Wan 2.2 I2V LoRA Trainer
Playground
Try it on WavespeedAI!Train custom Wan 2.2 LoRA models 10x faster. Style training, character training, object training. From concept to model in minutes, not hours. Upload a ZIP file containing videos to start!
Features
Wan 2.2 I2V LoRA Trainer
Wan 2.2 I2V (Image-to-Video) LoRA Trainer is a specialized training service for creating custom LoRA models optimized for image-to-video generation. Train personalized models 10x faster using video datasets to achieve consistent style transfer and character animation from static images.
Training Architecture
Built on Wan 2.2’s advanced MoE (Mixture of Experts) architecture, the trainer generates two specialized LoRA models:
- high_noise_lora: Optimized for high-noise denoising timesteps, handling initial motion planning and temporal structure
- low_noise_lora: Optimized for low-noise denoising timesteps, refining motion details and ensuring smooth transitions
This dual-model approach ensures superior image-to-video conversion quality with consistent temporal coherence.
Training Process
- Video Data Upload: Upload a ZIP file containing your training video sequences
- Temporal Analysis: The system analyzes motion patterns and temporal relationships
- Dual Model Training: Simultaneously trains both high_noise_lora and low_noise_lora models
- Motion Optimization: Fine-tunes models for smooth image-to-video transitions
- Model Delivery: Receive two specialized LoRA models optimized for I2V generation
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-i2v-lora-trainer" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"data": "",
"trigger_word": "p3r5on",
"steps": 100,
"learning_rate": 0.0004,
"lora_rank": 32
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
Parameter | Type | Required | Default | Range | Description |
---|---|---|---|---|---|
data | string | Yes | - | - | To train a WAN I2V LoRA, you need to upload a zip file containing videos. In addition to videos the archive can contain text files with captions. Each text file should have the same name as the video file it corresponds to. |
trigger_word | string | No | p3r5on | - | The phrase that will trigger the model to generate an video. |
steps | integer | No | 100 | 50 ~ 1500 | Number of steps to train the LoRA on. |
learning_rate | number | No | 0.0004 | 0.00000 ~ 1.00000 | |
lora_rank | integer | No | 32 | 1 ~ 128 |
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 Query Parameters
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 | 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 |