal_compression_ratio if self.audio_vae is not None else 4
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)
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self.transformer_spatial_patch_size = self.transformer.config.patch_size if self.transformer is not None else 1
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self.transformer_temporal_patch_size = (
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self.transformer.config.patch_size_t if self.transformer is not None else 1
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)
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self.audio_sampling_rate = self.audio_vae.config.sample_rate if self.audio_vae is not None else 16000
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self.audio_hop_length = self.audio_vae.config.mel_hop_length if self.audio_vae is not None else 160
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self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_compression_ratio)
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# Tokenizer max length
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tokenizer_max_length = 1024
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if self.tokenizer is not None:
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tokenizer_max_length = self.tokenizer.model_max_length
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if tokenizer_max_length is None or tokenizer_max_length > 100000:
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encoder_config = getattr(self.text_encoder, "config", None)
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config_max_len = getattr(encoder_config, "max_position_embeddings", None)
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if config_max_len is None:
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config_max_len = getattr(encoder_config, "max_seq_len", None)
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tokenizer_max_length = config_max_len or 1024
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self.tokenizer_max_length = int(tokenizer_max_length)
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# Pipeline state
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self._guidance_scale = None
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self._attention_kwargs = None
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self._interrupt = False
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self._num_timesteps = None
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self._current_timestep = None
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# ------------------------------------------------------------------
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# Text Encoding (LTX-2.3 specific)
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# ------------------------------------------------------------------
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def _get_gemma_prompt_embeds(
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self,
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prompt: str | list[str],
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num_videos_per_prompt: int = 1,
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max_sequence_length: int = 1024,
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device: torch.device | None = None,
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dtype: torch.dtype | None = None,
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):
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"""Encode prompts using Gemma-3-12B, returning ALL 49 hidden states flattened.
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LTX-2.3 differs from LTX-2 in text encoding:
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- LTX-2: uses _pack_text_embeds (layer selection + pooling)
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- LTX-2.3: stacks ALL 49 hidden states and flattens to [B, seq, 188160]
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The connectors unflatten, apply per_token_rms_norm, and project internally.
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"""
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device = device or self.device
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dtype = dtype or self.text_encoder.dtype
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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if self.tokenizer is not None:
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self.tokenizer.padding_side = "left"
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if self.tokenizer.pad_token is None:
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self.tokenizer.pad_token = self.tokenizer.eos_token
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prompt = [p.strip() for p in prompt]
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids.to(device)
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prompt_attention_mask = text_inputs.attention_mask.to(device)
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# Move text encoder to GPU for encoding
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self.text_encoder.to(device)
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text_encoder_outputs = self.text_encoder(
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input_ids=text_input_ids,
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attention_mask=prompt_attention_mask,
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output_hidden_states=True,
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)
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# Move text encoder back to CPU immediately
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self.text_encoder.to("cpu")
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torch.cuda.empty_cache()
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hidden_states = text_encoder_outputs.hidden_states
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# LTX-2.3: Stack ALL 49 hidden states and flatten
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# [49 x (B, seq, 3840)] -> [B, seq, 3840, 49] -> [B, seq, 188160]
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prompt_embeds = torch.stack(hidden_states, dim=-1).flatten(2, 3).to(dtype=dtype)
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_, seq_len, _ = prompt_embeds.shape
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prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
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prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
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prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
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return prompt_embeds, prompt_attention_mask
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def encode_prompt(
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self,
