backgroundremover
stable-diffusion
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| backgroundremover | stable-diffusion | |
|---|---|---|
| 86 | 382 | |
| 6,220 | 65,243 | |
| - | 2.0% | |
| 6.9 | 0.0 | |
| 5 days ago | 10 days ago | |
| Python | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
backgroundremover
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Ask HN: What side projects landed you a job?
Not a job I took. But when I launched https://github.com/nadermx/backgroundremover I got offered a high level position in a a photo company via my email which at the time was on my GitHub profile.
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Image Upscaler AI
UI looks identical to https://backgroundremoverai.com/ ?
- Ask HN: Would you pay for a SaaS even if its open source and can be selfhosted?
- Show HN: Image background removal without annoying subscriptions
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Open source Background Remover: Remove Background from images and video using AI
I think this is what you wanted? https://github.com/nadermx/backgroundremover/tree/main/dist
- Background Remover lets you Remove Background from images and video using AI with a simple command line interface that is free and open source.
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BackgroundRemover 0.2.1 - Remove Background from Video and Images using AI
This is a website.
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?
U-2-Net - The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
diffusers-uncensored - Uncensored fork of diffusers
rembg - Rembg is a tool to remove images background
diffusers - 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Linux-Fake-Background-Webcam - Faking your webcam background under GNU/Linux, now supports background blurring, animated background, colour map effect, hologram effect and on-demand processing.
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
obs-backgroundremoval - An OBS plugin for removing background in portrait images (video), making it easy to replace the background when recording or streaming.
onnx - Open standard for machine learning interoperability