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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3601–3625 of 6689 papers

TitleStatusHype
Triplet-Aware Scene Graph Embeddings—0
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation—0
MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices—0
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models—0
MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices—0
Triplet Synthesis For Enhancing Composed Image Retrieval via Counterfactual Image Generation—0
Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter—0
Model-Agnostic Human Preference Inversion in Diffusion Models—0
Model alignment using inter-modal bridges—0
Model as a Game: On Numerical and Spatial Consistency for Generative Games—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
Model Collapse Demystified: The Case of Regression—0
Model Discrepancy Learning: Synthetic Faces Detection Based on Multi-Reconstruction—0
Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization—0
Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message—0
cGANs with Conditional Convolution Layer—0
Modeling the Nonsmoothness of Modern Neural Networks—0
Approximate Caching for Efficiently Serving Diffusion Models—0
Moderating the Generalization of Score-based Generative Model—0
Modular Conversational Agents for Surveys and Interviews—0
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling—0
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer—0
Modulating human brain responses via optimal natural image selection and synthetic image generation—0
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis—0
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers—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