on_boarding.md

graph LR
    System_Configuration["System Configuration"]
    Data_Pipeline["Data Pipeline"]
    Core_ML_Components_Models_Optimizations_["Core ML Components (Models & Optimizations)"]
    Model_Management["Model Management"]
    Distributed_Execution["Distributed Execution"]
    Training_Orchestration["Training Orchestration"]
    Inference_Orchestration["Inference Orchestration"]
    External_Service_Interface["External Service Interface"]
    System_Configuration -- "Provides dataset paths and preprocessing parameters" --> Data_Pipeline
    System_Configuration -- "Specifies model architectures and checkpoint paths" --> Model_Management
    Data_Pipeline -- "Supplies preprocessed data batches for training" --> Training_Orchestration
    Data_Pipeline -- "Provides input data" --> Inference_Orchestration
    Model_Management -- "Loads model weights, VAE weights, and attention configurations" --> Core_ML_Components_Models_Optimizations_
    Training_Orchestration -- "Utilizes models for forward/backward passes and latent operations" --> Core_ML_Components_Models_Optimizations_
    Core_ML_Components_Models_Optimizations_ -- "Performs passes and latent operations" --> Training_Orchestration
    Inference_Orchestration -- "Utilizes models for core generation logic, latent encoding/decoding, and denoising steps" --> Core_ML_Components_Models_Optimizations_
    Core_ML_Components_Models_Optimizations_ -- "Executes generation logic and handles latent transformations" --> Inference_Orchestration
    Distributed_Execution -- "Facilitates parallel training and data synchronization" --> Training_Orchestration
    Training_Orchestration -- "Leverages for scaling and distributed execution" --> Distributed_Execution
    Distributed_Execution -- "Enables distributed inference execution" --> Inference_Orchestration
    Inference_Orchestration -- "Leverages for scaling and distributed execution" --> Distributed_Execution
    External_Service_Interface -- "Triggers video generation requests" --> Inference_Orchestration
    Inference_Orchestration -- "Returns generated video data" --> External_Service_Interface
    click System_Configuration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/System_Configuration.md" "Details"
    click Data_Pipeline href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/Data_Pipeline.md" "Details"
    click Core_ML_Components_Models_Optimizations_ href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/Core_ML_Components_Models_Optimizations_.md" "Details"
    click Model_Management href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/Model_Management.md" "Details"
    click Distributed_Execution href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/Distributed_Execution.md" "Details"
    click Training_Orchestration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/Training_Orchestration.md" "Details"
    click Inference_Orchestration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/Inference_Orchestration.md" "Details"
    click External_Service_Interface href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/FastVideo/External_Service_Interface.md" "Details"

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Details

The FastVideo architecture is designed as a high-performance, modular ML toolkit for video generation, structured around a clear pipeline flow. It starts with System Configuration to define operational parameters, which then guides the Data Pipeline in preparing input for either Training Orchestration or Inference Orchestration. Central to both workflows are the Core ML Components (Models & Optimizations), which include specialized attention kernels for accelerated processing. Model Management handles the loading and fine-tuning of these core models. For scalability, both training and inference leverage a Distributed Execution system. The generated videos are finally exposed via an External Service Interface. This architecture emphasizes distinct functional boundaries, optimized data flow, and extensibility, making it suitable for complex deep learning tasks on GPU hardware.

System Configuration [Expand]

Manages application-wide settings, command-line arguments, and model configurations, acting as the initial entry point for defining operational parameters.

Related Classes/Methods:

• fastvideo.fastvideo_args.prepare_fastvideo_args:524-541

Data Pipeline [Expand]

Handles the ingestion, transformation, and batching of video and text data, preparing raw data into a format suitable for model consumption in both training and inference.

Related Classes/Methods:

• fastvideo.dataset.parquet_dataset_iterable_style.__iter__:113-132
• fastvideo.pipelines.preprocess.preprocess_pipeline_base.preprocess_video_and_text:266-446

Core ML Components (Models & Optimizations) [Expand]

Encapsulates the primary deep learning models (Diffusion Transformers, Encoders, VAE) central to video generation, along with specialized attention mechanisms (STA, VSA) critical for performance and memory optimization.

Related Classes/Methods:

• fastvideo.models.dits.base.__init__:92-114
• fastvideo.models.vaes.common.encode:65-77
• fastvideo.models.vaes.common.decode:79-97
• fastvideo.attention.backends.video_sparse_attn.build:160-197
• fastvideo.attention.backends.sliding_tile_attn.build:77-88
• fastvideo.models.schedulers.scheduling_flow_match_euler_discrete.step:440-534

Model Management [Expand]

Manages the dynamic loading of pre-trained model weights, maintains a registry of available model architectures, and handles fine-tuning techniques like LoRA.

Related Classes/Methods:

• fastvideo.models.loader.component_loader.load_model:245-304

Distributed Execution [Expand]

Provides utilities and state management for distributed training and inference across multiple GPUs or compute nodes, coordinating individual GPU worker processes and inter-device communication.

Related Classes/Methods:

• fastvideo.distributed.parallel_state.initialize_model_parallel:844-916
• fastvideo.worker.gpu_worker.event_loop:120-180

Training Orchestration [Expand]

Orchestrates the complete training and knowledge distillation workflows, managing data iteration, forward/backward passes, and optimization steps for efficient model learning.

Related Classes/Methods:

• fastvideo.training.training_pipeline.train:412-522
• fastvideo.training.distillation_pipeline.train:882-1065

Inference Orchestration [Expand]

Defines and executes the sequential stages required to generate a video from input prompts and parameters, including the core denoising process and transforming latent representations into visual outputs.

Related Classes/Methods:

• fastvideo.pipelines.composed_pipeline_base.forward:332-358
• fastvideo.entrypoints.video_generator.generate_video:100-185

External Service Interface [Expand]

Provides user-facing interfaces (e.g., Gradio, Ray Serve) for interacting with the video generation capabilities, exposing core functionality to external applications or users.

Related Classes/Methods:

• examples.inference.gradio.ray_serve_backend.generate_video:252-281

FAQ