Ima All Ai

内容创作需配置 API Key

最全面的AI内容创作平台，统一接入所有领先模型，覆盖图像（通过KIE API的KIE Nano Banana 2）、视频（Wan 2.6、Kli...

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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 Banana2gemini-3.1-flash-image❌ NOT nano-banana-2, 预算选择 4-13 ptsNano Banana Progemini-3-pro-image❌ NOT nano-banana-pro, 高质量 10-18 ptsSeeDream 4.5doubao-seedream-4.5✅ Recommended default, 5 ptsMidjourneymidjourney✅ 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 2.6wan2.6-t2v``wan2.6-i2v⚠️ Note -t2v/-i2v suffixIMA Video Pro (Sevio 1.0)ima-pro``ima-pro✅ IMA native quality modelIMA Video Pro Fast (Sevio 1.0-Fast)ima-pro-fast``ima-pro-fast✅ IMA native low-latency modelKling O1kling-video-o1``kling-video-o1⚠️ Note video- prefixKling 2.6kling-v2-6``kling-v2-6⚠️ Note v prefixHailuo 2.3MiniMax-Hailuo-2.3``MiniMax-Hailuo-2.3⚠️ Note MiniMax- prefixHailuo 2.0MiniMax-Hailuo-02``MiniMax-Hailuo-02⚠️ Note 02 not 2.0Google Veo 3.1veo-3.1-generate-preview``veo-3.1-generate-preview⚠️ Note -generate-preview suffixSora 2 Prosora-2-pro``sora-2-pro✅ StraightforwardPixversepixverse``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 sonicDouBao BGMGenBGM❌ NOT doubao-bgmDouBao SongGenSong❌ NOT doubao-song

语音模型 (Speech/TTS Models)#

友好名称 (Friendly Name)model_id说明 (Notes)seed-tts-2.0seed-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:

bash

# ❌ 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 / keywordsMedia typetask_type examples画 / 生成图 / 图片 / image / 画一张 / 图生图imagetext_to_image, image_to_image视频 / 生成视频 / video / 图生视频 / 文生视频videotext_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musictext_to_music语音 / 朗读 / TTS / 语音合成 / 配音 / speech / read aloud / text-to-speechspeechtext_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 / phrasingParameter (if in form_config)Notes女声 / 女声朗读 / female voicevoice_id / voice_typeUse value from form_config options男声 / 男声朗读 / male voicevoice_id / voice_typeUse value from form_config options语速快/慢 / speed up/slowspeede.g. 0.8–1.2音调 / pitchpitchIf supported大声/小声 / volumevolumeIf 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#

DomainOwnerPurposeData SentPrivacyapi.imastudio.comIMA StudioMain API (product list, task creation, task polling)Prompts, model IDs, generation params, your API keyStandard HTTPS, data processed for AI generationimapi.liveme.comIMA StudioImage/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.comAlibaba 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:

bash

# ─── 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 TypeSent to IMA?Stored Locally?User ControlPrompts (image/video/music)✅ Yes (required for generation)❌ NoNone (required)API key✅ Yes (authentication header)❌ NoSet via env varuser_id (optional CLI arg)❌ Never (local preference key only)✅ Yes (as prefs file key)Change --user-id valueModel preferences❌ No✅ Yes (~~/.openclaw)Delete anytimeGeneration 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):

bash

# 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:

json

{
  "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)

python

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

Step 2: Check user preference

python

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

Step 3: Decide which model to use

python

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)

python

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 saysAction用XXX / 换成XXX / 改用XXXSwitch to model XXX + save as preference以后都用XXX / 默认用XXX / always use XXXSave + confirm: ✅ 已记住！以后图片生成默认用 [XXX]``我喜欢XXX / 我更喜欢XXXSave 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 saysAction用最好的 / 用最合适的 / best / recommendedClear 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:

python

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:

text

🎨 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:

