hao-ai-lab/FastVideo
Dec 2025 – Jun 2026
5 Months active
Languages Used
PythonBashTypeScript
Technical Skills
AI Model DevelopmentDeep LearningMachine LearningVideo ProcessingAI model integrationasync programming
Kaiqin Kong
PROFILE
Kaiqin Kong
Over five months, contributed to hao-ai-lab/FastVideo by developing advanced video generation features, including interactive gameplay with mouse and keyboard controls, real-time streaming, and camera trajectory-driven outputs. Leveraged Python, PyTorch, and React to integrate AI models, implement training pipelines such as self-forcing distillation, and enhance video quality through synchronization and style customization. Addressed training stability with bug fixes and improved maintainability via codebase refactoring. Introduced evaluation metrics for video quality and managed model lifecycle with enhanced logging and EMA handling. The work enabled production-ready, customizable video generation workflows, supporting both developer usability and robust, high-resolution content creation.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
8
Lines of code
22,608
Activity Months5
Your Network
214 peopleSame Organization
@ucsd.edu196
Work History
June 2026
5 Commits • 4 Features
Jun 1, 2026June 2026 — hao-ai-lab/FastVideo: Focused delivery across synchronization, customization, evaluation, and training stability to boost output quality and production reliability. Key deliverables: - Video position offset support in LTX2Transformer3DModel: added video_position_offset_sec to adjust video timing for improved audio-video synchronization; includes bugfix to honor the offset in DiT; Commit: 3f245781391922d8270ec3efcbcf130d8bd115bf - LoRA-based style adapters with adjustable strengths in Dreamverse: integrated LoRA controls enabling adjustable stylistic strengths for Dreamverse video generation; Commit: d3a821cdcfa695a58c366dc7fe0814988cbe3479 - New pairwise VLM judging metric for video generation quality: introduces a metric to evaluate video quality by foreground-background separation; Commit: efcc245c2e5627c3c81159a4561c87928d78561e - EMA management and logging improvements in distillation and MoE: defers EMA construction until training begins, improves weight handling, and enhances logging for EMA init/save; Commits: a931efe33a0ea611a9eb0f3289cc3009a02cbf7a and 9ea77d37f398ea55b7b8be6525f80e34b16cbdac Overall impact and accomplishments: - Improved synchronization and control over video processing leads to higher-quality, time-aligned outputs. - Enhanced customization and experimentation through LoRA-style adapters. - More relevant, objective evaluation of video generation quality via VLM-based judging. - More stable training pipelines with better observability thanks to improved EMA handling and logging. Technologies/skills demonstrated: - LTX2Transformer3DModel parameterization and bugfixing, audio-video synchronization. - LoRA integration and application in Dreamverse. - Pairwise VLM evaluation metric design and integration. - EMA management across distillation and MoE, with improved logging and lifecycle handling.
5 Commits • 4 Features
Jun 1, 2026June 2026 — hao-ai-lab/FastVideo: Focused delivery across synchronization, customization, evaluation, and training stability to boost output quality and production reliability. Key deliverables: - Video position offset support in LTX2Transformer3DModel: added video_position_offset_sec to adjust video timing for improved audio-video synchronization; includes bugfix to honor the offset in DiT; Commit: 3f245781391922d8270ec3efcbcf130d8bd115bf - LoRA-based style adapters with adjustable strengths in Dreamverse: integrated LoRA controls enabling adjustable stylistic strengths for Dreamverse video generation; Commit: d3a821cdcfa695a58c366dc7fe0814988cbe3479 - New pairwise VLM judging metric for video generation quality: introduces a metric to evaluate video quality by foreground-background separation; Commit: efcc245c2e5627c3c81159a4561c87928d78561e - EMA management and logging improvements in distillation and MoE: defers EMA construction until training begins, improves weight handling, and enhances logging for EMA init/save; Commits: a931efe33a0ea611a9eb0f3289cc3009a02cbf7a and 9ea77d37f398ea55b7b8be6525f80e34b16cbdac Overall impact and accomplishments: - Improved synchronization and control over video processing leads to higher-quality, time-aligned outputs. - Enhanced customization and experimentation through LoRA-style adapters. - More relevant, objective evaluation of video generation quality via VLM-based judging. - More stable training pipelines with better observability thanks to improved EMA handling and logging. Technologies/skills demonstrated: - LTX2Transformer3DModel parameterization and bugfixing, audio-video synchronization. - LoRA integration and application in Dreamverse. - Pairwise VLM evaluation metric design and integration. - EMA management across distillation and MoE, with improved logging and lifecycle handling.
