Text to Image Generation
458 papers with code • 1 benchmarks • 1 datasets
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 implementations
DiffusionDB
Most implemented papers
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
taoxugit/AttnGAN
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CVPR 2018
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
Zero-Shot Text-to-Image Generation
openai/DALL-E
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Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Adding Conditional Control to Text-to-Image Diffusion Models
lllyasviel/controlnet
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ICCV 2023
ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls.
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
rinongal/textual_inversion
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Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes.
InstructPix2Pix: Learning to Follow Image Editing Instructions
timothybrooks/instruct-pix2pix
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CVPR 2023
We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image.
Composer: Creative and Controllable Image Synthesis with Composable Conditions
Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability.
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.
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
luosiallen/latent-consistency-model
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Inspired by Consistency Models (song et al.), we propose Latent Consistency Models (LCMs), enabling swift inference with minimal steps on any pre-trained LDMs, including Stable Diffusion (rombach et al).
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.
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.
