Image Generation
1989 papers with code • 85 benchmarks • 67 datasets
Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
( Image credit: StyleGAN )
Benchmarks
These leaderboards are used to track progress in Image Generation
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Libraries
Use these libraries to find Image Generation models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
MNIST
Cityscapes
CelebA
Fashion-MNIST
CUB-200-2011
FFHQ
Oxford 102 Flower
Subtasks
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Image-to-Image Translation
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Image Inpainting
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Text-to-Image Generation
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Conditional Image Generation
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Conditional Image Generation
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Face Generation
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3D Generation
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Image Harmonization
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Pose Transfer
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3D-Aware Image Synthesis
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Facial Inpainting
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Layout-to-Image Generation
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ROI-based image generation
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Image Generation from Scene Graphs
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Pose-Guided Image Generation
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User Constrained Thumbnail Generation
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Handwritten Word Generation
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Chinese Landscape Painting Generation
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person reposing
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Infinite Image Generation
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Multi class one-shot image synthesis
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Single class few-shot image synthesis
Latest papers with no code
SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models
With the continuous advancement of vision language models (VLMs) technology, remarkable research achievements have emerged in the dermatology field, the fourth most prevalent human disease category.
From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation
Addressing this, we introduce Parts2Whole, a novel framework designed for generating customized portraits from multiple reference images, including pose images and various aspects of human appearance.
Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation
Recent studies have demonstrated the exceptional potentials of leveraging human preference datasets to refine text-to-image generative models, enhancing the alignment between generated images and textual prompts.
FINEMATCH: Aspect-based Fine-grained Image and Text Mismatch Detection and Correction
To address this, we propose FineMatch, a new aspect-based fine-grained text and image matching benchmark, focusing on text and image mismatch detection and correction.
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
The rapid development of diffusion models has triggered diverse applications.
GLoD: Composing Global Contexts and Local Details in Image Generation
However, simultaneous control over both global contexts (e. g., object layouts and interactions) and local details (e. g., colors and emotions) still remains a significant challenge.
Towards Better Text-to-Image Generation Alignment via Attention Modulation
To achieve this, we incorporate a temperature control mechanism within the early phases of the self-attention modules to mitigate entity leakage issues.
Accelerating Image Generation with Sub-path Linear Approximation Model
Diffusion models have significantly advanced the state of the art in image, audio, and video generation tasks.
Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models
We use compositional property of diffusion models, which allows to leverage multiple prompts in a single image generation.
LTOS: Layout-controllable Text-Object Synthesis via Adaptive Cross-attention Fusions
Controllable text-to-image generation synthesizes visual text and objects in images with certain conditions, which are frequently applied to emoji and poster generation.
