deeplab2
DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks. (by google-research)
latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models (by CompVis)
| deeplab2 | latent-diffusion | |
|---|---|---|
| 5 | 70 | |
| 982 | 10,622 | |
| 0.2% | 2.8% | |
| 4.0 | 0.0 | |
| about 1 year ago | 2 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
deeplab2
Posts with mentions or reviews of deeplab2.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-06.
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Meet MOAT: An Artificial Intelligence (AI) Model that Combines Convolution and Attention Operations to Achieve Powerful Vision Models
Quick Read: https://www.marktechpost.com/2022/12/30/meet-moat-an-artificial-intelligence-ai-model-that-combines-convolution-and-attention-operations-to-achieve-powerful-vision-models/ Paper: https://arxiv.org/pdf/2210.01820.pdf Github: https://github.com/google-research/deeplab2
- [D] Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
- Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
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[P] DeepLab2: A TensorFlow Library for Deep Labeling web demo
github: https://github.com/google-research/deeplab2
- DeepLab2 – New deep labeling library for TensorFlow
latent-diffusion
Posts with mentions or reviews of latent-diffusion.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-21.
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SDXL: The next generation of Stable Diffusion models for text-to-image synthesis
Stable Diffusion XL (SDXL) is the latest text-to-image generation model developed by Stability AI, based on the latent diffusion techniques. SDXL has the potential to create highly realistic images for media, entertainment, education, and industry domains, opening new ways in practical uses of AI imagery.
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Is it possible to create a checkpoint from scratch?
Here's a link to the early latent-diffusion git, that might be able to create a blank model (I haven't tested it): https://github.com/CompVis/latent-diffusion
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Anything better than pix2pixHD?
Latent diffusion could work for you: https://github.com/CompVis/latent-diffusion (https://arxiv.org/abs/2112.10752)
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Image Upscaler AI
There are a lot but the one implemented as LDSR in most stable guis is this one. https://github.com/CompVis/latent-diffusion
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I've been collecting millions of images of only public domain /cc0 licensing. I'd like to train a stable diffusion model on the collection. Could some one share their knowledge of what this would take? Otherwise, simply enjoy my library.
CompVis/latent-diffusion: High-Resolution Image Synthesis with Latent Diffusion Models (github.com)
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Run Clip on iPhone to Search Photos
The "retrieval based model" refers to https://github.com/CompVis/latent-diffusion#retrieval-augmen..., which uses ScaNN to train a knn embedding searcher.
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Class Action Lawsuit filed against Stable Diffusion and Midjourney.
Stability is basically https://github.com/CompVis/latent-diffusion + training data.
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[D] Influential papers round-up 2022. What are your favorites?
Found relevant code at https://github.com/CompVis/latent-diffusion + all code implementations here
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Can anyone explain differences between sampling methods and their uses to me in simple terms, because all the info I've found so far is either very contradicting or complex and goes over my head
DDIM and PLMS were the original samplers. They were part of Latent Diffusion's repository. They stand for the papers that introduced them, Denoising Diffusion Implicit Models and Pseudo Numerical Methods for Diffusion Models on Manifolds.
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AI art is very dystopian.
yes, https://github.com/CompVis/latent-diffusion
What are some alternatives?
When comparing deeplab2 and latent-diffusion you can also consider the following projects:
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
disco-diffusion
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
dalle-mini - DALL·E Mini - Generate images from a text prompt
multiface - Hosts the Multiface dataset, which is a multi-view dataset of multiple identities performing a sequence of facial expressions.
hent-AI - Automation of censor bar detection
Unicorn - [ECCV'22 Oral] Towards Grand Unification of Object Tracking
dalle-2-preview
NUWA - A unified 3D Transformer Pipeline for visual synthesis
stable-diffusion
Cream - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/AutoML]
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
deeplab2 vs yolov7
latent-diffusion vs disco-diffusion
deeplab2 vs XMem
latent-diffusion vs dalle-mini
deeplab2 vs multiface
latent-diffusion vs hent-AI
deeplab2 vs Unicorn
latent-diffusion vs dalle-2-preview
deeplab2 vs NUWA
latent-diffusion vs stable-diffusion
deeplab2 vs Cream
latent-diffusion vs DALLE2-pytorch