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

Showing 401425 of 6689 papers

TitleStatusHype
Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese DishesCode1
Flux Already Knows -- Activating Subject-Driven Image Generation without TrainingCode2
Towards Explainable Partial-AIGC Image Quality Assessment0
seg2med: a bridge from artificial anatomy to multimodal medical images0
CoProSketch: Controllable and Progressive Sketch Generation with Diffusion Model0
Generating Fine Details of Entity Interactions0
GigaTok: Scaling Visual Tokenizers to 3 Billion Parameters for Autoregressive Image GenerationCode3
Muon-Accelerated Attention Distillation for Real-Time Edge Synthesis via Optimized Latent Diffusion0
Discriminator-Free Direct Preference Optimization for Video Diffusion0
On the Design of Diffusion-based Neural Speech Codecs0
LMM4LMM: Benchmarking and Evaluating Large-multimodal Image Generation with LMMsCode1
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical ImagingCode1
MixDiT: Accelerating Image Diffusion Transformer Inference with Mixed-Precision MX Quantization0
Marmot: Multi-Agent Reasoning for Multi-Object Self-Correcting in Improving Image-Text Alignment0
POEM: Precise Object-level Editing via MLLM control0
DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows0
FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation0
PixelFlow: Pixel-Space Generative Models with FlowCode3
Model Discrepancy Learning: Synthetic Faces Detection Based on Multi-Reconstruction0
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning0
PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering0
DyDiT++: Dynamic Diffusion Transformers for Efficient Visual GenerationCode1
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability0
OmniCaptioner: One Captioner to Rule Them AllCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified