Wan 2.1 I2V 720P LoRA | Fast Image-to-Video API

Wan 2.1 Image-to-Video 720p LoRA

Wan 2.1 Image-to-Video 720p LoRA is a powerful image-to-video generation model that transforms static images into dynamic 720p HD videos. With full LoRA support, apply custom styles, artistic effects, or consistent character appearances to create unique animated content.

Why It Stands Out

Image-driven generation: Animate any image while preserving its original style and composition.
LoRA support: Apply custom LoRA models for specific styles, characters, or aesthetics.
Prompt-guided motion: Describe camera movements, actions, and atmospheric effects.
Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
Negative prompt support: Exclude unwanted elements for cleaner outputs.
HD 720p output: Generate crisp 1280×720 videos with rich detail.
Fine-tuned control: Adjust guidance scale and flow shift for precise results.
Reproducibility: Use the seed parameter to recreate exact results.

Parameters

Parameter	Required	Description
image	Yes	Source image to animate (upload or public URL).
prompt	Yes	Text description of desired motion and style.
negative_prompt	No	Elements to avoid in the output.
loras	No	LoRA models to apply (path and scale).
size	No	Output resolution (default: 1280×720).
num_inference_steps	No	Quality/speed trade-off (default: 30).
duration	No	Video length: 5 or 10 seconds (default: 5).
guidance_scale	No	Prompt adherence strength (default: 5).
flow_shift	No	Motion flow control (default: 5).
seed	No	Set for reproducibility; -1 for random.

How to Use

Upload your source image — drag and drop a file or paste a public URL.
Write a prompt describing the motion and action you want. Use the Prompt Enhancer for AI-assisted optimization.
Add LoRAs (optional) — select LoRA models and adjust their scale.
Add a negative prompt (optional) — specify elements to exclude.
Adjust parameters — set duration, guidance scale, and other settings as needed.
Click Run and wait for your video to generate.
Preview and download the result.

How to Use LoRA

LoRA (Low-Rank Adaptation) lets you apply custom styles without retraining the full model.

Add LoRA: Enter the LoRA path and adjust the scale (0.0–1.0).
Recommended LoRAs: Check the interface for suggested LoRAs with preview images.
Scale adjustment: Higher scale means stronger style effect.

Best Use Cases

Style Transfer — Convert images to anime, cartoon, or artistic video styles.
Creative Animation — Apply unique visual effects like crush, melt, or transform.
Social Media Content — Turn photos into engaging video posts.
Marketing & Advertising — Animate product images with custom brand styles.
Artistic Projects — Create unique animated content with specific aesthetics.

Pricing

Duration	Price
5 seconds	$0.30
10 seconds	$0.45

Pro Tips for Best Quality

Use high-resolution, well-lit source images for optimal results.
Be specific in your prompt — describe the action, motion, and effects you want.
Start with LoRA scale around 0.7–1.0 and adjust based on results.
Use negative prompts to reduce artifacts like blur, distortion, or unwanted motion.
Check recommended LoRAs for inspiration and proven style effects.
Fix the seed when iterating to compare different parameter settings.

Notes

Ensure uploaded image URLs are publicly accessible.
Higher num_inference_steps produces better quality but increases generation time.
Processing time varies based on parameters and current queue load.
Please ensure your content complies with usage guidelines.

Wan 2.1 I2v 720p Lora API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-720p-lora 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 I2v 720p Lora below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1/i2v-720p-lora" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "negative_prompt": "blurry, low quality, distorted",
    "loras": [
        {
            "path": "Remade-AI/Crush",
            "scale": 1
        }
    ],
    "size": "1280*720",
    "num_inference_steps": 30,
    "duration": 5,
    "guidance_scale": 5,
    "flow_shift": 5,
    "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/i2v-720p-lora", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://example.com/your-input.jpg",
        "negative_prompt": "blurry, low quality, distorted",
        "loras": [
                {
                        "path": "Remade-AI/Crush",
                        "scale": 1
                }
        ],
        "size": "1280*720",
        "num_inference_steps": 30,
        "duration": 5,
        "guidance_scale": 5,
        "flow_shift": 5,
        "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/i2v-720p-lora",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "negative_prompt": "blurry, low quality, distorted",
    "loras": [
        {
            "path": "Remade-AI/Crush",
            "scale": 1
        }
    ],
    "size": "1280*720",
    "num_inference_steps": 30,
    "duration": 5,
    "guidance_scale": 5,
    "flow_shift": 5,
    "seed": -1
}
)

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

Wan 2.1 I2v 720p Lora API — Frequently asked questions

What is the Wan 2.1 I2v 720p Lora API?

Wan 2.1 I2v 720p Lora is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. Wan 2.1 i2v-720p generates image-to-video outputs at 720p and supports custom LoRA adapters for personalized styles and fine-tuning. 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 I2v 720p Lora 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-i2v-720p-lora.

How much does Wan 2.1 I2v 720p Lora cost per run?

Wan 2.1 I2v 720p Lora starts at $0.30 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 I2v 720p Lora accept?

Key inputs: `prompt`, `image`, `duration`, `size`, `seed`, `guidance_scale`. 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-i2v-720p-lora.

How long does Wan 2.1 I2v 720p Lora take to generate?

Average end-to-end generation time on WaveSpeedAI is around 74 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 I2v 720p Lora 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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