IMA AI Creation

音乐模型 (Music Models)

语音模型 (Speech/TTS Models)

How to get the correct model_id:

1. Check this table first
2. Use --list-models --task-type <type> to query available models
3. Refer to command examples in this SKILL.md

Runtime truth source: GET /open/v1/product/list (or --list-models).
Any table in this document is guidance; actual availability depends on current product list.

Example:

# ❌ WRONG: Inferring from friendly name
--model-id nano-banana-pro

# ✅ CORRECT: Using exact model_id from table
--model-id gemini-3-pro-image

📚 Optional Knowledge Enhancement (ima-knowledge-ai)

This skill is fully runnable as a standalone package. If ima-knowledge-ai is installed, the agent may read its references for workflow decomposition and consistency guidance.

Recommended optional reads:

1. Check for workflow complexity — Read ima-knowledge-ai/references/workflow-design.md if:

   • User mentions: "MV"、"宣传片"、"完整作品"、"配乐"、"soundtrack"
   • Task spans multiple media types (image + video, video + music, etc.)
   • Complex multi-step workflows that need task decomposition

2. Check for visual consistency needs — Read ima-knowledge-ai/references/visual-consistency.md if:

   • User mentions: "系列"、"多张"、"同一个"、"角色"、"续"、"series"、"same"
   • Task involves: multiple images/videos, character continuity, product shots
   • Second+ request about same subject (e.g., "旺财在游泳" after "生成旺财照片")

3. Check video modes — Read ima-knowledge-ai/references/video-modes.md if:

   • Any video generation task
   • Need to understand: image_to_video vs reference_image_to_video difference

4. Check model selection — Read ima-knowledge-ai/references/model-selection.md if:

   • Unsure which model to use
   • Need cost/quality trade-off guidance
   • User specifies budget or quality requirements

Why this matters:

• Multi-media workflows need proper task sequencing (e.g., video duration → matching music duration)
• AI generation defaults to 独立生成 each time — without reference images, results will be inconsistent
• Wrong video mode = wrong result (image_to_video ≠ reference_image_to_video)
• Model choice affects cost and quality significantly

Example multi-media workflow:

User: "帮我做个产品宣传MV，有背景音乐，主角是旺财小狗"

❌ Wrong: 
  1. Generate dog image (random look)
  2. Generate video (different dog)
  3. Generate music (unrelated)

✅ Right:
  1. Read workflow-design.md + visual-consistency.md
  2. Generate Master Reference: 旺财小狗图片
  3. Generate video shots using image_to_video with 旺财 as first frame
  4. Get video duration (e.g., 15s)
  5. Generate BGM with matching duration and mood

How to check:

# Step 0: Determine media type first (image / video / music / speech)
# From user request: "画"/"生成图"/"image" → image; "视频"/"video" → video; "音乐"/"歌"/"music"/"BGM" → music; "语音"/"朗读"/"TTS"/"speech" → speech
# Then choose task_type and model from the corresponding section (image: text_to_image/image_to_image; video: text_to_video/...; music: text_to_music; speech: text_to_speech)

# Step 1: Read knowledge base based on task type
if multi_media_workflow:
    read("~/.openclaw/skills/ima-knowledge-ai/references/workflow-design.md")

if "same subject" or "series" or "character":
    read("~/.openclaw/skills/ima-knowledge-ai/references/visual-consistency.md")

if video_generation:
    read("~/.openclaw/skills/ima-knowledge-ai/references/video-modes.md")

# Step 2: Execute with proper sequencing and reference images
# (see workflow-design.md for specific patterns)

No exceptions — for simple single-media requests, you can proceed directly. For complex multi-media workflows, read the knowledge base first.

📥 User Input Parsing (Media Type & Task Routing)

Purpose: So that any agent parses user intent consistently, first determine the media type from the user's request, then choose task_type and model.

1. User phrasing → media type (do this first)

If the request mixes media (e.g. "宣传片+配乐"), treat as multi-media workflow: read workflow-design.md, then plan image → video → music steps and use the correct task_type for each step.

