OnnxDiffusersUI
Pytorch
OnnxDiffusersUI | Pytorch | |
---|---|---|
26 | 341 | |
189 | 78,436 | |
- | 1.9% | |
4.9 | 10.0 | |
12 months ago | about 24 hours ago | |
Python | Python | |
GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
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OnnxDiffusersUI
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AMD GPU
Sounds like Automatic1111 doesn't work with your AMD RX570 so it uses your CPU instead. There's a guy that tried a few workarounds on his github page that might help but you basically need to buy a GPU made in the last 3 years that has ray tracing capability. https://github.com/azuritecoin/OnnxDiffusersUI
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Useful Links
Onnx Diffusers UI
- AMD support for Microsoft® DirectML optimization of Stable Diffusion
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[Help] using custom model
Its this one: https://github.com/azuritecoin/OnnxDiffusersUI It works properly except i can't convert models other than the default it comes with.
- [Stable Diffusion] J'ai créé ma propre interface utilisateur pour exécuter SD sur AMD GPU sur Windows
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Best option for running on an AMD GPU.
https://github.com/azuritecoin/OnnxDiffusersUI this seems to work pretty good for me, has quite a bit of features and has a CPU only mode, only problem with it is, it hogs a lot of ram
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Best UI for AMD Card
for windows the onnx ui:https://github.com/azuritecoin/OnnxDiffusersUI, or you can try and fenagle your way into installing automatic1111 on linux
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How Nvidia’s CUDA Monopoly In Machine Learning Is Breaking - OpenAI Triton And PyTorch 2.0
Guide
- AMD on Windows and RX580 Failed
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AMD GPU, SD and Linux on WSL2, any success story?
I also have a 6800 and when I'm on Windows I mess around with this https://github.com/azuritecoin/OnnxDiffusersUI which is fine and works okay but yeah without ROCm it is very slow. I usually just dualboot into Linux if I want to spend more time generating things. It's just how it is for now with support for AMD, sadly.
Pytorch
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Clasificador de imágenes con una red neuronal convolucional (CNN)
PyTorch (https://pytorch.org/)
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AI enthusiasm #9 - A multilingual chatbot📣🈸
torch is a package to manage tensors and dynamic neural networks in python (GitHub)
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Einsum in 40 Lines of Python
PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832
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Library for Machine learning and quantum computing
TensorFlow
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...
Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....
> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():
What are some alternatives?
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
stable-diffusion-webui - Stable Diffusion web UI
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
AMD-Stable-Diffusion-ONNX-FP16 - Example code and documentation on how to get FP16 models running with ONNX on AMD GPUs [Moved to: https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16]
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
A1111-Web-UI-Installer - Complete installer for Automatic1111's infamous Stable Diffusion WebUI
flax - Flax is a neural network library for JAX that is designed for flexibility.
stable-diffusion-webui-directml - Stable Diffusion web UI
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
stable-diffusion-webui - Stable Diffusion web UI
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more