Conditional Image Generation
133 papers with code • 10 benchmarks • 8 datasets
Conditional image generation is the task of generating new images from a dataset conditional on their class.
( Image credit: PixelCNN++ )
Benchmarks
These leaderboards are used to track progress in Conditional Image Generation
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Libraries
Use these libraries to find Conditional Image Generation models and implementationsDatasets
CIFAR-10
MS COCO
CIFAR-100
Tiny ImageNet
ImageNet-LT
ArtBench-10 (32x32)
Latest papers with no code
CIMGEN: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data
Content creation and image editing can benefit from flexible user controls.
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation
We evaluate VIESCORE on seven prominent tasks in conditional image tasks and found: (1) VIESCORE (GPT4-v) achieves a high Spearman correlation of 0. 3 with human evaluations, while the human-to-human correlation is 0. 45.
Conditional Image Generation with Pretrained Generative Model
As a result, the research community has devised methods to leverage pre-trained unconditional diffusion models with additional guidance for the purpose of conditional image generative.
Unlocking Pre-trained Image Backbones for Semantic Image Synthesis
Semantic image synthesis, i. e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images.
ArcGAN: Generative Adversarial Networks for 3D Architectural Image Generation
Due to advancements in infrastructural modulations, architectural design is one of the most peculiar and tedious processes.
Manifold Preserving Guided Diffusion
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.
Guided Flows for Generative Modeling and Decision Making
Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks.
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
To this end, and capitalizing on the powerful fine-grained generative control offered by the recent diffusion-based generative models, we introduce Steered Diffusion, a generalized framework for photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation.
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
Therefore, Diff-Retinex formulates the low-light image enhancement problem into Retinex decomposition and conditional image generation.
Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data
Label-noise or curated unlabeled data is used to compensate for the assumption of clean labeled data in training the conditional generative adversarial network; however, satisfying such an extended assumption is occasionally laborious or impractical.
