The text description is encoded by a text encoder (e.g. a language model or CLIP) into a conditioning representation. A generative model — most often diffusion-based — iteratively denoises a latent video representation conditioned on the text. A temporal module (temporal attention / 3D convolutions) is key to keeping motion and content consistent across frames. Finally a decoder (e.g. a VAE) turns the latents into image frames. The process synchronizes the text input with the video frames to ensure alignment and continuity.
Enables the creation of video content without filming, animation or editing — directly from a text description. It addresses the time-consuming, costly nature of video production and the lack of temporal consistency that arises from naively generating video frame by frame.
Converts the natural-language description into a representation that conditions generation.
Iteratively denoises the text-guided latent video representation. Usually a U-Net or diffusion transformer (DiT).
Temporal attention or 3D convolutions that maintain motion and content consistency across frames.
Decodes the latent representation into full-resolution image frames.
Objects and background can change appearance across frames, causing flicker and content drift.
Quality declines as the length of the generated video increases.
The model may misinterpret the prompt, producing video that deviates from the intent.
Text and fine details in generated video often appear garbled.
Training and inference are very costly, limiting output quality and length.
One of the earliest text-to-video models (9.4B parameters), released on GitHub.
Text-to-video model presented by Meta Platforms.
Google Brain model using a 3D U-Net architecture.
One of the first commercially available models with a web interface and text-to-video capability.
OpenAI model generating longer, high-quality clips, markedly raising quality and consistency.
Google model notable for generating synchronized audio together with video.
Attention and computation span many frames at once, so cost grows with resolution, frame count and clip length. This is the main source of the heavy compute requirements.
Resolution of generated frames (e.g. 512×512, 720p, 4K).
Duration of the generated video; longer clips are harder to keep consistent.
Frames per second affecting motion smoothness.
Strength of text conditioning; trades prompt fidelity against diversity.
Number of denoising steps; more steps = higher quality at the cost of time.
Video diffusion models (U-Net/DiT) perform dense computation over the entire latent representation at each denoising step.
Frames are generated interdependently (temporal consistency limits full parallelism), but within-frame processing and training steps are highly parallel on GPUs.
Video diffusion models require massive matrix operations that GPU tensor cores are optimized for.
TPUs scale training and inference of large generative models well.
