IMA Studio All-in-One — Image, Video, Music, SeeDream, Veo, Suno. Banana

v1.0.10

Most comprehensive AI content creation platform with unified access to all leading models across images (SeeDream 4.5, Midjourney, Nano Banana 2, Nano Banana...

2· 1.8k· 9 versions· 5 current· 5 all-time· Updated 16h ago· MIT-0

by@allenfancy-gan

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Install

openclaw skills install ima-all-ai

IMA AI Creation

⚠️ 重要：模型 ID 参考

CRITICAL: When calling the script, you MUST use the exact model_id (second column), NOT the friendly model name. Do NOT infer model_id from the friendly name (e.g., ❌ nano-banana-pro is WRONG; ✅ gemini-3-pro-image is CORRECT).

Quick Reference Table:

图像模型 (Image Models)

友好名称 (Friendly Name)	model_id	说明 (Notes)
Nano Banana2	`gemini-3.1-flash-image`	❌ NOT nano-banana-2, 预算选择 4-13 pts
Nano Banana Pro	`gemini-3-pro-image`	❌ NOT nano-banana-pro, 高质量 10-18 pts
SeeDream 4.5	`doubao-seedream-4.5`	✅ Recommended default, 5 pts
Midjourney	`midjourney`	✅ Same as friendly name, 8-10 pts

视频模型 (Video Models)

友好名称 (Friendly Name)	model_id (t2v)	model_id (i2v)	说明 (Notes)
Wan 2.6	`wan2.6-t2v`	`wan2.6-i2v`	⚠️ Note -t2v/-i2v suffix
IMA Video Pro (Sevio 1.0)	`ima-pro`	`ima-pro`	✅ IMA native quality model
IMA Video Pro Fast (Sevio 1.0-Fast)	`ima-pro-fast`	`ima-pro-fast`	✅ IMA native low-latency model
Kling O1	`kling-video-o1`	`kling-video-o1`	⚠️ Note video- prefix
Kling 2.6	`kling-v2-6`	`kling-v2-6`	⚠️ Note v prefix
Hailuo 2.3	`MiniMax-Hailuo-2.3`	`MiniMax-Hailuo-2.3`	⚠️ Note MiniMax- prefix
Hailuo 2.0	`MiniMax-Hailuo-02`	`MiniMax-Hailuo-02`	⚠️ Note 02 not 2.0
Google Veo 3.1	`veo-3.1-generate-preview`	`veo-3.1-generate-preview`	⚠️ Note -generate-preview suffix
Sora 2 Pro	`sora-2-pro`	`sora-2-pro`	✅ Straightforward
Pixverse	`pixverse`	`pixverse`	✅ Same as friendly name

音乐模型 (Music Models)

友好名称 (Friendly Name)	model_id	说明 (Notes)
Suno (sonic v4)	`sonic`	⚠️ Simplified to sonic
DouBao BGM	`GenBGM`	❌ NOT doubao-bgm
DouBao Song	`GenSong`	❌ NOT doubao-song

语音模型 (Speech/TTS Models)

友好名称 (Friendly Name)	model_id	说明 (Notes)
seed-tts-2.0	`seed-tts-2.0`	✅ Same as friendly name (default)

How to get the correct model_id:

Check this table first
Use --list-models --task-type <type> to query available models
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.

📥 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)

User intent / keywords	Media type	task_type examples
画 / 生成图 / 图片 / image / 画一张 / 图生图	image	`text_to_image`, `image_to_image`
视频 / 生成视频 / video / 图生视频 / 文生视频	video	`text_to_video`, `image_to_video`, `first_last_frame_to_video`, `reference_image_to_video`
音乐 / 歌 / BGM / 背景音乐 / music / 作曲	music	`text_to_music`
语音 / 朗读 / TTS / 语音合成 / 配音 / speech / read aloud / text-to-speech	speech	`text_to_speech`

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

Domain	Owner	Purpose	Data Sent	Privacy
`api.imastudio.com`	IMA Studio	Main API (product list, task creation, task polling)	Prompts, model IDs, generation params, your API key	Standard HTTPS, data processed for AI generation
`imapi.liveme.com`	IMA Studio	Image/Video upload service (presigned URL generation)	Your API key, file metadata (MIME type, extension)	Standard HTTPS, used for image/video tasks only
`.aliyuncs.com`, `.esxscloud.com`	Alibaba Cloud (OSS)	Image/video storage (file upload, CDN delivery)	Raw image/video bytes (via presigned URL, NO API key)	IMA-managed OSS buckets, presigned URLs expire after 7 days

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:

Authorization: Bearer ima_xxx... → api.imastudio.com (main API)
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:

Fork the repository (don't modify the original)
Update your fork with your changes
Test thoroughly with limited API keys
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:

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

📋 Privacy & Data Handling Summary

What this skill does with your data:

Data Type	Sent to IMA?	Stored Locally?	User Control
Prompts (image/video/music)	✅ Yes (required for generation)	❌ No	None (required)
API key	✅ Yes (authentication header)	❌ No	Set via env var
user_id (optional CLI arg)	❌ Never (local preference key only)	✅ Yes (as prefs file key)	Change `--user-id` value
Model preferences	❌ No	✅ Yes (~/.openclaw)	Delete anytime
Generation logs	❌ No	✅ Yes (~/.openclaw)	Auto-cleanup 7 days

Privacy recommendations:

Use test/scoped API keys for initial testing
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
Review source code at scripts/ima_create.py to verify network calls (search for create_task function)
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
else:
    use_model = knowledge_recommended_model or fallback_default

Step 4: Check for mismatch (for later hint)

if user_pref exists and knowledge_recommended_model != user_pref["model_id"]:
    mismatch = True  # Will add hint in success message

When to Write (User Explicit Preference ONLY)

✅ Save preference when user explicitly specifies a model:

User says	Action
`用XXX` / `换成XXX` / `改用XXX`	Switch to model XXX + save as preference
`以后都用XXX` / `默认用XXX` / `always use XXX`	Save + confirm: `✅ 已记住！以后图片生成默认用 [XXX]`
`我喜欢XXX` / `我更喜欢XXX`	Save as preference

❌ Do NOT save when:

Agent auto-selects from knowledge-ai → not user preference
Agent uses fallback default → not user preference
User says generic quality requests (see "Clear Preference" below) → clear preference instead

When to Clear (User Abandons Preference)

🗑️ Clear preference when user wants automatic selection:

User says	Action
`用最好的` / `用最合适的` / `best` / `recommended`	Clear pref + use knowledge-ai recommendation
`推荐一个` / `你选一个` / `自动选择`	Clear pref + use knowledge-ai recommendation
`用默认的` / `用新的`	Clear pref + use knowledge-ai recommendation
`试试别的` / `换个试试` (without specific model)	Clear pref + use knowledge-ai recommendation
`重新推荐`	Clear pref + use knowledge-ai recommendation

Implementation:

del prefs[f"user_{user_id}"][task_type]
save_prefs(prefs)

⭐ Model Selection Priority (Image)

Selection flow:

User preference (if exists) → Highest priority, always respect
ima-knowledge-ai skill (if installed) → Professional recommendation based on task
Fallback defaults → Use table below (only if neither 1 nor 2 exists)

Important notes:

User preference is only saved when user explicitly specifies a model (see "When to Write" above)
Knowledge-ai is always consulted (even when user pref exists) to detect mismatches
When mismatch detected → add gentle hint in success message (does NOT interrupt generation)

The defaults below are FALLBACK only. User preferences have highest priority, then knowledge-ai recommendations.

When using user preference for image generation, show a line like:

🎨 根据你的使用习惯，将用 [Model Name] 帮你生成…
• 模型：[Model Name]（你的常用模型）
• 预计耗时：[X ~ Y 秒]
• 消耗积分：[N pts]

Preference Change Confirmation

When user switches to a different model than their saved preference:

💡 你之前喜欢用 [Old Model]，这次换成了 [New Model]。
要把 [New Model] 设为以后的默认吗？
回复「是」保存 / 回复「否」仅本次使用

⭐ Recommended Defaults

These are fallback defaults — only used when no user preference exists.
Always default to the newest and most popular model. Do NOT default to the cheapest.

Task Type	Default Model	model_id	version_id	Cost	Why
text_to_image	SeeDream 4.5	`doubao-seedream-4.5`	`doubao-seedream-4-5-251128`	5 pts	Latest doubao flagship, photorealistic 4K
text_to_image (budget)	Nano Banana2	`gemini-3.1-flash-image`	`gemini-3.1-flash-image`	4 pts	Fastest and cheapest option
text_to_image (premium)	Nano Banana Pro	`gemini-3-pro-image`	`gemini-3-pro-image-preview`	10/10/18 pts	Premium quality, 1K/2K/4K options
text_to_image (artistic)	Midjourney 🎨	`midjourney`	`v6`	8/10 pts	Artist-level aesthetics, creative styles
image_to_image	SeeDream 4.5	`doubao-seedream-4.5`	`doubao-seedream-4-5-251128`	5 pts	Latest, best i2i quality
image_to_image (budget)	Nano Banana2	`gemini-3.1-flash-image`	`gemini-3.1-flash-image`	4 pts	Cheapest option
image_to_image (premium)	Nano Banana Pro	`gemini-3-pro-image`	`gemini-3-pro-image-preview`	10 pts	Premium quality
image_to_image (artistic)	Midjourney 🎨	`midjourney`	`v6`	8/10 pts	Artist-level aesthetics, style transfer
text_to_video	Wan 2.6	`wan2.6-t2v`	`wan2.6-t2v`	25 pts	🔥 Most popular t2v, balanced cost
text_to_video (premium)	Hailuo 2.3	`MiniMax-Hailuo-2.3`	`MiniMax-Hailuo-2.3`	38 pts	Higher quality
text_to_video (budget)	Vidu Q2	`viduq2`	`viduq2`	5 pts	Lowest cost t2v
image_to_video	Wan 2.6	`wan2.6-i2v`	`wan2.6-i2v`	25 pts	🔥 Most popular i2v, 1080P
image_to_video (premium)	Kling 2.6	`kling-v2-6`	`kling-v2-6`	40-160 pts	Premium Kling i2v
first_last_frame_to_video	Kling O1	`kling-video-o1`	`kling-video-o1`	48 pts	Newest Kling reasoning model
reference_image_to_video	Kling O1	`kling-video-o1`	`kling-video-o1`	48 pts	Best reference fidelity
text_to_music	Suno (sonic-v4)	`sonic`	`sonic`	25 pts	Latest Suno engine, best quality
text_to_speech	(query product list)	—	—	—	Run `--task-type text_to_speech --list-models`; use first or user-preferred model_id

