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
Semantic Approach to Quantifying the Consistency of Diffusion Model Image Generation
brinnaebent/semantic-consistency-score
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In this study, we identify the need for an interpretable, quantitative score of the repeatability, or consistency, of image generation in diffusion models.
CAT: Contrastive Adapter Training for Personalized Image Generation
Finally, we mention the possibility of CAT in the aspects of multi-concept adapter and optimization.
Taming Stable Diffusion for Text to 360° Panorama Image Generation
Generative models, e. g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts.
Latent Guard: a Safety Framework for Text-to-image Generation
Hence, we propose Latent Guard, a framework designed to improve safety measures in text-to-image generation.
Deep Generative Data Assimilation in Multimodal Setting
To our knowledge, our work is the first to apply deep generative framework for multimodal data assimilation using real-world datasets; an important step for building robust computational simulators, including the next-generation Earth system models.
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sherman-Morrison inversion formula to calculate the inverse.
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation
Sharing synthetic medical images is a promising alternative to sharing real images that can improve patient privacy and data security.
StoryImager: A Unified and Efficient Framework for Coherent Story Visualization and Completion
3) The story visualization and continuation models are trained and inferred independently, which is not user-friendly.
GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis
mvrl/geosynth
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We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control.
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
bytedance/MoMA
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This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model.
