Wan 2.1 V2V 480P LoRA Ultra Fast | Custom LoRA Image API

Wan 2.1 V2V 480p LoRA Ultra Fast — wavespeed-ai/wan-2.1/v2v-480p-lora-ultra-fast

Wan 2.1 V2V 480p LoRA Ultra Fast is a speed-optimized video-to-video model for prompt-guided edits while preserving the original motion and timing of an input video. Upload a source video, describe what should change, and tune strength to control how closely the output follows the original footage. It supports up to 3 LoRAs to enforce a consistent style, character look, or branded aesthetic—now with lower latency for rapid iteration.

Key capabilities

Ultra-fast video-to-video transformation anchored to an input video (480p 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
Fine control over motion behavior via flow_shift

Use cases

Rapid V2V restyling for social clips and creative iteration
Apply a consistent “house style” across multiple clips using LoRAs
Lighting/mood changes (cinematic grade, neon, golden hour) without re-animating motion
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	$0.125
10s	$0.1875

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)

Write prompts that explicitly separate preservation from transformation:

Template: Keep the same camera motion and timing from the input video. Change [style/lighting/environment]. Keep faces natural and consistent. Avoid flicker and warping.

Example prompts

Keep the original motion and composition. Apply a candid, cinematic look with warm sunlight, soft depth of field, and natural skin texture.
Preserve timing and camera movement. Restyle into a clean anime look with consistent shading and no flicker.
Keep the same scene and people. Change the color grade to sunset golden hour, add subtle lens flare, maintain realistic shadows.

Wan 2.1 v2v 480p 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-480p-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 480p Lora Ultra Fast below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/v2v-480p-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-480p-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-480p-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 480p Lora Ultra Fast API — Frequently asked questions

What is the Wan 2.1 v2v 480p Lora Ultra Fast API?

Wan 2.1 v2v 480p Lora Ultra Fast is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Wan 2.1 V2V 480p is an ultra-fast video-to-video model that generates unlimited AI videos and supports custom LoRAs for personalization. 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 480p 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-480p-lora-ultra-fast.

How much does Wan 2.1 v2v 480p Lora Ultra Fast cost per run?

Wan 2.1 v2v 480p Lora Ultra Fast starts at $0.13 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 480p 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-480p-lora-ultra-fast.

How long does Wan 2.1 v2v 480p Lora Ultra Fast take to generate?

Average end-to-end generation time on WaveSpeedAI is around 50 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 480p 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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