vqgan-clip
There are 31 repositories under vqgan-clip topic.
rbbrdckybk/ai-art-generator
For automating the creation of large batches of AI-generated artwork locally.
MohamadZeina/Disco_Diffusion_Local
Getting the latest versions of Disco Diffusion to work locally, instead of colab. Including how I run this on Windows, despite some Linux only dependencies ;)
somewheresy/S2ML-Generators
Multiple notebooks which allow the use of various machine learning methods to generate or modify multimedia content
rkhamilton/vqgan-clip-generator
Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.
pytti-tools/pytti-notebook
Start here
tnwei/vqgan-clip-app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
bes-dev/pytorch_clip_guided_loss
A simple library that implements CLIP guided loss in PyTorch.
ryananan/ai-atelier
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. 在Disco Diffusion模型的基础上,我们开发了一款汉化版AI艺术创作软件“AI聊天画室”。我们同时提供了文本生成图像模型(Disco Diffusion与VQGAN+CLIP)及文本生成文本(GPT-J-6B及GPT-NEOX-20B)可供选择。
happy-jihye/Streamlit-Tutorial
Streamlit Tutorial (ex: stock price dashboard, cartoon-stylegan, vqgan-clip, stylemixing, styleclip, sefa)
dughogan/VQGAN_Prompts
Generates a random prompt for a VQGAN+CLIP
robobeebop/VQGAN-CLIP-Video
Traditional deepdream with VQGAN+CLIP and optical flow. Ready to use in Google Colab.
overshard/ai-art
Art generation using VQGAN + CLIP using docker containers. A simplified, updated, and expanded upon version of Kevin Costa's work. This project tries to make generating art as easy as possible for anyone with a GPU by providing a simple web UI.
SilentByte/artai
ArtAI is an interactive art installation that collects people's ideas in real-time from social media and uses deep learning and AI art generation to curate these ideas into a dynamic display.
yudhisteer/Generating-Design-Ideas-from-Keywords
The purpose of the project is to understand a basic GAN from scratch. A WGAN was built to generate people's faces based in the Celeba Dataset. VQGAN + CLIP model was used to generate unique designs that would be used in fashion.
krishnakaushik25/VQGAN-CLIP
Gradio Web app for running VQGAN-CLIP locally
bharathraj-v/art_project
Mozart - A Generative Art Platform
CorvaeOboro/gen_ability_icon
creates ability icon images utilizing procgen and neural networks
CorvaeOboro/gen_item
creates item images utilizing procgen and neural networks
rxchit/AI-art-gen
AI-powered art generator based on VQGAN+CLIP
Frikallo/YAKbot
⚠️ DEVELOPMENT REPO - NOT MAINTAINED OR EASILY DEPLOYABLE ⚠️
PRAN20/VQ-GAN
VQGAN and CLIP are actually two separate machine learning algorithms that can be used together to generate images based on a text prompt. VQGAN is a generative adversarial neural network that is good at generating images that look similar to others (but not from a prompt), and CLIP is another neural network that is able to determine how well a caption (or prompt) matches an image.
pandas9/txt2dream
machines vivid dreams
Yazdi9/Video-generator
Video generator using CLIP+VQGAN and sdvm
hululuzhu/cn-text-to-pic
趣味中文图片生成
LibreCS/vqgarm
VQGAN+CLIP implementation for aarch64 architecture testing and benchmarking with machine learning workloads
mahalrs/newsgen
Multi-Modal Image Generation for News Stories
mohamedsobhi777/COMP4971C---Independent-Study-Project
COMP4971C Independent Study Project Repository.
OmkarNarvekar001/ART_GENERATION_USING_SPEECH_EMOTIONS
Translation of speech to image directly without text is an interesting and useful topic due to the potential application in computer-aided design, human to computer interaction, creation of an art form, etc. So we have focused on developing Deep learning and GANs based model which will take speech as an input from the user, analyze the emotions associated with it and accordingly generate the artwork which has been demanded by the user which will in turn provide a personalized experience. The approach used here is convolutional VQGAN to learn a codebook of context-rich visual parts, whose composition is subsequently modeled with autoregressive transformer architecture. Concept of CLIP-Contrastive Language Image-Pre-Training, also uses transformers which is a model trained to determine which caption from a set of captions best fits with a given image is used in our project. The input speech is classified into 8 different emotions using MLP classifier trained of RAVDESS emotional speech audio dataset and this acts as a base filter for the VQGAN model. Text converted from speech plays an important role in producing the final output image using CLIP model. VQGAN+CLIP model together utilizes both emotions and text to generate a more personalized artwork.
Xibanya/VQGAN-CLIP
yet another VQGAN-CLIP variation
benckx/baudelaire-on-vqgan
Experiments with Baudelaire and a text-to-image GAN.
SanshruthR/VQGAN-CLIP
VQGAN-CLIP generate images from text prompts.