johnowhitaker / imstack
Optimizable stack of images at different resolutions, a useful representation of images for deep learning tasks. Docs: https://johnowhitaker.github.io/imstack/
☆11Updated 2 years ago
Alternatives and similar repositories for imstack:
Users that are interested in imstack are comparing it to the libraries listed below
- Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.☆60Updated 3 years ago
- Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP☆39Updated 2 years ago
- CLOOB training (JAX) and inference (JAX and PyTorch)☆71Updated 2 years ago
- CLOOB Conditioned Latent Diffusion training and inference code☆113Updated 3 years ago
- High-Resolution Image Synthesis with Latent Diffusion Models☆61Updated 3 years ago
- Finetune the 1.4B latent diffusion text2img-large checkpoint from CompVis using deepspeed. (work-in-progress)☆36Updated 3 years ago
- Experimental LDM uses of Paella's architecture☆34Updated 2 years ago
- Inverts CLIP text embeds to image embeds and visualizes with deep-image-prior.☆35Updated 2 years ago
- Visual Taste Approximator (VTA) is a very simple tool that helps anyone create an automatic replica of themselves that can approximate th…☆39Updated 2 years ago
- Yet Another Diffusion Automation☆13Updated 2 years ago
- checkpoints for glide finetuned on laion and other datasets. wip.☆50Updated 2 years ago
- Script and models for clustering LAION-400m CLIP embeddings.☆26Updated 3 years ago
- ☆28Updated 3 years ago
- openai guided diffusion tweaks☆52Updated 2 years ago
- Gradient-Free Textual Inversion for Personalized Text-to-Image Generation☆41Updated 2 years ago
- Image restoration with neural networks but without learning.☆46Updated 2 years ago
- ESGD-M is a stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch.☆56Updated 2 years ago
- The implementation for Accelerating Guided Diffusion Sampling with Splitting Numerical Methods (2023)☆48Updated 2 years ago
- Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt☆137Updated last year
- Official repository for MaGNET, ICLR 2022☆24Updated 2 years ago
- ☆31Updated last year
- GPU accelerated Perlin Noise in python☆9Updated 4 years ago
- ☆56Updated 2 years ago
- Refactoring dalle-pytorch and taming-transformers for TPU VM☆60Updated 3 years ago
- CLIP Guided Diffusion☆61Updated last year
- Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch☆89Updated 3 years ago
- [NeurIPS 2022: Score-Based Modeling Workshop] Multiresolution Textual Inversion☆99Updated 2 years ago
- CLIP and PASTE: Using AI to Create Photo Collages from Text Prompts☆29Updated 2 years ago
- cheap views of intermediate Stable Diffusion results☆45Updated 2 years ago
- PyTorch implementation of Contrastive Feature Loss for Image Prediction (AIM Workshop at ICCV 2021)☆53Updated 3 years ago