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DALLE-datasets
This is a summary of easily available datasets for generalized DALLE-pytorch training.
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DALLE2-pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
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DALLE-reproduction
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DALLE-pytorch
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DALLE-pytorch reviews and mentions
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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.
- 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.
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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.
Since then, several efforts have been organized to replicate DALL-E. People organized initially around this awesome dalle replication repository https://github.com/lucidrains/DALLE-pytorch with some nice results that can be seen in the readme. More recently as part of an huggingface events, new results have been achieved (see https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA ) and an online demo is now available https://huggingface.co/spaces/flax-community/dalle-mini
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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
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9 Command-Line Tools to Go to Infinity & Beyond
Currently there are several projects trying to replicate DALL-E, here’s another one.
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Text to Image Generation
I have a working repo for that
https://github.com/lucidrains/dalle-pytorch
It just needs to be trained
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Are we ever going to get access to DALL-E?
and this
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A note from our sponsor - InfluxDB
www.influxdata.com | 17 Apr 2024
Stats
lucidrains/DALLE-pytorch is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of DALLE-pytorch is Python.
Popular Comparisons
- DALLE-pytorch VS DALL-E
- DALLE-pytorch VS DALLE2-pytorch
- DALLE-pytorch VS DALLE-datasets
- DALLE-pytorch VS deep-daze
- DALLE-pytorch VS CoCa-pytorch
- DALLE-pytorch VS imagen-pytorch
- DALLE-pytorch VS DALLE-reproduction
- DALLE-pytorch VS big-sleep
- DALLE-pytorch VS TimeSformer-pytorch
- DALLE-pytorch VS open_clip