T2i Adapter Openpose Sdxl 1.0

🚀 T2I-Adapter-SDXL - Openpose

T2I Adapter is a network that provides additional conditioning to stable diffusion. Each T2I checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint.

This checkpoint offers conditioning on OpenPose for the StableDiffusionXL checkpoint. It was a collaborative effort between Tencent ARC and Hugging Face.

✨ Features

• Enables additional conditioning for Stable Diffusion models.
• Supports various types of conditioning inputs.
• Collaboratively developed by Tencent ARC and Hugging Face.

📦 Installation

To get started, first install the required dependencies:

pip install -U git+https://github.com/huggingface/diffusers.git
pip install -U controlnet_aux==0.0.7 # for conditioning models and detectors
pip install transformers accelerate safetensors

💻 Usage Examples

Basic Usage

1. Images are first downloaded into the appropriate control image format.
2. The control image and prompt are passed to the StableDiffusionXLAdapterPipeline.

Let's have a look at a simple example using the Openpose Adapter.

# Dependency
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
from controlnet_aux import OpenposeDetector
import torch
import numpy as np
from PIL import Image

# load adapter
adapter = T2IAdapter.from_pretrained(
  "TencentARC/t2i-adapter-openpose-sdxl-1.0", torch_dtype=torch.float16
).to("cuda")

# load euler_a scheduler
model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
    model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16", 
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()

open_pose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")

# Condition Image
url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/people.jpg"
image = load_image(url)
image = open_pose(image, detect_resolution=512, image_resolution=1024)
image = np.array(image)[:, :, ::-1]           
image = Image.fromarray(np.uint8(image)) 

# Generation
prompt = "A couple, 4k photo, highly detailed"
negative_prompt = "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"

gen_images = pipe(
  prompt=prompt,
  negative_prompt=negative_prompt,
  image=image,
  num_inference_steps=30,
  adapter_conditioning_scale=1,
  guidance_scale=7.5,  
).images[0]
gen_images.save('out_pose.png')

Advanced Usage

The model is trained on 3M high - resolution image - text pairs from LAION - Aesthetics V2 with specific training parameters. You can refer to the training section for more details.

📚 Documentation

Model Details

Checkpoints

Model Name	Control Image Overview	Control Image Example	Generated Image Example
[TencentARC/t2i - adapter - canny - sdxl - 1.0](https://huggingface.co/TencentARC/t2i - adapter - canny - sdxl - 1.0) Trained with canny edge detection	A monochrome image with white edges on a black background.
[TencentARC/t2i - adapter - sketch - sdxl - 1.0](https://huggingface.co/TencentARC/t2i - adapter - sketch - sdxl - 1.0) Trained with PidiNet edge detection	A hand - drawn monochrome image with white outlines on a black background.
[TencentARC/t2i - adapter - lineart - sdxl - 1.0](https://huggingface.co/TencentARC/t2i - adapter - lineart - sdxl - 1.0) Trained with lineart edge detection	A hand - drawn monochrome image with white outlines on a black background.
[TencentARC/t2i - adapter - depth - midas - sdxl - 1.0](https://huggingface.co/TencentARC/t2i - adapter - depth - midas - sdxl - 1.0) Trained with Midas depth estimation	A grayscale image with black representing deep areas and white representing shallow areas.
[TencentARC/t2i - adapter - depth - zoe - sdxl - 1.0](https://huggingface.co/TencentARC/t2i - adapter - depth - zoe - sdxl - 1.0) Trained with Zoe depth estimation	A grayscale image with black representing deep areas and white representing shallow areas.
[TencentARC/t2i - adapter - openpose - sdxl - 1.0](https://huggingface.co/TencentARC/t2i - adapter - openpose - sdxl - 1.0) Trained with OpenPose bone image	A [OpenPose bone](https://github.com/CMU - Perceptual - Computing - Lab/openpose) image.

Training

Our training script was built on top of the official training script that we provide here.

The model is trained on 3M high - resolution image - text pairs from LAION - Aesthetics V2 with:

• Training steps: 35000
• Batch size: Data parallel with a single gpu batch size of 16 for a total batch size of 256.
• Learning rate: Constant learning rate of 1e - 5.
• Mixed precision: fp16

📄 License

This model is licensed under the Apache 2.0 license.