comfyui-video-production

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ComfyUI Video Production Pipeline

ComfyUI视频制作流水线

End-to-end video production orchestration for ComfyUI with automatic error recovery, quality validation, and instance management.

具备自动错误恢复、质量验证和实例管理功能的ComfyUI端到端视频制作编排方案。

Quick Start: Which Pipeline?

快速入门：选择合适的流水线？

Creating a multi-shot narrative video? → Keyframe Pipeline - Generate keyframes → Animate → Stitch with transitions

Animating existing images? → I2V Batch Pipeline - Load images → Queue I2V jobs → Auto-validate → Combine

Need smooth transitions between scenes? → Transition Pipeline - Crossfades, motion blur, zoom effects via FFmpeg

ComfyUI stuck or crashed? → Instance Manager - Auto-restart, health checks, queue monitoring

Debugging video issues? → Validation Suite - Check resolution, FPS, codec, face consistency, color grading

要创建多镜头叙事视频？ → 关键帧流水线 - 生成关键帧 → 动画化 → 添加转场拼接

要将现有图像转为动画？ → I2V批量流水线 - 加载图像 → 排队I2V任务 → 自动验证 → 合并

需要场景间的平滑转场？ → 转场流水线 - 通过FFmpeg实现交叉淡入淡出、运动模糊、缩放效果

ComfyUI卡顿或崩溃？ → 实例管理器 - 自动重启、健康检查、队列监控

调试视频问题？ → 验证套件 - 检查分辨率、FPS、编码格式、面部一致性、色彩分级

Core Pipelines

核心流水线

Pipeline 1: Keyframe-to-Video (Complete Narrative)

流水线1：关键帧转视频（完整叙事）

Use when: Creating story-driven videos with multiple distinct shots

1. Keyframe Generation Phase
   - Generate consistent keyframes with IP-Adapter/LoRA
   - Validate face consistency, lighting, pose progression
   - Save to organized directory structure
   - Auto-retry failed generations

2. I2V Animation Phase
   - Queue each keyframe to I2V model (Wan 2.2, LTX-2, AnimateDiff)
   - Monitor progress via ComfyUI API
   - Validate each clip (resolution, fps, duration)
   - Auto-retry with different seeds if failed

3. Concatenation Phase
   - Pre-flight validation (ensure all clips match)
   - Apply transition effects (crossfade, motion blur)
   - FFmpeg encoding with proper codec
   - Export final video with metadata

4. Quality Assurance
   - Face consistency check across clips
   - Color grading consistency
   - Audio sync validation (if applicable)
   - Generate QA report

Expected output: Single cohesive video with smooth transitions

适用场景： 创建包含多个不同镜头的故事驱动型视频

1. 关键帧生成阶段
   - 使用IP-Adapter/LoRA生成一致性关键帧
   - 验证面部一致性、光线、姿态连贯性
   - 保存至结构化目录
   - 自动重试失败的生成任务

2. I2V动画化阶段
   - 将每个关键帧提交至I2V模型队列（Wan 2.2、LTX-2、AnimateDiff）
   - 通过ComfyUI API监控进度
   - 验证每个片段的分辨率、帧率、时长
   - 失败时自动使用不同种子重试

3. 拼接阶段
   - 预验证（确保所有片段参数匹配）
   - 应用转场效果（交叉淡入淡出、运动模糊）
   - 使用FFmpeg按正确编码格式转码
   - 导出带元数据的最终视频

4. 质量保证
   - 检查片段间的面部一致性
   - 色彩分级一致性
   - 音频同步验证（如有音频）
   - 生成质量报告

预期输出： 转场流畅的连贯视频

Pipeline 2: Batch I2V Processing

流水线2：批量I2V处理

Use when: You have multiple images to animate independently

1. Image Discovery
   - Scan directory for source images
   - Validate image specs (resolution, format)
   - Generate processing manifest

2. Parallel I2V Queue
   - Queue all images to ComfyUI with appropriate prompts
   - Stagger submissions to avoid overload
   - Monitor queue depth and ETA

3. Progressive Validation
   - Check each completed video immediately
   - Flag issues (wrong resolution, fps, corruption)
   - Auto-retry flagged videos