Premium options:

Image: Nano Banana Pro — Highest quality with size control (1K/2K/4K), higher cost (10-18 pts for text_to_image, 10 pts for image_to_image)
Video: Kling O1, Sora 2 Pro, Google Veo 3.1 — Premium quality with longer duration options

Quick selection guide (production as of 2026-02-27, sorted by popularity):

Image (4 models available) → SeeDream 4.5 (5, default); artistic → Midjourney 🎨 (8-10); budget → Nano Banana2 (4, 512px); premium → Nano Banana Pro (10-18)
🔥 Video from text (most popular) → Wan 2.6 (25, balanced); premium → Hailuo 2.3 (38); budget → Vidu Q2 (5)
🔥 Video from image (most popular) → Wan 2.6 (25)
Music → Suno (25); DouBao BGM/Song (30 each)
Cheapest → Nano Banana2 512px (4) for image; Vidu Q2 (5) for video

Selection guide by use case:

Image Generation:

General image generation → SeeDream 4.5 (5pts)
Custom aspect ratio (16:9, 9:16, 4:3, etc.) → SeeDream 4.5 🌟 or Nano Banana Pro/2 🆕 (native support)
Budget-conscious / fast generation → Nano Banana2 (4pts)
Highest quality with size control (1K/2K/4K) → Nano Banana Pro (text_to_image: 10-18pts, image_to_image: 10pts)
Artistic/creative styles, illustrations, paintings → Midjourney 🎨 (8-10pts)
Style transfer / image editing → SeeDream 4.5 (5pts) or Midjourney 🎨 (artistic)

Video Generation:

General video generation → Wan 2.6 (25pts, most popular)
Premium cinematic quality → Google Veo 3.1 (70-330pts) or Sora 2 Pro (122+pts)
Budget video → Vidu Q2 (5pts) or Hailuo 2.0 (5pts)
With audio support → Kling O1 (48+pts) or Google Veo 3.1 (70+pts)
First/last frame animation → Kling O1 (48+pts)
Reference image consistency → Kling O1 (48+pts) or Google Veo 3.1 (70+pts)

Music Generation:

Custom song with lyrics, vocals, style → Suno sonic-v5 (25pts, default, ~2min)
- Full control: custom_mode, lyrics, vocal_gender, tags, negative_tags
- Best for: complete songs, vocal tracks, artistic compositions
Background music / ambient loop → DouBao BGM (30pts, ~30s)
- Simplified: prompt-only, no advanced parameters
- Best for: video backgrounds, ambient music, short loops
Simple song generation → DouBao Song (30pts, ~30s)
- Simplified: prompt-only
- Best for: quick song generation, structured vocal compositions
User explicitly asks for cheapest → DouBao BGM/Song (6pts each) — only if explicitly requested

Speech (TTS) Generation:

Text-to-speech / 语音合成 / 朗读 → text_to_speech. Always query GET /open/v1/product/list?category=text_to_speech (or --list-models) to get current model_id and credit. No fixed default; use first available or user preference. Voice/speed/format parameters: see "Model and parameter parsing" (TTS table) and "Speech (TTS) — text_to_speech" in this document.

⚠️ Technical Note for Suno:

model_version inside parameters.parameters (e.g., "sonic-v5") is different from the outer model_version field (which is "sonic"). Always set both correctly when creating Suno tasks.

⚠️ Production Image Models (4 available):

SeeDream 4.5 (doubao-seedream-4.5) — 5 pts, default
Midjourney 🎨 (midjourney) — 8/10 pts for 480p/720p, artistic styles
Nano Banana2 (gemini-3.1-flash-image) — 4/6/10/13 pts for 512px/1K/2K/4K
Nano Banana Pro (gemini-3-pro-image) — 10/10/18 pts for 1K/2K/4K

All other image models mentioned in older documentation are no longer available in production.

🌟 Parameter Support Notes (All Task Types):

Image Models (text_to_image / image_to_image)

🆕 MAJOR UPDATE: Nano Banana series now has NATIVE aspect_ratio support!

Nano Banana Pro: ✅ Supports aspect_ratio (1:1, 16:9, 9:16, 4:3, 3:4) NATIVELY
Nano Banana2: ✅ Supports aspect_ratio (1:1, 16:9, 9:16, 4:3, 3:4) NATIVELY
SeeDream 4.5: ✅ Supports 8 ratios via virtual params (1:1, 16:9, 9:16, 4:3, 3:4, 2:3, 3:2, 21:9)
Midjourney: ❌ 1:1 only (fixed 1024x1024)

aspect_ratio support details:

✅ aspect_ratio:
- SeeDream 4.5: ✅ Supports 8 ratios via virtual params (1:1, 16:9, 9:16, 4:3, 3:4, 2:3, 3:2, 21:9)
- Nano Banana2: ✅ Native support for 5 ratios (1:1, 16:9, 9:16, 4:3, 3:4)
- Nano Banana Pro: ✅ Native support for 5 ratios (1:1, 16:9, 9:16, 4:3, 3:4)
- Midjourney: ❌ 1:1 only (fixed 1024x1024)
✅ size:
- Nano Banana2: 512px, 1K, 2K, 4K (via different attribute_ids, 4-13 pts)
- Nano Banana Pro: 1K, 2K, 4K (via different attribute_ids, 10-18 pts)
- SeeDream 4.5: Adaptive default (5 pts)
- Midjourney: 480p/720p (via attribute_id, 8/10 pts)
❌ 8K: No model supports 8K (max is 4K via Nano Banana Pro)
❌ Non-standard aspect ratios (7:3, 8:5, etc.): Not supported. Use closest supported ratio or video models.
✅ n: Multiple outputs supported (1-4), credit × n

When user requests unsupported combinations for images:

Midjourney + aspect_ratio (16:9, etc.): Recommend SeeDream 4.5 or Nano Banana series instead

❌ Midjourney 暂不支持自定义 aspect_ratio（仅支持 1024x1024 方形）

✅ 推荐方案：
  1. SeeDream 4.5（支持虚拟参数 aspect_ratio）
     • 支持比例：1:1, 16:9, 9:16, 4:3, 3:4, 2:3, 3:2, 21:9
     • 成本：5 积分（性价比最佳）
  2. Nano Banana Pro/2（原生支持 aspect_ratio）
     • 支持比例：1:1, 16:9, 9:16, 4:3, 3:4
     • 成本：4-18 积分（按尺寸）

需要我帮你用 SeeDream 4.5 生成吗？

Any model + 8K: Inform user no model supports 8K, max is 4K (Nano Banana Pro)
Any model + non-standard ratio (7:3, 8:5, etc.): Non-standard ratio, not supported. Suggest closest supported ratio (e.g., 21:9 for ultra-wide, 2:3 for portrait)

Video Models (text_to_video / image_to_video / first_last_frame / reference_image)

✅ resolution: 540P, 720P, 1080P, 2K, 4K (model-dependent, higher res = higher cost)
✅ aspect_ratio: 16:9, 9:16, 1:1, 4:3 (model-dependent, check form_config)
✅ duration: 4s, 5s, 10s, 15s (model-dependent, longer = higher cost)
⚠️ generate_audio: Supported by Veo 3.1, Kling O1, Hailuo (check form_config)
✅ prompt_extend: AI-powered prompt enhancement (most models support)
✅ negative_prompt: Content exclusion (most models support)
✅ shot_type: Single/multi-shot control (model-dependent)
✅ seed: Reproducibility control (most models support, -1 = random)
✅ n: Multiple outputs (1-4), credit × n

🆕 Special Case: Pixverse Model Parameter (v1.0.7+)

Auto-Inference Logic for Pixverse V5.5/V5/V4:

Problem: Pixverse V5.5, V5, V4 lack model field in form_config from Product List API
Backend Requirement: Backend requires model parameter (e.g., "v5.5", "v5", "v4")
Auto-Fix: System automatically extracts version from model_name and injects it
- Example: model_name: "Pixverse V5.5" → auto-inject model: "v5.5"
- Example: model_name: "Pixverse V4" → auto-inject model: "v4"
Note: V4.5 and V3.5 include model in form_config (no auto-inference needed)
Relevant Task Types: All video modes (text_to_video, image_to_video, first_last_frame_to_video, reference_image_to_video)

Error Prevention:

Without auto-inference: err_code=400017 err_msg=Invalid value for model
With auto-inference (v1.0.7+): Pixverse V5.5/V5/V4 work seamlessly ✅

Music Models (text_to_music)

Suno sonic-v5 (Full-Featured):

✅ custom_mode: Suno only (enables vocal_gender, lyrics, tags support)
✅ vocal_gender: Suno only (male/female/mixed, requires custom_mode=True)
✅ lyrics: Suno only (custom lyrics support, requires custom_mode=True)
✅ make_instrumental: Suno only (force instrumental, no vocals)
✅ auto_lyrics: Suno only (AI-generated lyrics)
✅ tags: Suno only (genre/style tags)
✅ negative_tags: Suno only (exclude unwanted styles)
✅ title: Suno only (song title)
❌ duration: Fixed-length output (DouBao ~30s, Suno ~2min, not user-controllable)
✅ n: Multiple outputs supported (1-2), credit × n

DouBao BGM/Song (Simplified):

✅ prompt: Text description only
❌ No advanced parameters (no custom_mode, lyrics, vocal control)
❌ duration: Fixed ~30s output

🎵 Suno Prompt Writing Guide (for gpt_description_prompt):

When using Suno, structure your prompt with these elements:

Genre/Style:
- Examples: "lo-fi hip hop", "orchestral cinematic", "upbeat pop", "dark ambient", "indie folk", "electronic dance"
Tempo/BPM:
- Examples: "80 BPM", "fast tempo", "slow ballad", "moderate pace 110 BPM"
Vocals Control:
- No vocals: "no vocals" → set make_instrumental=true
- With vocals: "female vocals" → set vocal_gender="female"
- Male vocals: "male vocals" → set vocal_gender="male"
- Mixed: Set vocal_gender="mixed"
Mood/Emotion:
- Examples: "happy and energetic", "melancholic", "tense and dramatic", "peaceful and calming"
Negative Tags (exclude styles):
- Use negative_tags: "heavy metal, distortion, screaming" to exclude unwanted elements
Duration Hint:
- Examples: "60 seconds", "30 second loop", "2 minute track"
- Note: Suno typically generates ~2min, not strictly controllable

Example Suno prompts:

"upbeat lo-fi hip hop, 90 BPM, no vocals, relaxed and chill"
→ Set: make_instrumental=true

"emotional pop ballad, slow tempo, female vocals, melancholic"
→ Set: vocal_gender="female"

"orchestral cinematic trailer music, epic and dramatic, 120 BPM, no vocals"
→ Set: make_instrumental=true, tags="orchestral,cinematic,epic"

"acoustic indie folk, gentle guitar, male vocals, warm and nostalgic"
→ Set: vocal_gender="male", tags="acoustic,indie,folk"

⚠️ Technical Note for Suno:

model_version inside parameters.parameters (e.g., "sonic-v5") is different from the outer model_version field (which is "sonic"). Always set both correctly.

Common Parameter Patterns

n (batch generation): Supported by ALL models. Cost = base_credit × n. Creates n independent resources.
seed: Supported by most models (-1 = random, >0 = reproducible results)
prompt_extend: AI-powered prompt enhancement (video models only)

Decision Tree: When User Requests Unsupported Features

User asks for custom aspect ratio image (e.g. "7:3 landscape")
  → ❌ Image models don't support custom ratios
  → ✅ Solution: "图片模型不支持自定义比例。建议用视频模型(Wan 2.6 t2v)生成16:9视频，然后截取首帧作为图片。"

User asks for 8K image
  → ❌ No model supports 8K
  → ✅ Solution: "当前最高支持4K分辨率(Nano Banana Pro，18积分)。要使用吗？"

User asks for video with audio
  → Check model: Veo 3.1 / Kling O1 / Hailuo have generate_audio
  → ✅ Solution: "Veo 3.1 和 Kling O1 支持音频生成(需在参数中设置 generate_audio=True)。要用哪个？"

User asks for long music (e.g. "5 minute track")
  → ❌ Duration not user-controllable
  → ✅ Solution: "Suno 生成约2分钟音乐。需要更长时长可以生成多段后拼接。"

User asks for 30s video
  → Check model: Most models max 15s
  → ✅ Solution: "当前最长15秒。可选模型：Wan 2.6(15s, 75积分), Kling O1(10s, 96积分)。"

When user requests unsupported combinations:

Video + audio (unsupported model) → "该模型不支持音频。建议用 Veo 3.1 或 Kling O1 (支持 generate_audio 参数)"
Music + custom duration → "音乐时长由模型固定(Suno约2分钟,DouBao约30秒),无法自定义"
Video duration > 15s → "当前最长15秒。可选模型：Wan 2.6(15s, 75积分), Kling O1(10s, 96积分)"

Note: Image-specific unsupported combinations (Midjourney + aspect_ratio, 8K, non-standard ratios) are documented in the "Image Models" section above.

🧠 User Preference Memory (Video)

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`

{
  "user_{user_id}": {
    "text_to_video":              { "model_id": "wan2.6-t2v",      "model_name": "Wan 2.6",  "credit": 25, "last_used": "..." },
    "image_to_video":             { "model_id": "wan2.6-i2v",      "model_name": "Wan 2.6",  "credit": 25, "last_used": "..." },
    "first_last_frame_to_video":  { "model_id": "kling-video-o1", "model_name": "Kling O1", "credit": 48, "last_used": "..." },
    "reference_image_to_video":   { "model_id": "kling-video-o1", "model_name": "Kling O1", "credit": 48, "last_used": "..." }
  }
}

Model Selection Flow (Video Generation)

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

knowledge_recommended_model = read_ima_knowledge_ai()  # e.g., "Wan 2.6"

Step 2: Check user preference

user_pref = load_prefs().get(f"user_{user_id}", {}).get(task_type)  # e.g., {"model_id": "kling-video-o1", ...}

Step 3: Decide which model to use

if user_pref exists:
    use_model = user_pref["model_id"]  # Highest priority
else:
    use_model = knowledge_recommended_model or fallback_default

Step 4: Check for mismatch (for later hint)

if user_pref exists and knowledge_recommended_model != user_pref["model_id"]:
    mismatch = True  # Will add hint in success message

When to Write (User Explicit Preference ONLY)

✅ Save preference when user explicitly specifies a model:

User says	Action
`用XXX` / `换成XXX` / `改用XXX`	Switch to model XXX + save as preference
`以后都用XXX` / `默认用XXX` / `always use XXX`	Save + confirm: `✅ 已记住！以后视频生成默认用 [XXX]`
`我喜欢XXX` / `我更喜欢XXX`	Save as preference

❌ Do NOT save when:

Agent auto-selects from knowledge-ai → not user preference
Agent uses fallback default → not user preference
User says generic quality requests (see "Clear Preference" below) → clear preference instead

When to Clear (User Abandons Preference)

🗑️ Clear preference when user wants automatic selection:

User says	Action
`用最好的` / `用最合适的` / `best` / `recommended`	Clear pref + use knowledge-ai recommendation
`推荐一个` / `你选一个` / `自动选择`	Clear pref + use knowledge-ai recommendation
`用默认的` / `用新的`	Clear pref + use knowledge-ai recommendation
`试试别的` / `换个试试` (without specific model)	Clear pref + use knowledge-ai recommendation
`重新推荐`	Clear pref + use knowledge-ai recommendation

Implementation:

del prefs[f"user_{user_id}"][task_type]
save_prefs(prefs)

⭐ Model Selection Priority (Video)

Selection flow:

User preference (if exists) → Highest priority, always respect
ima-knowledge-ai skill (if installed) → Professional recommendation based on task
Fallback defaults → Use table below (only if neither 1 nor 2 exists)

Important notes:

User preference is only saved when user explicitly specifies a model (see "When to Write" above)
Knowledge-ai is always consulted (even when user pref exists) to detect mismatches
When mismatch detected → add gentle hint in success message (does NOT interrupt generation)

The defaults below are FALLBACK only. User preferences have highest priority, then knowledge-ai recommendations.

💬 User Experience Protocol (IM / Feishu / Discord) v2.0 🆕

v2.0 Updates (aligned with ima-image-ai v1.3):

Added Step 0 for correct message ordering (fixes group chat bug)

Added Step 5 for explicit task completion

Enhanced Midjourney support with proper timing estimates

Now 6 steps total (0-5): Acknowledgment → Pre-Gen → Progress → Success/Failure → Done

This skill runs inside IM platforms (Feishu, Discord via OpenClaw).
Generation takes 10 seconds (music) up to 6 minutes (video). Never let users wait in silence.
Always follow all 6 steps below, every single time.

🗣️ User-Friendly First, Transparent on Request

Default to plain-language updates in normal user flows. If users ask for technical details, provide them transparently (script name, endpoints, and key parameters).

In standard progress messages, prioritize: model name, estimated/actual time, credits consumed, result URL, and natural-language status updates.

Estimated Generation Time (All Task Types)

Task Type	Model	Estimated Time	Poll Every	Send Progress Every
text_to_image	SeeDream 4.5	25~60s	5s	20s
	Nano Banana2 💚	20~40s	5s	15s
	Nano Banana Pro	60~120s	5s	30s
	Midjourney 🎨	40~90s	8s	25s
image_to_image	SeeDream 4.5	25~60s	5s	20s
	Nano Banana2 💚	20~40s	5s	15s
	Nano Banana Pro	60~120s	5s	30s
	Midjourney 🎨	40~90s	8s	25s
text_to_video	Wan 2.6, Hailuo 2.0/2.3, Vidu Q2, Pixverse	60~120s	8s	30s
	SeeDance 1.5 Pro, Kling 2.6, Veo 3.1	90~180s	8s	40s
	Kling O1, Sora 2 Pro	180~360s	8s	60s
image_to_video	Same ranges as text_to_video	—	8s	40s
first_last_frame / reference	Kling O1, Veo 3.1	180~360s	8s	60s
text_to_music	DouBao BGM / Song	10~25s	5s	10s
	Suno (sonic-v5)	20~45s	5s	15s
text_to_speech	(varies by model)	5~30s	3s	10s

estimated_max_seconds = upper bound of the range (e.g. 60 for SeeDream 4.5, 40 for Nano Banana2, 120 for Nano Banana Pro, 90 for Midjourney, 180 for Kling 2.6, 360 for Kling O1).

Step 0 — Initial Acknowledgment Reply (Normal Reply) 🆕

⚠️ CRITICAL: This step is essential for correct message ordering in IM platforms (Feishu, Discord).

Before doing anything else, reply to the user with a friendly acknowledgment message using your normal reply (not message tool). This reply will automatically appear FIRST in the conversation.

Example acknowledgment messages:

For images:

好的!来帮你画一只萌萌的猫咪 🐱

收到！马上为你生成一张 16:9 的风景照 🏔️

OK! Starting image generation with SeeDream 4.5 🎨

For videos:

好的!来帮你生成一段视频 🎬

收到！开始用 Wan 2.6 生成视频 🎥

For music:

好的!来帮你创作一首音乐 🎵

Rules:

Keep it short and warm (< 15 words)
Match the user's language (Chinese/English)
Include relevant emoji (🐱/🎨/🎬/🎵/✨)
This is your ONLY normal reply — all subsequent updates use message tool

Why this matters:

Normal replies automatically appear FIRST in the conversation thread
message tool pushes appear in chronological order AFTER your initial reply
This ensures users see: "好的!" → "🎨 开始生成..." → "⏳ 进度..." → "✅ 成功!" (correct order)
Without Step 0, the confirmation might appear LAST, confusing users

Step 1 — Pre-Generation Notification (Push via message tool)

After Step 0 reply, use the message tool to push a notification immediately:

[Emoji] 开始生成 [内容类型]，请稍候…
• 模型：[Model Name]
• 预计耗时：[X ~ Y 秒]
• 消耗积分：[N pts]

Emoji by content type:

图片 → 🎨
视频 → 🎬（加注:视频生成需要较长时间，我会定时汇报进度）
音乐 → 🎵

Cost transparency (new requirement):

Always show credit cost with model tier context
For expensive models (>50 pts), offer cheaper alternative proactively
Examples:
- Balanced (default): "使用 Wan 2.6（25 积分，最新 Wan）"
- Premium (user explicit): "使用高端模型 Kling O1（48-120 积分），质量最佳"
- Premium (auto-selected): "使用 Wan 2.6（25 积分）。若需更高质量可选 Kling O1（48 积分起）"
- Budget (user asked): "使用 Vidu Q2（5 积分，最省钱）"

Adapt language to match the user (Chinese / English). For video, always add a note that it takes longer. For expensive models, always mention cheaper alternatives unless user explicitly requested premium.

