Topic: vqgan Goto Github
Some thing interesting about vqgan
Some thing interesting about vqgan
vqgan,NTIRE 2022 - Image Inpainting Challenge
User: affromero
vqgan,Experiments with Baudelaire and a text-to-image GAN.
User: benckx
vqgan,Pytorch implementation of Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
Organization: casualganpapers
vqgan,PyTorch codes for "Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors", ACM MM2022 (Oral)
User: chaofengc
vqgan,Streamlit Tutorial (ex: stock price dashboard, cartoon-stylegan, vqgan-clip, stylemixing, styleclip, sefa)
User: happy-jihye
vqgan,Official Implement of Multi-Stage Multi-Codebook (MSMC) TTS
User: hhguo
vqgan,Implementation of Binary Latent Diffusion
User: jiauzhang
vqgan,OCR-VQGAN, a discrete image encoder (tokenizer and detokenizer) for figure images in Paper2Fig100k dataset. Implementation of OCR Perceptual loss for clear text-within-image generation. Fork from VQGAN in CompVis/taming-transformers
User: joanrod
Home Page: https://arxiv.org/abs/2210.11248
vqgan,Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized
User: kcosta42
vqgan,An unofficial PyTorch implementation of VQGAN
User: khanhvu207
vqgan,🇰🇷 Text to Image in Korean
Organization: kr-happyface
vqgan,Video generation of anime content based on the first and last frame
User: kurokabe
vqgan,[ICLR 2024] DAEFR: Dual Associated Encoder for Face Restoration
User: liagm
Home Page: https://liagm.github.io/DAEFR/
vqgan,Multi-Modal Image Generation for News Stories
User: mahalrs
vqgan,Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt
User: mehdidc
vqgan,VQGAN from LDM without hell of dependencies
User: mehdidc
vqgan,Colabs for text prompt steered image generators
User: olaviinha
vqgan,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.
User: overshard
Home Page: https://hub.docker.com/r/overshard/ai-art
vqgan,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.
User: pran20
vqgan,Start here
Organization: pytti-tools
Home Page: https://pytti-tools.github.io/pytti-book/intro.html
vqgan,Fast and controllable text-to-image model.
User: qiyuan-ge
vqgan,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.
User: rkhamilton
vqgan,Traditional deepdream with VQGAN+CLIP and optical flow. Ready to use in Google Colab.
User: robobeebop
vqgan,Implementation of Taming Transformers for High-Resolution Image Synthesis (https://arxiv.org/abs/2012.09841) in PyTorch
User: rosinality
vqgan,[CVPR 2023] | RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
User: rq-wu
Home Page: https://rq-wu.github.io/projects/RIDCP/index.html
vqgan,Vector-Quantized Generative Adversarial Networks
User: sayedmohamedscu
vqgan,Text-to-Image Synthesis using Multimodal (VQGAN + CLIP) Architectures
User: sbmagar
vqgan,Docker for VQGAN+CLIP (z+quantize method)
User: sborquez
vqgan,[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
User: sczhou
vqgan,VQ-VAE/GAN implementation in pytorch-lightning
User: serezd
vqgan,Pytorch implementation of "Taming transformer for high resolution image synthesis (VQGAN)"
User: vrushank264
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