4. Export & Organize
   - Move validated videos to output directory
   - Generate index with metadata
   - Create contact sheet (thumbnail preview grid)

Expected output: Directory of validated animated clips

适用场景： 需独立动画化多张图像

1. 图像识别
   - 扫描目录获取源图像
   - 验证图像规格（分辨率、格式）
   - 生成处理清单

2. 并行I2V队列
   - 将所有图像按对应提示词提交至ComfyUI
   - 错开提交时间避免过载
   - 监控队列深度和预计完成时间

3. 渐进式验证
   - 完成后立即检查每个视频
   - 标记问题（错误分辨率、帧率、损坏）
   - 自动重试标记的视频

4. 导出与整理
   - 将验证通过的视频移至输出目录
   - 生成带元数据的索引
   - 创建预览缩略图网格

预期输出： 包含验证通过动画片段的目录

Pipeline 3: Video Concatenation with Transitions

流水线3：带转场的视频拼接

Use when: Combining existing video clips with professional transitions

1. Clip Validation
   - Verify all clips exist and are readable
   - Check resolution, fps, codec consistency
   - Report mismatches with fix suggestions

2. Transition Planning
   - Detect scene changes (cut detection)
   - Recommend transition types (crossfade, zoom, pan)
   - Calculate transition timing

3. FFmpeg Pipeline
   - Apply transitions between clips
   - Re-encode with consistent settings
   - Preserve quality (high bitrate, proper codec)

4. Audio Handling
   - Extract audio from clips (if present)
   - Crossfade audio at transitions
   - Sync to final video timeline

Expected output: Polished video with seamless transitions

适用场景： 为现有视频片段添加专业转场

1. 片段验证
   - 验证所有片段存在且可读取
   - 检查分辨率、帧率、编码格式一致性
   - 报告不匹配问题并提供修复建议

2. 转场规划
   - 检测场景切换
   - 推荐转场类型（交叉淡入淡出、缩放、平移）
   - 计算转场时长

3. FFmpeg流水线
   - 在片段间应用转场
   - 按统一设置重新编码
   - 保留画质（高码率、正确编码格式）

4. 音频处理
   - 提取片段中的音频（如有）
   - 在转场处交叉淡入淡出音频
   - 同步至最终视频时间轴

预期输出： 转场无缝的精修视频

Model Support (2026)

模型支持（2026）

Image-to-Video Models

图像转视频模型

Model	Quality	Speed	VRAM	Best For	Notes
LTX-2	★★★★★	Medium	16GB+	Production 4K video	Native 4K, audio+video
Wan 2.2 MoE	★★★★★	Slow	24GB+	Film-quality aesthetics	First+last frame control
Wan 2.1 14B	★★★★	Slow	24GB	High quality	Proven, stable
Wan 2.1 1.3B	★★★	Fast	8GB	Quick iteration	Consumer-friendly
AnimateDiff V3	★★★	Fast	8GB	Infinite length	Motion LoRAs
SVD (Stable Video Diffusion)	★★★	Medium	12GB	Short clips	14-25 frames

Model	画质	速度	VRAM	适用场景	说明
LTX-2	★★★★★	中等	16GB+	专业4K视频制作	原生4K，支持音视频
Wan 2.2 MoE	★★★★★	较慢	24GB+	电影级画质	支持首尾帧控制
Wan 2.1 14B	★★★★	较慢	24GB	高质量	成熟稳定
Wan 2.1 1.3B	★★★	较快	8GB	快速迭代	面向普通用户
AnimateDiff V3	★★★	较快	8GB	无限时长	支持运动LoRA
SVD (Stable Video Diffusion)	★★★	中等	12GB	短片段	14-25帧

Transition Effects

转场效果

Effect	Use Case	Encoding Cost
Crossfade	General purpose	Low
Motion blur	High-motion scenes	Medium
Zoom in/out	Dramatic emphasis	Medium
Pan left/right	Scene establishment	Medium
Fade to/from black	Chapter breaks	Low
Custom LUT	Color grading	Low

效果	适用场景	编码成本
交叉淡入淡出	通用场景	低
运动模糊	高动态场景	中等
缩放	戏剧性强调	中等
左右平移	场景铺垫	中等
淡入淡出黑场	章节切换	低
自定义LUT	色彩分级	低

ComfyUI Instance Management

ComfyUI实例管理

Health Monitoring

健康监控

python

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Auto-detected issues:

自动检测的问题:

Queue stalled (no progress for 5+ minutes)
Memory leak (VRAM usage climbing)
Process crashed (connection refused)
API unresponsive (timeout on /queue endpoint)
Disk full (output directory at capacity)

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队列停滞（5分钟无进度）
内存泄漏（显存持续攀升）
进程崩溃（连接被拒绝）
API无响应（/queue端点超时）
磁盘已满（输出目录容量不足）

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Auto-Recovery Actions

自动恢复操作

python

1. Soft Recovery (no restart)
   - Clear stuck queue items
   - Force garbage collection
   - Unload models from VRAM

2. Hard Recovery (restart required)
   - Save current queue state
   - Kill ComfyUI process gracefully
   - Wait for port release
   - Restart with same config
   - Restore queue from saved state

3. Emergency Fallback
   - Switch to backup ComfyUI instance
   - Redirect queue to instance on different port
   - Continue processing without data loss

python

1. 软恢复（无需重启）
   - 清除停滞队列项
   - 强制垃圾回收
   - 从显存卸载模型

2. 硬恢复（需重启）
   - 保存当前队列状态
   - 优雅终止ComfyUI进程
   - 等待端口释放
   - 使用相同配置重启
   - 从保存的状态恢复队列

3. 紧急降级
   - 切换至备用ComfyUI实例
   - 将队列重定向至不同端口的实例
   - 无数据丢失继续处理

Multi-Instance Support

多实例支持

bash

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Run multiple ComfyUI instances for parallel processing

运行多个ComfyUI实例实现并行处理

Instance 1: localhost:8188 (primary - I2V generation) Instance 2: localhost:8189 (secondary - upscaling/post-processing) Instance 3: localhost:8190 (backup - standby for failover)

实例1: localhost:8188（主实例 - I2V生成）实例2: localhost:8189（副实例 - 超分辨率/后期处理）实例3: localhost:8190（备用实例 - 故障转移）

Load balancing strategy:

负载均衡策略:

Round-robin for equal workloads
Priority-based for mixed tasks
Failover for crashed instances

---

轮询分配实现负载均等
基于优先级处理混合任务
故障实例自动转移

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Validation Suite

验证套件

Pre-Generation Validation

生成前验证

python

✓ Check ComfyUI is running and responsive
✓ Verify models are loaded (UNET, VAE, CLIP)
✓ Confirm output directory has sufficient space
✓ Validate source images exist and are readable
✓ Check prompts are non-empty and formatted correctly
✓ Verify workflow JSON is valid

python

✓ 检查ComfyUI是否运行且响应正常
✓ 验证模型已加载（UNET、VAE、CLIP）
✓ 确认输出目录有足够空间
✓ 验证源图像存在且可读取
✓ 检查提示词非空且格式正确
✓ 验证工作流JSON有效

Post-Generation Validation

生成后验证

python

✓ Video file exists and is non-zero size
✓ Resolution matches expected (e.g., 768x1024)
✓ FPS matches expected (e.g., 16 or 25)
✓ Duration matches expected (e.g., 3-5 seconds)
✓ Codec is compatible (h264, h265)
✓ No corruption (can read all frames)
✓ Face consistency score >0.85 (if character video)
✓ Color histogram within expected range

python

✓ 视频文件存在且大小非零
✓ 分辨率符合预期（如768x1024）
✓ FPS符合预期（如16或25）
✓ 时长符合预期（如3-5秒）
✓ 编码格式兼容（h264、h265）
✓ 无损坏（可读取所有帧）
✓ 面部一致性得分>0.85（人物视频）
✓ 颜色直方图在预期范围内

