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API Reference
Models
Train

Action

models.train.action(strmodel_id, TrainActionParams**kwargs) -> TrainActionResponse
POST/models/{modelId}/train/action

Trigger an action on a model training: cancel

ParametersExpand Collapse
model_id: str
action: Literal["cancel"]

The action to perform on the model training

original_assets: Optional[bool]

If set to true, returns the original asset without transformation

ReturnsExpand Collapse
class TrainActionResponse: …
model: Model
id: str

The model ID (example: “model_eyVcnFJcR92BxBkz7N6g5w”)

capabilities: List[Literal["3d23d", "audio2audio", "audio2video", 29 more]]

List of model capabilities (example: [“txt2img”, “img2img”, “txt2img_ip_adapter”, …])

One of the following:
"3d23d"
"audio2audio"
"audio2video"
"controlnet"
"controlnet_img2img"
"controlnet_inpaint"
"controlnet_inpaint_ip_adapter"
"controlnet_ip_adapter"
"controlnet_reference"
"controlnet_texture"
"img23d"
"img2img"
"img2img_ip_adapter"
"img2img_texture"
"img2txt"
"img2video"
"inpaint"
"inpaint_ip_adapter"
"outpaint"
"reference"
"reference_texture"
"txt23d"
"txt2audio"
"txt2img"
"txt2img_ip_adapter"
"txt2img_texture"
"txt2txt"
"txt2video"
"video23d"
"video2audio"
"video2img"
"video2video"
collection_ids: List[str]

A list of CollectionId this model belongs to

created_at: str

The model creation date as an ISO string (example: “2023-02-03T11:19:41.579Z”)

custom: bool

Whether the model is a custom model and can be used only with POST /generate/custom/{modelId} endpoint

example_asset_ids: List[str]

List of all example asset IDs setup by the model owner

privacy: Literal["private", "public", "unlisted"]

The privacy of the model (default: private)

One of the following:
"private"
"public"
"unlisted"
source: Literal["civitai", "huggingface", "other", "scenario"]

The source of the model

One of the following:
"civitai"
"huggingface"
"other"
"scenario"
status: Literal["copying", "failed", "new", 3 more]

The model status

One of the following:
"copying"
"failed"
"new"
"trained"
"training"
"training-canceled"
tags: List[str]

The associated tags (example: [“sci-fi”, “landscape”])

training_images_number: float

The total number of training images

type: Literal["custom", "elevenlabs-voice", "flux.1", 34 more]

The model type (example: “flux.1-lora”)

One of the following:
"custom"
"elevenlabs-voice"
"flux.1"
"flux.1-composition"
"flux.1-kontext-dev"
"flux.1-kontext-lora"
"flux.1-krea-dev"
"flux.1-krea-lora"
"flux.1-lora"
"flux.1-pro"
"flux.1.1-pro-ultra"
"flux.2-dev-edit-lora"
"flux.2-dev-lora"
"flux.2-klein-4b-edit-lora"
"flux.2-klein-4b-lora"
"flux.2-klein-9b-edit-lora"
"flux.2-klein-9b-lora"
"flux.2-klein-base-4b-edit-lora"
"flux.2-klein-base-4b-lora"
"flux.2-klein-base-9b-edit-lora"
"flux.2-klein-base-9b-lora"
"flux1.1-pro"
"gpt-image-1"
"qwen-image-2512-lora"
"qwen-image-edit-2509-lora"
"qwen-image-edit-2511-lora"
"qwen-image-edit-lora"
"qwen-image-lora"
"sd-1_5"
"sd-1_5-composition"
"sd-1_5-lora"
"sd-xl"
"sd-xl-composition"
"sd-xl-lora"
"zimage-de-turbo-lora"
"zimage-lora"
"zimage-turbo-lora"
updated_at: str

The model last update date as an ISO string (example: “2023-02-03T11:19:41.579Z”)

access_restrictions: Optional[Literal[0, 100, 25, 2 more]]

The access restrictions of the model 0: Free plan 25: Creator plan 50: Pro plan 75: Team plan 100: Enterprise plan

One of the following:
0
100
25
50
75
author_id: Optional[str]

The author user ID (example: “user_VFhihHKMRZyDDnZAJwLb2Q”)

class_: Optional[ModelClass]

The class of the model

category: str

The category slug of the class (example: “art-style”)

concept_prompt: str

The concept prompt of the class (example: “a sks character design”)

model_id: str

The model ID of the class (example: “stable-diffusion-v1-5”)

name: str

The class name (example: “Character Design”)

prompt: str

The class prompt (example: “a character design”)

slug: str

The class slug (example: “art-style-character-design”)

status: Literal["published", "unpublished"]

