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 151200 of 6689 papers

TitleStatusHype
PromptFix: You Prompt and We Fix the PhotoCode4
ResAdapter: Domain Consistent Resolution Adapter for Diffusion ModelsCode4
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video GeneratorsCode4
DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World KnowledgeCode3
Behavior Generation with Latent ActionsCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360degCode3
Personalized Image Generation with Deep Generative Models: A Decade SurveyCode3
ART: Anonymous Region Transformer for Variable Multi-Layer Transparent Image GenerationCode3
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360^Code3
Personalize Segment Anything Model with One ShotCode3
DiM: Diffusion Mamba for Efficient High-Resolution Image SynthesisCode3
Optimal Stepsize for Diffusion SamplingCode3
DiLightNet: Fine-grained Lighting Control for Diffusion-based Image GenerationCode3
Ovis-U1 Technical ReportCode3
On Noise Injection in Generative Adversarial NetworksCode3
Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi DecodingCode3
Generating Long Sequences with Sparse TransformersCode3
One Transformer Fits All Distributions in Multi-Modal Diffusion at ScaleCode3
On the Trajectory Regularity of ODE-based Diffusion SamplingCode3
Paint by Example: Exemplar-based Image Editing with Diffusion ModelsCode3
On Distillation of Guided Diffusion ModelsCode3
AP-LDM: Attentive and Progressive Latent Diffusion Model for Training-Free High-Resolution Image GenerationCode3
Nexus-Gen: A Unified Model for Image Understanding, Generation, and EditingCode3
Multimodal Foundation Models: From Specialists to General-Purpose AssistantsCode3
MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout GuidanceCode3
ModelScope Text-to-Video Technical ReportCode3
Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion ModelsCode3
MoMA: Multimodal LLM Adapter for Fast Personalized Image GenerationCode3
MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages with Negligible CostCode3
Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image SynthesisCode3
MedSegDiff-V2: Diffusion based Medical Image Segmentation with TransformerCode3
MaskGIT: Masked Generative Image TransformerCode3
Magic-Me: Identity-Specific Video Customized DiffusionCode3
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
MultiDiffusion: Fusing Diffusion Paths for Controlled Image GenerationCode3
One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single PromptCode3
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
DF40: Toward Next-Generation Deepfake DetectionCode3
DesignEdit: Multi-Layered Latent Decomposition and Fusion for Unified & Accurate Image EditingCode3
An Image is Worth 32 Tokens for Reconstruction and GenerationCode3
Designing a Better Asymmetric VQGAN for StableDiffusionCode3
DICEPTION: A Generalist Diffusion Model for Visual Perceptual TasksCode3
LLMs can see and hear without any trainingCode3
Deep Generative Models on 3D Representations: A SurveyCode3
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion ModelsCode3
Autoregressive Image Generation using Residual QuantizationCode3
Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset GenerationCode3
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation ModelsCode3
AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image GenerationCode3
CtrLoRA: An Extensible and Efficient Framework for Controllable Image GenerationCode3
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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