How to use the API to fine-tune Wan image-to-video and image generation models by training a LoRA model?

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You can fine-tune the Wan image-to-video model using the Model Studio API. This guide details all API operations in the fine-tuning workflow, from dataset upload to model deployment.

Prerequisites

Region: The features described in this document are available only in the Singapore region. You must use an API key created in this region.
Environment variables: You have obtained an API key and configured it as an environment variable.
Preparations:
- You have read the Model Fine-tuning Guide for details on supported models, fine-tuning steps, data format, and billing.
- Download the sample datasets:
  - Image-to-video (based on the first frame) : training set, validation set.
  - Image-to-video (based on the first and last frames): training set, validation set.

Upload a dataset

API description: The operation returns a unique file ID (id) to use for subsequent tasks, such as creating a fine-tuning job.

Note

The maximum size for a .zip file uploaded via the API is 1 GB.

Request URL

http

POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/files
Content-type: multipart/form-data

Request parameters

Parameter	Parameter location	Type	Required	Description	Example


Parameter	Parameter location	Type	Required	Description	Example
file	Body (form-data)	file	Yes	The local dataset file in .zip format. When passing parameters, use the format `files=@"<file path>"`. The path can be a relative path or an absolute path.	@"./wan-i2v-training-dataset.zip"
purpose	Body (form-data)	string	Yes	For a fine-tuning task, set this to `fine-tune`.	fine-tune

Response fields

Field	Type	Description	Example


Field	Type	Description	Example
id	string	The unique file ID, which is used to create a fine-tuning job.	file-ft-b2416bacc4d742xxxx
object	string	The object type. The value is always `file`.	file
bytes	integer	The size of the uploaded file in bytes.	73310369
filename	string	The name of the file.	wan-i2v-training-dataset.zip
purpose	string	The purpose of the file. For a fine-tuning task, the value is `fine-tune`.	fine-tune
status	string	The status of the uploaded file. `processed` indicates that the upload was successful.	processed
created_at	integer	The creation time, as a Unix timestamp.	1766127125

Request example

curl

curl --location --request POST 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/files' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--form 'file=@"./wan-i2v-training-dataset.zip"' \
--form 'purpose="fine-tune"'

Response example

Copy and save the id, which is the unique identifier for your uploaded dataset.

json

{
    "id": "file-ft-b2416bacc4d742xxxx",
    "object": "file",
    "bytes": 73310369,
    "filename": "wan-i2v-training-dataset.zip",
    "purpose": "fine-tune",
    "status": "processed",
    "created_at": 1766127125
}

Create a fine-tuning job

API: Starts a model fine-tuning task with a base model and a dataset.

API request

http

POST https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes
Content-type: application/json

Input parameters

Parameter	Location	Type	Required	Description	Example value


Parameter	Location	Type	Required	Description	Example value
model	Body	string	Yes	Specifies the base model to use for fine-tuning. - Image-to-video (from first frame): - wan2.7-i2v: Recommended for fine-tuning based on the first frame. Not recommended for fine-tuning based on first and last frames or for video continuation scenarios. - wan2.6-i2v - wan2.5-i2v-preview - wan2.2-i2v-flash - Image-to-video (from first and last frames): - wan2.2-kf2v-flash	wan2.7-i2v
training_file_ids	Body	array[string]	Yes	An array of file IDs for the training set.	["file-ft-b2416bacc4d742xxxx"]
validation_file_ids	Body	array[string]	No	An array of file IDs for the validation set. If not provided, the system automatically creates a validation set by splitting the training set.	-
training_type	Body	string	Yes	The fine-tuning type. Currently, only `efficient_sft` (LoRA efficient fine-tuning) is supported.	efficient_sft
hyper_parameters	Body	object	No	Hyperparameter settings.	See the tables below

Hyperparameters (hyper_parameters)

For your first training job, we recommend using the default values. Because hyperparameters vary by model, be sure to select the configuration that matches your base model.

Video generation models

If the model performs poorly or fails to converge, consider adjusting parameters such as n_epochs or learning_rate.


