Wan 2.2 T2V 720p LoRA
Playground
Try it on WavespeedAI!Wan 2.2 T2V 720p with custom LoRA support turns text prompts into 720p AI videos and enables unlimited video generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Wan 2.2 Text-to-Video 720p LoRA
Wan 2.2 Text-to-Video 720p LoRA is a powerful text-to-video generation model that creates stunning 720p HD videos from text descriptions. With advanced LoRA support including high-noise and low-noise options, apply custom styles, artistic effects, or consistent character appearances to create unique video content.
Why It Stands Out
- HD 720p output: Generate crisp videos in landscape (1280×720) or portrait (720×1280) formats.
- Advanced LoRA support: Three types of LoRA inputs for precise style control.
- Prompt Enhancer: Built-in AI-powered prompt optimization for better results.
- Negative prompt support: Exclude unwanted elements for cleaner outputs.
- Flexible duration: Choose between 5 or 8 second video lengths.
- Reproducibility: Use the seed parameter to recreate exact results.
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the video you want to generate. |
| negative_prompt | No | Elements to avoid in the output. |
| size | No | Output resolution: 1280×720 or 720×1280 (default: 1280×720). |
| duration | No | Video length: 5 or 8 seconds (default: 5). |
| loras | No | Standard LoRA models to apply. |
| high_noise_loras | No | LoRAs applied during high-noise denoising steps. |
| low_noise_loras | No | LoRAs applied during low-noise denoising steps. |
| seed | No | Set for reproducibility; -1 for random. |
How to Use
- Write a prompt describing the scene, action, and style you want. Use the Prompt Enhancer for AI-assisted optimization.
- Add a negative prompt (optional) — specify elements to exclude.
- Select size — choose landscape (1280×720) or portrait (720×1280).
- Set duration — choose 5 or 8 seconds.
- Add LoRAs (optional) — apply standard, high-noise, or low-noise LoRAs.
- Click Run and wait for your video to generate.
- Preview and download the result.
Understanding LoRA Types
- Standard LoRAs: Applied throughout the generation process.
- High-Noise LoRAs: Applied during early denoising steps for structural/compositional effects.
- Low-Noise LoRAs: Applied during later denoising steps for fine detail and style refinement.
Combining different LoRA types gives you precise control over the final output.
Best Use Cases
- Creative Animation — Apply unique visual styles and effects.
- Social Media Content — Create platform-optimized videos for TikTok, Reels, and Shorts.
- Marketing & Advertising — Produce stylized promotional videos.
- Artistic Projects — Generate videos with specific aesthetic styles.
- Character Consistency — Maintain character appearance across multiple videos.
Pricing
| Duration | Price |
|---|---|
| 5 seconds | $0.35 |
| 8 seconds | $0.56 |
Pro Tips for Best Quality
- Be detailed in your prompt — describe subject, action, environment, lighting, and mood.
- Use negative prompts to reduce artifacts like blur, distortion, or unwanted motion.
- Experiment with different LoRA combinations for unique effects.
- Use high-noise LoRAs for overall style, low-noise LoRAs for detail refinement.
- Choose portrait (720×1280) for mobile-first platforms like TikTok.
- Fix the seed when iterating to compare different LoRA combinations.
Notes
- Ensure uploaded LoRA paths are correct and accessible.
- Processing time varies based on duration and current queue load.
- Please ensure your prompts comply with content guidelines.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/t2v-720p-lora" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"size": "1280*720",
"duration": 5,
"seed": -1
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| prompt | string | Yes | - | The positive prompt for the generation. | |
| negative_prompt | string | No | - | The negative prompt for the generation. | |
| size | string | No | 1280*720 | 1280*720, 720*1280 | The size of the generated media in pixels (width*height). |
| duration | integer | No | 5 | 5, 8 | The duration of the generated media in seconds. |
| loras | array | No | max 3 items | List of LoRAs to apply (max 3). | |
| loras[].path | string | Yes | - | Path to the LoRA model | |
| loras[].scale | float | Yes | - | 0.0 ~ 4.0 | Scale of the LoRA model |
| high_noise_loras | array | No | - | - | List of high noise LoRAs to apply (max 3). |
| low_noise_loras | array | No | - | - | List of low noise LoRAs to apply (max 3). |
| seed | integer | No | -1 | -1 ~ 2147483647 | The random seed to use for the generation. -1 means a random seed will be used. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |