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 4176–4200 of 6689 papers

TitleStatusHype
World Knowledge from AI Image Generation for Robot Control—0
Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction—0
Prompt-Softbox-Prompt: A free-text Embedding Control for Image Editing—0
Unmasking the Canvas: A Dynamic Benchmark for Image Generation Jailbreaking and LLM Content Safety—0
All but One: Surgical Concept Erasing with Model Preservation in Text-to-Image Diffusion Models—0
Prompt Tuning Inversion for Text-Driven Image Editing Using Diffusion Models—0
Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-based Hyperspectral Image Synthesis—0
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks—0
Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Protective Perturbations against Unauthorized Data Usage in Diffusion-based Image Generation—0
Prototype memory and attention mechanisms for few shot image generation—0
Provably Secure Robust Image Steganography via Cross-Modal Error Correction—0
Proxy-Tuning: Tailoring Multimodal Autoregressive Models for Subject-Driven Image Generation—0
Align Your Flow: Scaling Continuous-Time Flow Map Distillation—0
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping—0
Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models—0
Unpaired Deblurring via Decoupled Diffusion Model—0
Alignment-free HDR Deghosting with Semantics Consistent Transformer—0
Aligning Text-to-Image Models using Human Feedback—0
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability—0
Pyramidal Denoising Diffusion Probabilistic Models—0
(Un)paired signal-to-signal translation with 1D conditional GANs—0
Aligning Latent Spaces with Flow Priors—0
Aligned Novel View Image and Geometry Synthesis via Cross-modal Attention Instillation—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