Parameter	Type	Required	Description	Recommended value
batch_size	int	Yes	batch size. The number of data samples to process in a single training iteration. - wan2.7-i2v: Recommended value is 1. - wan2.6-i2v: Recommended value is 1. - wan2.5-i2v-preview: Recommended value is 2. - wan2.2-i2v-flash: Recommended value is 4. - wan2.2-kf2v-flash: Recommended value is 4.	Varies by model
n_epochs	int	Yes	Number of epochs. The total training steps are determined by the number of epochs (`n_epochs`), dataset size, and batch size (`batch_size`). The formula is: `steps` = `n_epochs` × ceil(`dataset_size` / `batch_size`). To ensure the model is fully trained, we recommend a minimum of 800 total training steps. You can estimate the minimum required number of epochs using this formula: `n_epochs = 800 / ceil(dataset_size / batch_size)`. > For example: If your dataset has 5 samples and the `batch_size` is 2, the number of steps per epoch is ceil(5/2) = 3. The minimum recommended `n_epochs` would be 800 / 3 ≈ 267. This is a minimum; you can increase it as needed.	400
learning_rate	float	Yes	learning rate. Controls the magnitude of model weight updates. A value that is too high can degrade model performance, while a value that is too low may result in insignificant changes.	2e-5
eval_epochs	int	Yes	validation interval. The value must be ≥ `n_epochs/10`. Specifies how often (in epochs) to evaluate the model on the validation set during training.	50
max_pixels	int	Yes	Maximum resolution for training videos. Sets the maximum resolution (width × height) for videos in the training set. Videos exceeding this limit are scaled down, while smaller videos remain unchanged. - wan2.7-i2v: Recommended 147456. Range: 36864 (192×192) to 147456 (384×384). - wan2.6-i2v: Recommended 36864. Range: 16384 (128×128) to 36864 (192×192). - wan2.5-i2v-preview: Recommended 36864. Range: 16384 (128×128) to 36864 (192×192). - wan2.2-i2v-flash: Recommended 262144. Range: 65536 (256×256) to 262144 (512×512). - wan2.2-kf2v-flash: Recommended 262144. Range: 65536 (256×256) to 262144 (512×512).	Varies by model
split	float	No	Training set split ratio. The value must be in the range (0, 1). This parameter applies only if `validation_file_ids` is not provided. This parameter automatically splits a portion of the training set to be used as a validation set. For example, a value of 0.9 means 90% of the data is used for training and 10% for validation. The final size of the validation set is also constrained by `max_split_val_dataset_sample`.	0.9
max_split_val_dataset_sample	int	No	Maximum number of validation samples to split from the training set. The value must be ≥ 1. This parameter applies only if `validation_file_ids` is not provided. This parameter sets an upper limit on the size of the validation set, calculated as follows: `validation_set_size = min(total_dataset_size × (1 − split), max_split_val_dataset_sample)` > For example: If a dataset has 100 samples, `split` is 0.9 (reserving 10% for validation), and `max_split_val_dataset_sample` is 5, the calculated validation set size is 10 (100 × 0.1). However, because the upper limit is 5, only 5 samples will be used for validation.	5
save_total_limit	int	No	Checkpoint save limit. The maximum number of model checkpoints to save. The system keeps only the N most recent checkpoints, where N is this value.	10
lora_rank	int	No	LoRA rank. The rank (dimension) of the LoRA low-rank matrices. This value determines the number of trainable parameters for fine-tuning. A larger value increases the model's fitting capability but slows down training. The value must be a power of 2 (e.g., 16, 32, 64).	32
lora_alpha	int	No	LoRA alpha. The scaling factor for LoRA weights. It adjusts the impact of the fine-tuned parameters on the original model weights and is typically used in conjunction with `lora_rank`. The value must be a power of 2 (e.g., 16, 32, 64).	32

Output parameters

Parameter	Type	Description	Example value


Parameter	Type	Description	Example value
request_id	string	The unique identifier for the request.	0eb05b0c-02ba-414a-9d0c-xxxxxxxxx
output	object	The job details.	-
output.job_id	string	The unique ID for the model fine-tuning job. Use this ID to query the job status.	ft-202511111122-xxxx
output.job_name	string	The name of the model fine-tuning job.	ft-202511111122-xxxx
output.status	string	The status of the fine-tuning job. Valid values: - `PENDING`: Waiting to start. - `QUEUING`: Queued. Only one fine-tuning job can run at a time. - `RUNNING`: In progress. - `CANCELING`: Being canceled. - `SUCCEEDED`: Completed successfully. - `FAILED`: The job failed. - `CANCELED`: Canceled.	PENDING
output.finetuned_output	string	The name of the fine-tuned model. This name is used for model deployment and inference.	wan2.5-i2v-preview-ft-202511111122-xxxx
output.model	string	See output.base_model.	wan2.5-i2v-preview
output.base_model	string	The base model used for fine-tuning.	wan2.5-i2v-preview
output.training_file_ids	array	File IDs of the training set.	["file-ft-b2416bacc4d742xxxx"]
output.validation_file_ids	array	File IDs of the validation set. This field is an empty array if no validation set is provided.	[]
output.hyper_parameters	object	The hyperparameters for the job.
output.training_type	string	The fine-tuning method used. The recommended value is `efficient_sft`.	efficient_sft
output.create_time	string	The creation time of the job.	2025-11-11 11:22:22
output.workspace_id	string	The ID of the workspace that contains the job. See Get a Workspace ID.	llm-xxxxxxxxx
output.user_identity	string	The Alibaba Cloud account ID of the job owner.	12xxxxxxx
output.modifier	string	The Alibaba Cloud account ID of the user who last modified the job.	12xxxxxxx
output.creator	string	The Alibaba Cloud account ID of the user who created the job.	12xxxxxxx
output.group	string	The group associated with the model fine-tuning job.	llm
output.max_output_cnt	integer	Maximum number of checkpoints to save during training. This value is equivalent to the `save_total_limit` hyperparameter.	8

Request example

Replace <your-training-dataset-file-id> with the file ID from the Upload Dataset operation.

Image-to-video: First frame

Image-to-video: First and last frames

Multiple training and validation sets

Wan2.7 model

Wan2.6 model

Wan2.5 model

Wan2.2 model

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.7-i2v",
    "training_file_ids": [\
        "<your-training-dataset-file-id>"\
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "n_epochs": 400,
        "batch_size": 1,
        "learning_rate": 2e-5,
        "split": 0.9,
        "max_split_val_dataset_sample": 5,
        "eval_epochs": 50,
        "max_pixels": 147456,
        "save_total_limit": 10,
        "lora_rank": 32,
        "lora_alpha": 32
    }
}'

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.6-i2v",
    "training_file_ids": [\
        "<your-training-dataset-file-id>"\
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "n_epochs": 400,
        "batch_size": 1,
        "learning_rate": 2e-5,
        "split": 0.9,
        "max_split_val_dataset_sample": 5,
        "eval_epochs": 50,
        "max_pixels": 36864,
        "save_total_limit": 10,
        "lora_rank": 32,
        "lora_alpha": 32
    }
}'

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.5-i2v-preview",
    "training_file_ids": [\
        "<your-training-dataset-file-id>"\
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "n_epochs": 400,
        "batch_size": 2,
        "learning_rate": 2e-5,
        "split": 0.9,
        "max_split_val_dataset_sample": 5,
        "eval_epochs": 50,
        "max_pixels": 36864,
        "save_total_limit": 10,
        "lora_rank": 32,
        "lora_alpha": 32
    }
}'

