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4k-vfx

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4k-vfx

4K VFX

Maps a video-to-video VFX move onto the Pika MCP's Seedance 4K reference-to-video. The user hands you a clip + the change they want; you read EVERY frame (locally extracted with ffmpeg and tiled into contact sheets) and understand the audio (speech + music + SFX + ambience), author a Seedance-faithful prompt that locks the original (face, gestures, camera move) and time-codes the change to the right beat, then re-render the same shot in 4K with the VFX baked in. One run produces one 4K clip for one requested change.
将视频转视频的VFX效果映射到Pika MCP的Seedance 4K参考转视频功能上。用户提供一段视频片段以及想要的修改效果；你需要读取每一帧（通过ffmpeg本地提取并拼接成联系表）并理解音频（语音、音乐、音效和环境音），编写符合Seedance规范的提示词，锁定原始画面的（面部、手势、镜头运动），并将修改效果与正确的节拍时间码对齐，然后重新渲染同一镜头的4K版本，将VFX效果内置其中。一次运行针对一项修改请求生成一个4K视频片段。

How it works — why reading EVERY frame (and the audio) is the mechanism (do not skip the reading pass)

工作原理——为何必须读取每一帧（和音频）（请勿跳过读取步骤）

Seedance treats the reference video as a motion/style anchor, NOT a pixel-locked base — it does not copy the input frame-for-frame, it re-generates the shot guided by your prompt. So fidelity comes from the PROMPT: the more exhaustively the prompt describes the original (subject identity, exact gestures, camera move, framing, lighting, palette, wardrobe) and the audio (dialogue + lip-sync, music/SFX beats), the more the 4K output reads as the same shot with the change layered on. Reading EVERY frame — not a sample — is how you build that exhaustive description: you locally extract all frames with ffmpeg, tile them 25-to-a-contact-sheet (5×5), and analyze every sheet so the prompt is built from the entire timeline rather than a handful of stills. Skip the read (or read only a sample) and the output drifts — a different face, a different camera move, a different room, a missed motion beat. The reading pass — all frames + the audio — IS the skill.
Seedance将参考视频视为运动/风格锚点，而非像素锁定的基础——它不会逐帧复制输入内容，而是根据你的提示词重新生成镜头。因此，还原度取决于提示词：提示词越详尽地描述原始内容（主体身份、精确手势、镜头运动、构图、光线、色调、服装）以及音频（对话与唇形同步、音乐/音效节拍），4K输出就越像是在原始镜头基础上叠加了修改效果。读取每一帧而非采样帧，是构建详尽描述的关键：你需要通过本地ffmpeg提取所有帧，将每25帧拼接成一张联系表（5×5网格），并分析每张联系表，这样提示词就能基于整个时间线而非少量静态帧构建。跳过读取步骤（或仅读取采样帧）会导致输出内容偏离——比如面部不同、镜头运动不同、场景不同、错过运动节拍。读取步骤——所有帧+音频——就是这项技能的核心。

Prerequisites

前置条件

pika MCP available, and local 
ffmpeg
 in the run environment (used to extract every frame and build the contact sheets). Two inputs from the user: (1) a video clip (a local file or a public URL) and (2) the VFX change they want made to it. Tools used: 
upload_asset
, 
probe_media
, 
analyze_media
, 
transcribe_audio
, 
estimate_cost
, 
generate_reference_video
, 
task_status
(frame extraction + tiling is done locally with ffmpeg, not an MCP tool).
需具备Pika MCP，且运行环境中拥有本地
ffmpeg
（用于提取所有帧并构建联系表）。需要用户提供两个输入：(1) 视频片段（本地文件或公共URL）和**(2) 想要添加的VFX修改效果**。使用的工具包括：
upload_asset
、
probe_media
、
analyze_media
、
transcribe_audio
、
estimate_cost
、
generate_reference_video
、
task_status
（帧提取与拼接通过本地ffmpeg完成，而非MCP工具）。

Stage 0 — Gather inputs (settle this first)

