Workflow · Production
Evaluating and Debugging Generative AI: training diffusion models with Weights & Biases
The problem
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.
Workflow diagram · grounded in source
1
Initialize W&B training run
trigger
“create a wandb run
run = wandb.init(project="dlai_sprite_diffusion",
job_type="train",
config=config)”
2
Train diffusion model
ai_action
“The model is trained by adding noise to images and forcing it to predict the noise present on the image”
3
Log loss and image samples
output
“log the loss, learning rate, and the current epoch at each iteration. We also log the image samples generated at the end of each epoch”
4
Save model checkpoints as artifacts
integration
“model checkpoints are saved every four epochs, providing a snapshot of the model's state at that point in time”
5
Publish to Model Registry
output
“we make it available for the rest of the team using the Model Registry feature in Weights & Biases. This allows team members to view all the best model versions, the lineage of the model, and get back to the training run that produced th…”
Reported 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.
Reported stack
Weights & Biases
Frequently asked questions
What did this team achieve with this AI workflow?
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.
What tools did this team use?
Weights & Biases.
How is this workflow AI workflow structured?
Initialize W&B training run → Train diffusion model → Log loss and image samples → Save model checkpoints as artifacts → Publish to Model Registry.