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31 lines
1.4 KiB
Python
31 lines
1.4 KiB
Python
import torch
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig
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vram_config = {
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"onload_dtype": torch.float8_e4m3fn,
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"onload_device": "cpu",
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"onload_dtype": torch.float8_e4m3fn,
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"onload_device": "cpu",
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"preparing_dtype": torch.float8_e4m3fn,
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"preparing_device": "cuda",
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"computation_dtype": torch.bfloat16,
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"computation_device": "cuda",
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}
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pipe = FluxImagePipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", **vram_config),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/*.safetensors", **vram_config),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", **vram_config),
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ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors", **vram_config)
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],
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vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
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)
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for i in [0.1, 0.3, 0.5, 0.7, 0.9]:
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image = pipe(prompt="a cat on the beach", seed=2, value_controller_inputs=[i])
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image.save(f"value_control_{i}.jpg")
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