Wan 2.2 T2V 5b 720p LoRA
Playground
Try it on WavespeedAI!Wan 2.2 T2V 5B is a 5B text-to-video model with LoRA support that generates 720p videos from text prompts for easy personalization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Features
Wan 2.2 t2v-5b-720p-lora
Wan 2.2 T2V 5B is a 5B-parameter text-to-video model with LoRA support that generates 5-second, 1280×720 videos from text prompts. It is built on WAN AI’s Mixture of Experts (MoE) architecture, combining high-noise and low-noise experts across denoising timesteps for sharp details, smooth motion, and strong cinematic style.
Why it looks great
- Cinematic-level aesthetic control Tuned to professional filmmaking standards, with rich control over lighting, color palette, composition, lens, and camera movement.
- Large-scale complex motion Handles dramatic actions, character interactions, and challenging camera paths while keeping motion stable and natural.
- Precise semantic compliance Strong scene understanding and multi-object generation, so shots stay close to your story beats and prompt.
- 5B parameter backbone Higher texture fidelity, better character consistency, and improved temporal coherence across frames.
Pricing
- Flat price: $0.10 per run
How to Use
-
In the prompt box, describe the scene, characters, motion, camera, lighting, and style in detail.
-
(Optional) Click Add Item under loras to attach up to 3 LoRA adapters.
- In each LoRA slot, paste the LoRA path (owner/model-name) or a direct
.safetensorsURL. - Use the scale slider to control strength (for example, 0.6–1.0 for most character or style LoRAs).
- In each LoRA slot, paste the LoRA path (owner/model-name) or a direct
-
Choose the size 1280×720 or 720×1280.
-
Set the seed
-
Click Run. After generation finishes, preview the video on the right panel and download it if you are satisfied.
LoRA Guides
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-5b-720p-lora" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"size": "1280*720",
"seed": -1
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${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. | |
| size | string | No | 1280*720 | 1280*720, 720*1280 | The size of the generated media in pixels (width*height). |
| 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 |
| 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 |