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https://github.com/modelscope/DiffSynth-Studio.git
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35 lines
1.3 KiB
Python
35 lines
1.3 KiB
Python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
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from modelscope import snapshot_download
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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="DiffSynth-Studio/Qwen-Image-Layered-Control", 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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processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
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)
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snapshot_download(
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model_id="DiffSynth-Studio/Qwen-Image-Layered-Control",
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allow_file_pattern="assets/image_1_input.png",
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local_dir="data/layered_input"
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)
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prompt = "A cartoon skeleton character wearing a purple hat and holding a gift box"
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input_image = Image.open("data/layered_input/assets/image_1_input.png").convert("RGBA").resize((1024, 1024))
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images = pipe(
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prompt,
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seed=0,
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num_inference_steps=30, cfg_scale=4,
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height=1024, width=1024,
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layer_input_image=input_image,
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layer_num=0,
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)
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images[0].save("image.png")
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