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prompt: str | list[str],
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negative_prompt: str | list[str] | None = None,
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do_classifier_free_guidance: bool = True,
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num_videos_per_prompt: int = 1,
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prompt_embeds: torch.Tensor | None = None,
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negative_prompt_embeds: torch.Tensor | None = None,
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prompt_attention_mask: torch.Tensor | None = None,
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negative_prompt_attention_mask: torch.Tensor | None = None,
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max_sequence_length: int = 1024,
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device: torch.device | None = None,
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dtype: torch.dtype | None = None,
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):
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device = device or self.device
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prompt = [prompt] if isinstance(prompt, str) else prompt
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if prompt is not None:
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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prompt_embeds, prompt_attention_mask = self._get_gemma_prompt_embeds(
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prompt=prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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max_sequence_length=max_sequence_length,
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device=device,
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dtype=dtype,
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)
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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negative_prompt = negative_prompt or ""
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negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
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if prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type as `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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if isinstance(negative_prompt, list) and batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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negative_prompt_embeds, negative_prompt_attention_mask = self._get_gemma_prompt_embeds(
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prompt=negative_prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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max_sequence_length=max_sequence_length,
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device=device,
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dtype=dtype,
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)
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return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
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# ------------------------------------------------------------------
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# Latent utilities (shared with LTX2Pipeline)
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# ------------------------------------------------------------------
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@staticmethod
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def _pack_latents(latents: torch.Tensor, patch_size: int = 1, patch_size_t: int = 1) -> torch.Tensor:
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batch_size, num_channels, num_frames, height, width = latents.shape
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post_patch_num_frames = num_frames // patch_size_t
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post_patch_height = height // patch_size
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post_patch_width = width // patch_size
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latents = latents.reshape(
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batch_size,
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-1,
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post_patch_num_frames,
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patch_size_t,
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post_patch_height,
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patch_size,
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post_patch_width,
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patch_size,
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)
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latents = latents.permute(0, 2, 4, 6, 1, 3, 5, 7).flatten(4, 7).flatten(1, 3)
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return latents
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@staticmethod
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def _unpack_latents(
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latents: torch.Tensor,
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num_frames: int,
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height: int,
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width: int,
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patch_size: int = 1,
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patch_size_t: int = 1,
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) -> torch.Tensor:
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batch_size = latents.size(0)
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latents = latents.reshape(batch_size, num_frames, height, width, -1, patch_size_t, patch_size, patch_size)
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latents = latents.permute(0, 4, 1, 5, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(2, 3)
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return latents
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@staticmethod
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def _normalize_latents(
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latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
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) -> torch.Tensor:
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latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
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latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
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latents = (latents - latents_mean) * scaling_factor / latents_std
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return latents
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@staticmethod
