# Glossary of realtime video and world model terms > Definitions for realtime video and world models: sessions, chunks, autoregressive diffusion, KV cache, visual drift, transport and ready-second billing. Source: https://windflow.online/glossary · Updated 2026-10-11 · Windflow Labs The vocabulary of realtime video generation, in the order a developer tends to meet it. Definitions are short on purpose; the guides go deeper. ## Terms ### Realtime model A model that produces output continuously while it is being steered, at a pace a viewer can watch live. It differs from a batch model, which takes a request and returns a finished file after a wait. Realtime video models, interactive world models and live video editors are all realtime models. ### Realtime video generation Generating video fast enough, and in small enough pieces, that frames can be delivered to a viewer while later frames are still being computed. It usually relies on autoregressive generation in chunks and on a client that buffers a short playout window. ### Interactive video Video that responds to input while it plays: a changed prompt, a camera move, a key press or a webcam frame. The response arrives within seconds or less, not after a re-render. ### World model A generative model that simulates an environment frame by frame, conditioned on what happened before and on the actions taken. A world model produces a scene you can act in rather than a clip you only watch. ### Navigable world model A world model controlled by movement and look controls, typically WASD keys plus a camera direction, usually starting from a seed image. Examples on Windflow are LingBot-World, Matrix-Game, DreamX-World and ABot-World. ### Seed image The starting image a world model or image-to-video model is conditioned on. The first frames, the style and the setting of the generated world follow from it. ### Session A held GPU assigned to one running model. You connect once, send controls and receive frames over the same connection while the model keeps running, then release it. A session is the unit that is started, steered and billed. ### Ready session-second Windflow's billing unit: one second during which a session is ready and returning frames. Queueing and worker startup are not counted. Rates are published per model; see [pricing](https://windflow.online/pricing). ### Track A named stream a session sends or receives. Windflow models return video on `main_video` and, for models that generate sound, audio on `main_audio`. Webcam input travels on a send-only track. ### Prompt steering Changing a text prompt while a session runs so the generated video follows the new direction. Depending on the model, the change blends in softly or waits for the next chunk or window boundary. ### Soft transition and scene cut A soft transition keeps the model's rolling history, so subjects, layout and camera motion continue as the prompt changes. A scene cut discards that history to start independent content, which is cleaner for a new shot but costs a restart of the history. ### Autoregressive video generation Generating video in sequence, each chunk conditioned on what was already produced, instead of generating every frame at once. It is what lets a stream continue indefinitely and accept new input between chunks. ### Chunk, block and window The unit a model generates at a time: a fixed number of frames, such as 12, 24 or 33, or a longer window of several seconds. Controls sent during generation typically take effect at the next boundary, so chunk length sets how quickly a model reacts. ### Diffusion transformer (DiT) A diffusion model whose denoising network is a transformer operating on video latents. Most current video generators, including Helios, LongLive, LingBot-World and LTX, are built this way. ### Denoising step One pass of the denoising network. Standard diffusion needs many; realtime models are distilled to a handful, which is the main lever on how fast a chunk is produced. ### Distillation Training a model to reach in a few steps what a teacher model reaches in many. Techniques named in realtime video work include Self-Forcing, which trains the model on its own autoregressive output, and DMD2, a few-step distribution-matching distillation. ### KV cache and rolling history The stored attention keys and values of frames already generated. An autoregressive model reuses them so each chunk does not recompute the past; a rolling cache drops the oldest frames to keep memory bounded, which is why very old content can be forgotten. ### Drift Gradual degradation over a long autoregressive run: identity, colour or geometry slowly wander because each chunk inherits the previous chunk's small errors. Long-running models counter it with anchoring, memory and re-conditioning. ### Re-anchoring Periodically refreshing a long run with a clean conditioning signal, such as the original reference frame, to pull it back toward the intended look. A refresh can be visible as a brief jump. ### Time to first frame The delay between starting a session and the first frame reaching the viewer. For windowed models it can include a whole window of generation, so it is longer than the steady-state delay between frames. ### Control latency The delay between sending a control and seeing its effect on screen. It has two parts: how long until the next chunk boundary, and how long the stream takes to reach the viewer. ### Playout buffer A short queue on the client that smooths uneven frame arrival. A bigger buffer hides stalls and adds latency; a smaller one does the opposite. ### WebRTC A real-time media protocol built on UDP, with congestion control, loss recovery and browser support. It suits clean links and interactive audio and video; on a lossy path, keyframe recovery can cause visible freezes. ### WebSocket (H.264) A single TCP connection carrying encoded video frames and control messages. It is simple to traverse and to debug, and it trades WebRTC's loss handling for in-order delivery. ### Super-resolution A learned upscaler that turns frames generated at a lower resolution into the delivery resolution, for example 896 × 512 into 1344 × 768. It lets a model generate faster and still deliver more detail. ### Image-to-video and text-to-video Image-to-video starts from a reference image; text-to-video starts from a prompt alone. Many realtime models accept either, and some take only one. ### Joint audio-video generation Generating sound together with the picture in one model, so dialogue, ambience and music follow the scene, as opposed to dubbing audio onto finished video afterwards. LTX-2.5 and MiniMax FastH3 work this way. ### Open weights A model whose trained parameters can be downloaded and run by anyone, under the licence its authors chose. Open weights are not the same as an unrestricted licence: terms on commercial use, territory and disclosure vary by model.