618 lines
27 KiB
Python
618 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Request normalization and validation shared by LTX pipeline variants."""
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from __future__ import annotations
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from collections.abc import Mapping
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from dataclasses import dataclass, replace
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from typing import Any, Literal
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import torch
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from vllm_omni.diffusion.data import DiffusionOutput
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from vllm_omni.diffusion.worker.request_batch import DiffusionRequestBatch
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from .ltx2_guidance import LTXGuidanceSpec, LTXModalityGuidance
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from .ltx2_recipes import LTXPipelineRecipe
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@dataclass
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class LTXRequestInputs:
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"""Resolved request values shared by all LTX execution variants."""
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prompt: str | list[str] | None
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negative_prompt: str | list[str] | None
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height: int
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width: int
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num_frames: int
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frame_rate: float
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num_inference_steps: int
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guidance: LTXGuidanceSpec
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num_videos_per_prompt: int
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generator: torch.Generator | list[torch.Generator] | None
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latents: torch.Tensor | None
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audio_latents: torch.Tensor | None
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prompt_embeds: torch.Tensor | None
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negative_prompt_embeds: torch.Tensor | None
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prompt_attention_mask: torch.Tensor | None
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negative_prompt_attention_mask: torch.Tensor | None
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decode_timestep: float | list[float]
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decode_noise_scale: float | list[float] | None
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output_type: str
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max_sequence_length: int
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audio_latents_normalized: bool = False
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image_crf: int | None = None
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@property
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def guidance_scale(self) -> float:
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"""Compatibility view for callers that only understand video CFG."""
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return self.guidance.video.cfg_scale
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LTXCheckpointKind = Literal["distilled", "regular"]
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def validate_ltx_checkpoint(
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scheduler_config: Mapping[str, Any],
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*,
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expected_kind: LTXCheckpointKind | None,
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pipeline_name: str,
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) -> None:
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"""Ensure scheduler metadata matches the checkpoint required by a profile."""
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if expected_kind is None:
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return
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if expected_kind == "distilled":
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if scheduler_config.get("use_dynamic_shifting", True) or scheduler_config.get("shift_terminal") is not None:
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raise ValueError(
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f"{pipeline_name} requires a merged distilled LTX2 checkpoint; "
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"the loaded scheduler metadata describes a non-distilled checkpoint."
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)
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return
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if not scheduler_config.get("use_dynamic_shifting", False) or scheduler_config.get("shift_terminal") is None:
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raise ValueError(
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f"{pipeline_name} requires a regular non-distilled LTX checkpoint; "
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"the loaded scheduler metadata describes a distilled checkpoint."
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)
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def validate_pipeline_request(
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request_inputs: LTXRequestInputs,
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*,
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pipeline_recipe: LTXPipelineRecipe,
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vae_spatial_compression_ratio: int,
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vae_temporal_compression_ratio: int,
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pipeline_name: str,
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request_sigmas: list[float] | None = None,
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request_phase_sigmas: tuple[list[float] | None, ...] | None = None,
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) -> None:
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"""Validate the request capabilities declared by an execution recipe."""
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alignment = vae_spatial_compression_ratio * pipeline_recipe.max_spatial_downscale
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if request_inputs.height % alignment != 0 or request_inputs.width % alignment != 0:
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raise ValueError(
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f"{pipeline_name} resolution must be divisible by {alignment}, got "
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f"{request_inputs.width}x{request_inputs.height}."
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)
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if request_inputs.num_frames < 1 or (request_inputs.num_frames - 1) % vae_temporal_compression_ratio != 0:
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raise ValueError(
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f"{pipeline_name} `num_frames` must be {vae_temporal_compression_ratio} * k + 1, "
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f"got {request_inputs.num_frames}."
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)
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if not pipeline_recipe.allow_request_latents and (
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request_inputs.latents is not None or request_inputs.audio_latents is not None
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):
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raise ValueError(f"{pipeline_name} does not accept request-provided video or audio latents.")
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if not pipeline_recipe.allow_request_sigmas and request_sigmas is not None:
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raise ValueError(f"{pipeline_name} uses fixed phase sigma schedules.")
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if request_sigmas is not None and len(request_sigmas) < 2:
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raise ValueError("A custom LTX sigma schedule must contain at least two values.")