Quality Metrics

质量指标

python

Metrics tracked:
- Face embedding distance (identity consistency)
- Optical flow magnitude (motion smoothness)
- Frame PSNR/SSIM (interpolation quality)
- Color histogram deviation (lighting consistency)
- Audio sync offset (if audio present)

python

跟踪的指标:
- 人脸嵌入距离（身份一致性）
- 光流幅度（运动平滑度）
- 帧PSNR/SSIM（插值质量）
- 颜色直方图偏差（光线一致性）
- 音频同步偏移（如有音频）

Error Handling & Recovery

错误处理与恢复

Retry Strategies

重试策略

python

1. Seed Randomization Retry
   - Failed generation? Try different seed
   - Max 3 attempts per keyframe
   - Track seeds that fail (avoid reuse)

2. Parameter Adjustment Retry
   - CFG too high causing artifacts? Lower it
   - Steps too low causing incompleteness? Increase
   - Resolution too high OOM? Downscale

3. Model Fallback Retry
   - Wan 2.2 14B OOM? Fall back to 1.3B
   - LTX-2 unavailable? Fall back to Wan 2.1
   - AnimateDiff motion broken? Switch motion LoRA

4. Checkpoint Resume
   - Save progress after each successful clip
   - Resume from last successful checkpoint
   - Skip already-generated clips

python

1. 种子随机化重试
   - 生成失败？尝试不同种子
   - 每个关键帧最多3次尝试
   - 记录失败种子（避免重复使用）

2. 参数调整重试
   - CFG过高导致伪影？降低参数
   - 步数太少导致不完整？增加步数
   - 分辨率过高导致显存不足？降低分辨率

3. 模型降级重试
   - Wan 2.2 14B显存不足？降级至1.3B
   - LTX-2不可用？降级至Wan 2.1
   - AnimateDiff运动异常？切换运动LoRA

4. 断点续传
   - 每个片段成功后保存进度
   - 从最后成功的断点恢复
   - 跳过已生成的片段

Failure Logging

失败日志

python

logs/
├── 2026-02-16_pipeline.log       # Main pipeline log
├── 2026-02-16_comfyui.log        # ComfyUI stdout/stderr
├── 2026-02-16_validation.json    # Validation results
├── 2026-02-16_failures.json      # Failed attempts with reasons
└── 2026-02-16_recovery.json      # Recovery actions taken

python

logs/
├── 2026-02-16_pipeline.log       # 主流水线日志
├── 2026-02-16_comfyui.log        # ComfyUI标准输出/错误日志
├── 2026-02-16_validation.json    # 验证结果
├── 2026-02-16_failures.json      # 失败尝试及原因
└── 2026-02-16_recovery.json      # 执行的恢复操作

Directory Structure

目录结构

Organized Output

结构化输出

project_name/
├── 00_keyframes/                 # Source keyframe images
│   ├── kf01_scene_description.png
│   ├── kf02_scene_description.png
│   └── ...
├── 01_clips/                     # Individual animated clips
│   ├── clip_001_kf01.mp4
│   ├── clip_002_kf02.mp4
│   └── ...
├── 02_validated/                 # Clips that passed validation
│   ├── clip_001_kf01.mp4
│   ├── clip_002_kf02.mp4
│   └── ...
├── 03_transitions/               # Intermediate files for transitions
│   ├── transition_001_002.mp4
│   └── ...
├── 04_final/                     # Final combined video
│   ├── final_video_v1.mp4
│   ├── final_video_v2.mp4        # After revisions
│   └── ...
├── logs/                         # Execution logs
├── metadata/                     # JSON metadata for each asset
└── manifest.json                 # Complete project manifest

project_name/
├── 00_keyframes/                 # 源关键帧图像
│   ├── kf01_scene_description.png
│   ├── kf02_scene_description.png
│   └── ...
├── 01_clips/                     # 独立动画片段
│   ├── clip_001_kf01.mp4
│   ├── clip_002_kf02.mp4
│   └── ...
├── 02_validated/                 # 验证通过的片段
│   ├── clip_001_kf01.mp4
│   ├── clip_002_kf02.mp4
│   └── ...
├── 03_transitions/               # 转场中间文件
│   ├── transition_001_002.mp4
│   └── ...
├── 04_final/                     # 最终合并视频
│   ├── final_video_v1.mp4
│   ├── final_video_v2.mp4        # 修订后版本
│   └── ...
├── logs/                         # 执行日志
├── metadata/                     # 各资源的JSON元数据
└── manifest.json                 # 完整项目清单

