Roadmap
Last Updated: 14 September 2026 · Current release: v0.0.5 · Next: v0.1.0 Fashion ML Toolkit Core
Canonical file: ROADMAP.md · Strategy: VISION.md
Audience priority: fashion AI/ML engineers · agent builders · app builders · fashion companies (train + MCP) · CLI-first developers.
v0.1.0 exit criteria: train pack → LoRA finetune → garment/identity eval + Fashion Bench v0 → invoke via CLI/MCP → one agentic fashion workflow — without assembling five research repos. Outline: Fashion ML Engineer Path.
Shipped — v0.0.3 / v0.0.4 / v0.0.5 (Phase 0: invoke layer)
- Unified CLI + FastMCP (shared registry /
invoke_model) - Broad cloud try-on / generate / edit / video / understand adapters
- v0.0.4: LTX-2.5 (API + local), Hailuo 2.3, Wan (API + local 2.2), Runway Gen-4.5, Qwen3.8 (API + local)
- v0.0.5: dedicated VTON (Google Vertex, OutfitAnyone-Plus, Photoroom, Leffa, CatVTON), ChatGPT Images 2.5, MiniMax H3 / H3 Max + Fal H3 Max, NVIDIA NIM, Hy4, Muse Image, Qwen-Image, Wan 3.0, planner as registry super-agent
- OpenAPI / Postman media snapshots, docs, Gradio demos
- Local extras (FLUX.2 Turbo, Kimi-VL, LLaVA-NeXT, BEN2, LTX-2.5, Wan 2.2, Qwen3.8, Qwen-Image, Leffa, CatVTON, MiniMax H3)
- Web UI in TryOn Studio over MCP
Next — Fashion ML Toolkit Core → v0.1.0
| Slice | Focus |
|---|---|
| A — Data & prompts | Versioned prompt packs, train-pack schema, data CLI helpers |
| B — Train | Brand-style LoRA + one VTON/local LoRA path; opentryon train |
| C — Eval | Fashion Bench v0, side-by-side reports; opentryon eval |
| D — Local VTON | Leffa + CatVTON shipped in v0.0.5 (opentryon[local]) |
| E — Workflows | MCP-native Try-On QA or Fine-Tune Coach (task agent, not chatbot) |
| F — Efficiency card | VRAM/latency table for the path we actually ship |
v0.0.4 and v0.0.5 shipped as intermediate tags; v0.1.0 when A–E work end-to-end.
Fashion-only for this phase. Prompt datasets, fashion datasets, workflows, and agentic fashion workflows are in scope. Generic multi-domain / full LLM–VLM platform work waits until fashion patterns prove out.
Later
- Broader quantization / distillation / serving recipes
- Full fashion agents suite (PDP, lookbook, router, return-risk, …)
- Async / batch / caching DX · video VTON / 3D VTON
- Adjacent domains only after fashion train/eval/agent recipes are solid
Contribute
Good first issues: prompt packs, bench pairs, docs, dry-run tests.
High-value: LoRA recipes, Fashion Bench metrics, first local VTON, first MCP workflow.
Vendor / model queue (separate from these slices): Integrate next.
Follow the new-model checklist or join Discord.