image-portrait

name	image-portrait
version	1.0.5
description	Identity-consistent portrait generation from a reference photo. Covers professional headshots, dating photos, style transfers, themed portraits, photo series, avatars, ID photos. Use when generating styled portraits from a reference photo (e.g. professional headshot, anime avatar, cyberpunk portrait, travel photo, dating profile photo, ID photo).
metadata	{"starchild":{"emoji":"📸","skillKey":"image-portrait"}}
user-invocable	true
disable-model-invocation	false

image-portrait

Use this skill for all identity-consistent portrait generation requests on Starchild.

Covers: professional headshots, dating/social photos, artistic style transfers, themed/holiday portraits, photo series, digital avatars, children/family photos, ID/passport photos.

Core principle: call the provided script. Do not re-implement proxy/billing plumbing.

---

1. Quick start — single portrait (most common)

⚠️ Execution context — read this first. The code blocks below are Python, not shell commands. Starchild's bash tool runs /bin/bash -c, which cannot parse exec(open(...)) — pasting them directly into a bash command will fail with syntax error near unexpected token 'open'. Also, exec(open(...)) inside python3 -c fails with NameError: __file__ because the script uses __file__ for path resolution.

Use python3 - <<'EOF' with from exports import when calling via the bash tool:

python3 - <<'EOF'
import sys
sys.path.insert(0, "skills/image-portrait")
from exports import generate_portrait
result = generate_portrait(
    image_path="path/to/user/photo.jpg",
    style="professional",
)
print(result)
EOF

The heredoc (<<'EOF') preserves all quotes and newlines — no escaping needed.

exec(open('skills/image-portrait/generate_portrait.py').read())
result = generate_portrait(
    image_path="path/to/user/photo.jpg",
    style="professional",
)
# result -> {"success": True, "images": [{"local_path": "output/images/..."}], ...}

The script reads the local file, base64-encodes it, and sends it to fal.ai as a data URI — no manual URL publishing needed.

2. Quick start — public URL

exec(open('skills/image-portrait/generate_portrait.py').read())
result = generate_portrait(
    face_image_url="https://example.com/photo.jpg",
    style="anime",
)

3. Quick start — text-to-image (no reference photo)

exec(open('skills/image-portrait/generate_portrait.py').read())
result = generate_portrait(
    prompt="a young woman in cyberpunk armor, neon city background, rain",
    model="nanopro",
)

When no image_path or face_image_url is provided, the script uses the text-to-image endpoint (no /edit suffix).

Delivering the result to the user — IMPORTANT

Never hand the user the raw fal.media URL. fal serves files with restrictive CSP headers. The only reliable delivery path is the already-downloaded local file:

1. Use each image's local_path (e.g. output/images/xxx.png) — the script always downloads on success.
2. Tell the user the files are saved to output/images/ and viewable in the workspace file panel.
3. On Web channel, embed inline so the user can preview in chat:

   ![photo](output/images/<filename>.png)
   

4. On Telegram / WeChat: send via send_to_telegram(file_path="output/images/...", message_type="image") or send_to_wechat(file_path="output/images/...", message_type="image").

---

4. Parameters

Parameter	Required	Default	Description
image_path	no	—	Local workspace file path to the user's face photo
face_image_url	no	—	Public HTTPS URL of the user's face photo
style	no	"professional"	Preset style key (see §5)
scene	no	None	Custom scene description (appended to style prompt)
prompt	no	None	Fully custom prompt — overrides style+scene when set
model	no	"nanopro"	Model: "nano2" (fastest ~15s), "nanopro" (balanced ~25s, default), or "gpt" (best quality ~150s)
count	no	1	Number of images to generate (1–8)
aspect_ratio	no	"1:1"	Output ratio: 1:1, 3:4, 4:3, 9:16, 16:9

Image input rules:

• Provide image_path OR face_image_url for identity-consistent generation (edit mode).
• If both are given, image_path takes priority.
• Omit both for pure text-to-image generation (generate mode).

Prompt priority: prompt > style + scene > style > default (professional).