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def _normalize_audio_latents(latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor):
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latents_mean = latents_mean.to(latents.device, latents.dtype)
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latents_std = latents_std.to(latents.device, latents.dtype)
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return (latents - latents_mean) / latents_std
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@staticmethod
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def _denormalize_latents(
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latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor, scaling_factor: float = 1.0
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) -> torch.Tensor:
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latents_mean = latents_mean.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
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latents_std = latents_std.view(1, -1, 1, 1, 1).to(latents.device, latents.dtype)
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latents = latents * latents_std / scaling_factor + latents_mean
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return latents
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@staticmethod
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def _denormalize_audio_latents(latents: torch.Tensor, latents_mean: torch.Tensor, latents_std: torch.Tensor):
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latents_mean = latents_mean.to(latents.device, latents.dtype)
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latents_std = latents_std.to(latents.device, latents.dtype)
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return (latents * latents_std) + latents_mean
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@staticmethod
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def _pack_audio_latents(
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latents: torch.Tensor, patch_size: int | None = None, patch_size_t: int | None = None
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) -> torch.Tensor:
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if patch_size is not None and patch_size_t is not None:
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batch_size, num_channels, latent_length, latent_mel_bins = latents.shape
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post_patch_latent_length = latent_length / patch_size_t
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post_patch_mel_bins = latent_mel_bins / patch_size
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latents = latents.reshape(
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batch_size, -1, post_patch_latent_length, patch_size_t, post_patch_mel_bins, patch_size
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)
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latents = latents.permute(0, 2, 4, 1, 3, 5).flatten(3, 5).flatten(1, 2)
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else:
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latents = latents.transpose(1, 2).flatten(2, 3)
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return latents
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@staticmethod
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def _unpack_audio_latents(
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latents: torch.Tensor,
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latent_length: int,
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num_mel_bins: int,
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patch_size: int | None = None,
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patch_size_t: int | None = None,
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) -> torch.Tensor:
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if patch_size is not None and patch_size_t is not None:
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batch_size = latents.size(0)
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|
latents = latents.reshape(batch_size, latent_length, num_mel_bins, -1, patch_size_t, patch_size)
|
|
latents = latents.permute(0, 3, 1, 4, 2, 5).flatten(4, 5).flatten(2, 3)
|
|
else:
|
|
latents = latents.unflatten(2, (-1, num_mel_bins)).transpose(1, 2)
|
|
return latents
|
|
|
|
@staticmethod
|
|
def _unpad_audio_latents(latents: torch.Tensor, num_frames: int) -> torch.Tensor:
|
|
return latents[:, :num_frames]
|
|
|
|
# ------------------------------------------------------------------
|
|
# Latent preparation
|
|
# ------------------------------------------------------------------
|
|
|
|
def prepare_latents(
|
|
self,
|
|
batch_size: int = 1,
|
|
num_channels_latents: int = 128,
|
|
height: int = 512,
|
|
width: int = 768,
|
|
num_frames: int = 121,
|
|
noise_scale: float = 0.0,
|
|
dtype: torch.dtype | None = None,
|
|
device: torch.device | None = None,
|
|
generator: torch.Generator | None = None,
|
|
latents: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
if latents is not None:
|
|
if latents.ndim == 5:
|
|
latents = self._normalize_latents(
|
|
latents, self.vae.latents_mean, self.vae.latents_std, self.vae.config.scaling_factor
|
|
)
|
|
latents = self._pack_latents(
|
|
latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size
|
|
)
|
|
if latents.ndim != 3:
|
|
raise ValueError(f"Provided `latents` has shape {latents.shape}, expected [batch, seq, features].")
|
|
noise = randn_tensor(latents.shape, generator=generator, device=latents.device, dtype=latents.dtype)
|
|
latents = noise_scale * noise + (1 - noise_scale) * latents
|
|
return latents.to(device=device, dtype=dtype)
|
|
|
|
height = height // self.vae_spatial_compression_ratio
|
|
width = width // self.vae_spatial_compression_ratio
|
|
num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
|
|
shape = (batch_size, num_channels_latents, num_frames, height, width)
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
latents = self._pack_latents(latents, self.transformer_spatial_patch_size, self.transformer_temporal_patch_size)
|
|
return latents
|
|
|
|
def prepare_audio_latents(
|
|
self,
|
|
batch_size: int = 1,
|
|
num_channels_latents: int = 8,
|
|
audio_latent_length: int = 1,
|
|
num_mel_bins: int = 64,
|
|
noise_scale: float = 0.0,
|
|
dtype: torch.dtype | None = None,
|
|
device: torch.device | None = None,
|
|
generator: torch.Generator | list[torch.Generator] | None = None,
|
|
latents: torch.Tensor | None = None,
|
|
) -> tuple[torch.Tensor, int, int]:
|
|
original_latent_length = audio_latent_length
|
|
padded_latent_length = original_latent_length
|
|
latent_mel_bins = num_mel_bins // self.audio_vae_mel_compression_ratio
|
|
|
|
if latents is not None:
|
|
if latents.ndim == 4:
|
|
latents = self._pack_audio_latents(latents)
|
|
if latents.ndim != 3:
|
|
raise ValueError(f"Provided `latents` has shape {latents.shape}, expected [batch, seq, features].")