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.2-i2v-flash",
    "training_file_ids": [\
        "<your-training-dataset-file-id>"\
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "n_epochs": 400,
        "batch_size": 4,
        "learning_rate": 2e-5,
        "split": 0.9,
        "max_split_val_dataset_sample": 5,
        "eval_epochs": 50,
        "max_pixels": 262144,
        "save_total_limit": 10,
        "lora_rank": 32,
        "lora_alpha": 32
    }
}'

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.2-kf2v-flash",
    "training_file_ids": [\
        "<your-training-dataset-file-id>"\
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "n_epochs": 400,
        "batch_size": 4,
        "learning_rate": 2e-5,
        "split": 0.9,
        "max_split_val_dataset_sample": 5,
        "eval_epochs": 50,
        "max_pixels": 262144,
        "save_total_limit": 10,
        "lora_rank": 32,
        "lora_alpha": 32
    }
}'

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.5-i2v-preview",
    "training_file_ids": [\
        "<your-training-set-file-id_1>",\
        "<your-training-set-file-id_2>"\
    ],
    "validation_file_ids": [\
         "<your-validation-set-file-id_1>",\
         "<your-validation-set-file-id_2>"\
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "n_epochs": 400,
        "batch_size": 2,
        "learning_rate": 2e-5,
        "split": 0.9,
        "max_split_val_dataset_sample": 5,
        "eval_epochs": 50,
        "max_pixels": 36864,
        "save_total_limit": 10,
        "lora_rank": 32,
        "lora_alpha": 32
    }
}'

Response example

Focus on these two parameters: output.job_id (job ID) andoutput.finetuned_output (fine-tuned model name).

Video generation model

json

{
    "request_id": "0eb05b0c-02ba-414a-9d0c-xxxxxxxxx",
    "output": {
        "job_id": "ft-202511111122-xxxx",
        "job_name": "ft-202511111122-xxxx",
        "status": "PENDING",
        "finetuned_output": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "model": "wan2.5-i2v-preview",
        "base_model": "wan2.5-i2v-preview",
        "training_file_ids": [\
            "xxxxxxxxxxxx"\
        ],
        "validation_file_ids": [],
        "hyper_parameters": {
            "n_epochs": 400,
            "batch_size": 2,
            "learning_rate": 2.0E-5,
            "split": 0.9,
            "eval_epochs": 50
        },
        "training_type": "efficient_sft",
        "create_time": "2025-11-11 11:22:22"
    }
}

Retrieve a fine-tuning job

API description: Retrieves the status of a fine-tuning job using its job_id. Poll this endpoint to monitor the job's progress. When the job's status becomes SUCCEEDED, the model has successfully completed training.

Note

The fine-tuning job in this example can take several hours to complete. The actual time required depends on the base model.

Request

http

GET https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/{job_id}

Request parameters

Parameter	Location	Type	Required	Description	Example


Parameter	Location	Type	Required	Description	Example
job_id	Path parameter	string	Yes	The ID of the fine-tuning job.	ft-202511111122-xxxx

Response parameters

Parameter	Type	Description	Example


Parameter	Type	Description	Example
request_id	string	The unique ID of the request.	0eb05b0c-02ba-414a-9d0c-xxxxxxxxx
output	object	The details of the job.	-
output.job_id	string	The ID of the fine-tuning job. You can use this ID to query the job status.	ft-202511111122-xxxx
output.job_name	string	The name of the fine-tuning job.	ft-202511111122-xxxx
output.status	string	The status of the fine-tuning job. Possible values include: - PENDING: The job is waiting to start. - QUEUING: The job is in the queue. Only one fine-tuning job can be trained at a time. - RUNNING: The job is in progress. - CANCELING: The job is being canceled. - SUCCEEDED: The job completed successfully. - FAILED: The job failed. - CANCELED: The job has been canceled.	PENDING
output.finetuned_output	string	The name of the model generated by the fine-tuning job.	wan2.5-i2v-preview-ft-202511111122-xxxx
output.model	string	The model used for fine-tuning. This is the same as `base_model`.	wan2.5-i2v-preview
output.base_model	string	The base model used for fine-tuning.	wan2.5-i2v-preview
output.training_file_ids	array	A list of file IDs for the training set.	["file-ft-b2416bacc4d742xxxx"]
output.validation_file_ids	array	A list of file IDs for the validation set. This list is empty if no validation set was provided.	[]
output.hyper_parameters	object	The hyperparameters used for the job.
output.training_type	string	The training method used for fine-tuning. The recommended method is `efficient_sft`.	efficient_sft
output.create_time	string	The time when the fine-tuning job was created.	2025-11-11 11:22:22
output.end_time	string	The time when the fine-tuning job completed.	2025-11-11 16:49:01
output.workspace_id	string	The ID of the Alibaba Cloud Model Studio workspace associated with the API key. See Get a Workspace ID.	llm-xxxxxxxxx
output.user_identity	string	The user's Alibaba Cloud account ID.	12xxxxxxx
output.modifier	string	The Alibaba Cloud account ID of the user who last modified the job.	12xxxxxxx
output.creator	string	The Alibaba Cloud account ID of the user who created the job.	12xxxxxxx
output.group	string	The group associated with the fine-tuning job.	llm
output.max_output_cnt	integer	The maximum number of checkpoints to save. This value is equivalent to the hyperparameter`save_total_limit`.	8
output.output_cnt	integer	The actual number of checkpoints saved. The value is less than or equal to `output.max_output_cnt`.	8
output.usage	integer	The total number of tokens used for training. This value is used for billing.	432000

Request example

Replace <your-job-id> in the URL with the job_id you received when you created the fine-tuning job.

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<your-job-id>' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json'

Response example

The key parameters in the response are output.status and output.usage . A status of SUCCEEDED means the training is complete . The usage value indicates the total number of tokens consumed for billing.

