ad_image_create

Ad Image Creation

What this skill does

Generate advertising images from product assets with two output styles:

• Single hero image
• Collage image (multiple sub-images stitched into one final canvas)

Core method: decide the final target ratio first, then compute sub-image sizes, and call image_generator directly with matching size (no manual pre-crop/pre-pad on source assets).

Required workflow

1. Understand final deliverable:
   • Final ratio and size (for example 16:9, 1920x1080)
   • Single image or collage layout (2x2, 1x3, 1x2)
2. Activate product understanding:
   • SKILL__active_skill(skill_name="media_comprehension")
   • Extract product style, tone, audience, and suitable scene category.
3. Design scenes that match product positioning:
   • Keep style consistent with product quality/tone.
   • Avoid mismatched backgrounds (for example: minimal product + ultra-baroque palace).
4. Generate each sub-image using image_generator with exact request params.
5. Stitch sub-images (if collage), then validate final size/ratio.
6. Social copy: After images are final, add one short line of ad copy per deliverable image—the same count as the exported ad files (one hero → one line; four separate exports → four lines; one stitched collage file usually → one line unless the user asked for per-panel copy). Keep each line simple, fun, and tightly tied to that image’s scene and benefit; aim for 小红书 / 抖音 scroll appeal, not generic brand platitudes.

Supporting actor references (Mode 2/3)

When the ad needs a supporting actor beyond the product—either because the user asked for one or because they supplied material—do not fetch companion assets from TikTok or similar platforms. Use what is already available:

• User supplied still image(s): Use the provided file path(s) as reference_images for image_generator after a quick media_comprehension check that the image shows the intended actor/look.
• User supplied video: Capture one or more frames (screenshots) from that video in the workspace, run SKILL__active_skill(skill_name="media_comprehension") on each candidate frame, and pick a frame where the model confirms the desired supporting actor/appearance. Use that frame image as reference_images.

If the user provides no usable image or video reference, you may still proceed: call image_generator without actor reference_images and describe the supporting actor so the model generates that character in-scene—still following the actor-count rules below.

image_generator request contract (keep these fields)

Common fields

image_generator(
    content="...",
    info={
        "image_url": "/path/to/product.jpg",
        "size": "960x540",
        "output_dir": "/path/to/output"
    }
)

• content: prompt describing scene and composition.
• info.image_url: primary product image path.
• info.size: output size string in "WIDTHxHEIGHT" format.
• info.output_dir: output directory.

Optional field for actor/reference inputs

"reference_images": ["/path/to/ref1.jpg", "/path/to/ref2.jpg"]

Input modes

Mode 1: Product only

• Input: one product image
• Output: product integrated into environment
• Use when emphasizing material, shape, and style fit.

Mode 2: Product + one actor reference

• Input: product image + one actor image
• Output: product and actor in one scene
• Use when showing usage context and emotional connection.

Mode 3: Product + multiple reference images

• Input: product image + multiple references
• Output: richer scene with better pose/style guidance
• Still enforce actor-count language in prompt.

Critical rule: actor-count control

When using Mode 2/3, model may generate too many actors unless count is explicit.

Required prompt pattern

• Use explicit count language:
  • Chinese: 只有一只/个, 最多两只/个
  • English: only one, a single, at most two
• Recommended actor count:
  • Ad focus: 1 actor (preferred)
  • Lifestyle scene: max 2 actors

Good vs bad prompt snippet

# Good
content = "Create a warm living-room scene. There is only one cat interacting with the cat tree."

# Bad
content = "Create a warm scene with cats interacting with the cat tree."

Size back-solving quick table

Minimal implementation template

from PIL import Image

# 1) Analyze product style first
SKILL__active_skill(skill_name="media_comprehension")

# 2) Decide target and layout
final_size = (1920, 1080)
layout = "2x2"
sub_size = (960, 540)

# 3) Generate sub-images
for scene in scenes:
    content = scene["prompt"]  # include explicit actor count for Mode 2/3
    info = {
        "image_url": product_image,
        "size": f"{sub_size[0]}x{sub_size[1]}",
        "output_dir": output_dir
    }
    if scene.get("reference_images"):
        info["reference_images"] = scene["reference_images"]
    image_generator(content=content, info=info)

# 4) Stitch
canvas = Image.new("RGB", final_size, (255, 255, 255))
# paste each sub-image by layout...
canvas.save("final_ad.png", quality=95)

Quality checks

• One social tagline per final ad image (step 6): tone fits 小红书/抖音 skim-reading; matches that image, not a generic slogan.
• Product style and environment are consistent.
• For Mode 2/3, actor count is explicitly constrained in content.
• size values match computed sub-image dimensions.
• Final stitched output matches requested ratio/size.
• If generator output has slight dimension drift (for example height offset), crop after stitching.

Notes

• No source pre-processing required by default; rely on size control in generation.
• Use high-quality product/reference inputs.
• Keep scene descriptions concrete (lighting, furniture, color palette, mood) instead of vague labels.
• Social taglines: default to concise Chinese for 小红书/抖音 unless the user specifies another language or brand voice.