# IMA Studio — All-in-One AI Creation

> All-in-One AI creation: images (SeeDream 4.5, Midjourney, Nano Banana 2), videos (Wan 2.6, Kling, Veo 3.1, Sora, Pixverse, Hailuo, SeeDance, Vidu), music (Suno sonic, DouBao), TTS (seed-tts-2.0). Unified access with intelligent model selection and cross-media workflow orchestration. BEFORE using: READ ima-knowledge-ai skill for workflow and best practices. Use for: any AI content creation including images, videos, music, text-to-speech, multi-media projects, character consistency, product demos, social campaigns, complete creative workflows.

- Skill: `dvcrn/ima-studio-all-in-one-ai-creation` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dvcrn/ima-studio-all-in-one-ai-creation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/ima-studio-all-in-one-ai-creation/raw
- Safety review: pending (external: skill-scanner PASS, skillspector WARNING)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/ima-studio-all-in-one-ai-creation

---


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

**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
```

---

## ⚠️ MANDATORY PRE-CHECK: Read Knowledge Base First!

**If ima-knowledge-ai is not installed:** Skip all "Read …" steps below; use only this SKILL's **📥 User Input Parsing** (media type → task_type) and the Recommended Defaults / model tables for each media type.

**BEFORE executing ANY multi-media generation task, you MUST:**

1. **Check for workflow complexity** — Read `ima-knowledge-ai/references/workflow-design.md` if:
   - User mentions: "MV"、"宣传片"、"完整作品"、"配乐"、"soundtrack"
   - Task spans multiple media types (image + video, video + music, etc.)
   - Complex multi-step workflows that need task decomposition

2. **Check for visual consistency needs** — Read `ima-knowledge-ai/references/visual-consistency.md` if:
   - User mentions: "系列"、"多张"、"同一个"、"角色"、"续"、"series"、"same"
   - Task involves: multiple images/videos, character continuity, product shots
   - Second+ request about same subject (e.g., "旺财在游泳" after "生成旺财照片")

3. **Check video modes** — Read `ima-knowledge-ai/references/video-modes.md` if:
   - Any video generation task
   - Need to understand: image_to_video vs reference_image_to_video difference

4. **Check model selection** — Read `ima-knowledge-ai/references/model-selection.md` if:
   - Unsure which model to use
   - Need cost/quality trade-off guidance
   - User specifies budget or quality requirements

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

**Example multi-media workflow:**
```
User: "帮我做个产品宣传MV，有背景音乐，主角是旺财小狗"

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

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

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

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

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

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

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

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

---

## 📥 User Input Parsing (Media Type & Task Routing)

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

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

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

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

**Why two domains?** IMA Studio uses a microservices architecture:
- **`api.imastudio.com`**: Core AI generation API
- **`imapi.liveme.com`**: Specialized image/video upload service (shared infrastructure)

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

### Agent Execution (Internal Reference)

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

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

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

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

### ❌ What to AVOID (Security Risks)

**Actions that could compromise security:**
- ❌ Sharing API keys publicly or in skill files
- ❌ Modifying API endpoints to unknown servers
- ❌ Disabling SSL/TLS certificate verification
- ❌ Logging sensitive user data (prompts, IDs, etc.)
- ❌ Bypassing authentication or billing mechanisms

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

### 📋 Privacy & Data Handling Summary

**What this skill does with your data:**

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

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

### 🔧 For Skill Maintainers Only

**Version control:**
- All changes must go through Git with proper version bumps (semver)
- CHANGELOG.md must document all changes
- Production deployments require code review

**File checksums (optional):**
```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 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:**
```python
del prefs[f"user_{user_id}"][task_type]
save_prefs(prefs)
```

---

## ⭐ Model Selection Priority (Image)

**Selection flow:**

1. **User preference** (if exists) → Highest priority, always respect
2. **ima-knowledge-ai skill** (if installed) → Professional recommendation based on task
3. **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_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
  ```
  ❌ 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:**
```
"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`

```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 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:**
```python
d

…(truncated)