Image Generation

1922 papers with code • 85 benchmarks • 67 datasets

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 )

Libraries

Use these libraries to find Image Generation models and implementations

Attention Calibration for Disentangled Text-to-Image Personalization

monalissaa/disendiff 27 Mar 2024

However, an intriguing problem persists: Is it possible to capture multiple, novel concepts from one single reference image?

3
27 Mar 2024

Ship in Sight: Diffusion Models for Ship-Image Super Resolution

luigisigillo/shipinsight 27 Mar 2024

In this context, our method explores in depth the problem of ship image super resolution, which is crucial for coastal and port surveillance.

1
27 Mar 2024

DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis

rapisurazurite/diffface 27 Mar 2024

The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks.

1
27 Mar 2024

Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance

KU-CVLAB/Perturbed-Attention-Guidance 26 Mar 2024

These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration.

74
26 Mar 2024

SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions

comfyanonymous/comfyui 25 Mar 2024

Recent advancements in diffusion models have positioned them at the forefront of image generation.

30,008
25 Mar 2024

Multi-Scale Texture Loss for CT denoising with GANs

francescodifeola/denotextureloss 25 Mar 2024

To grasp highly complex and non-linear textural relationships in the training process, this work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM).

0
25 Mar 2024

Long-CLIP: Unlocking the Long-Text Capability of CLIP

beichenzbc/long-clip 22 Mar 2024

Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities.

135
22 Mar 2024

Generative Active Learning for Image Synthesis Personalization

zhangxulu1996/gal4personalization 22 Mar 2024

The primary challenge in conducting active learning on generative models lies in the open-ended nature of querying, which differs from the closed form of querying in discriminative models that typically target a single concept.

2
22 Mar 2024

Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models

vpulab/ovam 21 Mar 2024

This approach limits the generation of segmentation masks derived from word tokens not contained in the text prompt.

15
21 Mar 2024

Diversity-aware Channel Pruning for StyleGAN Compression

jiwoogit/dcp-gan 20 Mar 2024

Specifically, by assessing channel importance based on their sensitivities to latent vector perturbations, our method enhances the diversity of samples in the compressed model.

6
20 Mar 2024