Workflow Examples

工作流示例

Example 1: 30-Second Narrative Video (5 keyframes)

示例1：30秒叙事视频（5个关键帧）

python

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python

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Configuration

配置

project_name = "sage_character_reveal" keyframes = 5 i2v_model = "wan_2.2_moe" target_duration = 30 # seconds fps = 16

project_name = "sage_character_reveal" keyframes = 5 i2v_model = "wan_2.2_moe" target_duration = 30 # 秒 fps = 16

Pipeline execution

流水线执行

Generate 5 keyframes (IP-Adapter + LoRA) → sage_kf01_over_shoulder.png → sage_kf02_turning.png → sage_kf03_cardigan_fallen.png → sage_kf04_removing_bra.png → sage_kf05_topless.png
Validate keyframes → Face consistency: 0.92 ✓ → Lighting consistency: 0.88 ✓ → Pose progression: logical ✓
Queue I2V for each keyframe → clip_001: 6s @ 16fps (96 frames) ✓ → clip_002: 6s @ 16fps (96 frames) ✓ → clip_003: 6s @ 16fps (96 frames) ✓ → clip_004: 6s @ 16fps (96 frames) ✓ → clip_005: 6s @ 16fps (96 frames) ✓
Apply 0.5s crossfade transitions → Total: 30s - 2s (4 transitions × 0.5s) = 28s net
Export final video → sage_character_reveal_final.mp4 (30s, 768x1024, 16fps)

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生成5个关键帧（IP-Adapter + LoRA） → sage_kf01_over_shoulder.png → sage_kf02_turning.png → sage_kf03_cardigan_fallen.png → sage_kf04_removing_bra.png → sage_kf05_topless.png
验证关键帧 → 面部一致性: 0.92 ✓ → 光线一致性: 0.88 ✓ → 姿态连贯性: 符合逻辑 ✓
为每个关键帧提交I2V任务 → clip_001: 6秒 @ 16fps (96帧) ✓ → clip_002: 6秒 @ 16fps (96帧) ✓ → clip_003: 6秒 @ 16fps (96帧) ✓ → clip_004: 6秒 @ 16fps (96帧) ✓ → clip_005: 6秒 @ 16fps (96帧) ✓
应用0.5秒交叉淡入淡出转场 → 总时长: 30秒 - 2秒（4次转场 × 0.5秒）= 28秒净时长
导出最终视频 → sage_character_reveal_final.mp4 (30秒, 768x1024, 16fps)

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Example 2: Batch Process 20 Images

示例2：批量处理20张图像

python

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Configuration

配置

input_dir = "E:/ComfyUI/input/character_expressions" i2v_model = "ltx_2" motion_prompt = "gentle breathing, subtle movement, natural" batch_size = 4 # Process 4 at a time

input_dir = "E:/ComfyUI/input/character_expressions" i2v_model = "ltx_2" motion_prompt = "gentle breathing, subtle movement, natural" batch_size = 4 # 每次处理4张

Pipeline execution

流水线执行

Scan input directory → Found 20 PNG files
Queue 4 at a time to ComfyUI → Batch 1: expr_001.png → expr_004.png ✓ → Batch 2: expr_005.png → expr_008.png ✓ → Batch 3: expr_009.png → expr_012.png ✓ → Batch 4: expr_013.png → expr_016.png ✓ → Batch 5: expr_017.png → expr_020.png ✓
Validate each output → 19/20 passed (expr_011 failed - wrong resolution) → Retry expr_011 with corrected settings ✓
Export batch → 20 validated clips in output/expressions/ → Generated contact sheet: expressions_preview.png

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扫描输入目录 → 发现20个PNG文件
每次向ComfyUI提交4张图像 → 批次1: expr_001.png → expr_004.png ✓ → 批次2: expr_005.png → expr_008.png ✓ → 批次3: expr_009.png → expr_012.png ✓ → 批次4: expr_013.png → expr_016.png ✓ → 批次5: expr_017.png → expr_020.png ✓
验证每个输出 → 19/20通过（expr_011失败 - 分辨率错误） → 调整设置后重试expr_011 ✓
导出批次结果 → 20个验证通过的片段存于output/expressions/ → 生成预览缩略图网格: expressions_preview.png

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Reference Files

参考文件

Detailed Guides

详细指南

```
references/keyframe-generation.md
```
- Keyframe creation with IP-Adapter, LoRA, consistency tips
```
references/i2v-workflows.md
```
- Wan 2.2, LTX-2, AnimateDiff, SVD workflow templates
```
references/concatenation.md
```
- FFmpeg commands, transition effects, audio handling
```
references/validation.md
```
- Quality metrics, validation thresholds, troubleshooting
```
references/instance-management.md
```
- ComfyUI health checks, restart scripts, multi-instance setup
```
references/api-reference.md
```
- ComfyUI API endpoints, queue management, workflow submission
```
references/troubleshooting.md
```
- Common issues and solutions

```
references/keyframe-generation.md
```
- 使用IP-Adapter、LoRA创建关键帧的一致性技巧
```
references/i2v-workflows.md
```
- Wan 2.2、LTX-2、AnimateDiff、SVD工作流模板
```
references/concatenation.md
```
- FFmpeg命令、转场效果、音频处理
```
references/validation.md
```
- 质量指标、验证阈值、故障排查
```
references/instance-management.md
```
- ComfyUI健康检查、重启脚本、多实例搭建
```
references/api-reference.md
```
- ComfyUI API端点、队列管理、工作流提交
```
references/troubleshooting.md
```
- 常见问题及解决方案

Integration with Other Skills

与其他技能集成

Pair with:

```
comfyui-character-gen
```
- For generating initial keyframes with identity preservation
```
video-assembly
```
- For advanced editing and post-production
```
youtube-uploader
```
- For direct upload to YouTube after production

搭配使用：

```
comfyui-character-gen
```
- 生成带身份保留的初始关键帧
```
video-assembly
```
- 高级编辑与后期制作
```
youtube-uploader
```
- 制作完成后直接上传至YouTube

Advanced Features

高级功能

Adaptive Quality

自适应画质

python

undefined

python

undefined

Automatically adjust settings based on available resources

根据可用资源自动调整设置

if vram_available > 24: use_model = "wan_2.2_moe_14b" resolution = (832, 1216) batch_size = 1 elif vram_available > 12: use_model = "wan_2.1_1.3b" resolution = (768, 1024) batch_size = 2 else: use_model = "animatediff_v3" resolution = (512, 768) batch_size = 4

undefined

undefined

Progress Tracking

进度跟踪

python

undefined

python

undefined

Real-time progress updates

实时进度更新

[Pipeline] Keyframe generation: 3/5 complete (60%) [Pipeline] ETA: 12 minutes remaining [I2V] clip_003 generating: 47/96 frames (49%) [I2V] Current speed: 0.42 it/s [Validation] clip_001: PASS ✓ [Validation] clip_002: PASS ✓

undefined

[Pipeline] 关键帧生成: 3/5完成 (60%) [Pipeline] 预计剩余时间: 12分钟 [I2V] clip_003生成中: 47/96帧 (49%) [I2V] 当前速度: 0.42 it/s [Validation] clip_001: 通过 ✓ [Validation] clip_002: 通过 ✓

undefined

Rollback & Versioning

回滚与版本控制

python

undefined

python

undefined

Automatically version outputs

自动为输出版本化

output/ ├── final_video_v1.mp4 # Initial render ├── final_video_v2.mp4 # After fixing clip_003 ├── final_video_v3.mp4 # After adding transitions └── final_video_final.mp4 # Approved version

output/ ├── final_video_v1.mp4 # 初始渲染版本 ├── final_video_v2.mp4 # 修复clip_003后版本 ├── final_video_v3.mp4 # 添加转场后版本 └── final_video_final.mp4 # 最终确认版本

Rollback to previous version

回滚至之前版本

rollback_to_version(2) # Restore v2 as current

---

rollback_to_version(2) # 将v2恢复为当前版本

---

Workflow Generation

工作流生成

When asked to create a video production workflow:

