IMA Studio Video Generation

IMA Video AI Creation

📋 Before you install

• Credentials: This skill requires an IMA API key at runtime (IMA_API_KEY or --api-key). The key is sent to api.imastudio.com (main API) and imapi.liveme.com (image uploads). Obtain keys at https://imastudio.com. Use a scoped or test key if you want to limit exposure.
• Local files: The skill reads local image files you provide (for image-to-video); it also writes logs under ~/.openclaw/logs/ima_skills/ and preferences to ~/.openclaw/memory/ima_prefs.json. Do not point it at sensitive paths.
• Cross-skill reads: If ima-knowledge-ai is installed, this skill instructs the agent to read that skill's reference files (~/.openclaw/skills/ima-knowledge-ai/references/*) for workflow and visual-consistency guidance. If you do not have or trust that skill, skip those steps and use this skill's built-in defaults and tables.

⚠️ 重要：模型 ID 参考

CRITICAL: When calling the script, you MUST use the exact model_id (second/third column), NOT the friendly model name. Do NOT infer model_id from the friendly name.

Quick Reference Table:

User Input Variations Handled by Agent:

• "万" / "万2.6" / "Wan" → Wan 2.6 → wan2.6-t2v / wan2.6-i2v
• "可灵" / "可灵O1" / "Kling O1" → kling-video-o1
• "可灵2.6" / "Kling 2.6" → kling-v2-6
• "海螺" / "海螺2.3" / "Hailuo" → MiniMax-Hailuo-2.3
• "Veo" / "Google Veo" → veo-3.1-generate-preview

How to get the correct model_id:

1. Check this table first
2. Use --list-models --task-type text_to_video (or image_to_video)
3. Refer to command examples below

Example:

# ❌ WRONG: Inferring from friendly name
--model-id kling-o1

# ✅ CORRECT: Using exact model_id from table
--model-id kling-video-o1

⚠️ MANDATORY PRE-CHECK: Read Knowledge Base First!

If ima-knowledge-ai is not installed: Skip all "Read …" steps below; use only this SKILL's default models and the 📥 User Input Parsing tables for task_type, model_id, and parameters.

BEFORE executing ANY video generation task, you MUST:

1. CRITICAL: Understand video modes — Read ima-knowledge-ai/references/video-modes.md:

   • image_to_video = first frame to video (输入图成为第1帧)
   • reference_image_to_video = reference appearance to video (输入图是视觉参考，不是第1帧)
   • These are COMPLETELY DIFFERENT concepts!
   • Wrong mode choice = wrong result

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

   • User mentions: "系列"、"分镜"、"同一个"、"角色"、"续"、"多个镜头"
   • Task involves: multi-shot videos, character continuity, scene consistency
   • Second+ request about same subject (e.g., "旺财在游泳" after "生成旺财照片")

3. Check workflow/model/parameters — Read relevant ima-knowledge-ai/references/ sections if:

   • Complex multi-step video production
   • Unsure which model to use
   • Need parameter guidance (duration, resolution, reference strength)

Why this matters:

• AI video generation defaults to 独立生成 (independent generation) each time
• Without reference images, "same character/scene" will look completely different
• Text-to-video CANNOT maintain visual consistency — must use image-based modes

Example failure case:

User: "生成一只小狗，叫旺财" 
  → You: generate dog image A

User: "生成旺财在游泳的视频"
  → ❌ Wrong: text_to_video "狗在游泳" (new dog, different from A)
  → ✅ Right: read visual-consistency.md + video-modes.md → 
             use image_to_video with image A as first frame

How to check:

# Step 1: Read knowledge base
read("~/.openclaw/skills/ima-knowledge-ai/references/video-modes.md")
read("~/.openclaw/skills/ima-knowledge-ai/references/visual-consistency.md")

# Step 2: Identify if reference image needed
if "same subject" or "series" or "character continuity":
    # Use image-based mode with previous result as reference
    reference_image = previous_generation_result
    
    # Choose mode based on requirement
    if "reference becomes first frame":
        use_image_to_video(prompt, reference_image)
    else:
        use_reference_image_to_video(prompt, reference_image, reference_strength=0.8)
else:
    # OK to use text-to-video
    use_text_to_video(prompt)

No exceptions — if you skip this check and generate visually inconsistent results, that's a bug.