text

💡 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 TypeDefault Modelmodel_idversion_idCostWhytext_to_image****SeeDream 4.5doubao-seedream-4.5``doubao-seedream-4-5-2511285 ptsLatest doubao flagship, photorealistic 4Ktext_to_image (budget)Nano Banana2gemini-3.1-flash-image``gemini-3.1-flash-image4 ptsFastest and cheapest optiontext_to_image (premium)Nano Banana Progemini-3-pro-image``gemini-3-pro-image-preview10/10/18 ptsPremium quality, 1K/2K/4K optionstext_to_image (artistic)Midjourney 🎨midjourney``v68/10 ptsArtist-level aesthetics, creative stylesimage_to_image****SeeDream 4.5doubao-seedream-4.5``doubao-seedream-4-5-2511285 ptsLatest, best i2i qualityimage_to_image (budget)Nano Banana2gemini-3.1-flash-image``gemini-3.1-flash-image4 ptsCheapest optionimage_to_image (premium)Nano Banana Progemini-3-pro-image``gemini-3-pro-image-preview10 ptsPremium qualityimage_to_image (artistic)Midjourney 🎨midjourney``v68/10 ptsArtist-level aesthetics, style transfertext_to_video****Wan 2.6wan2.6-t2v``wan2.6-t2v25 pts🔥 Most popular t2v, balanced costtext_to_video (premium)Hailuo 2.3MiniMax-Hailuo-2.3``MiniMax-Hailuo-2.338 ptsHigher qualitytext_to_video (budget)Vidu Q2viduq2``viduq25 ptsLowest cost t2vimage_to_video****Wan 2.6wan2.6-i2v``wan2.6-i2v25 pts🔥 Most popular i2v, 1080Pimage_to_video (premium)Kling 2.6kling-v2-6``kling-v2-640-160 ptsPremium Kling i2vfirst_last_frame_to_video****Kling O1kling-video-o1``kling-video-o148 ptsNewest Kling reasoning modelreference_image_to_video****Kling O1kling-video-o1``kling-video-o148 ptsBest reference fidelitytext_to_music****Suno (sonic-v4)sonic``sonic25 ptsLatest Suno engine, best qualitytext_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_id s, 4-13 pts)
- Nano Banana Pro: 1K, 2K, 4K (via different attribute_id s, 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 text

❌ 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:

1. **Genre/Style:**
   - Examples: `"lo-fi hip hop"`, `"orchestral cinematic"`, `"upbeat pop"`, `"dark ambient"`, `"indie folk"`, `"electronic dance"`
2. **Tempo/BPM:**
   - Examples: `"80 BPM"`, `"fast tempo"`, `"slow ballad"`, `"moderate pace 110 BPM"`
3. **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"`
4. **Mood/Emotion:**
   - Examples: `"happy and energetic"`, `"melancholic"`, `"tense and dramatic"`, `"peaceful and calming"`
5. **Negative Tags (exclude styles):**
   - Use `negative_tags`: `"heavy metal, distortion, screaming"` to exclude unwanted elements
6. **Duration Hint:**
   - Examples: `"60 seconds"`, `"30 second loop"`, `"2 minute track"`
   - Note: Suno typically generates ~2min, not strictly controllable

