3d-ken-burns
cupy
3d-ken-burns | cupy | |
---|---|---|
5 | 21 | |
1,496 | 7,787 | |
- | 1.0% | |
3.8 | 9.9 | |
2 months ago | about 19 hours ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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3d-ken-burns
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Making a trailer for my book with midjourney. What do you think?
I guess you could use a free video editor (Davinci resolve is free with less features than payed version). And then try to use this open source script: https://github.com/sniklaus/3d-ken-burns, but it would definitely be harder.
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It's time to upscale FSR 2 even further: Meet FSR 2.1
installing ROCm is bit of a pain (there is little packaging, so you have to rebuild it yourself)
Search who's running Stable Diffusion on Nvidia and who's running on AMD: if you are using AMD, you are kind of on your own.
Finally, you have model with custom CUDA code (e.g. https://github.com/sniklaus/3d-ken-burns )
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Are there any websites that create an .mp4 of simple parallax effect movement from a jpg using machine learning?
There's a great Github with a script that I run on Google Colab that does a decent parallax effect in about 30 seconds through ML... but it just takes a while to get the instance spun up, and I've yet to figure out how to push out a 4k .mp4 from it. Surely someone has coding chops and can do this for parallax and monetize it like the Dall-E bot?
- How are people taking still photos and making these stereoscopic videos out of them? [READ COMMENTS]
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Battle Round 1: 3D Ken Burns Effect using PyTorch
Making use of: https://github.com/sniklaus/3d-ken-burns
cupy
- CuPy: NumPy and SciPy for GPU
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Keras 3.0
I did not expect anything interesting, but this is actually cool.
> A full implementation of the NumPy API. Not something "NumPy-like" — just literally the NumPy API, with the same functions and the same arguments.
I suppose it's like https://cupy.dev/
- Progress on No-GIL CPython
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Fedora 40 Eyes Dropping Gnome X11 Session Support
What was the difference in runtime performance, and did you try CuPy?
https://github.com/cupy/cupy :
> CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.
Projects using CuPy:
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How does one optimize their functions?
It's more effort though. You will likely have to format your data in specific ways for the GPU to efficiently process it. I've done this kind of thing with PyTorch tensors, but there are also math-specific libraries like CuPy. If you only have millions, Numpy should be fine.
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Speed Up Your Physics Simulations (250x Faster Than NumPy) Using PyTorch. Episode 1: The Boltzmann Distribution
I'd also recommend checking out CuPy which aims to fully re-implement the Numpy api for CUDA GPUs, while taking advantage of Nvidia's specialized libraries like cuBLAS, cuRAND, cuSOLVER etc. The tradeoff being that it only works with Nvidia GPUs.
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ELI5: Why doesn't numpy work on GPUs?
u/Spataner's answer is great. If you WANT GPU-enabled numpy functions, I would check out CuPy: https://cupy.dev/
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Help!!! Training neural net in vs code
Not sure how VS Code is relevant here as it's just you IDE, shouldn't have any influence on this. Now, seeing as you're using numpy (which has no gpu support), you could try and use something like CuPy in place of numpy. I'm not sure about the interoperability because I've never used this myself, but if you're lucky it could be as simple as just replacing all numpy calls with the same CuPy calls (or replacing all import numpy as np with import cupy as np ).
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What's the best thing/library you learned this year ?
Cupy replicates the numpy and scipy APIs but runs on the GPU.
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Making Python fast for free – adventures with mypyc
For that, you can use cupy[0], PyTorch[1] or Tensorflow[2]. They all mimic the numpy's API with the possibility to use your GPU.
[0] https://cupy.dev/
What are some alternatives?
stable-diffusion-rocm
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Numba - NumPy aware dynamic Python compiler using LLVM
halutmatmul - Hashed Lookup Table based Matrix Multiplication (halutmatmul) - Stella Nera accelerator
scikit-cuda - Python interface to GPU-powered libraries
XNOR-popcount-GEMM-PyTorch-CPU-CUDA - A PyTorch implemenation of real XNOR-popcount (1-bit op) GEMM Linear PyTorch extension support both CPU and CUDA
TensorFlow-object-detection-tutorial - The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch
pyhpc-benchmarks - A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:
bottleneck - Fast NumPy array functions written in C
NewsMTSC - Target-dependent sentiment classification in news articles reporting on political events. Includes a high-quality data set of over 11k sentences and a state-of-the-art classification model.
dpnp - Data Parallel Extension for NumPy