What Is ControlNet?
ControlNet is a revolutionary extension that adds additional control layers to the Stable Diffusion image generation process. Developed by Lvmin Zhang in 2023, this technology allows you to control visual structure, pose, depth, and edge lines beyond text prompts. As a result, instead of generating random images, you can create images that preserve the composition, position, and structure you want.
To use ControlNet, you need an AUTOMATIC1111 WebUI or ComfyUI installation and must add the ControlNet extension. You can download models from Hugging Face; each control mode requires a separate model file.
Canny Edge Detection
Canny mode detects edge lines in the source image and ensures the new image conforms to these lines. This mode is especially useful for:
- **Architectural drawings:** Applying different styles while preserving the outline of a building or room - **Character redrawing:** Reproducing a character in different styles while preserving their general form - **Logo vectorization:** Generating professional visuals while preserving a drawing's structure
Canny parameters: - **Low threshold (40-100):** Lower values capture more detail - **High threshold (100-200):** Higher values only capture prominent edges - **Control weight (0.5-1.5):** ControlNet influence; 1.0 is default, lower values give freer results
OpenPose: Human Pose Control
The OpenPose model detects key points of the human body (joints, face, hands) to allow you to generate different characters in the same pose. It is particularly strong in these scenarios:
- Fashion shoots: Trying different outfits and styles in a specific pose - Action scenes: Producing dynamic poses in a repeatable manner - Group portraits: Controlling the positions of multiple people
OpenPose usage steps: 1. Upload your reference image (photo or drawing) 2. Select "openpose_full" as preprocessor (including face and hands) 3. Check in preview that the pose was detected correctly 4. Describe the character's appearance in your prompt 5. Generate the result
**Tip:** You can manually edit the stick figure using a pose editor and create the desired pose from scratch.
Depth Map: Depth Control
Depth mode extracts a map of objects' distances from the camera and ensures the new image preserves the same depth structure. Use cases:
- **Background replacement:** Changing scenes while preserving foreground-background separation - **Style transfer:** Converting a photo to painting or illustration without distorting 3D structure - **Scene consistency:** Generating versions of the same location at different time periods
Depth preprocessor options: - **MiDaS:** General purpose, sufficient for most scenes - **Zoe:** More precise depth estimates - **LeReS:** Optimized for interior scenes
Scribble and Other Modes
**Scribble:** Transforms a rough hand-drawn sketch into a professional image. You can make simple drawings with a tablet or mouse and turn them into fully detailed images. Perfect for concept generation at the very beginning of the design process.
**Lineart:** Provides more precise line art control than Scribble. Ideal for manga, comics, and technical illustration.
**Segmentation:** Divides the image into semantic regions (sky, building, road, tree, etc.) to control what each region will be. Very useful in urban planning and landscape design.
**Tile:** Used for detail enhancement and upscaling by dividing the image into small regions. Ideal for quality upscaling of low-resolution images.
Multi-ControlNet: Multiple Controls
You achieve the real power by using multiple ControlNet models simultaneously. For example:
- Depth + OpenPose: Control both scene depth and character pose - Canny + Tile: Detail enhancement while preserving edge structure - Scribble + Depth: Generate a scene with depth from a rough sketch
When using Multi-ControlNet, adjusting each model's weight is important. Generally, one control should be primary (weight: 1.0) and the other secondary (weight: 0.5-0.7). Find the best balance through trial and error to prevent conflicts.
Performance and Optimization
ControlNet requires additional VRAM. A single ControlNet runs comfortably on cards with 8GB VRAM, while 12GB+ is recommended for Multi-ControlNet. If you experience memory issues:
- Enable xFormers or Flash Attention - Keep image size at 512x512 - Activate ControlNet's "low_vram" mode - Lower the preprocessor resolution