CompVis/taming-transformers

Features

• High-Resolution Synthesis - Synthesizes high-fidelity, high-resolution visuals using a combination of discrete latent spaces and learned visual patterns.
• Autoregressive Visual Token Predictors - Implements an autoregressive transformer that sequentially predicts discrete visual tokens to generate high-resolution images.
• Discretized Visual Representations - Employs a discrete codebook to quantize continuous feature vectors into a grid of learnable tokens.
• Image Synthesis Models - Combines an autoregressive transformer with a convolutional generator to synthesize high-resolution visual content.
• Latent-to-Pixel Decoding - Uses a convolutional decoder to map discrete latent tokens back into pixel space for high-fidelity image reconstruction.
• Latent Space Encoders - Utilizes a VQ-GAN encoder to compress high-resolution images into a discrete grid of codebook tokens.
• Layout-Guided Synthesis - Allows for precise control of object placement in generated scenes via bounding box coordinates.
• Image Inpainting - Provides capabilities to fill missing or empty image sections by analyzing surrounding pixels and learned patterns.
• Hybrid Architectures - Combines convolutional networks for local texture and transformers for global composition in a hybrid architecture.
• Layout-Based Scene Synthesis - Produces complex images by placing specific objects according to defined bounding box coordinates.
• Grid-Based Image Layouts - Generates complex scenes by placing specific objects according to defined bounding box coordinates.

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