DALLE-pytorch
dalle-mini
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DALLE-pytorch | dalle-mini | |
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20 | 3,446 | |
5,492 | 14,636 | |
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2.5 | 5.2 | |
2 months ago | 6 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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DALLE-pytorch
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The Eleuther AI Mafia
It all started originally on lucidrains/dalle-pytorch in the months following the release of DALL-E (1). The group started as `dalle-pytorch-replicate` but was never officially "blessed" by Phil Wang who seems to enjoy being a free agent (can't blame him).
https://github.com/lucidrains/DALLE-pytorch/issues/116 is where the discord got kicked off originally. There's a lot of other interactions between us in the github there. You should be able to find when Phil was approached by Jenia Jitsev, Jan Ebert, and Mehdi Cherti (all starting LAION members) who graciously offered the chance to replicate the DALL-E paper using their available compute at the JUWELS and JUWELS Booster HPC system. This all predates Emad's arrival. I believe he showed up around the time guided diffusion and GLIDE, but it may have been a bit earlier.
Data work originally focused on amassing several of the bigger datasets of the time. Getting CC12M downloaded and trained on was something of an early milestone (robvanvolt's work). A lot of early work was like that though, shuffling through CC12M, COCO, etc. with the dalle-pytorch codebase until we got an avocado armchair.
Christophe Schumann was an early contributor as well and great at organizing and rallying. He focused a lot on the early data scraping work for what would become the "LAION5B" dataset. I don't want to credit him with the coding and I'm ashamed to admit I can't recall who did much of the work there - but a distributed scraping program was developed (the name was something@home... not scraping@home?).
The discord link on Phil Wang's readme at dalle-pytorch got a lot of traffic and a lot of people who wanted to pitch in with the scraping effort.
Eventually a lot of people from Eleuther and many other teams mingled with us, some sort of non-profit org was created in Germany I believe for legal purposes. The dataset continued to grow and the group moved from training DALLE's to finetuning diffusion models.
The `CompVis` team were great inspiration at the time and much of their work on VQGAN and then latent diffusion models basically kept us motivated. As I mentioned a personal motivation was Katherine Crowson's work on a variety of things like CLIP-guided vqgan, diffusion, etc.
I believe Emad Mostaque showed up around the time GLIDE was coming out? I want to say he donated money for scrapers to be run on AWS to speed up data collection. I was largely hands off for much of the data scraping process and mostly enjoyed training new models on data we had.
As with any online community things got pretty ill-defined, roles changed over, volunteers came/went, etc. I would hardly call this definitive and that's at least partially the reason it's hard to trace as an outsider. That much of the early history is scattered about GitHub issues and PR's can't have helped though.
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Thoughts on AI image generators from text
Here you go: https://github.com/lucidrains/DALLE-pytorch
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[P] DALL·E Mini & Mega demo and production API
Here are some other implementations of Dalle clones in Pytorch by various authors in the ML and DL community: https://github.com/lucidrains/DALLE-pytorch
- New text-to-image network from Google beats DALL-E
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[Project] DALL-3 - generate better images with fewer tokens through clip guided diffusion
If in general DDPM > GAN > VAE, why do transformer image generators all use VQVAE to decode images? Wouldn't it be better to use a diffusion model? I was wondering about this and started experimenting with different ways to decode vector-quantized embeddings with a diffusion model - see discussion here After a lot of trial and error I got something that works pretty well.
- Still waiting for dall-e
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Ask HN: Computer Vision Project Ideas?
- "Discrete VAE", used as the backbone for OpenAI's DALL-E, reimplimented here (and other places) https://github.com/lucidrains/DALLE-pytorch (code for training a discrete VAE)
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Crawling@Home: Help Build The Worlds Largest Image-Text Pair Dataset!
Here's the DALLE-pytorch git repo.
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(from the discord stream) I'm so hyped for this game. This generation is really good.
I am very excited, when AI Dungeon was released and seeing them filtering stuff, I thought that one day there will be an open source version of this without filters, the same goes for any future open sourced GPT-X. Now if we can get to train an open source DALL-E too and integrate it on NovelAI. Wouldn't that be even more awesome?
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Wann habt Ihr euch das letzte Mal wie ein Kind über eine Sache gefreut?
Vielleicht bei https://github.com/lucidrains/DALLE-pytorch und https://github.com/kobiso/DALLE-reproduction
dalle-mini
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Mini-Gemini: Mining the Potential of Multi-Modality Vision Language Models
Mini-Gemini is a bit of a confusing name.
Reminds me of how DALL·E Mini came out three years ago and eventually had to rename itself to Craiyon https://github.com/borisdayma/dalle-mini
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New Baby Kitten, what should i Name her?
I wouldnt consider Craiyon to be high tier equipment
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Annual meatball harvest in southern Italy. Mamma mia. 👌🤌
Made with : https://www.craiyon.com/
- Taylor Swift holding up a novel and reading it aloud in a beautiful library while standing behind a lectern #craiyon
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AI Eevee
AI Site
- Ai art The Thing
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Never underestimate a droid: robots gather at AI for Good summit in Geneva
https://www.craiyon.com/ try it, and it's not even that good a text to image generator.
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So simple, yet I can't get the prompt. Any Idea?
For example used just your prompt on Craiyon , guess you can try on replicate the free sd demos for example.
- Pedi para uma IA genérica fazer uma foto do Fernando Diniz, estes foram os resultados
- MADNESS AI ART
What are some alternatives?
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
dalle-2-preview
deep-daze - Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
DALLE-datasets - This is a summary of easily available datasets for generalized DALLE-pytorch training.
stable-diffusion - A latent text-to-image diffusion model
imagen-pytorch - Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
dalle-flow - 🌊 A Human-in-the-Loop workflow for creating HD images from text
CoCa-pytorch - Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement