Inference Phase

• Prompt Tuning and Optimization: The model processes user input text prompts, performs keyword analysis, and precisely associates language representations with visual content. This ensures the generated videos are accurate and reasonable.

• Long Video Representation and Processing: The self-developed DiT-MoE model overcomes challenges in long video representation and processing, enabling the generation of longer, more coherent video content.

• Dynamic Inference

Adapts computational processes dynamically based on input variations. By leveraging dynamic computation graphs (e.g., PyTorch’s computational graphs), the model selects different execution paths during inference.

Particularly effective for handling variable-length inputs (e.g., text, sequences), optimizing inference efficiency while maintaining model flexibility.

• Applications of the Video Large Model

General-Purpose Video Large Model

Text-to-Video: Our model allows users to generate high-quality videos by entering simple text prompts. Its unique text parsing technology transforms natural language into visual scenes, producing short videos (8 seconds, 720p, 25fps). Capable of generating natural landscapes, character actions, and complex dynamic scenes, delivering impressive results quickly.

Visual Effects and Cinematic Representation:

Excels in visual aesthetics, handling complex lighting effects, camera angles, and dynamic scenes.

Generates highly detailed visuals, such as sunlight filtering through forests, flowing rivers, or intense combat scenes.

Through precise light control and physical simulation, our model provides cinematic-quality video output, blending realism and virtual creativity. It conveys emotions, textures, camera language, and other creative nuances effortlessly.

Prompt Optimization: When user input descriptions are vague or imprecise, our AI’s prompt optimization feature automatically adjusts prompts to ensure high video quality. Users seeking more control can disable this feature to input precise descriptions manually.

Video Consistency: To ensure consistent video quality, the training model has evolved from the "pre-training + fine-tuning" approach to a generalized unified architecture, achieving generalization at the task level. By leveraging the "contextual memory" capabilities of language models, it has transitioned from single-image input to accepting multiple reference images as input. Our model is capable of understanding the precise meaning of multiple input images, their interrelationships, and generating consistent, coherent, and logically sound outputs based on this information. This ensures high consistency during the video generation process, smooth scene transitions, and harmonious coordination among elements.

Customized Small Models for Specific Scenarios

Character-Specific Effects: Includes effects like kissing, hugging, face pinching, body melting, dancing, costume changes, transformations, smiling, and shouting.

Small Scenario Effects: Supports effects such as pinching, inflating, and melting, tailored through specialized model training.