# 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 ``` 200 ``` ## 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.segmind.com/readme/flux-fine-tuning/flux-pro-fine-tuning-api.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.