tvm
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stable-diffusion | tvm | |
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8 | 15 | |
436 | 11,130 | |
- | 1.9% | |
0.0 | 9.9 | |
12 months ago | 3 days ago | |
Python | ||
GNU General Public License v3.0 or later | Apache License 2.0 |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
stable-diffusion
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DALL·E Now Available Without Waitlist
No, sorry, but there's a whole bunch of one-click things now, I think?
I'm running it on Windows 10 using (a modified version of) https://github.com/bfirsh/stable-diffusion.git and Anaconda to create the environment from their `environment.yaml` (all of which was done using the normal `cmd` shell). Then to use it, I activate that env from `cmd` and switch into cygwin `bash` to run the `txt2img.py` script (because it's easier to script, etc.)
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How do I save the arguments for images I create when using the terminal? (Apple M1 Pro)
I am using the bfirsh version. And yes, I run "pyhthon scripts/txt2imp.py" to generate an image.
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Current canonical way to install Stable Diffusion on Apple Silicon?
Specifically regarding the first option above, I see that the procedure clones the repository from: https://github.com/bfirsh/stable-diffusion.git
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One-Click Install Stable Diffusion GUI App for M1 Mac. No Dependencies Needed
Just done a run on my 3080 under Windows using https://github.com/bfirsh/stable-diffusion.git and it's about 8 iterations/sec when nothing else is using CPU or GPU.
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Using the same seed and same prompt is still resulting in two different images?
I've cloned this repository on my M1 Mac: https://github.com/bfirsh/stable-diffusion/tree/apple-silicon-mps-support
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Run Stable Diffusion on Your M1 Mac’s GPU
Boom - nice. Here's a fork with that: https://github.com/bfirsh/stable-diffusion/tree/lstein
Requirements are "requirements-mac.txt" which'll need subbing in the guide.
We're testing this out with a few people in Discord before shipping to the blog post.
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...)
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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?
stable_diffusion.openvino
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
sd-webui-colab - A repo for the maintenance of the Colab version of stable-diffusion-webui repo
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
stable-diffusion - A latent text-to-image diffusion model
invisible-watermark - python library for invisible image watermark (blind image watermark)
nebuly - The user analytics platform for LLMs
stable-diffusion-rocm
deepsparse - Sparsity-aware deep learning inference runtime for CPUs