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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4526–4550 of 6689 papers

TitleStatusHype
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis—0
Seeing Sound: Assembling Sounds from Visuals for Audio-to-Image Generation—0
Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models—0
Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering—0
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation—0
seg2med: a bridge from artificial anatomy to multimodal medical images—0
SegAttnGAN: Text to Image Generation with Segmentation Attention—0
VaLID: Variable-Length Input Diffusion for Novel View Synthesis—0
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis—0
Segment Anything for comprehensive analysis of grapevine cluster architecture and berry properties—0
Segmentation-Reconstruction-Guided Facial Image De-occlusion—0
SEIGAN: Towards Compositional Image Generation by Simultaneously Learning to Segment, Enhance, and Inpaint—0
PixCell: A generative foundation model for digital histopathology images—0
Self-conditioned Embedding Diffusion for Text Generation—0
Self-control: A Better Conditional Mechanism for Masked Autoregressive Model—0
PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment—0
Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects—0
Variational autoencoder-based neural network model compression—0
Self-Guidance: Boosting Flow and Diffusion Generation on Their Own—0
Variational Autoencoders Without the Variation—0
Self-Improving Diffusion Models with Synthetic Data—0
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation—0
Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables—0
Adversarial Identity Injection for Semantic Face Image Synthesis—0
Self-Rewarding Large Vision-Language Models for Optimizing Prompts in Text-to-Image Generation—0
Show:102550
← PrevPage 182 of 268Next →
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