voltaML-fast-stable-diffusion
Radiata
| voltaML-fast-stable-diffusion | Radiata | |
|---|---|---|
| 14 | 8 | |
| 941 | 982 | |
| 2.0% | - | |
| 9.7 | 8.1 | |
| about 2 months ago | 7 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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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
Radiata
- 🌠🌟Radiata TensorRT WebUI ⚡🏎️💨
- 🌠🌟Radiata Stable Diffusion with TensorRT WebUI🏎️💨
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Automatic1111 is still active
I didn't and don't! Are you saying that can be applied in the a1111 gui? The things I've found by googling it seem to be about a separate UI which uses this optimisation to radically speed up generation.
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I made a tutorial on how to speed up SD on windows
wow You might be into something here I am going to try this today. Have you tried Lsmith is MIA for 1 month now but is base on TensorRT and it was supper fast when I ran it you need to convert the models to tensorRT format but once they run they are blazingly fast https://github.com/ddPn08/Lsmith
- Stable Diffusion as a game renderer test
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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.
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TensorRT txt2img GUI: 20 to 30% speed boost
I just found this amazing GUI that uses the accelerated models of SD to get a speed boost of up to 30%. https://github.com/ddPn08/Lsmith
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What is the fastest stable diffusion text to image implementation?
just released https://github.com/ddPn08/Lsmith
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
was-node-suite-comfyui - An extensive node suite for ComfyUI with over 190 new nodes
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
a1111-batch-interrogate - Example batch scripts using the A1111 SD Webui API [Moved to: https://github.com/d3x-at/a1111-api-examples]
sd-extension-system-info - System and platform info and standardized benchmarking extension for SD.Next and WebUI
stable-diffusion-webui - Stable Diffusion web UI
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-rocm - A stable diffusion webui configuration for AMD ROCm
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.
a1111-api-batch-examples - Example batch scripts using the A1111 SD Webui API [Moved to: https://github.com/d3x-at/a1111-api-examples]
depthmap2mask - Create masks out of depthmaps in img2img