Fine-Tune: Image Editing

Overview

This schema is used for fine-tuning models with image editing capabilities.

Schema Type

When creating a fine-tune with this schema, use:

{
  "resource": "main/your-dataset.parquet",
  "base_model": "<model-canonical-name>",
  "script_type": "image_editing",
  "training_params": {
    ...
  }
}

Key Parameters:

script_type: image_editing (the fine-tune type)
base_model: One of the supported model canonical names below

Supported Models

Qwen Image Edit (Qwen/Qwen-Image-Edit)
FLUX.1-Kontext [dev] (black-forest-labs/FLUX.1-Kontext-dev)

Request Schema

Required Fields

Field	Type	Required	Description
`batch_size`	integer	No	Batch Size (default: 1) (min: 1)
`cache_text_embeddings`	boolean	No	Cache Text Embeddings
`caption_column`	string	Yes	Caption Column (prompt) (DataFrame column name)
`control_image_column`	string	Yes	Control Image Column (input) (DataFrame column name)
`gradient_accumulation`	integer	No	Gradient Accumulation (default: 1) (min: 1)
`image_column`	string	Yes	Target Image Column (output) (DataFrame column name)
`learning_rate`	number	No	Learning Rate (default: 0.0002)
`lora_alpha`	integer	No	LoRA Alpha (default: 16) (min: 1)
`lora_rank`	integer	No	LoRA Rank (default: 16) (min: 1)
`sample_every`	integer	No	Sample Every (default: 200) (min: 1)
`sample_height`	integer	No	Sample Height (default: 1024) (min: 1)
`sample_width`	integer	No	Sample Width (default: 1024) (min: 1)
`samples`	array	No	Samples (array of object)
`steps`	integer	No	Steps (default: 3000) (min: 1)
`timestep_type`	string	No	Timestep Type (options: weighted, sigmoid, linear)
`use_lora`	boolean	No	Use LoRA

Example Request

{
  "resource": "main/your-dataset.parquet",
  "base_model": "<model-canonical-name>",
  "script_type": "image_editing",
  "training_params": {
    "batch_size": 1,
    "cache_text_embeddings": false,
    "caption_column": "",
    "control_image_column": "",
    "gradient_accumulation": 1,
    "image_column": "",
    "learning_rate": 0.0002,
    "lora_alpha": 16,
    "lora_rank": 16,
    "sample_every": 200,
    "sample_height": 1024,
    "sample_width": 1024,
    "samples": [
      {
        "ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png",
        "prompt": "an ox holding a sign that says 'Oxen.ai'"
      },
      {
        "ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png",
        "prompt": "a herd of oxen running in a field"
      }
    ],
    "steps": 3000,
    "timestep_type": "weighted",
    "use_lora": true
  }
}

import requests

url = "https://hub.oxen.ai/api/repos/{namespace}/{repo_name}/fine_tunes"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {{
  "resource": "main/your-dataset.parquet",
  "base_model": "<model-canonical-name>",
  "script_type": "image_editing",
  "training_params": {{
    "batch_size": 1,
    "cache_text_embeddings": false,
    "caption_column": "",
    "control_image_column": "",
    "gradient_accumulation": 1,
    "image_column": "",
    "learning_rate": 0.0002,
    "lora_alpha": 16,
    "lora_rank": 16,
    "sample_every": 200,
    "sample_height": 1024,
    "sample_width": 1024,
    "samples": [
      {{
        "ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png",
        "prompt": "an ox holding a sign that says 'Oxen.ai'"
      }},
      {{
        "ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png",
        "prompt": "a herd of oxen running in a field"
      }}
    ],
    "steps": 3000,
    "timestep_type": "weighted",
    "use_lora": true
  }}
}}

response = requests.post(url, headers=headers, json=data)
print(response.json())

curl -X POST https://hub.oxen.ai/api/repos/{namespace}/{repo_name}/fine_tunes \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{   "resource": "main/your-dataset.parquet",   "base_model": "<model-canonical-name>",   "script_type": "image_editing",   "training_params": {     "batch_size": 1,     "cache_text_embeddings": false,     "caption_column": "",     "control_image_column": "",     "gradient_accumulation": 1,     "image_column": "",     "learning_rate": 0.0002,     "lora_alpha": 16,     "lora_rank": 16,     "sample_every": 200,     "sample_height": 1024,     "sample_width": 1024,     "samples": [       {         "ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png",         "prompt": "an ox holding a sign that says 'Oxen.ai'"       },       {         "ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png",         "prompt": "a herd of oxen running in a field"       }     ],     "steps": 3000,     "timestep_type": "weighted",     "use_lora": true   } }'

Field Details

`batch_size`

Batch Size Type: integer Default: 1 Minimum: 1

`cache_text_embeddings`

Cache Text Embeddings Type: boolean Default: false

`caption_column`

Caption Column (prompt) Type: string Default: ""

`control_image_column`

Control Image Column (input) Type: string Default: ""

`gradient_accumulation`

Gradient Accumulation Type: integer Default: 1 Minimum: 1

`image_column`

Target Image Column (output) Type: string Default: ""

`learning_rate`

Learning Rate Type: number Default: 0.0002

`lora_alpha`

LoRA Alpha Type: integer Default: 16 Minimum: 1

`lora_rank`

LoRA Rank Type: integer Default: 16 Minimum: 1

`sample_every`

Sample Every Type: integer How often to generate samples during training (n steps) Default: 200 Minimum: 1

`sample_height`

Sample Height Type: integer Default: 1024 Minimum: 1

`sample_width`

Sample Width Type: integer Default: 1024 Minimum: 1

`samples`

Samples Type: array Used to show progress during the fine-tuning process Default:

[{"ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png", "prompt": "an ox holding a sign that says 'Oxen.ai'"}, {"ctrl_img_url": "https://hub.oxen.ai/api/repos/ox/Oxen-Character-Simple-Vector-Graphic/file/main/images/reference/bloxy_white_bg.png", "prompt": "a herd of oxen running in a field"}]

`steps`

Steps Type: integer Default: 3000 Minimum: 1

`timestep_type`

Timestep Type Type: string Default: "weighted" Options: weighted, sigmoid, linear

`use_lora`

Use LoRA Type: boolean Default: true

Fine-Tuning API

API Reference

fine_tunes

evaluations

Fine-Tune: Image Editing

Overview

Schema Type

Supported Models

Request Schema

Required Fields

Example Request

Field Details

`batch_size`

`cache_text_embeddings`

`caption_column`

`control_image_column`

`gradient_accumulation`

`image_column`

`learning_rate`

`lora_alpha`

`lora_rank`

`sample_every`

`sample_height`

`sample_width`

`samples`

`steps`

`timestep_type`

`use_lora`

​Overview

​Schema Type

​Supported Models

​Request Schema

​Required Fields

​Example Request

​Field Details

​batch_size

​cache_text_embeddings

​caption_column

​control_image_column

​gradient_accumulation

​image_column

​learning_rate

​lora_alpha

​lora_rank

​sample_every

​sample_height

​sample_width

​samples

​steps

​timestep_type

​use_lora

Overview

Schema Type

Supported Models

Request Schema

Required Fields

Example Request

Field Details

`batch_size`

`cache_text_embeddings`

`caption_column`

`control_image_column`

`gradient_accumulation`

`image_column`

`learning_rate`

`lora_alpha`

`lora_rank`

`sample_every`

`sample_height`

`sample_width`

`samples`

`steps`

`timestep_type`

`use_lora`