intel-extension-for-pytorch
diffusers
intel-extension-for-pytorch | diffusers | |
---|---|---|
16 | 266 | |
1,365 | 22,763 | |
4.9% | 3.3% | |
9.7 | 9.9 | |
4 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
intel-extension-for-pytorch
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Intel Arc A770: Arrays larger than 4GB crashes
I have been playing around in pytorch with an a770 16GB card and hit this error. The response seems to be https://github.com/intel/intel-extension-for-pytorch/issues/... that larger than 4gb allocations aren't supported even though the card is 16gb. I haven't seen a ton of stuff on intel arc for machine learning so wanted to share my experience
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Efficient LLM inference solution on Intel GPU
OK I found it. Looks like they use SYCL (which for some reason they've rebranded to DPC++): https://github.com/intel/intel-extension-for-pytorch/tree/v2...
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Intel CEO: 'The entire industry is motivated to eliminate the CUDA market'
Just to point out it does, kind of: https://github.com/intel/intel-extension-for-pytorch
I've asked before if they'll merge it back into PyTorch main and include it in the CI, not sure if they've done that yet.
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Watch out AMD: Intel Arc A580 could be the next great affordable GPU
Intel already has a working GPGPU stack, using oneAPI/SYCL.
They also have arguably pretty good OpenCL support, as well as downstream support for PyTorch and Tensorflow using their custom extensions https://github.com/intel/intel-extension-for-tensorflow and https://github.com/intel/intel-extension-for-pytorch which are actively developed and just recently brought up-to-date with upstream releases.
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How to run Llama 13B with a 6GB graphics card
https://github.com/intel/intel-extension-for-pytorch :
> Intel® Extension for PyTorch extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*
https://pytorch.org/blog/celebrate-pytorch-2.0/ :
> As part of the PyTorch 2.0 compilation stack, TorchInductor CPU backend optimization brings notable performance improvements via graph compilation over the PyTorch eager mode.
The TorchInductor CPU backend is sped up by leveraging the technologies from the Intel® Extension for PyTorch for Conv/GEMM ops with post-op fusion and weight prepacking, and PyTorch ATen CPU kernels for memory-bound ops with explicit vectorization on top of OpenMP-based thread parallelization*
DLRS Deep Learning Reference Stack: https://intel.github.io/stacks/dlrs/index.html
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Train Lora's on Arc GPUs?
Install intel extensions for pytorch using docker. https://github.com/intel/intel-extension-for-pytorch
- Does it make sense to buy intel arc A770 16gb or AMD RX 7900 XT for machine learning?
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PyTorch Intel HD Graphics 4600 card compatibility?
There is: https://github.com/intel/intel-extension-for-pytorch for intel cards on GPUs, but I would assume this doesn't extend to integraded graphics
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Stable Diffusion Web UI for Intel Arc
Nonetheless, this issue might be relevant for your case.
- Does anyone uses Intel Arc A770 GPU for machine learning? [D]
diffusers
- StableDiffusionSafetyChecker
- 🧨 diffusers 0.24.0 is out with Kandinsky 3.0, IP Adapters, and others
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What am I missing here? wheres the RND coming from?
I'm missing something about the random factor, from the sample code from https://github.com/huggingface/diffusers/blob/main/README.md
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T2IAdapter+ControlNet at the same time
Hey people, I noticed that combining these two methods in a single forward pass increases the controllability of the generation quite a bit. I was kind of puzzled that sometimes ControlNet yielded better results than T2IAdapter for some cases, and sometimes it was the other way around, so I decided to test both at the same time, and results were quite nice. Some visuals and more motivation here: https://github.com/huggingface/diffusers/issues/5847 And it was already merged here: https://github.com/huggingface/diffusers/pull/5869
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Won't you benchmark me?
Open Parti Prompts: The better way to evaluate diffusion models (repo)
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kohya_ss error. How do I solve this?
You have disabled the safety checker for by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
- Making a ControlNet inpaint for sdxl
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Stable Diffusion Gets a Major Boost with RTX Acceleration
For developers, TensorRT support also exists for the diffusers library via community pipelines. [1] It's limited, but if you're only supporting a subset of features, it can help.
In general, these insane speed boosts comes at the cost of bleeding edge features.
[1] https://github.com/huggingface/diffusers/blob/28e8d1f6ec82a6...
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Mysterious weights when training UNET
I was training sdxl UNET base model, with the diffusers library, which was going great until around step 210k when the weights suddenly turned back to their original values and stayed that way. I also tried with the ema version, which didn't change at all. I also looked at the tensor's weight values directly which confirmed my suspicions.
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I Made Stable Diffusion XL Smarter by Finetuning It on Bad AI-Generated Images
Merging LoRAs is essentially taking a weighted average of the LoRA adapter weights. It's more common in other UIs.
diffusers is working on a PR for it: https://github.com/huggingface/diffusers/pull/4473
What are some alternatives?
llama-cpp-python - Python bindings for llama.cpp
stable-diffusion-webui - Stable Diffusion web UI
openai-whisper-cpu - Improving transcription performance of OpenAI Whisper for CPU based deployment
stable-diffusion - A latent text-to-image diffusion model
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
ROCm - AMD ROCmâ„¢ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
invisible-watermark - python library for invisible image watermark (blind image watermark)
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
rocm-examples
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.