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has_phase_sigmas = request_phase_sigmas is not None and any(item is not None for item in request_phase_sigmas)
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if request_sigmas is not None and has_phase_sigmas:
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raise ValueError("Use either `sigmas` or per-stage sigma schedules, not both.")
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if has_phase_sigmas and not pipeline_recipe.allow_request_phase_sigmas:
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raise ValueError(f"{pipeline_name} does not accept per-stage sigma schedules.")
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if request_phase_sigmas is not None and len(request_phase_sigmas) != len(pipeline_recipe.phases):
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raise ValueError(
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f"{pipeline_name} has {len(pipeline_recipe.phases)} phases but received "
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f"{len(request_phase_sigmas)} per-stage sigma schedules."
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)
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for index, phase_sigmas in enumerate(request_phase_sigmas or ()):
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if phase_sigmas is not None and len(phase_sigmas) < 2:
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raise ValueError(f"`stage_{index + 1}_sigmas` must contain at least two values.")
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if (
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request_sigmas is None
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and (request_phase_sigmas is None or request_phase_sigmas[0] is None)
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and pipeline_recipe.fixed_num_inference_steps
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and (request_inputs.num_inference_steps != pipeline_recipe.num_inference_steps)
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):
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raise ValueError(
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f"{pipeline_name} uses {pipeline_recipe.num_inference_steps} fixed Stage 1 denoise steps; "
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f"got {request_inputs.num_inference_steps}."
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)
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negative_prompt = request_inputs.negative_prompt
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has_negative_prompt = (
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any(bool(value) for value in negative_prompt) if isinstance(negative_prompt, list) else bool(negative_prompt)
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)
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if not pipeline_recipe.allow_negative_prompt and (
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has_negative_prompt or request_inputs.negative_prompt_embeds is not None
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):
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raise ValueError(f"{pipeline_name} uses positive-only guidance and does not accept negative conditioning.")
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first_phase = pipeline_recipe.phases[0]
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if not first_phase.allow_guidance_override and request_inputs.guidance != first_phase.guidance:
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raise ValueError(f"{pipeline_name} uses fixed positive-only guidance and cannot override it.")
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def _unwrap_request_tensor(value: Any) -> Any:
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if isinstance(value, list):
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return value[0] if value else None
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return value
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def _get_prompt_field(prompt: Any, *keys: str) -> Any:
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if isinstance(prompt, str):
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return None
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additional = prompt.get("additional_information")
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for key in keys:
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value = prompt.get(key)
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if value is None and isinstance(additional, dict):
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value = additional.get(key)
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if value is not None:
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return _unwrap_request_tensor(value)
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return None
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def _get_audio_latents_from_sampling(sampling: Any) -> torch.Tensor | None:
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if sampling.audio_latents is not None:
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return sampling.audio_latents
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return sampling.extra_args.get("audio_latents")
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def _get_extra_arg(sampling: Any, *names: str) -> Any:
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extra_args = sampling.extra_args or {}
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for name in names:
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value = extra_args.get(name)
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if value is not None:
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return value
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return None
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def _normalize_image_crf(value: Any) -> int | None:
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if value is None:
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return None
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if isinstance(value, bool) or not isinstance(value, int):
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raise ValueError("`image_crf` must be an integer from 0 through 51.")
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if not 0 <= value <= 51:
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raise ValueError("`image_crf` must be an integer from 0 through 51.")
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return value
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def _normalize_stg_blocks(value: Any, default: tuple[int, ...]) -> tuple[int, ...]:
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if value is None:
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return default
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if isinstance(value, int):
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return (value,)
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if not isinstance(value, (list, tuple)):
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raise ValueError(f"STG blocks must be an integer or a list of integers, got {value!r}.")
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return tuple(int(item) for item in value)
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def _resolve_negative_prompts(
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prompts: list[Any],
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fallback: str | list[str],
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) -> str | list[str]:
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if not prompts:
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return fallback
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if isinstance(fallback, list) and len(fallback) != len(prompts):
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raise ValueError(
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f"`negative_prompt` has batch size {len(fallback)}, but the request batch has size {len(prompts)}."