Video generation model

json

{
    "request_id": "9bbb953c-bef2-4b59-9fc5-xxxxxxxxx",
    "output": {
        "job_id": "ft-202511111122-xxxx",
        "status": "SUCCEEDED",
        "finetuned_output": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "model": "wan2.5-i2v-preview",
        "base_model": "wan2.5-i2v-preview",
        "training_file_ids": ["xxxxxxxxxxxx"],
        "validation_file_ids": [],
        "hyper_parameters": {
            "n_epochs": 400,
            "learning_rate": 2.0E-5,
            "split": 0.9,
            "eval_epochs": 50
        },
        "training_type": "efficient_sft",
        "create_time": "2025-11-11 11:22:22",
        "end_time": "2025-11-11 16:49:01",
        "usage": 432000,
        "output_cnt": 8
    }
}

Deploy a model

API description: Deploys a trained model as an API service. Before proceeding, query the fine-tuning job status to ensure the job'sstatus is SUCCEEDED.

Request URL

http

POST https://dashscope-intl.aliyuncs.com/api/v1/deployments
Content-Type: application/json

Request parameters

Parameter	Location	Type	Required	Description	Example


Parameter	Location	Type	Required	Description	Example
model_name	Body	string	Yes	The name of the model to deploy. - To deploy a model from a fine-tuning job, use the`output.finetuned_output` value from the Create a fine-tuning job response. - To deploy an exported model, use the`output[].model_name` value from the Retrieve exported model details response.	wan2.5-i2v-preview-ft-202511111122-xxxx
capacity	Body	integer	Yes	The number of model instances to deploy. The recommended value is 1.	1
plan	Body	string	Yes	The deployment plan. For LoRA efficient fine-tuning, the recommended value is`lora`.	lora
aigc_config	Body	object	Yes	The prompt configuration.	-
aigc_config.use_input_prompt	Body	boolean	Yes	Specifies the prompt generation logic for inference. - `false` (default): Automatic mode. The system ignores the prompt from the API request and generates one using the configured templates. - `true`: Manual mode. The system uses the prompt from the API request and ignores the configured templates.	false
aigc_config.prompt	Body	string	Yes	The preset prompt template. This parameter applies only when`use_input_prompt=false`. During inference, the system uses this template to automatically generate a full description from the input image, including special effect instructions. Note: This parameter automatically overrides the prompt parameter passed in the inference API call.	Provide a video description based on the image content... > See Request examples.
aigc_config.lora_prompt_default	Body	string	Yes	The fallback prompt template. This parameter applies only when`use_input_prompt=false`. If prompt generation using`aigc_config.prompt` fails, the system uses this parameter to generate the video.	Then the s86b5p money rain effect begins... > See Request examples.

About the aigc_config.prompt parameter

Why set this parameter?

Without this parameter, you must write a full prompt for each inference call. When this parameter is set, you only need to upload an image when calling the model. The system then uses the template to add special effect instructions, eliminating the need for manual prompt input.

How to set it?

A prompt consists of a task instruction (what the model should do), an output template (to standardize the format), and an example (to guide the model's structure).

plaintext

Provide a video description based on the image content. The description must include "Then the s86b5p money rain effect begins, countless huge-sized US dollar bills (beige background/dark green patterns) pour down like a torrential rain, densely hitting and surrounding them. The bills continue to fall, while the camera slowly zooms in, they stretch their arms upward, neck slightly tilted back, expression surprised, completely immersed in this wild money rain".
Output template:
The video begins with a shot of [subject description]. [Environment description]. Then the s86b5p money rain effect begins, countless huge-sized US dollar bills (beige background/dark green patterns) pour down like a torrential rain, densely hitting and surrounding them. The bills continue to fall, while the camera slowly zooms in, they stretch their arms upward, neck slightly tilted back, expression surprised, completely immersed in this wild money rain.
Example:
The video begins showing a young woman standing in front of a brick wall covered with ivy. She has long, smooth reddish-brown hair, wearing a white sleeveless dress, a shiny silver necklace, and a smile on her face. The brick wall in the background is covered with green vines, appearing rustic and natural. Then the s86b5p money rain effect begins, countless huge-sized US dollar bills (beige background/dark green patterns) pour down like a torrential rain, densely hitting and surrounding her. The bills continue to fall, while the camera slowly zooms in, she stretches her arms upward, neck slightly tilted back, expression surprised, completely immersed in this wild money rain.

For more information about the output template, see Video Annotation: Write Prompts for Videos.

Response fields

Field	Type	Description	Example


Field	Type	Description	Example
request_id	string	The unique identifier for the request.	0eb05b0c-02ba-414a-9d0c-xxxxxxxxx
output	object	Details about the deployment.	-
output.deployed_model	string	The unique ID of the deployed model. Use this ID to retrieve the deployment status and invoke the model.	wan2.5-i2v-preview-ft-202511111122-xxxx
output.model_name	string	The name of the model.	wan2.5-i2v-preview-ft-202511111122-xxxx
output.status	string	The deployment status: - PENDING: The model is being deployed. - RUNNING: The model is running. - FAILED: The deployment failed.	PENDING
output.base_model	string	The base model used.	wan2.5-i2v-preview
output.gmt_create	string	The creation timestamp of the deployment.	2025-11-11T17:46:53.294
output.gmt_modified	string	The timestamp of the last update.	2025-11-11T17:46:53.294
output.workspace_id	string	The ID of the workspace associated with the Alibaba Cloud Model Studio API key. See Get a Workspace ID.	llm-xxxxxxxxx
output.charge_type	string	The billing model. `post_paid` indicates the post-paid model.	post_paid
output.creator	string	The Alibaba Cloud account ID of the user who created the deployment.	12xxxxxxx
output.modifier	string	The Alibaba Cloud account ID of the user who last modified the deployment.	12xxxxxxx
output.plan	string	The deployment plan.	lora

Request examples

Replace <your-model-name> with thefinetuned_output value from the Create a fine-tuning job response.

