VQGAN-CLIP
fast-style-transfer
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| VQGAN-CLIP | fast-style-transfer | |
|---|---|---|
| 67 | 4 | |
| 2,563 | 10,866 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | 9 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | - |
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VQGAN-CLIP
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📚 Tutorials & 🎨 AI Art Generation Tool List Mega Thread
VQGAN-CLIP
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Which is your favorite text to image model overall?
I've screwed with many text-to-image models over the past couple of years, and I found that while I currently enjoy Stable Diffusion's coherency, I have a soft spot for the ImageNet model used by default for VQGAN+CLIP. It easily approaches the uncanny valley when generating people or animals, but makes for great abstract backgrounds and wallpapers. I already have nostalgia for generating images with it on my CPU overnight.
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Stable Diffusion Announcement
For someone only tangentially familiar with this space, how is this different than e.g. https://github.com/nerdyrodent/VQGAN-CLIP which you can also run at home? Is it the quality of the generated images?
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Medieval Noir - VQGAN-CLIP - COCO Checkpoint
Used https://github.com/nerdyrodent/VQGAN-CLIP
- Once have access, do you run it on your computer or over the internet on Open-AI's computers?
- How to get AI imaging effect in Premiere pro
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A Guide to Asking Robots to Design Stained Glass Windows
I don't have any of the DALL-Es but I do have a couple from github [1], [2] which gave these outputs[3]
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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.
- Ask HN: Is there a publicly available (not private beta) text-to-image API?
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Got a Machine Learning Algorithm to depict Aphex
For those that are interested, I used VQGAN-CLIP, specifically this GitHub repository
fast-style-transfer
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Where to learn how to turn photos into images in the style of famous painters?
Try: https://github.com/lengstrom/fast-style-transfer . There are links to the underlying papers.
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I had an AI generate a bunch of synthwave inspired art. This was the result.
The algorithm is called style transfer, here's a well known implementation https://github.com/lengstrom/fast-style-transfer
- 【Image Transformation】Convert pictures to add styles from famous paintings
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10 Deep Learning Projects (Beginner & Advanced)
TensorFlow Fast Style Transfer PyTorch Fast Neural Style
What are some alternatives?
CLIP-Guided-Diffusion - Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.
randomCNN-voice-transfer - Audio style transfer with shallow random parameters CNN.
DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.
animegan2-pytorch - PyTorch implementation of AnimeGANv2
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Styleformer - A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.
deep-daze - Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun
OpenCV - Open Source Computer Vision Library
waifu2x - Image Super-Resolution for Anime-Style Art
examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
stable-diffusion - A latent text-to-image diffusion model
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration