Conditional Image Generation
133 papers with code • 10 benchmarks • 8 datasets
Conditional image generation is the task of generating new images from a dataset conditional on their class.
( Image credit: PixelCNN++ )
Benchmarks
These leaderboards are used to track progress in Conditional Image Generation
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Libraries
Use these libraries to find Conditional Image Generation models and implementationsDatasets
CIFAR-10
MS COCO
CIFAR-100
Tiny ImageNet
ImageNet-LT
ArtBench-10 (32x32)
Latest papers
Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
Hosein47/IEA-GAN
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Simulating high-resolution detector responses is a storage-costly and computationally intensive process that has long been challenging in particle physics.
PE-GAN: Prior Embedding GAN for PXD images at Belle II
Hosein47/IEA-GAN
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However, data from the fine-grained PXD requires a substantial amount of storage.
MaskSketch: Unpaired Structure-guided Masked Image Generation
lllyasviel/controlnet
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CVPR 2023
We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation.
Simple diffusion: End-to-end diffusion for high resolution images
Currently, applying diffusion models in pixel space of high resolution images is difficult.
Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models
mischad/fobadiffusion
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ICCV 2023
Curating datasets for object segmentation is a difficult task.
Neural Characteristic Function Learning for Conditional Image Generation
zhangjialu126/ccf_gan
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ICCV 2023
The emergence of conditional generative adversarial networks (cGANs) has revolutionised the way we approach and control the generation, by means of adversarially learning joint distributions of data and auxiliary information.
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
alsdudrla10/DG
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In sample generation, we add an auxiliary term to the pre-trained score to deceive the discriminator.
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.
All are Worth Words: A ViT Backbone for Diffusion Models
We evaluate U-ViT in unconditional and class-conditional image generation, as well as text-to-image generation tasks, where U-ViT is comparable if not superior to a CNN-based U-Net of a similar size.
Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
Diffusion models (DMs) have shown great potential for high-quality image synthesis.
