Training Phase

Data Processing: Focuses on summarizing and modeling patterns from massive data, aiming to extract and simulate the fundamental principles and deep rules of world operations. On the basis of accurate and immediate data, it generates realistic, clear, and smooth video content.
Learning Strategies: Employs a combination of supervised and unsupervised learning strategies to explore and model fundamental elements in the training data. Covers essential components such as objects, environments, scenarios, and events. Establishes semantic alignment between multi-modal capabilities, associating visual features with semantic labels. This ensures that textual descriptions can be accurately mapped and linked to relevant visual units.
Network Architecture: Utilizes a Diffusion-Transformer fusion architecture with Mixture-of-Experts (MoE). Adopts a self-supervised video compression neural network to map raw videos into a specific latent space. Through the Diffusion Transformer, it performs fine-grained modeling of spatiotemporal segments within this space, capturing dynamic interaction mechanisms in scenes.
Optimizers and Learning Rate: Incorporates self-developed machine learning methods to update the model's probabilistic estimation, optimizing performance. Implements a dynamic learning rate adjustment mechanism to improve training efficiency.

Last updated 1 month ago

Data Processing: Focuses on summarizing and modeling patterns from massive data, aiming to extract and simulate the fundamental principles and deep rules of world operations. On the basis of accurate and immediate data, it generates realistic, clear, and smooth video content.
Learning Strategies: Employs a combination of supervised and unsupervised learning strategies to explore and model fundamental elements in the training data. Covers essential components such as objects, environments, scenarios, and events. Establishes semantic alignment between multi-modal capabilities, associating visual features with semantic labels. This ensures that textual descriptions can be accurately mapped and linked to relevant visual units.
Network Architecture: Utilizes a Diffusion-Transformer fusion architecture with Mixture-of-Experts (MoE). Adopts a self-supervised video compression neural network to map raw videos into a specific latent space. Through the Diffusion Transformer, it performs fine-grained modeling of spatiotemporal segments within this space, capturing dynamic interaction mechanisms in scenes.
Optimizers and Learning Rate: Incorporates self-developed machine learning methods to update the model's probabilistic estimation, optimizing performance. Implements a dynamic learning rate adjustment mechanism to improve training efficiency.

Last updated 1 month ago