d922ab2cbc
Co-authored-by: Gnav3852 <63612880+Gnav3852@users.noreply.github.com> Co-authored-by: Mac Lee <macthecadillac@gmail.com>
125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Run full Flux2 text-to-image generation through FastVideo.
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User story:
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"I have a local or HF Diffusers-format full Flux2 checkpoint and want a
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minimal text-to-image generation command that uses embedded guidance."
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"""
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import argparse
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import os
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from pathlib import Path
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from fastvideo import VideoGenerator
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from fastvideo.api import (
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ComponentConfig,
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EngineConfig,
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GenerationRequest,
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GeneratorConfig,
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OffloadConfig,
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OutputConfig,
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ParallelismConfig,
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PipelineSelection,
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SamplingConfig,
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)
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="Run full Flux2 text-to-image generation.")
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parser.add_argument(
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"--model-path",
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default="black-forest-labs/FLUX.2-dev",
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help="HF id or local diffusers-format full Flux2 weights directory.",
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)
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parser.add_argument(
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"--output",
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default="outputs/flux2/flux2.png",
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help="Output PNG path.",
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)
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parser.add_argument(
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"--prompt",
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default="a photo of a banana on a wooden table, studio lighting",
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help="Text prompt.",
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)
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parser.add_argument("--height", type=int, default=1024)
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parser.add_argument("--width", type=int, default=1024)
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parser.add_argument("--steps", type=int, default=50)
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parser.add_argument("--guidance-scale", type=float, default=4.0)
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parser.add_argument("--max-sequence-length", type=int, default=None)
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--num-gpus", type=int, default=1)
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parser.add_argument("--tp-size", type=int, default=None)
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parser.add_argument("--sp-size", type=int, default=None)
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parser.add_argument(
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"--backend",
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default=None,
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help="Set FASTVIDEO_ATTENTION_BACKEND, for example TORCH_SDPA.",
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)
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return parser.parse_args()
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def main() -> None:
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args = parse_args()
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if args.backend:
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os.environ["FASTVIDEO_ATTENTION_BACKEND"] = args.backend
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output = Path(args.output)
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output.parent.mkdir(parents=True, exist_ok=True)
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tp_size = args.tp_size if args.tp_size is not None else (
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args.num_gpus if args.num_gpus > 1 else 1
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)
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sp_size = args.sp_size if args.sp_size is not None else (
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1 if args.num_gpus > 1 else args.num_gpus
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)
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generator_config = GeneratorConfig(
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model_path=args.model_path,
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engine=EngineConfig(
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num_gpus=args.num_gpus,
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parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
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use_fsdp_inference=False,
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offload=OffloadConfig(
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dit=False,
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vae=True,
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text_encoder=True,
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pin_cpu_memory=False,
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),
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),
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pipeline=PipelineSelection(
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workload_type="t2i",
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components=ComponentConfig(override_pipeline_cls_name="Flux2Pipeline"),
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),
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)
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generator = VideoGenerator.from_config(generator_config)
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try:
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sampling = SamplingConfig(
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height=args.height,
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width=args.width,
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num_frames=1,
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fps=1,
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num_inference_steps=args.steps,
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guidance_scale=args.guidance_scale,
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seed=args.seed,
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)
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extensions = {}
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if args.max_sequence_length is not None:
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extensions["max_sequence_length"] = args.max_sequence_length
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request = GenerationRequest(
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prompt=args.prompt,
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sampling=sampling,
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output=OutputConfig(
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output_path=str(output),
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save_video=True,
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return_frames=False,
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),
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extensions=extensions,
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)
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generator.generate(request)
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finally:
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generator.shutdown()
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if __name__ == "__main__":
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main()
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