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)
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resolved = []
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for index, item in enumerate(prompts):
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request_value = None if isinstance(item, str) else item.get("negative_prompt")
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if request_value is not None:
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resolved.append(request_value)
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else:
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resolved.append(fallback[index] if isinstance(fallback, list) else fallback)
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return resolved
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def _resolve_modality_guidance(
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sampling: Any,
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modality: str,
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default: LTXModalityGuidance,
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) -> LTXModalityGuidance:
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cfg_aliases = (f"{modality}_cfg_scale", f"{modality}_cfg_guidance_scale")
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stg_aliases = (f"{modality}_stg_scale", f"{modality}_stg_guidance_scale")
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modality_aliases = (
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("video_modality_scale", "a2v_guidance_scale")
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if modality == "video"
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else ("audio_modality_scale", "v2a_guidance_scale")
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)
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cfg_scale = _get_extra_arg(sampling, *cfg_aliases)
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stg_scale = _get_extra_arg(sampling, *stg_aliases)
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modality_scale = _get_extra_arg(sampling, *modality_aliases)
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rescale_scale = _get_extra_arg(sampling, f"{modality}_rescale_scale")
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stg_blocks = _get_extra_arg(sampling, f"{modality}_stg_blocks")
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return replace(
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default,
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cfg_scale=default.cfg_scale if cfg_scale is None else float(cfg_scale),
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stg_scale=default.stg_scale if stg_scale is None else float(stg_scale),
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modality_scale=default.modality_scale if modality_scale is None else float(modality_scale),
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rescale_scale=default.rescale_scale if rescale_scale is None else float(rescale_scale),
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stg_blocks=_normalize_stg_blocks(stg_blocks, default.stg_blocks),
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)
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def _resolve_guidance_spec(
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sampling: Any,
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default: LTXGuidanceSpec,
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*,
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guidance_scale: float | None,
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) -> LTXGuidanceSpec:
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video = _resolve_modality_guidance(sampling, "video", default.video)
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audio = _resolve_modality_guidance(sampling, "audio", default.audio)
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common_cfg = sampling.guidance_scale if sampling.guidance_scale_provided else guidance_scale
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if common_cfg is not None:
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video = replace(video, cfg_scale=float(common_cfg))
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audio = replace(audio, cfg_scale=float(common_cfg))
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return LTXGuidanceSpec(video=video, audio=audio)
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class LTXRequestMixin:
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"""Normalize serving requests without coupling them to a model version."""
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supports_request_batch = False
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@staticmethod
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def _validate_forward_options(
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*,
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timesteps: list[int] | None,
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guidance_rescale: float | None,
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noise_scale: float,
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return_dict: bool,
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attention_kwargs: dict[str, Any] | None,
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) -> None:
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if timesteps is not None:
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raise ValueError("LTX does not support `timesteps`; use `sigmas` for a custom schedule.")
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if guidance_rescale not in (None, 0.0):
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raise ValueError(
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"LTX does not support the common `guidance_rescale`; use `video_rescale_scale` and "
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"`audio_rescale_scale`."
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)
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if noise_scale != 0.0:
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raise ValueError("LTX only supports `noise_scale=0.0`.")
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if return_dict is not True:
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raise ValueError("LTX only supports `return_dict=True`.")
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if attention_kwargs is not None:
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raise ValueError("LTX does not support public `attention_kwargs`.")
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@torch.no_grad()
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def forward(
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self,
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req: DiffusionRequestBatch,
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*,
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image: Any | None = None,
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image_crf: int | None = None,
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prompt: str | list[str] | None = None,
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negative_prompt: str | list[str] | None = None,
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height: int | None = None,
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width: int | None = None,
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num_frames: int | None = None,
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frame_rate: float | None = None,
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num_inference_steps: int | None = None,
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sigmas: list[float] | None = None,
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stage_1_sigmas: list[float] | None = None,
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stage_2_sigmas: list[float] | None = None,
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timesteps: list[int] | None = None,
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guidance_scale: float | None = None,
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guidance_rescale: float | None = None,
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noise_scale: float = 0.0,
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num_videos_per_prompt: int | None = 1,
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generator: torch.Generator | list[torch.Generator] | None = None,
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latents: torch.Tensor | None = None,
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audio_latents: torch.Tensor | None = None,
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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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decode_timestep: float | list[float] = 0.0,
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decode_noise_scale: float | list[float] | None = None,
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output_type: str = "np",
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return_dict: bool = True,
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attention_kwargs: dict[str, Any] | None = None,
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max_sequence_length: int | None = None,
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) -> DiffusionOutput | list[DiffusionOutput]:
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"""Validate and normalize the public LTX request surface."""