|
|
latents = self._normalize_audio_latents(latents, self.audio_vae.latents_mean, self.audio_vae.latents_std)
|
|
noise = randn_tensor(latents.shape, generator=generator, device=latents.device, dtype=latents.dtype)
|
|
latents = noise_scale * noise + (1 - noise_scale) * latents
|
|
return latents.to(device=device, dtype=dtype), original_latent_length, padded_latent_length
|
|
|
|
shape = (batch_size, num_channels_latents, padded_latent_length, latent_mel_bins)
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
latents = self._pack_audio_latents(latents)
|
|
return latents, original_latent_length, padded_latent_length
|
|
|
|
# ------------------------------------------------------------------
|
|
# Properties
|
|
# ------------------------------------------------------------------
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale is not None and self._guidance_scale > 1.0
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def current_timestep(self):
|
|
return self._current_timestep
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
# ------------------------------------------------------------------
|
|
# Input validation
|
|
# ------------------------------------------------------------------
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
prompt_attention_mask=None,
|
|
negative_prompt_attention_mask=None,
|
|
):
|
|
if height % 32 != 0 or width % 32 != 0:
|
|
raise ValueError(f"`height` and `width` must be divisible by 32 but are {height} and {width}.")
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError("Cannot forward both `prompt` and `prompt_embeds`.")
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError("Provide either `prompt` or `prompt_embeds`.")
|
|
elif prompt is not None and not isinstance(prompt, (str, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
|
|
|
if prompt_embeds is not None and prompt_attention_mask is None:
|
|
raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
|
|
|
|
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
|
|
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
|
|
raise ValueError(
|
|
"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when "
|
|
"passed directly, but got: `prompt_attention_mask` "
|
|
f"{prompt_attention_mask.shape} != `negative_prompt_attention_mask` "
|
|
f"{negative_prompt_attention_mask.shape}."
|
|
)
|
|
|
|
# ------------------------------------------------------------------
|
|
# Cache context
|
|
# ------------------------------------------------------------------
|
|
|
|
def _transformer_cache_context(self, context_name: str):
|
|
cache_context = getattr(self.transformer, "cache_context", None)
|
|
if callable(cache_context):
|
|
return cache_context(context_name)
|
|
return nullcontext()
|
|
|
|
# ------------------------------------------------------------------
|
|
# Forward pass
|
|
# ------------------------------------------------------------------
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
req: OmniDiffusionRequest,
|
|
prompt: str | list[str] | None = None,
|
|
negative_prompt: str | list[str] | None = None,
|
|
height: int | None = None,
|
|
width: int | None = None,
|
|
num_frames: int | None = None,
|
|
frame_rate: float | None = None,
|
|
num_inference_steps: int | None = None,
|
|
sigmas: list[float] | None = None,
|
|
timesteps: list[int] | None = None,
|
|
guidance_scale: float = 4.0,
|
|
noise_scale: float = 0.0,
|
|
num_videos_per_prompt: int | None = 1,
|
|
generator: torch.Generator | list[torch.Generator] | None = None,
|
|
latents: torch.Tensor | None = None,
|
|
audio_latents: torch.Tensor | None = None,
|
|
prompt_embeds: torch.Tensor | None = None,
|
|
negative_prompt_embeds: torch.Tensor | None = None,
|
|
prompt_attention_mask: torch.Tensor | None = None,
|
|
negative_prompt_attention_mask: torch.Tensor | None = None,
|
|
decode_timestep: float | list[float] = 0.0,
|
|
decode_noise_scale: float | list[float] | None = None,
|
|
output_type: str = "np",
|
|
return_dict: bool = True,
|
|
attention_kwargs: dict[str, Any] | None = None,
|
|
max_sequence_length: int | None = None,
|
|
) -> DiffusionOutput:
|
|
# ---- Extract from request ----
|
|
prompt = [p if isinstance(p, str) else (p.get("prompt") or "") for p in req.prompts] or prompt
|
|
if all(isinstance(p, str) or p.get("negative_prompt") is None for p in req.prompts):
|
|
negative_prompt = None
|
|
elif req.prompts:
|
|
negative_prompt = ["" if isinstance(p, str) else (p.get("negative_prompt") or "") for p in req.prompts]
|
|
|
|
height = req.sampling_params.height or height or 512
|
|
width = req.sampling_params.width or width or 768
|
|
num_frames = req.sampling_params.num_frames or num_frames or 121
|
|
frame_rate = req.sampling_params.resolved_frame_rate or frame_rate or 24.0
|
|
num_inference_steps = req.sampling_params.num_inference_steps or num_inference_steps or 40
|
|
# Enforce minimum of 2 timesteps for flow matching scheduler
|
|
if timesteps is None:
|
|
num_inference_steps = max(int(num_inference_steps), 2)
|
|
elif len(timesteps) < 2:
|
|
raise ValueError("`timesteps` must contain at least 2 values for FlowMatchEulerDiscreteScheduler.")
