SD3 ControlNet Softedge

# SD3 Controlnet softedge The softedge controlnet is finetuned based on SD3-medium. It is trained using 12M open source and internal e-commerce dataset, and achieve good performance on both general and e-commerce image generation. It supports preprocessors such as pidinet, hed as well as their safe mode. ## Examples From left to right: pidinet preprocessor, ours with pidinet, hed preprocessor, ours with hed. `pidinet`   |`controlnet`|`hed`       |`controlnet` :--:|:--:|:--:|:--:  |  |  |   |  |  |   |  |  |   |  |  |   |  |  |  ## Usage with Diffusers ```python import torch from diffusers.utils import load_image, check_min_version from diffusers.models import SD3ControlNetModel from diffusers import StableDiffusion3ControlNetPipeline from controlnet_aux import PidiNetDetector controlnet = SD3ControlNetModel.from_pretrained( "alimama-creative/SD3-Controlnet-Softedge",torch_dtype=torch.float16 ) pipe = StableDiffusion3ControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, variant="fp16", torch_dtype=torch.float16, ) pipe.text_encoder.to(torch.float16) pipe.controlnet.to(torch.float16) pipe.to("cuda") image = load_image( "https://huggingface.co/alimama-creative/SD3-Controlnet-Softedge/resolve/main/images/im1_0.png" ) prompt = "A dog sitting on a park bench." width = 1024 height = 1024 edge_processor = PidiNetDetector.from_pretrained('lllyasviel/Annotators') edge_image = edge_processor(image, detect_resolution=width, image_resolution=width) res_image = pipe( prompt=prompt, negative_prompt="deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, di...