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")