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 226250 of 6689 papers

TitleStatusHype
CtrLoRA: An Extensible and Efficient Framework for Controllable Image GenerationCode3
Intelligent Grimm - Open-ended Visual Storytelling via Latent Diffusion ModelsCode3
DDT: Decoupled Diffusion TransformerCode3
DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation ModelsCode3
Emu3: Next-Token Prediction is All You NeedCode3
ModelScope Text-to-Video Technical ReportCode3
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion ModelsCode3
Attentive Eraser: Unleashing Diffusion Model's Object Removal Potential via Self-Attention Redirection GuidanceCode3
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image GenerationCode3
ControlAR: Controllable Image Generation with Autoregressive ModelsCode3
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion TransformerCode3
Consistency Models Made EasyCode3
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image GenerationCode3
All are Worth Words: A ViT Backbone for Diffusion ModelsCode3
Consistency Flow Matching: Defining Straight Flows with Velocity ConsistencyCode3
Improved Denoising Diffusion Probabilistic ModelsCode3
Image and Video Tokenization with Binary Spherical QuantizationCode3
AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image GenerationCode3
ImageFolder: Autoregressive Image Generation with Folded TokensCode3
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion ModelsCode3
FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion ModelCode3
ImageInWords: Unlocking Hyper-Detailed Image DescriptionsCode3
Hierarchical Text-Conditional Image Generation with CLIP LatentsCode3
Flow Matching for Generative ModelingCode3
Highly Compressed Tokenizer Can Generate Without TrainingCode3
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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