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 with no code
SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions
Specifically, we compute the gradient of the perceptual loss using the predicted denoised images at each denoising step, providing meaningful guidance for achieving coherent montages.
Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation
In this work, we propose Cocktail, a pipeline to mix various modalities into one embedding, amalgamated with a generalized ControlNet (gControlNet), a controllable normalisation (ControlNorm), and a spatial guidance sampling method, to actualize multi-modal and spatially-refined control for text-conditional diffusion models.
DuDGAN: Improving Class-Conditional GANs via Dual-Diffusion
We evaluated our method using the AFHQ, Food-101, and CIFAR-10 datasets and observed superior results across metrics such as FID, KID, Precision, and Recall score compared with comparison models, highlighting the effectiveness of our approach.
Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation
Language-guided image generation has achieved great success nowadays by using diffusion models.
Collage Diffusion
We seek to give users precise control over diffusion-based image generation by modeling complex scenes as sequences of layers, which define the desired spatial arrangement and visual attributes of objects in the scene.
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images.
Exploring Intra-Class Variation Factors With Learnable Cluster Prompts for Semi-Supervised Image Synthesis
Semi-supervised class-conditional image synthesis is typically performed by inferring and injecting class labels into a conditional Generative Adversarial Network (GAN).
Zero-Shot Object Segmentation through Concept Distillation from Generative Image Foundation Models
Curating datasets for object segmentation is a difficult task.
Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis
Vector-Quantized (VQ-based) generative models usually consist of two basic components, i. e., VQ tokenizers and generative transformers.
A survey on knowledge-enhanced multimodal learning
Multimodal learning has been a field of increasing interest, aiming to combine various modalities in a single joint representation.
