Workflow · workflow
Evaluating and Debugging Generative AI: training diffusion models with Weights & Biases
Training and evaluating generative AI models is complex, with a specific pitfall being the loss curve flattening early—which can mislead practitioners into stopping training before image quality has fully improved.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Initialize W&B training run
A Weights & Biases run is initialized to track the training experiment from the start.
Tools used
Weights & Biases
Outcome
By logging image samples at regular intervals alongside the loss curve in Weights & Biases, practitioners can track model progress visually and publish trained models to the team via the Model Registry.
Grounding & classification
Source type: technical build writeup
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