2. Model and parameter parsing

• Image: For model name → model_id and size/aspect_ratio parsing, follow the same rules as in ima-image-ai skill (User Input Parsing section).

• Video: For task_type (t2v / i2v / first_last / reference), model alias → model_id, and duration/resolution/aspect_ratio, follow ima-video-ai skill (User Input Parsing section).
  Sevio alias normalization in ima-all-ai:

  • Ima Sevio 1.0 → ima-pro
  • Ima Sevio 1.0-Fast / Ima Sevio 1.0 Fast → ima-pro-fast Routing rule:
  • Normalize alias first
  • Then resolve against runtime product list for the selected task_type
  • If model is absent in current category, return available model_ids from --list-models

• Music: Suno (sonic) vs DouBao BGM/Song — infer from "BGM"/"背景音乐" → BGM; "带歌词"/"人声" → Suno or Song. Use model_id sonic, GenBGM, GenSong per "Recommended Defaults" and "Music Generation" tables below.

• Speech (TTS): Get model_id from GET /open/v1/product/list?category=text_to_speech or run script with --task-type text_to_speech --list-models. Map user intent to parameters using product form_config:

  User intent / phrasing	Parameter (if in form_config)	Notes
  女声 / 女声朗读 / female voice	voice_id / voice_type	Use value from form_config options
  男声 / 男声朗读 / male voice	voice_id / voice_type	Use value from form_config options
  语速快/慢 / speed up/slow	speed	e.g. 0.8–1.2
  音调 / pitch	pitch	If supported
  大声/小声 / volume	volume	If supported

  If the user does not specify, use form_config defaults. Pass extra params via --extra-params '{"speed":1.0}'. Only send parameters present in the product’s credit_rules/attributes or form_config (script reflection strips others on retry).

⚙️ How This Skill Works

For transparency: This skill uses a bundled Python script (scripts/ima_create.py) to call the IMA Open API. The script:

• Sends your prompt to two IMA-owned domains (see "Network Endpoints" below)
• Uses --user-id only locally as a key for storing your model preferences
• Returns image/video/music URLs when generation is complete

What gets sent to IMA servers:

• ✅ Your prompt/description (image/video/music)
• ✅ Model selection (SeeDream/Wan/Suno/etc.)
• ✅ Generation parameters (size, duration, style, etc.)
• ❌ NO API key in prompts (key is used for authentication only)
• ❌ NO user_id (it's only used locally)

What's stored locally:

• ~/.openclaw/memory/ima_prefs.json - Your model preferences (< 1 KB)
• ~/.openclaw/logs/ima_skills/ - Generation logs (auto-deleted after 7 days)

🌐 Network Endpoints Used

Key Points:

• Music tasks (text_to_music) and TTS tasks (text_to_speech) only use api.imastudio.com.
• Image/video tasks require imapi.liveme.com to obtain presigned URLs for uploading input images.
• Your API key is sent to both api.imastudio.com and imapi.liveme.com (both owned by IMA Studio).
• Verify network calls: tcpdump -i any -n 'host api.imastudio.com or host imapi.liveme.com'. See this document: 🌐 Network Endpoints Used and ⚠️ Credential Security Notice for full disclosure.

⚠️ Credential Security Notice

Your API key is sent to both IMA-owned domains:

1. Authorization: Bearer ima_xxx... → api.imastudio.com (main API)
2. Query param appUid=ima_xxx... → imapi.liveme.com (upload service)

Security best practices:

• 🧪 Use test keys for experiments: Generate a separate API key for testing.
• 🔍 Monitor usage: Check https://imastudio.com/dashboard for unauthorized activity.
• ⏱️ Rotate keys: Regenerate your API key periodically (monthly recommended).
• 📊 Review logs: Check ~/.openclaw/logs/ima_skills/ for unexpected API calls.

