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
Elucidating The Design Space of Classifier-Guided Diffusion Generation
alexmaols/elucd
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Guidance in conditional diffusion generation is of great importance for sample quality and controllability.
Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
fabianaltekrueger/conditional_mmd_flows
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We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modeling.
ImagenHub: Standardizing the evaluation of conditional image generation models
Recently, a myriad of conditional image generation and editing models have been developed to serve different downstream tasks, including text-to-image generation, text-guided image editing, subject-driven image generation, control-guided image generation, etc.
Diverse Semantic Image Editing with Style Codes
Semantic image editing requires inpainting pixels following a semantic map.
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models
In this study, we aim to extend the capabilities of diffusion-based text-to-image (T2I) generation models by incorporating diverse modalities beyond textual description, such as sketch, box, color palette, and style embedding, within a single model.
Late-Constraint Diffusion Guidance for Controllable Image Synthesis
Specifically, we train a lightweight condition adapter to establish the correlation between external conditions and internal representations of diffusion models.
SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views
xt4d/sparsegnv
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We study to generate novel views of indoor scenes given sparse input views.
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
val-iisc/NoisyTwins
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CVPR 2023
We find that one reason for degradation is the collapse of latents for each class in the $\mathcal{W}$ latent space.
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
mischad/chest-distillation
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Recent advancements in diffusion models have significantly impacted the trajectory of generative machine learning research, with many adopting the strategy of fine-tuning pre-trained models using domain-specific text-to-image datasets.
Polynomial Implicit Neural Representations For Large Diverse Datasets
rajhans0/poly_inr
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CVPR 2023
With much fewer training parameters and higher representative power, our approach paves the way for broader adoption of INR models for generative modeling tasks in complex domains.