June 2026
May 2026
4 Commits • 1 Features
May 1, 2026May 2026 (2026-05) monthly summary for hao-ai-lab/FastVideo: Delivered a major upgrade to the MatrixGame family with a rename to MatrixGame2, launched MatrixGame3.0, and introduced new training pipelines including self-forcing distillation and diffusion forcing methods. Implemented a bug fix for gradient checkpointing and caching in MatrixGame2 self-forcing distillation to improve training performance and memory usage. These changes enhanced video generation quality (higher resolution and action-input support), reduced training time, and improved maintainability across the repository. Commit activity included matrix changes across the upgrade and rename: d6dfe954669d64af45dea6476ac5ca1c552b511c, 773d44b875f81945a4f1e76179949c0002f9988f, afdb6fbfa5a6349c6e270fdd4d84ac2a9a9485d2; and SF distillation bug fix: a7901537059b3712e7c1196588273cad71d7d29e.
May 2026
4 Commits • 1 Features
May 1, 2026May 2026 (2026-05) monthly summary for hao-ai-lab/FastVideo: Delivered a major upgrade to the MatrixGame family with a rename to MatrixGame2, launched MatrixGame3.0, and introduced new training pipelines including self-forcing distillation and diffusion forcing methods. Implemented a bug fix for gradient checkpointing and caching in MatrixGame2 self-forcing distillation to improve training performance and memory usage. These changes enhanced video generation quality (higher resolution and action-input support), reduced training time, and improved maintainability across the repository. Commit activity included matrix changes across the upgrade and rename: d6dfe954669d64af45dea6476ac5ca1c552b511c, 773d44b875f81945a4f1e76179949c0002f9988f, afdb6fbfa5a6349c6e270fdd4d84ac2a9a9485d2; and SF distillation bug fix: a7901537059b3712e7c1196588273cad71d7d29e.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 focused on delivering camera-controlled video generation by porting LingBot-World-Base (Cam) into FastVideo. Implemented camera trajectory-driven generation, new configurations, model definitions, and camera embedding utilities to enhance the video generation pipeline. No critical bugs reported; stabilization work completed during port.
1 Commits • 1 Features
Feb 1, 2026February 2026 focused on delivering camera-controlled video generation by porting LingBot-World-Base (Cam) into FastVideo. Implemented camera trajectory-driven generation, new configurations, model definitions, and camera embedding utilities to enhance the video generation pipeline. No critical bugs reported; stabilization work completed during port.
February 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026Concise monthly summary for hao-ai-lab/FastVideo (January 2026). Highlights include delivery of real-time streaming video generation for Matrix-Game 2.0, improvements to streaming pipelines, and user-driven control over generation parameters. No major bug fixes were reported for this period; the focus was on delivering a robust streaming feature and UI enhancements.
January 2026
1 Commits • 1 Features
Jan 1, 2026Concise monthly summary for hao-ai-lab/FastVideo (January 2026). Highlights include delivery of real-time streaming video generation for Matrix-Game 2.0, improvements to streaming pipelines, and user-driven control over generation parameters. No major bug fixes were reported for this period; the focus was on delivering a robust streaming feature and UI enhancements.
December 2025
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12 — Hao-ai-lab/FastVideo. Concise delivery focused on expanding interactive video generation capabilities. Key features delivered: - Matrix-Game 2.0: Interactive Video Generation with Mouse/Keyboard Controls, enabling interactive gameplay experiences within the video generation pipeline. Major bugs fixed: - No major bugs reported or fixed this month. Overall impact and accomplishments: - Significantly expanded FastVideo capabilities by enabling interactive content generation, unlocking new use cases and potential business value (enhanced user engagement and diversification of content formats). Technologies/skills demonstrated: - End-to-end feature delivery in a video generation workflow, collaboration via PRs and commits, and integration of input controls for interactive media.
1 Commits • 1 Features
Dec 1, 2025Month: 2025-12 — Hao-ai-lab/FastVideo. Concise delivery focused on expanding interactive video generation capabilities. Key features delivered: - Matrix-Game 2.0: Interactive Video Generation with Mouse/Keyboard Controls, enabling interactive gameplay experiences within the video generation pipeline. Major bugs fixed: - No major bugs reported or fixed this month. Overall impact and accomplishments: - Significantly expanded FastVideo capabilities by enabling interactive content generation, unlocking new use cases and potential business value (enhanced user engagement and diversification of content formats). Technologies/skills demonstrated: - End-to-end feature delivery in a video generation workflow, collaboration via PRs and commits, and integration of input controls for interactive media.
December 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability81.6%
Architecture83.4%
Performance81.6%
AI Usage50.0%
Skills & Technologies
Programming Languages
BashPythonTypeScript
Technical Skills
AI Model DevelopmentAI model integrationAPI integrationData ProcessingDeep LearningFastAPIMachine LearningModel TrainingPyTorchPythonPython ScriptingReactVideo Processingasync programmingcomputer vision
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