Lists (2)
Stars
A latent text-to-image diffusion model
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
This repository contains implementations and illustrative code to accompany DeepMind publications
High-Resolution Image Synthesis with Latent Diffusion Models
PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)
The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate images with image prompt.
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
Hunyuan-DiT : A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
Official Pytorch Implementation for "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation" presenting "MultiDiffusion" (ICML 2023)
Representation Engineering: A Top-Down Approach to AI Transparency
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-…
Language-Driven Semantic Segmentation
Official Implementation for "Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models" (SIGGRAPH 2023)
A python wrapper for the Visual Genome API
Probing the representations of Vision Transformers.
Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
Open source traditional chinese handwriting dataset.
An end-to-end signature verification system to extract, clean and verify signatures in documents. Signatures are detected using YOLOv5. Noise is cleaned using a CycleGAN approach and verified. Kera…
Unified Concept Editing in Diffusion Models
Framework code with wandb, checkpointing, logging, configs, experimental protocols. Useful for fine-tuning models or training from scratch, and testing them on a variety of datasets (transfer learn…
[NeurIPS 2022] Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers t…