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https://github.com/modelscope/DiffSynth-Studio.git
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33 lines
1.5 KiB
Python
33 lines
1.5 KiB
Python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
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from PIL import Image
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import torch
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from modelscope import dataset_snapshot_download
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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", 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", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
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ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="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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pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Canny_lora/epoch-4.safetensors")
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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/example_image_dataset",
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local_dir="./data/example_image_dataset",
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allow_file_pattern="canny/image_1.jpg"
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)
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controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
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prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。"
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image = pipe(
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prompt, seed=0,
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blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
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)
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image.save("image.jpg")
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