controlnet-openpose-sdxl-1.0

These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with OpenPose (v2) conditioning. You can find some example images in the following. prompt: a ballerina, romantic sunset, 4k photo (Image is from ComfyUI, you can drag and drop in Comfy to use it as workflow) License: refers to the OpenPose's one. First, install all the libraries: pip install -q controlnet_aux transformers accelerate pip install -q git+https://github.com/huggingface/diffusers Now, we're ready to make Darth Vader dance: from diffusers import AutoencoderKL, StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler import torch from controlnet_aux import OpenposeDetector from diffusers.utils import load_image openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet") image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png" ) openpose_image = openpose(image) controlnet = ControlNetModel.from_pretrained("thibaud/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16) pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16 ) pipe.enable_model_cpu_offload() prompt = "Darth vader dancing in a desert, high quality" negative_prompt = "low quality, bad quality" images = pipe( prompt, negative_prompt=negative_prompt, num_inference_steps=25, num_images_per_prompt=4, image=openpose_image.resize((1024, 1024)), generator=torch.manual_seed(97), ).images images[0] Here are some gemerated examples: Use of the training script by HF🤗 [here](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md). This checkpoint was first trained for 15,000 steps on laion 6a resized to a max minimum dimension of 768. one 1xA100 machine (Thanks a lot HF🤗 to provide the compute!) Data parallel with a single gpu batch size of 2 with gradient accumulation 8. Constant learning rate of 8e-5 fp16