How Do AI Video Generators Work? Diffusion, Latents and Sound Explained (2026)

AI video generators work by starting from random noise and removing it step by step until a video appears, guided by your text prompt (and, if you give one, a start image or reference clip). Modern models such as Sora, Wan, Seedance and Veo do this inside a compressed "latent" version of the video using a diffusion transformer, then decode the result back into real frames, and some generate a matching soundtrack at the same time. The rest of this guide explains each step in plain language, as of 31 October 2026.

How does an AI video generator work, step by step?

Every mainstream text-to-video model in 2026 follows the same basic pipeline. Names and sizes differ, but the stages are close to identical.

1. Read the prompt. A text encoder (a language model) turns your words into a list of numbers, called embeddings, that capture meaning: "a red fox running through snow at dusk, low tracking shot".
2. Start from noise. The model creates a block of pure random noise. It isn't a set of video frames yet but a compressed grid of numbers in latent space (explained below).
3. Denoise in steps. A large neural network, usually a diffusion transformer, looks at the noisy block and the prompt embeddings and predicts how to make the block slightly less noisy and slightly more like a video that matches the prompt. It repeats this over many steps. The Wan paper describes sampling with around 50 steps.
4. Decode to pixels. When the denoising finishes, a decoder (the second half of a VAE) expands the clean latent back into full-resolution frames.
5. Add sound (on audio models). Models with native audio produce an audio track together with the frames, so footsteps, rain or a spoken line match what's on screen.
6. Post-process. The service encodes the frames into an MP4, and may also upscale, interpolate frames or add a watermark.

The key idea is that the model never "films" anything. It learned during training what videos tend to look like and how things tend to move, and it uses that knowledge to turn noise into a plausible clip.

What is a diffusion model, in simple terms?

A diffusion model is trained in reverse. During training, engineers take a real video clip, add a known amount of noise and ask the network to predict that noise (or, in newer "flow matching" setups, the direction from noise to clean data). After billions of examples, the network becomes very good at answering "given this noisy mess and this caption, what would the clean version look like?"

At generation time you feed it only noise plus your caption, and it walks the noise toward a clean video one step at a time. A different random starting point, called the seed, gives a different video for the same prompt. That's why the same prompt can give you five different clips.

Two terms you'll see in 2026 model papers:

• Flow matching / rectified flow. A training method that learns a straight path between noise and data. Alibaba's Wan paper (arXiv 2503.20314) says Wan is built on the Flow Matching framework and trained to predict velocity.
• Classifier-free guidance. A setting that controls how strongly the model follows your prompt compared with "just making any video". Higher values stick closer to the prompt, but pushed too far the picture can look oversaturated or stiff.

What are latents and VAEs, and why do video models need them?

Raw video is huge. Five seconds of 1080p video at 24 frames per second is 120 full images. Running a giant network over every pixel at every denoising step would be far too slow, so video models first compress the video with a VAE (variational autoencoder).

• The encoder squeezes video into a much smaller latent grid during training.
• The decoder expands a generated latent back into pixels at the end.

Wan's paper gives concrete numbers for its 3D causal VAE ("Wan-VAE"): it compresses 4× in time and 8× in height and width, with 16 latent channels, and the VAE itself has about 127 million parameters. In plain terms, every four frames become one latent frame, and every 8×8 block of pixels becomes one latent "pixel".

OpenAI's Sora technical report ("Video generation models as world simulators", February 2024) describes the same approach. It compresses video into a lower-dimensional latent space, then cuts that latent into spacetime patches that the transformer treats as tokens, much like words in a language model. Because patches can come from clips of any length or shape, the report says Sora was trained on videos and images of variable durations, resolutions and aspect ratios. This is part of why modern generators can output 16:9, 9:16 or 1:1 without cropping.

What is a diffusion transformer (DiT)?

Older image generators used a U-Net, a convolutional network. Most current video models use a transformer instead, the same building block behind chatbots, applied to video patches rather than words.

• Self-attention lets every patch "look at" every other patch, across space and time. This is what keeps a character's face and shirt consistent from frame 1 to frame 120.
• Cross-attention lets each patch look at the prompt embeddings, which is how the words steer the picture.

Size matters. Wan's open models come in 1.3B and 14B parameter versions, and the paper reports pre-training on "billions of images and videos". The Sora report says diffusion transformers "scale effectively as video models", meaning that more compute and data keep improving quality.

What's the difference between text-to-video, image-to-video and reference-to-video?

They all share the same denoising engine. The difference is what you condition it on.

In image-to-video, the start image is encoded into the latent space and fixed as the first frame, so the model only has to invent what happens next. That's why it keeps faces and products so much more faithfully than text alone. On Kenerate you can try both on the text-to-video and image-to-video tools, and our first and last frame guide covers giving the model both ends of a shot.

