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 6001–6025 of 6689 papers

TitleStatusHype
Text Guided Person Image Synthesis—0
FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting—0
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models—0
FREA-Unet: Frequency-aware U-net for Modality Transfer—0
A Comprehensive Survey on Data-Efficient GANs in Image Generation—0
Fréchet Distance for Offline Evaluation of Information Retrieval Systems with Sparse Labels—0
BeautyBank: Encoding Facial Makeup in Latent Space—0
FreeEnhance: Tuning-Free Image Enhancement via Content-Consistent Noising-and-Denoising Process—0
FreeFlux: Understanding and Exploiting Layer-Specific Roles in RoPE-Based MMDiT for Versatile Image Editing—0
Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation—0
Free-Lunch Color-Texture Disentanglement for Stylized Image Generation—0
FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models—0
Textile Pattern Generation Using Diffusion Models—0
FreeTuner: Any Subject in Any Style with Training-free Diffusion—0
FreeVS: Generative View Synthesis on Free Driving Trajectory—0
Frequency Autoregressive Image Generation with Continuous Tokens—0
Frequency-Aware Guidance for Blind Image Restoration via Diffusion Models—0
Text Image Generation for Low-Resource Languages with Dual Translation Learning—0
Frequency-Time Diffusion with Neural Cellular Automata—0
ViUniT: Visual Unit Tests for More Robust Visual Programming—0
FRMD: Fast Robot Motion Diffusion with Consistency-Distilled Movement Primitives for Smooth Action Generation—0
From Bird's-Eye to Street View: Crafting Diverse and Condition-Aligned Images with Latent Diffusion Model—0
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task—0
From Graph Diffusion to Graph Classification—0
TextPainter: Multimodal Text Image Generation with Visual-harmony and Text-comprehension for Poster Design—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