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28 lines
1.1 KiB
Python
28 lines
1.1 KiB
Python
from diffsynth.pipelines.stable_diffusion import StableDiffusionPipeline, ModelConfig
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from diffsynth.core import load_state_dict
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import torch
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pipe = StableDiffusionPipeline.from_pretrained(
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torch_dtype=torch.float32,
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model_configs=[
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ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="text_encoder/model.safetensors"),
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ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="unet/diffusion_pytorch_model.safetensors"),
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ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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],
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tokenizer_config=ModelConfig(model_id="AI-ModelScope/stable-diffusion-v1-5", origin_file_pattern="tokenizer/"),
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)
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state_dict = load_state_dict("./models/train/stable-diffusion-v1-5_full/epoch-1.safetensors", torch_dtype=torch.float32)
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pipe.unet.load_state_dict(state_dict)
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image = pipe(
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prompt="a dog",
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negative_prompt="blurry, low quality, deformed",
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cfg_scale=7.5,
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height=512,
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width=512,
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seed=42,
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rand_device="cuda",
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num_inference_steps=50,
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)
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image.save("image_stable-diffusion-v1-5_full.jpg")
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