A1111-Web-UI-Installer
stable-diffusion
A1111-Web-UI-Installer | stable-diffusion | |
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86 | 382 | |
1,816 | 65,504 | |
0.3% | 1.1% | |
3.3 | 0.0 | |
9 months ago | 21 days ago | |
PowerShell | Jupyter Notebook | |
- | GNU General Public License v3.0 or later |
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A1111-Web-UI-Installer
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SDXL is super sluggish on A1111?
GitHub - EmpireMediaScience/A1111-Web-UI-Installer: Complete installer for Automatic1111's infamous Stable Diffusion WebUI
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How do i get started with stable diffusion
You can use this launcher it will do everything for you, you just need to choice where to install it and wether you want to download the base model or not
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A1111 UI complete installers for Windows?
I've been out of the loop for a bit, and it seems that the web installer I ran the last time is not being updated anymore. Are there any other updated installers for Windows that will set up everything that people are using now?
- i want to start
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which AI is this?
Or this https://github.com/EmpireMediaScience/A1111-Web-UI-Installer seems like a simplified installer that might do the installation mostly for you. I have never tried it, so you are on your own with that. Be sure to read the whole page before attempting installation.
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How can I install DPM++ 2M SDE Karras?
I'm always install by this link: https://github.com/EmpireMediaScience/A1111-Web-UI-Installer/releases
- how the fuck do i install automatic1111
- Error completing request
- To all my haters .
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According to the poll on the recent thread, /r/dalle2 community decided to keep the subreddit restricted on Reddit.
This is a good place to start reading. Given the open-source nature of SD, there are setups of various difficulty available. A1111 is the "standard" people enjoy because it's easy to plug in new stuff (ControlNet, new models, etc.), but it's not inherently easy to set up and get going. There is an installer for it, but I haven't tried it.
stable-diffusion
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Go is bigger than crab!
Which is a 1-click install of Stable Diffusion with an alternative web interface. You can choose a different approach but this one is pretty simple and I am new to this stuff.
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Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
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How to create an Image generating AI?
It sounds like you just want to set up Stable Diffusion to run locally. I don't think your computer's specs will be able to do it. You need a graphics card with a decent amount of VRAM. Stable diffusion is in Python as is almost every AI open source project I've seen. If you can get your hands on a system with an Nvidia RTX card with as much VRAM as possible, you're in business. I have an RTX 3060 with 12 gigs of VRAM and I can run stable diffusion and a whole variety of open source LLMs as well as other projects like face swap, Roop, tortoise TTS, sadtalker, etc...
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Two video cards...one dedicated to Stable Diffusion...the other for everything else on my PC?
Use specific GPU on multi GPU systems · Issue #87 · CompVis/stable-diffusion · GitHub
- Automatic1111 - Multiple GPUs
- Ist Google inzwischen einfach unbrauchbar?
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Why are people so against compensation for artists?
I dealt with this in one of my posts. At least SD 1.1 till 1.5 are all trained on a batch size of 2048. The version pretty much everyone uses (1.5) is first pretrained at a resolution of 256x256 for 237K steps on laion2B-en, at the end of those training steps it will have seen roughly 500M images in laion2B-en. After that it is pre-trained for 194K steps on laion-high-resolution at a resolution of 512x512, which is a subset of 170M images from laion5B. Finally it is trained for 1.110K steps on LAION aesthetic v2 5+. This is easily verified by taking a glance at the model card of SD 1.5. Though that one doesn't specify for part of the training exactly which aesthetic set was used for part of the training, for that you have to look at the CompVis github repo. Thus at the end of it all both the most recent images and the majority of images will have come from LAION aesthetic v2 5+ (seeing every image approx 4 times). Realistically a lot of the weights obtained from pretraining on 2B will have been lost, and only provided a good starting point for the weights.
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Is SDXL really open-source?
stable diffusion · CompVis/stable-diffusion@2ff270f · GitHub
- I want to ask the AI to draw me as a Pokemon anime character then draw six of Pokemon of my choice next to me. What are my best free, 15$ or under and 30$ or under choices?
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how can i create my own ai image model
Here for example --> https://github.com/CompVis/stable-diffusion
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
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.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
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]
diffusers-uncensored - Uncensored fork of diffusers
Dreambooth-Stable-Diffusion - Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
OnnxDiffusersUI - UI for ONNX based diffusers
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
onnx - Open standard for machine learning interoperability