autograd
dalle-playground
autograd | dalle-playground | |
---|---|---|
6 | 35 | |
6,797 | 2,762 | |
0.7% | - | |
6.0 | 3.2 | |
7 days ago | 4 months ago | |
Python | JavaScript | |
MIT License | MIT License |
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autograd
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
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.
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Autodidax: Jax Core from Scratch (In Python)
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.
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Numba: A High Performance Python Compiler
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
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Run Your Own DALL·E Mini (Craiyon) Server on EC2
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.
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Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia
> 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
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JAX - COMPARING WITH THE BIG ONES
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.
dalle-playground
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Discord bot with a locally-hosted SD backend.
Built on dalle-playground because it is simple and I like it.
- Neural photo engine with Intel compute stick?
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Free/open-source AI Text-To-Image Models that can be run on AWS?
[1] https://github.com/saharmor/dalle-playground
- ai_irl
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Running Dall-e mini on Windows? (Or: Are there any equivalent text-to-image AI's I can run on a windows PC with a 2080 TI?)
If you decide to abandon the idea of running locally and want to run in the cloud instead, https://github.com/saharmor/dalle-playground has a Google Colab notebook that's relatively easy to run (although Google Colab's free tier is relatively limited).
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Run Your Own DALL·E Mini (Craiyon) Server on EC2
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.
- Dream's over guys...
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How to run DALLE-2 locally
Is their any way to run DALLE-2 inside of a docker container similarly to this DALLE-PLAYGROUND repo on github?
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How difficult would it be to set up your own DALL-E (mini/mega) API for side-projects?
I know there are open source projects like dalle-playground. Would it literally be enough to host this app on an EC2 instance with the mini model?
- an AI image generator capable of taking a prompt and making it come to life.
What are some alternatives?
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
dalle-mini - DALL·E Mini - Generate images from a text prompt
SwinIR - SwinIR: Image Restoration Using Swin Transformer (official repository)
Dannjs - Easy to use Deep Neural Network Library for JavaScript.
jaxonnxruntime - A user-friendly tool chain that enables the seamless execution of ONNX models using JAX as the backend.
CogVideo - Text-to-video generation. The repo for ICLR2023 paper "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers"
autodidact - A pedagogical implementation of Autograd
min-dalle - min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
fbpic - Spectral, quasi-3D Particle-In-Cell code, for CPU and GPU
nano-neuron - 🤖 NanoNeuron is 7 simple JavaScript functions that will give you a feeling of how machines can actually "learn"
pure_numba_alias_sampling - Pure numba version of Alias sampling algorithm from L. Devroye's, "Non-Uniform Random Random Variate Generation"
pollinations - Generate Art