Dream-VL AI: How Discrete Diffusion Models Are Revolutionizing Robot Vision and Planning
15 days ago 高效码农Dream-VL and Dream-VLA: A Unified Vision–Language and Vision–Language–Action Framework Based on Discrete Diffusion Language Models Snippet (50–80 words) Dream-VL is trained on over 12 million multimodal samples using discrete diffusion, demonstrating strong advantages in long-horizon visual planning and parallel action generation. Dream-VLA is pretrained on 970k robotic manipulation trajectories and achieves 97.2% average performance on LIBERO, 71.4% on SimplerEnv-Bridge, and 60.5% on SimplerEnv-Fractal benchmarks. Table of Contents Introduction Why Discrete Diffusion Language Models (dLLMs)? Dream-VL: Training Data, Capabilities, and Benchmarks Dataset Scale and Training Paradigm High-Level Planning: ViPlan Benchmark Low-Level Action Planning: Speed and Robustness Dream-VLA: Robot Pretraining and Downstream …
How Yume1.5’s Text-Driven Engine Turns Images Into Walkable Worlds
20 days ago 高效码农From a Single Image to an Infinite, Walkable World: Inside Yume1.5’s Text-Driven Interactive Video Engine What is the shortest path to turning one picture—or one sentence—into a living, explorable 3D world that runs on a single GPU? Yume1.5 compresses time, space, and channels together, distills 50 diffusion steps into 4, and lets you steer with everyday keyboard or text prompts. 1 The 30-Second Primer: How Yume1.5 Works and Why It Matters Summary: Yume1.5 is a 5-billion-parameter diffusion model that autoregressively generates minutes-long 720p video while you walk and look around. It keeps temporal consistency by jointly compressing historical frames along …
TurboDiffusion Explained: How It Achieves 100x Faster AI Video Generation
25 days ago 高效码农TurboDiffusion Demystified: How It Achieves 100x Faster Video Generation Have you ever marveled at beautifully AI-generated videos, only to be held back by the agonizing wait times stretching into dozens of minutes or even hours? While traditional video diffusion models have made monumental breakthroughs in quality, their staggering computational cost has kept real-time generation a distant dream. Today, we dive deep into a revolutionary framework—TurboDiffusion. It accelerates the end-to-end video generation process by 100 to 200 times, reducing a 184-second generation to a mere 1.9 seconds, and slashing a 4549-second marathon down to 38 seconds on a single RTX 5090 …
HY-World 1.5: How This Open-Source AI Model Builds Real-Time Interactive Worlds
1 months ago 高效码农Exploring HY-World 1.5: A Breakthrough in Real-Time Interactive World Modeling with Long-Term Geometric Consistency HY-World 1.5, also known as WorldPlay, is an open-source streaming video diffusion model that enables real-time interactive world modeling at 24 FPS while maintaining long-term geometric consistency. It supports keyboard and mouse inputs for navigation, generalizes across real-world and stylized scenes, and powers applications like 3D reconstruction, promptable events, and infinite world extension. Why HY-World 1.5 is a Game-Changer for Interactive 3D World Generation Imagine navigating a virtual 3D world in real time, using your keyboard and mouse, where the environment stays perfectly consistent—even when you …
SVG-T2I: Generate Images in DINOv3’s Semantic Space Without a VAE
1 months ago 高效码农SVG-T2I: Generating Images Directly in the Semantic Space of Visual Foundation Models—No VAE Required Have you ever wondered about the crucial “compression” step hidden behind the magic of AI image generation? Mainstream methods like Stable Diffusion rely on a component called a Variational Autoencoder (VAE). Its job is to compress a high-definition image into a low-dimensional, abstract latent space, where the diffusion model then learns and generates. However, the space learned by a VAE often sacrifices semantic structure for pixel reconstruction, resulting in a representation that is disconnected from human “understanding” of images. So, can we discard the VAE and …
How RealVideo’s WebSocket Engine Creates Real-Time AI Avatars on 80GB GPUs
1 months ago 高效码农Turn Chat into a Real Face: Inside RealVideo, the WebSocket Video-Calling Engine That Speaks Back A plain-language walkthrough for college-level readers: how to install, tune, and deploy a live text → speech → lip-sync pipeline on two 80 GB GPUs, without writing a single line of extra code. 1. What Exactly Does RealVideo Do? RealVideo is an open-source stack that lets you: Type a sentence in a browser. Hear an AI voice answer instantly. Watch a real photograph speak the answer with perfectly synced lip motion. All three events happen in <500 ms inside one browser tab—no plug-ins, no After …
Wan-Move: 5 Secrets to Precise Motion Control in AI Video Generation
1 months ago 高效码农Wan-Move: Motion-Controllable Video Generation via Latent Trajectory Guidance In a nutshell: Wan-Move is a novel framework for precise motion control in video generation. It injects motion guidance by projecting pixel-space point trajectories into a model’s latent space and copying the first frame’s features along these paths. This requires no architectural changes to base image-to-video models (like Wan-I2V-14B) and enables the generation of high-quality 5-second, 480p videos. User studies indicate its motion controllability rivals commercial tools like Kling 1.5 Pro’s Motion Brush. In video generation, the quest to animate a static image and control its motion with precision lies at the …
Live Avatar AI: How We Reached 20 FPS Real-Time Streaming with a 14B-Parameter Model
