Flux Pro fine tuning API

This documentation outlines the API endpoints for initiating and managing Flux Pro 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 uploaded 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. - File Size Limit: The ZIP file must be under 100 MB in size. Larger files will be rejected or failed by the system.

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": "fluxprotest",
    "data_source_path": "Segmind-hosted Private ZIP URL (uploaded via presigned link)" or "any public zip url",
    "instance_prompt": "1MAN, running in brown suit",
    "trigger_word": "1MAN",
    "base_model": "FLUX_PRO",
    "theme": "GENERAL",
    "segmind_public": false,
    "advance_parameters": {
        "iterations": 300,
        "captioning": true,
        "priority": "QUALITY",
        "finetune_type": "FULL",
        "lora_rank": 32,
        "learning_rate": 0.005
    }
}'

Sample Response

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

Advanced Parameters:

theme: Determines the finetuning approach based on your concept Options: "CHARACTER", "PRODUCT", "STYLE", "GENERAL"
iterations Minimum: 100 Default: 300 Purpose: Defines training duration. For fast exploration 100-150 iterations can be enough. For more complex concepts, larger datasets or extreme precision more iterations than the default can help
learning_rate Default: 0.00001 if finetune_type is "FULL" Default: 0.0001 if finetune_type is "LORA".
priority Options: "SPEED", "QUALITY", "HIGH_RES_ONLY" The speed priority will improve speed per training step Default: "QUALITY"
captioning Type: Boolean Default: True Purpose: Enables/disables automatic image captioning
trigger_word Default: "TOK" Purpose: Unique word/phrase that will be used in the captions, to reference the newly introduced concepts
lora_rank Default: 32 Choose between 32 and 16. A lora_rank of 16 can increase training efficiency and decrease loading times.
finetune_type Default: "FULL" Choose between “FULL” for a full finetuning + post hoc extraction of the trained weights into a LoRA or “LORA” for a raw LoRA training

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

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:\

TRAINING_COMPLETED : The training is completed on model, and finetuned model is available for download on the trained_model_url.
INFERENCE_QUEUED: Model is being deployed on Segmind inference engine.
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.

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Last updated 8 days ago