Step 2 — Progress Updates

Poll the task detail API every [Poll Every] seconds per the table.
Send a progress update every [Send Progress Every] seconds.

⏳ 正在生成中… [P]%
已等待 [elapsed]s，预计最长 [max]s

Progress formula:

P = min(95, floor(elapsed_seconds / estimated_max_seconds * 100))

Cap at 95% — never reach 100% until the API confirms success
If elapsed > estimated_max: freeze at 95%, append 「快了，稍等一下…」
For video with max=360s: at 120s → 33%, at 250s → 69%, at 400s → 95% (frozen)

Step 3 — Success Notification

When task status = success:

For Video Tasks (text_to_video / image_to_video / first_last_frame / reference_image)

3.1 Send video player first (IM platforms like Feishu will render inline player):

# Get result URL from script output or task detail API
result = get_task_result(task_id)
video_url = result["medias"][0]["url"]

# Build caption
caption = f"""✅ 视频生成成功！
• 模型：[Model Name]
• 耗时：预计 [X~Y]s，实际 [actual]s
• 消耗积分：[N pts]

[视频描述]"""

# Add mismatch hint if user pref conflicts with knowledge-ai recommendation
if user_pref_exists and knowledge_recommended_model != used_model:
    caption += f"""

💡 提示：当前任务也许用 {knowledge_recommended_model} 也会不错（{reason}，{cost} pts）"""

# Send video with caption (use message tool if available)
message(
    action="send",
    media=video_url,  # ⚠️ Use HTTPS URL directly, NOT local file path
    caption=caption
)

Important:

Hint is non-intrusive — does NOT interrupt generation
Only shown when user pref conflicts with knowledge-ai recommendation
User can ignore the hint; video is already delivered

3.2 Then send link as text (for copying/sharing):

# Send link message immediately after video
message(action="send", text=f"🔗 视频链接（可复制分享）：\n{video_url}")

⚠️ Critical for video:

Send video player FIRST (inline preview)
Send text link SECOND (for copying)
Include first-frame thumbnail URL if available: result["medias"][0]["cover"]

For Image Tasks (text_to_image / image_to_image)

# Build caption
caption = f"""✅ 图片生成成功！
• 模型：[Model Name]
• 耗时：预计 [X~Y]s，实际 [actual]s
• 消耗积分：[N pts]

🔗 原始链接：{image_url}"""

# Add mismatch hint if user pref conflicts with knowledge-ai recommendation
if user_pref_exists and knowledge_recommended_model != used_model:
    caption += f"""

💡 提示：当前任务也许用 {knowledge_recommended_model} 也会不错（{reason}，{cost} pts）"""

# Send image with caption
message(
    action="send",
    media=image_url,
    caption=caption
)

Important:

Hint is non-intrusive — does NOT interrupt generation
Only shown when user pref conflicts with knowledge-ai recommendation
User can ignore the hint; image is already delivered

For Music Tasks (text_to_music)

Send audio file with player:

✅ 音乐生成成功！
• 模型：[Model Name]
• 耗时：预计 [X~Y]s，实际 [actual]s
• 消耗积分：[N pts]
• 时长：约 [duration]

[音频URL或直接发送音频文件]

For TTS Tasks (text_to_speech) — Full UX Protocol (Steps 0–5)

Step 0 — Initial acknowledgment (normal reply)
First reply with a short acknowledgment, e.g.: 好的，正在帮你把这段文字转成语音。 / OK, converting this text to speech.

Step 1 — Pre-generation (message tool)
Push once:

🔊 开始语音合成，请稍候…
• 模型：[Model Name]
• 预计耗时：[X ~ Y 秒]
• 消耗积分：[N pts]

Step 2 — Progress
Poll every 2–5s. Every 10–15s send: ⏳ 语音合成中… [P]%，已等待 [elapsed]s，预计最长 [max]s. Cap progress at 95% until API returns success.

Step 3 — Success (message tool)
When resource_status == 1 and status != "failed", send media = medias[0].url and caption:

✅ 语音合成成功！
• 模型：[Model Name]
• 耗时：实际 [actual]s
• 消耗积分：[N pts]
🔗 原始链接：[url]

Use the URL from the API (do not use local file paths).

Step 4 — Failure (message tool)
On failure, send user-friendly message. TTS error translation (do not expose raw API errors):

Technical	✅ Say (CN)	✅ Say (EN)
401 Unauthorized	密钥无效或未授权，请至 imaclaw.ai 生成新密钥	API key invalid; generate at imaclaw.ai
4008 Insufficient points	积分不足，请至 imaclaw.ai 购买积分	Insufficient points; buy at imaclaw.ai
Invalid product attribute	参数配置异常，请稍后重试	Configuration error, try again later
Error 6006 / 6010	积分或参数不匹配，请换模型或重试	Points/params mismatch, try another model
resource_status == 2 / status failed	语音合成失败，建议换模型或缩短文本	Synthesis failed, try another model or shorter text
timeout	合成超时，请稍后重试	Timed out, try again later
Network error	网络不稳定，请检查后重试	Network unstable, check and retry
Text too long (TTS)	文本过长，请缩短后重试	Text too long, please shorten

Links: API key — https://www.imaclaw.ai/imaclaw/apikey ；Credits — https://www.imaclaw.ai/imaclaw/subscription

Step 5 — Done
After Step 0–4, no further reply needed. Do not send duplicate confirmations.

Step 4 — Failure Notification

When task status = failed or any API/network error, send:

❌ [内容类型]生成失败
• 原因：[natural_language_error_message]
• 建议改用：
  - [Alt Model 1]（[特点]，[N pts]）
  - [Alt Model 2]（[特点]，[N pts]）

需要我帮你用其他模型重试吗？

⚠️ CRITICAL: Error Message Translation

NEVER show technical error messages to users. Always translate API errors into natural language.
API key & credits: 密钥与积分管理入口为 imaclaw.ai（与 imastudio.com 同属 IMA 平台）。Key and subscription management: imaclaw.ai (same IMA platform as imastudio.com).

Technical Error	❌ Never Say	✅ Say Instead (Chinese)	✅ Say Instead (English)
`401 Unauthorized` 🆕	Invalid API key / 401 Unauthorized	❌ API密钥无效或未授权<br>💡 生成新密钥: https://www.imaclaw.ai/imaclaw/apikey	❌ API key is invalid or unauthorized<br>💡 Generate API Key: https://www.imaclaw.ai/imaclaw/apikey
`4008 Insufficient points` 🆕	Insufficient points / Error 4008	❌ 积分不足，无法创建任务<br>💡 购买积分: https://www.imaclaw.ai/imaclaw/subscription	❌ Insufficient points to create this task<br>💡 Buy Credits: https://www.imaclaw.ai/imaclaw/subscription
`"Invalid product attribute"` / `"Insufficient points"`	Invalid product attribute	生成参数配置异常，请稍后重试	Configuration error, please try again later
`Error 6006` (credit mismatch)	Error 6006	积分计算异常，系统正在修复	Points calculation error, system is fixing
`Error 6009` (no matching rule)	Error 6009	参数组合不匹配，已自动调整	Parameter mismatch, auto-adjusted
`Error 6010` (attribute_id mismatch)	Attribute ID does not match	模型参数不匹配，请尝试其他模型	Model parameters incompatible, try another model
`error 400` (bad request)	error 400 / Bad request	请求参数有误，请稍后重试	Invalid request parameters, please try again
`resource_status == 2`	Resource status 2 / Failed	生成过程遇到问题，建议换个模型试试	Generation failed, please try another model
`status == "failed"` (no details)	Task failed	这次生成没成功，要不换个模型试试？	Generation unsuccessful, try a different model?
`timeout`	Task timed out / Timeout error	生成时间过长已超时，建议用更快的模型	Generation took too long, try a faster model
Network error / Connection refused	Connection refused / Network error	网络连接不稳定，请检查网络后重试	Network connection unstable, check network and retry
Rate limit exceeded	429 Too Many Requests / Rate limit	请求过于频繁，请稍等片刻再试	Too many requests, please wait a moment
Prompt moderation (Sora only)	Content policy violation	提示词包含敏感内容，请修改后重试	Prompt contains restricted content, please modify
Model unavailable	Model not available / 503 Service Unavailable	当前模型暂时不可用，建议换个模型	Model temporarily unavailable, try another model
Lyrics format error (Suno only) 🎵	Invalid lyrics format	歌词格式有误，请调整后重试	Lyrics format error, adjust and retry
Prompt too short/long (Music) 🎵	Prompt length invalid	音乐描述过短或过长，请调整到合适长度 (建议20-100字)	Music description too short or long, adjust to appropriate length (20-100 chars recommended)
Text too long (TTS) 🔊	TTS text length	文本过长，请缩短后重试	Text too long, please shorten and retry

Generic fallback (when error is unknown):

Chinese: 生成过程遇到问题，请稍后重试或换个模型试试
English: Generation encountered an issue, please try again or use another model

Best Practices:

Focus on user action: Tell users what to do next, not what went wrong technically
Be reassuring: Use phrases like "建议换个模型试试" instead of "失败了"
Avoid blame: Never say "你的提示词有问题" → say "提示词需要调整一下"
Provide alternatives: Always suggest 1-2 alternative models in the failure message
🆕 Include actionable links (v1.0.8+): For 401/4008 errors, provide clickable links to API key generation or credit purchase pages
🎵 Music-specific (v1.2.0+):
- For Suno lyrics errors, suggest simplifying lyrics or using auto-generated lyrics (auto_lyrics=true)
- For prompt length errors, give example length (e.g., "建议20-100字")
- For BGM requests, recommend DouBao BGM over Suno
🔊 TTS-specific: Use the TTS error translation table in "For TTS Tasks (text_to_speech)" above; suggest another model via --list-models or shortening text.

