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 2126–2150 of 6689 papers

TitleStatusHype
ReDistill: Residual Encoded Distillation for Peak Memory Reduction—0
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference OptimizationCode3
Diffusion-based image inpainting with internal learningCode0
GenAI Arena: An Open Evaluation Platform for Generative ModelsCode2
BitsFusion: 1.99 bits Weight Quantization of Diffusion Model—0
Zero-Painter: Training-Free Layout Control for Text-to-Image SynthesisCode2
Coherent Zero-Shot Visual Instruction Generation—0
Quantum Implicit Neural RepresentationsCode1
Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiTCode7
Understanding the Limitations of Diffusion Concept Algebra Through Food—0
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity—0
Tackling Copyright Issues in AI Image Generation Through Originality Estimation and GenericizationCode0
Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive DiffusionCode2
Inv-Adapter: ID Customization Generation via Image Inversion and Lightweight Adapter—0
I4VGen: Image as Free Stepping Stone for Text-to-Video Generation—0
Analyzing the Feature Extractor Networks for Face Image SynthesisCode0
Plug-and-Play Diffusion Distillation—0
Stable-Pose: Leveraging Transformers for Pose-Guided Text-to-Image GenerationCode2
Enhance Image-to-Image Generation with LLaVA-generated Prompts—0
Guiding a Diffusion Model with a Bad Version of ItselfCode4
Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image GenerationCode4
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise—0
It's a Feature, Not a Bug: Measuring Creative Fluidity in Image Generators—0
Re-ReST: Reflection-Reinforced Self-Training for Language AgentsCode1
Layout Agnostic Scene Text Image Synthesis with Diffusion Models—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