SOTAVerified

Image Generation

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 )

Papers

Showing 1351–1400 of 6689 papers

TitleStatusHype
Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models—0
Physics Informed Distillation for Diffusion ModelsCode2
A Survey on Vision Autoregressive Model—0
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation—0
TIPO: Text to Image with Text Presampling for Prompt OptimizationCode2
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing—0
Emotion Classification of Children Expressions—0
Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution—0
Evaluating the Generation of Spatial Relations in Text and Image Generative Models—0
Leveraging Previous Steps: A Training-free Fast Solver for Flow Diffusion—0
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study—0
ENAT: Rethinking Spatial-temporal Interactions in Token-based Image SynthesisCode1
Layout Control and Semantic Guidance with Attention Loss Backward for T2I Diffusion Model—0
Token Merging for Training-Free Semantic Binding in Text-to-Image SynthesisCode2
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models—0
More Expressive Attention with Negative WeightsCode0
DDIM-Driven Coverless Steganography Scheme with Real Key—0
Region-Aware Text-to-Image Generation via Hard Binding and Soft RefinementCode4
PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation—0
Scalable, Tokenization-Free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-Size Reusable Structures for On-Device Image Generation—0
Autoregressive Models in Vision: A SurveyCode4
Improving image synthesis with diffusion-negative sampling—0
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models—0
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation ModelCode0
Conditional Diffusion Model for Longitudinal Medical Image Generation—0
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation—0
Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models—0
DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric FinetuningCode0
BendVLM: Test-Time Debiasing of Vision-Language EmbeddingsCode0
Image Understanding Makes for A Good Tokenizer for Image GenerationCode1
Taming Rectified Flow for Inversion and EditingCode4
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation ModelsCode5
SEE-DPO: Self Entropy Enhanced Direct Preference Optimization—0
DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image GenerationCode1
ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks—0
Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation—0
Textual Aesthetics in Large Language ModelsCode0
DiT4Edit: Diffusion Transformer for Image Editing—0
On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models—0
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Training-free Regional Prompting for Diffusion TransformersCode4
Towards Small Object Editing: A Benchmark Dataset and A Training-Free ApproachCode0
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning—0
DreamPolish: Domain Score Distillation With Progressive Geometry Generation—0
TypeScore: A Text Fidelity Metric for Text-to-Image Generative Models—0
Advancements in Data Processing and Calibration for the Hyperspectral Imaging Satellite (HySIS)—0
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems—0
Evaluation Metric for Quality Control and Generative Models in Histopathology Images—0
Randomized Autoregressive Visual GenerationCode5
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified