genmedia-workflow

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Genmedia workflow production

Genmedia工作流制作

Runtime: All endpoint calls run via the genmedia CLI. See the
genmedia
skill for command syntax; run
genmedia init
once if not yet installed.
Use this skill when a single model call is not enough. A workflow is a planned sequence of genmedia calls with clear inputs, outputs, dependencies, and quality checks.
Load references as needed:
  • references/pipeline-patterns.md
  • references/node-rules.md
  • references/utility-endpoints.md
  • references/recipes.md
  • model-routing
    for creative model defaults
Use
model-routing
for default creative model choices. Still inspect schemas, check pricing when cost matters, and use exact endpoint fields.
运行环境: 所有端点调用均通过genmedia CLI执行。有关命令语法,请查看
genmedia
技能;若尚未安装,请先运行
genmedia init
当单次模型调用无法满足需求时,请使用此技能。工作流是一系列规划好的genmedia调用序列,具备明确的输入、输出、依赖关系和质量检查机制。
按需加载参考文档:
  • references/pipeline-patterns.md
  • references/node-rules.md
  • references/utility-endpoints.md
  • references/recipes.md
  • model-routing
    用于创意模型默认配置
使用
model-routing
选择默认的创意模型。仍需检查模式、在关注成本时查看定价,并使用精确的端点字段。

Inputs to collect

需要收集的输入信息

Ask only for missing information that changes the pipeline:
  • Final deliverable: image set, video, clips, audio, subtitles, dataset, social batch, product campaign, storyboard, style exploration.
  • Source assets: product images, character references, first frames, video, audio, logo, transcript, brand guide.
  • Runtime limits: quality target, cost sensitivity, number of variants, duration, aspect ratios, deadline.
  • Continuity requirements: product identity, character face, scene layout, voice, color grade.
  • Model preference: ask the user only when quality, speed, cost, or audio tradeoffs are not clear from the brief.
仅询问会改变流水线的缺失信息:
  • 最终交付物:图像集、视频、剪辑、音频、字幕、数据集、社交平台批量内容、产品推广活动素材、分镜脚本、风格探索素材。
  • 源资产:产品图片、角色参考图、首帧画面、视频、音频、标志、文字稿、品牌指南。
  • 运行限制:质量目标、成本敏感度、变体数量、时长、宽高比、截止日期。
  • 连续性要求:产品标识、角色面部特征、场景布局、语音、色彩分级。
  • 模型偏好:仅当从需求简述中无法明确质量、速度、成本或音频方面的权衡时,才向用户询问。

