accelerate
stable-diffusion-webui
accelerate | stable-diffusion-webui | |
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18 | 2,808 | |
6,996 | 129,975 | |
2.9% | - | |
9.7 | 9.9 | |
1 day ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | MIT |
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accelerate
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
accelerate is a best-in-class lib for deploying models, especially across multi-gpu and multi-node.
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Code Llama - The Hugging Face Edition
In the coming days, we'll work on sharing scripts to train models, optimizations for on-device inference, even nicer demos (and for more powerful models), and more. Feel free to like our GitHub repos (transformers, peft, accelerate). Enjoy!
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What are the current fastest multi-gpu inference frameworks?
So I rent a cloud server today to try out some of the recent LLMs like falcon and vicuna. I started with huggingface's generate API using accelerate. It got about 2 instances/s with 8 A100 40GB GPUs which I think is a bit slow. I was using batch size = 1 since I do not know how to do multi-batch inference using the .generate API. I did torch.compile + bf16 already. Do we have an even faster multi-gpu inference framework? I have 8 GPUs so I was thinking about MUCH faster speed like ~10 or 20 instances per second (or is it possible at all? I am pretty new to this field).
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Looking at lefnire's suggestion of splitting huggingface batches per gradient_accumulation_steps
Looking through https://github.com/huggingface/accelerate/tree/main/src/accelerate/utils/ I think it might be feasible, but will require some modifications to:
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Have to abandon my (almost) finished LLaMA-API-Inference server. If anybody finds it useful and wants to continue, the repo is yours. :)
As /u/RabbitHole32 already mentioned, the speed increase stems from a patch which modifies, how a certain, large tensor is distributed between the GPU's. The patch was created by /u/emvw7yf. Here you can find the respective GitHub issue: https://github.com/huggingface/accelerate/issues/1394
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Help please! SD installation broken
::pip install git+https://github.com/huggingface/accelerate
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Batch Controlnet
pip install controlnet_aux pip install diffusers transformers git+https://github.com/huggingface/accelerate.git
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[D] Large Language Models feasible to run on 32GB RAM / 8 GB VRAM / 24GB VRAM
Try to use both GPUs with this one: https://github.com/huggingface/accelerate https://huggingface.co/docs/accelerate/usage_guides/big_modeling https://huggingface.co/blog/accelerate-large-models Maybe it will help (the last link is clearer IMHO).
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Fine Tuning Stable Diffusion with Dreambooth from Within My Python Code
I read through this page on accelerate, but it's not clear to me how the arguments such as instance_prompt gets passed in.
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What does ACCELERATE do in AUTOMATIC1111?
To activate it you have to uncomment webui-user.sh line 44 and adding set ACCELERATE="True" to webui-user.bat. It seems to use huggingface/accelerate (Microsoft DeepSpeed, ZeRO paper) ACCELERATE
stable-diffusion-webui
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Show HN: I made an app to use local AI as daily driver
* LLaVA model: I'll add more documentation. You are right Llava could not generate images. For image generation I don't have immediate plans, but checkout these projects for local image generation.
- https://diffusionbee.com/
- https://github.com/comfyanonymous/ComfyUI
- https://github.com/AUTOMATIC1111/stable-diffusion-webui
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
I would love to be able to have a native stable diffusion experience, my rx 580 takes 30s to generate a single image. But it does work after following https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...
I got this up and running on my windows machine in short order and I don't even know what stable diffusion is.
But again, it would be nice to have first class support to locally participate in the fun.
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Ask HN: What is the state of the art in AI photo enhancement?
In Auto1111, that just uses Image.blend. :)
https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob...
- How To Increase Performance Time on MacOS
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Can anyone suggest an AI model that can help me enhance a poorly drawn logo?
I used SDXL in automatic1111 webui for both images. Now that I think about it, the procedure I described was how I made this one, but the one that looks like an illustration was done in two steps. I used the canny ControlNet as I said for the outer part of the logo to preserve the shape of the fonts, but I had to turn it off for the boot to give SDXL leeway to add detail and make it look more like a boot.
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Seeking out an experienced and empathetic coding buddy.
That said, please do learn coding and don't get discouraged when somebody says to learn PyTorch or recommends using a Jupiter notebook with no further information on how to translate the skill into images. I would highly recommend some short term goals. Get your feet wet by taking apart the UIs. The comfy API documentation is here and the A1111 API documentation is here. There is a difference in completeness, welcome to programming. Writing nodes or plugins is also a good way to jump into this world. Custom wildcard logic might be very attractive to you if you aren't the type that want to deal with a nested file structure to simulate logic.
- can't get it working with an AMD gpu
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SD extension that allows for setting override
Possibly Unprompted? https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/8094
- Need to write an application to use Stable Diffusion on my desktop PC - which resource should I learn to use?
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4090 Speed Decrease on each Generation/Iteration
version: v1.6.1 ββ’β python: 3.10.13 ββ’β torch: 2.0.1+cu118 ββ’β xformers: 0.0.20 ββ’β gradio: 3.41.2 ββ’β checkpoint: 6e8d4871f8
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
stable-diffusion-ui - Easiest 1-click way to install and use Stable Diffusion on your computer. Provides a browser UI for generating images from text prompts and images. Just enter your text prompt, and see the generated image. [Moved to: https://github.com/easydiffusion/easydiffusion]
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
ComfyUI - The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
SHARK - SHARK - High Performance Machine Learning Distribution
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
lora - Using Low-rank adaptation to quickly fine-tune diffusion models.
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | εΌζΊεθ―ε―Ήθ―θ―θ¨ζ¨‘ε
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.
unsloth - Finetune Llama 3, Mistral & Gemma LLMs 2-5x faster with 80% less memory
safetensors - Simple, safe way to store and distribute tensors