WaveSpeedAI APIWavespeed AIWan 2.2 T2V 5b 720p LoRA

Wan 2.2 T2V 5b 720p LoRA

Wan 2.2 T2V 5b 720p LoRA

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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

  1. In the prompt box, describe the scene, characters, motion, camera, lighting, and style in detail.

  2. (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 .safetensors URL.
    • Use the scale slider to control strength (for example, 0.6–1.0 for most character or style LoRAs).
  3. Choose the size 1280×720 or 720×1280.

  4. Set the seed

  5. 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

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-The positive prompt for the generation.
sizestringNo1280*7201280*720, 720*1280The size of the generated media in pixels (width*height).
lorasarrayNomax 3 itemsList of LoRAs to apply (max 3).
loras[].pathstringYes-Path to the LoRA model
loras[].scalefloatYes-0.0 ~ 4.0Scale of the LoRA model
seedintegerNo-1-1 ~ 2147483647The random seed to use for the generation. -1 means a random seed will be used.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

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