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 5201–5225 of 6689 papers

TitleStatusHype
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping—0
Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models—0
Unpaired Deblurring via Decoupled Diffusion Model—0
Alignment-free HDR Deghosting with Semantics Consistent Transformer—0
Aligning Text-to-Image Models using Human Feedback—0
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability—0
Pyramidal Denoising Diffusion Probabilistic Models—0
(Un)paired signal-to-signal translation with 1D conditional GANs—0
Aligning Latent Spaces with Flow Priors—0
Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation—0
QID^2: An Image-Conditioned Diffusion Model for Q-space Up-sampling of DWI Data—0
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning—0
QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation—0
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping—0
Qua^2SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models—0
Qualitative Failures of Image Generation Models and Their Application in Detecting Deepfakes—0
Quality analysis of DCGAN-generated mammography lesions—0
Quality and Quantity: Unveiling a Million High-Quality Images for Text-to-Image Synthesis in Fashion Design—0
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations—0
Quantitative analysis of collagen remodeling in pancreatic lesions using computationally translated collagen images derived from brightfield microscopy images—0
Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Image Concepts—0
Quantitative Manipulation of Custom Attributes on 3D-Aware Image Synthesis—0
Zero-Shot Subject-Centric Generation for Creative Application Using Entropy Fusion—0
Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST—0
UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision—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