SteadyDancer in ComfyUI | I2V Human Animation Workflow

Turns portraits into smooth, lifelike motion videos instantly.

Who it's for: creators who want this pipeline in ComfyUI without assembling nodes from scratch. Not for: one-click results with zero tuning — you still choose inputs, prompts, and settings.

Open preloaded workflow on RunComfy

Open preloaded workflow on RunComfy (browser)

Why RunComfy first
- Fewer missing-node surprises — run the graph in a managed environment before you mirror it locally.
- Quick GPU tryout — useful if your local VRAM or install time is the bottleneck.
- Matches the published JSON — the zip follows the same runnable workflow you can open on RunComfy.

When downloading for local ComfyUI makes sense — you want full control over models on disk, batch scripting, or offline runs.

How to use (local ComfyUI)
1. Load inputs (images/video/audio) in the marked loader nodes.
2. Set prompts, resolution, and seeds; start with a short test run.
3. Export from the Save / Write nodes shown in the graph.

Expectations — First run may pull large weights; cloud runs may require a free RunComfy account.

Overview

This workflow helps you convert still images into fluid, expressive video animations with human-level realism. It preserves facial consistency, body alignment, and visual clarity across frames. SteadyDancer is a first-frame-preserving image-to-video animation framework built on the Wan 2.1 I2V model, enhancing it with pose-driven motion modeling to generate stable, identity-consistent character videos. You'll gain refined control over pose and rhythm without losing character identity. Perfect for dance, portrait, or character motion projects. Create high-fidelity, natural movement animations efficiently using next-gen I2V modeling.

Important nodes:

Key nodes in Comfyui SteadyDancer workflow

WanVideoAddSteadyDancerEmbeds (#71)

This node is the SteadyDancer heart of the graph. It fuses the image conditioning with pose latents and CLIP‑vision cues so the first frame locks identity while motion unfolds naturally. Adjust pose_strength_spatial to control how tightly limbs follow the detected skeleton and pose_strength_temporal to regulate motion smoothness over time. Use start_percent and end_percent to limit where pose control applies within the sequence for more natural intros and outros.

PoseAndFaceDetection (#89)

Runs YOLOv10 detection and ViTPose‑H keypoint estimation on the driving video. If poses miss small limbs or faces, increase input resolution upstream or choose footage with fewer occlusions and cleaner lighting. When multiple people are present, keep the target subject largest in frame so the detector and pose head remain stable.

VHS_LoadVideo (#75)

Controls what portion of the motion source you use. Increase the frame cap for longer outputs or lower it to prototype rapidly. The force_rate input aligns pose spacing with the generation rate and can help reduce stutter when the original clip’s FPS is unusual.

LayerUtility: ImageScaleByAspectRatio V2 (#146)

Keeps frames within a chosen long‑side limit while maintaining aspect ratio and bucketing to a divisible size. Match the scale here to the generation canvas so SteadyDancer does not need to upsample or crop aggressively. If you see soft results or edge artifacts, bring the long side closer to the model’s native training scale for a cleaner decode.

WanVideoSamplerSettings (#119)

Defines the denoising plan for the Wan 2.1 sampler. The scheduler and steps set overall quality versus speed, while cfg balances adherence to the image plus prompt against diversity. seed locks reproducibility, and denoise_strength can be lowered when you want to hew even closer to the reference image’s appearance.

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Notes

SteadyDancer in ComfyUI | I2V Human Animation Workflow — see RunComfy page for the latest node requirements.