openvinotoolkit/openvino.genai
May 2026 – May 2026
1 Month active
Languages Used
C++Python
Technical Skills
C++ programmingOpenCVPython programmingimage processingmachine learningvideo generation
Bita Yekrang
PROFILE
Bita Yekrang
Developed a cross-language visualization pipeline for denoising trajectories in the openvinotoolkit/openvino.genai repository, focusing on the text-to-image generation process. Delivered C++ and Python samples that capture and visualize per-step latents from the Text2ImagePipeline, exporting the denoising process as MJPG AVI videos using OpenCV. Ensured consistent video output by explicitly configuring the MJPEG backend and established robust end-to-end validation through cross-language parity checks and a 20-step test matrix. Enhanced the project’s reliability by updating continuous integration workflows, adding new test markers, and revising documentation to reflect the new sample outputs and validation criteria for image generation tasks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
333
Activity Months1
Work History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 focused on delivering a robust, cross-language visualization pipeline for denoising trajectories in openvino.genai. Key features delivered include two new samples (C++ and Python) that visualize per-step latents from the Text2ImagePipeline and output a denoising_process.avi, enabling end-to-end inspection of the text-to-image generation process. The work also strengthened reliability by explicitly selecting OpenCV's MJPEG backend for video output and by updating readmes, tests, and CI configurations.
1 Commits • 1 Features
May 1, 2026May 2026 focused on delivering a robust, cross-language visualization pipeline for denoising trajectories in openvino.genai. Key features delivered include two new samples (C++ and Python) that visualize per-step latents from the Text2ImagePipeline and output a denoising_process.avi, enabling end-to-end inspection of the text-to-image generation process. The work also strengthened reliability by explicitly selecting OpenCV's MJPEG backend for video output and by updating readmes, tests, and CI configurations.
May 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++ programmingOpenCVPython programmingimage processingmachine learningvideo generation
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