---

5. Style presets

A: Identity-consistent character styles

Style	Key	Best for
Professional headshot	professional	LinkedIn, resume, corporate
Artistic portrait	artistic	Creative portfolio, gallery
Anime	anime	Social media, fun avatar
Cyberpunk	cyberpunk	Gaming profile, sci-fi fan
Oil painting	oil_painting	Art gift, classical look
Watercolor	watercolor	Soft artistic portrait
Vintage	vintage	Retro aesthetic, nostalgia
Casual lifestyle	casual	Social media, personal blog

B: Personal showcase / dating / social

Style	Key	Best for
Dating — cafe	dating_cafe	Dating app, warm vibe
Dating — beach	dating_beach	Dating app, summer vibe
Dating — city	dating_city	Dating app, urban vibe
Dating — restaurant	dating_restaurant	Dating app, elegant vibe
Travel — Europe	travel_europe	Travel blog, social media
Travel — Japan	travel_japan	Travel blog, cultural
Travel — tropical	travel_tropical	Vacation, resort
Sports — gym	sports_gym	Fitness profile
Sports — running	sports_running	Athletic profile
Social media	social_media	Instagram, TikTok
LinkedIn	linkedin	Professional networking
Personal brand	personal_brand	Entrepreneur, creator

D: Themed / scene portraits

Style	Key	Best for
Christmas	christmas	Holiday greeting, social
Halloween	halloween	Holiday fun
Graduation	graduation	Milestone celebration
Wedding	wedding	Wedding planning, save-the-date
Business speech	business_speech	Speaker profile
Musician	musician	Music promotion
Chef	chef	Food blog, restaurant
Outdoor adventure	outdoor_adventure	Adventure blog
Pet together	pet_together	Pet lover profile
Reading	reading	Book club, literary
Night city	night_city	Urban lifestyle
Hanfu (Chinese traditional)	hanfu	Cultural, cosplay

O: Digital avatar

Style	Key	Best for
3D cartoon	avatar_3d	Social avatar, Pixar style
Gaming avatar	avatar_gaming	Game profile, RPG
VTuber	avatar_vtuber	Streaming, VTuber

T: Children & family

Style	Key	Best for
Child portrait	child_portrait	Family keepsake
Family photo	family_photo	Family portrait

U: ID / passport photos

Style	Key	Best for
ID photo (white bg)	id_photo_white	Passport, driver's license
ID photo (blue bg)	id_photo_blue	Visa, work permit

---

6. Model selection guide

Model	Key	Speed	Quality	Best for
Nano Banana 2	nano2	~15s	Good	Quick drafts, fast iteration, bulk generation.
NanoPro	nanopro	~25s	Better	Default for all requests. Balanced speed and quality.
GPT Image 2	gpt	~150s	Best	When user explicitly asks for "highest quality" or "best quality". Complex scenes.

Decision rules:

1. Default: always use nanopro unless the user explicitly requests otherwise.
2. Use nano2 when: user wants fastest results, is iterating on styles, generating many images, or says "quick", "draft", "fast".
3. Use gpt when: user says "highest quality", "best quality", "premium", or the scene is very complex with many specific details.

# Default (fast)
result = generate_portrait(image_path="photo.jpg", style="anime")

# High quality (user requested)
result = generate_portrait(image_path="photo.jpg", style="anime", model="gpt")

---

7. Custom scene examples

# Style + custom scene
result = generate_portrait(
    image_path="uploads/my_photo.jpg",
    style="professional",
    scene="in a modern office with city skyline view",
)

# Custom scene only (defaults to professional style base)
result = generate_portrait(
    image_path="uploads/my_photo.jpg",
    scene="standing on a beach at sunset, golden hour lighting",
)

# Fully custom prompt (overrides everything)
result = generate_portrait(
    image_path="uploads/my_photo.jpg",
    prompt="portrait of a person as a medieval knight, full plate armor, castle background, dramatic lighting, oil painting style",
)

# Different aspect ratio
result = generate_portrait(
    image_path="uploads/my_photo.jpg",
    style="cyberpunk",
    aspect_ratio="9:16",
)

# Multiple images
result = generate_portrait(
    image_path="uploads/my_photo.jpg",
    style="dating_cafe",
    count=4,
)

---

8. Prompt engineering best practices

When the user's request doesn't match any preset style, or when you need to construct a custom prompt, follow these guidelines (derived from reference skills: ai-headshot-generation, ai-avatar-generation, style-transfer, portrait-enhancement, character-design-sheet, avatar-portrait, nano-banana-pro, pet-portrait-generation).

Automatic likeness preservation

When a reference image is provided (edit mode), the script automatically prepends a likeness preservation instruction to every prompt. This ensures the generated portrait preserves the subject's facial identity. You do NOT need to add likeness instructions manually — the script handles it.

Exception: avatar styles (avatar_3d, avatar_gaming, avatar_vtuber) skip the likeness prefix because stylization takes priority over photographic likeness.

