AI-Hypercomputer/maxdiffusion
Jul 2026 – Jul 2026
1 Month active
Languages Used
No languages
Technical Skills
machine_learningperformance optimizationpythontestingyaml
HadarIngonyama
PROFILE
Hadaringonyama
Worked on the AI-Hypercomputer/maxdiffusion repository to deliver MagCache acceleration for Wan 2.2 dual-transformer pipelines, targeting both T2V and I2V inference. The approach involved implementing skip-path scheduling, per-phase forced-compute zones, and a unified mag_ratios_base curve to optimize performance and configurability. Configuration parameters such as use_magcache and magcache_thresh were integrated throughout the Python codebase and YAML files, with comprehensive validation and benchmarking using SSIM and PSNR metrics. Extensive testing and documentation updates ensured correctness and measurable speedups, resulting in faster inference, higher throughput, and reduced latency for Wan 2.2 workflows without reported bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
756
Activity Months1
Your Network
46 peopleSame Organization
@ingonyama.com6
Shared Repositories
40
Work History
July 2026
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for AI-Hypercomputer/maxdiffusion focused on advancing inference performance and configurability for Wan 2.2 pipelines. Delivered MagCache acceleration for Wan 2.2 dual-transformer pipelines (T2V and I2V) with skip-path scheduling, per-phase forced-compute zones, and a unified interleaved mag_ratios_base curve across phases. Implemented end-to-end configuration plumbing (use_magcache, magcache_thresh, magcache_K, retention_ratio) and propagated these settings through the 2.2 pipelines and YAML configurations. Expanded validation, tests, and documentation to ensure correctness and measurable speedups. Key updates touched Wan 2.2: wan_pipeline_2_2.py, wan_pipeline_i2v_2p2.py, generate_wan.py, base_wan_27b.yml, base_wan_i2v_27b.yml, tests/wan2_2_magcache_test.py, and README. No major bugs fixed reported this month. Business value: faster inference, higher throughput, reduced latency, and stronger configurability for Wan 2.2 workflows. Technologies/skills demonstrated: MagCache acceleration, dual-transformer pipeline tuning, skip-path scheduling, YAML/config plumbing, host and TPU testing, benchmarking (SSIM/PSNR) and comprehensive documentation.
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for AI-Hypercomputer/maxdiffusion focused on advancing inference performance and configurability for Wan 2.2 pipelines. Delivered MagCache acceleration for Wan 2.2 dual-transformer pipelines (T2V and I2V) with skip-path scheduling, per-phase forced-compute zones, and a unified interleaved mag_ratios_base curve across phases. Implemented end-to-end configuration plumbing (use_magcache, magcache_thresh, magcache_K, retention_ratio) and propagated these settings through the 2.2 pipelines and YAML configurations. Expanded validation, tests, and documentation to ensure correctness and measurable speedups. Key updates touched Wan 2.2: wan_pipeline_2_2.py, wan_pipeline_i2v_2p2.py, generate_wan.py, base_wan_27b.yml, base_wan_i2v_27b.yml, tests/wan2_2_magcache_test.py, and README. No major bugs fixed reported this month. Business value: faster inference, higher throughput, reduced latency, and stronger configurability for Wan 2.2 workflows. Technologies/skills demonstrated: MagCache acceleration, dual-transformer pipeline tuning, skip-path scheduling, YAML/config plumbing, host and TPU testing, benchmarking (SSIM/PSNR) and comprehensive documentation.
July 2026
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
No languages yet
Technical Skills
machine_learningperformance optimizationpythontestingyaml
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline