Orchestrates video generation across three engines, selecting the best one based on requirements and available resources.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncomfyui-video-pipelineExecute the skills CLI command in your project's root directory to begin installation:
Fetches comfyui-video-pipeline from mckruz/comfyui-expert and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate comfyui-video-pipeline. Access via /comfyui-video-pipeline in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Orchestrates video generation across three engines, selecting the best one based on requirements and available resources.
VIDEO REQUEST
|
|-- Need film-level quality?
| |-- Yes + 24GB+ VRAM → Wan 2.2 MoE 14B
| |-- Yes + 8GB VRAM → Wan 2.2 1.3B
|
|-- Need long video (>10 seconds)?
| |-- Yes → FramePack (60 seconds on 6GB)
|
|-- Need fast iteration?
| |-- Yes → AnimateDiff Lightning (4-8 steps)
|
|-- Need camera/motion control?
| |-- Yes → AnimateDiff V3 + Motion LoRAs
|
|-- Need first+last frame control?
| |-- Yes → Wan 2.2 MoE (exclusive feature)
|
|-- Default → Wan 2.2 (best general quality)
Prerequisites:
wan2.1_i2v_720p_14b_bf16.safetensors in models/diffusion_models/umt5_xxl_fp8_e4m3fn_scaled.safetensors in models/clip/open_clip_vit_h_14.safetensors in models/clip_vision/wan_2.1_vae.safetensors in models/vae/Settings:
| Parameter | Value | Notes |
|---|---|---|
| Resolution | 1280x720 (landscape) or 720x1280 (portrait) | Native training resolution |
| Frames | 81 (~5 seconds at 16fps) | Multiples of 4 + 1 |
| Steps | 30-50 | Higher = better quality |
| CFG | 5-7 | |
| Sampler | uni_pc | Recommended for Wan |
| Scheduler | normal |
Frame count guide:
| Duration | Frames (16fps) |
|---|---|
| 1 second | 17 |
| 3 seconds | 49 |
| 5 seconds | 81 |
| 10 seconds | 161 |
VRAM optimization:
Same as I2V but uses wan2.1_t2v_14b_bf16.safetensors and EmptySD3LatentImage instead of image conditioning.
Wan 2.2 MoE allows specifying both the first and last frame, enabling precise video planning:
VRAM usage is invariant to video length - generates 60-second videos at 30fps on just 6GB VRAM.
How it works:
| Parameter | Value | Notes |
|---|---|---|
| Resolution | 640x384 to 1280x720 | Depends on VRAM |
| Duration | Up to 60 seconds | VRAM-invariant |
| Quality | High (comparable to Wan) | Uses same base models |
| Parameter | Value (Standard) | Value (Lightning) |
|---|---|---|
| Motion Module | v3_sd15_mm.ckpt |
animatediff_lightning_4step.safetensors |
| Steps | 20-25 | 4-8 |
| CFG | 7-8 | 1.5-2.0 |
| Sampler | euler_ancestral | lcm |
| Resolution | 512x512 | 512x512 |
| Context Length | 16 | 16 |
| Context Overlap | 4 | 4 |
| LoRA | Motion |
|---|---|
| v2_lora_ZoomIn | Camera zooms in |
| v2_lora_ZoomOut | Camera zooms out |
| v2_lora_PanLeft | Camera pans left |
| v2_lora_PanRight | Camera pans right |
| v2_lora_TiltUp | Camera tilts up |
| v2_lora_TiltDown | Camera tilts down |
| v2_lora_RollingClockwise | Camera rolls clockwise |
After any video generation:
Doubles or quadruples frame count for smoother motion:
Input (16fps) → RIFE 2x → Output (32fps)
Input (16fps) → RIFE 4x → Output (64fps)
Use rife47 or rife49 model.
Apply FaceDetailer to each frame:
Reduces temporal inconsistencies between frames.
Maintain consistent color grading across frames.
Final output via VHS Video Combine:
frame_rate: 16 (native) or 24/30 (after interpolation)
format: "video/h264-mp4"
crf: 19 (high quality) to 23 (smaller file)
Complete pipeline for character dialogue:
1. Generate audio → comfyui-voice-pipeline
2. Generate base video → This skill (Wan I2V or AnimateDiff)
- Prompt: "{character}, talking naturally, slight head movement"
- Duration: match audio length
3. Apply lip-sync → Wav2Lip or LatentSync
4. Enhance faces → FaceDetailer + CodeFormer
5. Final output → video-assembly
Before marking video as complete:
references/workflows.md - Workflow templates for Wan and AnimateDiffreferences/models.md - Video model download linksreferences/research-log.md - Latest video generation advancesstate/inventory.json - Available video modelsPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Keeps context tight: comfyui-video-pipeline is the kind of skill you can hand to a new teammate without a long onboarding doc.
comfyui-video-pipeline is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend comfyui-video-pipeline for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: comfyui-video-pipeline is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: comfyui-video-pipeline is the kind of skill you can hand to a new teammate without a long onboarding doc.
comfyui-video-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.
comfyui-video-pipeline has been reliable in day-to-day use. Documentation quality is above average for community skills.
comfyui-video-pipeline fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for comfyui-video-pipeline matched our evaluation — installs cleanly and behaves as described in the markdown.
comfyui-video-pipeline reduced setup friction for our internal harness; good balance of opinion and flexibility.
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