Why two domains? IMA Studio uses a microservices architecture:

• api.imastudio.com: Core AI generation API
• imapi.liveme.com: Specialized image/video upload service (shared infrastructure)

Both domains are operated by IMA Studio. The same API key grants access to both services.

Agent Execution (Internal Reference)

Note for users: You can review the script source at scripts/ima_create.py anytime.
The agent uses this script to simplify API calls. Music tasks use only api.imastudio.com, while image/video tasks also call imapi.liveme.com for file uploads (see "Network Endpoints" above).

Use the bundled script internally for all task types — it ensures correct parameter construction:

# ─── Image Generation ──────────────────────────────────────────────────────────

# Basic text-to-image (default model)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_image \
  --model-id doubao-seedream-4.5 --prompt "a cute puppy on grass, photorealistic" \
  --user-id {user_id} --output-json

# Text-to-image with size override (Nano Banana2)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_image \
  --model-id gemini-3.1-flash-image --prompt "city skyline at sunset, 4K" \
  --size 2k --user-id {user_id} --output-json

# Image-to-image with input URL
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type image_to_image \
  --model-id doubao-seedream-4.5 --prompt "turn into oil painting style" \
  --input-images https://example.com/photo.jpg --user-id {user_id} --output-json

# ─── Video Generation ──────────────────────────────────────────────────────────

# Basic text-to-video (default model, 5s 720P)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_video \
  --model-id wan2.6-t2v --prompt "a puppy dancing happily, cinematic" \
  --user-id {user_id} --output-json

# Text-to-video with extra params (10s 1080P)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_video \
  --model-id wan2.6-t2v --prompt "dramatic ocean waves, sunset" \
  --extra-params '{"duration":10,"resolution":"1080P","aspect_ratio":"16:9"}' \
  --user-id {user_id} --output-json

# Image-to-video (animate static image)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type image_to_video \
  --model-id wan2.6-i2v --prompt "camera slowly zooms in, gentle movement" \
  --input-images https://example.com/photo.jpg --user-id {user_id} --output-json

# First-last frame video (two images)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type first_last_frame_to_video \
  --model-id kling-video-o1 --prompt "smooth transition between frames" \
  --input-images https://example.com/frame1.jpg https://example.com/frame2.jpg \
  --user-id {user_id} --output-json

# ─── Music Generation ──────────────────────────────────────────────────────────

# Basic text-to-music (Suno default)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_music \
  --model-id sonic --prompt "upbeat electronic music, 120 BPM, no vocals" \
  --user-id {user_id} --output-json

# Music with custom lyrics (Suno custom mode)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_music \
  --model-id sonic --prompt "pop ballad, emotional" \
  --extra-params '{"custom_mode":true,"lyrics":"Your custom lyrics here...","vocal_gender":"female"}' \
  --user-id {user_id} --output-json

# Background music (DouBao BGM)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_music \
  --model-id GenBGM --prompt "relaxing ambient music for meditation" \
  --user-id {user_id} --output-json

# ─── Text-to-Speech (TTS) ─────────────────────────────────────────────────────

# List TTS models first to get model_id, then generate speech
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_speech --list-models

# TTS: use model_id from list above (prompt = text to speak)
python3 {baseDir}/scripts/ima_create.py \
  --api-key $IMA_API_KEY --task-type text_to_speech \
  --model-id <model_id from list> --prompt "Text to be spoken here." \
  --user-id {user_id} --output-json

The script outputs JSON with url, model_name, credit — use these values in the UX protocol messages below. The script internals (product list query, parameter construction, polling) are invisible to users.

Overview

Call IMA Open API to create AI-generated content. All endpoints require an ima_* API key. The core flow is: query products → create task → poll until done.

🔒 Security & Transparency Policy

This skill is community-maintained and open for inspection.

