mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-18 22:08:13 +00:00
52 lines
2.5 KiB
Python
52 lines
2.5 KiB
Python
from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig, ControlNetInput
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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": torch.bfloat16,
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"offload_device": "cpu",
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"onload_dtype": torch.bfloat16,
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"onload_device": "cpu",
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"preparing_dtype": torch.bfloat16,
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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 = ZImagePipeline.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="PAI/Z-Image-Turbo-Fun-Controlnet-Union-2.1", origin_file_pattern="Z-Image-Turbo-Fun-Controlnet-Union-2.1-8steps.safetensors", **vram_config),
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ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors", **vram_config),
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ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors", **vram_config),
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ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
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],
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tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
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vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
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)
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# Control
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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="depth/image_1.jpg"
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)
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controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1024, 1024))
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prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
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image = pipe(prompt=prompt, seed=0, height=1024, width=1024, controlnet_inputs=[ControlNetInput(image=controlnet_image, scale=0.7)])
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image.save("image_control.jpg")
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# Inpaint
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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="inpaint/*.jpg"
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)
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inpaint_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024))
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inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024))
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prompt = "一只戴着墨镜的猫"
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image = pipe(prompt=prompt, seed=0, height=1024, width=1024, controlnet_inputs=[ControlNetInput(inpaint_image=inpaint_image, inpaint_mask=inpaint_mask, scale=0.7)])
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image.save("image_inpaint.jpg")
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