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self._validate_forward_options(
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timesteps=timesteps,
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guidance_rescale=guidance_rescale,
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noise_scale=noise_scale,
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return_dict=return_dict,
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attention_kwargs=attention_kwargs,
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)
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if image is not None and not self.support_image_input:
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raise ValueError(f"{self.__class__.__name__} does not support `image` input.")
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return self._forward_request(
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req,
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image=image,
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image_crf=image_crf,
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prompt=prompt,
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negative_prompt=negative_prompt,
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height=height,
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width=width,
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num_frames=num_frames,
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frame_rate=frame_rate,
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num_inference_steps=num_inference_steps,
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sigmas=sigmas,
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stage_1_sigmas=stage_1_sigmas,
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stage_2_sigmas=stage_2_sigmas,
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guidance_scale=guidance_scale,
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num_videos_per_prompt=num_videos_per_prompt,
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generator=generator,
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latents=latents,
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audio_latents=audio_latents,
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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prompt_attention_mask=prompt_attention_mask,
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negative_prompt_attention_mask=negative_prompt_attention_mask,
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decode_timestep=decode_timestep,
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decode_noise_scale=decode_noise_scale,
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output_type=output_type,
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max_sequence_length=max_sequence_length,
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)
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@staticmethod
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def _resolve_request_sigmas(
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req: DiffusionRequestBatch,
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fallback: list[float] | None,
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) -> list[float] | None:
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request_sigmas = [
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sampling.sigmas if sampling.sigmas is not None else _get_extra_arg(sampling, "sigmas")
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for sampling in req.sampling_params_list
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]
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first = request_sigmas[0] if request_sigmas else None
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if any(item != first for item in request_sigmas[1:]):
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raise ValueError("Batched LTX requests must use identical custom sigmas.")
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return first if first is not None else fallback
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@staticmethod
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def _resolve_request_phase_sigmas(
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req: DiffusionRequestBatch,
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stage_1_fallback: list[float] | None,
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stage_2_fallback: list[float] | None,
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) -> tuple[list[float] | None, list[float] | None] | None:
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resolved: list[list[float] | None] = []
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for name, fallback in (("stage_1_sigmas", stage_1_fallback), ("stage_2_sigmas", stage_2_fallback)):
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values = [_get_extra_arg(sampling, name) for sampling in req.sampling_params_list]
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first = values[0] if values else None
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if any(item != first for item in values[1:]):
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raise ValueError(f"Batched LTX requests must use identical `{name}` values.")
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resolved.append(first if first is not None else fallback)
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return tuple(resolved) if any(item is not None for item in resolved) else None
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def check_inputs(
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self,
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prompt: str | list[str] | None,
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height: int,
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width: int,
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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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) -> None:
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if height % 32 != 0 or width % 32 != 0:
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raise ValueError(f"`height` and `width` must be divisible by 32 but are {height} and {width}.")
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if prompt is not None and prompt_embeds is not None:
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raise ValueError("Cannot forward both `prompt` and `prompt_embeds`.")
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if prompt is None and prompt_embeds is None:
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raise ValueError("Provide either `prompt` or `prompt_embeds`.")
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if prompt is not None and not isinstance(prompt, (str, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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if prompt_embeds is not None and prompt_attention_mask is None:
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raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.")
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if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
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raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.")
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if prompt_embeds is not None and negative_prompt_embeds is not None:
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if prompt_embeds.shape != negative_prompt_embeds.shape:
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raise ValueError(
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"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
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f" got {prompt_embeds.shape} and {negative_prompt_embeds.shape}."
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)
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if prompt_attention_mask.shape != negative_prompt_attention_mask.shape:
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raise ValueError(
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"`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when"
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f" passed directly, but got {prompt_attention_mask.shape} and"
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f" {negative_prompt_attention_mask.shape}."