Core workflow

核心工作流

  1. Write a short pipeline graph before running anything.
    text
    input assets -> planner -> generation nodes -> utility nodes -> QA -> final outputs
  2. Resolve endpoints for each role. Check known endpoint IDs first.
    bash
    genmedia models --endpoint_id openai/gpt-image-2 --json
    genmedia models --endpoint_id fal-ai/nano-banana-pro/edit --json
    genmedia models --endpoint_id bytedance/seedance-2.0/image-to-video --json
    genmedia models --endpoint_id xai/grok-imagine-video/image-to-video --json
    genmedia models --endpoint_id veed/fabric-1.0 --json
    Use text search only as fallback discovery for roles not covered by
    model-routing
    or the utility reference:
    bash
    genmedia models "image generation product photography" --json
    genmedia models "image editing reference preservation" --json
    genmedia models "image to video" --json
    genmedia models "subtitle video utility" --json
    genmedia docs "fal.ai workflow utility endpoints" --json
  3. Inspect every endpoint before use.
    bash
    genmedia schema <endpoint_id> --json
    genmedia pricing <endpoint_id> --json
  4. Upload local files once and reuse returned URLs.
    bash
    genmedia upload ./input.png --json
    genmedia upload ./voiceover.wav --json
  5. Run each node with JSON output. Use async for slow generation.
    bash
    genmedia run <endpoint_id> --<field> "<value>" --json
    genmedia run <endpoint_id> --<field> "<value>" --async --json
    genmedia status <endpoint_id> <request_id> --download "./outputs/workflow/{request_id}_{index}.{ext}" --json
  6. For downstream nodes, pass the media URL from the previous
    result
    when it is available. If you only have a local file path, upload it first.
  7. Download final assets with templates that cannot collide.
    bash
    --download "./outputs/workflow/{request_id}_{index}.{ext}"
  8. Return a compact manifest.
    json
    {
      "goal": "short deliverable description",
      "nodes": [
        {
          "id": "shot_01",
          "role": "image_to_video",
          "endpoint_id": "...",
          "request_id": "...",
          "input_urls": ["..."],
          "output_urls": ["..."],
          "downloaded_files": ["..."],
          "notes": "continuity or defect notes"
        }
      ],
      "final_files": ["..."]
    }
  1. 在运行任何操作前,编写简短的流水线图。
    text
    输入资产 -> 规划器 -> 生成节点 -> 工具节点 -> 质量检查 -> 最终输出
  2. 为每个角色解析端点。首先检查已知的endpoint_id。
    bash
    genmedia models --endpoint_id openai/gpt-image-2 --json
    genmedia models --endpoint_id fal-ai/nano-banana-pro/edit --json
    genmedia models --endpoint_id bytedance/seedance-2.0/image-to-video --json
    genmedia models --endpoint_id xai/grok-imagine-video/image-to-video --json
    genmedia models --endpoint_id veed/fabric-1.0 --json
    仅当
    model-routing
    或工具参考未覆盖对应角色时,才使用文本搜索作为后备发现方式:
    bash
    genmedia models "image generation product photography" --json
    genmedia models "image editing reference preservation" --json
    genmedia models "image to video" --json
    genmedia models "subtitle video utility" --json
    genmedia docs "fal.ai workflow utility endpoints" --json
  3. 使用前检查每个端点。
    bash
    genmedia schema <endpoint_id> --json
    genmedia pricing <endpoint_id> --json
  4. 上传本地文件一次,重复使用返回的URL。
    bash
    genmedia upload ./input.png --json
    genmedia upload ./voiceover.wav --json
  5. 以JSON输出运行每个节点。对慢速生成任务使用异步模式。
    bash
    genmedia run <endpoint_id> --<field> "<value>" --json
    genmedia run <endpoint_id> --<field> "<value>" --async --json
    genmedia status <endpoint_id> <request_id> --download "./outputs/workflow/{request_id}_{index}.{ext}" --json
  6. 对于下游节点,在前一个节点的
    result
    可用时,传入媒体URL。如果只有本地文件路径,请先上传。
  7. 使用不会冲突的模板下载最终资产。
    bash
    --download "./outputs/workflow/{request_id}_{index}.{ext}"
  8. 返回简洁的清单。
    json
    {
      "goal": "简短的交付物描述",
      "nodes": [
        {
          "id": "shot_01",
          "role": "image_to_video",
          "endpoint_id": "...",
          "request_id": "...",
          "input_urls": ["..."],
          "output_urls": ["..."],
          "downloaded_files": ["..."],
          "notes": "连续性或缺陷说明"
        }
      ],
      "final_files": ["..."]
    }

Pipeline rules

流水线规则

  • Keep one node responsible for one clear transformation.
  • Fan out independent generation, crop, upscale, subtitle, or variation nodes.
  • Keep sequential chains only when node B needs node A output.
  • For consistency, prefer reference/edit or image-to-video over independent text-only generations.
  • For default creative model choices, follow
    model-routing
    unless the user names a model.
  • Use utility endpoints for deterministic work: crop, resize, grid, composite, audio merge, subtitle, speed change, compression.
  • Record endpoint, schema-relevant parameters, request ID, and output path for every node.
  • If a 422 error occurs, read
    validation_errors
    , inspect schema again, then fix the exact field.
  • 每个节点只负责一项明确的转换任务。
  • 并行处理独立的生成、裁剪、放大、字幕或变体节点。
  • 仅当节点B需要节点A的输出时,才使用顺序链。
  • 为保证一致性,优先使用参考/编辑或图像转视频功能,而非独立的纯文本生成。
  • 除非用户指定模型,否则遵循
    model-routing
    选择默认的创意模型。
  • 使用工具端点处理确定性工作:裁剪、调整大小、网格布局、合成、音频合并、字幕添加、速度调整、压缩。
  • 记录每个节点的端点、与模式相关的参数、请求ID和输出路径。
  • 如果出现422错误,请查看
    validation_errors
    ,再次检查模式,然后修复精确的字段。

Quality gate

质量检查关卡

Before returning, verify:
  • The pipeline graph matches the requested deliverable.
  • No generation model was chosen from memory alone.
  • All local source files were uploaded before use.
  • Final files were saved through
    --download
    .
  • Utility endpoints used exact schema fields.
  • Continuity anchors were repeated where identity or product fidelity matters.
  • Each node output is either accepted, retried, or marked with a defect.
If the workflow becomes too complex, stop expanding and ask the user to choose between faster iteration, higher fidelity, or broader variation.
返回结果前,验证以下内容:
  • 流水线图与请求的交付物匹配。
  • 所有生成模型均不是仅凭记忆选择的。
  • 所有本地源文件在使用前均已上传。
  • 最终文件已通过
    --download
    保存。
  • 工具端点使用了精确的模式字段。
  • 在涉及标识或产品保真度的地方,重复使用了连续性锚点。
  • 每个节点的输出要么被接受、重试,要么标记为有缺陷。
如果工作流变得过于复杂,请停止扩展并询问用户在更快迭代、更高保真度或更多变体之间进行选择。