**Example Suno prompts:**

text

"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

text

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`

json

```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)

python

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

Step 2: Check user preference

python

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

python

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)

python

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

Implementation:

python

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 TypeModelEstimated TimePoll EverySend Progress Everytext_to_imageSeeDream 4.525~~60s5s20sNano Banana2 💚20~~40s5s15sNano Banana Pro60~~120s5s30sMidjourney 🎨40~~90s8s25simage_to_imageSeeDream 4.525~~60s5s20sNano Banana2 💚20~~40s5s15sNano Banana Pro60~~120s5s30sMidjourney 🎨40~~90s8s25stext_to_videoWan 2.6, Hailuo 2.0/2.3, Vidu Q2, Pixverse60~~120s8s30sSeeDance 1.5 Pro, Kling 2.6, Veo 3.190~~180s8s40sKling O1, Sora 2 Pro180~~360s8s60simage_to_videoSame ranges as text_to_video—8s40sfirst_last_frame / referenceKling O1, Veo 3.1180~~360s8s60stext_to_musicDouBao BGM / Song10~~25s5s10sSuno (sonic-v5)20~~45s5s15stext_to_speech(varies by model)5~30s3s10s

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:

text

好的!来帮你画一只萌萌的猫咪 🐱

text

收到！马上为你生成一张 16:9 的风景照 🏔️

text

OK! Starting image generation with SeeDream 4.5 🎨

For videos:

text

好的!来帮你生成一段视频 🎬

text

收到！开始用 Wan 2.6 生成视频 🎥

For music:

text

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:

text

[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.

text

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

Progress formula:

text

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):

python

# 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):

python

# 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)

python

# 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:

text

✅ 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:

text

🔊 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:

text

✅ 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.ai4008 Insufficient points积分不足，请至 imaclaw.ai 购买积分Insufficient points; buy at imaclaw.aiInvalid product attribute参数配置异常，请稍后重试Configuration error, try again laterError 6006 / 6010积分或参数不匹配，请换模型或重试Points/params mismatch, try another modelresource_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 texttimeout合成超时，请稍后重试Timed out, try again laterNetwork error网络不稳定，请检查后重试Network unstable, check and retryText 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:

text

❌ [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

💡 生成新密钥: https://www.imaclaw.ai/imaclaw/apikey❌ API key is invalid or unauthorized

💡 Generate API Key: https://www.imaclaw.ai/imaclaw/apikey4008 Insufficient points 🆕Insufficient points / Error 4008❌ 积分不足，无法创建任务

💡 购买积分: https://www.imaclaw.ai/imaclaw/subscription❌ Insufficient points to create this task

💡 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 laterError 6006 (credit mismatch)Error 6006积分计算异常，系统正在修复Points calculation error, system is fixingError 6009 (no matching rule)Error 6009参数组合不匹配，已自动调整Parameter mismatch, auto-adjustedError 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 modelerror 400 (bad request)error 400 / Bad request请求参数有误，请稍后重试Invalid request parameters, please try againresource_status == 2Resource status 2 / Failed生成过程遇到问题，建议换个模型试试Generation failed, please try another modelstatus == "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?timeoutTask 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 modelNetwork error / Connection refusedConnection refused / Network error网络连接不稳定，请检查网络后重试Network connection unstable, check network and retryRate limit exceeded429 Too Many Requests / Rate limit请求过于频繁，请稍等片刻再试Too many requests, please wait a momentPrompt 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 modifyModel unavailableModel 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 modelLyrics format error (Suno only) 🎵Invalid lyrics format歌词格式有误，请调整后重试Lyrics format error, adjust and retryPrompt 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 TypeFailed ModelFirst AltSecond Alttext_to_imageSeeDream 4.5Nano Banana2 (4pts, fast)Nano Banana Pro (10-18pts, premium)text_to_imageNano Banana2SeeDream 4.5 (5pts, better quality)Nano Banana Pro (10-18pts)text_to_imageNano Banana ProSeeDream 4.5 (5pts)Nano Banana2 (4pts, budget)image_to_imageSeeDream 4.5Nano Banana2 (4pts, fast)Nano Banana Pro (10pts)image_to_imageNano Banana2SeeDream 4.5 (5pts)Nano Banana Pro (10pts)image_to_imageNano Banana ProSeeDream 4.5 (5pts)Nano Banana2 (4pts)text_to_videoKling O1Wan 2.6 (25pts)Vidu Q2 (5pts)text_to_videoGoogle Veo 3.1Kling O1 (48pts)Sora 2 Pro (122pts)text_to_videoAnyWan 2.6 (25pts, most popular)Hailuo 2.0 (5pts)image_to_videoWan 2.6Kling O1 (48pts)Hailuo 2.0 i2v (25pts)image_to_videoAnyWan 2.6 (25pts, most popular)Vidu Q2 Pro (20pts)first_last / referenceKling O1Kling 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 BGMDouBao Song (30pts)Suno (25pts, 功能最强)text_to_music 🎵DouBao SongDouBao BGM (30pts)****Suno (25pts, 功能最强)**text_to_speech 🔊(any)Query --list-models for alternativesUse 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)#