✅ What Users CAN Do

Full transparency:

• ✅ Review all source code: Check scripts/ima_create.py and ima_logger.py anytime
• ✅ Verify network calls: Music tasks use api.imastudio.com only; image/video tasks also use imapi.liveme.com (see "Network Endpoints" section)
• ✅ Inspect local data: View ~/.openclaw/memory/ima_prefs.json and log files
• ✅ Control privacy: Delete preferences/logs anytime, or disable file writes (see below)

Configuration allowed:

• ✅ Set API key in environment or agent config:
  • Environment variable: export IMA_API_KEY=ima_your_key_here
  • OpenClaw/MCP config: Add IMA_API_KEY to agent's environment configuration
  • Get your key at: https://imastudio.com
• ✅ Use scoped/test keys: Test with limited API keys, rotate after testing
• ✅ Disable file writes: Make prefs/logs read-only or symlink to /dev/null

Data control:

• ✅ View stored data: cat ~/.openclaw/memory/ima_prefs.json
• ✅ Delete preferences: rm ~/.openclaw/memory/ima_prefs.json (resets to defaults)
• ✅ Delete logs: rm -rf ~/.openclaw/logs/ima_skills/ (auto-cleanup after 7 days anyway)

⚠️ Advanced Users: Fork & Modify

If you need to modify this skill for your use case:

1. Fork the repository (don't modify the original)
2. Update your fork with your changes
3. Test thoroughly with limited API keys
4. Document your changes for troubleshooting

Note: Modified skills may break API compatibility or introduce security issues. Official support only covers the unmodified version.

❌ What to AVOID (Security Risks)

Actions that could compromise security:

• ❌ Sharing API keys publicly or in skill files
• ❌ Modifying API endpoints to unknown servers
• ❌ Disabling SSL/TLS certificate verification
• ❌ Logging sensitive user data (prompts, IDs, etc.)
• ❌ Bypassing authentication or billing mechanisms

Why this matters:

1. API Compatibility: Skill logic aligns with IMA Open API schema
2. Security: Malicious modifications could leak credentials or bypass billing
3. Support: Modified skills may not be supported
4. Community: Breaking changes affect all users

📋 Privacy & Data Handling Summary

What this skill does with your data:

Privacy recommendations:

1. Use test/scoped API keys for initial testing
2. Note: --user-id is never sent to IMA servers - it's only used locally as a key for storing preferences in ~/.openclaw/memory/ima_prefs.json
3. Review source code at scripts/ima_create.py to verify network calls (search for create_task function)
4. Rotate API keys after testing or if compromised

Get your IMA API key: Visit https://imastudio.com to register and get started.

🔧 For Skill Maintainers Only

Version control:

• All changes must go through Git with proper version bumps (semver)
• CHANGELOG.md must document all changes
• Production deployments require code review

File checksums (optional):

# Verify skill integrity
sha256sum SKILL.md scripts/ima_create.py

If users report issues, verify file integrity first.

🧠 User Preference Memory (Image)

User preferences have highest priority when they exist. But preferences are only saved when users explicitly express model preferences — not from automatic model selection.

Storage: ~/.openclaw/memory/ima_prefs.json

Single file, shared across all IMA skills:

{
  "user_{user_id}": {
    "text_to_image":  { "model_id": "doubao-seedream-4.5", "model_name": "SeeDream 4.5", "credit": 5,  "last_used": "2026-02-27T03:07:27Z" },
    "image_to_image": { "model_id": "doubao-seedream-4.5", "model_name": "SeeDream 4.5", "credit": 5,  "last_used": "2026-02-27T03:07:27Z" },
    "text_to_speech": { "model_id": "<from product list>", "model_name": "...", "credit": 2, "last_used": "..." }
  }
}

Model Selection Flow (Image Generation)

Step 1: Get knowledge-ai recommendation (if installed)

knowledge_recommended_model = read_ima_knowledge_ai()  # e.g., "SeeDream 4.5"

Step 2: Check user preference

user_pref = load_prefs().get(f"user_{user_id}", {}).get(task_type)  # e.g., {"model_id": "midjourney", ...}

Step 3: Decide which model to use

if user_pref exists:
    use_model = user_pref["model_id"]  # Highest priority