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)
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def _resolve_request_inputs(
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self,
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req: DiffusionRequestBatch,
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*,
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prompt: str | list[str] | None,
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negative_prompt: str | list[str] | None,
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height: int | None,
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width: int | None,
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num_frames: int | None,
|
|
frame_rate: float | None,
|
|
num_inference_steps: int | None,
|
|
guidance_scale: float | None,
|
|
num_videos_per_prompt: int | None,
|
|
generator: torch.Generator | list[torch.Generator] | None,
|
|
latents: torch.Tensor | None,
|
|
audio_latents: torch.Tensor | None,
|
|
prompt_embeds: torch.Tensor | None,
|
|
negative_prompt_embeds: torch.Tensor | None,
|
|
prompt_attention_mask: torch.Tensor | None,
|
|
negative_prompt_attention_mask: torch.Tensor | None,
|
|
decode_timestep: float | list[float],
|
|
decode_noise_scale: float | list[float] | None,
|
|
output_type: str,
|
|
max_sequence_length: int | None,
|
|
image_crf: int | None = None,
|
|
) -> LTXRequestInputs:
|
|
sampling_params_list = req.sampling_params_list
|
|
for sampling in sampling_params_list:
|
|
if sampling.timesteps is not None:
|
|
raise ValueError("LTX does not support `timesteps`; use `sigmas` for a custom schedule.")
|
|
if sampling.guidance_rescale:
|
|
raise ValueError(
|
|
"LTX does not support the common `guidance_rescale`; use `video_rescale_scale` and "
|
|
"`audio_rescale_scale`."
|
|
)
|
|
if _get_extra_arg(sampling, "flow_shift") is not None:
|
|
raise ValueError("LTX does not support `flow_shift`; use `sigmas` for a custom schedule.")
|
|
|
|
request_image_crfs = [_get_extra_arg(item, "image_crf") for item in sampling_params_list]
|
|
first_image_crf = request_image_crfs[0] if request_image_crfs else None
|
|
if any(item != first_image_crf for item in request_image_crfs[1:]):
|
|
raise ValueError("Batched LTX requests must use identical `image_crf` values.")
|
|
image_crf = _normalize_image_crf(first_image_crf if first_image_crf is not None else image_crf)
|
|
|
|
sampling = sampling_params_list[0]
|
|
is_dummy_run = req.is_dummy_run()
|
|
prompt = [item if isinstance(item, str) else (item.get("prompt") or "") for item in req.prompts] or prompt
|
|
negative_prompt = _resolve_negative_prompts(
|
|
req.prompts,
|
|
negative_prompt if negative_prompt is not None else self.pipeline_recipe.negative_prompt,
|
|
)
|
|
|
|
height = sampling.height or height or self.pipeline_recipe.height
|
|
width = sampling.width or width or self.pipeline_recipe.width
|
|
num_frames = sampling.num_frames or num_frames or self.pipeline_recipe.num_frames
|
|
frame_rate = sampling.resolved_frame_rate or frame_rate or self.pipeline_recipe.frame_rate
|
|
num_inference_steps = (
|
|
sampling.num_inference_steps or num_inference_steps or self.pipeline_recipe.num_inference_steps
|
|
)
|
|
if is_dummy_run and self.pipeline_recipe.fixed_num_inference_steps:
|
|
num_inference_steps = self.pipeline_recipe.num_inference_steps
|
|
num_inference_steps = max(int(num_inference_steps), 2)
|
|
|
|
num_videos_per_prompt = (
|
|
sampling.num_outputs_per_prompt if sampling.num_outputs_per_prompt > 0 else num_videos_per_prompt or 1
|
|
)
|
|
max_sequence_length = sampling.max_sequence_length or max_sequence_length or self.tokenizer_max_length
|
|
guidance_specs = [
|
|
_resolve_guidance_spec(
|
|
item,
|
|
self.pipeline_recipe.request_guidance,
|
|
guidance_scale=guidance_scale,
|
|
)
|
|
for item in sampling_params_list
|
|
]
|
|
guidance = guidance_specs[0]
|
|
if is_dummy_run and not self.pipeline_recipe.phases[0].allow_guidance_override:
|
|
guidance = self.pipeline_recipe.request_guidance
|
|
if any(item != guidance for item in guidance_specs[1:]):
|
|
raise ValueError("Batched LTX requests must use identical video/audio guidance parameters.")
