Eachlabs Fashion Ai

Training

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

AI Fashion Model 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-text-to-image",
    "version": "0.0.1",
    "input": {
      "prompt": "Professional fashion model wearing a tailored navy blazer, editorial photography, studio lighting, full body shot, neutral background",
      "image_size": "1024x1536",
      "quality": "high"
    }
  }'

Virtual Try-On

Combine a garment image with a model image:

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "wan-v2-6-image-to-image",
    "version": "0.0.1",
    "input": {
      "prompt": "The person in image 1 wearing the clothing from image 2, professional fashion photography, editorial style",
      "image_urls": ["https://example.com/model.jpg", "https://example.com/garment.jpg"],
      "image_size": "portrait_4_3"
    }
  }'

Runway / Catwalk Video

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/fashion-model.jpg",
      "prompt": "Fashion model walking confidently on a runway, camera follows from front, professional fashion show lighting",
      "duration": "5",
      "resolution": "1080p"
    }
  }'

Catwalk with Motion Reference

Use a real runway walk as motion reference:

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "kling-v2-6-pro-motion-control",
    "version": "0.0.1",
    "input": {
      "image_url": "https://example.com/fashion-model.jpg",
      "video_url": "https://example.com/runway-walk-reference.mp4",
      "character_orientation": "video"
    }
  }'

Brand Style Training

Train a LoRA on your brand's visual style for consistent campaign imagery:

curl -X POST https://api.eachlabs.ai/v1/prediction \
  -H "Content-Type: application/json" \
  -H "X-API-Key: $EACHLABS_API_KEY" \
  -d '{
    "model": "z-image-trainer",
    "version": "0.0.1",
    "input": {
      "image_data_url": "https://example.com/brand-photos.zip",
      "default_caption": "brand editorial fashion photography style",
      "training_type": "style",
      "steps": 1500
    }
  }'

Prompt Tips for Fashion

• Specify pose: "full body shot", "half body", "close-up on garment details"
• Include lighting: "editorial studio lighting", "natural light", "dramatic side lighting"
• Mention style: "editorial", "street style", "haute couture", "casual lookbook"
• For diversity: specify body types, skin tones, and ages in prompts
• For consistency: use the same style keywords across a campaign series

Parameter Reference

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