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

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Showing 3576–3600 of 6689 papers

TitleStatusHype
Boosting Medical Image Synthesis via Registration-guided Consistency and Disentanglement Learning—0
Sketch-Guided Scene Image Generation—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion—0
Spanish TrOCR: Leveraging Transfer Learning for Language AdaptationCode0
MMIS: Multimodal Dataset for Interior Scene Visual Generation and Recognition—0
GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing—0
JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation—0
MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices—0
Layered Diffusion Model for One-Shot High Resolution Text-to-Image Synthesis—0
Rethinking Image Skip Connections in StyleGAN2—0
An Improved Method for Personalizing Diffusion Models—0
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models—0
PROUD: PaRetO-gUided Diffusion Model for Multi-objective GenerationCode0
DiCTI: Diffusion-based Clothing Designer via Text-guided Input—0
Medical Image Fusion for High-Level Analysis: A Mutual Enhancement Framework for Unaligned PAT and MRICode0
Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling—0
Lateralization LoRA: Interleaved Instruction Tuning with Modality-Specialized Adaptations—0
Representation learning with CGAN for casual inference—0
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs—0
Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research OpportunitiesCode0
SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules—0
UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks—0
Label-free Neural Semantic Image Synthesis—0
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

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