ControlNet SD 1.5 Open Pose

ControlNet SD 1.5 Open Pose is a neural network designed to add conditional control to Stable Diffusion 1.5's text-to-image generation capabilities. Based on the architecture detailed in Adding Conditional Control to Text-to-Image Diffusion Models, the model specializes in replicating subject poses from input images into newly generated images while allowing for style and color transfer.

The model is part of the ControlNet 1.1 release, which maintains architectural consistency with ControlNet 1.0 while offering significant improvements in robustness and result quality. The technology leverages preprocessors to extract pose information, creating a "skeleton" of key body points that guides the Stable Diffusion generation process.

Variants and Capabilities

Multiple variants of the OpenPose model exist, each offering different levels of detail in pose detection:

• openpose: Focuses on basic body pose detection
• openpose_face: Includes facial feature detection
• openpose_faceonly: Specializes in facial feature detection
• openpose_full: Combines body pose, hand, and facial feature detection for comprehensive pose information

The ControlNet 1.1 developers recommend using primarily two options for optimal results:

1. "OpenPose" for body keypoints only
2. "OpenPose Full" for complete body, hand, and face keypoints

A notable advancement in version 1.1 is the improved implementation of the OpenPose algorithm, resulting in more accurate keypoint detection, particularly for hand poses. The model has also benefited from cleaned training data, addressing issues present in version 1.0 such as duplicate images and low-quality data.

Technical Implementation and Integration

The model can be implemented through various interfaces, with official support for combining multiple ControlNets limited to the Automatic1111 Stable Diffusion web UI. The extension includes a smart resampling algorithm that maintains pixel-perfect control images regardless of resolution changes, distinguishing it from other implementations.

For systems with varying VRAM capabilities:

• 8GB to 16GB VRAM: Use the --medvram-sdxl command-line flag
• Less than 8GB VRAM: Use the --lowvram flag

The model can be deployed in two directory locations:

• stable-diffusion-webui\extensions\sd-webui-controlnet\models
• stable-diffusion-webui\models\ControlNet

A more efficient Low-Rank Adaptation (LoRA) version has been developed by Stability AI, significantly reducing the model size:

• Original ControlNet models: 4.7GB
• Rank 256 LoRA version: ~738MB
• Experimental Rank 128 version: ~377MB

References

• ControlNet Github Repository: Main project repository
• Automatic1111 ControlNet Extension: Essential extension for web UI implementation
• Civitai Model Page for ControlNet 1.1 Models: Access to model variants
• HuggingFace ControlNet Model Page: Official model downloads
• Multi-ControlNet Documentation: Guide for using multiple ControlNets simultaneously
• ComfyUI: Alternative interface for model implementation
• StableSwarmUI: Additional implementation interface
• Stability AI Custom Nodes: Extended functionality for ComfyUI