First frame

First and last frames

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/deployments' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model_name": "<your-model-name>",
    "aigc_config": {
        "use_input_prompt": false,
        "prompt": "Provide a video description based on the image content. The description must include the phrase “Then the s86b5p money rain effect begins, countless huge-sized US dollar bills (beige background/dark green patterns) pour down like a torrential rain, densely hitting and surrounding them. The bills continue to fall, while the camera slowly zooms in, they stretch their arms upward, neck slightly tilted back, expression surprised, completely immersed in this wild money rain.”\nOutput Template:\nThe video begins with a shot of [subject description]. [Environment description]. Then the s86b5p money rain effect begins, countless huge-sized US dollar bills (beige background/dark green patterns) pour down like a torrential rain, densely hitting and surrounding them. The bills continue to fall, while the camera slowly zooms in, they stretch their arms upward, neck slightly tilted back, expression surprised, completely immersed in this wild money rain.\nExample:\nThe video begins showing a young woman standing in front of a brick wall covered with ivy. She has long, smooth reddish-brown hair, wearing a white sleeveless dress, a shiny silver necklace, and a smile on her face. The brick wall in the background is covered with green vines, appearing rustic and natural. Then the s86b5p money rain effect begins, countless huge-sized US dollar bills (beige background/dark green patterns) pour down like a torrential rain, densely hitting and surrounding her. The bills continue to fall, while the camera slowly zooms in, she stretches her arms upward, neck slightly tilted back, expression surprised, completely immersed in this wild money rain.",
        "lora_prompt_default": "Then the s86b5p money rain effect begins, countless huge-sized US dollar bills (beige background/dark green patterns) pour down like a torrential rain, densely hitting and surrounding the main character. The bills continue to fall, while the camera slowly zooms in, the main character stretches their arms upward, neck slightly tilted back, with a surprised expression, completely immersed in this wild money rain."
    },
    "capacity": 1,
    "plan": "lora"
}'

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/deployments' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model_name": "<your-model-name>",
    "aigc_config": {
        "use_input_prompt": false,
        "prompt": "Provide a video description based on the image content. The description must include the phrase “Then they begin the s86b5p transformation.”\nOutput Template:\nThe video begins with a shot of [subject description]. [Environment description]. Then they begin the s86b5p transformation.\nExample:\nThe video begins with a young woman in an outdoor setting. She has short, curly dark brown hair and a friendly smile. She is wearing a black Polo shirt with colorful floral embroidery. The background features green vegetation and distant mountains. Then she begins the s86b5p transformation.",
        "lora_prompt_default": "Then they begin the s86b5p transformation."
    },
    "capacity": 1,
    "plan": "lora"
}'

Response example

Note the following parameters in the response: output.deployed_model (the unique ID for the deployed model) and output.status (which shows PENDING while the deployment is in progress).

json

{
    "request_id": "96020b2e-9072-4c8a-9981-xxxxxxxxx",
    "output": {
        "deployed_model": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "gmt_create": "2025-11-11T17:46:53.294",
        "gmt_modified": "2025-11-11T17:46:53.294",
        "status": "PENDING",
        "model_name": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "base_model": "wan2.5-i2v-preview",
        "workspace_id": "llm-xxxxxxxxx",
        "charge_type": "post_paid",
        "creator": "12xxxxxxx",
        "modifier": "12xxxxxxx",
        "plan": "lora"
    }
}

Query model deployment status

API description: Poll this API. When the task status status changes to RUNNING, the model is successfully deployed.

Note

Deploying the fine-tuned model in this example takes approximately 5 to 10 minutes.

Request URL

http

GET https://dashscope-intl.aliyuncs.com/api/v1/deployments/{deployed_model}

Request parameters

Parameter	Location	Type	Required	Description	Example


Parameter	Location	Type	Required	Description	Example
deployed_model	Path parameter	string	Yes	The unique ID of the deployed model. Specify the value for the deployed model output parameter `output.deployed_model`.	wan2.5-i2v-preview-ft-202511111122-xxxx

Response fields

Field	Type	Description	Example


Field	Type	Description	Example
request_id	string	The unique identifier for the request.	0eb05b0c-02ba-414a-9d0c-xxxxxxxxx
output	object	Details about the deployment task.	-
output.deployed_model	string	The unique ID of the deployed model. Use this ID to invoke the model.	wan2.5-i2v-preview-ft-202511111122-xxxx
output.model_name	string	The model name.	wan2.5-i2v-preview-ft-202511111122-xxxx
output.status	string	The deployment status: - PENDING: Deploying - RUNNING: Ready - ARREARS_DOWN: Stopped (overdue payment) - ARREARS_RECOVERING: Resuming (overdue payment) - FAILED: Deployment failed - OFFLINING: Taking offline - UPDATING: Updating - UPDATING_FAILED: Update failed	RUNNING
output.base_model	string	The base model used for fine-tuning.	wan2.5-i2v-preview
output.gmt_create	string	The creation time of the deployment task.	2025-11-11T17:46:53.294
output.gmt_modified	string	The last update time of the deployment task.	2025-11-11T18:02:2
output.workspace_id	string	The ID of the workspace associated with the Model Studio API key. See Get Workspace ID.	llm-xxxxxxxxx
output.charge_type	string	The billing model. The value post_paid indicates that the service is post-paid.	post_paid
output.creator	string	The Alibaba Cloud account ID of the user who created the deployment.	12xxxxxxx
output.modifier	string	The Alibaba Cloud account ID of the user who last modified the deployment.	12xxxxxxx
output.plan	string	The deployment plan.	lora

Request example

Replace <replace_with_deployed_model> with the value of the deployed model output parameter output.deployed_model.

