gradio VS animegan2-pytorch

There's also that library all the AI models started using that gives you a public URL to share. After researching it: https://www.gradio.app/ is the link.

It's used specifically for making simple UIs for machine learning apps. But I guess technically you could use it for anything.

What is the business model for https://www.taipy.io/, https://streamlit.io/, or https://www.gradio.app/? These are nice tools - but how will the sponsoring businesses support themselves? I didn't see any mention of enterprise plans, etc. Is the answer simply that "we've not announced our revenue model yet"? What should one expect?

Repo : https://github.com/gradio-app/gradio

There's TensorFlow.js for running machine learning on JavaScript, but personally, I'd prefer using the Python Gradio package, which is designed for creating UIs for machine learning inference demos.

I found an issue on gradio github but looks like it's closed so I am not sure if it's still a common issue or only I am facing it due to certain settings/absence of a fix. ( https://github.com/gradio-app/gradio/issues/3060 )

I disagree hard with this – for instance I've recently needed to dig into the code for the Gradio library, and when PRs are like https://github.com/gradio-app/gradio/pull/3300 (and the merge commit's message is what it is) it's hard to understand why some decisions have been made when doing `git annotate` later on.

Gradio for a ui that your commanderGPT can visit and use

gradio is documented, it doesn't seem very complex, it would be something like moving this block under the other one. i think it's ui_extra_networks.py, the file you are looking to edit. (if you do it make a copy to restore when you go to update)

Gradio did have an issue for a while where your URL was guessable, so unless you had a password it was pretty easy to find, but as far as I know they've increased the complexity so much that it's no longer an issue.

My script wouldn't work on the langchain because it was literally made within the gradio "docs builder" script that afaik was made specifically for their website (repo)

rom PIL import Image import torch import IPython from IPython.display import display # upload images from google.colab import files uploaded = files.upload() # https://github.com/bryandlee/animegan2-pytorch # load models and face2paint utility function model_facev2 = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", pretrained="face_paint_512_v2") face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", size=512) for INPUT_IMG in ['KatLi.JPG']: img = Image.open(INPUT_IMG).convert("RGB") out_facev2 = face2paint(model_facev2, img) # display images display(img) display(out_facev2)

I'll point you towards Prism, then. As far as style transfer goes, it's the best I've found so far. Extremely simple to run in colab and offline, plus the results are quite good for what it's doing. I used it a ton before I started running Face Portrait v2 offline as a general "art" filter for things I'm working on. It does stylization and some facial morphing in one pass, which adds a lot more personality than style transfer alone. You can also use it on anything, not just faces.

I would recommend https://github.com/bryandlee/animegan2-pytorch and have used it for other videos, but I’m still working on my own model, the results of which you see above 👆🏻

Sure, I used this Python library and the Inshot video editor https://github.com/bryandlee/animegan2-pytorch

PyTorch Implementation of AnimeGANv2

(2021.02.21) The pytorch version of AnimeGANv2 has been released, Be grateful to @bryandlee for his contribution.

AnimeGAN2 PyTorch

github: https://github.com/bryandlee/animegan2-pytorch

source tweet: https://twitter.com/Yokohara\_h/status/1466521442686685188?s=20 web app(webcam): https://huggingface.co/spaces/nateraw/animegan-v2-for-videos web app(images): https://huggingface.co/spaces/akhaliq/AnimeGANv2 github: https://github.com/bryandlee/animegan2-pytorch