AI Toolbox
A curated collection of 759 free cutting edge AI papers with code and tools for text, image, video, 3D and audio generation and manipulation.





Text2Place can place any human or object realistically into diverse backgrounds. This enables scene hallucination by generating compatible scenes for the given pose of the human, text-based editing of the human and placing multiple persons into a scene.
One-DM can generate handwritten text from a single reference sample, mimicking the style of the input. It captures unique writing patterns and works well across multiple languages.
FlexiClip can generate smooth animations from clipart images while keeping key points in the right place.
Diffusion2GAN is a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference while preserving image quality. This enables one-step 512px/1024px image generation at an interactive speed of 0.09/0.16 second as well as 4k image upscaling!
LinFusion can generate high-resolution images up to 16K in just one minute using a single GPU. It improves performance on various Stable Diffusion versions and works with pre-trained components like ControlNet and IP-Adapter.
ViewCrafter can generate high-quality 3D views from single or few images using a video diffusion model. It allows for precise camera control and is useful for real-time rendering and turning text into 3D scenes.
CSGO can perform image-driven style transfer and text-driven stylized synthesis. It uses a large dataset with 210k image triplets to improve style control in image generation.
HumanVid can generate videos from a character photo while allowing users to control both human and camera motions. It introduces a large-scale dataset that combines high-quality real-world and synthetic data, achieving state-of-the-art performance in camera-controllable human image animation.
Follow-Your-Canvas can outpaint videos at higher resolutions, from 512x512 to 1152x2048.
LogoMotion can turn logos from layered PDF files into content-aware animated HTML canvas animations. Very cool!
KEEP can enhance video face super-resolution by maintaining consistency across frames. It uses Kalman filtering to improve facial details, working well on both synthetic and real-world videos.
tps-inbetween can generate high-quality intermediate frames for animation line art. It effectively connects lines and fills in missing details, even during fast movements, using a method that models keypoint relationships between frames.
STA-V2A can generate high-quality audio from videos by extracting important features and using text for guidance. It uses a Latent Diffusion Model for audio creation and a new metric called Audio-Audio Align to measure how well the audio matches the video timing.
TVG can create smooth transition videos between two images without needing training. It uses diffusion models and Gaussian Process Regression for high-quality results and adds controls for better timing.
Iterative Object Count Optimization can improve object counting accuracy in text-to-image diffusion models.
SparseCraft can reconstruct 3D shapes and appearances from just three colored images. It uses a Signed Distance Function (SDF) and a radiance field, achieving fast training times of under 10 minutes without needing pretrained models.
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.
Generative Photomontage can combine parts of multiple AI-generated images using a brush tool. It enables the creation of new appearance combinations, correct shapes and artifacts, and improve prompt alignment, outperforming existing image blending methods.
Filtered Guided Diffusion shows that image-to-image translation and editing doesn’t necessarily require additional training. FGD simply applies a filter to the input of each diffusion step based on the output of the previous step in an adaptive manner which makes this approach easy to implement.