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 201250 of 6689 papers

TitleStatusHype
Emu3: Next-Token Prediction is All You NeedCode3
PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language InstructionsCode3
Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion ModelsCode3
VILA-U: a Unified Foundation Model Integrating Visual Understanding and GenerationCode3
Anim-Director: A Large Multimodal Model Powered Agent for Controllable Animation Video GenerationCode3
Scaling Diffusion Transformers to 16 Billion ParametersCode3
Consistency Flow Matching: Defining Straight Flows with Velocity ConsistencyCode3
StyleShot: A Snapshot on Any StyleCode3
Consistency Models Made EasyCode3
DF40: Toward Next-Generation Deepfake DetectionCode3
GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual GenerationCode3
MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout GuidanceCode3
Image and Video Tokenization with Binary Spherical QuantizationCode3
An Image is Worth 32 Tokens for Reconstruction and GenerationCode3
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference OptimizationCode3
AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image GenerationCode3
Deciphering Oracle Bone Language with Diffusion ModelsCode3
DiM: Diffusion Mamba for Efficient High-Resolution Image SynthesisCode3
On the Trajectory Regularity of ODE-based Diffusion SamplingCode3
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion TransformerCode3
ImageInWords: Unlocking Hyper-Detailed Image DescriptionsCode3
U-DiTs: Downsample Tokens in U-Shaped Diffusion TransformersCode3
Taming Stable Diffusion for Text to 360° Panorama Image GenerationCode3
MoMA: Multimodal LLM Adapter for Fast Personalized Image GenerationCode3
Towards Realistic Scene Generation with LiDAR Diffusion ModelsCode3
Self-Rectifying Diffusion Sampling with Perturbed-Attention GuidanceCode3
FlashFace: Human Image Personalization with High-fidelity Identity PreservationCode3
DesignEdit: Multi-Layered Latent Decomposition and Fusion for Unified & Accurate Image EditingCode3
Generic 3D Diffusion Adapter Using Controlled Multi-View EditingCode3
Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion ModelCode3
Scaling Rectified Flow Transformers for High-Resolution Image SynthesisCode3
Behavior Generation with Latent ActionsCode3
ViewDiff: 3D-Consistent Image Generation with Text-to-Image ModelsCode3
VisionLLaMA: A Unified LLaMA Backbone for Vision TasksCode3
Trajectory Consistency Distillation: Improved Latent Consistency Distillation by Semi-Linear Consistency Function with Trajectory MappingCode3
Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image SynthesisCode3
Visual Style Prompting with Swapping Self-AttentionCode3
FiT: Flexible Vision Transformer for Diffusion ModelCode3
DiLightNet: Fine-grained Lighting Control for Diffusion-based Image GenerationCode3
Magic-Me: Identity-Specific Video Customized DiffusionCode3
Anatomically-Controllable Medical Image Generation with Segmentation-Guided Diffusion ModelsCode3
Intelligent Grimm - Open-ended Visual Storytelling via Latent Diffusion ModelsCode3
SEED-Bench: Benchmarking Multimodal Large Language ModelsCode3
Style Aligned Image Generation via Shared AttentionCode3
UniGS: Unified Representation for Image Generation and SegmentationCode3
VBench: Comprehensive Benchmark Suite for Video Generative ModelsCode3
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion ModelsCode3
Multimodal Foundation Models: From Specialists to General-Purpose AssistantsCode3
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image GenerationCode3
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
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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