Yazhou Xing's starred repositories
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Grounded-Segment-Anything
Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
ML-Papers-of-the-Week
🔥Highlighting the top ML papers every week.
Segment-Everything-Everywhere-All-At-Once
[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
Text2Video-Zero
[ICCV 2023 Oral] Text-to-Image Diffusion Models are Zero-Shot Video Generators
VideoCrafter
A Toolkit for Text-to-Video Generation and Editing
awesome-ai-art-image-synthesis
A list of awesome tools, ideas, prompt engineering tools, colabs, models, and helpers for the prompt designer playing with aiArt and image synthesis. Covers Dalle2, MidJourney, StableDiffusion, and open source tools.
Awesome-Anything
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
StableDiffusionReconstruction
Takagi and Nishimoto, CVPR 2023
pix2pix-zero
Zero-shot Image-to-Image Translation [SIGGRAPH 2023]
ImageReward
[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation
MultiDiffusion
Official Pytorch Implementation for "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation" presenting "MultiDiffusion" (ICML 2023)
paint-with-words-sd
Implementation of Paint-with-words with Stable Diffusion : method from eDiff-I that let you generate image from text-labeled segmentation map.
SceneDreamer
[TPAMI 2023] SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections
PAIR-Diffusion
[CVPR 2024] PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor
vid2vid-zero
Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models
Taming-Stable-Diffusion-with-Human-Ranking-Feedback
This is the official repo for the paper "Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oracles", Tang et al. https://arxiv.org/abs/2303.03751