Skill: allenfancy-gan/ima-sevio-ai (version 1.0.2)

--- name: IMA Sevio AI Generation version: 1.1.0 category: file-generation author: IMA Studio (imastudio.com) keywords: imastudio, video generation, text-to-video, image-to-video, IMA, Ima Sevio, Sevio, IMA Video Pro, IMA Video Pro Fast argument-hint: "[text prompt or media URL]" description: > IMA model generation with exactly two Sevio models: Ima Sevio 1.0 and Ima Sevio 1.0-Fast. Supports text-to-video, image-to-video, first-last-frame, and reference-image workflows. Keeps the same API flow, reflection retry mechanism, and interface contract as ima-video-ai. Requires IMA API key. requires: env: - IMA_API_KEY primaryCredential: IMA_API_KEY credentialNote: > IMA_API_KEY is sent to api.imastudio.com for product/task APIs and to imapi.liveme.com only when local media must be uploaded before task creation. persistence: readWrite: - ~/.openclaw/memory/ima_prefs.json - ~/.openclaw/logs/ima_skills/ retention: Logs are auto-cleaned after 7 days; preferences remain until user deletes them. instructionScope: crossSkillReadOptional: - ~/.openclaw/skills/ima-knowledge-ai/references/* --- # IMA Sevio AI Creation ## 🎯 Skill Capabilities 本技能是 **Ima Sevio 视频生成专用入口**。对外不是“模型 ID 映射器”，而是两档清晰的视频生成能力： - **Ima Sevio 1.0（质量优先）** - 定位：正式出片、质感优先、镜头语言要求高的任务。 - 适合：广告镜头、角色叙事、风格化短片、需要更高一致性的镜头段落。 - **Ima Sevio 1.0-Fast（速度优先）** - 定位：快速打样、批量试风格、创意迭代。 - 适合：提案阶段 A/B 版本、镜头预演、低延迟验证。 ### Ability Positioning (Top-Tier Video Model Class) 在公开视频能力维度上，Sevio 系列可按以下能力理解（用于用户预期管理）： - **时序与主体一致性**：在连续动作和主体保持上具备高稳定性表现。 - **镜头语言控制**：支持对推拉摇移、节奏和运动感的描述驱动。 - **多模态条件理解**：可结合文本与参考素材（image/reference/first-last frame）进行生成。 - **高质量输出取向**：面向高分辨率、高观感视频产出（以当次产品规则为准）。 ### Workflow Coverage - `text_to_video`：文本直接生成视频。 - `image_to_video`：以首帧图驱动动态生成。 - `first_last_frame_to_video`：以首尾帧约束过渡与收束。 - `reference_image_to_video`：以参考图约束风格/主体特征。 ### Input & Reliability - `prompt` 负责主体、动作、镜头、风格与节奏描述。 - `--input-images` 支持单个/多个输入，统一以字符串数组语义处理。 - 本地文件先上传再生成，远程 HTTP(S) 链接直接使用。 - 运行时动态匹配产品规则（credit_rules/form_config）并内置自动重试与降级策略。 - 轮询上限 40 分钟；若无明确报错但超时，会提示前往创作记录页查看。 ### Output - 返回可直接分发的视频结果 URL（含封面信息），可直接用于消息卡片或播放器。 --- ## ✨ Expected Outcomes & Boundaries (Outcomes, Timing, Scope Limits) ### Expected Outcomes - **质量预期（Sevio 1.0）**：在画面稳定、主体一致、镜头控制上，目标体验达到行业同级高水平能力带。 - **速度预期（Sevio 1.0-Fast）**：在保持可用画质与控制力的前提下，提供更快周转，适合多轮迭代。 - **模式预期**：图生/参考/首尾帧模式，相比纯文生视频更有利于主体连续性与风格一致性。 ### Timing Expectations | 模型（用户展示） | 典型耗时 | |---|---:| | Ima Sevio 1.0（IMA Video Pro） | 120~300s | | Ima Sevio 1.0-Fast（IMA Video Pro Fast） | 60~120s | 轮询超时上限：`40 分钟（2400s）`。 ### Capability Boundaries (Avoid Misunderstanding) - 本技能只做 **视频生成链路**，不负责后期剪辑、自动分镜编排、成片包装。 - 结果质量受提示词、参考素材质量、当次产品规则与积分策略影响，不保证每次一致。 - 仅支持本技能白名单模型；其他模型名会被拦截或映射后执行。 --- ## ⚠️ 内部调用：模型 ID 参考（不对用户展示） **User-facing rule:** In user messages, always use **Ima Sevio 1.0 / Ima Sevio 1.0-Fast** names. Do not expose raw `model_id` unless the user explicitly asks for technical details. **CRITICAL:** When calling the script, you MUST use exact `model_id` values. For `ima-sevio-ai`, only these two are allowed: | Friendly Name | model_id | Notes | |---|---|---| | IMA Pro | `ima-pro` | Default quality model | | IMA Pro Fast | `ima-pro-fast` | Faster / lower-latency model | | Ima Sevio 1.0 | `ima-pro` | Display-name alias | | Ima Sevio 1.0-Fast | `ima-pro-fast` | Display-name alias | ### 模型中文介绍（可公开口径） **IMA Video Pro（Ima Sevio 1.0）** 面向高质量视频创作的主力模型。 在时序一致性、镜头语言控制、多模态条件理解等核心维度上，能力定位达到行业同级高水平视频模型能力。 适合对质感、稳定性和镜头可控性要求更高的生产任务。 **核心优势（公开可查）** - 高帧率时序一致性 - 精准镜头语言控制 - 图像 / 音频 / 文本多模态输入 - 2K 级输出画质 **IMA Video Pro Fast（Ima Sevio 1.0-Fast）** 面向高频迭代场景的加速模型版本。 在保持主体可辨识与镜头可控的基础上，优先缩短生成时延，适合提案打样、快速试风格和实时创作流程。 **Rules:** - Do NOT infer model IDs from other IMA skills. - Do NOT use any model outside this allowlist. - If user asks for other models, map to one of the two allowed models with explanation. - Alias input `Ima Sevio 1.0` is auto-mapped to `ima-pro`. - Alias input `Ima Sevio 1.0-Fast` is auto-mapped to `ima-pro-fast`. --- ## 📚 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 better mode selection and consistency guidance. Recommended optional reads: 1. **Understand video modes** — Read `ima-knowledge-ai/references/video-modes.md`: - `image_to_video` = input image becomes frame 1 - `reference_image_to_video` = input image is visual reference, not frame 1 2. **Check 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" - multi-shot continuity, character consistency, repeated subject 3. **Check workflow/model/parameters** — Read related references when unsure about mode or parameters. **Why this matters:** - AI generation is independent by default. - Text-only generation cannot preserve visual continuity reliably. - Wrong mode choice causes wrong results. --- ## 📥 User Input Parsing (Model & Parameter Recognition) ### 1) User phrasing → `task_type` | User intent | task_type | |---|---| | Only text | `text_to_video` | | One image as first frame | `image_to_video` | | One image as reference | `reference_image_to_video` | | Two images as first+last frame | `first_last_frame_to_video` | ### 2) User phrasing → `model_id` Normalize case-insensitively and ignore spaces: | User says | model_id | |---|---| | `ima-pro`, `pro`, `专业版`, `高质量` | `ima-pro` | | `ima-pro-fast`, `fast`, `极速`, `快速` | `ima-pro-fast` | | `Ima Sevio 1.0` | `ima-pro` | | `Ima Sevio 1.0-Fast` | `ima-pro-fast` | | "默认" / "推荐" / "自动" | `ima-pro` | If user explicitly asks "faster", prefer `ima-pro-fast`. If user explicitly asks "best quality", prefer `ima-pro`. ### 3) User phrasing → duration / resolution / aspect_ratio | User says | Parameter | Normalized value | |---|---|---| | 5秒 / 5s | duration | 5 | | 10秒 / 10s | duration | 10 | | 15秒 / 15s | duration | 15 | | 横屏 / 16:9 | aspect_ratio | 16:9 | | 竖屏 / 9:16 | aspect_ratio | 9:16 | | 方形 / 1:1 | aspect_ratio | 1:1 | | 720P / 720p | resolution | 720P | | 1080P / 1080p | resolution | 1080P | | 4K / 4k | resolution | 4K (only if model/rule supports) | If unspecified, use product `form_config` defaults. --- ## ⚙️ How This Skill Works This skill uses bundled script `scripts/ima_video_create.py` and keeps original API workflow: - product list query - parameter resolution - create task - poll task detail - return video URL ### 🌐 Network Endpoints Used | Domain | Purpose | What's Sent | |---|---|---| | `api.imastudio.com` | task create + status polling | prompt, model params, task IDs, API key | | `imapi.liveme.com` | image upload (when image input exists) | image bytes, API key | **Privacy notes:** - API key is sent to both domains for auth. - `--user-id` is local-only and not sent to IMA servers. - Local files: preferences and logs in `~/.openclaw`. --- ## Agent Execution (Internal) ```bash # Text to video python3 {baseDir}/scripts/ima_video_create.py \ --api-key $IMA_API_KEY \ --task-type text_to_video \ --model-id ima-pro \ --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 ima-pro-fast \ --prompt "camera slowly zooms in" \ --input-images https://example.com/photo.jpg \ --user-id {user_id} \ --output-json # 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 ima-pro \ --prompt "smooth transition" \ --input-images https://example.com/first.jpg https://example.com/last.jpg \ --user-id {user_id} \ --output-json ``` `--input-images` accepts remote HTTP(S) links and local file paths. Local image files are uploaded to OSS first; non-local HTTP(S) links are assigned directly. CLI form is space-separated arguments; equivalent JSON form is: `["https://example.com/ref1.jpg","https://example.com/ref2.jpg"]`. --- ## 🚨 CRITICAL: How to send video to user Always send remote URL directly: ```python video_url = json_output["url"] message(action="send", media=video_url, caption="✅ 视频生成成功") ``` Do NOT download to local file before sending. --- ## 🧠 User Preference Memory Storage: `~/.openclaw/memory/ima_prefs.json` ```json { "user_{user_id}": { "text_to_video": {"model_id": "ima-pro", "model_name": "Ima Sevio 1.0", "credit": 0, "last_used": "..."