阶段0——收集输入（先完成此步骤）

You need exactly two things before generating:
  1. The video — the original clip to transform. If they gave a local file, you will upload it (Step 1); if they gave a public URL, keep it.
  2. The change / VFX — what should be different in the output (e.g. "turn the plaza into an open desert on a finger snap", "make it snow", "set the room on fire behind me", "morph my jacket into glowing armor").
If either is missing, ask for it before doing anything else — do not invent a change, and do not proceed on a clip you cannot reach. Once both are in hand, say in one line what you're about to do ("Reading every frame of your clip to build the desert-on-snap 4K prompt…") and run Steps 1–5 without further check-ins — but stop at the Step 6 agreement gate before generating: the 4K render is the expensive, irreversible step, and it only fires after the user approves the prompt and cost.
生成前你需要明确获取以下两项内容：
  1. 视频——需要转换的原始片段。如果用户提供的是本地文件，你需要上传它（步骤1）；如果是公共URL，直接使用即可。
  2. 修改/VFX效果——输出内容中需要改变的部分（例如：“打响指时将广场变成开阔沙漠”、“添加下雪效果”、“在我身后点燃房间”、“将我的夹克变形为发光盔甲”）。
如果任意一项缺失，请在进行其他操作前向用户索要——请勿自行编造修改效果，也不要处理无法获取的视频片段。获取两项内容后，用一句话告知用户你即将执行的操作（例如：“正在读取你视频的每一帧，以构建打响指变沙漠的4K提示词……”），然后无需进一步确认即可执行步骤1-5，但在执行步骤6生成前必须经过确认环节：4K渲染成本高昂且不可逆转，只有在用户批准提示词和成本后才能启动。

Step 1 — Upload the clip (
upload_asset
)

步骤1——上传视频片段（
upload_asset
）

Real video must be uploaded — inline base64 only works for tiny assets (<~3MB), so any actual clip goes through the presigned-upload path.
  1. Call 
    upload_asset
    with 
    filename
    , 
    mime_type
    (
    video/mp4
    for mp4), and 
    size_bytes
    (the file's byte size).
  2. PUT the raw bytes to the returned 
    presigned_url
    .
  3. Keep the returned 
    public_url
    → 
    state.clip_url
    .
If the user already gave a public URL, skip this step and set 
state.clip_url
to it.
真实视频必须上传——内嵌base64仅适用于极小资源（<约3MB），因此任何实际视频片段都需通过预签名上传路径处理。
  1. 调用
    upload_asset
    ，传入
    filename
    、
    mime_type
    （MP4文件为
    video/mp4
    ）和
    size_bytes
    （文件的字节大小）。
  2. 将原始字节内容PUT到返回的
    presigned_url
    中。
  3. 保存返回的
    public_url
    至
    state.clip_url
    。
如果用户已提供公共URL，跳过此步骤，直接将
state.clip_url
设为该URL。

Step 2 — Probe (
probe_media
)

步骤2——探测媒体信息（
probe_media
）

Call 
probe_media
on 
state.clip_url
to read duration, fps, dimensions, and aspect ratio. Use these to: (a) sanity-check the all-frame extraction in Step 3 (duration × fps ≈ total frame count ≈ 25 × number of sheets), and (b) choose the output 
aspect_ratio
and 
duration
here, matched to the source — the Step-6 gate quotes this choice to the user and Step 7 fires with it.
调用
probe_media
读取
state.clip_url
的时长、帧率、分辨率和宽高比。这些信息用于：(a) 验证步骤3中全帧提取的合理性（时长×帧率≈总帧数≈25×联系表数量），(b) 在此选择与源视频匹配的输出
aspect_ratio
和
duration
——步骤6的确认环节会向用户说明此选择，步骤7将按此参数执行。

Step 3 — Extract EVERY frame locally and tile into contact sheets (ffmpeg)

步骤3——本地提取每一帧并拼接成联系表（ffmpeg）

Read the whole video, not a sample. Use local ffmpeg to extract every frame at the source rate (no drops, no exceptions), then tile them into legible contact sheets at 25 frames per sheet (5×5 grid) so each 
analyze_media
call covers 25 consecutive frames at once.
Work in a relative working directory (do not use an absolute system temp path):
mkdir -p frames sheets
ffmpeg -i INPUT -vsync 0 frames/f_%05d.png            # every frame, no drops
Then build the 25-per-sheet contact sheets (5×5), scaling each frame down so the sheet is legible and stamping the frame index onto each tile:
ffmpeg -i INPUT -vf "scale=480:-1,drawtext=text='%{n}':x=4:y=4:fontsize=20:fontcolor=yellow:box=1:boxcolor=black@0.5,tile=5x5" sheets/sheet_%03d.png
drawtext=text='%{n}'
stamps the frame index on each tile so the analysis can reference exact frames; 
tile=5x5
groups 25 consecutive frames per sheet. If 
drawtext
is unavailable in the local ffmpeg build, fall back to plain 
scale=480:-1,tile=5x5
. 
INPUT
is the local clip (or a local copy of the URL). Confirm the sheet count lines up with the probe in Step 2 — every frame must land on a sheet, no exceptions.
读取完整视频，而非采样帧。使用本地ffmpeg以源帧率提取每一帧（无丢帧、无例外），然后将每25帧拼接成一张清晰的联系表（5×5网格），这样每次
analyze_media
调用就能一次性分析25帧连续画面。
在相对工作目录中操作（请勿使用绝对系统临时路径）：
mkdir -p frames sheets
ffmpeg -i INPUT -vsync 0 frames/f_%05d.png            # 提取每一帧，无丢帧
然后构建每25帧一张的联系表（5×5），缩小每帧尺寸以保证联系表清晰，并在每个帧块上标记帧索引：
ffmpeg -i INPUT -vf "scale=480:-1,drawtext=text='%{n}':x=4:y=4:fontsize=20:fontcolor=yellow:box=1:boxcolor=black@0.5,tile=5x5" sheets/sheet_%03d.png
drawtext=text='%{n}'
用于在每个帧块上标记帧索引，以便分析时能引用精确帧；
tile=5x5
将25帧连续画面分组为一张联系表。如果本地ffmpeg版本不支持
drawtext
，则退化为仅使用
scale=480:-1,tile=5x5
。
INPUT
为本地视频片段（或URL的本地副本）。确认联系表数量与步骤2的探测结果一致——每一帧都必须包含在联系表中，无例外。

Step 4 — Read EVERY sheet + understand the audio (
upload_asset
, 
analyze_media
, 
transcribe_audio
)

步骤4——读取所有联系表+理解音频（
upload_asset
、
analyze_media
、
transcribe_audio
）

This is the fidelity step, and it has two mandatory halves: read all the frames and understand the audio. Neither is optional.
这是保证还原度的步骤，包含两个强制部分：读取所有帧和理解音频。两者缺一不可。

4a — Read all the frames (every sheet)

4a——读取所有帧（所有联系表）

For each contact sheet from Step 3 (cover ALL of them — every frame is on a sheet, no exceptions): upload it with 
upload_asset
(the sheets are images, so upload to get a URL), then call 
analyze_media
on that sheet URL with a 
query
that reads the full progression across those 25 frames — pulling out everything the prompt must lock:
  • Subject / identity — who/what is in frame; face, hair, build, distinguishing features.
  • Exact motion & gestures — what the subject does, beat by beat, referencing the stamped frame indices (e.g. "raises right hand around frame 0048, snaps fingers around frame 0072").
  • Camera move — static / pan / push-in / handheld / orbit, and its timing.
  • Framing — shot size (close/medium/wide), subject position, headroom.
  • Lighting — direction, hardness, color temperature, time of day.
  • Palette — dominant colors, mood.
  • Wardrobe / props / setting — clothing, objects, background.
Carry the read across all sheets so the entire timeline is understood start-to-end, not just a sampled middle.
针对步骤3生成的每张联系表（覆盖全部——每一帧都在联系表中，无例外）：使用
upload_asset
上传（联系表为图片，需上传以获取URL），然后调用
analyze_media
分析该联系表URL，
query
需包含这25帧的完整变化过程——提取提示词中需要锁定的所有信息：
  • 主体/身份——画面中的人物/物体；面部、发型、体型、显著特征。
  • 精确动作与手势——主体的动作，逐节拍描述，并引用标记的帧索引（例如：“约第0048帧抬起右手，约第0072帧打响指”）。
  • 镜头运动——静态/摇镜头/推镜头/手持镜头/环绕镜头，及其时间点。
  • 构图——景别（近景/中景/远景）、主体位置、头部空间。
  • 光线——方向、硬度、色温、时段。
  • 色调——主导颜色、氛围。
  • 服装/道具/场景——衣物、物品、背景。
遍历所有联系表进行读取，确保整个时间线从开始到结束都被理解，而非仅采样中间部分。

4b — Understand the audio (mandatory, not just speech)

4b——理解音频（强制要求，不仅是语音）

Always read the audio — it is required, not optional. Two passes:
  • transcribe_audio
    for speech + timestamps (the words and when they're said), so the prompt can preserve dialogue and lip-sync beats.
  • analyze_media
    on the audio (or the video URL) to understand the NON-speech audio too — music, sound effects, ambience, tone, and rhythm/beats.
Fold the audio understanding into the prompt where it matters — preserve dialogue + lip-sync if anyone speaks, and time the VFX change to an audio beat / sound cue when the change should land on the music or a sound (e.g. snap, hit, downbeat) rather than a bare timestamp.
Assemble the all-frame read and the audio read into a single faithful description of the original — that description is the raw material for Step 5.
必须读取音频——这是必填项，而非可选项。分为两个步骤：
  • 使用
    transcribe_audio
    获取语音+时间戳（内容及其出现时间），以便提示词能保留对话和唇形同步节拍。
  • 调用
    analyze_media
    分析音频（或视频URL），理解非语音音频——音乐、音效、环境音、音调、节奏/节拍。
将音频理解融入提示词的相关部分——如果有人物对话则保留对话+唇形同步，并且当修改效果需要配合音乐或声音（例如响指、撞击、重拍）时，将VFX修改效果与音频节拍/声音提示时间码对齐，而非仅使用裸时间戳。
将全帧读取结果和音频读取结果整合为对原始内容的完整描述——该描述是步骤5的素材。

Step 5 — Write the Seedance prompt (the authoring step, NEVER skip it)

步骤5——编写Seedance提示词（创作步骤，请勿跳过）

Compose ONE prompt = a faithful, exhaustive description of the original (the locked elements) + the user's requested change, time-coded + a reference to the input clip via the 
@Video1
 token. Be thorough: every detail you read in Step 4 that you omit is a detail Seedance is free to change.
Template — fill the 
[SLOTS]
from your Step-4 read:
Re-render this exact shot @Video1 in cinematic 4K. LOCK the original: [SUBJECT/IDENTITY — face, hair, build, wardrobe], performing [EXACT GESTURES, beat by beat]. Keep the SAME camera move ([CAMERA: static / pan / push-in / handheld], [timing]), the SAME framing ([SHOT SIZE + subject position]), the SAME lighting ([direction, hardness, color temp, time of day]) and the SAME palette ([dominant colors / mood]). CHANGE: [the VFX], time-coded — at [t0]–[t1]s [what the scene looks like before the change], then at [t2]s [the change triggers / VFX appears] and [how the scene reads after]. Everything not described by the change stays identical to @Video1. Photorealistic, high detail, consistent identity throughout.
Time-code the change so Seedance knows when it happens relative to the locked motion (e.g. "at 0–3s the plaza is unchanged as the subject raises their hand; at 3s on the finger snap the plaza dissolves into an open desert, sand and heat-haze replacing the buildings while the subject, camera move, and framing stay identical"). The stronger and more specific the time-coded change clause, the less Seedance ignores it.
撰写一个提示词 = 对原始内容的完整详尽描述（锁定元素）+ 用户请求的修改效果（带时间码） + 通过**
@Video1
**标记引用输入视频。务必详尽：你在步骤4中读取但未写入提示词的任何细节，Seedance都可能随意修改。
模板——用步骤4的读取内容填充
[占位符]
：
以电影质感4K分辨率重新渲染此镜头 @Video1。锁定原始内容：[主体/身份——面部、发型、体型、服装]，做出[精确手势，逐节拍描述]。保持相同的镜头运动（[镜头类型：静态/摇镜头/推镜头/手持镜头]，[时间点]）、相同的构图（[景别 + 主体位置]）、相同的光线（[方向、硬度、色温、时段]）和相同的色调（[主导颜色/氛围]）。修改：[VFX效果]，带时间码——[t0]–[t1]秒 [修改前的场景状态]，然后在[t2]秒 [修改触发/VFX出现]，之后[修改后的场景状态]。未被修改描述覆盖的所有内容均与@Video1完全一致。照片级真实感，高细节，全程身份一致。
为修改效果添加时间码，让Seedance知道修改相对于锁定运动的发生时间（例如：“0–3秒广场保持不变，主体抬起手；3秒打响指时，广场溶解为开阔沙漠，沙子和热浪取代建筑，同时主体、镜头运动和构图保持不变”）。时间码修改条款越明确具体，Seedance就越不容易忽略它。