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/deployments/<your-deployed-model-id>' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json'

Response example

Monitor the status field. When the status changes to RUNNING , the model is successfully deployed and you can start calling it.

json

{
    "request_id": "66d15f35-0772-409f-bc70-xxxxxxxxx",
    "output": {
        "deployed_model": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "gmt_create": "2025-11-11T17:46:53",
        "gmt_modified": "2025-11-11T18:02:24",
        "status": "RUNNING",
        "model_name": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "base_model": "wan2.5-i2v-preview",
        "workspace_id": "llm-xxxxxxxxx",
        "charge_type": "post_paid",
        "creator": "12xxxxxxx",
        "modifier": "12xxxxxxxx",
        "plan": "lora"
    }
}

Generate videos

To invoke the fine-tuned LoRA model, see Invoke the model to generate videos.

Checkpoint

1. List checkpoints

API description: Retrieves a list of checkpoints that successfully generated preview videos from the validation set. The list excludes checkpoints that failed validation.

Usage limitations: Call this API after model fine-tuning is complete. Otherwise, the API returns an empty list.

Request URL

http

GET https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/{job_id}/validation-results

Request parameters

Parameter	Location	Type	Required	Description	Example


Parameter	Location	Type	Required	Description	Example
job_id	Path	string	Yes	The fine-tuning job ID.	ft-202511111122-xxxx

Response parameters

Parameter	Type	Description	Example


Parameter	Type	Description	Example
request_id	string	The request ID.	0eb05b0c-02ba-414a-9d0c-xxxxxxxxx
output	array[string]	A list of checkpoints.	-
output[].checkpoint	string	The checkpoint name.	checkpoint-160

Request example

Replace <job_id> in the URL with the value of the Create fine-tuning job output parameter: job_id.

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<job_id>/validation-results' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json'

Response example

json

{
    "request_id": "da1310f5-5a21-4e29-99d4-xxxxxx",
    "output": [\
        {\
            "checkpoint": "checkpoint-160"\
        },\
        {\
            "checkpoint": "checkpoint-20"\
        },\
        {\
            "checkpoint": "checkpoint-40"\
        },\
        {\
            "checkpoint": "checkpoint-60"\
        }\
    ]
}

2. Retrieve checkpoint validation results

API description: Retrieves the validation results of a specific checkpoint (e.g., "checkpoint-160") to review the generated video.

Request URL

http

GET https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/{job_id}/validation-details/{checkpoint}?page_no=1&page_size=10

Request parameters

Parameter	Location	Type	Required	Description	Example


Parameter	Location	Type	Required	Description	Example
job_id	Path	string	Yes	The fine-tuning job ID.	ft-202511111122-xxxx
checkpoint	Path	string	Yes	The checkpoint name.	checkpoint-160
page_no	Query	integer	No	The page number. Defaults to 1.	1
page_size	Query	integer	No	The page size. Defaults to 10.	10

Response parameters

Video generation model


Parameter	Type	Description	Example
request_id	string	The request ID.	375b3ad0-d3fa-451f-b629-xxxxxxx
output	object	The output object.	-
output.page_no	integer	The page number.	1
output.page_size	integer	The page size.	10
output.total	integer	The total number of entries in the validation set.	1
output.list	array[object]	A list of validation set results.	-
output.list[].video_path	string	The public URL of the preview video generated from the checkpoint. This URL expires in 24 hours. Download the video before it expires.	https://finetune-result.oss-cn-wulanchabu.aliyuncs.com/xxx.mp4?Expires=xxxx
output.list[].prompt	string	The prompt for the validation data, from the `data.jsonl` annotation file in the dataset.	-
output.list[].first_frame_path	string	The public URL of the first frame image for the validation sample, generated from the source image in the dataset.	https://finetune-result.oss-cn-wulanchabu.aliyuncs.com/xxx.jpeg

Request example

Replace <YOUR_JOB_ID> with the job_id from the Create a fine-tuning job operation.
Replace <YOUR_CHECKPOINT> with the desired checkpoint name, such as "checkpoint-160".

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<YOUR_JOB_ID>/validation-details/<YOUR_CHECKPOINT>?page_no=1&page_size=10' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

Response example

The preview URL expires in 24 hours. Download the file before it expires.

Video generation model

json

{
    "request_id": "375b3ad0-d3fa-451f-b629-xxxxxxx",
    "output": {
        "page_no": 1,
        "page_size": 10,
        "total": 1,
        "list": [\
            {\
                "video_path": "https://finetune-result.oss-cn-wulanchabu.aliyuncs.com/xxx.mp4?Expires=xxxx",\
                "prompt": "The video begins with a young man sitting in a cafe...",\
                "first_frame_path": "https://finetune-result.oss-cn-wulanchabu.aliyuncs.com/xxx.jpeg"\
            }\
        ]
    }
}

3. Export a checkpoint

API description: Exports a checkpoint as a deployable model.

Request URL

http

GET https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/{job_id}/export/{checkpoint}?model_name={model_name}

Request parameters

Parameter	Location	Type	Required	Description	Example


Parameter	Location	Type	Required	Description	Example
job_id	Path	string	Yes	The fine-tuning job ID.	ft-202511111122-xxxx
checkpoint	Path	string	Yes	The checkpoint name.	checkpoint-160
model_name	Query	string	Yes	The display name for the exported model in the console. This name must be globally unique. We recommend using English letters, digits, underscores (_), and hyphens (-). Note: This name is for display in the console only. The Get exported model details operation returns the actual model name in the `output[].model_name` parameter.	wan2.5-i2v-preview-ft-202511111122-xxxx

Response parameters

Parameter	Type	Description	Example


Parameter	Type	Description	Example
request_id	string	The request ID.	0eb05b0c-02ba-414a-9d0c-xxxxxxxxx
output	boolean	Indicates whether the export request was submitted successfully. - `true`: The request was submitted successfully. - `false`: The request failed. Retry the operation.	true

Request example

<The ID of the fine-tuning job>: Replace the entire placeholder with the value of the output parameter job_id from Create a fine-tuning job.
<checkpoint_to_export>: The value of the checkpoint to export, such as "checkpoint-160".
<exported_model_name>: The custom name of the exported model, used only for display in the console.

