autograd VS dalle-flow

Actually, that's never been a constraint for JAX autodiff. JAX grew out of the original Autograd (https://github.com/hips/autograd), so differentiating through Python control flow always worked. It's jax.jit and jax.vmap which place constraints on control flow, requiring structured control flow combinators like those.

I'm sure there's a lot of good material around, but here are some links that are conceptually very close to the linked Autodidax.

There's [Autodidact](https://github.com/mattjj/autodidact), a predecessor to Autodidax, which was a simplified implementation of [the original Autograd](https://github.com/hips/autograd). It focuses on reverse-mode autodiff, not building an open-ended transformation system like Autodidax. It's also pretty close to the content in [these lecture slides](https://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/slid...) and [this talk](http://videolectures.net/deeplearning2017_johnson_automatic_...). But the autodiff in Autodidax is more sophisticated and reflects clearer thinking. In particular, Autodidax shows how to implement forward- and reverse-modes using only one set of linearization rules (like in [this paper](https://arxiv.org/abs/2204.10923)).

Here's [an even smaller and more recent variant](https://gist.github.com/mattjj/52914908ac22d9ad57b76b685d19a...), a single ~100 line file for reverse-mode AD on top of NumPy, which was live-coded during a lecture. There's no explanatory material to go with it though.

XLA is "higher level" than what Numba produces.

You may be able to get the equivalent of jax via numba+numpy+autograd[1], but I haven't tried it before.

IMHO, jax is best thought of as a numerical computation library that happens to include autograd, vmapping, pmapping and provides a high level interface for XLA.

I have built a numerical optimisation library with it, and although a few things became verbose, it was a rather pleasant experience as the natural vmapping made everything a breeze, I didn't have to write the gradients for my testing functions, except for special cases that involved exponents and logs that needed a bit of delicate care.

[1] https://github.com/HIPS/autograd

Next, we want the code in the https://github.com/hrichardlee/dalle-playground repo, and we want to construct a pip environment from the backend/requirements.txt file in that repo. We were almost able to use the saharmor/dalle-playground repo as-is, but we had to make one change to add the jax[cuda] package to the requirements.txt file. In case you haven’t seen jax before, jax is a machine-learning library from Google, roughly equivalent to Tensorflow or PyTorch. It combines Autograd for automatic differentiation and XLA (accelerated linear algebra) for JIT-compiling numpy-like code for Google’s TPUs or Nvidia’s CUDA API for GPUs. The CUDA support requires explicitly selecting the [cuda] option when we install the package.

> fun fact, the Jax folks at Google Brain did have a Python source code transform AD at one point but it was scrapped essentially because of these difficulties

I assume you mean autograd?

https://github.com/HIPS/autograd

These four points lead to an enormous differentiation in the ecosystem: Keras, for example, was originally thought to be almost completely focused on point (4), leaving the other tasks to a backend engine. In 2015, on the other hand, Autograd focused on the first two points, allowing users to write code using only "classic" Python and NumPy constructs, providing subsequently many options for point (2). Autograd's simplicity greatly influenced the development of the libraries to follow, but it was penalized by the clear lack of the points (3) and (4), i.e. adequate techniques to speed up the code and sufficiently abstract modules for neural network development.

Jina AI is really into generative AI. It started out with DALL·E Flow, swiftly followed by DiscoArt. And then…🦗🦗*🦗🦗. At least for a while…

you can host your own https://github.com/jina-ai/dalle-flow

For example for all the multimodal stuff like clipseg and upscalers, I'm using isolated executors through jina flow: https://github.com/jina-ai/dalle-flow/tree/main/executors

clipseg is an image segmentation method used to find a mask for an image from a prompt. I implemented it as an executor for dalle-flow and added it to my bot yasd-discord-bot.

Merged into [dalle-flow](https://github.com/jina-ai/dalle-flow/pull/112) this morning and works on my Discord bot [yasd-discord-bot](https://github.com/AmericanPresidentJimmyCarter/yasd-discord-bot).

https://github.com/jina-ai/dalle-flow with ports of some code from https://github.com/lstein/stable-diffusion plus some stuff specific to my uses (mostly more exposed settings and meta data on the outputs).

dalle flow - a more advanced version of dall-e mini, running dall-e mega and a diffusion model (free colab), free

Another option is https://github.com/jina-ai/dalle-flow combines DALL-E Mini with some other image processing models, and they have a pre-built Docker image that you could run locally. However, because it loads additional image processing models, you'll need about 21 GB of GPU RAM which is more than a 2080 TI has. You could always try to edit their Dockerfile and re-build it to remove the other models.

For the second half of this article, we’ll use meadowdata/meadowrun-dallemini-demo which contains a notebook for running multiple models as sequential batch jobs to generate images using Meadowrun. The combination of models is inspired by jina-ai/dalle-flow.