Generative model projects are among the most frequently misscoped in deep learning freelancing.
The Demo-to-Deployment Gap
A GAN generating plausible product images in a Colab notebook looks finished. Packaging that into a stable API with reproducible outputs, latency under two seconds, and consistent quality across edge cases is a separate engineering problem. Clients who have seen a demo rarely budget for the second part.
Diffusion Models and Compute Expectations
Stable Diffusion fine-tuning on custom datasets has become a common freelance request. Running DreamBooth or LoRA training requires either significant local GPU memory or cloud costs the client may not anticipate. A single fine-tuning session on a custom 20-image dataset can take 40 minutes on an A100 instance, which has a direct cost that needs to appear in the project estimate.
Scope Clauses Worth Including in Contracts
- Number of model iterations covered by the fixed fee
- Who owns compute costs during development
- Acceptance criteria for output quality
Without these clauses, generative model projects tend to expand without a corresponding adjustment to compensation.