Finetune a Diffusion Model for Sustainable-Fashion Mockups
Overview
What this challenge is about.
You receive 1,200 product photos with paired captions and the brand's style guide. Fine-tune a Stable-Diffusion-class base model with LoRA (Low-Rank Adaptation, a parameter-efficient fine-tuning method) to capture the brand's earthy palette, natural-light look, and recurring crops. Build a Gradio app where the founder types a prompt and gets 4 candidate mockups. Quality-check via a 5-prompt holdout set scored by 3 internal raters on brand-fit and realism (1-5 scale). Write a 2-page memo on remaining gaps and what additional data (e.g., model-in-motion, more swatch types) would close them.
The Brief
What you'll do, and what you'll demonstrate.
Fine-tune a diffusion model to produce on-brand product mockups good enough for internal design iteration.
Earning criteria — what you'll demonstrate
- Apply parameter-efficient fine-tuning (LoRA) to a large generative model
- Build a usable interface around a generative model with Gradio
- Evaluate generative outputs with structured rater protocols
- Diagnose generative failure modes and prioritize data fixes
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Machine Learning Engineer
Owning the full training-to-demo loop with a rigorous rater rubric is the MLE pattern most early-stage product teams need.
This challenge sharpens
- lora-finetuning
- pytorch
- diffusion-models