Augmented CLIP - Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.

Related tags

Deep Learning Augmented_CLIP
Overview

Train aug_clip against laion400m-embeddings found here: https://laion.ai/laion-400-open-dataset/ - note that this used the base ViT-B/32 CLIP model.

download - 2022-01-02T144212 746

Sample notebook adapted from Sadnow's 360Diffusion repo, thanks to all involved!

Latest revision: Beta 1.52 (10/11/21): https://colab.research.google.com/github/sadnow/360Diffusion/blob/main/360Diffusion_Public.ipynb

Latest highlights: Full compatibility for both 256 and 512 model for upscaling to 256,512,1024,2048, and 4096px.

Note that 4096 files aren’t quite as pretty as 2048, and they’re massive in file size. 2048 is appealing in most cases. If you intend on upscaling to anything higher than 1024, I recommend using the 512 diffusion model found in the settings-

Credits & Acknowledgements

Prior release(s): Implemented Daniel Russ’s Perlin revisions, fixed init_bug, 4096 double-pass, VRAM fixes, practical debug_mode (set to higher skip_timestep)

All edits & additions are welcome and appreciated~

You might also like...
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption, CVPR 2021 (Oral)
TAP: Text-Aware Pre-training for Text-VQA and Text-Caption, CVPR 2021 (Oral)

TAP: Text-Aware Pre-training TAP: Text-Aware Pre-training for Text-VQA and Text-Caption by Zhengyuan Yang, Yijuan Lu, Jianfeng Wang, Xi Yin, Dinei Flo

Microsoft 61 Nov 14, 2022
This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models are Pix2Pix, Pix2PixHD, CycleGAN and PointWise.

RGB2NIR_Experimental This repository contains several image-to-image translation models, whcih were tested for RGB to NIR image generation. The models

null 5 Jan 4, 2023
CLIP (Contrastive Language–Image Pre-training) trained on Indonesian data

CLIP-Indonesian CLIP (Radford et al., 2021) is a multimodal model that can connect images and text by training a vision encoder and a text encoder joi

Galuh 17 Mar 10, 2022
A Jupyter notebook to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

A Jupyter notebook to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

Eugenio Herrera 175 Dec 29, 2022
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

Object Pose Estimation Demo This tutorial will go through the steps necessary to perform pose estimation with a UR3 robotic arm in Unity. You’ll gain

Unity Technologies 187 Dec 24, 2022
CLIP+FFT text-to-image
CLIP+FFT text-to-image

Aphantasia This is a text-to-image tool, part of the artwork of the same name. Based on CLIP model, with FFT parameterizer from Lucent library as a ge

vadim epstein 690 Jan 2, 2023
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized

VQGAN-CLIP-Docker About Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized This is a stripped and minimal dependency repository for running loca

Kevin Costa 73 Sep 11, 2022
PyTorch implementation of
PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)

PyTorch implementation of Conformer: Convolution-augmented Transformer for Speech Recognition. Transformer models are good at capturing content-based

Soohwan Kim 565 Jan 4, 2023
Styled Augmented Translation
Styled Augmented Translation

SAT Style Augmented Translation Introduction By collecting high-quality data, we were able to train a model that outperforms Google Translate on 6 dif

null 139 Dec 29, 2022
Comments
  • Cool idea, how have your results been?

    Cool idea, how have your results been?

    Sorry for posting an issue for this but was really curious about what the results are like :)

    I tried out your version to compare against a non-augmented embedding without diffusion/vqgan to see the "raw" results but I find it's not directly obvious with these things to see what works "better"

    opened by samedii 3
Owner
Peter Baylies
Peter Baylies
GitHub
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP

CLIP-GEN [简体中文][English] 本项目在萤火二号集群上用 PyTorch 实现了论文 《CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP》。 CLIP-GEN 是一个 Language-F

null 75 Dec 29, 2022
Python package to generate image embeddings with CLIP without PyTorch/TensorFlow

imgbeddings A Python package to generate embedding vectors from images, using OpenAI's robust CLIP model via Hugging Face transformers. These image em

Max Woolf 81 Jan 4, 2023
[EMNLP 2021] Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

RoSTER The source code used for Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training, p

Yu Meng 60 Dec 30, 2022
CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

CLIP (Contrastive Language–Image Pre-training) Experiments (Evaluation) Model Dataset Acc (%) ViT-B/32 (Paper) CIFAR100 65.1 ViT-B/32 (Our) CIFAR100 6

Myeongjun Kim 52 Jan 7, 2023
Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

null 458 Jan 2, 2023
Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

This codebase is being actively maintained, please create and issue if you have issues using it Basics All data files are included under losses and ea

J K Terry 32 Nov 9, 2021
Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)

Deep Daze mist over green hills shattered plates on the grass cosmic love and attention a time traveler in the crowd life during the plague meditative

Phil Wang 4.4k Jan 3, 2023
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN.

Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU.

Phil Wang 2.3k Jan 9, 2023
PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence) and pre-trained model on ImageNet dataset

Reference-Based-Sketch-Image-Colorization-ImageNet This is a PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization usin

Yuzhi ZHAO 11 Jul 28, 2022
Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in Pytorch

Retrieval-Augmented Denoising Diffusion Probabilistic Models (wip) Implementation of Retrieval-Augmented Denoising Diffusion Probabilistic Models in P

Phil Wang 55 Jan 1, 2023