|
|
num_videos_per_prompt = (
|
|
req.sampling_params.num_outputs_per_prompt
|
|
if req.sampling_params.num_outputs_per_prompt > 0
|
|
else num_videos_per_prompt or 1
|
|
)
|
|
max_sequence_length = (
|
|
req.sampling_params.max_sequence_length or max_sequence_length or self.tokenizer_max_length
|
|
)
|
|
|
|
if req.sampling_params.guidance_scale_provided:
|
|
guidance_scale = req.sampling_params.guidance_scale
|
|
|
|
if generator is None:
|
|
generator = req.sampling_params.generator
|
|
if generator is None and req.sampling_params.seed is not None:
|
|
generator = torch.Generator(device=self.device).manual_seed(req.sampling_params.seed)
|
|
|
|
latents = req.sampling_params.latents if req.sampling_params.latents is not None else latents
|
|
audio_latents = (
|
|
req.sampling_params.audio_latents
|
|
if req.sampling_params.audio_latents is not None
|
|
else req.sampling_params.extra_args.get("audio_latents", audio_latents)
|
|
)
|
|
|
|
# Override with pre-computed embeddings if provided in request
|
|
req_prompt_embeds = [_get_prompt_field(p, "prompt_embeds") for p in req.prompts]
|
|
if any(p is not None for p in req_prompt_embeds):
|
|
prompt_embeds = torch.stack(req_prompt_embeds)
|
|
|
|
req_negative_prompt_embeds = [_get_prompt_field(p, "negative_prompt_embeds") for p in req.prompts]
|
|
if any(p is not None for p in req_negative_prompt_embeds):
|
|
negative_prompt_embeds = torch.stack(req_negative_prompt_embeds)
|
|
|
|
req_prompt_attention_masks = [
|
|
_get_prompt_field(p, "prompt_attention_mask") or _get_prompt_field(p, "attention_mask") for p in req.prompts
|
|
]
|
|
if any(m is not None for m in req_prompt_attention_masks):
|
|
prompt_attention_mask = torch.stack(req_prompt_attention_masks)
|
|
|
|
req_negative_attention_masks = [
|
|
_get_prompt_field(p, "negative_prompt_attention_mask") or _get_prompt_field(p, "negative_attention_mask")
|
|
for p in req.prompts
|
|
]
|
|
if any(m is not None for m in req_negative_attention_masks):
|
|
negative_prompt_attention_mask = torch.stack(req_negative_attention_masks)
|
|
|
|
if req.sampling_params.decode_timestep is not None:
|
|
decode_timestep = req.sampling_params.decode_timestep
|
|
if req.sampling_params.decode_noise_scale is not None:
|
|
decode_noise_scale = req.sampling_params.decode_noise_scale
|
|
if req.sampling_params.output_type is not None:
|
|
output_type = req.sampling_params.output_type
|
|
|
|
self.check_inputs(
|
|
prompt=prompt,
|
|
height=height,
|
|
width=width,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._attention_kwargs = attention_kwargs
|
|
self._interrupt = False
|
|
self._current_timestep = None
|
|
|
|
if prompt is not None and isinstance(prompt, str):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self.device
|
|
|
|
# ---- Encode prompts ----
|
|
(
|
|
prompt_embeds,
|
|
prompt_attention_mask,
|
|
negative_prompt_embeds,
|
|
negative_prompt_attention_mask,
|
|
) = self.encode_prompt(
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
max_sequence_length=max_sequence_length,
|
|
device=device,
|
|
)