|
|
|
|
if self.supports_request_batch:
|
|
if generator is None:
|
|
generator = req.collate_request_generators(num_videos_per_prompt, generator)
|
|
latents = req.collate_request_tensors("latents", latents)
|
|
audio_latents = DiffusionRequestBatch.collate_tensors(
|
|
[_get_audio_latents_from_sampling(item) for item in sampling_params_list],
|
|
"audio_latents",
|
|
audio_latents,
|
|
)
|
|
prompt_fields = DiffusionRequestBatch.collate_prompt_field_map(
|
|
req.prompts,
|
|
{
|
|
"prompt_embeds": prompt_embeds,
|
|
"negative_prompt_embeds": negative_prompt_embeds,
|
|
"prompt_attention_mask": prompt_attention_mask,
|
|
"negative_prompt_attention_mask": negative_prompt_attention_mask,
|
|
},
|
|
field_aliases={
|
|
"prompt_attention_mask": ("prompt_attention_mask", "attention_mask"),
|
|
"negative_prompt_attention_mask": (
|
|
"negative_prompt_attention_mask",
|
|
"negative_attention_mask",
|
|
),
|
|
},
|
|
)
|
|
prompt_embeds = prompt_fields["prompt_embeds"]
|
|
negative_prompt_embeds = prompt_fields["negative_prompt_embeds"]
|
|
prompt_attention_mask = prompt_fields["prompt_attention_mask"]
|
|
negative_prompt_attention_mask = prompt_fields["negative_prompt_attention_mask"]
|
|
if prompt_embeds is not None:
|
|
prompt = None
|
|
if negative_prompt_embeds is not None:
|
|
negative_prompt = None
|
|
else:
|
|
if generator is None:
|
|
generator = sampling.generator
|
|
if generator is None and sampling.seed is not None:
|
|
generator = torch.Generator(device=self.device).manual_seed(sampling.seed)
|
|
latents = sampling.latents if sampling.latents is not None else latents
|
|
sampling_audio_latents = _get_audio_latents_from_sampling(sampling)
|
|
if sampling_audio_latents is not None:
|
|
audio_latents = sampling_audio_latents
|
|
|
|
request_prompt_embeds = [_get_prompt_field(item, "prompt_embeds") for item in req.prompts]
|
|
if any(value is not None for value in request_prompt_embeds):
|
|
prompt_embeds = torch.stack(request_prompt_embeds) # type: ignore[arg-type]
|
|
request_negative_embeds = [_get_prompt_field(item, "negative_prompt_embeds") for item in req.prompts]
|
|
if any(value is not None for value in request_negative_embeds):
|
|
negative_prompt_embeds = torch.stack(request_negative_embeds) # type: ignore[arg-type]
|
|
request_prompt_masks = [
|
|
_get_prompt_field(item, "prompt_attention_mask", "attention_mask") for item in req.prompts
|
|
]
|
|
if any(value is not None for value in request_prompt_masks):
|
|
prompt_attention_mask = torch.stack(request_prompt_masks) # type: ignore[arg-type]
|
|
request_negative_masks = [
|
|
_get_prompt_field(item, "negative_prompt_attention_mask", "negative_attention_mask")
|
|
for item in req.prompts
|
|
]
|
|
if any(value is not None for value in request_negative_masks):
|
|
negative_prompt_attention_mask = torch.stack(request_negative_masks) # type: ignore[arg-type]
|
|
if prompt_embeds is not None:
|
|
prompt = None
|
|
if negative_prompt_embeds is not None:
|
|
negative_prompt = None
|
|
|
|
if sampling.decode_timestep is not None:
|
|
decode_timestep = sampling.decode_timestep
|
|
if sampling.decode_noise_scale is not None:
|
|
decode_noise_scale = sampling.decode_noise_scale
|
|
if sampling.output_type is not None:
|
|
output_type = sampling.output_type
|
|
|
|
return LTXRequestInputs(
|
|
prompt=prompt,
|
|
negative_prompt=negative_prompt,
|
|
height=int(height),
|
|
width=int(width),
|
|
num_frames=int(num_frames),
|
|
frame_rate=float(frame_rate),
|
|
num_inference_steps=int(num_inference_steps),
|
|
guidance=guidance,
|
|
num_videos_per_prompt=int(num_videos_per_prompt),
|
|
generator=generator,
|
|
latents=latents,
|
|
audio_latents=audio_latents,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
prompt_attention_mask=prompt_attention_mask,
|
|
negative_prompt_attention_mask=negative_prompt_attention_mask,
|
|
decode_timestep=decode_timestep,
|
|
decode_noise_scale=decode_noise_scale,
|
|
output_type=output_type,
|
|
max_sequence_length=int(max_sequence_length),
|
|
image_crf=image_crf,
|
|
)
|