How do AI video generators make sound?

Until 2025 most generators made silent clips, and you added music or voice later. Several 2026 models now generate native audio in the same pass as the video. The model learns how sound and image line up (a door slam with the door closing, lips with speech).

Google DeepMind's Veo page says Veo can add "sound effects, ambient noise, and even dialogue" and generates the audio natively. It also notes that natural spoken audio, especially short speech, is still an area of active development. On Kenerate, models with a sound toggle include Veo 3.1, Seedance 2.5, Seedance 2.0, Wan 3.0 and Kling 3.0. Turning sound off is cheaper on some of them (for example, Veo 3.1 is billed at half rate when silent).

Why are AI video clips so short?

Most models cap a single generation at somewhere between 5 and 30 seconds. There are three reasons:

1. Attention cost grows fast. Every patch attends to every other patch, so doubling the clip length roughly quadruples the attention work.
2. Memory. All those tokens have to fit on the GPU at once during each denoising step.
3. Drift. The longer the clip, the more chances small errors have to build up (a hand that slowly changes shape, a background that wanders).

Here are the real per-generation limits of popular models on Kenerate, taken from our model config as of 31 October 2026:

To get past the cap, creators chain clips with "extend" or end-frame tricks. Our guide to making longer AI videos walks through three methods, and the full model list shows every model's limits side by side.

Why does AI video generation take minutes, not seconds?

A quick back-of-the-envelope calculation shows why. Wan's VAE turns roughly 81 frames of 720p video (about 5 seconds at 16 fps) into 21 latent frames of 90 × 160. After the transformer groups those into 2 × 2 patches, that's on the order of 75,000 tokens. The transformer then has to process all of them, with attention between them, at every one of ~50 denoising steps, plus a text encoder pass and a VAE decode at the end.

That's billions of times more arithmetic than a chatbot reply of a few hundred words. Providers speed it up with tricks the Wan paper lists (diffusion caching, 8-bit attention, splitting work across GPUs), and "fast" or "turbo" variants such as Veo 3.1 Fast use fewer steps or smaller networks. Even so, a high-resolution clip usually takes a few minutes, and a queue at busy times adds more. It also explains price: you're paying for GPU time, which is why cost scales with seconds × resolution. Our cost-per-second breakdown compares models in detail.

Why do hands, text and physics still go wrong?

The model has no physics engine and no 3D model of the scene. It predicts pixels that look right based on patterns in its training data. The Sora report itself says Sora models short- and long-range dependencies "often, though not always".

Common failure points, and why they happen:

• Hands and fingers. Small, fast-moving and often hidden, so the training signal is weak, and a 4× temporal compression can blur quick finger motion.
• Text and signs. Letters need pixel-exact shapes that diffusion struggles to hold across frames.
• Object permanence. Something that leaves the frame may come back looking different.
• Physics. Glass, liquids, collisions and cause and effect (a bite that leaves no mark) are only learned statistically.
• Many people at once. Wan's own benchmark shows its 14B model scoring below some rivals on human artifacts and large motion.

Practical fixes: use image-to-video so the hard details are already in the start frame, keep hands simple in the prompt, generate a few seeds and keep the best, and keep text overlays for editing instead of the generator.

Where does the training data come from, and are AI videos labelled?

Models learn from very large collections of captioned images and videos. The Wan paper describes billions of items, filtered for quality and recaptioned by a separate vision-language model so captions are detailed. The Sora report says OpenAI recaptioned its training videos with descriptive captions too. Training data and licensing differ by company, and it's an active legal debate.

On the output side, Google says every Veo video carries an invisible SynthID watermark for AI detection, and platforms like YouTube and TikTok ask creators to label realistic AI content. See are AI video generators safe for privacy and labelling rules.

How can I try this myself?

1. Open the Kenerate AI video generator and pick a model. Wan 3.0 at 480p is the cheapest way to draft.
2. Write a prompt with subject, action, setting, camera and light: "a red fox trotting through fresh snow at dusk, low tracking shot, soft blue light".
3. Choose text-to-video, or upload a start image for image-to-video if you need an exact subject.
4. Set length and resolution. Start short (5 s) and low-res, then upscale the take you keep.
5. Turn sound on if the model supports it and you want ambience or dialogue.
6. Generate two or three seeds, keep the best and extend it if you need a longer shot.

Sources

• OpenAI, "Video generation models as world simulators" (Sora technical report, February 2024): https://openai.com/index/video-generation-models-as-world-simulators/
• Wan Team, Alibaba Group, "Wan: Open and Advanced Large-Scale Video Generative Models", arXiv 2503.20314: https://arxiv.org/abs/2503.20314
• Google DeepMind, Veo model page: https://deepmind.google/models/veo/
• Kenerate model config (clip lengths, resolutions, credit rates), checked 31 October 2026.