video-to-video
Wan 2.1 V2V converts source videos into AI 720p outputs for scalable video-to-video production and unlimited video generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
Your request will cost $0.3 per run.
For $10 you can run this model approximately 33 times.
One more thing::
ExamplesView all
README
Wan 2.1 V2V 720p — wavespeed-ai/wan-2.1/v2v-720p
Wan 2.1 V2V 720p is a video-to-video model for prompt-guided transformations while preserving the original motion and timing of an input clip. Upload a source video, describe the desired changes (style, lighting, atmosphere, details), and tune strength to control how closely the output follows the original footage. It’s a solid default choice when you want higher clarity than 480p and don’t need LoRA.
Key capabilities
- Video-to-video transformation anchored to an input video (720p output)
- Preserves motion continuity and pacing from the original clip
- Prompt-guided edits for style, lighting, mood, and scene details
- Strength control to balance “keep original” vs. “transform”
- Motion behavior tuning via flow_shift
Use cases
- Restyle a video while keeping original motion (cinematic, dreamy, stylized looks)
- Improve mood/lighting (sunset grade, neon night, warm indoor) without changing timing
- Brand-safe refresh: keep composition, update colors/textures/details
- Social and marketing variations from the same base footage
- Quick iteration: change prompts and seeds to generate multiple creative takes
Pricing
| Duration | Price per video |
|---|---|
| 5s | $0.30 |
| 10s | $0.45 |
Inputs
- video (required): source video to transform
- prompt (required): what to change and how the result should look
- negative_prompt (optional): what to avoid (artifacts, jitter, unwanted elements)
Parameters
- num_inference_steps: sampling steps
- duration: output duration (seconds)
- strength: how strongly to transform the input video (lower = preserve more; higher = change more)
- guidance_scale: prompt adherence strength
- flow_shift: motion/flow behavior tuning
- seed: random seed (-1 for random; fixed for reproducible results)
Prompting guide (V2V)
Write prompts that explicitly separate preservation from transformation:
Template: Keep the original motion and timing. Change [style/lighting/environment/details]. Keep faces stable and natural. Avoid flicker, warping, and jitter.
Example prompts
- Keep the original dance motion and camera timing. Apply a warm golden-hour cinematic color grade, soft bloom from streetlights, subtle film grain, and realistic shadows.
- Preserve motion and composition. Restyle the scene into a clean illustrated look with stable edges and no flicker.
- Keep the same choreography and timing. Add light rain, wet pavement reflections, and moody neon lighting while maintaining natural skin tones.