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<your-fine-tuning-job-id>/export/<your-checkpoint-to-export>?model_name=<your-exported-model-display-name>' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

Response example

json

{
    "request_id": "0817d1ed-b6b6-4383-9650-xxxxx",
    "output": true
}

4. Retrieve exported model details

API description: Retrieves the status of all checkpoints for a fine-tuning job. Use this operation to confirm that a checkpoint export is complete and to get the uniquemodel_name required for model deployment and invocation.

Request API

http

GET https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/{job_id}/checkpoints

Request parameters

Parameter	Location	Type	Required	Description	Example


Parameter	Location	Type	Required	Description	Example
job_id	Path parameter	string	Yes	The fine-tuning job ID.	ft-202511111122-xxxx

Response fields

Field	Type	Description	Example


Field	Type	Description	Example
request_id	string	The unique request ID.	0eb05b0c-02ba-414a-9d0c-xxxxxxxxx
output	array[object]	A list of checkpoint details.	-
output[].create_time	string	The creation time.	2025-11-11T13:27:29
output[].job_id	string	The fine-tuning job ID.	ft-202511111122-xxxx
output[].checkpoint	string	The checkpoint name.	checkpoint-160
output[].full_name	string	The full checkpoint ID.	ft-202511111122-496e-checkpoint-160
output[].model_name	string	The name of the exported model, used for model deployment and invocation. Returned only when the `status` is SUCCEEDED.	wan2.5-i2v-preview-ft-202511111122-xxxx-c160
output[].model_display_name	string	The display name of the model.	wan2.5-i2v-preview-ft-202511111122-xxxx
output[].status	string	The model export status: - PENDING: Queued for export. - PROCESSING: Export in progress. - SUCCEEDED: Export successful. - FAILED: Export failed. - UNSUPPORTED: Export not supported.	SUCCEEDED

Request example

Replace <your-fine-tuning-job-id> with thejob_id returned from the Create a fine-tuning job operation.

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<your-fine-tuning-job-id>/checkpoints' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

Response example

In the returned list, locate your target checkpoint, such as checkpoint-160. When its status becomes SUCCEEDED, the export is complete. Save the model_name, which is the unique identifier for subsequent model deployment and invocation.

json

{
    "request_id": "b0e33c6e-404b-4524-87ac-xxxxxx",
    "output": [\
         ......,\
        {\
            "create_time": "2025-11-11T13:42:31",\
            "full_name": "ft-202511111122-496e-checkpoint-180",\
            "job_id": "ft-202511111122-496e",\
            "checkpoint": "checkpoint-180",\
            "status": "PENDING" // An unexported checkpoint does not have a model_name field.\
        },\
        {\
            "create_time": "2025-11-11T13:27:29",\
            "full_name": "ft-202511111122-496e-checkpoint-160",\
            "job_id": "ft-202511111122-496e",\
            "checkpoint": "checkpoint-160",\
            "model_name": "wan2.5-i2v-preview-ft-202511111122-xxxx-c160", // Important field used for model deployment and invocation.\
            "model_display_name": "wan2.5-i2v-preview-ft-202511111122-xxxx",\
            "status": "SUCCEEDED" // A successfully exported checkpoint.\
        },\
        ......\
\
    ]
}

5. Deploy and call the model

After you export a checkpoint and retrieve its model_name, follow these steps:

Deploy the model: For the model_name parameter, enter the value from the export.
Query deployment status
Call the model to generate videos/images

Fine-tuning job management

Query fine-tuning job logs

Request example

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<fine-tuning-job-id>/logs' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

Response example

json

{
    "request_id": "b7ecb456-6dd1-4f35-a581-xxxxxx",
    "output": {
        "total": 25,
        "logs": [\
            "2025-11-11 11:23:37,315 - INFO - data process succeeded, start to fine-tune",\
            " Actual number of consumed tokens is 215040 !",\
            " Actual number of consumed tokens is 419840 !",\
            " Actual number of consumed tokens is 624640 !",\
            " Actual number of consumed tokens is 829440 !",\
            " Actual number of consumed tokens is 1034240 !",\
            " Actual number of consumed tokens is 1239040 !",\
            " Actual number of consumed tokens is 1443840 !",\
            " Actual number of consumed tokens is 1648640 !",\
            " Actual number of consumed tokens is 1853440 !",\
            " Actual number of consumed tokens is 2058240 !",\
            " Actual number of consumed tokens is 2263040 !",\
            " Actual number of consumed tokens is 2467840 !",\
            " Actual number of consumed tokens is 2672640 !",\
            " Actual number of consumed tokens is 2877440 !",\
            " Actual number of consumed tokens is 3082240 !",\
            " Actual number of consumed tokens is 3287040 !",\
            " Actual number of consumed tokens is 3491840 !",\
            " Actual number of consumed tokens is 3696640 !",\
            " Actual number of consumed tokens is 3901440 !",\
            "2025-11-11 16:31:40,760 - INFO - fine-tuned output got, start to transfer it for inference",\
            "2025-11-11 16:32:29,162 - INFO - transfer for inference succeeded, start to deliver it for inference",\
            "2025-11-11 16:40:28,784 - INFO - start to save checkpoint",\
            "2025-11-11 16:49:01,738 - INFO - finetune-job succeeded",\
            "2025-11-11 16:49:02,234 - INFO - ##FT_COMPLETE##"\
        ]
    }
}

