Transform videos into stunning artistic styles with perfect motion flow.
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 transform existing or generated videos into new artistic styles while keeping motion stable and structure accurate. You can apply cinematic, painterly, or abstract visual effects directly in your video pipeline. It offers strong temporal coherence for smooth transitions between frames. With intuitive controls, it streamlines your creative process and ensures consistent, high-quality results. Perfect for editors and designers seeking refined, stylized video outputs.
Important nodes:
Key nodes in Comfyui Wan 2.1 Ditto workflow
WanVideoVACEModelSelect (#128)
Choose which Ditto weights to use for stylization. The default global Ditto model is a balanced choice for most footage. If your goal is anime‑to‑real conversion, select the sim‑to‑real Ditto variant referenced in the node note. Switching Ditto variants changes the character of the restyle without touching other settings.
WanVideoVACEEncode (#126)
Builds the visual conditioning from your input frames. The key controls are width, height, and num_frames, which should match the prepared video for best results. Use strength to adjust how assertively Ditto’s style influences the edit, and vace_start_percent and vace_end_percent to limit when conditioning applies across the diffusion trajectory. Enable tiled_vae on very large resolutions to reduce memory pressure.
WanVideoTextEncode (#111)
Encodes positive and negative prompts via the mT5‑XXL encoder to guide style and content. Keep positive prompts concise and descriptive, and use negatives to suppress artifacts such as flicker or over‑saturation. The force_offload and device options let you trade speed for memory if you are running large models.
WanVideoSampler (#119)
Runs the Wan 2.1 backbone with Ditto stylization to generate the final latents. The most impactful settings are steps, cfg, scheduler, and seed. Use denoise_strength when you want to preserve more of the original structure, and keep slg_args connected to balance content fidelity against style strength. Increasing steps or guidance may improve detail at the cost of time.
ImageScaleByAspectRatio V2 (#76)
Sets a stable target size for all frames before conditioning. Drive the long‑side target with the standalone integer so you can test small, fast previews and then increase resolution for final renders. Keep the scale consistent between iterations to make A/B comparisons meaningful.
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Notes
Wan 2.1 Ditto in ComfyUI | Video Stylization and Motion Consistency — see RunComfy page for the latest node requirements.