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30 lines
1.3 KiB
Python
30 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 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="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"),
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],
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)
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pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_lora/epoch-4.safetensors", alpha=1)
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image = pipe(
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prompt="a cat sitting on a chair, wearing sunglasses",
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controlnet_inputs=[ControlNetInput(
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image=Image.open("data/example_image_dataset/inpaint/image_1.jpg"),
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inpaint_mask=Image.open("data/example_image_dataset/inpaint/mask.jpg"),
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scale=0.9
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)],
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height=1024, width=1024,
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seed=0, rand_device="cuda",
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)
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image.save("image_FLUX.1-dev-Controlnet-Inpainting-Beta_lora.jpg")
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