thibaud / controlnet-openpose-sdxl-1.0

SDXL-controlnet: OpenPose (v2)


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






Comfy Workflow



(Image is from ComfyUI, you can drag and drop in Comfy to use it as workflow)
License: refers to the OpenPose's one.





Using in 🧨 diffusers


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


# Compute openpose conditioning 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)

# Initialize ControlNet pipeline.
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()


# Infer.
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:






Training


Use of the training script by HF🤗 here.





Training data


This checkpoint was first trained for 15,000 steps on laion 6a resized to a max minimum dimension of 768.





Compute


one 1xA100 machine (Thanks a lot HF🤗 to provide the compute!)





Batch size


Data parallel with a single gpu batch size of 2 with gradient accumulation 8.





Hyper Parameters


Constant learning rate of 8e-5





Mixed precision


fp16