open_clip VS DALLE-pytorch

While OpenAI’s CLIP model has garnered a lot of attention, it is far from the only game in town—and far from the best! On the OpenCLIP leaderboard, for instance, the largest and most capable CLIP model from OpenAI ranks just 41st(!) in its average zero-shot accuracy across 38 datasets.

Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.

It is a misconception that Adobe's models have not been trained on copyrighted work. Nobody should be repeating their marketing claims.

Adobe has not shown how they train the text encoders in Firefly, or what images were used for the text-based conditioning (i.e. "text to image") part of their image generation model. They are almost certainly using CLIP or T5, which are trained on LAION2b, an image dataset with the very problems they are trying to address, C4 (a text dataset similarly encumbered) and similar.

I welcome anyone who works at Adobe to simply answer this question of how they trained the text encoders for text conditioning and put it to rest. There is absolutely nothing sensitive about the issue, unless it exposes them in a lie.

So no chance. I think it's a big fat lie. They'd have to have made some other scientific breakthrough, which they didn't.

Using information from https://openai.com/research/clip and https://github.com/mlfoundations/open_clip, it's possible to investigate the likelihood that using just their stock image dataset, can they make a working text encoder?

It's certainly not impossible, but it's impracticable. On 248m images (roughly the size of Adobe Stock), CLIP gets 37% on ImageNet, and on the 2000m from LAION, it performs 71-80%. And even with 2000m images, CLIP is substantially worse performing than the approach that Imagen uses for "text comprehension," which relies on essentially many billions more images and text tokens.

https://github.com/mlfoundations/open_clip/blob/main/docs/op...

in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).

also, if you describe your task a bit more, we might be able to direct you to a fairly out-of-the-box solution, e.g. you might be able to use one of the pretrained models supported by https://github.com/mlfoundations/open_clip without any additional training

It says on the Stable Diffusion Github repo that it uses the “OpenCLIP-ViT/H” https://github.com/mlfoundations/open_clip model as a text encoder, and from my prior experience with CLIP, I have found that it is very easy to generate image and text embeddings (because CLIP is a multimodal model).

https://replicate.com/pharmapsychotic/clip-interrogator

using:

cfg.apply_low_vram_defaults()

interrogate_fast()

I tried lighter models like vit32/laion400 and others etc all are very very slow to load or use (model list: https://github.com/mlfoundations/open_clip)

I'm desperately looking for something more modest and light.

A library that is very useful for this kind of application is timm. You may also find the feature representation provided by a CLIP model particularly powerful.

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.

Here you go: https://github.com/lucidrains/DALLE-pytorch

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

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.

- "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)

Here's the DALLE-pytorch git repo.

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?

Vielleicht bei https://github.com/lucidrains/DALLE-pytorch und https://github.com/kobiso/DALLE-reproduction