The class status (only published classes are listed, but unpublished classes can still appear in existing models)

One of the following:
"published"
"unpublished"
thumbnails: List[str]

Some example images URLs to showcase the class

compliant_model_ids: Optional[List[str]]

List of base model IDs compliant with the model (example: [“flux.1-dev”, “flux.1-schnell”]) This attribute is mainly used for Flux LoRA models

concepts: Optional[List[ModelConcept]]

The concepts is required for the type model: composition

model_id: str

The model ID (example: “model_eyVcnFJcR92BxBkz7N6g5w”)

scale: float

The scale of the model (example: 1.0) For Flux Kontext Prompt Editing, the scale is between 0 and 2.

maximum2
minimum-2
model_epoch: Optional[str]

The epoch of the model (example: “000001”) Only available for Flux Lora Trained models

epoch: Optional[str]

The epoch of the model. Only available for Flux Lora Trained models. If not set, uses the final model epoch (latest)

epochs: Optional[List[ModelEpoch]]

The epochs of the model. Only available for Flux Lora Trained models.

epoch: str

The epoch hash to identify the epoch

assets: Optional[List[ModelEpochAsset]]

The assets of the epoch if sample prompts as been supplied during training

asset_id: str

The AssetId of the image during training (example: “asset_GTrL3mq4SXWyMxkOHRxlpw”)

url: str

The url of the asset

inputs: Optional[List[ModelInput]]

The inputs of the model. Only used for custom models. To retrieve this list, get it by modelId with GET /models/{modelId}

name: str

The name that must be user to call the model through the API

type: Literal["boolean", "file", "file_array", 7 more]

The data type of the input

One of the following:
"boolean"
"file"
"file_array"
"inputs_array"
"model"
"model_array"
"number"
"number_array"
"string"
"string_array"
allowed_values: Optional[List[object]]

The allowed values for the input. For `string` or `number` types, creates a single-select dropdown. For `string_array` type, creates a multi-select dropdown.

background_behavior: Optional[Literal["opaque", "transparent"]]

Specifies the background behavior for the input. Only available for `file` and `file_array` input types with kind `image`.

One of the following:
"opaque"
"transparent"
color: Optional[bool]

Whether the input is a color or not. Only available for `string` input type.

cost_impact: Optional[bool]

Whether this input affects the model’s cost calculation

default: Optional[object]

The default value for the input

description: Optional[str]

Help text displayed in the UI to provide additional information about the input

group: Optional[str]

Used to visually group inputs together in the UI. Inputs with the same group value appear consecutively in the UI.

hint: Optional[str]

Hint text displayed in the UI as a tooltip to guide the user

inputs: Optional[List[Dict[str, object]]]

The list of inputs which form an object within a container array. All inputs are the same as the current object. This is only available for type inputs_array inputs.

kind: Optional[Literal["3d", "audio", "document", 4 more]]

The asset kind of the input. Only taken into account for `file` and `file_array` input types. If model provides multiple kinds, the input will be not able to create the asset on the flight on API side with dataurl without data:kind, prefix

One of the following:
"3d"
"audio"
"document"
"image"
"image-hdr"
"json"
"video"
label: Optional[str]

The label displayed in the UI for this input

mask_from: Optional[str]

The name of the file input field to use as the mask source

max: Optional[float]

The maximum allowed value. Only available for `number` and `array` input types.

max_length: Optional[float]

The maximum allowed length for `string` inputs. Also applies to each item in `string_array`.

max_size: Optional[float]

The maximum allowed file size in bytes. Only applies to `file` and `file_array` input types. Validated against `asset.properties.size` at job creation time.

min: Optional[float]

The minimum allowed value. Only available for `number` and array input types.

min_length: Optional[float]

The minimum allowed length for string inputs. Also applies to each item in `string_array`.

model_types: Optional[List[Literal["custom", "elevenlabs-voice", "flux.1", 34 more]]]

The allowed model types for this input. Example: `[“flux.1-lora”]`. Only available for `model_array` input type.

