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 101150 of 6689 papers

TitleStatusHype
InstanceDiffusion: Instance-level Control for Image GenerationCode4
T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion ModelsCode4
ImgEdit: A Unified Image Editing Dataset and BenchmarkCode4
SDXS: Real-Time One-Step Latent Diffusion Models with Image ConditionsCode4
SEED-Story: Multimodal Long Story Generation with Large Language ModelCode4
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and GenerationCode4
Story-Adapter: A Training-free Iterative Framework for Long Story VisualizationCode4
T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoTCode4
ResAdapter: Domain Consistent Resolution Adapter for Diffusion ModelsCode4
Guiding a Diffusion Model with a Bad Version of ItselfCode4
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent DiffusionCode4
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step InferenceCode4
Region-Aware Text-to-Image Generation via Hard Binding and Soft RefinementCode4
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video GeneratorsCode4
High-Resolution Image Synthesis with Latent Diffusion ModelsCode4
ArchiSound: Audio Generation with DiffusionCode4
Language Model Beats Diffusion -- Tokenizer is Key to Visual GenerationCode4
Training-free Regional Prompting for Diffusion TransformersCode4
PromptFix: You Prompt and We Fix the PhotoCode4
Prompt-to-Prompt Image Editing with Cross Attention ControlCode4
GLIGEN: Open-Set Grounded Text-to-Image GenerationCode4
LCM-LoRA: A Universal Stable-Diffusion Acceleration ModuleCode4
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language ModelsCode4
AnyText: Multilingual Visual Text Generation And EditingCode4
Long-CLIP: Unlocking the Long-Text Capability of CLIPCode4
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANsCode4
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image SynthesisCode4
Fine-Tuning Image-Conditional Diffusion Models is Easier than You ThinkCode4
Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual GenerationCode4
Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image GenerationCode4
Elucidating the Design Space of Diffusion-Based Generative ModelsCode4
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision ApplicationsCode4
One Diffusion to Generate Them AllCode4
Phased Consistency ModelsCode4
Diffusion Models: A Comprehensive Survey of Methods and ApplicationsCode4
Null-text Inversion for Editing Real Images using Guided Diffusion ModelsCode4
AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video DataCode4
Diffusion Model-Based Image Editing: A SurveyCode4
Moûsai: Text-to-Music Generation with Long-Context Latent DiffusionCode4
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion ModelsCode4
Autoregressive Models in Vision: A SurveyCode4
Autoregressive Video Generation without Vector QuantizationCode4
MIGC++: Advanced Multi-Instance Generation Controller for Image SynthesisCode4
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image EditingCode4
MIGC: Multi-Instance Generation Controller for Text-to-Image SynthesisCode4
Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal InteractionCode4
A Survey on Video Diffusion ModelsCode4
3D-aware Conditional Image SynthesisCode4
Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by StepCode4
Ming-Omni: A Unified Multimodal Model for Perception and GenerationCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified