StyleCLIP
CLIP
StyleCLIP | CLIP | |
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23 | 103 | |
3,899 | 22,316 | |
- | 3.0% | |
0.0 | 1.2 | |
11 months ago | 3 days ago | |
HTML | Jupyter Notebook | |
MIT License | MIT License |
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StyleCLIP
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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.
CLIP
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How to Cluster Images
We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:
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Show HN: Memories, FOSS Google Photos alternative built for high performance
Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
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How to Build a Semantic Search Engine for Emojis
Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
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Where can this be used? I have seen some tutorials to run deepfloyd on Google colab. Any way it can be done on local?
pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade
What are some alternatives?
encoder4editing - Official implementation of "Designing an Encoder for StyleGAN Image Manipulation" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02766
open_clip - An open source implementation of CLIP.
compare_gan - Compare GAN code.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
NVAE - The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
stylegan2-pytorch - Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
disco-diffusion
pixel2style2pixel - Official Implementation for "Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation" (CVPR 2021) presenting the pixel2style2pixel (pSp) framework
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
alias-free-gan - Alias-Free GAN project website and code
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation