voltaML-fast-stable-diffusion
automatic
voltaML-fast-stable-diffusion | automatic | |
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14 | 185 | |
941 | 4,745 | |
2.0% | - | |
9.7 | 9.9 | |
about 2 months ago | 1 day ago | |
Python | Python | |
GNU General Public License v3.0 only | GNU Affero General Public License v3.0 |
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voltaML-fast-stable-diffusion
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Show HN: Shortbread, a web app that helps you create AI comics in minutes
Also, VoltaML has a good reference GPU AITemplate SD 1.5 implementation:
https://github.com/VoltaML/voltaML-fast-stable-diffusion/tre...
The speed jump is massive on my desktop GPU, probably even more dramatic on cloud hardware, and it may support some things (weight swapping/lora swapping/resolution changing) better than JAX.
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AI Horde’s AGPL3 hordelib receives DMCA take-down from hlky
This kind of drama is just sad.
I dont know if you are OP, but plenty of other UIs have interrogator code, like https://github.com/VoltaML/voltaML-fast-stable-diffusion/tre...
- What is the text-to-image AI tool?
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WIP - TensorRT accelerated stable diffusion img2img from mobile camera over webrtc + whisper speech to text. Interdimensional cable is here! Code: https://github.com/venetanji/videosd
If you just want an accelerated ui, you can check https://github.com/ddPn08/Lsmith/ or https://github.com/VoltaML/voltaML-fast-stable-diffusion which also use the same origina nvidia code. These projects don't do img2img though, you can check in my repo for the img2img pipeline if you need. You need to compile the tensorrt engines for the models first. There are a few steps you can check in their script: export onnx, optimize onnx, compile engine for optimized onnx. I streamlined that a bit and I normally just run my compile.py in docker to build engines.
- 4090, 33 it/s, Windows 10
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RTX 4090 12.5it/s ... can this be even faster?
Try https://github.com/VoltaML/voltaML-fast-stable-diffusion
- When will the 30 img per 1 second model happen?
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Get in the robot, Harry
This one: VoltaML/voltaML-fast-stable-diffusion: Lightweight library to accelerate Stable-Diffusion, Dreambooth into fastest inference models with single line of code 🔥 🔥 (github.com)
- Anyone tried this VoltaML fast stable diffusion. I thought they were gonna add support for automatic1111.
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Me waiting for A1111 Depth2img to officially support custom depth maps.
You will be waiting a lot longer for this to be implemented: https://github.com/VoltaML/voltaML-fast-stable-diffusion
automatic
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Open-source project ZLUDA lets CUDA apps run on AMD GPUs
> it won't ever be a viable option
For production workloads, I generally agree. It's an unsupported hack with a questionable future, I wouldn't do anything money-making with it.
However, for tinkering and consumer workloads, it already works pretty well. Enough of cuDNN and cuBLAS work to run PyTorch and in turn, Stable Diffusion with https://github.com/lshqqytiger/ZLUDA - there's even a fairly user-friendly setup process already in https://github.com/vladmandic/automatic .
I was able to get a personal non-ML related project working on my AMD card in just a few minutes, which saved me a lot of development time before I then deployed the production workload on NV hardware (this is probably why AMD pulled the plug on the project - it's almost more of a boost to NV than anything else, AMD really need people to be writing code on ROCm to deploy on AMD datacenter hardware).
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Show HN: Comflowy – A ComfyUI Tutorial for Beginners
While I currently use SD.Next[1], I have tested ComfyUI locally with my AMD card. The UI can be daunting, but you learn quite a great deal about how a Stable Diffusion pipeline works. In addition some innovations and advances find their way into ComfyUI first.
[1] https://github.com/vladmandic/automatic
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Just me or SDXL is bad for rendering trees, grasses, vegetation in general ? Looks a stop motion or unfinished painting. How can I fix it ?
I used SD.NEXT ( https://github.com/vladmandic/automatic ) and https://civitai.com/models/82098/add-more-details-detail-enhancer-tweaker-lora and epicphotogasm_lastUnicorn
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Is SDXL supposed to be this slow on my system?
I found this thread on GitHub talking about how this was fixed in the latest version with an optional setting. I tried enabling it, as they mentioned, but it just resulted in an immediate CUDA out of memory error when starting generation. So it seems I'm actually needing the shared memory, which I assume is my issue.
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Another Monday, another big release from SDNext!
As always, do check out our more detailed changelog, give us a quick install from our Repo, and stop by our Discord Server for any questions or help you may need.
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What's the best stable diffusion client for base m1 MacBook air?
SD.Next
- Intel Arc 770 with Linux Mint, support requested!
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SDNext - Controlnet keeps being disabled after installing SDXL ?
Today I finally wanted to give SDXL a chance, so I set everythin up according to Vladmandic's Wiki https://github.com/vladmandic/automatic/wiki/SD-XL
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Vlad SD.Next SDXL DirectML: 'StableDiffusionXLPipeline' object has no attribute 'alphas_cumprod'
I'm trying to get SDXL working on Vlad's SDNext, but I keep getting the error in the title when trying to run basic operations. I'm not sure what's going on, I followed his guide for it to a T.
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[P] Stable Diffusion XL (SDXL) Benchmark - 769 images per dollar on consumer GPUs
We used an inference container based on SDNext, along with a custom worker written in Typescript that implemented the job processing pipeline. The worker used HTTP to communicate with both the SDNext container and with our batch framework.
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
SHARK - SHARK - High Performance Machine Learning Distribution
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
stable-diffusion-webui-colab - stable diffusion webui colab
sd-extension-system-info - System and platform info and standardized benchmarking extension for SD.Next and WebUI
kohya_ss
diffusionbee-stable-diffusion-ui - Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
stable-diffusion-webui-ux - Stable Diffusion web UI UX
AITemplate - AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.
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.
depthmap2mask - Create masks out of depthmaps in img2img
stable-diffusion-webui-wd14-tagger - Labeling extension for Automatic1111's Web UI