CategoryNamemodel_idCosttext_to_imageSeeDream 4.5 🌟doubao-seedream-4.55 ptstext_to_imageMidjourney 🎨midjourney8/10 pts (480p/720p)text_to_imageNano Banana2 💚gemini-3.1-flash-image4/6/10/13 ptstext_to_imageNano Banana Progemini-3-pro-image10/10/18 ptsimage_to_imageSeeDream 4.5 🌟doubao-seedream-4.55 ptsimage_to_imageMidjourney 🎨midjourney8/10 pts (480p/720p)image_to_imageNano Banana2 💚gemini-3.1-flash-image4/6/10/13 ptsimage_to_imageNano Banana Progemini-3-pro-image10 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.

ModelCustom Aspect RatioMax ResolutionSize OptionsNotesSeeDream 4.5✅ (via virtual params)4K (adaptive)8 aspect ratiosSupports 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 ratiosSupports 1:1, 16:9, 9:16, 4:3, 3:4; size via attribute_idNano Banana Pro✅ Native support 🆕4K (4096×4096)1K/2K/4K + aspect ratiosSupports 1:1, 16:9, 9:16, 4:3, 3:4; size via attribute_idMidjourney 🎨❌ (1:1 only)1024px (square)480p/720p via attribute_idFixed 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_id s
✅ 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)

Namemodel_idTypical positioningtask_type supportIMA Video Pro (Sevio 1.0)ima-proHigher quality / consistencytext_to_video, image_to_video, first_last_frame_to_video, reference_image_to_videoIMA Video Pro Fast (Sevio 1.0-Fast)ima-pro-fastLower latency / faster iterationtext_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.

CategoryNamemodel_idCost Range**text_to_video (14)Wan 2.6 🔥wan2.6-t2v25-120 ptsHailuo 2.3MiniMax-Hailuo-2.332+ ptsHailuo 2.0MiniMax-Hailuo-025+ ptsVidu Q2viduq25-70 ptsSeeDance 1.5 Prodoubao-seedance-1.5-pro20+ ptsSora 2 Prosora-2-pro122+ ptsKling O1kling-video-o148-120 ptsKling 2.6kling-v2-680+ ptsGoogle Veo 3.1veo-3.1-generate-preview70-330 ptsPixverse V5.5 / V5 / V4.5 / V4 / V3.5pixverse12-48 ptsimage_to_video (14)Wan 2.6 🔥wan2.6-i2v25-120 ptsHailuo 2.3 / 2.0MiniMax-Hailuo-2.3/0225-32 ptsVidu Q2 Providuq2-pro20-70 ptsSeeDance 1.5 Prodoubao-seedance-1.5-pro47+ ptsSora 2 Prosora-2-pro122+ ptsKling O1 / 2.6kling-video-o1/v2-648-120 ptsGoogle Veo 3.1veo-3.1-generate-preview70-330 ptsPixverse V5.5-V3.5pixverse12-48 ptsfirst_last_frame (11)**Kling O1 🌟kling-video-o148-120 ptsKling 2.6kling-v2-680+ ptsOthers (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-o148-120 ptsGoogle Veo 3.1veo-3.1-generate-preview70-330 ptsOthers (4)Vidu Q2, Pixverse V5.5/V5/V4.5—

Music Generation#

CategoryNamemodel_idCostNotestext_to_musicSunosonic25 ptssonic-v5; custom_mode, lyrics, vocal_gendertext_to_musicDouBao BGMGenBGM30 ptsBackground musictext_to_musicDouBao SongGenSong30 ptsSong 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:

FieldTypeMeaningresource_statusint or null0=处理中, 1=可用, 2=失败, 3=已删除；null 视为 0statusstring"pending" / "processing" / "success" / "failed"urlstringAudio URL when resource_status=1 (mp3/wav)duration_strstringOptional, e.g. "12s"formatstringOptional, 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

MistakeFixprompt at top levelPut prompt inside parameters[].parameters (script does this)Wrong or missing attribute_idAlways call product list first; use credit_rulesSingle pollPoll until all medias have resource_status == 1Ignoring status when resource_status=1Check status != "failed"Sending params not in form_config/credit_rulesUse 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:

RouteAuthUse Case/open/v1/``Authorization: Bearer ima_* onlyThird-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:

HeaderRequiredValueNotesAuthorization✅Bearer ima_your_api_key_hereAPI key authenticationx-app-source✅ima_skillsFixed value — identifies skill-originated requestsx_app_languagerecommendeden / zhProduct label language; defaults to en if omittedtext

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 TypeInput Required?Upload Before Create?Notestext_to_image❌ No—Prompt onlyimage_to_image✅ Yes (1 image)✅ Upload firstSingle input imagetext_to_video❌ No—Prompt onlyimage_to_video✅ Yes (1 image)✅ Upload firstSingle input imagefirst_last_frame_to_video✅ Yes (2 images)✅ Upload firstFirst + last framereference_image_to_video✅ Yes (1+ images)✅ Upload firstReference image(s)text_to_music❌ No—Prompt onlytext_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):

python

# 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)#

python

# 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.

ModelTask Typemodel_idattribute_idcreditNotestext_to_imageSeeDream 4.5text_to_imagedoubao-seedream-4.523415 ptsDefault, balancedNano Banana Pro (1K)text_to_imagegemini-3-pro-image239910 pts1024×1024Nano Banana Pro (2K)text_to_imagegemini-3-pro-image240010 pts2048×2048Nano Banana Pro (4K)text_to_imagegemini-3-pro-image240118 pts4096×4096text_to_videoWan 2.6 (720P, 5s)text_to_videowan2.6-t2v205725 ptsDefault, balancedWan 2.6 (1080P, 5s)text_to_videowan2.6-t2v205840 pts—Wan 2.6 (720P, 10s)text_to_videowan2.6-t2v205950 pts—Wan 2.6 (1080P, 10s)text_to_videowan2.6-t2v206080 pts—Wan 2.6 (720P, 15s)text_to_videowan2.6-t2v206175 pts—Wan 2.6 (1080P, 15s)text_to_videowan2.6-t2v2062120 pts—Kling O1 (5s, std)text_to_videokling-video-o1231348 ptsLatest KlingKling O1 (5s, pro)text_to_videokling-video-o1231460 pts—Kling O1 (10s, std)text_to_videokling-video-o1231596 pts—Kling O1 (10s, pro)text_to_videokling-video-o12316120 pts—text_to_musicSuno (sonic-v4)text_to_musicsonic237025 ptsDefaultDouBao BGMtext_to_musicGenBGM439930 pts—DouBao Songtext_to_musicGenSong439830 pts—All othersany—→ 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)#

