product-photo-studio

Transform a real product photo into a studio-quality ecommerce image, lifestyle scene, or marketplace-ready hero shot. This AI product photography tool replaces backgrounds, improves lighting, and stages scenes while using the source photo and confirmed product details as the visual anchor. Create clean white-background listings, contextual lifestyle compositions, and premium ad visuals from a single phone snap for Amazon, Taobao, Shopify, and social media. Start from one product photo, combine several references, or refine a selected draft toward a polished listing image.

AI Product Photo Studio

Transform one real product photo into a studio-quality listing image, lifestyle scene, or marketplace-ready hero shot. Reuse decisions already present in the conversation and move by the shortest route that completes the requested image.

Choose the route

• Clean background: with one product photo, remove the original background and place the product on a clean white, light-gray, or studio-gradient background using beatra.images.transform. This is the default for marketplace main images.
• Scene and lifestyle: with one product photo and a scene description, place the product in a contextual lifestyle setting—on a kitchen counter, a wooden table, a marble shelf, or a seasonal backdrop—using beatra.images.transform.
• Refine an accepted draft: use beatra.images.edit with the accepted image as images[0] to fix a shadow, remove a reflection, adjust color temperature, or clean up a small defect without changing the composition.

Follow product routing for the precise branch and scene craft when turning the request into a visual specification anchored to the source product.

Shape one product brief

The hard input is a real product photo. Scene and lighting must not rewrite a confirmed color, accessory, or quantity.

Reuse the user's product type, intended marketplace, background preference, and any style references. Ask only when a missing decision materially changes the result. For a standard marketplace main image, propose a clean white background as the default; for a lifestyle request, propose a scene that matches the product's category.

Build the brief around:

• the product itself—what it is, its category, and any key visual details (label text, brand logo, shape, color);
• one target marketplace or use (Amazon, Taobao, Shopify, social media, ad campaign) when it determines format rules;
• one background or scene direction (clean white, studio gradient, lifestyle context, seasonal);
• ordered visual references when available (style inspiration, background reference, angle reference).

If the user has already stated the target marketplace or background type, reuse it. If that choice is genuinely missing, propose the best default and include it in the single paid-call confirmation.

Prepare the call

Use only this Skill's bundled scripts/mcp_client.py for every remote MCP operation. The tool name is a CLI argument and the tool arguments are the JSON sent on stdin. Do not configure or call a host Beatra Connector, and do not use REST/OpenAPI as a fallback. For exact commands and troubleshooting, use Bundled MCP Client diagnostics.

• Upload the product photo through the bundled client helpers first, then call beatra.images.transform with the uploaded artifact as the first ordered reference. Label the product image's role explicitly in the prompt so the model treats it as the visual anchor.
• For a clean background, set an explicit square or marketplace-ratio canvas.
• For a lifestyle scene, describe the scene, lighting direction, and surface material in the prompt while identifying the source product details that should carry into the result.
• For an accepted draft, call beatra.images.edit. Use at most two normalized edit_regions on image_index=0 for localized fixes; omit regions for a whole-image adjustment.

Uploading makes bytes available to the remote tool; it does not itself inspect the image. Review only visual facts the host can actually see.

Keep model=auto and count=1 unless the user explicitly chooses otherwise. Call beatra.models.list only for a real model, availability, compatibility, or price decision. The detailed request shapes and examples are in workflow.

Confirm and execute once

Planning and brief preparation are free. Before the paid image call, show and freeze the final prompt, ordered references, canvas, background or scene direction, model, controls, and output count. Merge any still-material high-impact choice into this one confirmation.

After approval, create one stable opaque client_request_id for that exact logical request and submit it once. A changed prompt, reference or order, canvas, scene direction, model, count, or control is new paid work and needs a new confirmation and a new ID.

Track, review, and deliver

After receiving a task_id, poll only that task with beatra.tasks.get. If the ID is lost, use beatra.tasks.list to find candidates and verify the selected one with tasks.get. Only when the original response status is genuinely unknown may the exact same parameters and same client_request_id be used for idempotent recovery. Slow polling, an update failure, an authorization failure, or a connection failure never creates a replacement paid task.

Use beatra.tasks.cancel only when the user asks. If cancellation returns 409, continue tracking the original task. See review and recovery for the full recovery contract.

When the result is visible, review product fidelity against the source photo, background quality (clean edges, consistent lighting, natural shadow), color accuracy (do product colors match the original?), canvas fit, and the marketplace's current image guidance if applicable. Deliver the artifact links, observed dimensions, task ID, and billing.net_charged_credits. Offer at most one focused, unexecuted revision. Generated assets can also be viewed and managed at beatra.ai.

References by task

• Choosing among clean background, lifestyle scene, and detail edit, or planning for a specific marketplace: product routing
• Turning a request into a scene specification anchored to the source product: scene craft
• Exact request shapes, ordered-reference labeling, and JSON examples for each route: workflow
• Lost task, slow task, cancellation, result review, or planning a revision: review and recovery
• First install or expired authorization: installation and authentication
• Bundled MCP Client commands and diagnostics: Bundled MCP Client diagnostics
• Installation registration: installation registration
• Task lookup, polling, and result fields: tasks and results
• Balance, validation, and structured errors: billing, errors, and recovery
• Disconnecting the installation: uninstall and disconnect
• Official sources, integrity checks, and update controls: automatic updates and safety

Installation, updates, and account operations

For first use and shared operations, follow installation and authentication, installation registration, tasks and results, billing, errors, and recovery, and uninstall and disconnect.

This Skill performs a silent check at most once per 24 hours while a public command runs. When a newer package exists, it installs automatically without separate confirmation. Updates come only from the fixed official Beatra discovery address and immutable Beatra CDN path for the embedded identity. Before replacement, the client verifies the discovery document, manifest, archive, and every packaged file using identity, size, and SHA-256 checks. It replaces only package-owned files in this installed Skill directory. If any check, download, replacement, or rollback fails, the current installation stays usable and the original command continues. Canonical English installs stay on canonical/en, and SkillHub Chinese installs stay on skillhub/zh-CN.

The user can persistently control automatic updates:

text

python3 scripts/mcp_client.py update --auto off
python3 scripts/mcp_client.py update --auto on
python3 scripts/mcp_client.py update --check

Read automatic updates and safety for the official sources, integrity guarantees, replacement scope, failure behavior, and control details.