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from diffsynth import ModelManager, SDImagePipeline, ControlNetConfigUnit, download_models | |
import torch | |
# Download models (automatically) | |
# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575?type=Model&format=SafeTensor&size=full&fp=fp16) | |
# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) | |
# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) | |
# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) | |
# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) | |
download_models(["AingDiffusion_v12", "ControlNet_v11p_sd15_lineart", "ControlNet_v11f1e_sd15_tile"]) | |
# Load models | |
model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", | |
file_path_list=[ | |
"models/stable_diffusion/aingdiffusion_v12.safetensors", | |
"models/ControlNet/control_v11f1e_sd15_tile.pth", | |
"models/ControlNet/control_v11p_sd15_lineart.pth" | |
]) | |
pipe = SDImagePipeline.from_model_manager( | |
model_manager, | |
[ | |
ControlNetConfigUnit( | |
processor_id="tile", | |
model_path=rf"models/ControlNet/control_v11f1e_sd15_tile.pth", | |
scale=0.5 | |
), | |
ControlNetConfigUnit( | |
processor_id="lineart", | |
model_path=rf"models/ControlNet/control_v11p_sd15_lineart.pth", | |
scale=0.7 | |
), | |
] | |
) | |
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," | |
negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," | |
torch.manual_seed(0) | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
cfg_scale=7.5, clip_skip=1, | |
height=512, width=512, num_inference_steps=80, | |
) | |
image.save("512.jpg") | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
cfg_scale=7.5, clip_skip=1, | |
input_image=image.resize((1024, 1024)), controlnet_image=image.resize((1024, 1024)), | |
height=1024, width=1024, num_inference_steps=40, denoising_strength=0.7, | |
) | |
image.save("1024.jpg") | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
cfg_scale=7.5, clip_skip=1, | |
input_image=image.resize((2048, 2048)), controlnet_image=image.resize((2048, 2048)), | |
height=2048, width=2048, num_inference_steps=20, denoising_strength=0.7, | |
) | |
image.save("2048.jpg") | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
cfg_scale=7.5, clip_skip=1, | |
input_image=image.resize((4096, 4096)), controlnet_image=image.resize((4096, 4096)), | |
height=4096, width=4096, num_inference_steps=10, denoising_strength=0.5, | |
tiled=True, tile_size=128, tile_stride=64 | |
) | |
image.save("4096.jpg") | |