PROMPT RADAR / VIDEO视频叙事
One of our creative technologists @hellorob just ran a full production p
One of our creative technologists @hellorob just ran a full production pipeline without touching a single node - using Comfy MCP + Claude Fable 5. Production pipeline: → Shot pulled from web → scene detection + trimming
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编辑先看
它为什么值得进入灵感库
它最有价值的部分，是把一个模糊想法推进到了可以被模型执行的程度。 把一段描述拆成起始状态、动作变化、镜头响应和结束画面，连续性比形容词更重要。
01 / 原始方法
动作与镜头 Prompt
One of our creative technologists @hellorob just ran a full production pipeline without touching a single node - using Comfy MCP + Claude Fable 5. Production pipeline: → Shot pulled from web → scene detection + trimming → DepthAnything V3 + OpenPose ref video → frame scrubbing → character swap via gpt-image-2 → Seedance 2.0 prompts + gens → 3-panel comparison The preprocessing and prompt writing - usually the hardest, most time-consuming part - handled automatically. This is what the MCP was built for. Try the Comfy MCP today👇
02 / 复刻路径
不要一次改完所有变量
用 GPT Image 2 复刻时，建议先单独生成稳定首帧，再把动作拆成“开始、变化、收束”三段；每段只保留一个主要运动。
- 主体：One of our creative technologists @hellorob just ran a full production pipeline without touching a single node - using Comfy MCP + Claude Fable 5. Production pipeline: → Shot pulled from web → scene detection + trimming
- 视觉方向：实时 AI 视频案例；查看原帖媒体与作者说明
- 镜头与构图：按原帖镜头流程执行；查看原帖媒体与工作流
- 交付约束：auto，保留原作者方法与归属；复刻时避免水印、低清、畸变、主体漂移和无关文字。
负向约束
保留原作者方法与归属；复刻时避免水印、低清、畸变、主体漂移和无关文字。
模型建议
GPT Image 2 / Seedance / Veo / Kling。先用原始 Prompt 建立基准结果，再根据模型对自然语言、镜头运动或文字排版的能力做局部改写。
来源与编辑原则