ControlNet SD 1.5 Tile

ControlNet SD 1.5 Tile is an experimental model in the ControlNet 1.1 family, designed specifically to work with Stable Diffusion 1.5. It shares the same underlying architecture as other ControlNet 1.0 models but features significant improvements in robustness and quality. The model's development is based on the research detailed in Adding Conditional Control to Text-to-Image Diffusion Models.

Key Features and Capabilities

The Tile model's primary innovation lies in its ability to process very large images (4k or 8k) by diffusing individual tiles while maintaining coherence across the entire image. This approach solves a common problem where global prompts might override local tile semantics, resulting in more consistent and high-quality outputs. The model exhibits two distinct behaviors:

1. The ability to generate new details while ignoring existing ones, which is particularly useful for fixing image imperfections such as blur from resizing
2. Prioritization of local tile context over global prompts when conflicts arise

A notable technical feature is the implementation of a smart resampling algorithm that maintains pixel-perfect input control images regardless of the output resolution. This is especially valuable when working with manually created or carefully tuned control images, as discussed in the ControlNet Tile implementation details.

Implementation and Usage

The model can be implemented through various interfaces, with the most common being the Automatic1111 ControlNet extension. For users with limited GPU resources, the model has been tested successfully on both 8GB and 6GB VRAM GPUs, with the latter requiring float16 support. Recommended command-line flags include:

• --medvram-sdxl for 8GB-16GB VRAM
• --lowvram for systems with less than 8GB VRAM

For optimal performance on 8GB GPUs, users can set save_memory = True in the configuration file. The model can be installed in either stable-diffusion-webui\extensions\sd-webui-controlnet\models or stable-diffusion-webui\models\ControlNet.

Comparison with Other ControlNet Models

Within the ControlNet 1.1 family, the Tile model stands alongside several specialized variants, including:

• Depth (MiDaS and ClipDrop)
• Canny Edge
• Normal
• Scribble
• Soft Edge
• Openpose
• Inpaint
• Instruct Pix2Pix

While these models excel in their specific use cases, the Tile model distinguishes itself through its superior handling of large images and ability to maintain consistency across tiled sections. This makes it particularly valuable for high-resolution image processing and super-resolution applications.

A lighter version of the technology is available through Stability AI's Control-LoRAs, which come in Rank 256 (~738MB) and Rank 128 (~377MB) variants, significantly reducing the model size from the original 4.7GB while maintaining performance.

References and Additional Resources

• ControlNet 1.1 Github Repository - Main repository for ControlNet 1.1 models and code
• Automatic1111 ControlNet Plugin - Plugin for implementing ControlNet in the popular Automatic1111 interface
• ControlNet Models on HuggingFace - Official model downloads
• Civitai ControlNet 1.1 Models - Community resource for ControlNet models
• ControlNet Tile Discussion - Technical discussion and examples of the Tile model's implementation
• Original Research Paper - Academic foundation for ControlNet technology