Step 5 — Done (No Further Action Needed) 🆕

After sending Step 3 (success) or Step 4 (failure):

DO NOT send any additional messages unless the user asks a follow-up question
The task is complete — wait for the user's next request
User preference has been saved (if generation succeeded)
The conversation is ready for the next generation request

Why this step matters:

Prevents unnecessary "anything else?" messages that clutter the chat
Allows users to naturally continue the conversation when ready
Respects the asynchronous nature of IM platforms

Exception: If the user explicitly asks "还有别的吗？" or similar, then respond naturally.

🆕 Enhanced Error Handling (v1.0.8):

The Reflection mechanism (3 automatic retries) now provides specific, actionable suggestions for common errors:

401 Unauthorized: System suggests generating a new API key with clickable link
4008 Insufficient Points: System suggests purchasing credits with clickable link
500 Internal Server Error: Automatic parameter degradation (size, resolution, duration, quality)
6009 No Rule Match: Automatic parameter completion from credit_rules
6010 Attribute Mismatch: Automatic credit_rule reselection
Timeout: Helpful info with dashboard link for background task status

All error handling is automatic and transparent — users receive natural language explanations with next steps.

Failure fallback by task type:

Task Type	Failed Model	First Alt	Second Alt
text_to_image	SeeDream 4.5	Nano Banana2 (4pts, fast)	Nano Banana Pro (10-18pts, premium)
text_to_image	Nano Banana2	SeeDream 4.5 (5pts, better quality)	Nano Banana Pro (10-18pts)
text_to_image	Nano Banana Pro	SeeDream 4.5 (5pts)	Nano Banana2 (4pts, budget)
image_to_image	SeeDream 4.5	Nano Banana2 (4pts, fast)	Nano Banana Pro (10pts)
image_to_image	Nano Banana2	SeeDream 4.5 (5pts)	Nano Banana Pro (10pts)
image_to_image	Nano Banana Pro	SeeDream 4.5 (5pts)	Nano Banana2 (4pts)
text_to_video	Kling O1	Wan 2.6 (25pts)	Vidu Q2 (5pts)
text_to_video	Google Veo 3.1	Kling O1 (48pts)	Sora 2 Pro (122pts)
text_to_video	Any	Wan 2.6 (25pts, most popular)	Hailuo 2.0 (5pts)
image_to_video	Wan 2.6	Kling O1 (48pts)	Hailuo 2.0 i2v (25pts)
image_to_video	Any	Wan 2.6 (25pts, most popular)	Vidu Q2 Pro (20pts)
first_last / reference	Kling O1	Kling 2.6 (80pts)	Veo 3.1 (70pts+)
text_to_music 🎵	Suno	DouBao BGM (30pts, 背景音乐)	DouBao Song (30pts, 歌曲生成)
text_to_music 🎵	DouBao BGM	DouBao Song (30pts)	Suno (25pts, 功能最强)
text_to_music 🎵	DouBao Song	DouBao BGM (30pts)	Suno (25pts, 功能最强)
text_to_speech 🔊	(any)	Query `--list-models` for alternatives	Use another model_id from product list

Music-specific failure guidance:

If Suno fails → Recommend DouBao BGM (for background music) or DouBao Song (for songs)
If DouBao BGM fails → Try DouBao Song first (similar pricing), then Suno (more powerful)
If DouBao Song fails → Try DouBao BGM first (similar pricing), then Suno (more powerful)
For lyrics errors in Suno → Suggest simplifying lyrics or using auto_lyrics=true
For prompt length errors → Recommend 20-100 characters

TTS-specific failure guidance:

If TTS fails → Run --task-type text_to_speech --list-models and suggest another model_id; or shorten text / simplify content. Use the TTS error translation table in "For TTS Tasks" above for user-facing messages.

Supported Models at a Glance

Source: production GET /open/v1/product/list (2026-02-27). Model count reduced significantly. Always query product list API at runtime.

Image Generation (4 models each)

Category	Name	model_id	Cost
text_to_image	SeeDream 4.5 🌟	`doubao-seedream-4.5`	5 pts
text_to_image	Midjourney 🎨	`midjourney`	8/10 pts (480p/720p)
text_to_image	Nano Banana2 💚	`gemini-3.1-flash-image`	4/6/10/13 pts
text_to_image	Nano Banana Pro	`gemini-3-pro-image`	10/10/18 pts
image_to_image	SeeDream 4.5 🌟	`doubao-seedream-4.5`	5 pts
image_to_image	Midjourney 🎨	`midjourney`	8/10 pts (480p/720p)
image_to_image	Nano Banana2 💚	`gemini-3.1-flash-image`	4/6/10/13 pts
image_to_image	Nano Banana Pro	`gemini-3-pro-image`	10 pts

Midjourney attribute_ids: 5451/5452 (text_to_image), 5453/5454 (image_to_image)
Nano Banana2 size options: 512px (4pts), 1K (6pts), 2K (10pts), 4K (13pts)
Nano Banana Pro size options: 1K (10pts), 2K (10pts), 4K (18pts for t2i / 10pts for i2i)

Image Model Capabilities (Parameter Support)

⚠️ Critical: Models have varying parameter support. Custom aspect ratios are now supported by multiple models.

Model	Custom Aspect Ratio	Max Resolution	Size Options	Notes
SeeDream 4.5	✅ (via virtual params)	4K (adaptive)	8 aspect ratios	Supports 1:1, 16:9, 9:16, 4:3, 3:4, 2:3, 3:2, 21:9 (5 pts)
Nano Banana2	✅ Native support 🆕	4K (4096×4096)	512px/1K/2K/4K + aspect ratios	Supports 1:1, 16:9, 9:16, 4:3, 3:4; size via `attribute_id`
Nano Banana Pro	✅ Native support 🆕	4K (4096×4096)	1K/2K/4K + aspect ratios	Supports 1:1, 16:9, 9:16, 4:3, 3:4; size via `attribute_id`
Midjourney 🎨	❌ (1:1 only)	1024px (square)	480p/720p via `attribute_id`	Fixed 1024x1024, artistic style focus

Key Capabilities:

✅ Aspect ratio control: SeeDream 4.5 (virtual params), Nano Banana Pro/2 (native support)
❌ 8K: Not supported by any model (max is 4K)
✅ Size control: Nano Banana2, Nano Banana Pro, and Midjourney support multiple size options via different attribute_ids
✅ Budget option: Nano Banana2 is the cheapest at 4 pts for 512px, but 4K costs 13pts
🎨 Artistic styles: Midjourney excels at creative, artistic, and illustration styles
💡 Best value: SeeDream 4.5 at 5pts offers aspect ratio flexibility; Nano Banana2 512px at 4pts for fastest/cheapest

Video Generation

IMA Sevio Family (Dynamic Routing)

Name	model_id	Typical positioning	task_type support
IMA Video Pro (Sevio 1.0)	`ima-pro`	Higher quality / consistency	`text_to_video`, `image_to_video`, `first_last_frame_to_video`, `reference_image_to_video`
IMA Video Pro Fast (Sevio 1.0-Fast)	`ima-pro-fast`	Lower latency / faster iteration	`text_to_video`, `image_to_video`, `first_last_frame_to_video`, `reference_image_to_video`

Notes:

ima-all-ai does not hardcode Sevio availability; it resolves by runtime product/list.
If a Sevio model is temporarily absent for a category, use --list-models result as final source.

Category	Name	model_id	Cost Range
text_to_video (14)	Wan 2.6 🔥	`wan2.6-t2v`	25-120 pts
	Hailuo 2.3	`MiniMax-Hailuo-2.3`	32+ pts
	Hailuo 2.0	`MiniMax-Hailuo-02`	5+ pts
	Vidu Q2	`viduq2`	5-70 pts
	SeeDance 1.5 Pro	`doubao-seedance-1.5-pro`	20+ pts
	Sora 2 Pro	`sora-2-pro`	122+ pts
	Kling O1	`kling-video-o1`	48-120 pts
	Kling 2.6	`kling-v2-6`	80+ pts
	Google Veo 3.1	`veo-3.1-generate-preview`	70-330 pts
	Pixverse V5.5 / V5 / V4.5 / V4 / V3.5	`pixverse`	12-48 pts
image_to_video (14)	Wan 2.6 🔥	`wan2.6-i2v`	25-120 pts
	Hailuo 2.3 / 2.0	`MiniMax-Hailuo-2.3/02`	25-32 pts
	Vidu Q2 Pro	`viduq2-pro`	20-70 pts
	SeeDance 1.5 Pro	`doubao-seedance-1.5-pro`	47+ pts
	Sora 2 Pro	`sora-2-pro`	122+ pts
	Kling O1 / 2.6	`kling-video-o1/v2-6`	48-120 pts
	Google Veo 3.1	`veo-3.1-generate-preview`	70-330 pts
	Pixverse V5.5-V3.5	`pixverse`	12-48 pts
first_last_frame (11)	Kling O1 🌟	`kling-video-o1`	48-120 pts
	Kling 2.6	`kling-v2-6`	80+ pts
	Others (9)	Hailuo 2.0, Vidu Q2 Pro, SeeDance 1.5 Pro, Veo 3.1, Pixverse V5.5-V3.5	—
reference_image (6)	Kling O1 🌟	`kling-video-o1`	48-120 pts
	Google Veo 3.1	`veo-3.1-generate-preview`	70-330 pts
	Others (4)	Vidu Q2, Pixverse V5.5/V5/V4.5	—

Music Generation

Category	Name	model_id	Cost	Notes
text_to_music	Suno	`sonic`	25 pts	sonic-v5; custom_mode, lyrics, vocal_gender
text_to_music	DouBao BGM	`GenBGM`	30 pts	Background music
text_to_music	DouBao Song	`GenSong`	30 pts	Song generation

Speech (TTS) — text_to_speech

Models and credits are not fixed. Always call GET /open/v1/product/list?category=text_to_speech (or run the script with --task-type text_to_speech --list-models) to get current model_id, attribute_id, and credit.

ima-all-ai has complete TTS capability: This document and the bundled ima_create.py provide full TTS support (routing, parameters, create/poll, UX protocol Steps 0–5, error translation). The ima-tts-ai skill is an optional standalone package with the same specification.

