mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 06:23:43 +00:00
76 lines
3.1 KiB
Python
76 lines
3.1 KiB
Python
from diffsynth import ModelManager, SDImagePipeline, ControlNetConfigUnit, download_models
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import torch
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# Download models (automatically)
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# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575?type=Model&format=SafeTensor&size=full&fp=fp16)
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# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth)
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# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth)
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# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth)
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# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth)
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download_models(["AingDiffusion_v12", "ControlNet_v11p_sd15_lineart", "ControlNet_v11f1e_sd15_tile"])
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# Load models
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model_manager = ModelManager(torch_dtype=torch.float16, device="cuda",
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file_path_list=[
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"models/stable_diffusion/aingdiffusion_v12.safetensors",
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"models/ControlNet/control_v11f1e_sd15_tile.pth",
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"models/ControlNet/control_v11p_sd15_lineart.pth"
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])
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pipe = SDImagePipeline.from_model_manager(
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model_manager,
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[
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ControlNetConfigUnit(
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processor_id="tile",
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model_path=rf"models/ControlNet/control_v11f1e_sd15_tile.pth",
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scale=0.5
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),
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ControlNetConfigUnit(
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processor_id="lineart",
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model_path=rf"models/ControlNet/control_v11p_sd15_lineart.pth",
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scale=0.7
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),
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]
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)
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prompt = "masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait,"
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negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw,"
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torch.manual_seed(0)
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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cfg_scale=7.5, clip_skip=1,
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height=512, width=512, num_inference_steps=80,
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)
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image.save("512.jpg")
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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cfg_scale=7.5, clip_skip=1,
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input_image=image.resize((1024, 1024)), controlnet_image=image.resize((1024, 1024)),
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height=1024, width=1024, num_inference_steps=40, denoising_strength=0.7,
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)
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image.save("1024.jpg")
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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cfg_scale=7.5, clip_skip=1,
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input_image=image.resize((2048, 2048)), controlnet_image=image.resize((2048, 2048)),
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height=2048, width=2048, num_inference_steps=20, denoising_strength=0.7,
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)
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image.save("2048.jpg")
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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cfg_scale=7.5, clip_skip=1,
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input_image=image.resize((4096, 4096)), controlnet_image=image.resize((4096, 4096)),
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height=4096, width=4096, num_inference_steps=10, denoising_strength=0.5,
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tiled=True, tile_size=128, tile_stride=64
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)
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image.save("4096.jpg")
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