I have a complete reference paper for a Denoising Diffusion Probabilistic Model and a new idea that targets model-parameter quantization to slash computational cost without sacrificing generation quality. What I need is a clean, well-documented Python implementation of this idea that can stand up to peer-review scrutiny and be the backbone of a publishable paper. You will start from the provided baseline code and literature, integrate the proposed quantization scheme, run controlled experiments against the un-quantized DDPM, and demonstrate measurable savings in FLOPs and memory. Results must be reproducible on a single high-end GPU and come with clear evaluation scripts (FID, IS or equivalent) plus a succinct technical write-up that I can fold into the methodology section of a paper. Deliverables • Modular Python code (preferably PyTorch, yet any mainstream Python DL framework is fine) implementing the quantized DDPM • Training & inference scripts with command-line configs • Comparative benchmarks highlighting the reduced computational cost and any side effects on sample quality • A brief report (Markdown or LaTeX) detailing methodology, hyper-parameters, and results Acceptance criteria • At least one quantized model variant that achieves ≥ original sample quality while cutting runtime or memory by a meaningful margin (to be finalised together) • Code runs end-to-end from dataset preparation through sampling with a single command • All functions and classes are fully doc-stringed and PEP8 compliant If you enjoy pushing diffusion models toward efficient deployment, let’s collaborate and make this ready for submission.