June 2026 monthly summary focused on delivering high-impact features, stabilizing core inference paths, and expanding model support across containers and hardware. The work emphasized business value through reliability improvements, performance gains, and new capabilities in video generation and FP8-precision workflows.

May 2026 performance summary focused on reliability, correctness, and expanded hardware and model coverage across LightX2V and MNN. Delivered targeted fixes and feature expansions with clear business value in inference reliability, quantized compute, and service capabilities. The work drove more robust deployments, faster debugging, and broader hardware support while maintaining strict validation and test discipline. Key outcomes include: (1) reliability and correctness improvements in Wan feature caching for ModelTC/LightX2V by aligning CFG state to scheduler.infer_condition and removing the legacy infer_conditional flag; updated caching paths and regression tests to prevent caching inconsistencies; (2) strengthened INT8 Triton GEMM with a tightened EVEN_K decision logic, expanded CUDA coverage for bias/no-bias, and non-tile safety across M/N, preserving default torch.float16 dtype and adding tolerance-aware tests; (3) BAGEL image generation stabilization and feature expansion enabling dynamic output shapes, seed control, PNG saving for T2I, and single-image editing I2I with enhanced context handling and validation; (4) ONNX Einsum 4D fast-path fix in MNN to ensure correct output order with non-aligned batch dims, avoiding runtime errors and falling back to the general path when necessary; (5) robustness improvement by making the llm submodule import optional, reducing import-time failures when submodules are unavailable. Technologies/skills demonstrated: CUDA, Triton kernels, PyTorch dtype handling, ONNX and Einsum path reasoning, build and test hygiene (pre-commit, pytest, regression tests), and broader hardware support (Blackwell sm_120) planning acknowledged as ongoing in related work.