Skills comfyui-video

Automate AI video generation with ComfyUI and LTX-2.3. Supports text-to-video (T2V), image-to-video (I2V), batch scene rendering for music videos, and multi-scene workflows. Includes progress monitoring, fault recovery, and performance tuning. Use when generating AI videos with ComfyUI, creating MV scenes in batch, troubleshooting video rendering, or optimizing generation speed.

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/a3165458/comfyui-video" ~/.claude/skills/clawdbot-skills-comfyui-video && rm -rf "$T"
manifest: skills/a3165458/comfyui-video/SKILL.md
source content

ComfyUI Video Generation

Automate AI video generation using ComfyUI + LTX-2.3 model. Ideal for music video (MV) production, multi-scene batch rendering, and AI video content creation.

Requirements

ItemSpec
GPU≥24GB VRAM (Turing/Ampere/Ada)
ComfyUI0.17+
PyTorch2.6+cu124
AccessSSH tunnel forwarding port 18188

Model Setup

ModelSizePath
LTX-2.3 dev (bf16)43GB
models/checkpoints/ltx-2.3-22b-dev.safetensors
Gemma 3 12B23GB
models/text_encoders/comfy_gemma_3_12B_it.safetensors
Distilled LoRA7.1GB
models/loras/ltxv/ltx2/ltx-2.3-22b-distilled-lora-384.safetensors
Video VAE (bf16)-
models/vae/LTX23_video_vae_bf16.safetensors

Turing GPUs (e.g., Quadro RTX 8000) do NOT support

fp8_e4m3fn
. Use bf16/fp16 models only.

Performance Baseline

Per-step time: ~221s (constant, regardless of frame count!)
15 steps: ~57 min
25 steps: ~1h45m
Frames: 72=3s, 121=5s, 480=20s (24fps)

Key insight: Frame count does NOT affect total time. Bottleneck is model forward pass.

Workflow Node Reference

NodeIDPurpose
LoadImage2004I2V reference input
CLIPTextEncode (positive)2483Positive prompt
CLIPTextEncode (negative)2612Negative prompt
EmptyLTXVLatentVideo3059Empty latent
LTXVScheduler4966Steps/length params
LoraLoaderModelOnly4922+LoRA loader
SaveVideo4823/4852Output mp4

Quick Start

Generate a Single Video (I2V)

  1. Load workflow:
    /workspace/ComfyUI/custom_nodes/ComfyUI-LTXVideo/example_workflows/2.3/LTX-2.3_T2V_I2V_Single_Stage_Distilled_Full.json
  2. Set params using
    scripts/batch_scenes.js
  3. Click Run
  4. Wait ~1 hour
  5. Download from
    /workspace/ComfyUI/output/

Batch Scene Generation

Use

scripts/batch_scenes.js
for automation:

// Load script first, then configure each scene:
await comfyui_batch.configureScene({
  name: "scene_01",
  prompt: "A lonely girl running through rain at night, neon reflections",
  image: "unified_ref.png",
  steps: 15,
  frames: 72
});
// Click Run, repeat for next scene

Step Count Guide

StepsQualityTime/SceneUse Case
8Rough~30minQuick preview
15Good~57minRecommended sweet spot
25Best~1h45mFinal quality output

I2V + LoRA at 15 steps achieves ~90% of 25-step quality with 40% less time.

Troubleshooting

VAEDecode Validation Failed

Error:

Exception when validating node: 'VAEDecode'
Cause: VAE load timing or insufficient VRAM Fix: Reload the entire workflow (fetch + loadGraphData), wait for models to fully load, then run. Never reload during execution.

Browser Tab Lost

Cause: SSH tunnel disconnected Fix:

  1. Rebuild tunnel:
    ssh -f -N -L 18188:localhost:18188 user@host -p port
  2. Navigate to ComfyUI
  3. Reload workflow

Inconsistent Characters Across Scenes

Cause: Different reference images per scene Fix: Use the SAME reference image for all scenes. Extract a clear frame from an existing video if needed. The I2V input image dictates the visual baseline.

Output Video Not Saved

Check:

ssh -p PORT root@HOST "ls -lht /workspace/ComfyUI/output/*.mp4"
Fix: Check for VAEDecode errors in log, then re-run.

Monitoring Progress

# Current sampling progress
ssh -p PORT root@HOST "grep 'it/s' /tmp/comfy.log | tail -1"

# Completion check
ssh -p PORT root@HOST "grep 'Prompt executed' /tmp/comfy.log | tail -1"

# Output files
ssh -p PORT root@HOST "ls -lht /workspace/ComfyUI/output/*.mp4"

Best Practices

  1. 15 steps is the sweet spot — I2V converges at 15-20 steps, 25 has diminishing returns
  2. Unified reference image — Same input image for all scenes ensures character consistency
  3. Reload workflow every time — Avoids VAEDecode validation failures
  4. Never reload during execution — Current run will fail
  5. Frame selection — 72 frames (3s) for testing, 480 frames (20s) for final output
  6. VRAM management — Wait for each generation to complete before starting next

T2V vs I2V Comparison

ModeStepsQualityNotes
T2V (no LoRA)15❌ Very blurryNot recommended
I2V + LoRA25✅ ExcellentMajor quality improvement
I2V + LoRA15✅ Very goodBest time/quality ratio

Conclusion: I2V + LoRA is the recommended combination.

Resources

  • scripts/batch_scenes.js
    — Batch scene automation
  • references/workflow_nodes.md
    — Full node ID mapping
  • references/tips.md
    — Prompt tips, VRAM management, optimization