Wan 2.1 T2V 720p LoRA
Playground
Try it on WavespeedAI!Wan 2.1 Text-to-Video 720P creates 720P videos from text prompts and supports custom LoRAs for personalized styles. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Wan 2.1 Text-to-Video 720p LoRA
Generate stunning videos from text descriptions with Wan 2.1 Text-to-Video 720p LoRA. This powerful model transforms your written prompts into high-quality 720p videos with smooth motion and cinematic quality — plus full LoRA support for custom styles, characters, and aesthetics.
Why It Stands Out
- Pure text-to-video generation: No source image needed — describe your vision and watch it come to life.
- LoRA support: Load custom LoRA models to apply specific styles, maintain character consistency, or match brand aesthetics.
- Prompt-guided creation: Control scenes, actions, camera movements, and atmosphere through natural language.
- Negative prompt support: Exclude unwanted elements for cleaner, more controlled outputs.
- Flexible duration: Generate 5-second or 10-second clips depending on your needs.
- Reproducibility: Use the seed parameter to recreate exact results or iterate on variations.
Pricing
| Duration | Price |
|---|---|
| 5 seconds | $0.30 |
| 10 seconds | $0.45 |
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the video you want to generate. |
| negative_prompt | No | Elements to avoid in the generated video. |
| lora_url | No | URL to your custom LoRA model file. |
| lora_strength | No | LoRA influence strength (typically 0.5–1.0). |
| size | No | Output resolution (default: 1280×720). |
| num_inference_steps | No | Quality/speed trade-off (default: 30). |
| duration | No | Video length in seconds: 5 or 10 (default: 5). |
| guidance_scale | No | Prompt adherence strength (default: 5). |
| flow_shift | No | Motion intensity control (default: 5). |
| seed | No | Set for reproducibility; -1 for random. |
How to Use
- Write a prompt describing the scene, action, and style you want.
- Add a LoRA (optional) — paste the URL to your custom LoRA and set the strength.
- Set parameters — adjust duration, guidance scale, and other settings as needed.
- Add a negative prompt (optional) to exclude unwanted elements.
- Click Run and wait for your video to generate.
- Preview and download the result.
How to Use LoRA
LoRA (Low-Rank Adaptation) lets you customize the model’s output style without retraining the full model.
- Use your LoRA: Host your .safetensors file at a public URL and paste it into the lora_url field.
- Train your LoRA: Learn how to create custom LoRAs in our guide: Train Your Own LoRA Model
Common LoRA use cases: consistent character appearance, specific art styles, brand-aligned aesthetics, anime/cartoon styles.
Best Use Cases
- Social Media Content — Create scroll-stopping video content from scratch.
- Marketing & Advertising — Produce concept videos and ad creatives without filming.
- Storytelling & Animation — Generate scenes for short films, music videos, or narrative projects.
- Game & App Development — Create promotional trailers and UI animations.
- Personalized Content — Use custom LoRAs for branded or character-consistent videos.
Pro Tips for Best Quality
- Be specific in your prompt — describe subject, action, environment, lighting, and camera movement.
- Use negative prompts to reduce common artifacts: blur, distortion, jitter, or watermarks.
- Start with lower inference steps (20–25) for quick previews, then increase for final renders.
- When using LoRA, start with strength around 0.7 and adjust based on results.
- Fix the seed when iterating to isolate the effect of parameter changes.
Notes
- Ensure any LoRA URLs are publicly 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-2.1/t2v-720p-lora" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"loras": [
{
"path": "Remade-AI/Fire",
"scale": 1
}
],
"size": "1280*720",
"num_inference_steps": 30,
"duration": 5,
"guidance_scale": 5,
"flow_shift": 5,
"seed": -1
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${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. | |
| 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 |
| size | string | No | 1280*720 | 1280*720, 720*1280 | The size of the generated media in pixels (width*height). |
| num_inference_steps | integer | No | 30 | 1 ~ 40 | The number of inference steps to perform. |
| duration | integer | No | 5 | 5 ~ 10 | The duration of the generated media in seconds. |
| guidance_scale | number | No | 5 | 0.00 ~ 20.00 | The guidance scale to use for the generation. |
| flow_shift | number | No | 5 | 1.0 ~ 10.0 | The shift value for the timestep schedule for flow matching. |
| 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 |