Image AI Tools
Free image AI tools for generating and editing visuals, creating 3D assets for games, films, and more, optimizing your creative projects.
FlipSketch can generate sketch animations from static drawings by allowing users to describe the desired motion. It uses motion priors from text-to-video diffusion models to create smooth animations while keeping the original sketch’s look.
StyleCodes can encode the style of an image into a 20-symbol base64 code for easy sharing and use in image generation. It allows users to create style-reference codes (srefs) from their own images, helping to control styles in diffusion models with high quality.
SGEdit can add, remove, replace, and adjust objects in images while keeping the quality of the image consistent.
MagicQuill enables efficient image editing with a simple interface that lets users easily insert elements and change colors. It uses a large language model to understand editing intentions in real time, improving the quality of the results.
RayGauss can create realistic new views of 3D scenes, using Gaussian-based ray casting! It produces high-quality images quickly, running at 25 frames per second, and avoids common picture problems that older methods had.
Regional-Prompting-FLUX adds regional prompting capabilities to diffusion transformers like FLUX. It effectively manages complex prompts and works well with tools like LoRA and ControlNet.
ZIM can generate precise matte masks from segmentation labels, enabling zero-shot image matting.
Face Anon can anonymize faces in images while keeping original facial expressions and head positions. It uses diffusion models to achieve high-quality image results and can also perform face swapping tasks.
ScalingConcept can enhance or suppress existing concepts in images and audio without adding new elements. It can generate poses, enhance object stitching and reduce fuzziness in anime productions.
ControlAR adds controls like edges, depths, and segmentation masks to autoregressive models like LlamaGen.
State of the art diffusion models are trained on square images. FiT is a new transformer architecture specifically designed for generating images with unrestricted resolutions and aspect ratios (similar to what Sora does). This enables a flexible training strategy that effortlessly adapts to diverse aspect ratios during both training and inference phases, thus promoting resolution generalization and eliminating biases induced by image cropping.
From Text to Pose to Image can generate high-quality images from text prompts by first creating poses and then using them to guide image generation. This method improves control over human poses and enhances image fidelity in diffusion models.
FreCaS can generate high-resolution images quickly using a method that breaks the process into stages with increasing detail. It is about 2.86× to 6.07× faster than other tools for creating 2048×2048 images and improves image quality significantly.
Stable-Hair can robustly transfer a diverse range of real-world hairstyles onto user-provided faces for virtual hair try-on. It employs a two-stage pipeline that includes a Bald Converter for hair removal and specialized modules for high-fidelity hairstyle transfer.
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.
HART is an autoregressive transformer model that can generate high-quality 1024x1024 images from text 3x times faster than SD3-Medium.
EfficientViT can speed up high-resolution diffusion models by compressing data with a ratio of up to 128 while keeping good image quality. It achieves a 19.1x speed increase for inference and a 17.9x speed increase for training on ImageNet 512x512 compared to other autoencoders.
CtrLoRA can adapt a base ControlNet for image generation with just 1,000 data pairs in under one hour of training on a single GPU. It reduces learnable parameters by 90%, making it much easier to create new guidance conditions.