mcpField
E
eachlabs-product-visuals

eachlabs-product-visuals

github.com/eachlabsMarketing & growth44312 installsUpdated today
Repository

About eachlabs-product-visuals

A skill for creating product photography, e-commerce visuals, and product videos through EachLabs AI models. It guides users through model selection and prediction handling, with workflows for backgrounds, lifestyle scenes, multiple angles, and image-to-video output.

Key features

  1. 01Model recommendations by product task
  2. 02Prediction creation, polling, and output extraction
  3. 03White-background and lifestyle product imagery
  4. 04Multiple-angle product views
  5. 05Background removal and replacement
  6. 06Image-to-video generation

Use cases

  1. 01Prepare clean product images for store listings
  2. 02Create lifestyle scenes for product campaigns
  3. 03Generate several views of a product image
  4. 04Turn product imagery into a video

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/eachlabs/skills --skill eachlabs-product-visuals

After installing, type/eachlabs-product-visualsin Claude Code to confirm it loaded.

Bundled filesreferences/MODELS.md

Details

From the project README

EachLabs Product Visuals

Generate professional product photography, e-commerce visuals, and product videos using EachLabs AI models.

Authentication

Header: X-API-Key: <your-api-key>

Set the EACHLABS_API_KEY environment variable. Get your key at eachlabs.ai.

Recommended Models

E-Commerce Specific
TaskModelSlug
Product photoshootProduct to Photoshootproduct-to-photoshoot
Product on modelProduct Photo to Modelshootproduct-photo-to-modelshoot
Color variationsProduct Colorsproduct-colors
Food photographyFood Photosfood-photos
Background removalProduct Background Removerproduct-backround-remover
Image upscalingProduct Photo Upscalerproduct-photo-upscaler
Home scene placementProduct Home Viewproduct-home-view
Product shotBria Product Shotbria-product-shot
Product shootProduct Shootproduct-shoot
Product arc shotEachlabs Product Arc Shoteachlabs-product-arc-shot-v1
Product zoom inEachlabs Product Zoom Ineachlabs-product-zoom-in-v1
General Purpose
TaskModelSlug
Product photographyGPT Image v1.5gpt-image-v1-5-text-to-image
Background replacementGPT Image v1.5 Editgpt-image-v1-5-edit
Product editingFlux 2 Turbo Editflux-2-turbo-edit
Multi-angle viewsQwen Image Editqwen-image-edit-2511-multiple-angles
Background removalRembg Enhancerembg-enhance
Background removalEachlabs BG Removereachlabs-bg-remover-v1
Image upscalingEachlabs Upscaler Proeachlabs-image-upscaler-pro-v1
Ad inpaintingSDXL Ad Inpaintsdxl-ad-inpaint
Custom product styleZ Image Trainerz-image-trainer
Product videoPixverse v5.6 Image-to-Videopixverse-v5-6-image-to-video

Prediction Flow

  1. Check model GET https://api.eachlabs.ai/v1/model?slug=<slug> — validates the model exists and returns the request_schema with exact input parameters. Always do this before creating a prediction to ensure correct inputs.
  2. POST https://api.eachlabs.ai/v1/prediction with model slug, version "0.0.1", and input matching the schema
  3. Poll GET https://api.eachlabs.ai/v1/prediction/{id} until status is "success" or "failed"
  4. Extract output URL from response

Workflows

Product on White Background
curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "gpt-image-v1-5-edit",
    "version": "0.0.1",
    "input": {
      "prompt": "Place this product on a clean white background with soft studio lighting and subtle shadows",
      "image_urls": ["https://example.com/product.jpg"],
      "background": "opaque",
      "quality": "high"
    }
  }'
Lifestyle Scene Generation
curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "gpt-image-v1-5-edit",
    "version": "0.0.1",
    "input": {
      "prompt": "Place this coffee mug on a cozy wooden desk in a modern home office with warm morning light, lifestyle photography",
      "image_urls": ["https://example.com/mug.jpg"],
      "quality": "high"
    }
  }'
Multi-Angle Product Views
curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "qwen-image-edit-2511-multiple-angles",
    "version": "0.0.1",
    "input": {
      "image_urls": ["https://example.com/product.jpg"],
      "horizontal_angle": 45,
      "vertical_angle": 15,
      "zoom": 5
    }
  }'

Generate multiple angles by running separate predictions with different horizontal_angle values (0, 45, 90, 135, 180, 225, 270, 315 for a full 360).

Product with Transparent Background
curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "gpt-image-v1-5-edit",
    "version": "0.0.1",
    "input": {
      "prompt": "Remove the background from this product image",
      "image_urls": ["https://example.com/product.jpg"],
      "background": "transparent",
      "output_format": "png"
    }
  }'
Product Video from Image
curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "pixverse-v5-6-image-to-video",
    "version": "0.0.1",
    "input": {
      "image_url": "https://example.com/product-studio.jpg",
      "prompt": "Slow cinematic camera rotation around the product with dramatic studio lighting",
      "duration": "5",
      "resolution": "1080p"
    }
  }'

Prompt Tips for Products

  • Specify lighting: "soft studio lighting", "dramatic side lighting", "natural window light"
  • Mention surface: "marble surface", "wooden table", "clean white background"
  • Include shadows: "soft shadows", "reflection on surface"
  • Add context: "lifestyle setting", "in-use shot", "flat lay arrangement"
  • For batch catalog shots, maintain consistency with similar prompts

Batch Processing

For catalog-scale processing, create multiple predictions in parallel by sending separate POST requests for each product. Poll each prediction independently.

Parameter Reference

See the eachlabs-image-generation and eachlabs-video-generation references for complete model parameters.

Comments

Markdown supported