jd-opensource/xllm
Sep 2025 – Sep 2025
1 Month active
Languages Used
C++Pythonprotobuf
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentBatch ProcessingC++C++ Development
yiming-l21
PROFILE
Yiming-l21
Developed and stabilized a production-ready Flux Multimodal Image Generation Pipeline for the jd-opensource/xllm repository, delivering end-to-end image generation using DiT-based diffusion models. Integrated CLIP text models, T5 encoders, VAEs, and DiT diffusion within a dedicated API, enabling scalable batched inference and efficient prompt management. Addressed scheduler and input handling bugs in the Flux pipeline, improving reliability for batch processing and user-facing workflows. Leveraged C++, Python, and PyTorch to implement robust data structures, backend systems, and runtime components, resulting in a scalable, API-driven image generation solution that supports multimodal AI and advanced deep learning model inference capabilities.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
7Total
Bugs
1
Commits
7
Features
1
Lines of code
12,141
Activity Months1
Your Network
112 peopleSame Organization
@jd.com71
chenda14Member
chenjiajun79Member
chenxiaoyu8Member
cuimaoxin1Member
dengfei.59Member
Yingxu DengMember
2extliuweijian32Member
yq33victorMember
gechunfa1Member
Shared Repositories
41
Work History
September 2025
7 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm focused on delivering a scalable, end-to-end image generation capability and stabilizing the Flux-based pipeline. Highlights include the first production-ready Flux Multimodal Image Generation Pipeline (DiT diffusion) with API access and batched inference, alongside robust input handling and bug fixes that improve reliability for batch prompts.
7 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for jd-opensource/xllm focused on delivering a scalable, end-to-end image generation capability and stabilizing the Flux-based pipeline. Highlights include the first production-ready Flux Multimodal Image Generation Pipeline (DiT diffusion) with API access and batched inference, alongside robust input handling and bug fixes that improve reliability for batch prompts.
September 2025
Activity
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Quality Metrics
Correctness87.0%
Maintainability81.4%
Architecture87.2%
Performance77.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Pythonprotobuf
Technical Skills
API DesignAPI DevelopmentBackend DevelopmentBatch ProcessingC++C++ DevelopmentCMakeCUDAComputer VisionData StructuresDeep LearningDiffusion ModelsDistributed SystemsImage GenerationMachine Learning
Repositories Contributed To
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