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21 lines
960 B
Python
21 lines
960 B
Python
from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig
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from diffsynth.core import load_state_dict
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import torch
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pipe = ZImagePipeline.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="Tongyi-MAI/Z-Image", origin_file_pattern="transformer/*.safetensors"),
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ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
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ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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],
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tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
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)
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state_dict = load_state_dict("./models/train/Z-Image_full/epoch-1.safetensors", torch_dtype=torch.bfloat16)
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pipe.dit.load_state_dict(state_dict)
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prompt = "a dog"
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image = pipe(prompt=prompt, seed=42, rand_device="cuda", num_inference_steps=50, cfg_scale=4)
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image.save("image.jpg")
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