Gif Sticker Maker

Trigger: Use when converting photos of people, pets, objects, or logos into animated GIF stickers with captions and effects using image and video generation AI.

GIF Sticker Maker

Convert user photos into 4 animated GIF stickers (Funko Pop / Pop Mart style).

Style Spec

• Funko Pop / Pop Mart blind box 3D figurine
• C4D / Octane rendering quality
• White background, soft studio lighting
• Caption: black text + white outline, bottom of image

Prerequisites

Before starting any generation step, ensure:

1. Python venv is activated with dependencies from requirements.txt installed
2. MINIMAX_API_KEY is exported (e.g. export MINIMAX_API_KEY='your-key')
3. ffmpeg is available on PATH (for Step 3 GIF conversion)

If any prerequisite is missing, set it up first. Do NOT proceed to generation without all three.

Workflow

Step 0: Collect Captions

Ask user (in their language):

"Would you like to customize the captions for your stickers, or use the defaults?"

• Custom: Collect 4 short captions (1–3 words). Actions auto-match caption meaning.
• Default: Look up captions table by detected user language. Never mix languages.

Step 1: Generate 4 Static Sticker Images

Tool: scripts/minimax_image.py

1. Analyze the user's photo — identify subject type (person / animal / object / logo).
2. For each of the 4 stickers, build a prompt from image-prompt-template.txt by filling {action} and {caption}.
3. If subject is a person: pass --subject-ref <user_photo_path> so the generated figurine preserves the person's actual facial likeness.
4. Generate (all 4 are independent — run concurrently):

python3 scripts/minimax_image.py "<prompt>" -o output/sticker_hi.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_laugh.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_cry.png --ratio 1:1 --subject-ref <photo>
python3 scripts/minimax_image.py "<prompt>" -o output/sticker_love.png --ratio 1:1 --subject-ref <photo>

--subject-ref only works for person subjects (API limitation: type=character). For animals/objects/logos, omit the flag and rely on text description.

Step 2: Animate Each Image → Video

Tool: scripts/minimax_video.py with --image flag (image-to-video mode)

For each sticker image, build a prompt from video-prompt-template.txt, then:

python3 scripts/minimax_video.py "<prompt>" --image output/sticker_hi.png -o output/sticker_hi.mp4
python3 scripts/minimax_video.py "<prompt>" --image output/sticker_laugh.png -o output/sticker_laugh.mp4
python3 scripts/minimax_video.py "<prompt>" --image output/sticker_cry.png -o output/sticker_cry.mp4
python3 scripts/minimax_video.py "<prompt>" --image output/sticker_love.png -o output/sticker_love.mp4

All 4 calls are independent — run concurrently.

Step 3: Convert Videos → GIF

Tool: scripts/convert_mp4_to_gif.py

python3 scripts/convert_mp4_to_gif.py output/sticker_hi.mp4 output/sticker_laugh.mp4 output/sticker_cry.mp4 output/sticker_love.mp4

Outputs GIF files alongside each MP4 (e.g. sticker_hi.gif).

Step 4: Deliver

Output format (strict order):

1. Brief status line (e.g. "4 stickers created:")
2. <deliver_assets> block with all GIF files
3. NO text after deliver_assets

<deliver_assets>
<item><path>output/sticker_hi.gif</path></item>
<item><path>output/sticker_laugh.gif</path></item>
<item><path>output/sticker_cry.gif</path></item>
<item><path>output/sticker_love.gif</path></item>
</deliver_assets>

Default Actions

See references/captions.md for multilingual caption defaults.

Rules

• Detect user's language, all outputs follow it
• Captions MUST come from captions.md matching user's language column — never mix languages
• All image prompts must be in English regardless of user language (only caption text is localized)
• <deliver_assets> must be LAST in response, no text after

Pitfalls

• Image aspect ratio: stickers work best at 1:1 (square) — crop before processing.
• File size: GIFs over 15MB may not render on some platforms — optimize frame count and resolution.
• Caption readability: light text on light backgrounds is unreadable — always add text shadow or backdrop.

Verification

• Is the output GIF under 15MB?
• Is the caption readable against the background?
• Does the animation loop cleanly (no jump at loop point)?