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Merge pull request #70 from modelstudioai/feat/switch-model
feat(video): upgrade happyhorse model from 1.0 to 1.1, video-edit has not been updated and is still 1.0.
2 parents 1ffcbdd + af524e5 commit 19c4f5f

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‎CHANGELOG.md‎

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[中文版](CHANGELOG.zh.md) · [README](README.md) · [Contributing](CONTRIBUTING.md)
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## [1.4.1] - 2026-06-22
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### Changed
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- Video generation now defaults to the upgraded HappyHorse 1.1 model for better quality. The 1.0 models are still available via `--model`.
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- `bl update` now keeps the agent skill in sync across all your agent apps (Claude Code, Cursor, etc.), and refreshes it even when the CLI is already up to date.
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## [1.4.0] - 2026-06-17
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### Added

‎CHANGELOG.zh.md‎

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[English](CHANGELOG.md) · [README](README.zh.md) · [参与贡献](CONTRIBUTING.zh.md)
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## [1.4.1] - 2026-06-22
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### 变更
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- 视频生成默认升级到 HappyHorse 1.1 模型,画面质量更佳。如需使用 1.0 模型,可通过 `--model` 指定。
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- `bl update` 现在会把 agent skill 同步更新到所有 agent 应用(Claude Code、Cursor 等),即使 CLI 已是最新版本也会刷新 skill。
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## [1.4.0] - 2026-06-17
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### 新增

‎README.md‎

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@@ -27,7 +27,7 @@ Equip your AI Agent out-of-the-box with these capabilities, composable across co
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- **Text chat** — Qwen3.7-max: major gains in agentic coding, frontend coding, and vibe coding
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- **Multimodal (Omni)** — Full omni-modal support across text + image + audio + video
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- **Image generation & editing** — Qwen-Image 2.0: pro text rendering, photorealism, strong semantic adherence, multi-image composition
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- **Video generation & editing** — HappyHorse-1.0 series: text-/image-/reference-to-video and natural-language video editing (up to 9-image reference)
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- **Video generation & editing** — happyhorse-1.1 series: text-/image-/reference-to-video and natural-language video editing (up to 9-image reference)
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- **Speech synthesis & recognition** — CosyVoice streaming TTS, voice cloning from 5–20s samples; FunAudio-ASR covers 30 languages including 7 Chinese dialects and 20+ Mandarin accents
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- **Image & video understanding** — Qwen-VL: long-form video analysis, chart/document parsing, visual reasoning, multilingual OCR
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@@ -54,7 +54,7 @@ Equip your AI Agent out-of-the-box with these capabilities, composable across co
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A complete **2-minute, 16:9 cinematic short film** — produced end-to-end from a single natural-language sentence, with **zero manual editing**. This showcase demonstrates how an AI Agent can compose a multi-step creative pipeline by orchestrating three primitives:
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- **[Qwen Code](https://github.com/QwenLM/qwen-code)** — the agentic coding model that interprets the user's intent and drives the workflow
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- **[Aliyun Model Studio CLI](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)** — invokes **HappyHorse 1.0**, Aliyun Model Studio's text-/image-/reference-to-video generation model
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- **[Aliyun Model Studio CLI](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)** — invokes **HappyHorse 1.1**, Aliyun Model Studio's text-/image-/reference-to-video generation model
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- **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** — handles scene decomposition, storyboarding, shot continuity, and final stitching
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### The single prompt
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1. **Qwen Code** parses the request, plans the narrative beats, and decides which tools to call.
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2. The **spark-video Skill** breaks the story into shots, writes per-shot prompts, and enforces visual continuity (characters, lighting, palette, lens language).
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3. **`bl video generate`** dispatches each shot to **HappyHorse 1.0** in parallel.
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3. **`bl video generate`** dispatches each shot to **HappyHorse 1.1** in parallel.
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4. The skill stitches all clips back together into a single 16:9 / ~2-min deliverable.
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No timeline scrubbing. No frame-by-frame editing. Just one sentence → one video.

