Flux fine Tuning API

This documentation outlines the API endpoints for initiating and managing 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.

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_A100_40GB", "NVIDIA_H100", "NVIDIA_L40S" Descriptions: - NVIDIA_A100_40GB (Balanced performance) - NVIDIA_H100 (Fastest training) - NVIDIA_L40S (Cost-efficient)

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": "fluxtest1",
    "data_source_path": "https://bucket.s3.amazonaws.com/path/to/dataset.zip" or "any public zip url",
    "instance_prompt": "1MAN, running in brown suit",
    "trigger_word": "1MAN",
    "base_model": "FLUX",
    "train_type": "LORA",
    "machine_type": "NVIDIA_A100_40GB",
    "theme": "FLUX",
    "segmind_public": false,
    "advance_parameters": {
        "auto_caption": true,
        "batch_size": 2,
        "bucket_steps": 64,
        "center_crop": false,
        "content_or_style": "balanced",
        "gradient_accumulation_steps": 2,
        "learning_rate": 0.0004,
        "linear": 16,
        "linear_alpha": 16,
        "lora_rank": 128,
        "lr_scheduler": "constant",
        "max_grad_norm": 0,
        "noise_offset": 0,
        "num_train_epochs": 1000,
        "optimizer_type": "Adafactor",
        "prompt": "driving a truck",
        "relative_step": false,
        "repeats": 100,
        "resolution": 1024,
        "steps": 1000,
        "weight_decay": 0.01
    }
}'

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 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.