stable-diffusion-webui-feature-showcase
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4 | 33 | |
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- | 8 months ago | |
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stable-diffusion-embeddings
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Aerith and Tifa being super cute in the city
My own dataset consists of 107 high resolution input images and I attempted training it for more than 50k steps mulitple times with varying parameters. One would assume that it should work; AUTOMATIC1111 trained his embeddings here quite similarly, but my own results looked quite plastic-like.
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Close-up portrait upscaled to 2432x3072
Warning: NSFW https://gitlab.com/16777216c/stable-diffusion-embeddings
- Idiot's guide to sticking your head in stuff using AUTOMATIC1111's repo
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Parameters for textual inversion on AUTOMATIC1111?
Yeah, that's what i've been training on but the step count was 3k to 7k not 100k, with that many steps you might need more data but i'm not sure. Check Automatic's TI wiki, those are the ranges he's using, there are also some nsfw embbedings on gitlab trained like that.
stable-diffusion-webui-feature-showcase
- How to turn anime image to realistic image in stable diffusion?
- [Stable Diffusion] inversion textuelle avec AUTOMATIC1111 webui
- [Ainudes] Comment créer des nus IA ?
- Is there any documentation for Automatic1111 WebUI?
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Is there a properly comprehensive guide on prompt syntax?
A1111 https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase
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Which one is the "official" version
Here's a quick rundown on a few of the most popular ones with links. I started out using CMDR2, which is very easy to get running as a newbie. Then I kind of graduated to NMKD because I wanted something a little more mainstream but still easy to use. Then, I finally decided I was hungry for all the strange and exotic bells and whistles that SD had to offer me, and so I installed Automatic1111. I also wanted something that would work well with my 4GB GTX 1650 laptop card, because that's considered "low ram" and kind of on the edge for running SD- Automatic1111 fit the bill there, too.
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At your service...
All generations were on the "Berry's Mix" model, which is made by combining NAI-final, Zenith's F111, r34 and SD1.4 according to this recipe. I used 30ish steps when generating images and inpainting, but 70-80 steps when outpainting because I read here that outpainting really benefits from extra steps. When outpainting I would generate 2-4 versions and pick the least broken one, then tidy up with inpainting.
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What's the name of this feature?
sounds like "outpainting", one of the very first features listed on 1111 repo with some instructions: https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase
- How do you expand an image? (image to image)
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Running neural networks locally.
I have no idea what you're talking about. Just get Automatic1111
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
CogVideo - Text-to-video generation. The repo for ICLR2023 paper "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers"
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
glid-3-xl-stable - stable diffusion training
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
stable-diffusion - Optimized Stable Diffusion modified to run on lower GPU VRAM
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.
StableDiffusion-Windows-GUI
txt2imghd - A port of GOBIG for Stable Diffusion
diffusers-interpret - Diffusers-Interpret 🤗🧨🕵️♀️: Model explainability for 🤗 Diffusers. Get explanations for your generated images.
diffusion-ui - Frontend for deeplearning Image generation