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 16511700 of 6689 papers

TitleStatusHype
From Face to Natural Image: Learning Real Degradation for Blind Image Super-ResolutionCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
Continuous-Time Functional Diffusion ProcessesCode1
Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect DetectionCode1
Paragraph-to-Image Generation with Information-Enriched Diffusion ModelCode1
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-TrainingCode1
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation LearningCode1
Partition-Guided GANsCode1
PatchDPO: Patch-level DPO for Finetuning-free Personalized Image GenerationCode1
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image ManipulationCode1
Frido: Feature Pyramid Diffusion for Complex Scene Image SynthesisCode1
Continuous Speculative Decoding for Autoregressive Image GenerationCode1
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANsCode1
Continuous Language Generative FlowCode1
Diffusion Deformable Model for 4D Temporal Medical Image GenerationCode1
Personalized Text-to-Image Generation with Auto-Regressive ModelsCode1
3D-Aware Semantic-Guided Generative Model for Human SynthesisCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Frequency Domain Image Translation: More Photo-realistic, Better Identity-preservingCode1
Causal Inference via Style Transfer for Out-of-distribution GeneralisationCode1
DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery AnalysisCode1
FunkNN: Neural Interpolation for Functional GenerationCode1
GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data SynthesisCode1
GeNIe: Generative Hard Negative Images Through DiffusionCode1
HWD: A Novel Evaluation Score for Styled Handwritten Text GenerationCode1
Continual Learning of Diffusion Models with Generative DistillationCode1
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
Attribute-guided image generation from layoutCode1
Contextual Convolutional Neural NetworksCode1
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image SynthesisCode1
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Frame Interpolation with Consecutive Brownian Bridge DiffusionCode1
Context-Aware Layout to Image Generation with Enhanced Object AppearanceCode1
ForkGAN: Seeing into the Rainy NightCode1
AITTI: Learning Adaptive Inclusive Token for Text-to-Image GenerationCode1
Forward-only Diffusion Probabilistic ModelsCode1
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic DataCode1
Diffusion Models Beat GANs on Image ClassificationCode1
FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded SamplingCode1
FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptionsCode1
Foreground-Background Separation through Concept Distillation from Generative Image Foundation ModelsCode1
Can MLLMs Perform Text-to-Image In-Context Learning?Code1
Diffusion Models for Interferometric Satellite Aperture RadarCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial NetworksCode1
Focal Frequency Loss for Image Reconstruction and SynthesisCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
Content-Aware GAN CompressionCode1
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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