sd-model-preview-xd
stable-diffusion
sd-model-preview-xd | stable-diffusion | |
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4 | 383 | |
70 | 65,739 | |
- | 1.5% | |
6.3 | 0.0 | |
3 months ago | 5 days ago | |
Python | Jupyter Notebook | |
MIT License | GNU General Public License v3.0 or later |
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sd-model-preview-xd
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Upcoming updates in AUTOMATIC1111 webui
Here you are -> https://github.com/CurtisDS/sd-model-preview-xd 馃槉
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LORA works in some checkpoints, but not others with all the same parameters
Wouldn't be a bad thing to do. 馃榿 If you want to completely organize it, like me, you put every different model into it's own folder, together with preview images and description to use with this -> https://github.com/CurtisDS/sd-model-preview-xd
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guide to matching ckpt models and VAEs to LORAs and embeddings in Automatic1111 for better results
I suggest using sd-model-review-xd to make a nice description, your personal tips to remember, and previews of each model for easy reference.
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Model Preview - How to use Extra Networks tab to visually organize and choose your models by custom previews in AUTOMATIC1111
This is better than nothing, but not much. Model Preview XD is vastly superior. It allows for multiple sample images, a text or HTML description, and filtering models by tags. Your method is fine if you only have a handful of models, but if you have hundreds, you need something a lot more comprehensive.
stable-diffusion
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Top 7 Text-to-Image Generative AI Models
Stable Diffusion: It is based on a kind of diffusion model called a latent diffusion model, which is trained to remove noise from images in an iterative process. It is one of the first text-to-image models that can run on consumer hardware and has its code and model weights publicly available.
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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 路 Issue #87 路 CompVis/stable-diffusion 路 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 路 CompVis/stable-diffusion@2ff270f 路 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?
What are some alternatives?
sd-dynamic-thresholding - Dynamic Thresholding (CFG Scale Fix) for Stable Diffusion (StableSwarmUI, ComfyUI, and Auto WebUI)
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
sd_civitai_extension - All of the Civitai models inside Automatic 1111 Stable Diffusion Web UI
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
stable-diffusion-webui - Stable Diffusion web UI
diffusers-uncensored - Uncensored fork of diffusers
civitai - A repository of models, textual inversions, and more
diffusers - 馃 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
onnx - Open standard for machine learning interoperability
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
dalle-mini - DALL路E Mini - Generate images from a text prompt