clip-glass
Repository for "Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search" (by galatolofederico)
StyleCLIP
Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral) (by orpatashnik)
| clip-glass | StyleCLIP | |
|---|---|---|
| 13 | 23 | |
| 177 | 3,899 | |
| - | - | |
| 0.0 | 0.0 | |
| over 2 years ago | 11 months ago | |
| Python | HTML | |
| GNU General Public License v3.0 only | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
clip-glass
Posts with mentions or reviews of clip-glass.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-03.
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test
(Added Feb. 5, 2021) CLIP-GLaSS.ipynb - Colaboratory by Galatolo. Uses BigGAN (default) or StyleGAN to generate images. The GPT2 config is for image-to-text, not text-to-image. GitHub.
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Image to text models
After a cursory search I found CLIP-GLaSS and CLIP-cap. I've used CLIP-GLaSS in a previous experiment, but found the captions for digital/CG images quite underwhelming. This is understandable since this is not what the model was trained on, but still I'd like to use a better model.
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[R] end-to-end image captioning
CLIP-GLaSS
- What CLIP-GLaSS thinks Ancient Egyptian computers would look like
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Texttoimage 3 Images For Text Photo Of Donald
The images were generated using this notebook.
- CLIP-GLaSS prompt: "Screenshot of a video game from the 1930s"
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[P] List of sites/programs/projects that use OpenAI's CLIP neural network for steering image/video creation to match a text description
The CLIP-GLaSS project has image-to-text functionality (I haven't tried it.)
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For educational purposes: Text-to-image (3 runs with no cherry-picking, 6 images each) for text "Photo of a Lamborghini painted purple and red" generated using CLIP-GLaSS. config=StyleGAN2_car_d. save_each=50. generations=1000
Link to notebook.
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Sharing CLIP magic based on OpenAI's blog post via a bit more accessible YT medium. Lmk what u think 🙈 ❤️
CLIP-GLaSS
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[R] [P] Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search. Link to code and Google Colab notebook for project CLIP-GLaSS is in a comment.
Github for CLIP-GLaSS is here.
StyleCLIP
Posts with mentions or reviews of StyleCLIP.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-13.
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A History of CLIP Model Training Data Advances
While CLIP on its own is useful for applications such as zero-shot classification, semantic searches, and unsupervised data exploration, CLIP is also used as a building block in a vast array of multimodal applications, from Stable Diffusion and DALL-E to StyleCLIP and OWL-ViT. For most of these downstream applications, the initial CLIP model is regarded as a “pre-trained” starting point, and the entire model is fine-tuned for its new use case.
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[D] What is the largest / most diverse GAN model currently out there?
I'm currently building a fork for StyleCLIP global directions which allows you to control multiple semantic parameters simoultaneously to generate and edit an image with StyleGAN and CLIP in realtime. I want to showcase its potential as a design tool. Unfortunately, GAN weights are trained on very domain-specific (faces, cars, churches) data. This makes them inferior to modern diffusion models which I can use to generate whatever comes to mind. Although I know we won't have a GAN-based DALL-E counterpart anytime soon, I still would love to use my system with weights that can output a wide variety of things.
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test
(Added Feb. 15, 2021) StyleCLIP - Colaboratory by orpatashnik. Uses StyleGAN to generate images. GitHub. Twitter reference. Reddit post.
- I am David Bau, and I study the structure of the complex computations learned within deep neural networks.
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Dragon Age Origins Companions as Photorealistic People.
I used StyleCLIP. I purchased some Google Colab time to use their GPUs. I'll probably do some more later this week.
- Turning BDO characters into blursed people with AI
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I used AI to generate real life for honor character faces
Link for Styleclip
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AI-generated 'real' faces of CGI characters - description in comments
So, I watched this Corridor Crew video on generating realistic faces from CG characters, and I wanted to try it out on the RDR2 models. The github link for the original work is here. If you guys are interested I can generate the faces of more characters from RDR2 and RDR1. I can even try some from RD Revolver.
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AI Generated Art Scene Explodes as Hackers Create Groundbreaking New Tools - New AI tools CLIP+VQ-GAN can create impressive works of art based on just a few words of input.
Combining these methods with CLIP allows you to generate images based on text. This one uses a face generator. https://github.com/orpatashnik/StyleCLIP
- [D] How to save latent code edited from StyleClip.
What are some alternatives?
When comparing clip-glass and StyleCLIP you can also consider the following projects:
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
encoder4editing - Official implementation of "Designing an Encoder for StyleGAN Image Manipulation" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02766
meshed-memory-transformer - Meshed-Memory Transformer for Image Captioning. CVPR 2020
compare_gan - Compare GAN code.
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
NVAE - The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)
aphantasia - CLIP + FFT/DWT/RGB = text to image/video
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
stylized-neural-painting - Official Pytorch implementation of the preprint paper "Stylized Neural Painting", in CVPR 2021.
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
StyleCLIP - Using CLIP and StyleGAN to generate faces from prompts.
alias-free-gan - Alias-Free GAN project website and code
clip-glass vs a-PyTorch-Tutorial-to-Image-Captioning
StyleCLIP vs encoder4editing
clip-glass vs meshed-memory-transformer
StyleCLIP vs compare_gan
clip-glass vs deep-daze
StyleCLIP vs NVAE
clip-glass vs aphantasia
StyleCLIP vs stylegan2-pytorch
clip-glass vs stylized-neural-painting
StyleCLIP vs pixel2style2pixel
clip-glass vs StyleCLIP
StyleCLIP vs alias-free-gan