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Detailer

Detailer is an optional workflow step that can be enabled from the menu for text, image, and control generation.

What Detailer does

Detect objects

Using selected model(s) and settings, Detailer detects objects in the image.

Settings: - Minimum confidence of detected object - Maximum number of detected objects - Max overlap between detected objects - Minimum and Maximum size of detected objects

Detail object

  1. Crop each detected object and resize it to a resolution suitable for the loaded model.
  2. Run inpaint for each cropped object using configured rules.
  3. Resize the result back to original size and place it into the original image.

Settings: - Prompt and Negative prompt
- Steps and Strength of inpaint operation
- Edge padding and Edge blur

Notes

Prompt

If detailer prompt is specified, it will be used, otherwise primary prompt will be used. In case you want to add detailer-specific prompts but also keep the primary prompt, you can use [prompt] in the detailer prompt text box to refer to it.

For example, [prompt]. blue eyes

Advanced Usage

Separating prompts

If Detailer prompt contains multiple lines, each line is used separately: - For each detailer model (in order) - For each detected object (in order)

Example with single model and 3 detected objects:

Base Prompt:
  three woman walking down the city street  
Detailer prompt:  
  pink makeup and red lipstick  
  green eyeshadow  
  blue eye contacts

If you have 2 models and 3 detected objects, the first model uses the first 3 lines (one per object), and the second model uses the next 3 lines (one per object).

The same rule applies to negative prompts.

Segmentation

Some Detailer models can output segmentation masks, not just bounding boxes. To use segmentation masks, enable Use segmentation. If the selected model does not support segmentation, SD.Next falls back to normal bounding boxes.

Tip

Models that have segmentation capabilities often have seg in the name

Multi-class models

Most object detection models are trained on one class, for example face or hands. Some models support multiple classes, and you can choose which classes to detect. yolo11m is one example of a multi-class model. When using a multi-class model, available classes are listed in logs at runtime.

Models

SD.Next includes several pre-defined single-class Detailer models: face-yolo8n, hand_yolov8n, person_yolov8n-seg, eyes-v1, eyes-full-v1
And one multi-class model: yolo11m

Additional models can be added by downloading a pretrained model and placing it in the folder specified in Settings -> System paths -> Yolo models (default: models/yolo).

Compatible models are from the YOLO model family. Models from other families, such as MediaPipe, are not supported.

Expert Mode

The model list is usually shown as a dropdown where one or more models can be selected. You can also use the button next to the list to switch to a text box for manual model input and optional parameters. Text is parsed as a model list separated by comma ,, newline \n, or semicolon ;. Each model can include parameters separated by colon :.

Example:

face-yolo8n:steps=5:denoising_strength=0.3,
eyes-v1:mask_blur=0.5
hand_yolov8n:sampler_name=DDIM

For full list of parameters, see Parameters page

Warning

Expert mode is intended for advanced use and SD.Next does not validate the input. Overriding some parameter values may result in unexpected behavior or errors