1 months ago 高效码农LiveAvatar under the hood: how a 14-billion-parameter diffusion model now runs live, lip-synced avatars at 20 FPS on five GPUs A plain-language walk-through of the paper, code and benchmarks—no hype, no hidden plugs. “We want an avatar that can talk forever, look like the reference photo, and run in real time.” —Authors’ opening line, arXiv:2512.04677 1. The problem in one sentence Big diffusion models give great faces, but they are slow (0.25 FPS) and drift out of look after a few hundred frames. LiveAvatar keeps the quality, removes the lag, and stops the drift—so you can stream an avatar for …
Crisp Text-to-Image Generation: How Ovis-Image 7B Delivers 20B-Level Performance on One GPU
1 months ago 高效码农Ovis-Image: A 7-Billion-Parameter Text-to-Image Model That Punches at 20-Billion Scale—While Running on One GPU “ What makes a compact 7 B model able to render crisp, bilingual, layout-heavy text previously dominated by 20 B+ giants, and how can you deploy it today? TL;DR (the 30-second take) Architecture: 2 B multimodal Ovis 2.5 encoder frozen for alignment, 7 B MMDiT diffusion decoder trained from scratch, FLUX.1-schnell VAE stays frozen—10 B total, <24 GB VRAM. Training: four-stage pipeline (pre-train → instruction fine-tune → DPO preference → GRPO text-specialist) steadily improves word accuracy from 87 % → 92 %. Benchmarks: leads CVTG-2K English …
ViBT Image Generation: How Brownian Bridge Models Achieve 4× Faster AI Inference
1 months ago 高效码农ViBT: Vision Bridge Transformer at Scale – A Practical Deep Dive What is ViBT and why does it achieve up to 4× faster inference than token-heavy conditional diffusion models while maintaining comparable quality? ViBT is the first large-scale realization of Brownian Bridge generative models for vision tasks. Instead of the classic “noise-to-data” paradigm, it directly learns stochastic trajectories from a structured source (image/video) to a structured target, eliminating most conditioning tokens and dramatically reducing compute. Figure: Example results of ViBT across instruction-based editing, stylization, colorization, and frame interpolation. Why the Noise-to-Data Paradigm Feels Wrong for Conditional Generation Most modern image …
Decoupled DMD: How 8-Step Diffusion Outperforms 100-Step Models Without Extra Parameters
1 months ago 高效码农Decoupled DMD: Why 8-Step Diffusion Can Outperform 100-Step Teachers Without Extra Parameters Central question: How can a student network with no additional parameters generate images that look better than its 100-step teacher in only 8 forward passes? Short answer: By decomposing the training objective into two cooperative mechanisms—CFG Augmentation (the engine) and Distribution Matching (the seat-belt)—and giving each its own noise schedule. 1. The Misleading Success of DMD Core question: If DMD was supposed to match distributions, why does it only work when you add an asymmetric CFG term that breaks the theory? Short answer: Theory describes the DM term; …
Why Fourier Space Reveals the Hidden Truth About Diffusion Models’ Detail Generation
7 months ago 高效码农Fourier Space Perspective on Diffusion Models: Why High-Frequency Detail Generation Matters 1. Fundamental Principles of Diffusion Models Diffusion models have revolutionized generative AI across domains like image synthesis, video generation, and protein structure prediction. These models operate through two key phases: 1.1 Standard DDPM Workflow Forward Process (Noise Addition): x_t = √(ᾱ_t)x_0 + √(1-ᾱ_t)ε Progressively adds isotropic Gaussian noise Controlled by decreasing noise schedule ᾱ_t Reverse Process (Denoising): Starts from pure noise (x_T ∼ N(0,I)) Uses U-Net to iteratively predict clean data 2. Key Insights from Fourier Analysis Transitioning to Fourier space reveals critical frequency-dependent behaviors: 2.1 Spectral Properties of Natural Data Data Type …
Image Stylization Breakthrough: How OmniConsistency Solves Diffusion Model Challenges
7 months ago 高效码农Mastering Image Stylization: How OmniConsistency Solves Consistency Challenges in Diffusion Models Understanding the Evolution of Image Stylization In the rapidly evolving landscape of digital art and AI-driven creativity, image stylization has emerged as a transformative technology. From converting ordinary photographs into oil paintings to transforming real-world scenes into anime-style visuals, this field has seen remarkable advancements. However, the journey hasn’t been without challenges. Two critical issues have persisted in image stylization: maintaining consistent styling across complex scenes and preventing style degradation during iterative editing processes. Recent breakthroughs in diffusion models have significantly improved image generation capabilities. These models learn to …
MMaDA: How This Unified Multimodal Diffusion Model Transforms AI Generation?
7 months ago 高效码农MMaDA: A Breakthrough in Unified Multimodal Diffusion Models 1. What Is MMaDA? MMaDA (Multimodal Large Diffusion Language Models) represents a groundbreaking family of foundation models that unify text reasoning, cross-modal understanding, and text-to-image generation through an innovative diffusion architecture. Unlike traditional single-modal AI systems, its core innovation lies in integrating diverse modalities (text, images, etc.) into a shared probabilistic framework—a design philosophy its creators term “modality-agnostic diffusion.” 2. The Three Technical Pillars of MMaDA 2.1 Unified Diffusion Architecture Traditional multimodal models often adopt modular designs (text encoder + vision encoder + fusion modules). MMaDA revolutionizes this paradigm by: Processing all …