Wan2.1 Stand In in ComfyUI | Character-Consistent Video Workflow

Keeps characters consistent across video from just one reference image.

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 produce character-driven videos where identity remains consistent from frame to frame. With just one image as input, it generates outputs that preserve facial features, style, and personality across sequences. Ideal for animators, storytellers, and creators of avatars, it ensures both high fidelity and strong continuity. You gain stable results without needing multiple reference images. It saves time, reduces rework, and improves reliability for long-form creative projects. Designed for practical usability, it gives you control over story-driven visuals with dependable results.

Important nodes:

Key nodes in Comfyui Wan2.1 Stand In workflow

• WanVideoModelLoader (#22). Loads Wan 2.1 14B and applies the Stand In LoRA at model initialization. Keep the Stand In adapter connected here rather than later in the graph so identity is enforced throughout the denoising path. Pair with WanVideoVAELoader (#38) for the matching Wan‑VAE.

• WanVideoAddStandInLatent (#102). Fuses your encoded reference image latent into the image embeddings. If identity drifts, increase its influence; if motion seems overly constrained, reduce it slightly.

• WanVideoSampler (#27). The main generator. Tuning steps, scheduler choice, and guidance strategy here has the largest impact on detail, motion richness, and temporal stability. When pushing resolution or length, consider adjusting sampler settings before changing anything upstream.

• WanVideoSetBlockSwap (#70) with WanVideoBlockSwap (#39). Trades GPU memory for speed by swapping attention blocks between devices. If you see out‑of‑memory errors, increase offloading; if you have headroom, reduce offloading for faster iteration.

• ImageRemoveBackground+ (#128) and ImageCompositeMasked (#108). These ensure the subject is cleanly isolated and placed on a neutral canvas, which reduces color contamination and improves the Stand In identity lock across frames.

• VHS_VideoCombine (#180). Controls encoding, frame rate, and file naming for the main MP4 output. Use it to set your preferred FPS and quality target for delivery.

Notes

Wan2.1 Stand In in ComfyUI | Character-Consistent Video Workflow — see RunComfy page for the latest node requirements.