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 426–450 of 6689 papers

TitleStatusHype
OmniCaptioner: One Captioner to Rule Them AllCode2
Compass Control: Multi Object Orientation Control for Text-to-Image Generation—0
HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned GuidanceCode2
Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models—0
Transfer between Modalities with MetaQueries—0
An Empirical Study of GPT-4o Image Generation CapabilitiesCode1
D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition—0
A Training-Free Style-aligned Image Generation with Scale-wise Autoregressive Model—0
DDT: Decoupled Diffusion TransformerCode3
CDM-QTA: Quantized Training Acceleration for Efficient LoRA Fine-Tuning of Diffusion Model—0
Storybooth: Training-free Multi-Subject Consistency for Improved Visual Storytelling—0
Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive JailbreakingCode1
Gaussian Mixture Flow Matching ModelsCode2
Generative Adversarial Networks with Limited Data: A Survey and Benchmarking—0
Multimodal Cinematic Video Synthesis Using Text-to-Image and Audio Generation Models—0
UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual EncodingCode2
Thermoxels: a voxel-based method to generate simulation-ready 3D thermal models—0
Digital Gene: Learning about the Physical World through Analytic Concepts—0
A Hybrid Wavelet-Fourier Method for Next-Generation Conditional Diffusion Models—0
Dynamic Importance in Diffusion U-Net for Enhanced Image SynthesisCode0
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning—0
MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models—0
FLAIRBrainSeg: Fine-grained brain segmentation using FLAIR MRI only—0
Detection Limits and Statistical Separability of Tree Ring Watermarks in Rectified Flow-based Text-to-Image Generation ModelsCode0
Bias in Large Language Models Across Clinical Applications: A Systematic Review—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