One of the following:
"custom"
"elevenlabs-voice"
"flux.1"
"flux.1-composition"
"flux.1-kontext-dev"
"flux.1-kontext-lora"
"flux.1-krea-dev"
"flux.1-krea-lora"
"flux.1-lora"
"flux.1-pro"
"flux.1.1-pro-ultra"
"flux.2-dev-edit-lora"
"flux.2-dev-lora"
"flux.2-klein-4b-edit-lora"
"flux.2-klein-4b-lora"
"flux.2-klein-9b-edit-lora"
"flux.2-klein-9b-lora"
"flux.2-klein-base-4b-edit-lora"
"flux.2-klein-base-4b-lora"
"flux.2-klein-base-9b-edit-lora"
"flux.2-klein-base-9b-lora"
"flux1.1-pro"
"gpt-image-1"
"qwen-image-2512-lora"
"qwen-image-edit-2509-lora"
"qwen-image-edit-2511-lora"
"qwen-image-edit-lora"
"qwen-image-lora"
"sd-1_5"
"sd-1_5-composition"
"sd-1_5-lora"
"sd-xl"
"sd-xl-composition"
"sd-xl-lora"
"zimage-de-turbo-lora"
"zimage-lora"
"zimage-turbo-lora"
parent: Optional[bool]

Whether this input represents a parent asset to assign to the produced assets. Only available for `file` and `file_array` input types.

For `file_array`, the parent asset is the first item in the array.

placeholder: Optional[str]

Placeholder text for the input. Only available for ‘string’ input type.

prompt: Optional[bool]

Whether the input is a prompt. When true, displays as a text area with prompt spark feature. Only available for `string` input type.

prompt_spark: Optional[bool]

Whether the input is used with prompt spark. Only available for `string` input type.

required: Optional[ModelInputRequired]

Set of rules that describes when this input is required:

  • `always`: Input is always required
  • `ifNotDefined`: Input is required when another specified input is not defined
  • `ifDefined`: Input is required when another specified input is defined
  • `conditionalValues`: Input is required when another input has a specific value

By default, the input is not required.

always: Optional[bool]

Whether the input is always required

conditional_values: Optional[object]

Makes this input required when another input has a specific value:

  • Key: name of the input to check
  • Value: operation and allowed values that trigger the requirement
if_defined: Optional[object]

Makes this input required when another input is defined:

  • Key: name of the input that must be defined
  • Value: message to display when this input is required
if_not_defined: Optional[object]

Makes this input required when another input is not defined:

  • Key: name of the input that must be undefined
  • Value: message to display when this input is required
step: Optional[float]

The step increment for numeric inputs. Only available for `number` input type.

minimum1
model_keyword: Optional[str]

The model keyword, this is a legacy parameter, please use conceptPrompt in parameters

name: Optional[str]

The model name (example: “Cinematic Realism”)

negative_prompt_embedding: Optional[str]

Fine-tune the model’s inferences with negative prompt embedding

owner_id: Optional[str]

The owner ID (example: “team_VFhihHKMRZyDDnZAJwLb2Q”)

parameters: Optional[ModelParameters]

The parameters of the model

age: Optional[str]

Age group of the voice (for professional cloning)

Only available for ElevenLabs voice training

batch_size: Optional[float]

The batch size Less steps, and will increase the learning rate

Only available for Flux LoRA training

maximum4
minimum1
class_prompt: Optional[str]

The prompt to specify images in the same class as provided instance images

Only available for SD15 training

clone_type: Optional[str]

Type of voice cloning: “instant” (fast) or “professional” (higher quality, requires captcha)

Only available for ElevenLabs voice training

concept_prompt: Optional[str]

The prompt with identifier specifying the instance (or subject) of the class (example: “a daiton dog”)

Default value varies depending on the model type:

  • For SD1.5: “daiton” if no class is associated with the model
  • For SDXL: “daiton”
  • For Flux: ""
gender: Optional[str]

Gender of the voice (for professional cloning)

Only available for ElevenLabs voice training

language: Optional[str]

Language of the audio samples (ISO 639-1 code)

Only available for ElevenLabs voice training

learning_rate: Optional[float]

Initial learning rate (after the potential warmup period)

Default value varies depending on the model type:

  • For SD1.5 and SDXL: 0.000005
  • For Flux: 0.0001
exclusiveMinimum
minimum0
learning_rate_text_encoder: Optional[float]

Initial learning rate (after the potential warmup period) for the text encoder

Maximum [Flux LoRA: 0.001] Default [SDXL: 0.00005 | Flux LoRA: 0.00001] Minimum [SDXL: 0 | Flux LoRA: 0.000001]

exclusiveMinimum
maximum0.001
minimum0
learning_rate_unet: Optional[float]

Initial learning rate (after the potential warmup period) for the UNet

Only available for SDXL LoRA training

exclusiveMinimum
minimum0
lr_scheduler: Optional[Literal["constant", "constant-with-warmup", "cosine", 3 more]]