‎README.zh.md‎

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@@ -27,7 +27,7 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_
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- **文本对话** — Qwen3.7-max：Agentic coding、前端编程、Vibe coding 等能力显著增强
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- **全模态对话** — 文本 + 图像 + 音频 + 视频全模态支持
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- **图像生成与编辑** — Qwen-Image 2.0：专业文字渲染、真实质感、强语义遵循、多图合成
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- **视频生成与编辑** — HappyHorse-1.0 系列，支持文生 / 图生 / 参考生（最多 9 张图参考）/ 自然语言视频编辑
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- **视频生成与编辑** — happyhorse-1.1 系列，支持文生 / 图生 / 参考生（最多 9 张图参考）/ 自然语言视频编辑
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- **语音合成与识别** — CosyVoice 实时流式合成，5-20s 样本即可克隆；FunAudio-ASR 覆盖 30 种语种，含汉语七大方言与 20+ 口音官话
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- **图像与视频理解** — Qwen-VL：长视频解析、复杂图表与文档识别、视觉推理、多语种 OCR
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@@ -54,7 +54,7 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_
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一部完整的 **2 分钟、16:9 电影感短片** —— 由一句自然语言端到端生成,**全程零手动剪辑**。这个示例展示了 AI Agent 如何把三个基础能力编排成一条多步创作流水线:
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- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— Agentic coding 模型,解析用户意图、驱动整个工作流
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- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.0**,百炼的文生/图生/参考生视频模型
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- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.1**,百炼的文生/图生/参考生视频模型
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- **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** —— 负责场景拆分、分镜设计、镜头连贯性和最终拼接
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### 唯一的提示词
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1. **Qwen Code** 解析需求、规划叙事节奏,决定要调用哪些工具。
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2. **spark-video Skill** 把故事拆成镜头、为每个镜头写提示词,并保证视觉连贯性(角色、光线、色调、镜头语言)。
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3. **`bl video generate`** 把每个镜头并行下发给 **HappyHorse 1.0**。
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3. **`bl video generate`** 把每个镜头并行下发给 **HappyHorse 1.1**。
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4. Skill 把所有片段拼成最终的 16:9 / 约 2 分钟成片。
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没有时间线拖拽,没有逐帧剪辑。一句话 → 一部短片。

‎packages/cli/README.md‎

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@@ -27,7 +27,7 @@ Equip your AI Agent out-of-the-box with these capabilities, composable across co
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- **Text chat** — Qwen3.7-max: major gains in agentic coding, frontend coding, and vibe coding
2828
- **Multimodal (Omni)** — Full omni-modal support across text + image + audio + video
2929
- **Image generation & editing** — Qwen-Image 2.0: pro text rendering, photorealism, strong semantic adherence, multi-image composition
30-
- **Video generation & editing** — HappyHorse-1.0 series: text-/image-/reference-to-video and natural-language video editing (up to 9-image reference)
30+
- **Video generation & editing** — happyhorse-1.1 series: text-/image-/reference-to-video and natural-language video editing (up to 9-image reference)
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- **Speech synthesis & recognition** — CosyVoice streaming TTS, voice cloning from 5–20s samples; FunAudio-ASR covers 30 languages including 7 Chinese dialects and 20+ Mandarin accents
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- **Image & video understanding** — Qwen-VL: long-form video analysis, chart/document parsing, visual reasoning, multilingual OCR
3333

@@ -54,7 +54,7 @@ Equip your AI Agent out-of-the-box with these capabilities, composable across co
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A complete **2-minute, 16:9 cinematic short film** — produced end-to-end from a single natural-language sentence, with **zero manual editing**. This showcase demonstrates how an AI Agent can compose a multi-step creative pipeline by orchestrating three primitives:
5555

5656
- **[Qwen Code](https://github.com/QwenLM/qwen-code)** — the agentic coding model that interprets the user's intent and drives the workflow
57-
- **[Aliyun Model Studio CLI](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)** — invokes **HappyHorse 1.0**, Aliyun Model Studio's text-/image-/reference-to-video generation model
57+
- **[Aliyun Model Studio CLI](https://bailian.console.aliyun.com/cli?source_channel=cli_github&)** — invokes **HappyHorse 1.1**, Aliyun Model Studio's text-/image-/reference-to-video generation model
5858
- **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** — handles scene decomposition, storyboarding, shot continuity, and final stitching
5959

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### The single prompt
@@ -67,7 +67,7 @@ A complete **2-minute, 16:9 cinematic short film** — produced end-to-end from
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1. **Qwen Code** parses the request, plans the narrative beats, and decides which tools to call.
6969
2. The **spark-video Skill** breaks the story into shots, writes per-shot prompts, and enforces visual continuity (characters, lighting, palette, lens language).
70-
3. **`bl video generate`** dispatches each shot to **HappyHorse 1.0** in parallel.
70+
3. **`bl video generate`** dispatches each shot to **HappyHorse 1.1** in parallel.
7171
4. The skill stitches all clips back together into a single 16:9 / ~2-min deliverable.
7272

7373
No timeline scrubbing. No frame-by-frame editing. Just one sentence → one video.

‎packages/cli/README.zh.md‎

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -27,7 +27,7 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_
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- **文本对话** — Qwen3.7-max：Agentic coding、前端编程、Vibe coding 等能力显著增强
2828
- **全模态对话** — 文本 + 图像 + 音频 + 视频全模态支持
2929
- **图像生成与编辑** — Qwen-Image 2.0：专业文字渲染、真实质感、强语义遵循、多图合成
30-
- **视频生成与编辑** — HappyHorse-1.0 系列，支持文生 / 图生 / 参考生（最多 9 张图参考）/ 自然语言视频编辑
30+
- **视频生成与编辑** — happyhorse-1.1 系列，支持文生 / 图生 / 参考生（最多 9 张图参考）/ 自然语言视频编辑
3131
- **语音合成与识别** — CosyVoice 实时流式合成，5-20s 样本即可克隆；FunAudio-ASR 覆盖 30 种语种，含汉语七大方言与 20+ 口音官话
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- **图像与视频理解** — Qwen-VL：长视频解析、复杂图表与文档识别、视觉推理、多语种 OCR
3333

