Text-to-Image Generation
276 papers with code • 11 benchmarks • 18 datasets
عکس یه بخیه در حال خون ریزی
Benchmarks
These leaderboards are used to track progress in Text-to-Image Generation
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Libraries
Use these libraries to find Text-to-Image Generation models and implementationsDatasets
MS COCO
CUB-200-2011
Oxford 102 Flower
Conceptual Captions
Fashion-Gen
Multi-Modal CelebA-HQ
LHQ
Pick-a-Pic
T2I-CompBench
Human-Art
Subtasks
Most implemented papers
MaskGIT: Masked Generative Image Transformer
google-research/maskgit
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CVPR 2022
At inference time, the model begins with generating all tokens of an image simultaneously, and then refines the image iteratively conditioned on the previous generation.
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions.
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis
MinfengZhu/DM-GAN
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CVPR 2019
If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality.
CogView: Mastering Text-to-Image Generation via Transformers
THUDM/CogView
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NeurIPS 2021
Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding.
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
ofa-sys/ofa
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In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization.
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
dome272/paella
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Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity.
Muse: Text-To-Image Generation via Masked Generative Transformers
Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Conditional text-to-image generation is an active area of research, with many possible applications.
ManiGAN: Text-Guided Image Manipulation
mrlibw/ManiGAN
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The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e. g., texture, colour, and background), while preserving other contents that are irrelevant to the text.
Conditional Image Generation and Manipulation for User-Specified Content
IIGROUP/Multi-Modal-CelebA-HQ-Dataset
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This can be done by conditioning the model on additional information.