TTS Task Detail — Response Shape

Poll POST /open/v1/tasks/detail until completion. For TTS, medias[] uses the same structure as other IMA audio tasks:

Field	Type	Meaning
`resource_status`	int or null	0=处理中, 1=可用, 2=失败, 3=已删除；null 视为 0
`status`	string	"pending" / "processing" / "success" / "failed"
`url`	string	Audio URL when resource_status=1 (mp3/wav)
`duration_str`	string	Optional, e.g. "12s"
`format`	string	Optional, e.g. "mp3", "wav"

Success example: When all medias have resource_status == 1 and status != "failed", read medias[0].url (or watermark_url). Example: {"medias":[{"resource_status":1,"status":"success","url":"https://cdn.../output.mp3","duration_str":"12s","format":"mp3"}]}.

TTS Create Task — Request Shape

task_type: "text_to_speech". No image input: src_img_url: [], input_images: []. prompt (text to speak) must be inside parameters[].parameters, not at top level. Extra fields (e.g. voice_id, speed) come from product form_config; pass via --extra-params and only include params present in the product’s credit_rules/form_config.

TTS Common Mistakes

Mistake	Fix
prompt at top level	Put prompt inside `parameters[].parameters` (script does this)
Wrong or missing attribute_id	Always call product list first; use credit_rules
Single poll	Poll until all medias have resource_status == 1
Ignoring status when resource_status=1	Check status != "failed"
Sending params not in form_config/credit_rules	Use only params from product list; script reflection strips others on retry

Always call GET /open/v1/product/list?category=<type> first to get the live attribute_id and form_config defaults required for task creation.

There are two equivalent route systems serving the same backend logic:

Route	Auth	Use Case
`/open/v1/`	`Authorization: Bearer ima_*` only	Third-party / agent access
`/api/v3/`	Token + API Key (dual auth)	Frontend App

This skill documents the /open/v1/ Open API. All business logic (credit validation, N-flattening, risk control) runs identically on both paths.

Environment

Base URL: https://api.imastudio.com

Required/recommended headers for all /open/v1/ endpoints:

Header	Required	Value	Notes
`Authorization`	✅	`Bearer ima_your_api_key_here`	API key authentication
`x-app-source`	✅	`ima_skills`	Fixed value — identifies skill-originated requests
`x_app_language`	recommended	`en` / `zh`	Product label language; defaults to `en` if omitted

Authorization: Bearer ima_your_api_key_here
x-app-source: ima_skills
x_app_language: en

📤 When to Upload Images (Quick Reference)

The IMA Open API does NOT accept raw bytes or base64 images. All image inputs must be public HTTPS URLs.

Task Type	Input Required?	Upload Before Create?	Notes
text_to_image	❌ No	—	Prompt only
image_to_image	✅ Yes (1 image)	✅ Upload first	Single input image
text_to_video	❌ No	—	Prompt only
image_to_video	✅ Yes (1 image)	✅ Upload first	Single input image
first_last_frame_to_video	✅ Yes (2 images)	✅ Upload first	First + last frame
reference_image_to_video	✅ Yes (1+ images)	✅ Upload first	Reference image(s)
text_to_music	❌ No	—	Prompt only
text_to_speech	❌ No	—	Prompt only (text to speak)

Upload flow:

User provides local file path or bytes → call prepare_image_url() (see section below)
User provides HTTPS URL → use directly, no upload needed
Use the returned CDN URL (fdl) as the value for input_images / src_img_url

Example workflow (image_to_image):

# User provides local file
image_url = prepare_image_url("/path/to/photo.jpg", api_key)
# → Returns: https://ima-ga.esxscloud.com/webAgent/privite/2026/02/27/..._uuid.jpeg

# Then create task with this URL
create_task(
    task_type="image_to_image",
    input_images=[image_url],  # Use uploaded URL
    prompt="turn into oil painting"
)

⚠️ MANDATORY: Always Query Product List First

CRITICAL: You MUST call /open/v1/product/list BEFORE creating any task.
The attribute_id field is REQUIRED in the create request. If it is 0 or missing, you get:
"Invalid product attribute" → "Insufficient points" → task fails completely.
NEVER construct a create request from the model table alone. Always fetch the product first.

How to get attribute_id (all task types)

# Query product list with the correct category
GET /open/v1/product/list?app=ima&platform=web&category=<task_type>
# task_type: text_to_image | image_to_image | text_to_video | image_to_video |
#            first_last_frame_to_video | reference_image_to_video | text_to_music | text_to_speech

# Walk the V2 tree to find your target model (type=3 leaf nodes only)
for group in response["data"]:
    for version in group.get("children", []):
        if version["type"] == "3" and version["model_id"] == target_model_id:
            attribute_id  = version["credit_rules"][0]["attribute_id"]
            credit        = version["credit_rules"][0]["points"]
            model_version = version["id"]    # = version_id / model_version
            model_name    = version["name"]
            form_defaults = {f["field"]: f["value"] for f in version["form_config"]}
            break

Quick Reference: Known attribute_ids

Pre-queried values for convenience. Always call the product list at runtime for accuracy.

Model	Task Type	model_id	attribute_id	credit	Notes
text_to_image
SeeDream 4.5	text_to_image	`doubao-seedream-4.5`	2341	5 pts	Default, balanced
Nano Banana Pro (1K)	text_to_image	`gemini-3-pro-image`	2399	10 pts	1024×1024
Nano Banana Pro (2K)	text_to_image	`gemini-3-pro-image`	2400	10 pts	2048×2048
Nano Banana Pro (4K)	text_to_image	`gemini-3-pro-image`	2401	18 pts	4096×4096
text_to_video
Wan 2.6 (720P, 5s)	text_to_video	`wan2.6-t2v`	2057	25 pts	Default, balanced
Wan 2.6 (1080P, 5s)	text_to_video	`wan2.6-t2v`	2058	40 pts	—
Wan 2.6 (720P, 10s)	text_to_video	`wan2.6-t2v`	2059	50 pts	—
Wan 2.6 (1080P, 10s)	text_to_video	`wan2.6-t2v`	2060	80 pts	—
Wan 2.6 (720P, 15s)	text_to_video	`wan2.6-t2v`	2061	75 pts	—
Wan 2.6 (1080P, 15s)	text_to_video	`wan2.6-t2v`	2062	120 pts	—
Kling O1 (5s, std)	text_to_video	`kling-video-o1`	2313	48 pts	Latest Kling
Kling O1 (5s, pro)	text_to_video	`kling-video-o1`	2314	60 pts	—
Kling O1 (10s, std)	text_to_video	`kling-video-o1`	2315	96 pts	—
Kling O1 (10s, pro)	text_to_video	`kling-video-o1`	2316	120 pts	—
text_to_music
Suno (sonic-v4)	text_to_music	`sonic`	2370	25 pts	Default
DouBao BGM	text_to_music	`GenBGM`	4399	30 pts	—
DouBao Song	text_to_music	`GenSong`	4398	30 pts	—
All others	any	—	→ query `/open/v1/product/list`	—	Always runtime query

⚠️ Production warning: attribute_id and credit values change frequently in production. Always call /open/v1/product/list at runtime; above table is pre-queried reference only (2026-02-27).

Common Mistakes (and resulting errors)

Mistake	Error
`attribute_id` is `0` or missing	`"Invalid product attribute"` + `"Insufficient points"`
`attribute_id` outdated (production changed)	Same errors; always query product list first
`attribute_id` doesn't match parameter combination	Error 6010: "Attribute ID does not match the calculated rule"
`prompt` at outer `parameters[]` level	Prompt ignored; wrong routing
`cast` missing from inner `parameters.parameters`	Billing validation failure
`credit` value wrong or missing	Error 6006
`model_name` / `model_version` missing	Wrong backend routing
Skipped product list, used table values directly	All of the above

⚠️ Critical for Google Veo 3.1 and multi-rule models:

Models like Google Veo 3.1 have multiple credit_rules, each with a different attribute_id for different parameter combinations:

720p + 4s + optimized → attribute_id A
720p + 8s + optimized → attribute_id B
4K + 4s + high → attribute_id C

The script automatically selects the correct attribute_id by matching your parameters (duration, resolution, compression_quality, generate_audio) against each rule's attributes. If the match fails, you get error 6010.

Fix: The bundled script now checks these video-specific parameters for smart credit_rule selection. Always use the script, not manual API construction.

Core Flow

1. GET /open/v1/product/list?app=ima&platform=web&category=<type>
   → REQUIRED: Get attribute_id, credit, model_version, model_name, form_config defaults

[If input image required]
2. Upload image → get public HTTPS URL
   → See "Image Upload" section below

3. POST /open/v1/tasks/create
   → Must include: attribute_id, model_name, model_version, credit, cast, prompt (nested!)

4. POST /open/v1/tasks/detail  {"task_id": "..."}
   → Poll until medias[].resource_status == 1
   → Extract url from completed media

Image Upload (Required Before Image Tasks)

The IMA Open API does NOT accept raw bytes or base64 images. All image inputs must be public HTTPS URLs.

When a user provides an image (local file, bytes, base64), you must upload it first and get a URL. This is exactly what the IMA frontend does before every image task.

Real Upload Flow (from IMA Frontend Source)

The frontend uses a two-step presigned URL flow via the IM platform:

Step 1: GET /api/rest/oss/getuploadtoken   → returns { ful, fdl }
          ful = presigned PUT URL (upload destination, expires ~7 days)
          fdl = final CDN download URL (use this as input_images value)

Step 2: PUT {ful}  with raw image bytes + Content-Type header
          → image is stored in Aliyun OSS: zhubite-imagent-bot.oss-us-east-1.aliyuncs.com
          → accessible via CDN: https://ima-ga.esxscloud.com/...

