name: image-to-video displayName: "Image-to-Video — Pro Pack on RunComfy" description: > Animate any still image on RunComfy — this skill is a smart router that matches the user's intent to the right i2v model in the RunComfy catalog. Picks HappyHorse 1.0 I2V (Arena #1, native audio, identity preservation) for general animations, Wan 2.7 with audio_url for custom-voiceover lip-sync, or Seedance 2.0 Pro for multi-modal animation from image + reference video + reference audio. Bundles each model's documented prompting patterns so the caller gets sharper output without burning iterations on the wrong model. Calls runcomfy run <vendor>/<model>/image-to-video (or endpoint variant) through the local RunComfy CLI. Triggers on "image to video", "image-to-video", "i2v", "animate image", "make this move", or any explicit ask to turn a still into video. homepage: https://www.runcomfy.com license: MIT

Image-to-Video — Pro Pack on RunComfy

runcomfy.com · HappyHorse I2V · Wan 2.7 · Seedance 2.0 Pro · GitHub

Image-to-video, intent-routed. This skill doesn't lock you to one model — it picks the right i2v model in the RunComfy catalog based on what the user actually wants: portrait animation, custom-voiceover lip-sync, or multi-modal composition.

npx skills add agentspace-so/runcomfy-skills --skill image-to-video -g

Pick the right model for the user's intent

The agent reads this table, classifies the user's intent, and picks the matching subsection below.

Route 1: HappyHorse 1.0 I2V — default for portrait / product / general animation

Model: happyhorse/happyhorse-1-0/image-to-video · Arena rank: #1 (Elo 1392)

Schema

Output aspect = input aspect. No independent reframing.

Invoke

runcomfy run happyhorse/happyhorse-1-0/image-to-video \
  --input '{
    "image_url": "https://.../portrait.jpg",
    "prompt": "Gentle camera drift around the subject'\''s face, subtle breathing motion, identity-stable features, soft natural light."
  }' \
  --output-dir <absolute/path>

Prompting tips

• Lead with motion verbs: "drift", "dolly in", "orbit", "tilt up", "reveal", "blink", "breathe". Front-load what's MOVING.
• Don't restate the image — the model sees it. Focus tokens on what changes.
• Preservation goals explicit: "identity-stable features", "packaging unchanged", "background geometry stable".
• Lighting evolution: "rim light intensifying", "shadows shortening as camera rises".
• One beat per clip — single primary motion (orbit OR dolly OR tilt OR character action).

Route 2: Wan 2.7 + audio_url — when the user has a custom voiceover

Model: wan-ai/wan-2-7/text-to-video (NOT /image-to-video — Wan 2.7's t2v endpoint accepts an audio_url that drives lip-sync)

Note on i2v with Wan 2.7: Wan 2.7's primary i2v animation isn't on a dedicated endpoint here. For pure i2v (image animated by motion prompt only), prefer HappyHorse i2v. Use Wan 2.7 specifically when the user has a custom audio track they want lip-synced to a generated talking-head clip.

Schema (Wan 2.7 t2v with audio)

Invoke

runcomfy run wan-ai/wan-2-7/text-to-video \
  --input '{
    "prompt": "Medium close-up of a confident spokesperson in a softly-lit recording booth, leaning slightly toward the camera, locked tripod, shallow DOF, warm key light from camera-left.",
    "audio_url": "https://.../voiceover-en.mp3",
    "duration": 12,
    "aspect_ratio": "9:16"
  }' \
  --output-dir <absolute/path>

Prompting tips

• Describe the talking-head shot — framing, lighting, lens feel. The audio drives the lip-sync; the prompt builds the visual frame around it.
• Match duration to audio length — clip will be silent past the audio if too long.
• Use negative_prompt for issues: "no subtitles, no flicker, no distorted hands".
• For multi-language dubs — same prompt, swap audio_url per call. Lock seed for visual consistency across languages.

Route 3: Seedance 2.0 Pro — multi-modal animation (image + ref video + ref audio)

Model: bytedance/seedance-v2/pro

Use when the user wants a single clip that combines: a subject image + scene from a reference video + voice tone from a reference audio.

Schema (Seedance 2.0 Pro, i2v-relevant fields)

Invoke

runcomfy run bytedance/seedance-v2/pro \
  --input '{
    "prompt": "Subject from image 1 walks through the café in video 1, voice tone matches audio 1. Medium close-up, slow push-in, warm light, gentle ambience.",
    "image_url": ["https://.../subject.jpg"],
    "video_url": ["https://.../cafe-locked-shot.mp4"],
    "audio_url": ["https://.../voice-tone.mp3"],
    "duration": 8
  }' \
  --output-dir <absolute/path>

Prompting tips

• Image vs text division — use image_url for what must stay stable (face, costume, brand); use prompt for what should evolve (action, mood, lighting).
• Number the refs in the prompt: "subject from image 1, lighting from video 1, voice from audio 1". Seedance routes cues correctly.
• Reference media specs — videos / audio must be 2–15s; audio < 15MB.
• Don't mix radically different aesthetics — if image 1 is a watercolor and video 1 is photoreal, output drifts.

Exit codes

Full reference: docs.runcomfy.com/cli/troubleshooting.

How it works

The skill picks one of HappyHorse 1.0 I2V / Wan 2.7 t2v+audio / Seedance 2.0 Pro based on user intent and invokes runcomfy run <model_id> with the matching JSON body. The CLI POSTs to the Model API, polls the request, fetches the result, and downloads any .runcomfy.net/.runcomfy.com URL into --output-dir. Ctrl-C cancels the remote request before exit.

Security & Privacy

• Token storage: runcomfy login writes the API token to ~/.config/runcomfy/token.json with mode 0600 (owner-only read/write). Set RUNCOMFY_TOKEN env var to bypass the file entirely in CI / containers.
• Input boundary: the user prompt is passed as a JSON string to the CLI via --input. The CLI does NOT shell-expand the prompt; it transmits the JSON body directly to the Model API over HTTPS. No shell injection surface from prompt content.
• Third-party content: image / mask / video URLs you pass are fetched by the RunComfy model server, not by the CLI on your machine. Treat external URLs as untrusted; image-based prompt injection is a known risk for any image-edit / video-edit model.
• Outbound endpoints: only model-api.runcomfy.net (request submission) and *.runcomfy.net / *.runcomfy.com (download whitelist for generated outputs). No telemetry, no callbacks.
• Generated-file size cap: the CLI aborts any single download > 2 GiB to prevent disk-fill from a malicious or runaway model output.