Step 6 — Agreement gate (
estimate_cost
) — MANDATORY before generating

步骤6——确认环节（
estimate_cost
）——生成前必须执行

The 4K render is the expensive step, and once fired it can't be un-spent. Never call 
generate_reference_video
without explicit user approval in this session. Present, in one message:
  1. The full Step-5 prompt — verbatim, so the user can catch a wrong detail (face, gesture, timing) before it costs money.
  2. The generation recipe — provider 
    seedance
    , model 
    standard
    , resolution 
    4k
    , plus the 
    aspect_ratio
    and 
    duration
    you chose from the Step-2 probe.
  3. The estimated cost — call 
    estimate_cost
    for the Step-7 
    generate_reference_video
    call and quote the result. If the estimate errors, say the cost is unknown and flag that 4K standard is the top-price tier — do not silently skip the number.
Then wait. Three outcomes:
  • Approve → proceed to Step 7 unchanged.
  • Revise — the user corrects a detail or the change clause → update the Step-5 prompt (re-reading specific sheets if the correction demands it) and re-present the gate.
  • Abort / no answer → do not generate. Never treat silence as approval.
Only skip this gate if the user explicitly pre-authorized the spend in this session ("just run it, don't ask", a standing instruction to render without confirmation). Having provided the clip and the change is NOT pre-authorization — that's just Stage 0 input.
4K渲染成本高昂，一旦启动就无法撤销。在本次会话中，未经用户明确批准，请勿调用
generate_reference_video
。用一条消息展示以下内容：
  1. 步骤5的完整提示词——原文展示，以便用户在产生费用前发现错误细节（面部、手势、时间点）。
  2. 生成配置——服务商
    seedance
    ，模型
    standard
    ，分辨率
    4k
    ，以及步骤2探测后选择的
    aspect_ratio
    和
    duration
    。
  3. 预估成本——调用
    estimate_cost
    获取步骤7
    generate_reference_video
    的成本并告知用户。如果预估出错，说明成本未知，并标注4K标准档为最高价格层级——请勿跳过成本说明。
然后等待用户回应。可能的三种结果：
  • 批准——直接执行步骤7。
  • 修改——用户修正细节或修改条款→更新步骤5的提示词（如需修正则重新读取特定联系表），并重新展示确认环节。
  • 终止/无回应——请勿生成。永远不要将沉默视为批准。
仅当用户在本次会话中明确预先授权费用（例如：“直接运行，不用问”、“无需确认直接渲染”的长期指令）时，才可跳过此确认环节。用户提供视频和修改效果并不等同于预先授权——这只是阶段0的输入。

Step 7 — Generate (Seedance 4K reference-to-video) (
generate_reference_video
)

