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 76–100 of 6689 papers

TitleStatusHype
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation—0
Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis—0
A Watermark for Auto-Regressive Image Generation Models—0
Edit360: 2D Image Edits to 3D Assets from Any Angle—0
Pisces: An Auto-regressive Foundation Model for Image Understanding and Generation—0
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models—0
High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model—0
Symmetrical Flow Matching: Unified Image Generation, Segmentation, and Classification with Score-Based Generative Models—0
The Role of Generative AI in Facilitating Social Interactions: A Scoping Review—0
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning—0
MMMG: A Massive, Multidisciplinary, Multi-Tier Generation Benchmark for Text-to-Image Reasoning—0
Marrying Autoregressive Transformer and Diffusion with Multi-Reference AutoregressionCode2
SAGE: Exploring the Boundaries of Unsafe Concept Domain with Semantic-Augment ErasingCode0
SPARKE: Scalable Prompt-Aware Diversity Guidance in Diffusion Models via RKE Score—0
Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation—0
Only-Style: Stylistic Consistency in Image Generation without Content Leakage—0
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations—0
Ming-Omni: A Unified Multimodal Model for Perception and GenerationCode4
ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models—0
Consistent Story Generation with Asymmetry Zigzag SamplingCode0
Geometric Regularity in Deterministic Sampling of Diffusion-based Generative Models—0
Noise Conditional Variational Score DistillationCode1
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand BetterCode2
Diffuse and Disperse: Image Generation with Representation Regularization—0
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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