Unicorn
latent-diffusion
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Unicorn | latent-diffusion | |
---|---|---|
7 | 70 | |
942 | 10,575 | |
- | 5.4% | |
0.0 | 0.0 | |
over 1 year ago | 2 months ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
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.
Unicorn
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need help with object detection and object tracking using yolov4
Also check out Unicorn - https://github.com/MasterBin-IIAU/Unicorn
- [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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Researchers from Bytedance and Dalian University Propose 🦄 ‘Unicorn’: a Unified Computer Vision Approach to Address Four Tracking Tasks Using a Single Model with the Same Model Parameters
Continue reading | Checkout the paper and github link
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking(Video Demo)
Brief Overview We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. For the first time, we accomplished the great unification of the tracking network architecture and learning paradigm. Unicorn performs on-par or better than its task-specific counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. Our work is accepted to ECCV 2022 as an oral presentation ! Paper: https://arxiv.org/abs/2207.07078 Code: https://github.com/MasterBin-IIAU/Unicorn
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking
Code for https://arxiv.org/abs/2207.07078 found: https://github.com/MasterBin-IIAU/Unicorn
latent-diffusion
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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?
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.
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
theseus - A library for differentiable nonlinear optimization
dalle-2-preview
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
hent-AI - Automation of censor bar detection
NUWA - A unified 3D Transformer Pipeline for visual synthesis
stable-diffusion
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch