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Found 14 papers, 7 papers with code
MaxFusion: Plug&Play Multi-Modal Generation in Text-to-Image Diffusion Models
Large diffusion-based Text-to-Image (T2I) models have shown impressive generative powers for text-to-image generation as well as spatially conditioned image generation.
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers
As these parameters are independent, a single diffusion model with these task-specific parameters can be used to perform multiple tasks simultaneously.
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.
AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation
However, SAM does not generalize well to the medical domain as is without utilizing a large amount of compute resources for fine-tuning and using task-specific prompts.
$CrowdDiff$: Multi-hypothesis Crowd Density Estimation using Diffusion Models
Furthermore, as the intermediate time steps of the diffusion process are noisy, we incorporate a regression branch for direct crowd estimation only during training to improve the feature learning.
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors
The improvements obtained by our method suggest that the priors can be incorporated as a general plugin for improving conditional diffusion models.
Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
We also introduce a novel reliability parameter that allows using different off-the-shelf diffusion models trained across various datasets during sampling time alone to guide it to the desired outcome satisfying multiple constraints.
Ranked #1 on
Face Sketch Synthesis
on Multi-Modal CelebA-HQ
NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images
This results in a pair of images with colored noise.
T2V-DDPM: Thermal to Visible Face Translation using Denoising Diffusion Probabilistic Models
In this paper, we propose a Denoising Diffusion Probabilistic Model (DDPM) based solution for T2V translation specifically for facial images.
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
In recent years, various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed in the literature which attempt to remove the distortion in the image.
DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection
However, in this work, our focus is not on image synthesis but on utilizing it as a pre-trained feature extractor for the downstream application of change detection.
Ranked #1 on
Change Detection
on DSIFN-CD
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models
Based on the fact that the distribution over each time step in the diffusion model is Gaussian, in this work we show that there exists a closed-form expression to the generate the image corresponds to the given modalities.
SAR Despeckling using a Denoising Diffusion Probabilistic Model
The despeckled image is recovered by a reverse process which iteratively predicts the added noise using a noise predictor which is conditioned on the speckled image.
A comparison of different atmospheric turbulence simulation methods for image restoration
In this paper, we systematically evaluate the effectiveness of various turbulence simulation methods on image restoration.