MistakeErrorattribute_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[] levelPrompt ignored; wrong routingcast missing from inner parameters.parametersBilling validation failurecredit value wrong or missingError 6006model_name / model_version missingWrong backend routingSkipped product list, used table values directlyAll 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#

text

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:

text

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#

text

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

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

ParameterExampleDescriptionappUid``ima_xxx...Use IMA API key directly — no separate login neededappId``webAgentApp identifier (fixed)appKey``32jdskjdk320eewApp secret (fixed, used for sign generation)cmimToken``ima_xxx...Use IMA API key directly — same as appUidsign``117CF6CF...IM auth HMAC: SHA1("webAgent|32jdskjdk320eew|{timestamp}|{nonce}").upper()``timestamp``1772042430Unix timestamp (seconds), generated per requestnonce``CxI1FLI5ajLJZ1jlxZmegRandom nonce string, generated per requestfService``priviteFixed: storage service typefType``picture``picture for images, video, audio``fSuffix``jpegFile extension: jpeg, png, mp4, mp3``fContentType``image/jpegMIME 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:

json

{
  "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#

text

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#

python

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)#

categoryCapabilityInputtext_to_imageText → Imagepromptimage_to_imageImage → Imageprompt + input_imagestext_to_videoText → Videopromptimage_to_videoImage → Videoprompt + input_imagesfirst_last_frame_to_videoFirst+Last Frame → Videoprompt + src_img_url[2]reference_image_to_videoReference Image → Videoprompt + src_img_url[1+]text_to_musicText → Musicprompttext_to_speechText → Speechprompt (text to speak)

Detail API status values#

Each media in medias[] has two fields:

FieldTypeValuesDescription**resource_statusint (or null)0, 1, 2, 30=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``statusMeaningAction0 or nullpending / 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 11``failed失败 (status 优先)Stop, handle error2any失败Stop, handle error3any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#

text

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.

json

[
  {
    "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#

text

POST /open/v1/tasks/create

Request Structure#

json

{
  "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#

FieldRequiredDescriptiontask_type✅Must match parameters[].category``parameters[].attribute_id✅From credit_rules[].attribute_id in product listparameters[].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 serviceparameters[].parameters.cast✅{"points": N, "attribute_id": N} — mirrors creditparameters[].parameters.n✅Number of outputs (usually 1). Gateway flattens N>1 into separate resources.parameters[].parameters.input_imagesimage tasksArray of input image URLstop-level src_img_urlmulti-imageArray 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#

json

{
  "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)#

text

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

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

json

{
  "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:

json

{
  "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#

json

{
  "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#

json

{
  "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#

MistakeFixattribute_id not from credit_rulesAlways fetch product list firstcredit value wrongMust exactly match credit_rules[].points — error 6006prompt at wrong locationPut prompt in parameters[].parameters.prompt (nested), not only at top levelPolling biz_id instead of idUse id (task ID) for /tasks/detailSingle-poll instead of loopPoll until resource_status == 1 for ALL mediasMissing app / platform in parametersRequired fields — use ima / web``category mismatchparameters[].category must match top-level task_type``resource_status == 2 not handledCheck for failure, don't loop foreverstatus == "failed" ignoredresource_status=1 + status="failed" means actual failuren > 1 and only checking first mediaAll 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.

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Ima All Ai

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)#

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#

Quick Reference#

Task Types (category values)#

Detail API status values#

API 1: Product List#

API 2: Create Task#

Request Structure#

Field Reference#

N-Field Flattening (Gateway Internal Logic)#

Response#

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

Step 3: Use `fdl` as the Image URL#