The scheduler type to use (default: “constant”)

Only available for SD15 and SDXL LoRA training

One of the following:
"constant"
"constant-with-warmup"
"cosine"
"cosine-with-restarts"
"linear"
"polynomial"
max_train_steps: Optional[float]

Maximum number of training steps to execute (default: varies depending on the model type)

For SDXL LoRA training, please use numTextTrainSteps and numUNetTrainSteps instead

Default value varies depending on the model type:

  • For SD1.5: round((number of training images * 225) / 3)
  • For SDXL: number of training images * 175
  • For Flux: number of training images * 100

Maximum value varies depending on the model type:

  • For SD1.5 and SDXL: [0, 40000]
  • For Flux: [0, 10000]
maximum40000
minimum0
nb_epochs: Optional[float]

The number of epochs to train for

Only available for Flux LoRA training

maximum30
minimum1
nb_repeats: Optional[float]

The number of times to repeat the training

Only available for Flux LoRA training

maximum30
minimum1
num_text_train_steps: Optional[float]

The number of training steps for the text encoder

Only available for SDXL LoRA training

maximum40000
minimum0
num_u_net_train_steps: Optional[float]

The number of training steps for the UNet

Only available for SDXL LoRA training

maximum40000
minimum0
optimize_for: Optional[Literal["likeness"]]

Optimize the model training task for a specific type of input images. The available values are:

  • “likeness”: optimize training for likeness or portrait (targets specific transformer blocks)
  • “all”: train all transformer blocks
  • “none”: train no specific transformer blocks

This parameter controls which double and single transformer blocks are trained during the LoRA training process.

Only available for Flux LoRA training

prior_loss_weight: Optional[float]

The weight of prior preservation loss

Only available for SD15 and SDXL LoRA training

exclusiveMinimum
maximum1.7976931348623157
minimum0
random_crop: Optional[bool]

Whether to random crop or center crop images before resizing to the working resolution

Only available for SD15 and SDXL LoRA training

random_crop_ratio: Optional[float]

Ratio of random crops

Only available for SD15 and SDXL LoRA training

maximum1
minimum0
random_crop_scale: Optional[float]

Scale of random crops

Only available for SD15 and SDXL LoRA training

maximum1
minimum0
rank: Optional[float]

The dimension of the LoRA update matrices

Only available for SDXL (deprecated), Flux LoRA and Musubi training

Default value varies depending on the model type:

  • For SDXL (deprecated): 64
  • For Flux: 16
  • For Musubi: 64

Each trainer enforces its own tighter limit (Flux LoRA: [2; 64], Musubi: [2; 128])

maximum128
minimum2
remove_background_noise: Optional[bool]

Whether to remove background noise from audio samples before cloning. When enabled, each sample must be at least 5 seconds long.

Only available for ElevenLabs voice training

sample_prompts: Optional[List[str]]

The prompts to use for each epoch Only available for Flux LoRA training

sample_source_images: Optional[List[str]]

The sample prompt images (AssetIds) paired with samplePrompts Only available for Flux LoRA training Must be the same length as samplePrompts

scale_lr: Optional[bool]

Whether to scale the learning rate

Note: Legacy parameter, will be ignored

Only available for SD15 and SDXL LoRA training

seed: Optional[float]

Used to reproduce previous results. Default: randomly generated number.

Only available for SD15 and SDXL LoRA training

maximum9007199254740991
minimum0
text_encoder_training_ratio: Optional[float]

Whether to train the text encoder or not

Example: For 100 steps and a value of 0.2, it means that the text encoder will be trained for 20 steps and then the UNet for 80 steps

Note: Legacy parameter, please use numTextTrainSteps and numUNetTrainSteps

Only available for SD15 and SDXL LoRA training

maximum0.99
minimum0
validation_frequency: Optional[float]

Validation frequency. Cannot be greater than maxTrainSteps value

Only available for SD15 and SDXL LoRA training

minimum0
validation_prompt: Optional[str]

Validation prompt

Only available for SD15 and SDXL LoRA training

voice_description: Optional[str]

Description of the voice characteristics

Only available for ElevenLabs voice training

wandb_key: Optional[str]

The Weights And Bias key to use for logging. The maximum length is 40 characters

parent_model_id: Optional[str]

The id of the parent model

performance_stats: Optional[ModelPerformanceStats]

Aggregated performance stats

variants: List[ModelPerformanceStatsVariant]

