ControlNet SD 1.5 Segmentation

ControlNet SD 1.5 Segmentation is a neural network model that adds conditional control to Stable Diffusion 1.5 text-to-image generation. It's part of the larger ControlNet 1.1 family, which includes fourteen different control models designed to guide image generation through various input sources. The model was developed based on research detailed in "Adding Conditional Control to Text-to-Image Diffusion Models".

Architecture and Training

The model leverages Stable Diffusion 1.5 as its base architecture and was trained on a combination of COCO and ADE20K datasets. Version 1.1 introduced significant improvements over its predecessor, notably adding support for both ADE20K and COCO annotation protocols, which enhanced the model's overall performance and versatility. The model accepts semantic segmentation maps as input, allowing precise control over object boundaries and classifications in the generated images.

A notable advancement in the ControlNet family is the Control-LoRA variant, which offers a parameter-efficient implementation. While the original ControlNet models are approximately 4.7GB in size, Control-LoRA versions reduce this significantly - Rank 256 variants are about 738MB, while Rank 128 versions are roughly 377MB, making them more accessible for consumer GPUs.

Features and Capabilities

The model's primary feature is its ability to process various types of segmentation maps through multiple preprocessors:

• Seg_OFADE20K
• Seg_OFCOCO
• Seg_UFADE20K
• Manually created masks

ControlNet SD 1.5 Segmentation can be used in conjunction with other ControlNet models through the multi-ControlNet feature, allowing for complex control over image generation. The "Pixel Perfect" resampling algorithm ensures that input control images maintain their detail and resolution during the upscaling process, regardless of the target image resolution.

Implementation and Usage

The model is primarily implemented through two main interfaces:

1. Automatic1111's Stable Diffusion WebUI (via the sd-webui-controlnet extension)
2. ComfyUI

Performance may vary depending on available VRAM, and users can optimize performance using command-line flags such as --medvram-sdxl and --lowvram. The model can be used alongside other ControlNet variants, though compatibility testing is recommended, particularly with extensions like Deforum.

Reference Links

• ControlNet Github Repository - Main project repository
• Automatic1111 ControlNet Extension - WebUI integration
• ControlNet 1.1 Models - Download page for various ControlNet 1.1 models
• HuggingFace Model Page - Official model downloads
• Research Paper - Original research documentation
• Control-LoRA Models - Parameter-efficient implementations
• ComfyUI Documentation - Alternative interface documentation