SD3 ControlNet Depth

--- license: apache-2.0 library_name: diffusers --- # SD3-ControlNet-Depth <img src="./assets/teaser.png"/> # Demo ```python import torch from diffusers import StableDiffusion3ControlNetPipeline from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel from diffusers.utils import load_image # load pipeline controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Depth") pipe = StableDiffusion3ControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet ) pipe.to("cuda", torch.float16) # config control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Depth/resolve/main/images/depth.jpeg") prompt = "a panda cub, captured in a close-up, in forest, is perched on a tree trunk. good composition, Photography, the cub's ears, a fluffy black, are tucked behind its head, adding a touch of whimsy to its appearance. a lush tapestry of green leaves in the background. depth of field, National Geographic" n_prompt = "bad hands, blurry, NSFW, nude, naked, porn, ugly, bad quality, worst quality" # to reproduce result in our example generator = torch.Generator(device="cpu").manual_seed(4000) image = pipe( prompt, negative_prompt=n_prompt, control_image=control_image, controlnet_conditioning_scale=0.5, guidance_scale=7.0, generator=generator ).images[0] image.save('image.jpg') ``` # Limitation Due to the fact that only 1024*1024 pixel resolution was used during the training phase, the inference performs best at this size, with other sizes yielding suboptimal results.