Stable-Diffusion-ONNX-FP16 VS stylegan2-projecting-images

There are a few versions of stable diffusion that someone ported and you can use them, I think I used https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16 with my amd card, then there's something like shark stable diffusion

Yes, the FP16 stuff in this latest release is a big part of that, and should tentatively support 8GB cards. I haven't pushed things quite as far as https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16 yet, but support for 4GB cards is possible and we've been discussing how it works: https://github.com/ssube/onnx-web/issues/241

Second option is: https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16

A Colab notebook.

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.

Please head over to the Google Colab

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.

访问 Google Colab 。

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.

Open your web browser and navigate to Google Colab.

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.