73 lines
3.8 KiB
Python
73 lines
3.8 KiB
Python
import argparse
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import time
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from pathlib import Path
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from mflux import Flux1Controlnet, ConfigControlnet, ModelConfig, StopImageGenerationException
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def main():
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# fmt: off
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parser = argparse.ArgumentParser(description="Generate an image based on a prompt.")
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parser.add_argument("--prompt", type=str, required=True, help="The textual description of the image to generate.")
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parser.add_argument("--controlnet-image-path", type=str, required=True, help="Local path of the image to use as input for controlnet.")
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parser.add_argument("--controlnet-strength", type=float, default=0.4, help="Controls how strongly the control image influences the output image. A value of 0.0 means no influence. (Default is 0.4)")
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parser.add_argument("--controlnet-save-canny", action="store_true", help="If set, save the Canny edge detection reference input image.")
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parser.add_argument("--output", type=str, default="image.png", help="The filename for the output image. Default is \"image.png\".")
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parser.add_argument("--model", "-m", type=str, required=True, choices=["dev", "schnell"], help="The model to use (\"schnell\" or \"dev\").")
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parser.add_argument("--seed", type=int, default=None, help="Entropy Seed (Default is time-based random-seed)")
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parser.add_argument("--height", type=int, default=1024, help="Image height (Default is 1024)")
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parser.add_argument("--width", type=int, default=1024, help="Image width (Default is 1024)")
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parser.add_argument("--steps", type=int, default=None, help="Inference Steps")
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parser.add_argument('--stepwise-image-output-dir', type=str, default=None, help='Output dir to write step-wise images and their final composite image to.')
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parser.add_argument("--guidance", type=float, default=3.5, help="Guidance Scale (Default is 3.5)")
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parser.add_argument("--quantize", "-q", type=int, choices=[4, 8], default=None, help="Quantize the model (4 or 8, Default is None)")
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parser.add_argument("--path", type=str, default=None, help="Local path for loading a model from disk")
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parser.add_argument("--lora-paths", type=str, nargs="*", default=None, help="Local safetensors for applying LORA from disk")
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parser.add_argument("--lora-scales", type=float, nargs="*", default=None, help="Scaling factor to adjust the impact of LoRA weights on the model. A value of 1.0 applies the LoRA weights as they are.")
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parser.add_argument("--metadata", action="store_true", help="Export image metadata as a JSON file.")
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# fmt: on
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args = parser.parse_args()
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if args.path and args.model is None:
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parser.error("--model must be specified when using --path")
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if args.steps is None:
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args.steps = 4 if args.model == "schnell" else 14
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# Load the model
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flux = Flux1Controlnet(
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model_config=ModelConfig.from_alias(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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try:
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# Generate an image
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image = flux.generate_image(
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seed=int(time.time()) if args.seed is None else args.seed,
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prompt=args.prompt,
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output=args.output,
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controlnet_image_path=args.controlnet_image_path,
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controlnet_save_canny=args.controlnet_save_canny,
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stepwise_output_dir=Path(args.stepwise_image_output_dir) if args.stepwise_image_output_dir else None,
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config=ConfigControlnet(
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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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# Save the image
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image.save(path=args.output, export_json_metadata=args.metadata)
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except StopImageGenerationException as stop_exc:
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print(stop_exc)
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if __name__ == "__main__":
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main()
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