Step 1: Get Upload Token

GET https://imapi.liveme.com/api/rest/oss/getuploadtoken

Required query parameters (11 total — sourced directly from frontend generateUploadInfo):

Parameter	Example	Description
`appUid`	`ima_xxx...`	Use IMA API key directly — no separate login needed
`appId`	`webAgent`	App identifier (fixed)
`appKey`	`32jdskjdk320eew`	App secret (fixed, used for `sign` generation)
`cmimToken`	`ima_xxx...`	Use IMA API key directly — same as appUid
`sign`	`117CF6CF...`	IM auth HMAC: `SHA1("webAgent\|32jdskjdk320eew\|{timestamp}\|{nonce}").upper()`
`timestamp`	`1772042430`	Unix timestamp (seconds), generated per request
`nonce`	`CxI1FLI5ajLJZ1jlxZmeg`	Random nonce string, generated per request
`fService`	`privite`	Fixed: storage service type
`fType`	`picture`	`picture` for images, `video`, `audio`
`fSuffix`	`jpeg`	File extension: `jpeg`, `png`, `mp4`, `mp3`
`fContentType`	`image/jpeg`	MIME type of the file

简化认证：直接使用 IMA API key 填充 appUid 和 cmimToken 参数，无需单独获取凭证。

Response:

{
  "ful": "https://zhubite-imagent-bot.oss-us-east-1.aliyuncs.com/webAgent/privite/2026/02/26/..._uuid.jpeg?Expires=...&OSSAccessKeyId=...&Signature=...",
  "fdl": "https://ima-ga.esxscloud.com/webAgent/privite/2026/02/26/..._uuid.jpeg",
  "ful_expire": "...",
  "fdl_expire": "...",
  "fdl_key": "..."
}

Step 2: Upload Image via Presigned URL

PUT {ful}
Content-Type: image/jpeg
Body: [raw image bytes]

No auth headers needed — the presigned URL already encodes the credentials.

Step 3: Use `fdl` as the Image URL

After the PUT succeeds, use fdl (the CDN URL) as the value for input_images / src_img_url.

Python Implementation

import hashlib, time, uuid, requests, mimetypes

# ── 🌐 IMA Upload Service Endpoint (IMA-owned, for image/video uploads) ──────
IMA_IM_BASE = "https://imapi.liveme.com"

# ── 🔑 Hardcoded APP_KEY (Public, Shared Across All Users) ──────────────────
# This APP_KEY is a PUBLIC identifier used by IMA Studio's image/video upload 
# service. It is NOT a secret—it's intentionally shared across all users and 
# embedded in the IMA web frontend. This key is used to generate HMAC signatures 
# for upload token requests, but your IMA API key (ima_xxx...) is the ACTUAL 
# authentication credential. Think of APP_KEY as a "client ID" rather than a 
# "client secret."
#
# ⚠️ Security Note: Your ima_xxx... API key is the sensitive credential. It is 
# sent to imapi.liveme.com as query parameters (appUid, cmimToken). Always use 
# test keys for experiments and rotate your API key regularly.
#
# 📖 See SECURITY.md for complete disclosure and network verification guide.
APP_ID    = "webAgent"
APP_KEY   = "32jdskjdk320eew"   # Public shared key (used for HMAC sign generation)
APP_UID   = "<your_app_uid>"    # POST /api/v3/login/app → data.user_id
APP_TOKEN = "<your_app_token>"  # POST /api/v3/login/app → data.token


def _gen_sign() -> tuple[str, str, str]:
    """Generate per-request (sign, timestamp, nonce)."""
    nonce = uuid.uuid4().hex[:21]
    ts    = str(int(time.time()))
    raw   = f"{APP_ID}|{APP_KEY}|{ts}|{nonce}"
    sign  = hashlib.sha1(raw.encode()).hexdigest().upper()
    return sign, ts, nonce


def get_upload_token(app_uid: str, app_token: str,
                     suffix: str, content_type: str) -> dict:
    """Step 1: Get presigned upload URL from IMA's upload service.
    
    Calls GET imapi.liveme.com/api/rest/oss/getuploadtoken with exactly 11 params.
    Returns: { "ful": "<presigned PUT URL>", "fdl": "<CDN download URL>" }
    
    Args:
        app_uid: Your IMA API key (ima_xxx...), used as appUid parameter
        app_token: Your IMA API key (ima_xxx...), used as cmimToken parameter
        suffix: File extension (jpeg, png, mp4, mp3)
        content_type: MIME type (image/jpeg, video/mp4, etc.)
    
    Security Note:
        Your IMA API key (ima_xxx...) is sent to imapi.liveme.com as query 
        parameters (appUid, cmimToken). This is IMA Studio's image/video upload 
        service, separate from the main api.imastudio.com API. Both domains are 
        owned by IMA Studio—this is part of IMA's microservices architecture.
        
        Why two domains?
        - api.imastudio.com: Core AI generation API (product list, task creation)
        - imapi.liveme.com: Specialized upload service (presigned URL generation)
        
        Your API key grants access to both services. For security verification, 
        see SECURITY.md section "Network Traffic Verification."
    """
    sign, ts, nonce = _gen_sign()
    r = requests.get(
        f"{IMA_IM_BASE}/api/rest/oss/getuploadtoken",
        params={
            # App Key params
            "appUid":       app_uid,       # APP_UID
            "appId":        APP_ID,
            "appKey":       APP_KEY,
            "cmimToken":    app_token,     # APP_TOKEN
            "sign":         sign,
            "timestamp":    ts,
            "nonce":        nonce,
            # File params
            "fService":     "privite",     # fixed
            "fType":        "picture",     # picture / video / audio
            "fSuffix":      suffix,        # jpeg / png / mp4 / mp3
            "fContentType": content_type,
        },
    )
    r.raise_for_status()
    return r.json()["data"]


def upload_image_to_oss(image_bytes: bytes, content_type: str, ful: str) -> None:
    """Step 2: PUT image bytes to the presigned OSS URL. No auth needed."""
    resp = requests.put(ful, data=image_bytes, headers={"Content-Type": content_type})
    resp.raise_for_status()


def prepare_image_url(source, api_key: str) -> str:
    """
    Full workflow: upload any image and return the CDN URL (fdl).
    
    Args:
        source: file path (str), raw bytes, or already-public HTTPS URL
        api_key: IMA API key for upload authentication
    
    Returns: public HTTPS CDN URL ready to use as input_images value
    """
    # Already a public URL → use directly, no upload needed
    if isinstance(source, str) and source.startswith("https://"):
        return source
    
    # Read file bytes
    if isinstance(source, str):
        ext = source.rsplit(".", 1)[-1].lower() if "." in source else "jpeg"
        with open(source, "rb") as f:
            image_bytes = f.read()
        content_type = mimetypes.guess_type(source)[0] or "image/jpeg"
    else:
        image_bytes = source
        ext = "jpeg"
        content_type = "image/jpeg"

    # Step 1: Get presigned URL using API key directly
    token_data = get_upload_token(api_key, ext, content_type)
    ful = token_data["ful"]
    fdl = token_data["fdl"]

    # Step 2: Upload to OSS
    upload_image_to_oss(image_bytes, content_type, ful)

    # Step 3: Return CDN URL
    return fdl   # use this as input_images / src_img_url value

OSS path format: webAgent/privite/{YYYY}/{MM}/{DD}/{timestamp}_{uid}_{uuid}.{ext} CDN base: https://ima-ga.esxscloud.com/ OSS bucket: zhubite-imagent-bot.oss-us-east-1.aliyuncs.com

Quick Reference

Task Types (category values)

category	Capability	Input
`text_to_image`	Text → Image	prompt
`image_to_image`	Image → Image	prompt + input_images
`text_to_video`	Text → Video	prompt
`image_to_video`	Image → Video	prompt + input_images
`first_last_frame_to_video`	First+Last Frame → Video	prompt + src_img_url[2]
`reference_image_to_video`	Reference Image → Video	prompt + src_img_url[1+]
`text_to_music`	Text → Music	prompt
`text_to_speech`	Text → Speech	prompt (text to speak)

Detail API status values

Each media in medias[] has two fields:

Field	Type	Values	Description
`resource_status`	int (or `null`)	`0`, `1`, `2`, `3`	0=处理中, 1=可用, 2=失败, 3=已删除。API 可能返回 `null`，需当作 0。
`status`	string	`"pending"`, `"processing"`, `"success"`, `"failed"`	任务状态文案。轮询时以 `resource_status` 为准；`status == "failed"` 表示失败。

Poll on resource_status first, then ensure status is not "failed":

`resource_status`	`status`	Meaning	Action
`0` or `null`	`pending` / `processing`	处理中	Keep polling; do not stop (null = 0)
`1`	`success` (or `completed`)	完成	Read `url`; stop only when all medias are 1
`1`	`failed`	失败 (status 优先)	Stop, handle error
`2`	any	失败	Stop, handle error
`3`	any	已删除	Stop

Important: (1) Treat resource_status: null as 0. (2) Stop only when all medias have resource_status == 1. (3) When resource_status=1, still check status != "failed".

API 1: Product List

GET /open/v1/product/list?app=ima&platform=web&category=text_to_image

Internally calls downstream /v1/products/listv2. Returns a V2 tree structure: type=2 nodes are model groups, type=3 nodes are versions (leaves). Only type=3 nodes contain credit_rules and form_config.

webAgent is auto-converted to ima by the gateway — you can use either value for app.

[
  {
    "id": "SeeDream",
    "type": "2",
    "name": "SeeDream",
    "model_id": "",
    "children": [
      {
        "id": "doubao-seedream-4-0-250828",
        "type": "3",
        "name": "SeeDream 4.0",
        "model_id": "doubao-seedream-4.0",
        "credit_rules": [
          { "attribute_id": 332, "points": 5, "attributes": { "default": "enabled" } }
        ],
        "form_config": [
          { "field": "size", "type": "tags", "value": "1K",
            "options": [{"label":"1K","value":"1K"}, {"label":"2K","value":"2K"}] }
        ]
      }
    ]
  }
]

How to pick a version for task creation:

Traverse nodes to find type=3 leaves (versions)
Use model_id and id (= model_version) from the leaf
Pick credit_rules[].attribute_id matching your desired quality/size (attributes field shows the config)
Use form_config[].value as default parameters values

credit_rules[].attribute_id → required for task creation as attribute_id. credit_rules[].points → required for task creation as credit and cast.points.

