Wan 2.1 V2V 720P LoRA Ultra Fast API

Wan 2.1 V2V 720p LoRA Ultra Fast — wavespeed-ai/wan-2.1/v2v-720p-lora-ultra-fast

Wan 2.1 V2V 720p LoRA Ultra Fast is a speed-optimized video-to-video model that transforms an input video using a text prompt while preserving the original motion and timing. Upload a source video, describe the desired style or changes, and tune strength to balance between “keep the original” and “apply the edit.” It supports up to 3 LoRAs for consistent styling, character look, or branded aesthetics—now with faster turnaround for rapid iteration at 720p.

Key capabilities

Ultra-fast video-to-video transformation anchored to an input video (720p output)
Prompt-guided edits while keeping motion continuity and pacing
Strength control to balance preservation vs. transformation
LoRA support (up to 3) for stable style/identity steering across clips
Fine motion behavior tuning via flow_shift

Use cases

Rapid 720p V2V restyling for social, ads, and creative iteration
Apply a consistent “house style” across multiple clips using LoRAs
Upgrade mood and color grade (cinematic, warm window light, neon, noir)
Brand-safe refresh: keep composition and timing, update textures/colors/details
Quick A/B testing by changing prompts, LoRAs, or seed

Pricing

Duration	Price per video
5s	$0.225
10s	$0.3375

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)
loras (optional): up to 3 LoRA items for style/identity steering

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)

LoRA (up to 3 items):

loras: list of LoRA entries (max 3)
path: owner/model-name or a direct.safetensors URL
scale: LoRA strength

Prompting guide (V2V + LoRA)

A reliable structure is “preserve + edit + style”:

Template: Keep the original motion and timing. Apply [style/look] and adjust [lighting/colors/textures]. Keep faces natural and stable. Avoid flicker, warping, and jitter.

Example prompts

Keep the original motion and composition. Apply a warm, cozy studio look with soft window light, visible dust particles, gentle film grain, and natural skin tones.
Preserve camera motion and timing. Restyle the clip into a flat-color illustration look while keeping clean edges and stable shading.
Keep the scene and movement. Shift the color grade to golden hour, add subtle bloom and soft shadows, maintain realism.

Wan 2.1 v2v 720p Lora Ultra Fast API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/v2v-720p-lora-ultra-fast with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Wan 2.1 v2v 720p Lora Ultra Fast below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/v2v-720p-lora-ultra-fast" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "video": "https://example.com/your-input.mp4",
    "loras": [
        {
            "path": "motimalu/wan-flat-color-v2",
            "scale": 1
        }
    ],
    "negative_prompt": "blurry, low quality, distorted",
    "num_inference_steps": 30,
    "duration": 5,
    "strength": 0.9,
    "guidance_scale": 5,
    "flow_shift": 3,
    "seed": -1
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/wan-2.1/v2v-720p-lora-ultra-fast", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "video": "https://example.com/your-input.mp4",
        "loras": [
                {
                        "path": "motimalu/wan-flat-color-v2",
                        "scale": 1
                }
        ],
        "negative_prompt": "blurry, low quality, distorted",
        "num_inference_steps": 30,
        "duration": 5,
        "strength": 0.9,
        "guidance_scale": 5,
        "flow_shift": 3,
        "seed": -1
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/wan-2.1/v2v-720p-lora-ultra-fast",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "video": "https://example.com/your-input.mp4",
    "loras": [
        {
            "path": "motimalu/wan-flat-color-v2",
            "scale": 1
        }
    ],
    "negative_prompt": "blurry, low quality, distorted",
    "num_inference_steps": 30,
    "duration": 5,
    "strength": 0.9,
    "guidance_scale": 5,
    "flow_shift": 3,
    "seed": -1
}
)

print(output["outputs"][0])  # → URL of the generated output

Wan 2.1 v2v 720p Lora Ultra Fast API — Frequently asked questions

What is the Wan 2.1 v2v 720p Lora Ultra Fast API?

Wan 2.1 v2v 720p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Wan 2.1 V2V 720p LoRA Ultra-Fast converts videos to 720p with custom LoRA support and lets you generate unlimited AI videos. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Wan 2.1 v2v 720p Lora Ultra Fast API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.1-v2v-720p-lora-ultra-fast.

How much does Wan 2.1 v2v 720p Lora Ultra Fast cost per run?

Wan 2.1 v2v 720p Lora Ultra Fast starts at $0.23 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Wan 2.1 v2v 720p Lora Ultra Fast accept?

Key inputs: `prompt`, `video`, `duration`, `seed`, `guidance_scale`, `num_inference_steps`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/wan-2.1-v2v-720p-lora-ultra-fast.

How long does Wan 2.1 v2v 720p Lora Ultra Fast take to generate?

Average end-to-end generation time on WaveSpeedAI is around 158 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Wan 2.1 v2v 720p Lora Ultra Fast outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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