Self-Attention Decomposition For Training Free Diffusion Editing

arXiv — cs.CV•Tuesday, October 28, 2025 at 4:00:00 AM

A recent study introduces a novel method for enhancing the controllability of diffusion models in image synthesis. By focusing on self-attention decomposition, researchers aim to identify interpretable directions in the model's latent representations that correspond to specific semantic attributes. This advancement is significant as it addresses the challenges of precise editing in generated images, paving the way for more targeted and effective applications in creative fields.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Synthesia

Create realistic AI videos with custom avatars and voiceovers in minutes.

AI & DataTry the app

Dyad

Build and deploy free, local AI applications with open-source tools.

AI & DataTry the app

PicPicAI

Transform your images with powerful AI editing tools, all in one place.

AI & DataTry the app

Continue Readings

arXiv — cs.CVa day ago

FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning

PositiveArtificial Intelligence

A new framework called FeRA has been introduced to enhance the adaptation of diffusion models for generative tasks. By focusing on the frequency energy mechanism during denoising, FeRA aligns parameter updates with the intrinsic energy progression of diffusion, comprising components like a frequency energy indicator and a soft frequency router.

Read full article

via arXiv — cs.CV

arXiv — cs.CVa day ago

Zero-Shot Video Deraining with Video Diffusion Models

PositiveArtificial Intelligence

A new zero-shot video deraining method has been introduced, leveraging a pretrained text-to-video diffusion model to effectively remove rain from complex dynamic scenes without the need for synthetic data or model fine-tuning. This approach marks a significant advancement in video deraining technology, addressing limitations of existing methods that rely on paired datasets or static camera setups.

Read full article

via arXiv — cs.CV

arXiv — cs.CVa day ago

Evaluating Dataset Watermarking for Fine-tuning Traceability of Customized Diffusion Models: A Comprehensive Benchmark and Removal Approach

NeutralArtificial Intelligence

A recent study has introduced a comprehensive evaluation framework for dataset watermarking in fine-tuning diffusion models, addressing the need for traceability in customized image generation. This framework assesses methods based on Universality, Transmissibility, and Robustness, revealing vulnerabilities in existing watermarking techniques under real-world scenarios.

Read full article

via arXiv — cs.CV

arXiv — cs.CVa day ago

MatMart: Material Reconstruction of 3D Objects via Diffusion

PositiveArtificial Intelligence

MatMart has introduced a novel material reconstruction framework for 3D objects, utilizing diffusion models to enhance material estimation and generation. This two-stage process begins with accurate material prediction and is followed by prior-guided material generation for unobserved views, resulting in high-fidelity outcomes. The framework demonstrates strong scalability by allowing reconstruction from an arbitrary number of input images.

Read full article

via arXiv — cs.CV

arXiv — cs.CV2 days ago

HDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates

PositiveArtificial Intelligence

A new approach to image compression, known as Hybrid-Diffusion Image Compression (HDCompression), has been introduced to tackle the challenges of achieving high fidelity and perceptual quality at ultra-low bitrates. This dual-stream framework combines generative vector-quantized modeling, diffusion models, and conventional learned image compression techniques to enhance image quality while minimizing artifacts caused by heavy quantization.

Read full article

via arXiv — cs.CV

arXiv — cs.LG2 days ago

SCALEX: Scalable Concept and Latent Exploration for Diffusion Models

PositiveArtificial Intelligence

SCALEX has been introduced as a framework for scalable and automated exploration of latent spaces in diffusion models, addressing the limitations of existing methods that often rely on predefined categories or manual interpretation. This framework utilizes natural language prompts to extract semantically meaningful directions, enabling zero-shot interpretation and systematic comparisons across various concepts.

Read full article

via arXiv — cs.LG