Image and video decision
TryHijab
A single developer can implement the core try-on workflow using open-source components (diffusers / MagicClothing) but reproducing the hosted product's polish, scalability, privacy/legal posture, and user trust is non-trivial, so building a narrow self-hosted replacement is realistic but replacing the full paid service is not.
Visit website↗Built by Adel Ljaljic, who ships 5 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off49 h to build
$500/mo10 h/mo upkeep
No published price to break even against.
Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All TryHijab alternatives, with the arithmetic →
What a replacement has to do
- User uploads a selfie → system detects and aligns the face → run an image-generation/compositing model to produce a hijab-on-photo → post-process and return a downloadable HD image.
What it still won’t have
- Polished consumer UX and branding present on the hosted product
- Built-in trust signals (privacy/legal workflow, refunds/policies) and customer support
- High-throughput managed GPU inference and optimized latency at scale
- Any proprietary training or dataset improvements the vendor may have made
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
TryHijab does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal self-hosted Hijab try-on web service using: React (or Svelte) frontend, FastAPI backend, PostgreSQL (for job metadata) or Redis (for queue), Celery/RQ workers, and PyTorch with HuggingFace Diffusers for image inpainting/garment synthesis. Core features in scope: secure image upload (single selfie), face detection and landmark alignment, a reproducible image edit pipeline that applies a hijab (use an existing controllable clothing repo or diffusion inpainting), simple job queue and status API, downloadable HD output, automatic deletion of uploaded/generated images after 1 hour, basic error handling, and unit/integration tests for the API and worker. Out of scope: training large custom models from scratch, multi-tenant billing, a production user dashboard, and large-scale autoscaling. Require clear error messages, input validation, rate limiting, test coverage for main flows, and documentation for deployment (including GPU instance type).
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- 3/3 assessment runs agreed+4
- Evidence score64
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 4
Every page the run actually retrieved.
- official productTryHijab
- official productTryHijab FAQ excerpt
- official productTryHijab FAQ excerpt
- open sourcehacksider/Deep-Live-Cam
Integrity checks
What held up, and what did not.
