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26 lines
1.0 KiB
Python
26 lines
1.0 KiB
Python
import torch
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from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
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from diffsynth.core.loader.file import load_state_dict
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pipe = ErnieImagePipeline.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="PaddlePaddle/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
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ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="PaddlePaddle/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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],
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)
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lora_state_dict = load_state_dict("./models/train/Ernie-Image-T2I_lora/epoch-4.safetensors", torch_dtype=torch.bfloat16, device="cuda")
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pipe.load_lora(pipe.dit, state_dict=lora_state_dict, alpha=1.0)
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image = pipe(
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prompt="a professional photo of a cute dog",
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seed=0,
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num_inference_steps=50,
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cfg_scale=4.0,
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)
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image.save("image_lora.jpg")
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print("LoRA validation image saved to image_lora.jpg")
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