Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion

[TPAMI'23] Unifying Flow, Stereo and Depth Estimation

Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️

T-GATE: Temporally Gating Attention to Accelerate Diffusion Model for Free!

Implementation of CALM from the paper "LLM Augmented LLMs: Expanding Capabilities through Composition", out of Google Deepmind

🚀 Cross attention map tools for huggingface/diffusers

1-shot image segmentation using Stable Diffusion

Code on selecting an action based on multimodal inputs. Here in this case inputs are voice and text.

The official repository of "Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models".

A lightweight PyTorch implementation of the Transformer-XL architecture proposed by Dai et al. (2019)

[ITSC-2023] HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection

Tensorflow implementation of 'Robust Image Watermarking based on Cross-Attention and Invariant Domain Learning'

This is the implementation of the paper Enhanced Photovoltaic Power Forecasting: An iTransformer and LSTM-Based Model Integrating Temporal and Covariate Interactions

TGRS: Code for "Unsupervised Hybrid Network of Transformer and CNN for Blind Hyperspectral and RGB Image Fusion"

This model synthesises high-fidelity fashion videos from single images featuring spontaneous and believable movements.

Segment-Like-Me: 1-shot image segmentation using Stable Diffusion

Transcription factor binding site prediction for novel DNA sequence data aiding in mutation identification and drug discovery

3D Human-Object Interaction in Video A New Approach to Object Tracking via Cross-Modal Attention

[ISMB 2024] Official PyTorch Code for "PhiHER2: Phenotype-informed weakly supervised model for HER2 status prediction from WSIs"

Official source code of the paper: "Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson’s Diagnosis Support"