Performance metrics per variant

capability: str

The generation capability (example: “txt2img”, “img2video”, “txt2audio”)

computed_at: str

When these stats were last computed (ISO date)

variant_key: str

Unique variant identifier (example: “txt2img:1K”, “img2video:2K”, “txt2audio”)

arena_score: Optional[ModelPerformanceStatsVariantArenaScore]

External quality score from arena.ai leaderboard

arena_category: str

Arena category (example: “text_to_image”, “image_to_video”)

arena_model_name: str

Model name on arena.ai

fetched_at: str

When this score was last fetched (ISO date)

rank: float

Rank in the arena category

rating: float

ELO rating

rating_lower: float

ELO rating confidence interval lower bound

rating_upper: float

ELO rating confidence interval upper bound

votes: float

Number of human votes

cost_per_asset_max_cu: Optional[float]

Maximum cost per output asset (CU)

cost_per_asset_min_cu: Optional[float]

Minimum cost per output asset (CU)

cost_per_asset_p50_cu: Optional[float]

Median cost per output asset (CU)

inference_latency_p50_sec: Optional[float]

Inference latency P50 per output asset (seconds)

inference_latency_p75_sec: Optional[float]

Inference latency P75 per output asset (seconds)

resolution: Optional[str]

The resolution bucket (example: “0.5K”, “1K”, “2K”, “4K”)

total_latency_p50_sec: Optional[float]

Total latency P50 per output asset, including queue time (seconds)

total_latency_p75_sec: Optional[float]

Total latency P75 per output asset, including queue time (seconds)

default: Optional[str]

Default variant key for quick model comparison

prompt_embedding: Optional[str]

Fine-tune the model’s inferences with prompt embedding

short_description: Optional[str]

The model short description (example: “This model generates highly detailed cinematic scenes.”)

soft_deletion_on: Optional[str]

The date when the model will be soft deleted (only for Free plan)

thumbnail: Optional[ModelThumbnail]

A thumbnail for your model

asset_id: str

The AssetId of the image used as a thumbnail for your model (example: “asset_GTrL3mq4SXWyMxkOHRxlpw”)

url: str

The url of the image used as a thumbnail for your model

training_image_pairs: Optional[List[ModelTrainingImagePair]]

Array of training image pairs

instruction: Optional[str]

The instruction for the image pair, source to target

source_id: Optional[str]

The source asset ID (must be a training asset)

target_id: Optional[str]

The target asset ID (must be a training asset)

training_images: Optional[List[ModelTrainingImage]]

The URLs of the first 3 training images of the model. To retrieve the full set of images, get it by modelId

id: str

The training image ID (example: “asset_GTrL3mq4SXWyMxkOHRxlpw”)

automatic_captioning: str

Automatic captioning of the image

created_at: str

The training image upload date as an ISO string (example: “2023-02-03T11:19:41.579Z”)

description: str

Description for the image

download_url: str

The URL of the image

name: str

The original file name of the image (example: “my-training-image.jpg”)

training_progress: Optional[ModelTrainingProgress]

Additional information about the training progress of the model

stage: Literal["pending", "pending-captcha", "queued-for-train", 2 more]

The stage of the request

One of the following:
"pending"
"pending-captcha"
"queued-for-train"
"running-train"
"starting-train"
updated_at: float

Timestamp in milliseconds of the last time the training progress was updated

position: Optional[float]

Position of the job in the queue (ie. the number of job in the queue before this one)

progress: Optional[float]

The progress of the job

maximum1
minimum0
remaining_time_ms: Optional[float]

The remaining time in milliseconds

started_at: Optional[float]

The timestamp in millisecond marking the start of the process

training_stats: Optional[ModelTrainingStats]

Additional information about the model’s training

ended_at: Optional[str]

The training end time as an ISO date string

queue_duration: Optional[float]

The training queued duration in seconds

started_at: Optional[str]

The training start time as an ISO date string

train_duration: Optional[float]

The training duration in seconds

ui_config: Optional[ModelUiConfig]

The UI configuration for the model

input_properties: Optional[Dict[str, ModelUiConfigInputProperties]]

Configuration for the input properties

collapsed: Optional[bool]
loras_component: Optional[ModelUiConfigLorasComponent]

Configuration for the loras component

label: str

The label of the component

model_input: str

The input name of the model (model_array)

scale_input: str

The input name of the scale (number_array)

model_id_input: Optional[str]

The input model id (example: a composition or a single LoRA modelId) If specified, the model id will be attached to the output asset as a metadata If the model-decomposer parser is specified on it, modelInput and scaleInput will be automatically populated

presets: Optional[List[ModelUiConfigPreset]]