|
|
|
|
# ---- Connectors (LTX-2.3: padding_side API) ----
|
|
# Concatenate negative + positive embeddings BEFORE connector call,
|
|
# matching diffusers which calls connectors once with batch=2.
|
|
# This ensures batch-dependent operations produce identical results.
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
|
|
|
|
self.connectors.to(device)
|
|
tokenizer_padding_side = getattr(self.tokenizer, "padding_side", "left")
|
|
connector_prompt_embeds, connector_audio_prompt_embeds, connector_attention_mask = self.connectors(
|
|
prompt_embeds, prompt_attention_mask, padding_side=tokenizer_padding_side
|
|
)
|
|
self.connectors.to("cpu")
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
|
|
# ---- Prepare latents ----
|
|
latent_num_frames = (num_frames - 1) // self.vae_temporal_compression_ratio + 1
|
|
latent_height = height // self.vae_spatial_compression_ratio
|
|
latent_width = width // self.vae_spatial_compression_ratio
|
|
if latents is not None and latents.ndim == 5:
|
|
_, _, latent_num_frames, latent_height, latent_width = latents.shape
|
|
|
|
num_channels_latents = self.transformer.config.in_channels
|
|
latents = self.prepare_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
num_channels_latents,
|
|
height,
|
|
width,
|
|
num_frames,
|
|
noise_scale,
|
|
torch.float32,
|
|
device,
|
|
generator,
|
|
latents,
|
|
)
|
|
|
|
duration_s = num_frames / frame_rate
|
|
audio_latents_per_second = (
|
|
self.audio_sampling_rate / self.audio_hop_length / float(self.audio_vae_temporal_compression_ratio)
|
|
)
|
|
audio_num_frames = round(duration_s * audio_latents_per_second)
|
|
if audio_latents is not None and audio_latents.ndim == 4:
|
|
_, _, audio_num_frames, _ = audio_latents.shape
|
|
|
|
num_mel_bins = self.audio_vae.config.mel_bins if self.audio_vae is not None else 64
|
|
latent_mel_bins = num_mel_bins // self.audio_vae_mel_compression_ratio
|
|
num_channels_latents_audio = self.audio_vae.config.latent_channels if self.audio_vae is not None else 8
|
|
audio_latents, original_audio_num_frames, padded_audio_num_frames = self.prepare_audio_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
num_channels_latents=num_channels_latents_audio,
|
|
audio_latent_length=audio_num_frames,
|
|
num_mel_bins=num_mel_bins,
|
|
noise_scale=noise_scale,
|
|
dtype=torch.float32,
|
|
device=device,
|
|
generator=generator,
|
|
latents=audio_latents,
|
|
)
|
|
|
|
# ---- Scheduler setup ----
|
|
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
|
|
# Use max_image_seq_len (not actual video_sequence_length) for mu calculation,
|
|
# matching diffusers' LTX2Pipeline which hardcodes this value.
|
|
mu = calculate_shift(
|
|
self.scheduler.config.get("max_image_seq_len", 4096),
|
|
self.scheduler.config.get("base_image_seq_len", 1024),
|
|
self.scheduler.config.get("max_image_seq_len", 4096),
|
|
self.scheduler.config.get("base_shift", 0.95),
|
|
self.scheduler.config.get("max_shift", 2.05),
|
|
)
|
|
audio_scheduler = copy.deepcopy(self.scheduler)
|
|
_ = retrieve_timesteps(audio_scheduler, num_inference_steps, device, timesteps, sigmas=sigmas, mu=mu)
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler,
|
|
num_inference_steps,
|
|
device,
|
|
timesteps,
|
|
sigmas=sigmas,
|
|
mu=mu,
|
|
)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# ---- RoPE coordinates ----
|
|
video_coords = self.transformer.rope.prepare_video_coords(
|
|
latents.shape[0],
|
|
latent_num_frames,
|
|
latent_height,
|
|
latent_width,
|
|
latents.device,
|
|
fps=frame_rate,
|
|
)
|
|
audio_coords = self.transformer.audio_rope.prepare_audio_coords(
|
|
audio_latents.shape[0],
|
|
padded_audio_num_frames,
|
|
audio_latents.device,
|
|
)
|
|
|
|
# ---- CFG: duplicate coords for batch=2 ----
|
|
# Connector outputs are already batch=2 (neg+pos concatenated before connector call)
|
|
if self.do_classifier_free_guidance:
|
|
video_coords = video_coords.repeat((2,) + (1,) * (video_coords.ndim - 1))
|
|
audio_coords = audio_coords.repeat((2,) + (1,) * (audio_coords.ndim - 1))
