textual_inversion
stylegan2-projecting-images
textual_inversion | stylegan2-projecting-images | |
---|---|---|
30 | 135 | |
2,743 | 288 | |
- | - | |
0.8 | 10.0 | |
about 1 year ago | about 1 year ago | |
Jupyter Notebook | 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.
textual_inversion
- FLiP Stack Weekly for 06 February 2023
- Loading textual inversion embeddings in vanilla SD library?
- Embeddings without using AUTO1111
-
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?
-
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
-
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.
-
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
-
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.
stylegan2-projecting-images
-
Getting Started with Gemma Models
A Colab notebook.
- Welcome to Colaboratory
-
A playground to practice differential privacy - Antigranular
To play with the dataset, we first must create a Jupyter notebook, a powerful and popular tool among data engineers. I created mine on Google Colab.
-
Topic and Subtopic Extraction with the Google Gemini Pro
Please head over to the Google Colab
-
How do I begin building AI tools for myself?
But regardless of what you want to do, you'll probably use Python. In this context, a good way to work with Python is using Jupyter Notebooks. So you should start with installing Python and Jupyter and go from there. If you want to get started without installing anything, Google Colab gives you a remote Jupyter Notebook which runs in the browser for free.
-
教程:使用 Google Colab 安全地转发 B 站视频
访问 Google Colab 。
-
Journey into Jupyter Notebooks: A Beginner's Guide
Remember school days when you'd share notes with classmates? Jupyter takes that spirit and amplifies it. Once you've crafted your Notebook, you can share it with peers, collaborators, and the world. Platforms like GitHub and Google's Colab natively render Jupyter Notebooks. It's like penning an open letter to the world but in a delightful mix of code, text, and visuals.
- This feels like an obvious question, but if I load a pickle file that is 1GB in size, is it taking up 1GB of memory?
-
Leveraging Google Colab to run Postgres: A Comprehensive Guide
Open your web browser and navigate to Google Colab.
-
No excuses to start working with Python
Using Google Colab you can develop Python codes, similar to Jupyter Notebooks. You will have an environment prepared with various Python libraries. In addition you have tips on small codes for development, some tutorials, gihub connection, cloud -saved notebooks and more.
What are some alternatives?
stable-diffusion - A latent text-to-image diffusion model
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
stable-diffusion-webui-colab - stable diffusion webui colab
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.
gimp-stable-diffusion
stable-diffusion-webui - Stable Diffusion web UI
discoart - 🪩 Create Disco Diffusion artworks in one line
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]
quickstart-android - Firebase Quickstart Samples for Android
VideoX - VideoX: a collection of video cross-modal models
comfyui-colab - comfyui colabs templates new nodes