textual_inversion
MinVIS
| textual_inversion | MinVIS | |
|---|---|---|
| 30 | 5 | |
| 2,743 | 261 | |
| - | 0.0% | |
| 0.8 | 0.0 | |
| about 1 year ago | over 1 year ago | |
| Jupyter Notebook | Python | |
| MIT License | GNU General Public License v3.0 or later |
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textual_inversion
- FLiP Stack Weekly for 06 February 2023
- Loading textual inversion embeddings in vanilla SD library?
- Embeddings without using AUTO1111
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How to use embeddings with PyTorch
Checking out https://github.com/rinongal/textual_inversion, which has some possibly informative examples and scripts.
- Textual Inversion
- Advice on Automatic1111 textual inversion tuning?
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Hi. Is training my own textual inversion feasible on one 1070? &how long does it take?
I think currently you will need about 20GB VRam..., options are: 1. https://github.com/rinongal/textual_inversion - localy
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Question About Running Local Textual Inversion
Rinongal and nicolai256 versions, the latter of which is also the one explained in Nerdy Rodent's youtube video https://www.youtube.com/watch?v=WsDykBTjo20, work but they also have an issue of lacking editability in comparison to one made by huggingface's collab which is followed up in a very long issue on Rinongal's Github. You can add accumulate_grad_batches: 4 to the end of the finetune files like shown in Nerdy Rodent's video at this time stamp to try to alleviate this issue, but the quality isn't as good as one made in the online collab.
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How close are we to full movie generation from a technical standpoint?
That may mostly solve that but it’s too early right now: https://github.com/rinongal/textual_inversion
For fun I tried to make an entire animated music video but it took over one week of processing and basically fell apart coherently by 30 seconds so just did one third:
https://youtu.be/f3GfUKJBUYA
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Easy Textual Inversion tutorial. How To Train Stable Diffusion With Your Own Art.
The huggingface models don't work with the local stable diffusion, only the models trained locally with this repo https://github.com/rinongal/textual_inversion can be installed, at least for now.
MinVIS
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Using image based models for videos
This is basically that but for instance segmentation https://github.com/NVlabs/MinVIS
- [D] Most Popular AI Research Aug 2022 - Ranked Based On GitHub Stars
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Most Popular AI Research Aug 2022 pt. 2 - Ranked Based On GitHub Stars
https://arxiv.org/abs/2208.02245 https://github.com/nvlabs/minvis
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NVIDIA AI Researchers Propose ‘MinVIS,’ A Minimal Video Instance Segmentation (VIS) Framework That Achieves SOTA Performance With Neither Video-Based Architectures Nor Training Procedures
Continue reading | Check out the Preprint/Under review paper and github link.
- A Minimal Video Instance Segmentation Framework Without Video-Based Training
What are some alternatives?
stable-diffusion - A latent text-to-image diffusion model
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..)
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
Cold-Diffusion-Models - Official implementation of Cold-Diffusion for different transformations in pytorch.
InvokeAI - InvokeAI is a leading creative engine for Stable Diffusion models, empowering professionals, artists, and enthusiasts to generate and create visual media using the latest AI-driven technologies. The solution offers an industry leading WebUI, supports terminal use through a CLI, and serves as the foundation for multiple commercial products.
stable-diffusion-webui - Stable Diffusion web UI
PeRFception - [NeurIPS2022] Official implementation of PeRFception: Perception using Radiance Fields.
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
walk_in_the_park
VideoX - VideoX: a collection of video cross-modal models
Awesome-Dataset-Distillation - Awesome Dataset Distillation Papers