61 lines
2.7 KiB
Python
61 lines
2.7 KiB
Python
import argparse
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import os
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import sys
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import time
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'src')))
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from flux_1.config.model_config import ModelConfig
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from flux_1.config.config import Config
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from flux_1.flux import Flux1
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def main():
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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('--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=4, help='Inference Steps')
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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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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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# Load the model
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flux = Flux1(
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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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# 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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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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)
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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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if __name__ == '__main__':
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main()
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