I am seeking a skilled deep learning researcher/engineer to collaborate on a new project. I have a foundational paper and codebase on the quantization of diffusion models (BiDM) and the official Flow Matching for Generative Models implementation from Facebook Research. The objective is to apply the binarization/quantization concept from BiDM to Flow Matching models to achieve compact, efficient, and high-quality generative performance. The goal is to produce results suitable for top-tier academic publications. Key Objectives • Integrate BiDM-style binarization into the Flow Matching framework. • Evaluate the impact of quantization on efficiency, generation quality, and training stability. • Establish a comprehensive benchmark for quantized Flow Matching models. Key Requirements and Deliverables 1. Implementation • Develop a quantized Flow Matching model in PyTorch and CUDA. • Incorporate binarized weights and/or activations with minimal performance loss. • Implement mechanisms for quantization-aware fine-tuning and efficient inference. 2. Experimental Validation • Train and evaluate on CIFAR-10, CelebA-HQ, FFHQ, and optionally LSUN-Church or AFHQ. • Report FID, IS, LPIPS, and KID scores. • Provide visual and numerical comparisons with full-precision models. 3. Performance Analysis • Measure model size reduction, inference speedup, and memory usage. • Analyze the trade-off between compression and generative quality. 4. Comparative Study • Compare against full-precision Flow Matching, BiDM, and other quantized generative baselines. 5. Reproducibility • Deliver reusable scripts/notebooks for full pipeline execution. • Include detailed documentation of hyperparameters, environment setup, and dependencies. Technical Stack • Frameworks: PyTorch, CUDA • Datasets: CIFAR-10, CelebA-HQ, FFHQ, LSUN-Church, AFHQ • Metrics: FID, IS, LPIPS, KID • Hardware: GPU (A100/V100 or similar) Expected Outcomes • A quantized Flow Matching model demonstrating significant compression and speedup with minimal quality degradation. • Comprehensive benchmarking and reproducible experimental setup suitable for publication.