List fine-tuning jobs

Request example

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

Response example

json

{
    "request_id": "bf4d3475-f50c-42e2-a263-xxxxxxxxx",
    "output": {
        "page_no": 1,
        "page_size": 10,
        "total": 1,
        "jobs": [\
            {\
                "job_id": "ft-202511111122-xxxx",\
                "job_name": "ft-202511111122-xxxx",\
                "status": "SUCCEEDED",\
                "finetuned_output": "wan2.5-i2v-preview-ft-202511111122-xxxx",\
                "model": "wan2.5-i2v-preview",\
                "base_model": "wan2.5-i2v-preview",\
                "training_file_ids": [\
                    "xxxxxxxxx"\
                ],\
                "validation_file_ids": [],\
                "hyper_parameters": {\
                    "n_epochs": 400,\
                    "batch_size": 2,\
                    "learning_rate": 2.0E-5,\
                    "split": 0.9,\
                    "eval_epochs": 50\
                },\
                "training_type": "efficient_sft",\
                "create_time": "2025-11-11 11:22:22",\
                "workspace_id": "llm-xxxxxxxxx",\
                "user_identity": "xxxxxxxxx",\
                "modifier": "xxxxxxxxx",\
                "creator": "xxxxxxxxx",\
                "end_time": "2025-11-11 16:49:01",\
                "group": "llm",\
                "usage": 432000,\
                "max_output_cnt": 8,\
                "output_cnt": 8\
            }\
        ]
    }
}

Cancel fine-tuning job

Request example

curl

curl --location --request POST 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<fine-tuning-job-id>/cancel' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json'

Response example

json

{
    "request_id": "d8dab938-e32e-40bf-83ab-xxxxxx",
    "output": {
        "status": "success"
    }
}

Delete fine-tuning job

Request example

curl

curl --location --request DELETE 'https://dashscope-intl.aliyuncs.com/api/v1/fine-tunes/<fine-tuning-job-id>' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json'

Response example

json

{
    "request_id": "1301136c-12f2-4504-880a-xxxxxx",
    "output": {
        "status": "success"
    }
}

Manage model deployments

Delete a deployment

Important

This operation immediately stops the model deployment service and is irreversible:

The model is no longer available for inference.
Billing for the deployment stops.

Request example

curl

curl --request DELETE 'https://dashscope-intl.aliyuncs.com/api/v1/deployments/<your-deployed-model-id>' \
    --header "Authorization: Bearer $DASHSCOPE_API_KEY" \
    --header 'Content-Type: application/json'

Response example

A status of DELETING in the output.status field indicates the deletion is in progress.

json

{
    "request_id": "c2ed2aa2-39b8-4a86-b79e-xxxxxx",
    "output": {
        "deployed_model": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "gmt_create": "2025-11-11T17:46:53",
        "gmt_modified": "2025-12-22T11:18:27.532",
        "status": "DELETING",
        "model_name": "wan2.5-i2v-preview-ft-202511111122-xxxx",
        "base_model": "wan2.5-i2v-preview",
        "workspace_id": "llm-xxxxxx",
        "charge_type": "post_paid",
        "creator": "xxxxxx",
        "modifier": "xxxxxx",
        "plan": "lora"
    }
}

To verify the deletion, call the Query model deployment status operation. A NotFound error in the response confirms the deployment has been deleted.

json

{
    "request_id": "eb619064-0c4£-4d29-aa49-xxxxxx",
    "message": "Not found.",
    "code": "NotFound"
}

Previous: Create a tuning jobNext: Get fine-tuning job details

Is this page helpful?

Prerequisites

Upload a dataset

Request parameters

Response fields

Request example

Response example

Create a fine-tuning job

Input parameters

Output parameters

Request example

Response example

Retrieve a fine-tuning job

Request parameters

Response parameters

Request example

Response example

Deploy a model

Request parameters

Response fields

Request examples

Response example

Query model deployment status

Request parameters

Response fields

Request example

Response example

Generate videos

Checkpoint

1. List checkpoints

2. Retrieve checkpoint validation results

3. Export a checkpoint

4. Retrieve exported model details

5. Deploy and call the model

Fine-tuning job management

Query fine-tuning job logs

List fine-tuning jobs

Cancel fine-tuning job

Delete fine-tuning job

Manage model deployments

Delete a deployment

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How to use the API to fine-tune Wan image-to-video and image generation models by training a LoRA model? ​

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Request ​

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How to use the API to fine-tune Wan image-to-video and image generation models by training a LoRA model?

Models

Models

GenAI Application

AI Service

AI Use Case

Overview

Why Alibaba Cloud

Customers and Insights

What's New

Products

Featured Products

Related Programs

Solutions

Pricing

Overview & Tools

Optimize your cost

Resources

Technical Resources

Training&Certification

Developer Hub

Partners

Marketplace

Find a Partner

Become a Partner

Support

Support & Professional Services

Contact us

Language

Locale

Prerequisites

Upload a dataset

Request URL

Request parameters

Response fields

Request example

Response example

Create a fine-tuning job

API request

Input parameters

Hyperparameters (hyper_parameters)

Output parameters

Request example

Response example

Retrieve a fine-tuning job

Request

Request parameters

Response parameters

Request example

Response example

Deploy a model

Request URL

Request parameters

Response fields

Request examples

Response example

Query model deployment status

Request URL

Request parameters

Response fields

Request example

Response example

Generate videos

Checkpoint

1. List checkpoints

Request URL

Request parameters

Response parameters

Request example

Response example

2. Retrieve checkpoint validation results

Request URL

Request parameters

Response parameters

Request example

Response example

3. Export a checkpoint

Request URL

Request parameters

Response parameters