API 2: Create Task

POST /open/v1/tasks/create

Request Structure

{
  "task_type": "text_to_image",
  "enable_multi_model": false,
  "src_img_url": [],
  "upload_img_src": "",
  "parameters": [
    {
      "attribute_id": 8538,
      "model_id":      "doubao-seedream-4.5",
      "model_name":    "SeeDream 4.5",
      "model_version": "doubao-seedream-4-5-251128",
      "app":           "ima",
      "platform":      "web",
      "category":      "text_to_image",
      "credit":        5,
      "parameters": {
        "prompt":       "a beautiful mountain sunset, photorealistic",
        "size":         "4k",
        "n":            1,
        "input_images": [],
        "cast":         {"points": 5, "attribute_id": 8538}
      }
    }
  ]
}

Field Reference

Field	Required	Description
`task_type`	✅	Must match `parameters[].category`
`parameters[].attribute_id`	✅	From `credit_rules[].attribute_id` in product list
`parameters[].model_id`	✅	From `type=3` leaf node `model_id`
`parameters[].model_version`	✅	From `type=3` leaf node `id`
`parameters[].app`	✅	Use `ima` (or `webAgent`, auto-converted)
`parameters[].platform`	✅	Use `web`
`parameters[].category`	✅	Must match top-level `task_type`
`parameters[].credit`	✅	Must equal `credit_rules[].points`. Error 6006 if wrong.
`parameters[].parameters.prompt`	✅	The actual prompt text used by downstream service
`parameters[].parameters.cast`	✅	`{"points": N, "attribute_id": N}` — mirrors credit
`parameters[].parameters.n`	✅	Number of outputs (usually `1`). Gateway flattens N>1 into separate resources.
`parameters[].parameters.input_images`	image tasks	Array of input image URLs
top-level `src_img_url`	multi-image	Array for first_last_frame / reference tasks

N-Field Flattening (Gateway Internal Logic)

When n > 1, the gateway automatically:

Generates n independent resourceBizId values
Deducts credits n times (one per resource)
Creates n separate tasks in the downstream service

Response medias[] will contain n items. Poll until all have resource_status == 1.

Response

{
  "code": 0,
  "data": {
    "id": "task_abc123",
    "biz_id": "biz_xxx",
    "task_type": "text_to_image",
    "medias": [],
    "generate_count": 1,
    "created_at": 1700000000000,
    "timeout_at": 1700000300000
  }
}

data.id = task ID for polling. timeout_at = Unix ms deadline.

API 3: Task Detail (Poll)

POST /open/v1/tasks/detail
{"task_id": "<id from create response>"}

Poll every 2–5s (8s+ for video). Completed response:

{
  "id": "task_abc",
  "medias": [{
    "resource_status": 1,
    "status": "success",
    "url": "https://cdn.../output.jpg",
    "cover": "https://cdn.../cover.jpg",
    "format": "jpg",
    "width": 1024,
    "height": 1024
  }]
}

Polling stop condition (must implement exactly):

Treat resource_status: null (or missing) as 0 (processing). Do not stop when you see null; backend may serialize Go *int as null.
Stop only when ALL medias[].resource_status == 1 and no status == "failed". If you return on the first media with resource_status == 1 while others are still 0, the task is not fully done and you will keep polling or get inconsistent state.
Stop immediately if any status == "failed" or resource_status == 2 or resource_status == 3.

Task Type Examples

text_to_image ✅ Verified

No image input. src_img_url: [], input_images: []. See API 2 for full example.

text_to_video ✅ Verified

Extra fields vs text_to_image — all from form_config defaults:

{
  "task_type": "text_to_video",
  "src_img_url": [],
  "parameters": [{
    "attribute_id":  4838,
    "model_id":      "wan2.6-t2v",
    "model_name":    "Wan 2.6",
    "model_version": "wan2.6-t2v",
    "category":      "text_to_video",
    "credit":        3,
    "app": "ima", "platform": "web",
    "parameters": {
      "prompt":          "a puppy dancing happily, sunny meadow",
      "negative_prompt": "",
      "prompt_extend":   false,
      "duration":        5,
      "resolution":      "1080P",
      "aspect_ratio":    "16:9",
      "shot_type":       "single",
      "seed":            -1,
      "n":               1,
      "input_images":    [],
      "cast":            {"points": 3, "attribute_id": 4838}
    }
  }]
}

Video-specific fields from form_config: duration (seconds), resolution, aspect_ratio, shot_type, negative_prompt, prompt_extend. Poll every 8s (video generation is slower). Response medias[].cover = first-frame thumbnail.

text_to_music

No image input. src_img_url: [], input_images: [].

image_to_image ✅ Verified

{
  "task_type": "image_to_image",
  "src_img_url": ["https://...input.jpg"],
  "parameters": [{
    "attribute_id":  8560,
    "model_id":      "doubao-seedream-4.5",
    "model_version": "doubao-seedream-4-5-251128",
    "category":      "image_to_image",
    "credit":        5,
    "app": "ima", "platform": "web",
    "parameters": {
      "prompt":       "turn into oil painting style",
      "size":         "4k",
      "n":            1,
      "input_images": ["https://...input.jpg"],
      "cast":         {"points": 5, "attribute_id": 8560}
    }
  }]
}

⚠️ size must be from form_config options (e.g. "2k", "4k", "2048x2048"). "adaptive" is NOT valid for SeeDream 4.5 i2i — causes error 400. Top-level src_img_url and parameters.input_images must both contain the input image URL. Some i2i models (e.g. doubao-seededit-3.0-i2i) may not be available in test environments — fall back to SeeDream 4.5.

image_to_video / first_last_frame_to_video / reference_image_to_video

{
  "src_img_url": ["https://first-frame.jpg", "https://last-frame.jpg"]
}

Index 0 = first frame (or reference), index 1 = last frame (first_last_frame only).

Common Mistakes

Mistake	Fix
`attribute_id` not from `credit_rules`	Always fetch product list first
`credit` value wrong	Must exactly match `credit_rules[].points` — error 6006
`prompt` at wrong location	Put prompt in `parameters[].parameters.prompt` (nested), not only at top level
Polling `biz_id` instead of `id`	Use `id` (task ID) for `/tasks/detail`
Single-poll instead of loop	Poll until `resource_status == 1` for ALL medias
Missing `app` / `platform` in parameters	Required fields — use `ima` / `web`
`category` mismatch	`parameters[].category` must match top-level `task_type`
`resource_status == 2` not handled	Check for failure, don't loop forever
`status == "failed"` ignored	`resource_status=1` + `status="failed"` means actual failure
`n > 1` and only checking first media	All `n` media items must reach `resource_status == 1`

Complete Python Example

See the Python example sections throughout this documentation for implementation guidance covering all 7 task types.

Version tags

latestvk974sn803v0h72aqyd6q28db6d83230x

Runtime requirements

EnvIMA_API_KEY

IMA Studio All-in-One — Image, Video, Music, SeeDream, Veo, Suno. Banana

Security Scans

Install

IMA AI Creation

⚠️ 重要：模型 ID 参考

图像模型 (Image Models)

视频模型 (Video Models)

音乐模型 (Music Models)

语音模型 (Speech/TTS Models)

📚 Optional Knowledge Enhancement (ima-knowledge-ai)

📥 User Input Parsing (Media Type & Task Routing)

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

2. Model and parameter parsing

⚙️ How This Skill Works

🌐 Network Endpoints Used

⚠️ Credential Security Notice

Agent Execution (Internal Reference)

Overview

🔒 Security & Transparency Policy

✅ What Users CAN Do

⚠️ Advanced Users: Fork & Modify

❌ What to AVOID (Security Risks)

📋 Privacy & Data Handling Summary

🔧 For Skill Maintainers Only

🧠 User Preference Memory (Image)

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

Model Selection Flow (Image Generation)

When to Write (User Explicit Preference ONLY)

When to Clear (User Abandons Preference)

⭐ Model Selection Priority (Image)

Preference Change Confirmation

⭐ Recommended Defaults

Image Models (text_to_image / image_to_image)

Video Models (text_to_video / image_to_video / first_last_frame / reference_image)

🆕 Special Case: Pixverse Model Parameter (v1.0.7+)

Music Models (text_to_music)

Common Parameter Patterns

Decision Tree: When User Requests Unsupported Features

🧠 User Preference Memory (Video)

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

Model Selection Flow (Video Generation)

When to Write (User Explicit Preference ONLY)

When to Clear (User Abandons Preference)

⭐ Model Selection Priority (Video)

💬 User Experience Protocol (IM / Feishu / Discord) v2.0 🆕

🗣️ User-Friendly First, Transparent on Request

Estimated Generation Time (All Task Types)

Step 0 — Initial Acknowledgment Reply (Normal Reply) 🆕

Step 1 — Pre-Generation Notification (Push via message tool)

Step 2 — Progress Updates

Step 3 — Success Notification

For Video Tasks (text_to_video / image_to_video / first_last_frame / reference_image)

For Image Tasks (text_to_image / image_to_image)

For Music Tasks (text_to_music)

For TTS Tasks (text_to_speech) — Full UX Protocol (Steps 0–5)

Step 4 — Failure Notification

Step 5 — Done (No Further Action Needed) 🆕

Supported Models at a Glance

Image Generation (4 models each)

Image Model Capabilities (Parameter Support)

Video Generation

IMA Sevio Family (Dynamic Routing)

Music Generation

Speech (TTS) — text_to_speech

TTS Task Detail — Response Shape

TTS Create Task — Request Shape

TTS Common Mistakes

Environment

📤 When to Upload Images (Quick Reference)

⚠️ MANDATORY: Always Query Product List First

How to get attribute_id (all task types)

Quick Reference: Known attribute_ids

Common Mistakes (and resulting errors)

Core Flow

Image Upload (Required Before Image Tasks)

Real Upload Flow (from IMA Frontend Source)

Step 1: Get Upload Token

Step 2: Upload Image via Presigned URL

Step 3: Use fdl as the Image URL

Python Implementation

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

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

Step 3: Use `fdl` as the Image URL