Conditional Image Generation
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Benchmarks
CIFAR-10
ImageNet 128x128
CIFAR-100
ArtBench-10 (32x32)
ImageNet 256x256
ImageNet 64x64
COCO-Animals
CIFAR-10 LT
CelebAMask-HQ
ImageNet-LT
Tiny ImageNet
Most implemented
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Analyzing and Improving the Image Quality of StyleGAN
Improved Training of Wasserstein GANs
Self-Attention Generative Adversarial Networks
Improved Techniques for Training GANs
Conditional Image Synthesis With Auxiliary Classifier GANs
Papers
A Plug-in Interpretation of Conditioning in Score-Based Diffusion Models
We propose a conditioning mechanism for diffusion models based on multi-speed joint diffusion of the target and the condition. The mechanism learns an unconditional joint score network and enforces conditioning at infere…
Conditional Image GenerationEnergy-Guided Flow Matching
Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a f…
Conditional Image GenerationText-to-Image GenerationInference-Time Scaling of Diffusion Models via Progressive Seed Pruning
Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for autoregressive language models. Because final quality is highly sensiti…
Conditional Image GenerationDICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and…
Conditional Image GenerationImage Super-ResolutionImage DeblurringStyle TransferShift-and-Sum Quantization for Visual Autoregressive Models
Post-training quantization (PTQ) enables efficient deployment of deep networks using a small set of data. Its application to visual autoregressive models (VAR), however, remains relatively unexplored. We identify two key…
Conditional Image GenerationDivide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models
The abundance of pre-trained diffusion models provides an opportunity for composition. Combining several models, however, runs the risk of one model dominating or models disagreeing with each other. Here, we propose Divi…
Conditional Image Generation