Configuration for the presets

fields: List[str]
presets: object
resolution_component: Optional[ModelUiConfigResolutionComponent]

Configuration for the resolution component

height_input: str

The input name of the height

label: str

The label of the component

presets: List[ModelUiConfigResolutionComponentPreset]

The resolution presets

height: float
label: str
width: float
width_input: str

The input name of the width

selects: Optional[Dict[str, object]]

Configuration for the selects

trigger_generate: Optional[ModelUiConfigTriggerGenerate]

Configuration for the trigger generate button

label: str
after: Optional[str]

The ‘name’ of the input where the trigger generate button will be displayed (after the input). Do not specify both position and after.

position: Optional[Literal["bottom", "top"]]

The position of the trigger generate button. If position specified, the button will be displayed at the specified position. Do not specify both position and after.

One of the following:
"bottom"
"top"
user_id: Optional[str]

(Deprecated) The user ID (example: “user_VFhihHKMRZyDDnZAJwLb2Q”)

Action

import os
from scenario_sdk import Scenario

client = Scenario(
    api_key=os.environ.get("SCENARIO_SDK_API_KEY"),  # This is the default and can be omitted
    api_secret=os.environ.get("SCENARIO_SDK_API_SECRET"),  # This is the default and can be omitted
)
response = client.models.train.action(
    model_id="modelId",
    action="cancel",
)
print(response.model)
{
  "model": {
    "id": "id",
    "capabilities": [
      "3d23d"
    ],
    "collectionIds": [
      "string"
    ],
    "createdAt": "createdAt",
    "custom": true,
    "exampleAssetIds": [
      "string"
    ],
    "privacy": "private",
    "source": "civitai",
    "status": "copying",
    "tags": [
      "string"
    ],
    "trainingImagesNumber": 0,
    "type": "custom",
    "updatedAt": "updatedAt",
    "accessRestrictions": 0,
    "authorId": "authorId",
    "class": {
      "category": "category",
      "conceptPrompt": "conceptPrompt",
      "modelId": "modelId",
      "name": "name",
      "prompt": "prompt",
      "slug": "slug",
      "status": "published",
      "thumbnails": [
        "string"
      ]
    },
    "compliantModelIds": [
      "string"
    ],
    "concepts": [
      {
        "modelId": "modelId",
        "scale": -2,
        "modelEpoch": "modelEpoch"
      }
    ],
    "epoch": "epoch",
    "epochs": [
      {
        "epoch": "epoch",
        "assets": [
          {
            "assetId": "assetId",
            "url": "url"
          }
        ]
      }
    ],
    "inputs": [
      {
        "name": "name",
        "type": "boolean",
        "allowedValues": [
          {}
        ],
        "backgroundBehavior": "opaque",
        "color": true,
        "costImpact": true,
        "default": {},
        "description": "description",
        "group": "group",
        "hint": "hint",
        "inputs": [
          {
            "foo": "bar"
          }
        ],
        "kind": "3d",
        "label": "label",
        "maskFrom": "maskFrom",
        "max": 0,
        "maxLength": 0,
        "maxSize": 0,
        "min": 0,
        "minLength": 0,
        "modelTypes": [
          "custom"
        ],
        "parent": true,
        "placeholder": "placeholder",
        "prompt": true,
        "promptSpark": true,
        "required": {
          "always": true,
          "conditionalValues": {},
          "ifDefined": {},
          "ifNotDefined": {}
        },
        "step": 1
      }
    ],
    "modelKeyword": "modelKeyword",
    "name": "name",
    "negativePromptEmbedding": "negativePromptEmbedding",
    "ownerId": "ownerId",
    "parameters": {
      "age": "age",
      "batchSize": 1,
      "classPrompt": "classPrompt",
      "cloneType": "cloneType",
      "conceptPrompt": "conceptPrompt",
      "gender": "gender",
      "language": "language",
      "learningRate": 1,
      "learningRateTextEncoder": 0.0005,
      "learningRateUnet": 1,
      "lrScheduler": "constant",
      "maxTrainSteps": 0,
      "nbEpochs": 1,
      "nbRepeats": 1,
      "numTextTrainSteps": 0,
      "numUNetTrainSteps": 0,
      "optimizeFor": "likeness",
      "priorLossWeight": 1,
      "randomCrop": true,
      "randomCropRatio": 0,
      "randomCropScale": 0,
      "rank": 2,
      "removeBackgroundNoise": true,
      "samplePrompts": [
        "string"
      ],
