VQGAN-CLIP-Video
vqgan-clip-app
| VQGAN-CLIP-Video | vqgan-clip-app | |
|---|---|---|
| 1 | 3 | |
| 22 | 101 | |
| - | - | |
| 1.8 | 0.0 | |
| about 2 years ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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VQGAN-CLIP-Video
vqgan-clip-app
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How not to waste $1600?
If you want to try your hand at buggering your whole system - try playing with AI image generation as it uses all possible computer assets :D . There is a lot of forms and installations for those but I VQGANs from github the easiest. Problem is that some require familarity with shell, python and in some cases - you need to enable the Linux subsystem in Windows (is it called a subsystem? it is not exactly a VM). This one is the easiest to install out of all I tried. But I liked the results of Pixray most but I wrecked it. I use this one nowadays.
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[P] Nvidia releases web app for GauGAN2, which generates landscape images via text description, inpainting, sketch, object type segmentation map, and style image
My attempt at centralizing models to be run locally looks like this: https://github.com/tnwei/vqgan-clip-app/, currently supports VQGAN-CLIP models and CLIP guided diffusion models.
- App for running VQGAN-CLIP and CLIP guided diffusion locally
What are some alternatives?
frame-interpolation - FILM: Frame Interpolation for Large Motion, In ECCV 2022.
streamlit - Streamlit — A faster way to build and share data apps.
optical.flow.demo - A project that uses optical flow and machine learning to detect aimhacking in video clips.
CLIP-Guided-Diffusion - Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.
feed_forward_vqgan_clip - Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt
jina-app-store-example - App store search example, using Jina as backend and Streamlit as frontend [Moved to: https://github.com/jina-ai/example-app-store]
moviepy - Video editing with Python
ai-art-generator - For automating the creation of large batches of AI-generated artwork locally.
AI-Art - PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!