Types and Applications of ControlNet
Stable Diffusion’s ControlNet is a powerful tool that allows for fine-grained control over the image generation process. This article provides an in-depth explanation of each ControlNet type available and their respective applications. Discover what functions each type offers and in what scenarios they are useful.
1. Canny Edge Detection
- Description: Canny Edge Detection detects curves and straight outlines in the input image and generates images based on them. While it is sensitive to noise, it fully replicates shapes and patterns.
- Usage: Useful for generating images with clear boundaries or retaining the details of outlines.
- Example: Used to emphasize the outlines of cartoon characters.
- Preprocessing: Outline extraction
- Result: Shape and pattern replication
2. Depth Estimation
- Description: Uses depth information from the input image to add 3D effects or adjust depth perception. It generally replicates shapes well, but patterns may differ.
- Usage: Ideal for generating images that emphasize depth and perspective.
- Example: Used to add depth to landscape photos, making them more realistic.
- Preprocessing: Depth extraction (Midas, LeReS, LeReS++, Zoe)
- Result: Shapes are well replicated, patterns may change
3. IP-Adapter
- Description: An image processing adapter that preserves the overall mood of the generated image using the input image as a prompt.
- Usage: Useful for adjusting the color, brightness, and contrast of an image.
- Example: Used to change the tone of an image to create a specific mood.
- Preprocessing: Image prompt
- Result: Overall mood of the image is preserved
4. Inpaint
- Description: Provides the ability to overwrite or modify specific parts of an image. When using img2img inpainting, it enhances the overall consistency of the image.
- Usage: Used to partially modify an image or correct defects.
- Example: Used to remove unwanted elements from a photo and replace them with other elements.
- Preprocessing: Image modification
- Result: High consistency in edits
5. Instant-ID
- Description: Identifies specific objects within an image and controls the image based on this identification.
- Usage: Useful for emphasizing or transforming the characteristics of specific objects through object recognition.
- Example: Used to identify a face in a portrait and change the background.
6. Instruct P2P
- Description: Allows users to give commands in text to transform or control the image. For example, commands like “turn him into a cyborg” can be used.
- Usage: Used to directly input user commands to control the details of the image.
- Example: Used to modify specific elements of an image with commands like “make the sky bluer.”
- Preprocessing: Command input
- Result: Image edited according to the command
7. Lineart
- Description: Extracts the line art from the input image and generates images based on it. Supports various styles.
- Usage: Useful for generating images in cartoon or illustration styles.
- Example: Used to create a colored image based on a sketch of a cartoon character.
- Preprocessing: Outline detection
- Result: Converted to line art form (Line art anime, Line art anime denoise, Line art coarse, Line art realistic, Line art standard)
8. MLSD (Multiple Layer Stroke Detection)
- Description: Quickly detects straight lines and controls the image by detecting various layers in the input image. It is advantageous for detecting outlines of interiors, buildings, streets, etc.
- Usage: Useful when dealing with multiple layers in a complex image.
- Example: Used to separate layers in architectural drawings and transform them into different styles.
- Preprocessing: Straight line detection
- Result: Multiple layers detected
9. Normal Map
- Description: Emphasizes texture and surface details by using the normal map of the input image.
- Usage: Used to enhance 3D effects and emphasize texture details.
- Example: Used to realistically represent the texture of a surface in 3D modeling.
- Preprocessing: 3D shape extraction (Normal Bae, Normal Midas)
- Result: Surface details emphasized
10. OpenPose
- Description: Detects human poses and controls the image based on these poses. There are preprocessing tools available for handling faces and hands.
- Usage: Useful for generating images that emphasize specific poses or actions.
- Example: Used to detect a dancing person’s movements and change the background.
- Preprocessing: Pose detection (OpenPose, OpenPose_face, OpenPose_hand, OpenPose_faceonly, OpenPose_full)
- Result: Pose-based image generation
11. Recolor
- Description: Provides the ability to repaint or change the colors of the input image.
- Usage: Used to create a new atmosphere by adjusting the colors of an image.
- Example: Used to change the tone of a photo to create an autumn atmosphere.
12. Reference
- Description: Controls the style or composition of the generated image using a reference image. It is particularly effective when creating images similar to the reference when using the same model.
- Usage: Useful when you want to maintain a specific style or composition.
- Example: Used to create new images inspired by the style of a famous painter.
- Preprocessing: Use of reference image (reference adain, reference adain+attn, reference only)
- Result: Image similar to the reference image
13. Scribble
- Description: Generates images based on doodles drawn by the user. Supports various styles of doodle preprocessing.
- Usage: Useful for creating images with freeform shapes.
- Example: Used to create a finished image based on a simple sketch.
- Preprocessing: Doodle style conversion (scribble hed, scribble pidinet, scribble xdog)
- Result: Doodle-based image generation
14. Segmentation (Seg)
- Description: Divides the input image into multiple segments and controls the image based on these segments. It maintains the characteristics of the same type.
- Usage: Used to emphasize or transform specific parts of an image.
- Example: Used to highlight specific areas of an image for advertising purposes.
- Preprocessing: Segment classification (seg_ofade20k, seg_ofcoco, seg_uface20k)
- Result: Segment-based image generation
15. Soft Edge
- Description: Softens the curves and straight edges of an image. Supports various soft edge preprocessing tools.
- Usage: Used to create soft and natural images.
- Example: Used to soften the skin tone in portrait photos.
- Preprocessing: Soft edge processing (Softedge hed, Softedge hedsafe, Softedge pidinet, Softedge pidisafe)
- Result: Soft edge image generation
16. Shuffle
- Description: Shuffles the input image, generating a new image that only retains the colors without maintaining the original form.
- Usage: Useful for generating creative and unpredictable images.
- Example: Used to create artistic and abstract images.
- Preprocessing: Image shuffling
- Result: Original form not retained, only colors used
17. Tile Resample
- Description: Divides the image into tiles and adds detailed elements. Useful when used in conjunction with AI upscaling tools.
- [Usage
- :@b] Useful for generating tile-patterned images or emphasizing details.
- Example: Used to create mosaic-style images.
- Preprocessing: Tile splitting
- Result: Detail addition
18. Color Grid T2I Adapter
- Description: Reduces the image to 1/64th of its size and then enlarges it to create a color grid, using this grid for the average color in that region.
- Usage: Useful for creating artistic images using only colors.
- Example: Used to create art pieces with a variety of color tones.
- Preprocessing: Color grid creation
- Result: Image generated using only colors
By utilizing these various ControlNet types, users can more precisely control the style and content of the images they create. Each type is designed for specific purposes and greatly aids in creative image generation.