Fast Flux fine tuning API

This documentation outlines the API endpoints for initiating and managing Fast Flux 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.

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": "fastfluxtest1",
    "data_source_path": "any public zip url",
    "instance_prompt": "1MAN, running in brown suit",
    "trigger_word": "1MAN",
    "base_model": "FAST_FLUX",
    "theme": "subject",
    "segmind_public": false,
    "advance_parameters": {
      "steps": 2000,
      "face_crop": false,
      "finetune_type": "LORA"
    }
}'

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://your-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 public 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 public 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",
    "extension": ".zip",
    "public": true
}'

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"
}

Upload File Using the Pre-Signed URL

Once you have the presigned_url, upload your dataset file using the following curl command:

Request

curl -X PUT \
  -T your_dataset.zip \
  -H "Content-Type: application/zip" \
  -H "x-amz-acl: public-read" \
  "PRESIGNED_URL"

Note

• Replace your_dataset.zip with your actual local file name.

• Replace PRESIGNED_URL with the exact presigned_url value received in the previous step.

• The x-amz-acl: public-read header is required if the pre-signed URL is generated with public read access.

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

Fast Fine-tuned model weight files (.safetensors) are now publicly accessible via the cloud_storage_url. You can download them directly without generating a pre-signed URL or using AWS credentials.

Request

curl -L "https://storage.googleapis.com/<bucket-name>/<model-id>.safetensors" -o model.safetensors

Sample Response

https://storage.googleapis.com/my-finetune-bucket/abc12345/model.safetensors

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.