Articles on: Segmind Developer Platform

Flux Kontext fine tuning API

This documentation outlines the API endpoints for initiating and managing Flux Kontext fine-tuning requests in Segmind.


Base URL


https://api.segmind.com


Authentication


All requests require an API key for authentication. Include the API key in the headers as follows:


--header 'x-api-key: YOUR_API_KEY'


1. Initiate Fine-Tune Request

Description


Initiate a new fine-tuning request.


Request


Headers


  • x-api-key: Your API key.
  • Content-Type: Should be application/json.


Dataset (data_source_path)


  • data_source_path: A URL pointing to a ZIP file containing your training dataset. Purpose: Specifies the dataset source for fine-tuning. Must be a valid public URL or a private Segmind URL. Options: "Public ZIP URL (must support GET & HEAD requests)", "Segmind Private URL using Presigned" Descriptions: - Public ZIP URL: Must be directly accessible and respond properly to both HEAD and GET requests with headers like Content-Length. - Segmind Presigned URL: Use Segmind Presigned URL endpoint to upload as private zip file


GPU Selection


  • machine_type: Specifies the GPU used for the fine-tuning job Purpose: Defines the hardware performance tier for training in request submit endpoint Options: "NVIDIA_H100" Descriptions: - NVIDIA_H100 (Fastest training) \


Body


The request body must be in JSON format.


Request


curl --location 'https://api.segmind.com/finetune/request/submit' \
--header 'x-api-key: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "name": "fluxkontexttest1",    "data_source_path": "Segmind-hosted Private ZIP URL (uploaded via presigned link)" or "any public zip url",    "instance_prompt": "Change the woman's hair color to bright red",    "base_model": "FLUX",    "train_type": "LORA",    "machine_type": "NVIDIA_H100",    "theme": "KONTEXT",    "segmind_public": false,    "advance_parameters": {        "learning_rate": 0.0004,        "steps": 300    }
}'


Sample Response


{
    "status": "REQUESTED",    "finetune_id": "uuid",    "name": "fine-tune-job-name"
}


2. Get the Details of Individual Fine-Tune Request

Description


Retrieve a fine-tuning request along with details.


Request


curl --location --request GET 'https://api.segmind.com/finetune/request/details' \
--header 'x-api-key: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "request_id": "FINETUNE_ID"
}'


Sample Response


{
  "finetune": {    "id": "uuid",    "finetune_id": "uuid",    "name": "fine-tune-job-name",    "data_source_path": "https://bucket.s3.amazonaws.com/path/to/dataset.zip",    "instance_prompt": "sample instance prompt",    "status": "AVAILABLE",    "source_type": "AWS_S3",    "base_model": "BASE_MODEL_NAME",    "slug": "model-slug",    "public_model": false,    "error_message": null,    "cloud_storage_url": "https://your-bucket.s3.amazonaws.com/path/to/model.safetensors",    "created_ts": "2025-01-01T00:00:00Z",    "updated_ts": "2025-01-01T00:00:00Z"  }
}


3. Get the List of Fine-Tune Requests

Description


Retrieve a list of fine-tuning requests along with their details.


Request


curl --location 'https://api.segmind.com/finetune/request/list' \
--header 'x-api-key: YOUR_API_KEY'


Sample Response


[
  {    "id": "uuid",    "data_source_path": "https://your-bucket.s3.amazonaws.com/path/to/dataset.zip",    "name": "fine-tune-job-name",    "status": "AVAILABLE",    "error_message": null,    "segmind_model_path": null,    "advance_parameters": {      "steps": 10,      "learning_rate": 0.0001,      "prompt": "sample prompt",      "theme": "sample-theme"    },    "segmind_public_model": false,    "train_type": "LORA",    "source_type": "AWS_S3",    "base_model": "BASE_MODEL_NAME",    "theme": "sample-theme",    "cloud_storage_url": null,    "finetune_id": "uuid",    "model_information": {}  }
]


4. Get Fine-Tune Data Upload Pre-Signed URL

Description


Obtain a pre-signed URL to securely upload fine-tuning data to cloud storage. This URL allows you to upload data directly from your local system or application without needing AWS credentials.


Usage


Call this endpoint to generate a temporary pre-signed URL. Use the returned URL to upload your dataset file to the specified location via a PUT request.


Request


curl --location --request GET 'https://api.segmind.com/finetune/request/upload/pre-signed-url' \
--header 'x-api-key: YOUR_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
    "name": "NAME_OF_THE_FILE"
}'


Sample Response


{
  "presigned_url": "https://finetune-pipeline.s3.amazonaws.com/uploads/{user_id}/{file_id}-{filename}.zip?X-Amz-Algorithm=...&X-Amz-Signature=...",  "s3_url": "https://finetune-pipeline.s3.amazonaws.com/uploads/{user_id}/{file_id}-{filename}.zip"
}


5. Update Fine-Tuned Model Access

Description


Update the access settings of a fine-tuned model (public/private).


Request


curl --location --request PUT 'https://api.segmind.com/finetune/request/access-update' \
--header 'x-api-key: YOUR_API_KEY' \
--form 'request_id="FINETUNE_REQUEST_ID"' \
--form 'segmind_public="True"'


Sample Response


200


6. Download the Fine-Tuned Safetensor File

Description


Generate a time-limited pre-signed URL to securely download the fine-tuned model file (.safetensors) from cloud storage. The URL is valid for 1 hour and allows direct download without requiring AWS credentials.


Request


curl --location --request GET 'https://api.segmind.com/finetune/request/file/download' \
--header 'x-api-key: YOUR_API_KEY' \
--form 'cloud_storage_url="CLOUD_STORAGE_URL"'


Sample Response


https://segmind-sd-models.s3.amazonaws.com/finetune/finetuned_models/job_id/filename.safetensors?AWSAccessKeyId=***&Signature=***&Expires=***


Webhooks


Webhooks provide a way to get real-time updates about finetuning jobs programmatically. You can register a webhook for finetuning jobs in the Developer tab on console.


Once a webhook is created, test it, by having it send a sample payload to verify delivery. Once its up, create a finetuning job to receive status updates. Events are sent to webhooks on 3 status changes:\


  1. TRAINING_COMPLETED : The training is completed on model, and finetuned model is available for download on the trained_model_url.
  2. INFERENCE_QUEUED: Model is being deployed on Segmind inference engine.
  3. AVAILABLE: Model is deployed, and ready for inferences on inference_api_url


Note: You can create only 1 webhook at a time for finetune jobs. If you want to change the webhook, please delete the old webhook before creating a new one.

Updated on: 29/10/2025

Was this article helpful?

Share your feedback

Cancel

Thank you!