ControlNet SDXL Diffusers Depth is a specialized AI model designed to add conditional control to Stable Diffusion XL (SDXL) image generation through the use of depth maps. It represents a significant advancement in controlled image generation, building upon the architecture of the ControlNet family while specifically focusing on depth-based image manipulation.
Architecture and Training
The model exists in multiple variants: diffusers_xl_depth_full.safetensors, diffusers_xl_depth_mid.safetensors, and diffusers_xl_depth_small.safetensors. These variants offer different size-performance tradeoffs, making the technology accessible to users with varying computational resources. The model was trained on a comprehensive dataset combining depth maps from multiple sources, including Midas, Leres, and Zoe depth maps at multiple resolutions (256, 384, and 512 pixels).
The architecture employs data augmentation techniques to improve robustness and reduce overfitting to specific depth estimation methods. This includes random left-right flipping and other augmentation strategies that help create a more generalized model. The training process utilized an unbiased dataset approach, which helped minimize the model's tendency to overfit to particular depth estimation techniques.
Features and Capabilities
The model processes depth maps, which are grayscale images representing distance from the camera, through specialized preprocessors (annotators) before feeding them into the ControlNet model. Several depth preprocessors are available, including:
depth_midasdepth_zoedepth_leres++depth_leres
Each preprocessor offers different approaches to depth information extraction and representation, affecting the final output quality. The model supports multiple control modes:
- Balanced
- My prompt is more important
- ControlNet is more important
These modes allow users to fine-tune the balance between the text prompt and the depth map's influence on the generated image. As detailed in the Civitai guide, users can adjust parameters such as "Control Weight," "Starting Control Step," and "Ending Control Step" to precisely control the depth map's influence during the diffusion process.
Implementation and Technical Requirements
The model is supported by the sd-webui-controlnet extension (version 1.1.400 and later) and has been tested across various VRAM configurations. For optimal performance:
- 8GB-16GB VRAM: Use
--medvram-sdxl
- Less than 8GB VRAM: Use
--lowvram
The extension implements a smart resampling algorithm that ensures pixel-perfect control images regardless of resolution. Multiple ControlNet instances can be used simultaneously, allowing for complex control over image generation through the combination of different control types.
References
- ControlNet Repository - Main repository for ControlNet implementation
- Hugging Face ControlNet Model Collection - Download location for model variants
- SD-WebUI-ControlNet Discussion - Technical implementation details and usage guidelines
- ControlNet v1.1 Documentation - Detailed technical documentation and model specifications
- Civitai Guide to ControlNet - User guide and practical implementation examples
- ComfyUI FAQ - Additional implementation guidance and troubleshooting
- StabilityAI Control-LoRA - Information about related Control-LoRA implementations