步骤7——生成（Seedance 4K参考转视频）（
generate_reference_video
）

Call 
generate_reference_video
with the exact 4K recipe:
  • provider
    : 
    seedance
  • seedance_model
    : 
    standard
     — REQUIRED for 4k (fast/mini cap at 720p).
  • resolution
    : 
    4k
  • reference_videos
    : 
    [state.clip_url]
     — the original footage as the motion/style anchor (Seedance accepts up to 3 videos).
  • reference_images
    : optional — style/creature/identity anchors (e.g. a generated reference image), only if you have one.
  • prompt
    : the Step-5 prompt (uses the 
    @Video1
     token to reference the input clip; no max length, so be exhaustive).
  • aspect_ratio
    : 
    auto
     (or match the source from Step 2).
  • duration
    : matched to the source, OR set 
    auto_duration
    : 
    true
    .
Do NOT pass 
negative_prompt
 (not supported on seedance — the call is rejected) and do NOT pass any fps param (it doesn't exist).
调用
generate_reference_video
，使用精确的4K配置：
  • provider
    : 
    seedance
  • seedance_model
    : 
    standard
    ——4K分辨率必填（fast/mini模型最高支持720p）。
  • resolution
    : 
    4k
  • reference_videos
    : 
    [state.clip_url]
    ——原始素材作为运动/风格锚点（Seedance最多支持3个视频）。
  • reference_images
    : 可选——风格/生物/身份锚点（例如生成的参考图），仅当你有相关素材时使用。
  • prompt
    : 步骤5的提示词（使用**
    @Video1
    **标记引用输入视频；无长度限制，请尽量详尽）。
  • aspect_ratio
    : 
    auto
    （或匹配步骤2中源视频的参数）。
  • duration
    : 匹配源视频，或设置
    auto_duration
    : 
    true
    。
请勿传入
negative_prompt
（Seedance不支持——调用会被拒绝），且请勿传入任何fps参数（该参数不存在）。

Step 8 — Deliver

步骤8——交付

If the call returns inline, you have the 4K video URL. If it returns 
{ task_id, status: running/background }
, poll 
task_status(task_id)
in a tight loop until 
completed
/ 
failed
/ 
cancelled
. On completion, return the 4K video URL to the user. Note: the output CDN may be egress-blocked for in-session download — so deliver the URL itself rather than trying to fetch the bytes locally.
如果调用直接返回结果，你将获得4K视频URL。如果返回
{ task_id, status: running/background }
，则循环调用
task_status(task_id)
直到状态变为
completed
/
failed
/
cancelled
。完成后，将4K视频URL返回给用户。注意：输出CDN可能限制会话内下载——因此直接交付URL即可，无需尝试本地获取字节内容。

