VideoX
Cold-Diffusion-Models
VideoX | Cold-Diffusion-Models | |
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7 | 14 | |
930 | 933 | |
1.3% | - | |
3.9 | 0.0 | |
about 1 month ago | over 1 year ago | |
Python | Python | |
GNU General Public License v3.0 or later | - |
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VideoX
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Generative AI
Input Output Theme Use Case Example Platforms Text Text Marketing Copywriting, creative personalisation, SEO optimisation Jasper AI (https://www.jasper.ai/) WRITER (https://writer.com/) Video Video / Text Sales Business development agumentation, Sales coaching https://www.regie.ai/ https://www.oliv.a/ Audio Text / Audio CRM Customer service chatbots (answering tickets and queries) https://symbl.ai/ https://forethought.ai/ Audio / Text Audio / Text Talent Management Performance management, Job interviews, Coaching, training https://www.onloop.com/ https://www.converzai.com/ Text Text Legal Contract drafting, Legal validation / citations https://casetext.com/ Text Text Code creation Natural language to generate code for softwares. AI pair programmer https://mayalabs.io/ https://github.com/features/copilot https://debuild.app/ https://kombai.io/ Text Text Code Documentation Natural language to generate code for softwares. AI pair programmer https://mayalabs.io/ https://github.com/features/copilot Text Image Art Generation AI system that can create realistic images and art from a description in natural language. https://openai.com/dall-e-2/ https://midjourney.com/home/?callbackUrl=%2Fapp%2F Text Text ML Model Build, train and deploy AI models https://huggingface.co/ https://replicate.com/ Text Audio Voice Synthesis Educational platform, training, Cognitive coaching https://synthesys.io/ https://speechify.com/ Text Video Video Educational platform, training, Cognitive coaching https://www.synthesia.io/ https://github.com/microsoft/VideoX/tree/master/X-CLIP Text Image 3D modelling Storyboarding for games, 3D video modelling https://nv-tlabs.github.io/GET3D/ https://dreamfusion3d.github.io/
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General Video Recognition with AI (How AI Understands Videos)
►Code: https://github.com/microsoft/VideoX/tree/master/X-CLIP
- [D] Most Popular AI Research Aug 2022 - Ranked Based On GitHub Stars
- Most Popular AI Research Aug 2022 pt. 2 - Ranked Based On GitHub Stars
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Stable Diffusion: Is Video Coming Soon?
The pieces are coming into place https://github.com/microsoft/VideoX/tree/master/X-CLIP
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Latest Computer Vision Research At Microsoft Explains How This Proposed Method Adapts The Pretrained Language Image Models To Video Recognition
Continue reading | Check out the paper and github link
Cold-Diffusion-Models
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[Discussion] training a diffusion model with a destructive process other than gaussian noise
Sure you can. You might be interested in cold diffusion (https://arxiv.org/abs/2208.09392) which tries doing a bunch of different kinds of degradation processes besides adding gaussian noise. You can kind of choose whatever input corruption process you want and teach the model to reverse it, and it works kinda well (I think gaussian noise might be better though)
- [D]eterministic diffusion models
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The Uncanny Failures of A.I.-Generated Hands
I wrote a response yesterday but did not post it or send it, ops.
I still don't understand the problem, if you ask model trained on a noise pattern "trees" for a forest it will still give you a random forest, that's what it was trained on, also: https://arxiv.org/abs/2208.09392, to see the diffusion process applied to processes other than Gaussian noise.
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when will be get away from noise based diffusion
What about this research: https://arxiv.org/abs/2208.09392
- Becoming a machine learning Engineer.
- About art AIs, how noise works?
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Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Found relevant code at https://github.com/arpitbansal297/Cold-Diffusion-Models + all code implementations here
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Denoising Diffusion models from first principle in Julia
This claims to explain diffusion models from first principles, but the issue with explaining how they work is we don't know how they work.
The explanation in the original paper turns out not to be true; you can get rid of most of their assumptions and it still works: https://arxiv.org/abs/2208.09392
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[D] Has anyone tried coding latent diffusion from scratch? or tried other conditioning information aside from image classes and text?
Check out the cold-diffusion repo, which has nice clean implementations, and also is useful in pointing out that the multi-step computation idea isn’t limited to denoising. https://github.com/arpitbansal297/Cold-Diffusion-Models
- [D] Most Popular AI Research August 2022 - Ranked By Twitter Likes
What are some alternatives?
textual_inversion
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
Intrusion-Detection-System-Using-Machine-Learning - Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
MinVIS
frame-interpolation - FILM: Frame Interpolation for Large Motion, In ECCV 2022.
ddsp-singing-vocoders - Official implementation of SawSing (ISMIR'22)
Awesome-Dataset-Distillation - Awesome Dataset Distillation Papers
civitai - A repository of models, textual inversions, and more
PeRFception - [NeurIPS2022] Official implementation of PeRFception: Perception using Radiance Fields.