Training Infrastructure of a Text-to-Video Generation System

Text-to-video models represent the next frontier in generative AI. These models are designed to interpret textual inputs and create videos that adhere to specified prompts in terms of content, style, and motion dynamics. Their applications span diverse fields, including entertainment, education, marketing, and virtual reality, offering a revolutionary toolkit for storytelling, simulation, and content personalization.

The complexity of video generation demands an advanced understanding of both NLP and video synthesis. Unlike text-to-image systems, which output a single image, text-to-video systems must consider spatial and temporal consistency, requiring sophisticated modeling of motion, transitions, and interactions over time. Prominent examples of video generation models include Open-SORA, Mochi 1, and SORA, each excelling in different aspects of video synthesis, such as realism, smooth transitions, and interpretive fidelity.

Let’s explore how to build an advanced and reliable text-to-video system. We’ll focus on creating a system that takes text inputs and generates realistic, high-quality videos.

Requirements

Designing a text-to-video system involves addressing functional and nonfunctional requirements to ensure the system performs effectively and reliably. Let’s break these down:

Functional requirements

The core functionalities that our text-to-video system should support include:

• Natural language understanding: The system must include a strong natural language understanding component to accurately interpret and extract meaningful information from text inputs.

• Video generation:  The system should produce high-quality videos that align with text prompts. These outputs must support specific characteristics, including resolution, frame rate, video length, and smooth transitions between frames.

Note: You may want to design a system with specific output requirements, like a 720pA video resolution is often called high-definition (HD) video. This means the video has 1280x720 pixels (horizontally and vertically, respectively). video with 30fpsFrames per second.. This is the phase where you can decide the requirements that will later shape the system design and influence the design decisions.

• Input formats: The system should handle a variety of input formats, such as JSON and plain text, to allow flexibility in integrating with different applications.

• Customization: Users should have fine-grained control over the generated videos, allowing them to specify desired styles, camera angles, emotional tones, or visual themes directly within their text prompts.

• Output formats: The system should support multiple video formats (e.g., MP4, AVI, or WebM) to ensure compatibility with various platforms and use cases.

Nonfunctional requirements

The nonfunctional requirements ensure that the video generation model performs reliably, scales effectively, and maintains security:

• Scalability: The system should handle varying workloads, from individual users to high-demand scenarios, without degradation in performance.

• Performance: Video generation should occur within a reasonable timeframe, maintaining low latency even for longer or high-resolution outputs.

• Reliability: The system should consistently generate videos that accurately reflect the input prompts, ensuring predictable and stable behavior.

• Availability: The system should prioritize high availability, achieved through a robust infrastructure that includes redundancy and failover mechanisms.

• Security and privacy: User inputs and generated outputs should be securely handled, with safeguards against unauthorized access or data leaks, particularly when dealing with sensitive or proprietary prompts. We also want to secure user data to achieve security and privacy.

Model selection

Text-to-video generation systems require a model combining natural language understanding (interpreting text) and video synthesis (creating dynamic visual content). Common architectures include:

1. Transformer-based models: These models, such as those leveraging GPT-style or BERTBERT (Bidirectional Encoder Representations from Transformers) is a transformer-based language model developed by Google that processes text bidirectionally, meaning it considers a word’s context from both its left and right sides.-style transformers, are adept at handling complex text inputs and aligning them with visual outputs. By pairing transformers with video generation modules, these architectures excel in maintaining temporal consistencyThe consistency and smoothness of visual changes over time in a video ensure that frames flow naturally without abrupt or unrealistic transitions. across frames.

2. Generative adversarial networks (GANs): GANs are effective for video generation due to their ability to create realistic content. Specialized conditional GANs can map textual prompts to video sequences in the text-to-video context. However, they often require additional tuning for temporal coherence.

3. Diffusion models: Building on their success in text-to-image generation, diffusion models have begun to extend to video generation. These models iteratively refine frames from noise while maintaining temporal relationships, offering a promising approach for producing high-quality videos.

4. Hybrid architectures: Combining elements from different architectures, such as a transformer for text comprehension and a GAN or diffusion model for video synthesis, can offer a balanced approach, taking advantage of the strengths of each model type. Note that hybrid architectures combine any two or more independent architectures, such as GANs, diffusion models, VAEs, etc.

Let’s compare some of the common state-of-the-art text-to-video models in the table below:

We will use Mochi 1, a diffusion-based model that generates high-quality stylized videos for our text-to-video generation system. Mochi 1 uses ~10 B parameters in the diffusion process and ~300 M for the VAE to ensure detail, temporal consistency, and customization flexibility. We will keep it simple and assume a model with 10 B parameters.

Note: We will not be training the text encoder here (it will be treated as a frozen modelDuring training, if we have multiple models and are only training some of them, we can call the others frozen models.). However, due to the size of the encoder used (T5-XXL(Wikipedia) https://en.wikipedia.org/wiki/T5_(language_model) has ~4.7 B parameters in its encoder), we must consider this when estimating the time it takes to train the model.

The Mochi 1 model starts with two main inputs: a text prompt (processed by the T5-XXL language model) and video input. The text prompt is encoded into text tokens and passed to the text processing stream with a smaller hidden dimension. Simultaneously, the video input is compressed by the video VAE, reducing its size through spatial compressionA technique to reduce the spatial resolution of video frames (e.g., compressing an image from 64x64 to 8x8) to make processing more efficient. (8x8) and temporal compressionReducing the number of frames in a video sequence by summarizing or encoding temporal information to decrease the computational load. (6x) to a compact 12-channel latent spaceA condensed video version where the essential information is encoded into 12 distinct channels. This is similar to how colors in an image are represented by red, green, and blue channels. This latent space allows the model to efficiently process and understand the visual information.. This output is then processed by the visual processing stream, which has a larger hidden dimension because video data requires more capacity to handle complexity.

The text and video streams come together in the multimodal self-attention module, where the system learns to unify and relate information from both modalities. The model uses an asymmetric diffusion transformer (AsymmDiT)https://www.genmo.ai/blog with specialized layers like non-square QKVQuery key value and projection layers to efficiently manage the differences between text and video data. This design ensures efficient memory usage and balances processing power.

After this, the architecture applies full 3D attention to process a large number of video tokensCompact representations of video data are created by compressing raw video into smaller, manageable units for processing in a model. (44,520) within a single context window, enhancing spatial and temporal coherence. It leverages 3D positional embeddings to position each token accurately in space and time. The model then uses techniques like SwiGLUSwiGLU feedforward layers, query-key normalizationThis is a stabilization technique for attention mechanisms to normalize the interaction between queries (inputs) and keys (context) to improve model performance., and sandwich normalizationA method where normalization layers ...