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Rice University
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This repository contains the implementation for the paper "AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA", accepted by ICML 2024.
[ICLR2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
helper functions for processing and integrating visual language information with Qwen-VL Series Model
Chromium Embedded Framework (CEF). A simple framework for embedding Chromium-based browsers in other applications.
Official implementation of paper AdaReTaKe: Adaptive Redundancy Reduction to Perceive Longer for Video-language Understanding
SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models
Proof of concept on how to implement the Reed Solomon class of error correcting codes in Python
⏳🛡 Pythonic universal errors-and-erasures Reed-Solomon codec to protect your data from errors and bitrot. Includes a future-proof zero-dependencies pure-python implementation 🔮 and an optional spee…
Tensors and Dynamic neural networks in Python with strong GPU acceleration
An open source re-implementation of RollerCoaster Tycoon 2 🎢
(2024CVPR) MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
Open deep learning compiler stack for cpu, gpu and specialized accelerators
Artifact from "Hardware Compute Partitioning on NVIDIA GPUs". THIS IS A FORK OF BAKITAS REPO
[ICML 2024] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference
Disaggregated serving system for Large Language Models (LLMs).
Home of the WebKit project, the browser engine used by Safari, Mail, App Store and many other applications on macOS, iOS and Linux.
The official GitHub mirror of the Chromium source
[CVPR 2025] Adaptive Keyframe Sampling for Long Video Understanding
Cource project of Comp 646
[CVPR 2025] Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Pytorch implementation of paper "HiDDeN: Hiding Data With Deep Networks" by Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei
Code of the paper: A Recipe for Watermarking Diffusion Models