The 7-element prompt structure

Every effective portrait prompt should include these elements (from nano-banana-pro skill):

[subject], [outfit/attire], [pose/action], [expression], [background/setting], [lighting], [style/quality modifiers]

Key principles

1. Likeness vs. style balance (from avatar-portrait skill):

   • Too photorealistic = ignores requested style
   • Too stylized = loses resemblance to source person
   • For stylized portraits: emphasize "stylized but maintains individual features"
   • For photorealistic: emphasize "keep facial features recognizable"

2. Lighting is critical — always specify lighting type:

   • Studio: "soft diffused studio lighting", "Rembrandt chiaroscuro lighting"
   • Natural: "golden hour warm light", "dappled sunlight through trees"
   • Dramatic: "dramatic rim lighting", "volumetric light beams", "neon glow"
   • Flat: "even flat lighting with no shadows" (for ID photos)

3. Background specificity — vague backgrounds produce poor results:

   • ❌ "nice background"
   • ✅ "blurred modern office with glass windows and city view"
   • ✅ "clean neutral gray gradient studio background"
   • ✅ "background style should match the character style" (for avatars)

4. Lens/camera hints — help the model understand framing:

   • "85mm lens look, shallow depth of field" (portrait)
   • "head and shoulders framing" (headshot)
   • "full body, clean white background" (character design)
   • "close-up face, portrait orientation" (expression/avatar)

5. Quality anchors — add style quality references:

   • "professional photography quality", "magazine cover quality"
   • "National Geographic photography style" (adventure)
   • "League of Legends splash art style" (gaming)
   • "Pixar and Disney animation style" (3D avatar)
   • "Studio Ghibli inspired" (anime)
   • "fine art watercolor painting look" (watercolor)

6. Texture and material — for artistic styles, specify medium:

   • "visible impasto brushstrokes, canvas texture" (oil painting)
   • "loose expressive watercolor style, soft edges, beautiful color bleeds and washes" (watercolor)
   • "natural film grain, Kodak Portra emulation" (vintage)
   • "cel-shaded, clean line art, bold outlines" (anime)
   • "visible pixels but NOT a pixelated photo filter" (pixel art)

7. Expression guidance — be specific about mood:

   • ❌ "smiling"
   • ✅ "warm genuine smile, confident approachable expression"
   • ✅ "neutral calm expression with mouth closed" (ID photo)
   • ✅ "passionate expression, energetic" (musician)

Example: building a custom prompt

User: "I want a photo of me as a wizard in a magical forest"

result = generate_portrait(
    image_path="uploads/photo.jpg",
    prompt=(
        "fantasy wizard portrait, wearing mystical purple robes with glowing runes, "
        "ancient wooden staff with crystal orb, wise powerful expression, "
        "enchanted forest background with bioluminescent plants and floating particles, "
        "dramatic magical lighting with ethereal glow, "
        "high fantasy art style, detailed digital painting quality"
    ),
)
# Note: likeness prefix is auto-added because image_path is provided

Example: pixel art avatar (from avatar-portrait skill)

User: "Make me a retro pixel art avatar"

result = generate_portrait(
    image_path="uploads/photo.jpg",
    prompt=(
        "retro 16-bit pixel art portrait, visible pixels with clean lines, "
        "rich colors, consistent shading, stylized but maintains individual features, "
        "warm sunset cityscape background in matching pixel art style, "
        "head and shoulders, square format"
    ),
)

---

9. Photo series

Generate a coordinated set of themed portraits in one call. Pass a custom list of styles/scenes — the agent assembles the list based on the user's request.

exec(open('skills/image-portrait/generate_portrait.py').read())
result = generate_series(
    image_path="uploads/my_photo.jpg",
    series=[
        {"style": "professional"},
        {"style": "casual", "scene": "at a rooftop bar, sunset"},
        {"style": "anime"},
        {"prompt": "portrait as a superhero, cape flowing, city skyline"},
    ],
)
# result -> {"success": True, "images": [...4 images...], "series": "custom"}

Each item in the list is a dict with optional keys:

• style — any style key from §7 (e.g. "professional", "anime", "cyberpunk")
• scene — override the scene description (combined with the style template)
• prompt — fully custom prompt (ignores style/scene)

---

10. Intent recognition guide

Use this table to map user requests to the correct style/parameters:

