b26a1d66b3
Co-authored-by: Anthony Wu <pls-file-gh-issue@users.noreply.github.com> Co-authored-by: filipstrand <strand.filip@gmail.com>
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
from mflux import Config, Flux1Controlnet, ModelConfig, StopImageGenerationException
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from mflux.callbacks.callback_manager import CallbackManager
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from mflux.error.exceptions import PromptFileReadError
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from mflux.ui import defaults as ui_defaults
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from mflux.ui.cli.parsers import CommandLineParser
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from mflux.ui.prompt_utils import get_effective_prompt
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def main():
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# 0. Parse command line arguments
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parser = CommandLineParser(description="Generate an image based on a prompt and a controlnet reference image.") # fmt: off
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parser.add_general_arguments()
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parser.add_model_arguments(require_model_arg=True)
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parser.add_lora_arguments()
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parser.add_image_generator_arguments(supports_metadata_config=False)
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parser.add_controlnet_arguments(mode="canny")
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parser.add_output_arguments()
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args = parser.parse_args()
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# 0. Set default guidance value if not provided by user
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if args.guidance is None:
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args.guidance = ui_defaults.GUIDANCE_SCALE
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# 1. Load the model
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flux = Flux1Controlnet(
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model_config=_get_controlnet_model_config(args.model),
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quantize=args.quantize,
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local_path=args.path,
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lora_paths=args.lora_paths,
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lora_scales=args.lora_scales,
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)
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# 2. Register callbacks
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memory_saver = CallbackManager.register_callbacks(args=args, flux=flux, enable_canny_saver=True)
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try:
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for seed in args.seed:
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# 3. Generate an image for each seed value
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image = flux.generate_image(
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seed=seed,
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prompt=get_effective_prompt(args),
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controlnet_image_path=args.controlnet_image_path,
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config=Config(
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num_inference_steps=args.steps,
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height=args.height,
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width=args.width,
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guidance=args.guidance,
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controlnet_strength=args.controlnet_strength,
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),
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)
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# 4. Save the image
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image.save(path=args.output.format(seed=seed), export_json_metadata=args.metadata)
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except (StopImageGenerationException, PromptFileReadError) as exc:
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print(exc)
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finally:
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if memory_saver:
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print(memory_saver.memory_stats())
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def _get_controlnet_model_config(model_name: str) -> ModelConfig:
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if model_name == "schnell":
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return ModelConfig.schnell_controlnet_canny()
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return ModelConfig.dev_controlnet_canny()
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if __name__ == "__main__":
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main()
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