“Built this custom node for batching prompts, saves a ton of time since models stay loaded between generations. About 50% faster than queuing individually.”

“This article is perfect for those familiar with Python and image generation AI, including users of Stable Diffusion, FLUX, ComfyUI, and Diffusers.”

“ELYZA Lab is introducing models that apply the techniques of image generation AI to text.”

“I was able play with Flux Klein before release and it's a blast.”

“Unlike prior single-paradigm approaches, which achieve <75% accuracy on out-of-distribution datasets, our method maintains 86.8% average accuracy across seven diverse test sets...”

“Keep creating and sharing, let Wan team see it.”

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“The findings indicate that while current generative models can simulate surface-level document aesthetics, they fail to reproduce structural and forensic authenticity.”

“One of the inventors of the transformer (the basis of chatGPT aka Generative Pre-Trained Transformer) says that it is now holding back progress.”

“The models are fully compatible with the LightX2V lightweight video/image generation inference framework.”

“Stay faithful to the original methods Minimize boilerplate while remaining readable Be easy to run and inspect as standalone files Reproduce key qualitative or quantitative results where feasible”

“The article doesn't contain a direct quote, but rather reports on a statement made by Sundar Pichai.”

“"全てを実装しない」「無闇に行動しない」「動きすぎない」ということについて考えていて"”

“SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous and arbitrary exploration across space and time.”

“GaMO expands the field of view from existing camera poses, which inherently preserves geometric consistency while providing broader scene coverage.”

“The self-bootstrapping framework reframes visual dubbing from an ill-posed inpainting task into a well-conditioned video-to-video editing problem.”

“Generative classifiers...can avoid this issue by modeling all features, both core and spurious, instead of mainly spurious ones.”

“DLMs augmented with polynomial-length chain-of-thought (CoT) can simulate any parallel sampling algorithm using an optimal number of sequential steps.”

“The paper demonstrates the superiority of HaineiFRDM in defect restoration ability over existing open-source methods.”

“The proposed sampler consistently improves sample quality under the same NFE budget and can be competitive with, and sometimes outperform, state-of-the-art higher-order samplers.”

“AODDiff inherently enables uncertainty quantification via multiple sampling, offering critical confidence metrics for downstream applications.”

“The paper demonstrates a proof-of-concept generative surrogate for reconstructing coherent turbulent dynamics between sparse snapshots.”

“The study substitutes thermal diffusion with mass diffusion and extends the usual scheme of mass diffusion to comprehend also the anomalous phenomena of superdiffusion or subdiffusion.”

“画像生成モデルもだいぶ進化を成し遂げており, それに伴って概念消去（unlearningに仮に分類しておきます）の研究も段々広く行われるようになってきました.”

“MDiffFR employs a tailored diffusion model on the server to generate embeddings for new items, which are then distributed to clients for cold-start inference.”

“ADS drives decoder success rates to near zero with minimal perceptual impact.”

“The paper introduces an SISR network built on a FM with lower-level feature conditioning, specifically DINOv2 features, which we call a Feature-to-Image Diffusion (F2IDiff) Foundation Model (FM).”

“The study finds that the GPA does not generally hold for these systems under moderate experimental conditions.”

“The paper demonstrates that implicit score matching achieves the same rates of convergence as denoising score matching and allows for Hessian estimation without the curse of dimensionality.”

“Mirage achieves high realism and temporal consistency across diverse editing scenarios.”

“CorGi and CorGi+ achieve up to 2.0x speedup on average, while preserving high generation quality.”

“LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19.”

“DiffThinker significantly outperforms leading closed source models including GPT-5 (+314.2%) and Gemini-3-Flash (+111.6%), as well as the fine-tuned Qwen3-VL-32B baseline (+39.0%), highlighting generative multimodal reasoning as a promising approach for vision-centric reasoning.”

“The model achieves comparable performance with less than 5% of the task-specific data required by dedicated expert models.”

“D^2-Align achieves superior alignment with human preference.”

“The paper presents an activation-steering framework for MDLMs that computes layer-wise steering vectors from a single forward pass using contrastive examples, without simulating the denoising trajectory.”

“GARDO's key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty.”

“The framework incorporates a Salient Region Selection module and a Jacobian Vector Product Guidance mechanism to generate physically plausible adversarial objects.”

“RainFusion2.0 can achieve 80% sparsity while achieving an end-to-end speedup of 1.5~1.8x without compromising video quality.”

“The diffusion actions selected by deep Q-learning at different iterations indeed composite a stochastic anisotropic diffusion process with strong adaptivity to different image structures, which enjoys improvement over the traditional ones.”

“CVC rethinks the role of velocity in inter-distribution transformation by introducing a dual-perspective velocity conversion mechanism.”

“Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime, while outperforming Community-IM and degree heuristics.”

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“Even minor prompt modifications, such as the substitution or insertion of a single word, can cause substantial degradation in semantic fidelity and temporal dynamics, highlighting critical vulnerabilities in current T2V diffusion models.”

“Our approach relies on nonlinear analysis, topological methods, and the theory of strongly continuous semigroups, yielding results applicable to a wide range of models.”

“The paper derives a fundamental identity that connects the (path-) derivative of a (possibly) non-Euclidean energy score to the score of the noisy marginal.”

“"Diffusion knows transparency." Generative video priors can be repurposed, efficiently and label-free, into robust, temporally coherent perception for challenging real-world manipulation.”

“The distilled model matches the visual quality of full-step, bidirectional baselines with 20x less inference cost and latency.”

“ThinkGen employs a decoupled architecture comprising a pretrained MLLM and a Diffusion Transformer (DiT), wherein the MLLM generates tailored instructions based on user intent, and DiT produces high-quality images guided by these instructions.”

“PurifyGen offers a plug-and-play solution with theoretical grounding and strong generalization to unseen prompts and models.”