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 3526–3550 of 6689 papers

TitleStatusHype
GazeCLIP: Towards Enhancing Gaze Estimation via Text Guidance—0
GazeFusion: Saliency-Guided Image Generation—0
GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation—0
GBSD: Generative Bokeh with Stage Diffusion—0
gCoRF: Generative Compositional Radiance Fields—0
EIUP: A Training-Free Approach to Erase Non-Compliant Concepts Conditioned on Implicit Unsafe Prompts—0
Gen2Det: Generate to Detect—0
BATINet: Background-Aware Text to Image Synthesis and Manipulation Network—0
BallGAN: 3D-aware Image Synthesis with a Spherical Background—0
GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing—0
GenCO: Generating Diverse Designs with Combinatorial Constraints—0
Text-to-Image Synthesis: A Decade Survey—0
Text-to-Image Synthesis Based on Machine Generated Captions—0
Generalizable Object Keypoint Localization from Generative Priors—0
Text-to-Image Synthesis Based on Object-Guided Joint-Decoding Transformer—0
Generalized Diffusion Model with Adjusted Offset Noise—0
Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through f-divergence Minimization—0
StyleForge: Enhancing Text-to-Image Synthesis for Any Artistic Styles with Dual Binding—0
Generated Contents Enrichment—0
Text-to-image synthesis method evaluation based on visual patterns—0
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts—0
Generate What You Can't See - a View-dependent Image Generation—0
Text to Image Synthesis Using Generative Adversarial Networks—0
Generating a Temporally Coherent Image Sequence for a Story by Multimodal Recurrent Transformers—0
Generating a Temporally Coherent Visual Story by Multimodal Recurrent Transformers—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