stable-diffusion
tvm
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stable-diffusion | tvm | |
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142 | 15 | |
2,438 | 11,156 | |
- | 2.1% | |
9.8 | 9.9 | |
over 1 year ago | 7 days ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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stable-diffusion
- [Stable Diffusion] Aide nécessaire à l'augmentation de la taille du fichier maximum sur l'installation locale
- [Machine Learning] [P] Exécutez une diffusion stable sur le GPU de votre M1 Mac
- Its time!
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Anybody running SD on a Macbook Pro? What are you using and how did you install it?
Yes, you can install it with Python! https://github.com/lstein/stable-diffusion works with macOS, and you can control all the common parameter via their WebUI or CLI :)
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How do I save the arguments for images I create when using the terminal? (Apple M1 Pro)
I'm using lstein fork ("dream") and when I create an image from the terminal, it also writes back to the terminal like this:
- I Resurrected “Ugly Sonic” with Stable Diffusion Textual Inversion
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AI Seamless Texture Generator Built-In to Blender
> Whenever I ask for something like ‘seamless tiling xxxxxx’ it kinda sorta gets the idea, but the resulting texture doesn’t quite tile right.
Getting seamless tiling requires more than just have "seamless tiling" in the prompt. It also depends on if the fork you're using has that feature at all.
https://github.com/lstein/stable-diffusion has the feature, but you need to pass it outside the prompt. So if you use the `dream.py` prompt cli, you can pass it `"Hats on the ground" --seamless` and it should be perfectly tilable.
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Auto SD Workflow - Update 0.2.0 - "Collections", Password Protection, Brand new UI + more
From https://github.com/lstein/stable-diffusion
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Stable Diffusion GUIs for Apple Silicon
Stable Diffusion Dream Script: This is the original site/script for supporting macOS. I found this soon after Stable Diffusion was publicly released and it was the site which inspired me to try out using Stable Diffusion on a mac. They have a web-based UI (as well as command-line scripts) and a lot of documentation on how to get things working.
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Still can't believe this technology is real. My talentless 2 minute sketch on the left.
I’m pretty sure it works for M2 as well - basically the newer ARM-based Macs. The instructions to get it working are detailed! https://github.com/lstein/stable-diffusion
tvm
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Making AMD GPUs competitive for LLM inference
Yes, this is coming! Myself and others at OctoML and in the TVM community are actively working on multi-gpu support in the compiler and runtime. Here are some of the merged and active PRs on the multi-GPU (multi-device) roadmap:
Support in TVM’s graph IR (Relax) - https://github.com/apache/tvm/pull/15447
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VSL; Vlang's Scientific Library
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
- Apache TVM
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MLC LLM - "MLC LLM is a universal solution that allows any language model to be deployed natively on a diverse set of hardware backends and native applications, plus a productive framework for everyone to further optimize model performance for their own use cases."
I have tried the iPhone app. It's fast. They're using Apache TVM which should allow better use of native accelerators on different devices. Like using metal on Apple and Vulcan or CUDA or whatever instead of just running the thing on the CPU like llama.cpp.
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ONNX Runtime merges WebGPU back end
I was going to answer the same, I find the approach of machine learning compilers that directly compile models to host and device code better than having to bring a huge runtime. There are exciting projects in this area like TVM Unity, IREE [2], or torch.export [3]
[1] https://github.com/apache/tvm/tree/unity
[2] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
[3] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
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Esp32 tensorflow lite
Apache TVM home page: https://tvm.apache.org/
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Decompiling x86 Deep Neural Network Executables
It's pretty clear its referring to the output of Apache TVM and Meta's Glow
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Run Stable Diffusion on Your M1 Mac’s GPU
As mentioned in sibling comments, Torch is indeed the glue in this implementation. Other glues are TVM[0] and ONNX[1]
These just cover the neural net though, and there is lots of surrounding code and pre-/post-processing that isn't covered by these systems.
For models on Replicate, we use Docker, packaged with Cog for this stuff.[2] Unfortunately Docker doesn't run natively on Mac, so if we want to use the Mac's GPU, we can't use Docker.
I wish there was a good container system for Mac. Even better if it were something that spanned both Mac and Linux. (Not as far-fetched as it seems... I used to work at Docker and spent a bit of time looking into this...)
[0] https://tvm.apache.org/
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How to get started with machine learning.
Or use TVM, the idea is to compile your model into code that you can load at runtime. Similar to onnxruntime, it only does DNN inference; so you need domain-specific code.
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An open-source library for optimizing deep learning inference. (1) You select the target optimization, (2) nebullvm searches for the best optimization techniques for your model-hardware configuration, and then (3) serves an optimized model that runs much faster in inference
Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.
What are some alternatives?
waifu-diffusion - stable diffusion finetuned on weeb stuff
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
taming-transformers - Taming Transformers for High-Resolution Image Synthesis
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
stable-diffusion-webui - Stable Diffusion web UI
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
diffusers-uncensored - Uncensored fork of diffusers
stable-diffusion
txt2imghd - A port of GOBIG for Stable Diffusion
nebuly - The user analytics platform for LLMs
dream-textures - Stable Diffusion built-in to Blender
deepsparse - Sparsity-aware deep learning inference runtime for CPUs