Failure modes

故障排查

symptomcause → fix
Clip too long / too largeUpload still works for large files; if Seedance rejects on duration, trim the source to the segment that matters, or set 
auto_duration: true
and match a shorter 
duration
.
No face / subject found in the readA sheet may not show the subject clearly — re-run 
analyze_media
on the specific sheet(s) covering the frames where the subject is visible (use the stamped frame indices), or rebuild the sheets without downscaling so detail is preserved. Do NOT drop to a sampled extraction — every frame stays on a sheet.
ffmpeg missing / 
drawtext
unavailable		The run environment needs local 
ffmpeg
; if the build lacks the 
drawtext
filter, fall back to plain 
scale=480:-1,tile=5x5
(sheets without stamped indices — still read every frame).
Identity / shot drift (different face, room, or camera move in the output)The prompt description was not exhaustive enough. Go back to Step 4–5 and add the missing locked details (more on face, gestures, camera move, lighting) from the contact-sheet read — Seedance only preserves what the prompt names. Re-render = new spend: re-pass the Step-6 gate with the revised prompt before firing again.
4k rejected / output downgradedEnsure 
seedance_model: standard
(fast/mini cap at 720p) and 
resolution: 4k
. If the wrong-param call already completed (and was charged), the corrected call is a second render — re-pass the Step-6 gate before re-firing; if the call was rejected outright (nothing charged), re-fire the corrected call under the original approval.
Speech / audio not preserved or change off-beatRun BOTH 
transcribe_audio
(speech + timing) and 
analyze_media
on the audio (music/SFX/ambience), then describe the dialogue + lip-movement beats and pin the time-coded change to the right audio cue/beat in the prompt so the re-render stays in sync.
Seedance ignores the requested changeStrengthen the time-coded change clause in Step 5 — make it more specific and explicitly time-anchored ("at Ns the change triggers"). The revised prompt is a new, never-approved prompt and the re-render is new spend: re-pass the Step-6 gate before firing again.
Job fails after a long runIf it ran several minutes before failing, the content already cleared the safety pass — the failure is infrastructural; re-fire the same call (the user's Step-6 approval covers retrying the identical, already-approved call — no need to re-gate).
症状原因→解决方法
视频过长/过大大文件仍可上传；如果Seedance因时长拒绝，将源视频裁剪至关键片段，或设置
auto_duration: true
并匹配更短的
duration
。
读取时未检测到面部/主体某张联系表可能未清晰展示主体——重新调用
analyze_media
分析覆盖主体可见帧的特定联系表（使用标记的帧索引），或不缩小帧尺寸重新构建联系表以保留细节。请勿改为采样提取——每一帧都必须包含在联系表中。
ffmpeg缺失/
drawtext
不可用		运行环境需要本地ffmpeg；如果版本不支持
drawtext
滤镜，退化为仅使用
scale=480:-1,tile=5x5
（无帧索引标记的联系表——仍需读取每一帧）。
身份/镜头偏离（输出中面部、场景或镜头运动不同）提示词描述不够详尽。回到步骤4-5，从联系表读取结果中添加缺失的锁定细节（更多关于面部、手势、镜头运动、光线的描述）——Seedance仅保留提示词中明确提及的内容。重新渲染会产生新费用：重新执行步骤6的确认环节，获得批准后再启动。
4K被拒绝/输出降级确保设置
seedance_model: standard
（fast/mini模型最高支持720p）和
resolution: 4k
。如果错误参数的调用已完成（且已扣费），修正后的调用属于第二次渲染——重新执行步骤6的确认环节后再启动；如果调用被直接拒绝（未扣费），则在原批准下重新发起修正后的调用。
语音/音频未保留或修改效果节拍错位同时运行
transcribe_audio
（语音+时间戳）和
analyze_media
分析音频（音乐/音效/环境音），然后在提示词中描述对话+唇形运动节拍，并将带时间码的修改效果与正确的音频提示/节拍绑定，确保重新渲染保持同步。
Seedance忽略请求的修改效果强化步骤5中的时间码修改条款——使其更具体并明确时间锚点（“在N秒时触发修改”）。修改后的提示词属于新的未批准提示词，重新渲染会产生新费用：重新执行步骤6的确认环节后再启动。
长时间运行后任务失败如果运行数分钟后失败，说明内容已通过安全审核——故障源于基础设施；重新发起相同调用（用户在步骤6的批准涵盖重试相同的已批准调用——无需重新确认）。

One-liner recap

一句话总结

Extract EVERY frame locally with ffmpeg → tile into 25-per-sheet (5×5) contact sheets and analyze every sheet → understand the audio (transcribe speech + analyze music/SFX/ambience) → write an exhaustive Seedance-faithful prompt that locks the original and time-codes the change to the right audio beat → agreement gate: show the prompt + recipe + 
estimate_cost
and wait for approval → re-render the same shot in 4K via 
generate_reference_video
(
seedance
/ 
standard
/ 
4k
, original as 
reference_videos
, 
@Video1
in the prompt, no 
negative_prompt
).
通过本地ffmpeg提取每一帧→拼接成每25帧一张（5×5）的联系表并分析所有联系表→理解音频（转录语音+分析音乐/音效/环境音）→编写详尽的符合Seedance规范的提示词，锁定原始内容并将修改效果与正确音频节拍时间码对齐→确认环节：展示提示词+配置+
estimate_cost
并等待批准→通过
generate_reference_video
重新渲染4K镜头（
seedance
/
standard
/
4k
，原始视频作为
reference_videos
，提示词中使用
@Video1
，不传入
negative_prompt
）。