PASTA (Prompt-Aware Shared-Latent Training-Free Annealed Diffusion Sampling) is a new framework for diffusion-based image generation that preserves clean, reserved regions such as headlines, logos, or UI elements. It enforces these constraints by selectively applying semantic guidance only to object-allowed areas during sampling, using a shared latent trajectory and a mask-guided fusion of full-prompt and context-prompt guidance. An annealed schedule suppresses semantic leakage early in denoising and gradually restores visual consistency, leading to improved reserved-region fidelity while maintaining overall image realism and perceptual consistency.
arXiv:2610.01723v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual tra...
arXiv:2507. 17853v2 Announce Type: replace-cross Abstract: Recent advances in text-to-image (T2I) generation have led to impressive visual results.
arXiv:2511.19811v2 Announce Type: replace-cross Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive output...
Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent source-image conditioning can limit how fully an edit is executed and how natural the result appears, especially when the target scene diverges substantially from the input.
The paper introduces "overpainting," a localized, context-aware image editing technique that allows users to specify precise or loose editing regions via a trimap. The method adapts a pretrained diffusion model with joint attention and low‑rank adaptation, incorporating attention‑dropout to balance noise, source, and mask inputs. An automated pipeline generates training data by pairing images from language‑based editing models, curating them, and extracting trimaps, enabling the model to perform a wide range of editing tasks.
The paper investigates how long, richly detailed prompts cause modern text-to-image models to lose diversity, even when many visual aspects are unspecified. It introduces PromptMoG, a training‑free method that samples prompt embeddings from a Mixture‑of‑Gaussians distribution to restore diversity while preserving semantic fidelity. The authors also present LPD‑Bench, a benchmark for evaluating fidelity and diversity under long, semantically dense prompts, and demonstrate PromptMoG’s effectiveness on four large diffusion models.
arXiv:2510. 05356v2 Announce Type: replace-cross Abstract: Hallucinations in diffusion models are samples with structural inconsistencies that can emerge due to the excessive smoothing of the learned score function, which in turn leads to interpolations between modes of the data distribution.
arXiv:2608. 03284v1 Announce Type: cross Abstract: Ensuring safety and policy compliance in text-to-image diffusion models remains a critical challenge, as benign or adversarial prompts can often elicit prohibited content, e.
arXiv:2607. 19344v1 Announce Type: cross Abstract: Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identities, and spatial arrangements that cannot be reliably achieved through text prompting alone.
QK-Edit introduces a training‑free query‑key manipulation strategy for Multimodal Diffusion Transformers (MM‑DiTs), enabling precise text‑guided image and video editing. By isolating and adjusting critical attention components, it balances prompt fidelity with structural consistency, supporting tasks such as object addition, removal, replacement, background changes, material and color adjustments, and style transformation. The method works seamlessly with state‑of‑the‑art models like FLUX and HunyuanVideo, improving editing performance without requiring additional training.
EOS-Story is a training‑free framework that improves visual storytelling by balancing identity consistency with scene‑specific adaptation. It separates narrative prompts into identity and scene sub‑prompts and introduces Cross‑Contextual Bridging to inject appearance cues into the scene embedding. In the vision space, Adaptive Identity Preservation aligns identity features to the scene scale and uses Identity‑Aware Self‑Attention to avoid structural artifacts, achieving competitive results on the extended Consistory+ benchmark.
arXiv:2605. 31162v1 Announce Type: cross Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored.