clip-glass
CLIP_prefix_caption
| clip-glass | CLIP_prefix_caption | |
|---|---|---|
| 13 | 2 | |
| 177 | 1,211 | |
| - | - | |
| 0.0 | 0.0 | |
| over 2 years ago | 3 months ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 only | MIT License |
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clip-glass
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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.
CLIP_prefix_caption
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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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[P] Fast and Simple Image Captioning model using CLIP and GPT-2
Image Captioning used to be a very complicated task, but now all you need is some pretrained CLIP and GPT-2. Check out my project repo for code and inference notebook, including our pretrained models. You can easily try on arbitrary images, please share your results :).
What are some alternatives?
a-PyTorch-Tutorial-to-Image-Captioning - Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
meshed-memory-transformer - Meshed-Memory Transformer for Image Captioning. CVPR 2020
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
aphantasia - CLIP + FFT/DWT/RGB = text to image/video
stylized-neural-painting - Official Pytorch implementation of the preprint paper "Stylized Neural Painting", in CVPR 2021.
StyleCLIP - Using CLIP and StyleGAN to generate faces from prompts.
CLIP-Style-Transfer - Doing style transfer with linguistic features using OpenAI's CLIP.
StyleCLIP - Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)
stylegan2-clip-approach - Navigating StyleGAN2 w latent space using CLIP
AuViMi - AuViMi stands for audio-visual mirror. The idea is to have CLIP generate its interpretation of what your webcam sees, combined with the words thare are spoken.
VectorAscent - Generate vector graphics from a textual caption