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32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
import torch
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from diffsynth.pipelines.flux_image import FluxImagePipeline, ModelConfig, ControlNetInput
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from diffsynth import load_state_dict
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from PIL import Image
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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"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/*.safetensors"),
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ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
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ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"),
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],
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)
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state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Union-alpha_full/epoch-0.safetensors")
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pipe.controlnet.models[0].load_state_dict(state_dict)
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image = pipe(
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prompt="a dog",
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controlnet_inputs=[ControlNetInput(
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image=Image.open("data/example_image_dataset/canny/image_1.jpg"),
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scale=0.9,
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processor_id="canny",
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)],
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height=768, width=768,
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seed=0, rand_device="cuda",
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)
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image.save("image_FLUX.1-dev-Controlnet-Union-alpha_full.jpg")
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