      "sampleSourceImages": [
        "string"
      ],
      "scaleLr": true,
      "seed": 0,
      "textEncoderTrainingRatio": 0,
      "validationFrequency": 0,
      "validationPrompt": "validationPrompt",
      "voiceDescription": "voiceDescription",
      "wandbKey": "wandbKey"
    },
    "parentModelId": "parentModelId",
    "performanceStats": {
      "variants": [
        {
          "capability": "capability",
          "computedAt": "computedAt",
          "variantKey": "variantKey",
          "arenaScore": {
            "arenaCategory": "arenaCategory",
            "arenaModelName": "arenaModelName",
            "fetchedAt": "fetchedAt",
            "rank": 0,
            "rating": 0,
            "ratingLower": 0,
            "ratingUpper": 0,
            "votes": 0
          },
          "costPerAssetMaxCU": 0,
          "costPerAssetMinCU": 0,
          "costPerAssetP50CU": 0,
          "inferenceLatencyP50Sec": 0,
          "inferenceLatencyP75Sec": 0,
          "resolution": "resolution",
          "totalLatencyP50Sec": 0,
          "totalLatencyP75Sec": 0
        }
      ],
      "default": "default"
    },
    "promptEmbedding": "promptEmbedding",
    "shortDescription": "shortDescription",
    "softDeletionOn": "softDeletionOn",
    "thumbnail": {
      "assetId": "assetId",
      "url": "url"
    },
    "trainingImagePairs": [
      {
        "instruction": "instruction",
        "sourceId": "sourceId",
        "targetId": "targetId"
      }
    ],
    "trainingImages": [
      {
        "id": "id",
        "automaticCaptioning": "automaticCaptioning",
        "createdAt": "createdAt",
        "description": "description",
        "downloadUrl": "downloadUrl",
        "name": "name"
      }
    ],
    "trainingProgress": {
      "stage": "pending",
      "updatedAt": 0,
      "position": 0,
      "progress": 0,
      "remainingTimeMs": 0,
      "startedAt": 0
    },
    "trainingStats": {
      "endedAt": "endedAt",
      "queueDuration": 0,
      "startedAt": "startedAt",
      "trainDuration": 0
    },
    "uiConfig": {
      "inputProperties": {
        "foo": {
          "collapsed": true
        }
      },
      "lorasComponent": {
        "label": "label",
        "modelInput": "modelInput",
        "scaleInput": "scaleInput",
        "modelIdInput": "modelIdInput"
      },
      "presets": [
        {
          "fields": [
            "string"
          ],
          "presets": {}
        }
      ],
      "resolutionComponent": {
        "heightInput": "heightInput",
        "label": "label",
        "presets": [
          {
            "height": 0,
            "label": "label",
            "width": 0
          }
        ],
        "widthInput": "widthInput"
      },
      "selects": {
        "foo": {}
      },
      "triggerGenerate": {
        "label": "label",
        "after": "after",
        "position": "bottom"
      }
    },
    "userId": "userId"
  }
}
Returns Examples
{
  "model": {
    "id": "id",
    "capabilities": [
      "3d23d"
    ],
    "collectionIds": [
      "string"
    ],
    "createdAt": "createdAt",
    "custom": true,
    "exampleAssetIds": [
      "string"
    ],
    "privacy": "private",
    "source": "civitai",
    "status": "copying",
    "tags": [
      "string"
    ],
    "trainingImagesNumber": 0,
    "type": "custom",
    "updatedAt": "updatedAt",
    "accessRestrictions": 0,
    "authorId": "authorId",
    "class": {
      "category": "category",
      "conceptPrompt": "conceptPrompt",
      "modelId": "modelId",
      "name": "name",
      "prompt": "prompt",
      "slug": "slug",
      "status": "published",
      "thumbnails": [
        "string"
      ]
    },
    "compliantModelIds": [
      "string"
    ],
    "concepts": [
      {
        "modelId": "modelId",
        "scale": -2,
        "modelEpoch": "modelEpoch"
      }
    ],
    "epoch": "epoch",
    "epochs": [
      {
        "epoch": "epoch",
        "assets": [
          {
            "assetId": "assetId",
            "url": "url"
          }
        ]
      }
    ],
    "inputs": [
      {
        "name": "name",
        "type": "boolean",
        "allowedValues": [
          {}
        ],
        "backgroundBehavior": "opaque",
        "color": true,
        "costImpact": true,
        "default": {},
        "description": "description",
        "group": "group",
        "hint": "hint",
        "inputs": [
