mirror of
https://github.com/modelscope/DiffSynth-Studio.git
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55 lines
2.1 KiB
Python
55 lines
2.1 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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vram_config = {
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"offload_dtype": "disk",
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"offload_device": "disk",
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"onload_dtype": torch.float8_e4m3fn,
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"onload_device": "cpu",
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"preparing_dtype": torch.float8_e4m3fn,
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"preparing_device": "cuda",
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"computation_dtype": torch.bfloat16,
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"computation_device": "cuda",
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}
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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", **vram_config),
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ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", **vram_config),
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ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
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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, ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control-V2", origin_file_pattern="model.safetensors", **vram_config))
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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="layer_v2/*.png"
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)
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prompt = "Text 'APRIL'"
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input_image = Image.open("data/example_image_dataset/layer_v2/image_1.png").convert("RGBA").resize((1024, 1024))
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image = pipe(
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prompt, seed=0,
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height=1024, width=1024,
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layer_input_image=input_image, layer_num=0,
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num_inference_steps=10, cfg_scale=4,
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)
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image[0].save("image_prompt.png")
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mask_image = Image.open("data/example_image_dataset/layer_v2/mask_2.png").convert("RGBA").resize((1024, 1024))
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input_image = Image.open("data/example_image_dataset/layer_v2/image_2.png").convert("RGBA").resize((1024, 1024))
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image = pipe(
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prompt, seed=0,
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height=1024, width=1024,
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layer_input_image=input_image, layer_num=0,
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context_image=mask_image,
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num_inference_steps=10, cfg_scale=1.0,
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)
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image[0].save("image_mask.png")
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