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 2376–2400 of 6689 papers

TitleStatusHype
Are conditional GANs explicitly conditional?—0
Adversarially Perturbed Wavelet-based Morphed Face Generation—0
Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands—0
DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows—0
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation—0
A Recipe for Scaling up Text-to-Video Generation with Text-free Videos—0
PixCell: A generative foundation model for digital histopathology images—0
Diverse and Tailored Image Generation for Zero-shot Multi-label Classification—0
DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation—0
DivCon: Divide and Conquer for Progressive Text-to-Image Generation—0
DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder—0
Ditto: Accelerating Diffusion Model via Temporal Value Similarity—0
DiTFastAttnV2: Head-wise Attention Compression for Multi-Modality Diffusion Transformers—0
Chain-of-Jailbreak Attack for Image Generation Models via Editing Step by Step—0
FreqPolicy: Efficient Flow-based Visuomotor Policy via Frequency Consistency—0
Generative Modeling of Individual Behavior at Scale—0
DiTFastAttn: Attention Compression for Diffusion Transformer Models—0
Adversarially Approximated Autoencoder for Image Generation and Manipulation—0
DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to Image Generation—0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution—0
CGOF++: Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields—0
EIUP: A Training-Free Approach to Erase Non-Compliant Concepts Conditioned on Implicit Unsafe Prompts—0
DiT4Edit: Diffusion Transformer for Image Editing—0
CG-NeRF: Conditional Generative Neural Radiance Fields—0
Adversarial Learning of Semantic Relevance in Text to Image Synthesis—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