Fashion ML Engineer Path
This page is the outline for AI/ML engineers who want to train, evaluate, and operate fashion models with OpenTryOn — not only call cloud APIs.
Status: target path for v0.1.0 (Fashion ML Toolkit Core). Invoke-layer pieces below work today on v0.0.5; train/eval/workflow sections are planned. See the Roadmap.
Who this is for
- Fashion ML engineers (LoRA / local VTON / VLM finetunes)
- Fashion agent builders (MCP + CLI workflows)
- Fashion companies customizing models for brand style and catalog quality
If you only need to call providers, start with Installation → CLI → MCP instead.
Path overview (target)
data / prompts → train (LoRA) → eval / bench → invoke (CLI/MCP) → fashion workflow
| Step | Goal | Today (v0.0.5) | Toward v0.1.0 |
|---|---|---|---|
| 1. Data & prompts | Train packs + versioned prompt packs | Dataset loaders (VITON-HD, …) | Schema, validate CLI, prompt packs |
| 2. Train | Brand-style / VTON LoRA | TryOnDiffusion research code | opentryon train + recipes/notebooks |
| 3. Eval | Garment/identity quality | Manual / ad-hoc | Fashion Bench v0 + opentryon eval |
| 4. Invoke | Same path for cloud & local | CLI + MCP + Leffa / CatVTON local VTON | Finetuned adapters on the same invoke path |
| 5. Workflow | Task agents, not chatbots | Planner over live MCP registry | Try-On QA or Fine-Tune Coach via MCP |
What you can do now
pip install -U opentryon
# local/GPU models when needed:
pip install -U "opentryon[local]"
opentryon vton --model flux-vto --help
opentryon generate --model p-image --help
# MCP: see getting-started/mcp
- Cloud try-on / generate / edit / video / understand via one registry
- Local extras: FLUX.2-dev Turbo, Kimi-VL, LLaVA-NeXT, BEN2
- Docs per provider under API Reference
Planned package layout
tryon/train/ # recipes, configs, runners
tryon/eval/ # metrics, Fashion Bench, reports
tryon/workflows/ # agentic fashion graphs
tryon/prompts/ # versioned prompt packs
tryon/datasets/ # loaders + train packs / cards
Suggested reading order
- Roadmap — next slices A–F
- CLI · MCP · Configuration
- New model checklist (when adding local/train adapters)
VISION.md(product system)
Contribute
High-value PRs for this path: prompt packs, bench pairs, LoRA recipe hardening, first local VTON adapter, Try-On QA / Fine-Tune Coach workflow.
Join Discord or open a GitHub issue tagged for train/eval/workflows.