📥 User Input Parsing (Model & Parameter Recognition)

Purpose: So that any agent (Claude or other models) parses user intent consistently, follow these rules when deriving task_type, model_id, and parameters from natural language. Do not guess — normalize first, then map.

1. User phrasing → task_type

When in doubt: "把图动起来" / "图动" → image_to_video; "参考这张图" / "按这张风格" → reference_image_to_video.

2. Model name / alias → model_id (normalize then lookup)

Normalize user wording (case-insensitive, ignore spaces), then map to model_id:

If the user names a model not in the table, match by Name in the "Supported Models" tables below and use its model_id for the chosen task_type.

3. User phrasing → duration / resolution / aspect_ratio

If the user does not specify duration/resolution/aspect_ratio, use form_config defaults from the product list for the chosen model (e.g. 5s, 720P or 1080P, 16:9).

⚙️ How This Skill Works

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

• Sends your prompt to IMA's servers (two domains, see below)
• Uses --user-id only locally as a key for storing your model preferences
• Returns a video URL when generation is complete

🌐 Network Endpoints Used

This skill connects to two domains owned by IMA Studio for complete functionality:

Why two domains?

• api.imastudio.com: IMA's video generation API (handles task orchestration)
• imapi.liveme.com: IMA's media storage infrastructure (handles large file uploads)
• Both services are owned and operated by IMA Studio

Privacy implications:

• Your IMA API key is sent to both domains for authentication
• Image files are uploaded to imapi.liveme.com to obtain CDN URLs (for image_to_video, first_last_frame_to_video, reference_image_to_video tasks)
• Video generation happens on api.imastudio.com using the CDN URLs
• For text_to_video tasks (no image input), only api.imastudio.com is contacted

Security verification:

# List all network endpoints in the code:
grep -n "https://" scripts/ima_video_create.py

# Expected output:
# 57: DEFAULT_BASE_URL = "https://api.imastudio.com"
# 58: DEFAULT_IM_BASE_URL = "https://imapi.liveme.com"

If you're concerned about the two-domain architecture:

1. Review IMA Studio's privacy policy at https://imastudio.com/privacy
2. Contact IMA technical support to confirm domain ownership: support@imastudio.com
3. Use a test/scoped API key first (see security notice below)

⚠️ Credential Security Notice

Your IMA API key is sent to TWO domains:

1. api.imastudio.com — Main video generation API
2. imapi.liveme.com — Image upload service (only when using image-to-video tasks)

Both domains are owned by IMA Studio, but if you're concerned about credential exposure:

✅ Best practices:

• Use a test/scoped API key for initial testing (create at https://imastudio.com/api-keys)
• Set a low quota (e.g., 100 credits) for the test key
• Rotate your key after testing if needed
• Contact IMA support to confirm domain ownership: support@imastudio.com

❌ Do NOT:

• Use a production key if you're uncomfortable with the two-domain architecture
• Share your API key with others
• Commit your API key to version control

What gets sent to IMA servers:

• ✅ Your video prompt/description
• ✅ Model selection (Wan/Hailuo/Kling/etc.)
• ✅ Video parameters (duration, resolution, etc.)
• ✅ Image files (for image-to-video tasks, uploaded to imapi.liveme.com)
• ✅ IMA API key (for authentication to both domains)
• ❌ 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)

Agent Execution (Internal Reference)

Note for users: You can review the script source at scripts/ima_video_create.py anytime.
The agent uses this script to simplify API calls. Network requests go to two IMA Studio domains: api.imastudio.com (API) and imapi.liveme.com (image uploads).

Use the bundled script internally to ensure correct parameter construction:

# Text to video
python3 {baseDir}/scripts/ima_video_create.py \
  --api-key  $IMA_API_KEY \
  --task-type text_to_video \
  --model-id  wan2.6-t2v \
  --prompt   "a puppy runs across a sunny meadow, cinematic" \
  --user-id  {user_id} \
  --output-json

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

✅ Local images: --input-images accepts both HTTPS URLs and local file paths. Local files are automatically uploaded to IMA CDN by the script (no need to host them first).

# First-last frame to video
python3 {baseDir}/scripts/ima_video_create.py \
  --api-key      $IMA_API_KEY \
  --task-type    first_last_frame_to_video \
  --model-id     kling-video-o1 \
  --prompt       "smooth transition" \
  --input-images https://example.com/first.jpg https://example.com/last.jpg \
  --user-id      {user_id} \
  --output-json

The script outputs JSON — parse it to get the result URL and pass it to the user via the UX protocol messages below.

