Wan 2.2 I2V 5b 720p LoRA

Wan 2.2 I2V 5b 720p LoRA

Playground

Wan 2.2 i2v-5B-720p is a 5B image-to-video model producing 720p videos with LoRA support for style customization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Wan 2.2 5B is a new-generation multimodal video model built by WAN AI. It uses an MoE (Mixture of Experts) architecture with high-noise and low-noise experts allocated across denoising timesteps, enabling sharper detail, cleaner motion, and richer cinematic style in short video clips.

Why it looks great

• Cinematic-level aesthetic control Aligned with professional film-making standards, with fine control over lighting, color palette, composition, camera lens and movement.
• Large-scale complex motion Handles dramatic actions, crowd scenes, character interactions, and challenging camera paths with smooth, stable motion.
• Precise semantic compliance Strong scene understanding and multi-object generation, producing shots that stay close to the creative brief.
• 5B parameter scale The 5-billion-parameter backbone brings higher fidelity textures, more consistent character details, and better temporal coherence.

Pricing

• Just $0.1 per run!

How to Use

1. In the prompt box, describe the scene, characters, motion, camera, lighting, and style.
2. (Optional) Add up to 3 LoRA adapters in the LoRA section.
3. Set the seed (−1 for random, or a fixed number for reproducible runs).
4. Click Run and wait for the video to render, then preview and download.

For detailed LoRA tutorials:

• How to train your LoRA?
• How to use your LoRA?

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
  "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/i2v-5b-720p-lora" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "${TASK}" | jq -r '.urls.get // empty')
if [ -z "${RESULT_URL}" ]; then RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"; fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}")
  RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "${STATUS}" >&2; exit 1 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters