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29 lines
1.2 KiB
Python
29 lines
1.2 KiB
Python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
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from diffsynth import load_state_dict
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from PIL import Image
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import torch
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pipe = QwenImagePipeline.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="Qwen/Qwen-Image-Layered", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
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ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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],
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tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
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)
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state_dict = load_state_dict("models/train/Qwen-Image-Layered_full/epoch-1.safetensors")
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pipe.dit.load_state_dict(state_dict)
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prompt = "a poster"
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input_image = Image.open("data/example_image_dataset/layer/image.png").convert("RGBA").resize((864, 480))
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images = pipe(
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prompt, seed=0,
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height=480, width=864,
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layer_input_image=input_image, layer_num=3,
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)
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for i, image in enumerate(images):
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if i == 0: continue # The first image is the input image.
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image.save(f"image_{i}.png")
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