Image AI Tools

MuDI can generate high-quality images of multiple subjects without mixing their identities. It has a 2x higher success rate for personalizing images and is preferred by over 70% of users in evaluations.

NeRF2Physics can predict the physical properties (mass, friction, hardness, thermal conductivity and Young’s modulus) of objects from a collection of images. This makes it possible to simulate the physical behavior of digital twins in a 3D scene.

LCM-Lookahead is another attempted LoRA killer with an LCM-based approach for identity transfer in text-to-image generations.

InstantStyle can separate style and content from images in text-to-image generation without tuning. It improves visual style by using features from reference images while keeping text control and preventing style leaks.

CosmicMan can generate high-quality, photo-realistic human images that match text descriptions closely. It uses a unique method called Annotate Anyone and a training framework called Decomposed-Attention-Refocusing (Daring) to improve the connection between text and images.

Following spatial instructions in text-to-image prompts is hard! SPRIGHT-T2I can finally do it though, resulting in more coherent and accurate compositions.

ID2Reflectance can generate high-quality facial reflectance maps from a single image.

Learning Inclusion Matching for Animation Paint Bucket Colorization can colorize line art in animations by allowing artists to colorize just one frame. The algorithm then automatically applies the color to the rest of the frames, using a learning-based inclusion matching pipeline for more accurate results.

PAID is a method that enables smooth high consistency image interpolation for diffusion models. GANs have been the king in that field so far, but this method shows promising results for diffusion models.

Attribute Control enables fine-grained control over attributes of specific subjects in text-to-image models. This lets you modify attributes like age, width, makeup, smile and more for each subject independently.

FlashFace can personalize photos by using one or a few reference face images and a text prompt. It keeps important details like scars and tattoos while balancing text and image guidance, making it useful for face swapping and turning virtual characters into real people.

ReNoise can be used to reconstruct an input image that can be edited using text prompts.

FouriScale can generate high-resolution images from pre-trained diffusion models with various aspect ratios and achieve an astonishing capacity of arbitrary-size, high-resolution, and high-quality generation.

You Only Sample Once can quickly create high-quality images from text in one step. It combines diffusion processes with GANs, allows fine-tuning of pre-trained models, and works well at higher resolutions without extra training.

StyleSketch is a method for extracting high-resolution stylized sketches from a face image. Pretty cool!

Desigen can generate high-quality design templates, including background images and layout elements. It uses advanced diffusion models for better control and has been tested on over 40,000 advertisement banners, achieving results similar to human designers.

DEADiff can synthesize images that combine the style of a reference image with text prompts. It uses a Q-Former mechanism to separate style and meaning.

ELLA is a lightweight approach to equip existing CLIP-based diffusion models with LLMs to improve prompt-understanding and enables long dense text comprehension for text-to-image models.

The PixArt model family got a new addition with PixArt-Σ. The model is capable of directly generating images at 4K resolution. Compared to its predecessor, PixArt-α, it offers images of higher fidelity and improved alignment with text prompts.

ResAdapter can generate images with any resolution and aspect ratio for diffusion models. It works with various personalized models and processes images efficiently, using only 0.5M parameters while keeping the original style.