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Overview

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

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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

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Supported Models

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

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Request Schema

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Required Fields

FieldTypeRequiredDescription
batch_sizeintegerNoBatch Size (default: 1) (min: 1)
cache_text_embeddingsbooleanNoCache Text Embeddings
caption_columnstringYesCaption Column (prompt) (DataFrame column name)
control_image_columnstringYesControl Image Column (input) (DataFrame column name)
gradient_accumulationintegerNoGradient Accumulation (default: 1) (min: 1)
image_columnstringYesTarget Image Column (output) (DataFrame column name)
learning_ratenumberNoLearning Rate (default: 0.0002)
lora_alphaintegerNoLoRA Alpha (default: 16) (min: 1)
lora_rankintegerNoLoRA Rank (default: 16) (min: 1)
sample_everyintegerNoSample Every (default: 200) (min: 1)
sample_heightintegerNoSample Height (default: 1024) (min: 1)
sample_widthintegerNoSample Width (default: 1024) (min: 1)
samplesarrayNoSamples (array of object)
stepsintegerNoSteps (default: 3000) (min: 1)
timestep_typestringNoTimestep Type (options: weighted, sigmoid, linear)
use_lorabooleanNoUse LoRA

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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
  }
}

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Field Details

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batch_size

Batch Size Type: integer Default: 1 Minimum: 1

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cache_text_embeddings

Cache Text Embeddings Type: boolean Default: false

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caption_column

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

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control_image_column

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

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gradient_accumulation

Gradient Accumulation Type: integer Default: 1 Minimum: 1

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image_column

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

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learning_rate

Learning Rate Type: number Default: 0.0002

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lora_alpha

LoRA Alpha Type: integer Default: 16 Minimum: 1

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lora_rank

LoRA Rank Type: integer Default: 16 Minimum: 1

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sample_every

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

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sample_height

Sample Height Type: integer Default: 1024 Minimum: 1

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sample_width

Sample Width Type: integer Default: 1024 Minimum: 1

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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"}]

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steps

Steps Type: integer Default: 3000 Minimum: 1

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timestep_type

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

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use_lora

Use LoRA Type: boolean Default: true