a1111-batch-interrogate
Radiata
| a1111-batch-interrogate | Radiata | |
|---|---|---|
| 1 | 8 | |
| 0 | 982 | |
| - | - | |
| 10.0 | 8.1 | |
| about 1 year ago | 8 months ago | |
| Python | Python | |
| - | Apache License 2.0 |
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a1111-batch-interrogate
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[Python] Is there a way to interface with my local StableDiffusion install through Python commands? Currently I use Automatic1111's GUI
As an example: https://github.com/d3x-at/a1111-batch-interrogate
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?
sd-webui-segment-everything - Segment Anything for Stable Diffusion Webui [Moved to: https://github.com/continue-revolution/sd-webui-segment-anything]
voltaML-fast-stable-diffusion - Beautiful and Easy to use Stable Diffusion WebUI
automatic - SD.Next: Advanced Implementation of Stable Diffusion and other Diffusion-based generative image models
was-node-suite-comfyui - An extensive node suite for ComfyUI with over 210 new nodes
docker-prompt-generator - Using a Model to generate prompts for Model applications. / 使用模型来生成作图咒语的偷懒工具，支持 MidJourney、Stable Diffusion 等。
stable-diffusion-webui - Stable Diffusion web UI
sd-webui-segment-anything - Segment Anything for Stable Diffusion WebUI
stable-diffusion-webui-rocm - A stable diffusion webui configuration for AMD ROCm
multidiffusion-upscaler-for-automatic1111 - Tiled Diffusion and VAE optimize, licensed under CC BY-NC-SA 4.0
a1111-api-batch-examples - Example batch scripts using the A1111 SD Webui API [Moved to: https://github.com/d3x-at/a1111-api-examples]
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-controlnet - WebUI extension for ControlNet