stable-diffusion-webui
Radiata
stable-diffusion-webui | Radiata | |
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10 | 8 | |
191 | 981 | |
- | - | |
9.8 | 8.1 | |
8 months ago | 8 months ago | |
Python | Python | |
GNU Affero General Public License v3.0 | Apache License 2.0 |
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stable-diffusion-webui
- Help with Install and Extensions
- AUTOMATIC1111 updated to 1.3.0 version
- Need help- mac M1 Pro
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Can’t connect to the Server
There seems to be some Mac related issue right now I think. You can try running this https://github.com/brkirch/stable-diffusion-webui/releases/tag/v1.1.1-RC but I have no idea about macs really other than reported issues with it crashing on generate.
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Python quit unexpectedly
Consider using this fork of A1111: https://github.com/brkirch/stable-diffusion-webui. It has Mac-specific improvements and is prepackaged so that changes to dependencies don't break the software.
- Help with installing on an intel mac?
- RuntimeError: “upsample_nearest2d_channels_last” not implemented for ‘Half’
- What am I doing wrong please?
- Automatic1111 is still active
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Turn on a Mac?
Yep, you could try this https://github.com/brkirch/stable-diffusion-webui/releases
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?
MochiDiffusion - Run Stable Diffusion on Mac natively
voltaML-fast-stable-diffusion - Beautiful and Easy to use Stable Diffusion WebUI
tomesd - Speed up Stable Diffusion with this one simple trick!
was-node-suite-comfyui - An extensive node suite for ComfyUI with over 190 new nodes
sd-ui-plugins
a1111-batch-interrogate - Example batch scripts using the A1111 SD Webui API [Moved to: https://github.com/d3x-at/a1111-api-examples]
stable-diffusion-webui-directml - Stable Diffusion web UI
stable-diffusion-webui-rocm - A stable diffusion webui configuration for AMD ROCm
a1111-api-batch-examples - Example batch scripts using the A1111 SD Webui API [Moved to: https://github.com/d3x-at/a1111-api-examples]
stable-diffusion-webui - Stable Diffusion web UI
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.