Personalized Image Generation

ID-Patch can generate personalized group photos by matching faces with specific positions. It reduces problems like identity leakage and visual errors, achieving high accuracy and speed—seven times faster than other methods.

SISO can generate and edit images using just one subject image without any training. It improves image quality, keeps the subject clear, and preserves the background better than other methods.

InfiniteYou can generate high-quality images with FLUX and retain a person’s identity.

Dream Engine can generate images by combining different concepts from reference images.

ImageRAG can find relevant images based on a text prompt to improve image generation. It helps create rare and detailed concepts without needing special training, making it useful for different image models.

MIGE can generate images from text prompts and reference images and edit existing images based on instructions.

Leffa can generate person images based on reference images, allowing for precise control over appearance and pose.

MagicTailor can reuse specific parts of images in text-to-image diffusion models. It improves image quality and keeps the subject’s identity clear while reducing semantic pollution.

DisEnvisioner can generate customized images from a single visual prompt and extra text instructions. It filters out irrelevant details and provides better image quality and speed without needing extra tuning.

UniPortrait can customize images of one or more people with high quality. It allows for detailed face editing and uses free-form text descriptions to guide changes.

TweedieMix can generate images and videos that combine multiple personalized concepts.

OmniBooth can generate images with precise control over their layout and style. It allows users to customize images using masks and text or image guidance, making the process flexible and personal.

ProCreate boosts the diversity and creativity of diffusion-based image generation while avoiding the replication of training data. By pushing generated image embeddings away from reference images, it improves the quality of samples and lowers the risk of copying copyrighted content.

StoryMaker can generate a series of images with consistent characters across multiple images. It keeps the same facial features, clothing, hairstyles, and body types, allowing for cohesive storytelling.

TextBoost can enable one-shot personalization of text-to-image models by fine-tuning the text encoder. It generates diverse images from a single reference image while reducing overfitting and memory needs.

MagicFace can generate high-quality images of people in any style without needing extra training.

MagicFace can generate high-quality images of people in any style without needing training. It uses special attention methods for precise attribute alignment and feature injection, working for both single and multi-concept customization.

ViPer can personalize image generation by capturing individual user preferences through a one-time commenting process on a selection of images. It utilizes these preferences to guide a text-to-image model, resulting in generated images that align closely with users’ visual tastes.

MasterWeaver can generate photo-realistic images from a single reference image while keeping the person’s identity and allowing for easy edits. It uses an encoder to capture identity features and a unique editing direction loss to improve text control, enabling changes to clothing, accessories, and facial features.

IMAGDressing-v1 can generate human try-on images from input garments. It is able to control different scenes through text and can be combined with IP-Adapter and ControlNet pose to enhance the diversity and controllability of generated images.