Code DMD from Scratch, to Prove You’re a Real AI Scientist

2026-02-20 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, short

Summary

The article details the challenging experience of implementing and training a Denoising Diffusion Model (DMD) from scratch, highlighting its practical effectiveness as demonstrated by the SDXL Turbo model's DMD version having over a million downloads compared to the original's few thousand. It explains that DMD aims to distill a one-step inference model matching a teacher model's distribution, despite diffusion models not being true probability distributions. The core training involves calculating the gradient difference between teacher and fake scorers, performing gradient descent iterations, and adding noise to samples before gradient calculation. The author also clarifies that DMD's single-step forward pass directly computes X0 from X1 via flow, unlike cumulative regular diffusion models, and emphasizes crucial gradient detachment details during generator and fake scorer training.

Key takeaway

For AI Engineers implementing or debugging advanced diffusion models, understanding DMD's unique training paradigm is critical. Your traditional loss monitoring methods may not apply, as DMD lacks a true loss function; instead, focus on tracking validation set metrics like FID, which should consistently decrease. This approach will help you identify and resolve subtle code issues that are otherwise difficult to detect.

Key insights

DMD distills multi-step diffusion models into efficient one-step inference models by matching scorer gradients.

Principles

• True loss functions are not always available for complex models.
• Gradient differences can drive optimization without explicit loss.
• Validation metrics are crucial for debugging non-decreasing loss models.

Method

DMD training involves sampling timesteps, adding noise, running the model forward, and calculating the gradient difference between teacher and fake scorers for gradient descent iterations, carefully managing gradient detachment.

In practice

• Reference open-source DMD implementations like CausVid or Cosmos.
• Use `detach` to control gradient flow during training.
• Monitor validation set metrics (e.g., FID) for debugging.

Topics

• Denoising Diffusion Models
• Model Distillation
• Gradient Descent
• SDXL Turbo
• Score Matching

Best for: AI Scientist, AI Engineer, Deep Learning Engineer

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• DASH: Dual-Branch Score Distillation for Guidance-Calibrated Compact Diffusion Models — Unsupervised unconditional score branches in diffusion model distillation cause guidance failure; dual-branch supervision is essential for effective compression.
• Continuous Language Diffusion as a Decoder-Interface Problem — Continuous language diffusion models succeed by guiding latent states into "decoder-basin" regions where decoders can reliably interpret tokens.
• Diffusion Language Models: An Experimental Analysis — Diffusion Language Models' performance and efficiency are critically shaped by generation-time design choices.
• Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation — Data-Forcing Distillation uses teacher score discrepancy to restore diversity and fidelity in few-step video generation, outperforming prior distillation methods.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.