autograd
SwinIR
autograd | SwinIR | |
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6 | 28 | |
6,797 | 4,091 | |
0.7% | - | |
6.0 | 0.0 | |
8 days ago | about 1 month ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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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.
SwinIR
- A smooth and sharp image interpolation you probably haven't heard of
- Certain directories (e.g. SwinIR) are empty (version: Empire Media Science A1111 Web UI Installer)
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I used Real-ESRGAN to upscale my image, but if you zoom in you can see that “water particles” looks like some random lines and image overall looks cartoonish. Is there a way to fix it?
003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth
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Any luck changing the upscaler? They seem to be hard coded
I was trying to get a new upscaler working, as someone pointed me to one that did a good job of preserving and creating new details: https://github.com/JingyunLiang/SwinIR
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Spatial-temporal denoising
SwinIR: https://github.com/JingyunLiang/SwinIR
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A Monster Hunter: World Virtual Photography Tutorial - YouTube
Upscalers that I use SwinIR https://github.com/JingyunLiang/SwinIR https://github.com/AUTOMATIC1111/stable-diffusion-webui (Use 'extras' tab for the upscaler function) Topaz Gigapixel AI https://www.topazlabs.com/gigapixel-ai
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what are the alternatives to letsenhance.io?
You could try out chaiNNer, it is a free local/offline application. There are a lot of (upscaling) models which you can download an use with it. You can for example try out SwinIR-L (link will start a model download) or any other model you like depending on your input images.
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[R] Swin transformer while using a rectangular attention window
the relative attention bias can be made non-square in the original implementation, there is a parameter window_size, at 7, that is forced to (7,7) directly, but you can change it easily. https://github.com/JingyunLiang/SwinIR/blob/main/models/network_swinir.py
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Robot dance animation with Robo-Diffusion (1024x576)
Use SwinIR medium model to upscale by 2 times. This will result in a video of 2048x1152.
- Help Need to get my VQGAN images to 10000 x 10000
What are some alternatives?
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
jaxonnxruntime - A user-friendly tool chain that enables the seamless execution of ONNX models using JAX as the backend.
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
autodidact - A pedagogical implementation of Autograd
Real-ESRGAN-ncnn-vulkan - NCNN implementation of Real-ESRGAN. Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration.
fbpic - Spectral, quasi-3D Particle-In-Cell code, for CPU and GPU
Ne2Ne-Image-Denoising - Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training
pure_numba_alias_sampling - Pure numba version of Alias sampling algorithm from L. Devroye's, "Non-Uniform Random Random Variate Generation"
chaiNNer - A node-based image processing GUI aimed at making chaining image processing tasks easy and customizable. Born as an AI upscaling application, chaiNNer has grown into an extremely flexible and powerful programmatic image processing application.
qha - A Python package for calculating thermodynamic properties under quasi-harmonic approximation, using data from ab-initio calculations
MPRNet - [CVPR 2021] Multi-Stage Progressive Image Restoration. SOTA results for Image deblurring, deraining, and denoising.