🚨 CRITICAL: How to send the video to user (Feishu/Discord/IM)

# ✅ CORRECT: Use the remote URL directly
video_url = json_output["url"]
message(
    action="send",
    media=video_url,  # Direct HTTPS URL → renders inline video player
    caption="✅ 视频生成成功！\n• 模型：[Model Name]\n• 耗时：[X]s\n• 消耗积分：[N pts]"
)

# ❌ WRONG: Download to local file first
# curl -o /tmp/video.mp4 {video_url}
# message(media="/tmp/video.mp4")  # Shows as file attachment (📎 path), NOT playable

Why this matters:

• ✅ Remote URL → Feishu renders inline video player with ▶ button
• ❌ Local file path → Feishu shows file attachment (📎 /tmp/...), not playable

Always use the remote URL directly. Never download the video to local storage.

Overview

🛡️ Model-Specific Notes

Sora 2 Pro — Content Safety Policy

⚠️ Important: Sora 2 Pro has strict content safety policies (OpenAI policy).

Content Restrictions:

• ❌ Cannot generate: people, celebrities, IP assets (e.g., Mickey Mouse)
• ❌ Strict prompt moderation
• ✅ Safe themes: landscapes, abstract patterns, animals, nature scenes

Recommended Prompts:

• ✅ "A sunset over mountains"
• ✅ "Abstract colorful flowing patterns"
• ✅ "A bird flying through clouds"

Avoid:

• ❌ "A person walking" (people)
• ❌ "Mickey Mouse dancing" (IP asset)
• ❌ Celebrity names or recognizable figures

If your prompt is rejected, try using more abstract or nature-focused descriptions.

Call IMA Open API to create AI-generated videos. 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_video_create.py and ima_logger.py anytime
• ✅ Verify network calls: Network requests go to two IMA Studio domains: api.imastudio.com (API) and imapi.liveme.com (image uploads). See "🌐 Network Endpoints Used" section above for full details.
• ✅ Inspect local data: View ~/.openclaw/memory/ima_prefs.json and log files
• ✅ Control privacy: Delete preferences/logs anytime, or disable file writes (see below)

Configuration allowed:

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

Data control:

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

⚠️ Advanced Users: Fork & Modify

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

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

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

❌ What to AVOID (Security Risks)

Actions that could compromise security:

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

Why this matters:

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

📋 Privacy & Data Handling Summary

What this skill does with your data:

Privacy recommendations:

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

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

🔧 For Skill Maintainers Only

Version control:

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

File checksums (optional):

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

If users report issues, verify file integrity first.

🧠 User Preference Memory

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

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

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

Model Selection Flow (Every Generation)

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

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

Step 2: Check user preference

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

Step 3: Decide which model to use

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

Step 4: Check for mismatch (for later hint)

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

When to Write (User Explicit Preference ONLY)

✅ Save preference when user explicitly specifies a model:

❌ 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:

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

⭐ Model Selection Priority

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.
Always default to the newest and most popular model. Do NOT default to the cheapest.

Selection guide (production credits, sorted by popularity):

• 🔥 Most popular text-to-video → Wan 2.6 (25 pts, balanced cost & quality)
• Premium text-to-video → Hailuo 2.3 (38 pts, higher quality)
• Budget text-to-video → Vidu Q2 (5 pts) or Hailuo 2.0 (12 pts)
• 🔥 Most popular image_to_video → Wan 2.6 (25 pts)
• first_last_frame / reference → Kling O1 (48 pts)
• User specifies cheapest → Vidu Q2 (5 pts) — only if explicitly requested

🆕 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 ✅

Why This Matters: Some Pixverse models (V5.5/V5/V4) have inconsistent form_config in the Product List API response. The auto-inference ensures all Pixverse versions work correctly without requiring users to manually specify the model parameter.

💬 User Experience Protocol (IM / Feishu / Discord)

Video generation takes 1~6 minutes. Never let users wait in silence.
Always follow all 4 steps below, every single time.

🚫 Never Say to Users

Only tell users: model name · estimated time · credits · result URL · plain-language status.

Estimated Generation Time per Model

estimated_max_seconds = upper bound of the range (e.g. 180 for Kling 2.1 Master, 360 for Kling O1).

