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https://github.com/modelscope/DiffSynth-Studio.git
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37 lines
1.8 KiB
Python
37 lines
1.8 KiB
Python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
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from modelscope import dataset_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="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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processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
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)
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dataset_snapshot_download(
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"DiffSynth-Studio/example_image_dataset",
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allow_patterns="layer/image.png",
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local_dir="data/example_image_dataset"
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)
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# Prompt should be provided to the pipeline. Our pipeline will not generate the prompt.
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prompt = 'A cheerful child with brown hair is waving enthusiastically under a bright blue sky filled with colorful confetti and balloons. The word "HELLO!" is prominently displayed in bold red letters above the child, while "Have a Great Day!" appears in elegant cursive at the bottom right corner. The scene is vibrant and festive, with a mix of pastel colors and dynamic shapes creating a joyful atmosphere.'
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# Height and width should be consistent with input_image and be divided evenly by 16
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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,
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seed=1, num_inference_steps=50,
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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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