ConvNeXt
stable-diffusion
ConvNeXt | stable-diffusion | |
---|---|---|
7 | 383 | |
5,009 | 65,504 | |
- | 1.1% | |
3.4 | 0.0 | |
over 1 year ago | 23 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.
ConvNeXt
-
Sunday Daily Thread: What's everyone working on this week?
Excited to share a python package that I've released (working on it for a while now): git: https://github.com/sashank-tirumala/yaml_config_override pypi: https://pypi.org/project/yaml-config-override/ The idea is simple, often you need to write hundred lines of 'argparse' code for deep learning and machine learning projects (example). To avoid that we create config files (yaml) but then there are times when you just want to overwrite the config values with 'argparse'. This package automates that process. It automatically adds command line arguments from config file definitions and then also overwrites config file arguments with your command line arguments. So for example:
-
[D] Influential papers round-up 2022. What are your favorites?
Found relevant code at https://github.com/facebookresearch/ConvNeXt + all code implementations here
- Are transformers taking over CNNs in the computer vision field ?
-
[D] What is your setup for setting up and monitoring experiments in the cloud?
Have you considered using monitor services such as Wandb? https://github.com/facebookresearch/ConvNeXt provides the code for distributed training, which you can refer to. It is a big tricky to make it work when using distributed training, but obviously it is better than regularly checking progress.
-
[R] Facebook AI & UC Berkeley’s ConvNeXts Compete Favourably With SOTA Hierarchical ViTs on CV Benchmarks
The ConvNeXt code is available on the project’s GitHub. The paper A ConvNet for the 2020s is on arXiv.
The ConvNeXt code is available on the project’s GitHub. The paper A ConvNet for the 2020s is on arXiv.
-
Convolution is not dead. (A ConvNet for the 2020s)
Code: https://github.com/facebookresearch/ConvNeXt
stable-diffusion
-
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.
-
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.
-
Why & How to check Invisible Watermark
an invisible watermarking of the outputs, to help viewers identify the images as machine-generated.
-
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...
-
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?
-
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.
-
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?
Swin-Transformer - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
GFPGAN - GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
Planet-Adventure- - Take a trip through our solar system and visit the planets!
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
GMAIL_TO_EXCEL_KEYWORD_SENDER_LIST - Get an EXCEL file of senders emailing you keywords [Moved to: https://github.com/daefv/Lights]
diffusers-uncensored - Uncensored fork of diffusers
diffusers - 🤗 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·E Mini - Generate images from a text prompt
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models