Step 1 — Pre-Generation Notification (with Cost Transparency)

Before calling the create API, send this message immediately:

🎬 开始生成视频，请稍候…
• 模型：[Model Name]
• 预计耗时：[X ~ Y 秒]（约 [X/60 ~ Y/60] 分钟）
• 消耗积分：[N pts]

视频生成需要一定时间，我会每隔一段时间汇报进度 🙏

Cost transparency (critical for video):

• For balanced/default models (25 pts): "使用 Wan 2.6（25 积分，最新 Wan）"
• For premium models (>50 pts):
  • If auto-selected: "使用 Wan 2.6（25 积分）。若需更高质量可选 Kling 2.1 Master（150 积分）"
  • If user explicit: "使用高端模型 Kling 2.1 Master（150 积分），质量最佳"
• For budget (user explicit): "使用 Vidu Q2（5 积分，最省钱选项）"

Adapt language to match the user. For expensive models (>50 pts), always mention cheaper alternatives unless user explicitly requested premium quality.

Adapt language to match the user. English → 🎬 Starting video generation, this may take [X~Y] seconds. I'll update you on progress…

Step 2 — Progress Updates

Poll the task detail API every 8s.
Send a progress update message every [Send Progress Every] seconds per the table above.

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

Progress formula:

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

• Cap at 95% — never show 100% until the API returns success
• If elapsed > estimated_max: keep P at 95% and append 「快了，稍等一下…」
• Example: elapsed=120s, max=180s → P = min(95, floor(120/180*100)) = min(95, 66) = 66%
• Example: elapsed=200s, max=180s → P = 95%（冻结 + 「快了，稍等一下…」）

Step 3 — Success Notification (Push video via message tool)

When task status = success:

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

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

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

[视频描述]"""

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

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

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

Mismatch hint example:

✅ 视频生成成功！
• 模型：Midjourney（你的偏好模型）
• 耗时：45s
• 消耗积分：8 pts

💡 提示：当前任务也许用 Wan 2.6 也会不错（写实风格更合适，25 pts）

[视频]

Important:

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

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

# Send link message immediately after
message(
    action="send",
    message=f"""🔗 视频链接（方便复制分享）：
{video_url}"""
)

Critical:

• Use the remote HTTPS URL directly as media parameter. Do NOT download to local file first.
• Send video first (for inline playback), then send link text (for copying/sharing).

For Feishu: Direct video URL → inline video player with play button. Local file path → file attachment (📎 path).

Step 4 — Failure Notification

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

❌ 视频生成失败
• 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).

Generic fallback (when error is unknown):

• Chinese: 视频生成遇到问题，请稍后重试或换个模型试试
• English: Video generation encountered an issue, please try again or use another model

Best Practices:

1. Focus on user action: Tell users what to do next, not what went wrong technically
2. Be reassuring: Use phrases like "建议换个模型试试" instead of "生成失败了"
3. Avoid blame: Never say "你的提示词有问题" → say "提示词需要调整一下"
4. Provide alternatives: Always suggest 1-2 alternative models in the failure message
5. Video-specific:
   • For Sora content policy errors, recommend Wan 2.6 or Kling O1 (more permissive)
   • For timeout errors, recommend faster models (Vidu Q2, Hailuo 2.0)
   • For image input errors, suggest checking image format (HTTPS URL, valid JPEG/PNG)
6. 🆕 Include actionable links (v1.0.8+): For 401/4008 errors, provide clickable links to API key generation or credit purchase pages

🆕 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 (resolution: 1080P → 720P → 540P, duration: 15 → 10 → 5)
• 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
• 🆕 Pixverse Model Parameter (v1.0.7+): Auto-inference for missing model parameter (V5.5/V5/V4)

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

Failure fallback table:

Supported Models

⚠️ Production Environment: Model availability validated against production API on 2026-02-27.

text_to_video (14 models)

image_to_video (14 models)

first_last_frame_to_video (10 models)

reference_image_to_video (9 models)

Production Notes (2026-02-27):

• ✅ Active models: 14 t2v, 14 i2v, 10 first_last_frame, 9 reference_image
• 🔥 Most popular: Wan 2.6 (both t2v and i2v)
• 🌟 Recommended defaults: Wan 2.6 (balanced), Kling O1 (premium with audio)

Environment

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

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

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

⚠️ 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 miss

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