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

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Showing 3951–4000 of 6689 papers

TitleStatusHype
NeAT: Neural Artistic Tracing for Beautiful Style TransferCode1
Mask-conditioned latent diffusion for generating gastrointestinal polyp imagesCode1
Improving Vision-and-Language Navigation by Generating Future-View Image Semantics—0
HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image ModelsCode1
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Diffusion Recommender ModelCode2
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model—0
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion—0
Binary Latent DiffusionCode1
Reflected Diffusion ModelsCode1
A Cheaper and Better Diffusion Language Model with Soft-Masked NoiseCode1
Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide VisualizationCode2
HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image GenerationCode2
Deep Generative Modeling with Backward Stochastic Differential EquationsCode0
Efficient Multimodal Sampling via Tempered Distribution FlowCode1
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image SynthesisCode1
InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning—0
Uncurated Image-Text Datasets: Shedding Light on Demographic BiasCode1
Zero-shot Generative Model Adaptation via Image-specific Prompt LearningCode1
Few-shot Semantic Image Synthesis with Class Affinity Transfer—0
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models—0
A Diffusion-based Method for Multi-turn Compositional Image Generation—0
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models—0
Cross-modulated Few-shot Image Generation for Colorectal Tissue ClassificationCode1
Cross-modal tumor segmentation using generative blending augmentation and self trainingCode0
EGC: Image Generation and Classification via a Diffusion Energy-Based ModelCode1
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation—0
Generative Multiplane Neural Radiance for 3D-Aware Image GenerationCode1
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free VideosCode3
CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images—0
MetaHead: An Engine to Create Realistic Digital Head—0
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis—0
Textile Pattern Generation Using Diffusion Models—0
Learning Dynamic Style Kernels for Artistic Style Transfer—0
Subject-driven Text-to-Image Generation via Apprenticeship Learning—0
PrefGen: Preference Guided Image Generation with Relative AttributesCode0
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image GenerationCode2
GVP: Generative Volumetric Primitives—0
3D-aware Image Generation using 2D Diffusion Models—0
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and InterpretabilityCode0
Social Biases through the Text-to-Image Generation Lens—0
Masked and Adaptive Transformer for Exemplar Based Image TranslationCode1
Semantic Image Translation for Repairing the Texture Defects of Building Models—0
Token Merging for Fast Stable DiffusionCode4
LayoutDiffusion: Controllable Diffusion Model for Layout-to-image GenerationCode2
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
DiffCollage: Parallel Generation of Large Content with Diffusion Models—0
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation—0
Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakes—0
Instant Photorealistic Neural Radiance Fields StylizationCode0
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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