User says	Style	Notes
"professional photo", "headshot", "LinkedIn photo"	professional or linkedin
"dating photo", "dating app", "Tinder photo"	dating_cafe / dating_beach / dating_city	Ask which vibe
"anime me", "anime version", "cartoon me"	anime
"cyberpunk", "sci-fi portrait"	cyberpunk
"oil painting", "classical portrait"	oil_painting
"watercolor portrait"	watercolor
"vintage photo", "retro"	vintage
"casual photo", "lifestyle"	casual
"travel photo in Paris/Europe"	travel_europe
"travel photo in Japan/Tokyo/Kyoto"	travel_japan
"beach photo", "tropical"	travel_tropical or dating_beach
"gym photo", "fitness"	sports_gym
"Christmas photo"	christmas
"Halloween photo"	halloween
"graduation photo"	graduation
"wedding photo"	wedding
"chef photo", "cooking"	chef
"musician", "on stage"	musician
"with my dog/pet"	pet_together
"reading", "bookish"	reading
"night city", "urban night"	night_city
"hanfu", "Chinese traditional"	hanfu
"3D avatar", "Pixar style"	avatar_3d
"gaming avatar", "RPG character"	avatar_gaming
"VTuber avatar"	avatar_vtuber
"kid photo", "children's portrait"	child_portrait
"family photo"	family_photo
"passport photo", "ID photo"	id_photo_white	White bg default
"visa photo"	id_photo_blue	Blue bg
"photo series", "set of photos"	Use generate_series()	Assemble custom list from styles
"highest quality", "best quality"	Any style + model="gpt"
Custom scene not in presets	Use scene= or prompt=

When NOT to use this skill (routing)

This skill's core contract is identity preservation: whenever a reference photo is provided, a likeness prefix ("preserve the subject's exact facial features…") is prepended to every prompt (except the 3 avatar_* styles). This means:

• User wants to drastically change the face/identity or fully re-imagine the person (e.g. "make me look like a different person", heavy character redesign) → route to image-create (text-to-image) instead. The likeness prefix will fight the stylization and iterations won't converge.
• User wants strong stylization but still recognizable → stay here; use anime / avatar_3d etc.
• User wants to edit a non-person photo → image-edit.

If a request keeps failing to move away from the reference photo's look after 2+ iterations, that's the likeness contract working as designed — switch skills rather than re-prompting.

---

11. Provided scripts

File	Purpose
generate_portrait.py	Core script: submit → poll → download. Handles local files (base64) and URLs, all styles, custom scenes, three models (nano2/nanopro/gpt).
exports.py	Re-exports generate_portrait, generate_series, STYLE_PROMPTS for programmatic use by other skills.
_cost_track.py	Cost tracking helper — records per-call costs via sc-proxy headers.

---

12. Local testing

Set FAL_KEY env var to call fal.ai directly (bypasses sc-proxy):

# Single portrait
FAL_KEY=your-fal-key python3 skills/image-portrait/generate_portrait.py photo.jpg anime 1 nanopro

# Args: <image_path_or_url> [style] [count] [model]

---

13. Troubleshooting

Problem	Fix
File not found: ...	Check the workspace path; the file must exist
Unsupported image format	Use .jpg, .jpeg, .png, .webp, or .bmp
Image too large	Resize to under 10 MB before uploading
face_image_url must be a public HTTP(S) URL	Use image_path for local files, or provide a valid https:// URL
HTTP 402 insufficient_credits	Top up balance; cost is pre-charged on submit
HTTP 403 endpoint_not_allowed	sc-proxy only allows approved fal endpoints; contact admin
Generation FAILED upstream	Simplify prompt, ensure face photo is clear and well-lit, retry
Job stuck IN_PROGRESS >10 min	Save request_id, retry later
Poor face consistency	Use a clear, front-facing photo with good lighting; avoid group photos
gpt model too slow	Switch to nanopro (default) for faster results

---

14. Infrastructure (reference)

• Caller → sc-proxy → queue.fal.run/{model} → fal model providers
• All requests must include Authorization: Key fake-falai-key-12345 (proxy injects the real FAL_KEY)
• Pre-charge happens at submit. Poll/result calls are free.
• Local files are base64-encoded as data URIs — no separate upload step needed.
• Final images live at https://*.fal.media/... — public CDN, no auth needed for download.
• Cost tracking via _cost_track.py — records X-Credits-Used from sc-proxy response headers.

Model endpoints

Model	Edit (with ref image)	Generate (text only)
nano2	fal-ai/nano-banana-2/edit	fal-ai/nano-banana-2
nanopro	fal-ai/nano-banana-pro/edit	fal-ai/nano-banana-pro
gpt	openai/gpt-image-2/edit	openai/gpt-image-2

---