@@ -54,7 +54,7 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_
5454
一部完整的 **2 分钟、16:9 电影感短片** —— 由一句自然语言端到端生成,**全程零手动剪辑**。这个示例展示了 AI Agent 如何把三个基础能力编排成一条多步创作流水线:
5555

5656
- **[Qwen Code](https://github.com/QwenLM/qwen-code)** —— Agentic coding 模型,解析用户意图、驱动整个工作流
57-
- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.0**,百炼的文生/图生/参考生视频模型
57+
- **[阿里云百炼 CLI](https://github.com/modelstudioai/cli/)** —— 调用 **HappyHorse 1.1**,百炼的文生/图生/参考生视频模型
5858
- **[spark-video Skill](https://github.com/JohnKeating1997/spark-video)** —— 负责场景拆分、分镜设计、镜头连贯性和最终拼接
5959

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### 唯一的提示词
@@ -65,7 +65,7 @@ _专为 AI Agent 打造，每个命令均可作为结构化工具调用。_
6565

6666
1. **Qwen Code** 解析需求、规划叙事节奏,决定要调用哪些工具。
6767
2. **spark-video Skill** 把故事拆成镜头、为每个镜头写提示词,并保证视觉连贯性(角色、光线、色调、镜头语言)。
68-
3. **`bl video generate`** 把每个镜头并行下发给 **HappyHorse 1.0**。
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3. **`bl video generate`** 把每个镜头并行下发给 **HappyHorse 1.1**。
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4. Skill 把所有片段拼成最终的 16:9 / 约 2 分钟成片。
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没有时间线拖拽,没有逐帧剪辑。一句话 → 一部短片。

‎packages/cli/package.json‎

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{
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"name": "bailian-cli",
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"version": "1.4.0",
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"version": "1.4.1",
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"description": "CLI for Aliyun Model Studio (DashScope) AI Platform.",
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"keywords": [
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"agent",

‎packages/cli/src/commands/video/generate.ts‎

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export default defineCommand({
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name: "video generate",
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description:
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"Generate a video from text or image (happyhorse-1.0-t2v / happyhorse-1.0-i2v / wan2.6-t2v)",
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"Generate a video from text or image (happyhorse-1.1-t2v / happyhorse-1.1-i2v / wan2.6-t2v)",
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usage: "bl video generate --prompt <text> [--image <url>] [flags]",
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options: [
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{
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flag: "--model <model>",
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description: "Model ID (default: happyhorse-1.0-t2v, or happyhorse-1.0-i2v with --image)",
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description: "Model ID (default: happyhorse-1.1-t2v, or happyhorse-1.1-i2v with --image)",
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},
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{ flag: "--prompt <text>", description: "Video description", required: true },
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{ flag: "--image <url>", description: "Input image URL for image-to-video generation" },
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const model =
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(flags.model as string) ||
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((flags.image as string) ? "happyhorse-1.0-i2v" : "happyhorse-1.0-t2v");
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((flags.image as string) ? "happyhorse-1.1-i2v" : "happyhorse-1.1-t2v");
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const format = detectOutputFormat(config.output);
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const imageUrl = flags.image as string | undefined;
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input: {
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prompt: prompt!,
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negative_prompt: (flags.negativePrompt as string) || undefined,
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// i2v models (happyhorse-1.0-i2v) require input.media with type 'first_frame'
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// i2v models (happyhorse-1.1-i2v) require input.media with type 'first_frame'
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...(resolvedImageUrl
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? { media: [{ type: "first_frame" as const, url: resolvedImageUrl }] }
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: {}),

‎packages/cli/src/commands/video/ref.ts‎

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export default defineCommand({
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description:
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"Reference-to-video generation (happyhorse-1.0-r2v / wan2.6-r2v): multi-subject, multi-shot with voice",
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"Reference-to-video generation (happyhorse-1.1-r2v / wan2.6-r2v): multi-subject, multi-shot with voice",
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usage: "bl video ref --prompt <text> --image <url>... [--ref-video <url>...] [flags]",
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options: [
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{ flag: "--model <model>", description: "Model ID (default: happyhorse-1.0-r2v)" },
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{ flag: "--model <model>", description: "Model ID (default: happyhorse-1.1-r2v)" },
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{
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flag: "--prompt <text>",
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description: "Video description with reference markers (image1, video1, etc.)",
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const imageVoices = (flags.imageVoice as string[] | undefined) || [];
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const videoVoices = (flags.videoVoice as string[] | undefined) || [];
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const model = (flags.model as string) || "happyhorse-1.0-r2v";
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const model = (flags.model as string) || "happyhorse-1.1-r2v";
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const format = detectOutputFormat(config.output);
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// --- Resolve file URLs (auto-upload local files) ---

‎packages/cli/src/pipeline/steps/bl-api.ts‎

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const model = input.model || (input.image ? "happyhorse-1.0-i2v" : "happyhorse-1.0-t2v");
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const model = input.model || (input.image ? "happyhorse-1.1-i2v" : "happyhorse-1.1-t2v");
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let resolvedImageUrl: string | undefined;
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if (input.image) {

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