|
|
|
|
# ---- Denoising loop ----
|
|
# Uses x0-space CFG (delta formulation) matching diffusers' LTX2Pipeline.
|
|
# The velocity predictions are converted to x0, guidance is applied in x0
|
|
# space, then converted back to velocity for the scheduler step.
|
|
with self.progress_bar(total=len(timesteps)) as pbar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
self._current_timestep = t
|
|
|
|
# Duplicate latents for CFG (uncond + cond)
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
latent_model_input = latent_model_input.to(connector_prompt_embeds.dtype)
|
|
audio_latent_model_input = (
|
|
torch.cat([audio_latents] * 2) if self.do_classifier_free_guidance else audio_latents
|
|
)
|
|
audio_latent_model_input = audio_latent_model_input.to(connector_prompt_embeds.dtype)
|
|
ts = t.expand(latent_model_input.shape[0])
|
|
|
|
with self._transformer_cache_context("cond_uncond"):
|
|
noise_pred_video, noise_pred_audio = self.transformer(
|
|
hidden_states=latent_model_input,
|
|
audio_hidden_states=audio_latent_model_input,
|
|
encoder_hidden_states=connector_prompt_embeds,
|
|
audio_encoder_hidden_states=connector_audio_prompt_embeds,
|
|
timestep=ts,
|
|
sigma=ts, # LTX-2.3: sigma for prompt_adaln
|
|
encoder_attention_mask=connector_attention_mask,
|
|
audio_encoder_attention_mask=connector_attention_mask,
|
|
num_frames=latent_num_frames,
|
|
height=latent_height,
|
|
width=latent_width,
|
|
fps=frame_rate,
|
|
audio_num_frames=padded_audio_num_frames,
|
|
video_coords=video_coords,
|
|
audio_coords=audio_coords,
|
|
attention_kwargs=attention_kwargs,
|
|
return_dict=False,
|
|
)
|
|
|
|
noise_pred_video = noise_pred_video.float()
|
|
noise_pred_audio = noise_pred_audio.float()
|
|
|
|
# CFG in x0-space (delta formulation matching diffusers)
|
|
if self.do_classifier_free_guidance:
|
|
noise_pred_video_uncond, noise_pred_video_cond = noise_pred_video.chunk(2)
|
|
# Convert velocity to x0: x0 = sample - velocity * sigma
|
|
x0_video_cond = latents - noise_pred_video_cond * self.scheduler.sigmas[i]
|
|
x0_video_uncond = latents - noise_pred_video_uncond * self.scheduler.sigmas[i]
|
|
video_cfg_delta = (guidance_scale - 1) * (x0_video_cond - x0_video_uncond)
|
|
x0_video_guided = x0_video_cond + video_cfg_delta
|
|
|
|
noise_pred_audio_uncond, noise_pred_audio_cond = noise_pred_audio.chunk(2)
|
|
x0_audio_cond = audio_latents - noise_pred_audio_cond * audio_scheduler.sigmas[i]
|
|
x0_audio_uncond = audio_latents - noise_pred_audio_uncond * audio_scheduler.sigmas[i]
|
|
audio_cfg_delta = (guidance_scale - 1) * (x0_audio_cond - x0_audio_uncond)
|
|
x0_audio_guided = x0_audio_cond + audio_cfg_delta
|
|
|
|
# Convert x0 back to velocity: v = (sample - x0) / sigma
|
|
noise_pred_video = (latents - x0_video_guided) / self.scheduler.sigmas[i]
|
|
noise_pred_audio = (audio_latents - x0_audio_guided) / audio_scheduler.sigmas[i]
|
|
|
|
latents = self.scheduler.step(noise_pred_video, t, latents, return_dict=False)[0]
