artroom-stable-diffusion
stable-diffusion
artroom-stable-diffusion | stable-diffusion | |
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8 | 383 | |
219 | 65,624 | |
- | 1.3% | |
0.0 | 0.0 | |
about 1 year ago | about 1 month ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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artroom-stable-diffusion
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Easy-to-use local install of Stable Diffusion released
Github Repo: https://github.com/artmamedov/artroom-stable-diffusion
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Ran an image of the boys through a few different AI models. Here are a few of the better outcomes.
I use Artroom (Alternative download link), mostly because I'm incompetent and it's the easiest one to set up from all of the things I've found.
- Which is your favorite text to image model overall?
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Anyone have an idea what the issue is here, attempting to run the optimized scripts but keep hitting the same error, no problem running the normal scripts. Thanks.
wild guess after looking at this.
- I’m buying a 12 GB card for this, how big can I expect to be able to go?
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image2image throwing errors, unsure how to get it to run
I used a new project called Artroom that did everything for me. I already had the 1.4 model downloaded so I just needed to rename it to model.ckpt and put in the right directory. The creator just added experimental image2image support in the 0.3.0 release, but you can only get that on the authors discord channel at the moment.
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Cats sitting at a table playing poker
Other pictures are from Pic 1's prompt but with varying parameters. Created using Artroom
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Stable Diffusion One-Click Install Local GUI
You can get latest from: https://github.com/artmamedov/artroom-stable-diffusion/releases
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 · 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?
What are some alternatives?
disco-diffusion
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
stable-diffusion-webui - Stable Diffusion web UI
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
stable-diffusion-webui-docker - Easy Docker setup for Stable Diffusion with user-friendly UI
diffusers-uncensored - Uncensored fork of diffusers
mindall-e - PyTorch implementation of a 1.3B text-to-image generation model trained on 14 million image-text pairs
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
CLIP-Guided-Diffusion - Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
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.
onnx - Open standard for machine learning interoperability