swift-diffusion VS open_clip

I am going to put model related code we use in a public repo soon (it is very similar to https://github.com/liuliu/swift-diffusion but in NHWC format). ANE will be around 25s if it runs. DT's default only uses GPUs and 35s is on GPU (yes, like you said, upscaling would take extra 10s).

The text encoder uses a new vocabulary set, make sure you copied them from open_clip repo: https://github.com/mlfoundations/open_clip (I have these also available at: https://github.com/liuliu/swift-diffusion/tree/liu/unet/examples/open_clip

Should be able too, if there is a need. I am more interested to support training hypernetwork from the app directly. The conversion script itself is open-source (https://github.com/liuliu/swift-diffusion/blob/main/examples/unet/main.swift), but not polished, and because Apple doesn't allow you to run Python on device, so I cannot make it as easy as typing a URL and get done. Need to figure out what the UX looks like without me providing a networked services ...

Hi, this is the first app in a while (probably 10 years) that I submitted to AppStore. I've done this app in 3 weeks, so there are a lot to be polished. The technology that enables this I discussed in depth in an accompanied blog post: https://liuliu.me/eyes/stretch-iphone-to-its-limit-a-2gib-mo...

Some parts of it (or major parts) is also available at https://github.com/liuliu/swift-diffusion. I plan to port more stuff back to swift-diffusion and make a CLI tool out of it (it is a bit more work than the app because I need to consider CUDA compatibility there).

AMA!

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.