          {
            "foo": "bar"
          }
        ],
        "kind": "3d",
        "label": "label",
        "maskFrom": "maskFrom",
        "max": 0,
        "maxLength": 0,
        "maxSize": 0,
        "min": 0,
        "minLength": 0,
        "modelTypes": [
          "custom"
        ],
        "parent": true,
        "placeholder": "placeholder",
        "prompt": true,
        "promptSpark": true,
        "required": {
          "always": true,
          "conditionalValues": {},
          "ifDefined": {},
          "ifNotDefined": {}
        },
        "step": 1
      }
    ],
    "modelKeyword": "modelKeyword",
    "name": "name",
    "negativePromptEmbedding": "negativePromptEmbedding",
    "ownerId": "ownerId",
    "parameters": {
      "age": "age",
      "batchSize": 1,
      "classPrompt": "classPrompt",
      "cloneType": "cloneType",
      "conceptPrompt": "conceptPrompt",
      "gender": "gender",
      "language": "language",
      "learningRate": 1,
      "learningRateTextEncoder": 0.0005,
      "learningRateUnet": 1,
      "lrScheduler": "constant",
      "maxTrainSteps": 0,
      "nbEpochs": 1,
      "nbRepeats": 1,
      "numTextTrainSteps": 0,
      "numUNetTrainSteps": 0,
      "optimizeFor": "likeness",
      "priorLossWeight": 1,
      "randomCrop": true,
      "randomCropRatio": 0,
      "randomCropScale": 0,
      "rank": 2,
      "removeBackgroundNoise": true,
      "samplePrompts": [
        "string"
      ],
      "sampleSourceImages": [
        "string"
      ],
      "scaleLr": true,
      "seed": 0,
      "textEncoderTrainingRatio": 0,
      "validationFrequency": 0,
      "validationPrompt": "validationPrompt",
      "voiceDescription": "voiceDescription",
      "wandbKey": "wandbKey"
    },
    "parentModelId": "parentModelId",
    "performanceStats": {
      "variants": [
        {
          "capability": "capability",
          "computedAt": "computedAt",
          "variantKey": "variantKey",
          "arenaScore": {
            "arenaCategory": "arenaCategory",
            "arenaModelName": "arenaModelName",
            "fetchedAt": "fetchedAt",
            "rank": 0,
            "rating": 0,
            "ratingLower": 0,
            "ratingUpper": 0,
            "votes": 0
          },
          "costPerAssetMaxCU": 0,
          "costPerAssetMinCU": 0,
          "costPerAssetP50CU": 0,
          "inferenceLatencyP50Sec": 0,
          "inferenceLatencyP75Sec": 0,
          "resolution": "resolution",
          "totalLatencyP50Sec": 0,
          "totalLatencyP75Sec": 0
        }
      ],
      "default": "default"
    },
    "promptEmbedding": "promptEmbedding",
    "shortDescription": "shortDescription",
    "softDeletionOn": "softDeletionOn",
    "thumbnail": {
      "assetId": "assetId",
      "url": "url"
    },
    "trainingImagePairs": [
      {
        "instruction": "instruction",
        "sourceId": "sourceId",
        "targetId": "targetId"
      }
    ],
    "trainingImages": [
      {
        "id": "id",
        "automaticCaptioning": "automaticCaptioning",
        "createdAt": "createdAt",
        "description": "description",
        "downloadUrl": "downloadUrl",
        "name": "name"
      }
    ],
    "trainingProgress": {
      "stage": "pending",
      "updatedAt": 0,
      "position": 0,
      "progress": 0,
      "remainingTimeMs": 0,
      "startedAt": 0
    },
    "trainingStats": {
      "endedAt": "endedAt",
      "queueDuration": 0,
      "startedAt": "startedAt",
      "trainDuration": 0
    },
    "uiConfig": {
      "inputProperties": {
        "foo": {
          "collapsed": true
        }
      },
      "lorasComponent": {
        "label": "label",
        "modelInput": "modelInput",
        "scaleInput": "scaleInput",
        "modelIdInput": "modelIdInput"
      },
      "presets": [
        {
          "fields": [
            "string"
          ],
          "presets": {}
        }
      ],
      "resolutionComponent": {
        "heightInput": "heightInput",
        "label": "label",
        "presets": [
          {
            "height": 0,
            "label": "label",
            "width": 0
          }
        ],
        "widthInput": "widthInput"
      },
      "selects": {
        "foo": {}
      },
      "triggerGenerate": {
        "label": "label",
        "after": "after",
        "position": "bottom"
      }
    },
    "userId": "userId"
  }
}