|
|
audio_latents = audio_scheduler.step(noise_pred_audio, t, audio_latents, return_dict=False)[0]
|
|
|
|
pbar.update()
|
|
|
|
# ---- Unpack and denormalize ----
|
|
latents = self._unpack_latents(
|
|
latents,
|
|
latent_num_frames,
|
|
latent_height,
|
|
latent_width,
|
|
self.transformer_spatial_patch_size,
|
|
self.transformer_temporal_patch_size,
|
|
)
|
|
latents = self._denormalize_latents(
|
|
latents,
|
|
self.vae.latents_mean,
|
|
self.vae.latents_std,
|
|
self.vae.config.scaling_factor,
|
|
)
|
|
|
|
audio_latents = self._unpad_audio_latents(audio_latents, original_audio_num_frames)
|
|
audio_latents = self._denormalize_audio_latents(
|
|
audio_latents,
|
|
self.audio_vae.latents_mean,
|
|
self.audio_vae.latents_std,
|
|
)
|
|
audio_latents = self._unpack_audio_latents(
|
|
audio_latents,
|
|
original_audio_num_frames,
|
|
num_mel_bins=latent_mel_bins,
|
|
)
|
|
|
|
# ---- Decode ----
|
|
if output_type == "latent":
|
|
video = latents
|
|
audio = audio_latents
|
|
else:
|
|
latents = latents.to(connector_prompt_embeds.dtype)
|
|
|
|
if not self.vae.config.timestep_conditioning:
|
|
timestep_decode = None
|
|
else:
|
|
noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)
|
|
if not isinstance(decode_timestep, list):
|
|
decode_timestep = [decode_timestep] * batch_size
|
|
if decode_noise_scale is None:
|
|
decode_noise_scale = decode_timestep
|
|
elif not isinstance(decode_noise_scale, list):
|
|
decode_noise_scale = [decode_noise_scale] * batch_size
|
|
timestep_decode = torch.tensor(decode_timestep, device=device, dtype=latents.dtype)
|
|
decode_noise_scale_t = torch.tensor(decode_noise_scale, device=device, dtype=latents.dtype)[
|
|
:, None, None, None, None
|
|
]
|
|
latents = (1 - decode_noise_scale_t) * latents + decode_noise_scale_t * noise
|
|
|
|
# Move VAE, audio_vae, vocoder to GPU for decoding
|
|
self.vae.to(device)
|
|
latents = latents.to(self.vae.dtype)
|
|
video = self.vae.decode(latents, timestep_decode, return_dict=False)[0]
|
|
video = self.video_processor.postprocess_video(video, output_type=output_type)
|
|
self.vae.to("cpu")
|
|
|
|
self.audio_vae.to(device)
|
|
audio_latents = audio_latents.to(self.audio_vae.dtype)
|
|
generated_mel_spectrograms = self.audio_vae.decode(audio_latents, return_dict=False)[0]
|
|
self.audio_vae.to("cpu")
|
|
|
|
self.vocoder.to(device)
|
|
audio = self.vocoder(generated_mel_spectrograms)
|
|
self.vocoder.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
|
|
return DiffusionOutput(output=(video, audio))
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|
|
|
|
|
|
class LTX23ImageToVideoPipeline(nn.Module):
|
|
"""LTX-2.3 image-to-video pipeline placeholder."""
|
|
|
|
def __init__(self, *, od_config: OmniDiffusionConfig, prefix: str = ""):
|
|
super().__init__()
|
|
raise NotImplementedError(
|
|
"LTX23ImageToVideoPipeline is not yet implemented. "
|
|
"Use LTX23Pipeline for single-stage text-to-video generation."
|
|
)
|