DALLE-pytorch
latent-diffusion
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DALLE-pytorch | latent-diffusion | |
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20 | 70 | |
5,468 | 10,234 | |
- | 4.7% | |
2.5 | 0.0 | |
about 1 month ago | 28 days ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
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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.
- 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
latent-diffusion
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Image Upscaler AI
There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion
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I've been collecting millions of images of only public domain /cc0 licensing. I'd like to train a stable diffusion model on the collection. Could some one share their knowledge of what this would take? Otherwise, simply enjoy my library.
CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)
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Run Clip on iPhone to Search Photos
The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.
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Class Action Lawsuit filed against Stable Diffusion and Midjourney.
Stability is basically https://github.com/CompVis/latent-diffusion + training data.
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[D] Influential papers round-up 2022. What are your favorites?
Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here
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Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head
DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.
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[Hobby Scuffles] Week of October 10, 2022
Auto refuses to comply, and explains that the code he wrote is based upon research and development that was done quite some time ago already and is open-source. The function in question was published on December 21, 2021 here: https://github.com/CompVis/latent-diffusion/commit/e66308c7f2e64cb581c6d27ab6fbeb846828253b. But that is in fact still not the original source. The original source code was published on On August 3, 2021 here: https://github.com/lucidrains/perceiver-pytorch. The original code's license allows commercial use, so nobody is wrong for using it. The license can be read here: https://github.com/lucidrains/perceiver-pytorch/blob/main/LICENSE.
- Creators of AI Art generator exclude Automatic1111 when his work eclipses their own in popularity. Appearing to be moving to /r/sdforall (+2000 users in 8 hours). Automatic1111's windows install supports features unpopular with the original authors that are ultimately open source (sources below).
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AUTOMATIC111 Code reference
So am I. Latent Diffusion.
What are some alternatives?
disco-diffusion
DALL-E - PyTorch package for the discrete VAE used for DALL·E.
dalle-mini - DALL·E Mini - Generate images from a text prompt
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
dalle-2-preview
hent-AI - Automation of censor bar detection
stable-diffusion
stable-diffusion - A latent text-to-image diffusion model
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality