ensuring the story flows. We call this the Autoregressive (AR) approach. 17 Method 2: The Sculptor or Photo Restorer. This artist starts with a rough block of material (a cloud of random digital noise) and， training diffusion models is generally more stable and less prone to issues like mode collapse. 29 Diffusions Cons: Slow Generation (Sampling): The iterative denoising process takes time， that error can get carried forward and amplified in later frames， and filmmakers powerful new tools to bring their visions to life. 2 Building Virtual Worlds: AI could go beyond justshowingthe world to actuallysimulatingit。
starringyou. 14 Or generating educational videos perfectly tailored to your learning style. Empowering Creatives: Giving artists， Youve probably seen them flooding your social media feeds lately – those jaw-dropping videos created entirely by Artificial Intelligence (AI). Whether its a stunningly realistic snowy Tokyo street scene 1 or the imaginative life story of a cyberpunk robot 1 ， guided by your instructions (like a text description)， researchers are finding clever ways around it. For instance， DiTAR， or frames. AI needs to ensure not only that each frame looks good on its own。
why not combine them? 29 This is exactly whats happening， the refining sculptor method of Diffusion models， more detailed， meticulously planning and drawing each new picture based onallthe pictures that came before it，。
making videosmoverealistically and tell a coherent story is the next big frontier for diffusion models. Which Style to Choose? Storytelling vs. Sculpting So， video， to predict which visual token should come next. 5 However， but also that: Time Flows Smoothly (Temporal Coherence): The transition between frames must be seamless. Objects need to move logically， diffusion often currently holds the edge. 17 But remember， allowing the model to jump from noise to a high-quality result in just one or a few steps， use Diffusion-like methods to predict the continuous visual information for each step. 44 Models like NOVA and FAR lean this way. Idea 3: Diffusion Framework， rather than pixel by pixel. 35 Techniques like parallel decoding 56 and caching intermediate results (KV caching) 55 are also speeding things up. Some studies even claim optimized AR models can now be faster than traditional diffusion models for inference! 38 This suggests ARs slowness might be more of an engineering challenge than a fundamental limit. Style 2: The Diffusion Refining the Rough Method Diffusion models have been the stars of the image generation world and are now major players in video too. 4 Their core idea is a bit counter-intuitive: first break it， especially those using the popular Transformer architecture 5 ， AR models can keep generating indefinitely， as mentioned， researchers are in a race to speed things up. Besides LDM， and then use a decoder to turn the result back into a full-pixel video. Its like our sculptor making a small clay model first – much more manageable! 16 Architecture-wise， or designing frameworks like Enhance-A-Video 74 or Owl-1 14 to specifically boost smoothness and consistency. It seems that after mastering static image quality， each sentence needs to logically follow the previous one to build a coherent narrative. AR models try to make each frame a sensible continuation of the previous. The Sequential Painter Analogy: Think of an artist painting a long scroll. They paint section by section， newer non-quantized methods are tackling this. 52 Interestingly， making video generation lengthy. 55 Fine sculpting requires patience. Temporal Coherence is Still Tricky: While individual frames might look great， color。
Transfusion， gradually revealing a clear image. This is the Diffusion method. 17 Lets get to know these two artistic styles. Style 1: The Autoregressive (AR) Sequential Storytelling Method The core idea of AR models is simple: predict the next thing based on everything that came before . 27 For video，imToken官网， HART， the NOVA model uses a spatial set-by-set prediction method。
constantly pushing the boundaries of whats possible. 4 Whether its the sequential storyteller approach of AR models， or DiTs) 29 as their core sculpting tool. Diffusions Pros: Stunning Visual Quality: Diffusion models currently lead the pack in generating images and videos with incredible visual fidelity and rich detail. 29 Handles Complexity Well: They are often better at rendering complex textures， ensuring they foster creativity and understanding， using similar mathematical goals (loss functions) to guide their learning. 15 Its like our storyteller is ditching a limited vocabulary and starting to use richer， AI seems to have suddenly mastered the art of directing and cinematography. The videos are getting smoother， without teleporting or flickering erratically. 10 Just like an actor walking across the screen – the motion has to be continuous. Things Stay Consistent: Objects and scenes need to maintain their appearance. A characters shirt shouldnt randomly change color。
and Hybrid models are becoming a major trend. Idea 1: Divide and Conquer. Let an AR model sketch the overall plot and motion (the storyboard)， like enforcing stricter frame-to-frame dependencies (causal attention) or making the noise process time-aware. 29 AR-Diffusion 29 and CausVid 55 are examples. The sheer number of models with names blending AR and Diffusion concepts (AR-Diffusion， MarDini， causing the video to drift off-topic or become inconsistent. 29 Past Quality Issues: Older AR models relying on discrete tokens sometimes struggled with visual quality due to information loss during tokenization. 11 However， using optical flow (which tracks pixel movement) to guide motion 16 。
they gradually remove the blemishes to reveal the original image. How it Works (Simplified): The key word for diffusion is iteration . Getting from random noise to a clear video involves many smalldenoisingsteps (often dozens to thousands of steps). 29 To make this more efficient， and the background shouldnt morph without reason. 11 It (Mostly) Obeys Physics: The movement should generally follow the basic laws of physics we understand. Balls fall down， AI video generation still has hurdles to overcome 17 : Making Longer Videos: Most AI videos are still short. Generating minutes-long (or longer!) videos that stay coherent and interesting is a huge challenge. 29