}, "image_to_video": {"model_id": "ima-pro-fast", "model_name": "Ima Sevio 1.0-Fast", "credit": 0, "last_used": "..."}, "first_last_frame_to_video": {"model_id": "ima-pro", "model_name": "Ima Sevio 1.0", "credit": 0, "last_used": "..."}, "reference_image_to_video": {"model_id": "ima-pro", "model_name": "Ima Sevio 1.0", "credit": 0, "last_used": "..."} } } ``` Model selection priority: 1. user preference 2. knowledge-ai recommendation 3. fallback default (`ima-pro`) ### Defaults | Task | Default | Alt (fast) | |---|---|---| | text_to_video | `ima-pro` | `ima-pro-fast` | | image_to_video | `ima-pro` | `ima-pro-fast` | | first_last_frame_to_video | `ima-pro` | `ima-pro-fast` | | reference_image_to_video | `ima-pro` | `ima-pro-fast` | --- ## 💬 User Experience Protocol (IM / Feishu / Discord) ### Estimated Generation Time | Model | Estimated Time | Poll Every | Send Progress Every | |---|---:|---:|---:| | ima-pro | 120~300s | 8s | 45s | | ima-pro-fast | 60~120s | 8s | 30s | Polling timeout upper bound: **40 minutes** (`2400s`). Use: - Step 1: pre-generation notice (model/time/credits) - Step 2: progress updates - Step 3: success push (video first, then shareable link) - Step 4: failure message with actionable retry options Progress formula: ```text P = min(95, floor(elapsed_seconds / estimated_max_seconds * 100)) ``` --- ## Step 4 — Failure Notification Translate technical errors to user language. For 401/4008 include links: - API key: https://www.imaclaw.ai/imaclaw/apikey - credits: https://www.imaclaw.ai/imaclaw/subscription ### Enhanced Error Handling (Reflection) The script keeps the same reflection mechanism (up to 3 retries): - `500` → parameter degradation - `6009` → auto-complete missing params from matched rules - `6010` → reselect matching credit rule - timeout → actionable guidance ### Fallback suggestion table | Failed model | First alt | Second alt | |---|---|---| | `ima-pro` | `ima-pro-fast` | `ima-pro` (retry with downgraded params) | | `ima-pro-fast` | `ima-pro` | `ima-pro-fast` (retry with defaults) | | unknown | `ima-pro` | `ima-pro-fast` | --- ## Supported Models Only two models are exposed by this skill: - `ima-pro` - `ima-pro-fast` Supported categories: - `text_to_video` - `image_to_video` - `first_last_frame_to_video` - `reference_image_to_video` > Attribute rules, points, and exact parameter combinations must be queried at runtime from product list. --- ## Environment Base URL: `https://api.imastudio.com` Required headers: - `[REDACTED_SECRET]` - `x-app-source: ima_skills` - `x_app_language: en` (or `zh`) --- ## ⚠️ MANDATORY: Always Query Product List First You MUST call `/open/v1/product/list` before creating tasks. `attribute_id` and `credit` must match current rule set. Common failures if skipped: - invalid product attribute - insufficient points - `6006`, `6010` --- ## Core Flow ```text 1) GET /open/v1/product/list 2) (if image input) upload image(s) -> HTTPS CDN URL(s) 3) POST /open/v1/tasks/create 4) POST /open/v1/tasks/detail (poll every 8s) ``` --- ## Image Upload For image tasks, source images must resolve to public HTTPS URLs. Bundled script supports local file path and uploads automatically. --- ## API 