Wan 2.2 Image LoRA Trainer

Playground

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 images to start!

Features

Wan 2.2 image LoRA Trainer

Wan 2.2 LoRA Trainer is a high-performance custom model training service for the Wan 2.2 text-to-video generation model. Train personalized LoRA (Low-Rank Adaptation) models 10x faster than traditional methods, enabling custom styles, characters, and objects for video generation.

Training Architecture

The trainer leverages Wan 2.2’s innovative MoE (Mixture of Experts) architecture, producing two specialized LoRA models:

high_noise_lora: Optimized for high-noise denoising timesteps, handling initial structure and composition
low_noise_lora: Optimized for low-noise denoising timesteps, refining details and final output quality

This dual-model approach ensures superior training efficiency and generation quality across all denoising stages.

Training Process

Data Upload: Upload a ZIP file containing your training images
Automatic Processing: The system automatically processes and optimizes your dataset
Dual Model Training: Simultaneously trains both high_noise_lora and low_noise_lora models
Model Delivery: Receive two specialized LoRA models ready for video 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-image-lora-trainer" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "data": "",
    "trigger_word": "p3r5on",
    "steps": 1000,
    "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 T2V LoRA, you need to upload a zip file containing at least 10 images. In addition to images the archive can contain text files with captions. Each text file should have the same name as the image file it corresponds to.
trigger_word	string	No	p3r5on	-	The phrase that will trigger the model to generate an video.
steps	integer	No	1000	1000 ~ 10000	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

Wan 2.2 I2V LoRA Trainer Wan 2.2 T2V 480p