Direct your next collection's campaign with the Lehenga AI On-model Photography Generator.

Never—you direct every output with sliders, presets, and clicks on the garment, not typed prompts. That UI control is consistent across GUI and REST API payloads, which is why ecommerce teams onboard buyers without rewriting creative briefs as chat threads.

For catalog teams, reliability matters more than model cleverness; RAWSHOT keeps tokens, timings, refund rules, commercial rights framing, provenance signalling, watermarking cues, REST surface, and SKU-scale batch patterns explicit so operations can rehearse PDP launches without hallucinated garment inventions.

You stop waiting for sample shipments and studio schedules to refresh visuals. With garment-led generation, you build on-model photos that represent cut, colour, pattern, logo placement, and fabric drape for PDPs and collection pages.

RAWSHOT gives you controllable camera, framing, lighting, background, and 150+ visual styles—so variants stay consistent across your catalog. Outputs include C2PA-signed provenance, visible and cryptographic watermarking, and a clear full commercial-rights story for merchant use.

Because SKU updates usually require reshoots, re-edits, and retakes that don’t scale with how fast collections change. RAWSHOT keeps the garment as the brief, so updates focus on the product details instead of reworking an entire photoshoot workflow.

You can generate in 2K or 4K, pick aspect ratios per platform, and reuse consistent models across your catalog to avoid drift. When you generate, you also get signed audit trail metadata that operations teams can manage for compliance and publishing.

You don’t translate a written idea into a text command. You upload the garment-led input and then direct the shoot with camera, pose, angle, lighting, background, and style preset controls until the result matches your product presentation goals.

Once the controls are set, generating variants is just switching those UI settings—not rewriting language. That approach keeps your results consistent across iterations and supports catalog-scale production with the REST API when you’re ready to automate.

Because the biggest risk in DIY prompting is instability: the garment details drift, logos can be invented, and faces can change across outputs. Click-driven controls keep your iterations grounded in product representation and consistent model identity, which matters for shopper trust.

RAWSHOT also carries labelled outputs, C2PA-signed provenance, and watermarking cues for traceability. You get a predictable commercial-rights story so publishing teams can move without legal uncertainty.

Yes. Every RAWSHOT output is full-commercial and permanent worldwide, and it includes C2PA-signed provenance plus visible and cryptographic watermarking with AI labelling.

This means your catalog and campaign teams can publish product imagery with clearer traceability than typical generic generators. It also supports compliance workflows that expect signed attribution and audit-ready records per image.

Check garment fidelity first: confirm cut, colour, pattern, logo placement, and fabric drape read correctly for your product. Then verify consistency across variants by saving and reusing the same model so your face and body stay aligned across SKUs.

Finally, confirm publish-readiness signals: outputs carry C2PA-signed provenance, watermarking, and per-image audit trail records. If anything looks off, regenerate by adjusting the relevant UI controls rather than restarting with new language.

For photos, pricing is flat per image and generation typically lands around 30–40 seconds. Tokens never expire, failed generations refund their tokens, and you can cancel in one click from the pricing page.

That structure helps commerce teams plan throughput for launches and reworks, because costs map directly to images rather than retry loops. For batch work, you can use the REST API to schedule catalog-scale generation with predictable pricing and rules.

Yes. RAWSHOT supports a REST API designed for catalog-scale pipelines, while the browser GUI stays available for single-shoot work. The key is that both routes use the same garment-led engine and consistent control set.

That makes it easier to automate SKU generation, schedule variations by aspect ratio, and keep model reuse stable across the catalog. Your pipeline can also preserve the signed provenance and audit trail metadata per image.

Operators across roles can use it because the creative decisions are UI controls rather than prompt writing. Designers can click camera, lighting, and style presets; ops can manage token rules, refunds, and cancellation; and catalog teams can standardize models across the whole assortment.

You’ll typically run single-look explorations in the browser GUI and graduate to REST API automation for nightly SKU throughput. Either way, the same provenance, watermarking, and commercial-rights framework stays attached to every output.