Direct your next drop's campaign with the Espadrilles 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 go from scheduling studio days to generating publishable on-model imagery on demand for every colorway and variant. The garment-led controls keep cut, color, pattern, logo, and drape faithful, so your product photos match the real inventory customers expect.

Practically, you click lens, framing, lighting, and style presets, then generate in 2K or 4K. Save a model once and reuse it, so your PDP gallery stays consistent across SKUs instead of changing faces and proportions between refreshes.

Because prompt-based DIY outputs often drift—garments mutate, faces change, and branding details can get invented. That forces retakes or manual fixes just to restore catalog consistency and brand integrity.

RAWSHOT is built for repeatability: the same saved model and garment-led settings produce consistent on-model results. You also get C2PA-signed provenance and a signed audit trail per image, so updates ship with a clean compliance story.

In RAWSHOT, you don’t type anything—you select camera and framing, choose pose and angle, and pick lighting, background, and a visual style preset that matches your channel. Those controls are the creative brief, captured in a repeatable UI.

Because the platform is engineered around the garment, the composition stays faithful to your real product attributes. Generate a still in 2K or 4K, then publish with full commercial rights and provenance metadata included for review workflows.

Generic image AI starts from a text idea and then tries to guess what your product should look like, which is where garment drift and invented branding tend to happen. RAWSHOT instead anchors output to garment fidelity with click-driven settings and explicit provenance.

For commerce, that means fewer surprises across a catalog: consistent faces via saved models and a repeatable look via the same UI controls. You also avoid prompt-engineering overhead every time you change a color or material.

You publish with clarity built in: RAWSHOT outputs are watermarked (visible plus cryptographic) and labelled, and they carry C2PA-signed provenance metadata. That gives your team an auditable record for marketing approvals and marketplace requirements.

For commercial use, every output includes full commercial rights—permanent and worldwide. The goal is honest packaging for your creative workflow, not a last-minute legal scramble after you’ve generated thousands of assets.

Start with garment fidelity and presentation: verify cut, color, pattern, logo, and fabric drape against the real product. Then check framing and aspect ratio for each placement (PDP, category tiles, campaign hero) so your espadrilles look intentional, not cropped.

Finally, confirm provenance signalling is present through the signed audit trail and C2PA metadata, and make sure watermarking meets your publishing standards. With RAWSHOT, those metadata elements are part of the output pipeline, so checks stay consistent across batches.

For stills, pricing is per image (~$0.55) and generation typically takes about 30–40 seconds. Tokens never expire, so you can schedule bursts around launches without worrying about losing credits.

If a generation fails, tokens are refunded, and you can cancel in one click from the pricing page. This makes production planning simpler for both small DTC teams and catalog operations that need predictable per-SKU costs.

Yes. RAWSHOT provides a REST API designed for catalog-scale pipelines, so you can trigger on-demand generation and ingest outputs into your existing CMS or product workflow. The same garment-led controls available in the browser GUI apply to the API workflow as well.

For teams running nightly SKU updates, this reduces manual effort and keeps look consistency high. You also get labelled, provenance-bearing outputs so downstream systems and reviewers don’t have to reverse-engineer where images came from.

Save the model and reuse it across every SKU so the face and body stay consistent between generations. That eliminates the common DIY issue where faces and proportions change from output to output, creating catalog inconsistency and extra QA work.

Then keep your look repeatable by selecting the same visual style presets, lighting system, and aspect ratios per channel. Combine that with C2PA-signed provenance and watermarking so your full catalog maintains both aesthetic continuity and trustable publishing signals.