[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
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Updated
Aug 11, 2024 - Python
[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
Pytorch implementation of Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
PyTorch codes for "Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors", ACM MM2022 (Oral)
[CVPR 2023] | RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
Official Implement of Multi-Stage Multi-Codebook (MSMC) TTS
Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt
Zero-Shot Text-to-Image Generation VQGAN+CLIP Dockerized
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
Implementation of Binary Latent Diffusion
Fast and controllable text-to-image model.
Streamlit Tutorial (ex: stock price dashboard, cartoon-stylegan, vqgan-clip, stylemixing, styleclip, sefa)
VQ-VAE/GAN implementation in pytorch-lightning
[ICLR 2024] DAEFR: Dual Associated Encoder for Face Restoration
Traditional deepdream with VQGAN+CLIP and optical flow. Ready to use in Google Colab.
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.
Vector-Quantized Generative Adversarial Networks
VQGAN from LDM without hell of dependencies
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