Assess Requirements
- Number of shots/keyframes
- Target duration per shot
- I2V model preference
- Transition style
- Quality vs speed tradeoff
Generate Pipeline Config
- Model selection based on VRAM/quality needs
- Resolution and FPS settings
- Validation thresholds
- Retry policies
Provide Execution Scripts
- Python scripts for API submission
- FFmpeg commands for concatenation
- Validation checks
- Recovery procedures
Monitor & Adapt
- Track progress in real-time
- Detect failures early
- Apply recovery strategies
- Report final metrics

当被要求创建视频制作工作流时：

评估需求
- 镜头/关键帧数量
- 单镜头目标时长
- I2V模型偏好
- 转场风格
- 画质与速度的权衡
生成流水线配置
- 根据显存/画质需求选择模型
- 分辨率和FPS设置
- 验证阈值
- 重试策略
提供执行脚本
- API提交用Python脚本
- 拼接用FFmpeg命令
- 验证检查脚本
- 恢复流程
监控与适配
- 实时跟踪进度
- 早期检测失败
- 应用恢复策略
- 报告最终指标

Best Practices

最佳实践

For Keyframe Videos

关键帧视频

Use same seed across all keyframes (consistency)
IP-Adapter weight 0.75-0.85 (strong but not rigid)
Validate keyframes before I2V (saves compute)
Keep clips 4-8 seconds each (sweet spot)
Use 0.5-1s crossfade transitions (smooth but not slow)

所有关键帧使用相同种子（保证一致性）
IP-Adapter权重设为0.75-0.85（强约束但不过度）
I2V前先验证关键帧（节省算力）
单片段时长保持4-8秒（最优区间）
使用0.5-1秒交叉淡入淡出转场（流畅不拖沓）

For Batch Processing

批量处理

Process in small batches (4-8 at a time)
Validate immediately after each batch
Save checkpoint after each successful batch
Use priority queue for important clips

小批量处理（每次4-8张）
每批次完成后立即验证
每批次成功后保存断点
为重要片段设置优先级队列

For Instance Management

实例管理

Monitor queue depth every 30s
Restart if no progress for 5 minutes
Keep backup instance ready on different port
Log all restart events for debugging

每30秒监控队列深度
5分钟无进度则重启
在不同端口准备备用实例
记录所有重启事件用于调试

Performance Optimization

性能优化

RTX 50 Series (2026)

RTX 50系列（2026）

bash

undefined

bash

undefined

ComfyUI launch flags for optimal performance

最优性能的ComfyUI启动参数

--highvram
--fp8_e4m3fn-unet
--reserve-vram 7
--use-pytorch-cross-attention

Expected performance:

预期性能:

Wan 2.2 14B: ~2-3 min per 5s clip (832x1216)
LTX-2 4K: ~4-5 min per 5s clip (1920x1080)
Wan 2.1 1.3B: ~1-2 min per 5s clip (768x1024)

undefined

Wan 2.2 14B: 每5秒片段约2-3分钟（832x1216）
LTX-2 4K: 每5秒片段约4-5分钟（1920x1080）
Wan 2.1 1.3B: 每5秒片段约1-2分钟（768x1024）

undefined

AMD GPUs (ROCm)

AMD GPU（ROCm）

bash

undefined

bash

undefined

ComfyUI v0.8.1+ has native ROCm support

ComfyUI v0.8.1+原生支持ROCm

No special flags needed, just install ROCm drivers

无需特殊参数，只需安装ROCm驱动

---

---

Skill Evolution

技能演进

This skill adapts to new I2V models and techniques. When new models release:

Add model specs to
```
references/i2v-workflows.md
```
Create workflow template for new model
Update model selection logic in main pipeline
Test with sample project
Document performance characteristics

See

references/evolution.md

for update protocol.

本技能会适配新的I2V模型和技术。新模型发布时：

将模型规格添加至
```
references/i2v-workflows.md
```
为新模型创建工作流模板
更新主流水线中的模型选择逻辑
用示例项目测试
记录性能特征

更新流程详见

references/evolution.md

。