Davids048andClaude Opus 5.5
e263249175
Every parameter, attribute, and keyword argument that carries the runtime config is named resolved_config instead of fastvideo_args, including the RPC kwargs keys between the executors and the workers. The module path fastvideo.fastvideo_args is unchanged. The rename is mechanical (a token-level rewrite) plus yapf line wrapping. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_012M91wnVFmPEvJ9h39r5BH7
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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Diffusion pipelines for fastvideo.
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This package contains diffusion pipelines for generating videos and images.
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"""
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from typing import cast
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from fastvideo.api.resolution import ResolvedGeneratorConfig
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from fastvideo.fastvideo_args import FastVideoArgs
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from fastvideo.logger import init_logger
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from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
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from fastvideo.pipelines.lora_pipeline import LoRAPipeline
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from fastvideo.pipelines.pipeline_batch_info import ForwardBatch, TrainingBatch
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from fastvideo.pipelines.pipeline_registry import PipelineType
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from fastvideo.registry import get_model_info
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from fastvideo.utils import maybe_download_model
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logger = init_logger(__name__)
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class PipelineWithLoRA(LoRAPipeline, ComposedPipelineBase):
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"""Type for a pipeline that has both ComposedPipelineBase and LoRAPipeline functionality."""
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pass
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def build_pipeline(resolved_config: ResolvedGeneratorConfig | FastVideoArgs,
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pipeline_type: PipelineType | str = PipelineType.BASIC) -> PipelineWithLoRA:
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"""
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Only works with valid hf diffusers configs. (model_index.json)
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We want to build a pipeline based on the inference args mode_path:
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1. resolve the pipeline class from the small Hub manifest
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2. download the required model components if needed
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3. verify the selected model components and build the pipeline
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"""
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# Resolve the concrete pipeline from the small Hub manifest before
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# downloading large component weights.
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model_path = resolved_config.model_path
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logger.info("Building pipeline of type: %s",
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pipeline_type.value if isinstance(pipeline_type, PipelineType) else pipeline_type)
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model_info = get_model_info(
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model_path=model_path,
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pipeline_type=pipeline_type,
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workload_type=resolved_config.workload_type,
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override_pipeline_cls_name=resolved_config.override_pipeline_cls_name,
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revision=resolved_config.revision,
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)
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pipeline_cls = model_info.pipeline_cls
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model_path = maybe_download_model(
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model_path,
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revision=resolved_config.revision,
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allow_patterns=pipeline_cls.get_hf_download_allow_patterns(),
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)
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logger.info("Model path: %s", model_path)
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# instantiate the pipelines
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pipeline = pipeline_cls(model_path, resolved_config)
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logger.info("Pipelines instantiated")
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return cast(PipelineWithLoRA, pipeline)
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__all__ = [
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"build_pipeline",
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"ComposedPipelineBase",
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"ForwardBatch",
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"LoRAPipeline",
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"TrainingBatch",
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]
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