Segmind FaceSwap Comic v1

Segmind FaceSwap Comic v1 is a specialized face-swapping model designed to maintain the artistic integrity of illustrated or stylized target images—making it ideal for use cases like personalized storybooks for children. This model allows users to transfer a face from a real photo onto a drawn or cartoon-style target image while preserving the original’s visual style.

It supports optional text prompts to guide expressions or facial details (e.g., “happy,” “eyes closed”), which improves alignment in stylized compositions.

Using parameters effectively

Optional prompt that can be used to guide facial expressions or moods (such as happy, crying, sleeping, or closed eyes). While it's not mandatory, adding a descriptive prompt can help improve results, especially when the face swap doesn't align well with the expected outcome.

The face_strength parameter controls how strongly the source face is preserved in the final image. A higher value ensures that the generated face closely matches the source, but may override the target image's artistic or stylized look. In cases where the target image is a comic or illustration, using a lower face strength can help blend the styles better.

The style_strength influences how much the output should adapt to the target image's visual style. A higher value makes the final result more consistent with the target’s style, which is useful for cartoonized or illustrated faces. Lower values favor realism from the source.

CFG (Classifier-Free Guidance) affects how strongly the model adheres to the internal style logic, and also influences the brightness and tone of the output skin. A higher CFG scale can result in darker skin tones, while lower values may keep the tones lighter and closer to the original.

Pipeline Limitations

The model currently supports only single-face swapping: both the source and target images should ideally have just one prominent face. If the target contains multiple faces, the model may randomly apply the swap to all or fail to detect the intended face properly.

It is strongly recommended to match gender between source and target images. While the model can technically swap any face, female-specific hairstyles like buns, braids, or ribbons may not carry over accurately onto male faces and vice versa.

The model generally handles accessories like eyeglasses, beards, and facial structure well. However, in cases of small or distant faces, it might fail to classify the gender or features correctly. This can be mitigated by providing an explicit description in the prompt.