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 3926–3950 of 6689 papers

TitleStatusHype
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion ModelsCode1
UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose TransferCode1
Modulating human brain responses via optimal natural image selection and synthetic image generation—0
Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models—0
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation—0
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and EditingCode2
Exploring Incompatible Knowledge Transfer in Few-shot Image GenerationCode1
Magnitude Invariant Parametrizations Improve Hypernetwork LearningCode1
Text-Conditional Contextualized Avatars For Zero-Shot Personalization—0
Identity Encoder for Personalized Diffusion—0
M2T: Masking Transformers Twice for Faster Decoding—0
AutoSplice: A Text-prompt Manipulated Image Dataset for Media ForensicsCode1
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image GenerationCode1
Control3Diff: Learning Controllable 3D Diffusion Models from Single-view Images—0
ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis—0
What does CLIP know about a red circle? Visual prompt engineering for VLMs—0
Expressive Text-to-Image Generation with Rich TextCode2
Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and ReconstructionCode2
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image GenerationCode3
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face InpaintingCode1
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANsCode1
Gradient-Free Textual Inversion—0
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI—0
Diffusion Models for Constrained DomainsCode1
Unified Multi-Modal Image Synthesis for Missing Modality Imputation—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