1: Product List `GET /open/v1/product/list?app=ima&platform=web&category=<task_type>` Use type=3 leaf nodes to read: - `model_id` - `id` (`model_version`) - `credit_rules[]` - `form_config[]` --- ## API 2: Create Task `POST /open/v1/tasks/create` ### text_to_video (example) ```json { "task_type": "text_to_video", "enable_multi_model": false, "src_img_url": [], "parameters": [ { "attribute_id": 1234, "model_id": "ima-pro", "model_name": "Ima Sevio 1.0", "model_version": "ima-pro", "app": "ima", "platform": "web", "category": "text_to_video", "credit": 25, "parameters": { "prompt": "a puppy dancing happily", "duration": 5, "resolution": "1080P", "aspect_ratio": "16:9", "n": 1, "input_images": [], "cast": {"points": 25, "attribute_id": 1234} } } ] } ``` For image tasks, keep top-level `src_img_url` and nested `input_images` consistent. --- ## API 3: Task Detail `POST /open/v1/tasks/detail` with `{ "task_id": "..." }` Status interpretation: - `resource_status`: `0/null` processing, `1` ready, `2` failed, `3` deleted - Stop only when all medias have `resource_status == 1` and none failed --- ## Common Mistakes - Polling too fast (use 8s) - Missing required nested fields (`prompt`, `cast`, `n`) - Credit/attribute mismatch (`6006` / `6010`) - Inconsistent `src_img_url` and `input_images` - Wrong mode choice (`image_to_video` vs `reference_image_to_video`) --- ## Python Example ```python import time import requests BASE_URL = "https://api.imastudio.com" [REDACTED_SECRET]" HEADERS = { "Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json", "x-app-source": "ima_skills", "x_app_language": "en", } ALLOWED = {"ima-pro", "ima-pro-fast"} def get_products(category: str) -> list: r = requests.get( f"{BASE_URL}/open/v1/product/list", headers=HEADERS, params={"app": "ima", "platform": "web", "category": category}, ) r.raise_for_status() nodes = r.json().get("data", []) leaves = [] def walk(items): for n in items: if n.get("type") == "3" and n.get("model_id") in ALLOWED: leaves.append(n) walk(n.get("children") or []) walk(nodes) return leaves def create_video_task(task_type: str, prompt: str, product: dict, src_img_url=None, **extra) -> str: src_img_url = src_img_url or [] rule = product["credit_rules"][0] defaults = {f["field"]: f["value"] for f in product.get("form_config", []) if f.get("value") is not None} params = { "prompt": prompt, "n": 1, "input_images": src_img_url, "cast": {"points": rule["points"], "attribute_id": rule["attribute_id"]}, **defaults, } params.update(extra) payload = { "task_type": task_type, "enable_multi_model": False, "src_img_url": src_img_url, "parameters": [{ "attribute_id": rule["attribute_id"], "model_id": product["model_id"], "model_name": product["name"], "model_version": product["id"], "app": "ima", "platform": "web", "category": task_type, "credit": rule["points"], "parameters": params, }], } r = requests.post(f"{BASE_URL}/open/v1/tasks/create", headers=HEADERS, json=payload) r.raise_for_status() return r.json()["data"]["id"] def poll(task_id: str, interval: int = 8, timeout: int = 600) -> dict: deadline = time.time() + timeout while time.time() < deadline: r = requests.post(f"{BASE_URL}/open/v1/tasks/detail", headers=HEADERS, json={"task_id": task_id}) r.raise_for_status() task = r.json().get("data", {}) medias = task.get("medias", []) if medias: rs = lambda m: m.get("resource_status") if m.get("resource_status") is not None else 0 if any(rs(m) in (2, 3) or (m.get("status") == "failed") for m in medias): raise RuntimeError(f"Task failed: {task_id}") if all(rs(m) == 1 for m in medias): return task time.sleep(interval) raise TimeoutError(f"Task timed out: {task_id}") ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)