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.
Siap
$0.225per run·~44 / $10
ContohLihat semua
A teenage girl painting on a canvas in a cozy studio, wearing an oversized shirt with paint stains, warm lighting from a side window.She dips her brush and continues to paint, her hand moving with precision, dust particles dance in the light beam, canvas shimmers with evolving color
flat color, no lineart, A woman races up the staircase as rain pours down outside at night.
The man casually adjusts his sunglasses and looks to the side, a slight breeze moves his jacket, light reflections flicker across the car surface, subtle movement in the background crowd
A female astronaut in a sleek space suit, standing in front of a large observation window showing a galaxy view, cold lighting.The stars slowly drift across the window, her eyes track the movement, slight motion in her suit's light indicators, a sense of vastness and quiet tension
A highly detailed shot of a futuristic racing car speeding on a neon-lit track. The car's tires leave a blue light trail. The background is blurred, motion blur effect, cyberpunk aesthetic, high speed, dynamic angle, 8K, cinematic.
A majestic magic dragon, a close-up shot, breathing fire over an ancient castle on a mountain cliff, surrounded by epic mountains and clouds. Dramatic lighting, fantasy art, cinematic, hyper-realistic, volumetric light, 4K, wide shot.
A futuristic soldier, equipped with advanced gear, running through a neon-lit, cyberpunk city at night. The camera is in a handheld shot, giving it a shaky, first-person perspective. Fast-paced action, blurred lights, high-energy, cinematic.
A striking view of a young female fashion model with flowing blonde hair, wearing a vibrant emerald green dress, dramatically twirling on a rooftop overlooking a bustling cityscape at sunset. The camera is a sweeping orbital shot circling her, capturing the movement of her dress and hair against the urban backdrop. Golden hour light casting long shadows, wind gently blowing her hair, creating a sense of freedom and energy, high fashion editorial, cinematic, 8k, ultra-detailed.
A cute fawn running through a sun-drenched forest with blooming wildflowers. The sun shines through the trees, casting beautiful light rays. Soft focus, macro photography, fairytale vibe, dreamy atmosphere, vibrant colors, UHD, slow motion.
Model Terkait
README
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 outputPython 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 outputWan 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.