# Recommendations ## List Models `recommendations.list_models(RecommendationListModelsParams**kwargs) -> SyncRecommendationsCursor[RecommendationListModelsResponse]` **get** `/recommendations/models` List recommended models matching the given filters ### Parameters - `capabilities: Optional[List[Literal["txt2img", "txt2img_ip_adapter", "txt2img_texture", 30 more]]]` Filter models by capabilities. Multiple values comma-separated. Can be any of the following values: txt2img, txt2img_ip_adapter, txt2img_texture, img2img, img2img_ip_adapter, img2img_texture, inpaint, outpaint, inpaint_ip_adapter, controlnet, controlnet_ip_adapter, reference, reference_texture, controlnet_reference, controlnet_img2img, controlnet_inpaint, controlnet_inpaint_ip_adapter, controlnet_texture, img23d, txt23d, video23d, 3d23d, img2video, txt2video, video2video, video2img, txt2audio, audio2audio, audio2video, video2audio, txt2txt, img2txt, audio2txt. Examples: `txt2img`, `img2img`. Also accepts prefixes or suffixes values such as `txt2`, `img2`, `inpaint`, `control`. Default: no filter - `"txt2img"` - `"txt2img_ip_adapter"` - `"txt2img_texture"` - `"img2img"` - `"img2img_ip_adapter"` - `"img2img_texture"` - `"inpaint"` - `"outpaint"` - `"inpaint_ip_adapter"` - `"controlnet"` - `"controlnet_ip_adapter"` - `"reference"` - `"reference_texture"` - `"controlnet_reference"` - `"controlnet_img2img"` - `"controlnet_inpaint"` - `"controlnet_inpaint_ip_adapter"` - `"controlnet_texture"` - `"img23d"` - `"txt23d"` - `"video23d"` - `"3d23d"` - `"img2video"` - `"txt2video"` - `"video2video"` - `"video2img"` - `"txt2audio"` - `"audio2audio"` - `"audio2video"` - `"video2audio"` - `"txt2txt"` - `"img2txt"` - `"audio2txt"` - `exclude_model_ids: Optional[str]` Exclude specific models by their IDs. Multiple IDs comma-separated. Example: `model1,model2,model3`. Default: no exclusions - `exclude_types: Optional[str]` Exclude models by type. Multiple types comma-separated. Example: `flux.1,flux.1-lora`. Default: no exclusions - `limit: Optional[float]` The maximum number of models to return. Default: `10`, Maximum: `30` - `next_token: Optional[str]` Pagination token to retrieve the next page of results. Use the `nextToken` from the previous response. Default: first page - `original_assets: Optional[bool]` If set to true, returns the original asset without transformation - `privacy: Optional[Literal["private", "public"]]` Filter models by privacy level. Default: `private`. Values: `private`, `public` - `"private"` - `"public"` - `project_id: Optional[str]` The projectId used for ownership resource management. Either to assert ownership or to set the owner of the resource(s) - `tags: Optional[str]` Filter models by tags. Multiple tags comma-separated. Example: `anime,portrait,style`. Default: no filter - `type: Optional[Literal["flux.1", "flux.1-lora", "flux.1-kontext-dev", 28 more]]` Filter models by type. Examples: `flux.1`, `flux.1-lora`, etc. Default: no filter - `"flux.1"` - `"flux.1-lora"` - `"flux.1-kontext-dev"` - `"flux.1-krea-dev"` - `"flux.1-kontext-lora"` - `"flux.1-krea-lora"` - `"flux.1-composition"` - `"flux.1-pro"` - `"flux1.1-pro"` - `"flux.1.1-pro-ultra"` - `"flux.2-dev-lora"` - `"flux.2-dev-edit-lora"` - `"flux.2-klein-4b-lora"` - `"flux.2-klein-9b-lora"` - `"flux.2-klein-base-4b-lora"` - `"flux.2-klein-base-9b-lora"` - `"flux.2-klein-4b-edit-lora"` - `"flux.2-klein-9b-edit-lora"` - `"flux.2-klein-base-4b-edit-lora"` - `"flux.2-klein-base-9b-edit-lora"` - `"gpt-image-1"` - `"qwen-image-lora"` - `"qwen-image-2512-lora"` - `"qwen-image-edit-lora"` - `"qwen-image-edit-2509-lora"` - `"qwen-image-edit-2511-lora"` - `"zimage-lora"` - `"zimage-turbo-lora"` - `"zimage-de-turbo-lora"` - `"custom"` - `"elevenlabs-voice"` ### Returns - `class RecommendationListModelsResponse: …` - `id: str` The model ID (example: "model_eyVcnFJcR92BxBkz7N6g5w") - `capabilities: List[Literal["3d23d", "audio2audio", "audio2txt", 30 more]]` List of model capabilities (example: ["txt2img", "img2img", "txt2img_ip_adapter", ...]) - `"3d23d"` - `"audio2audio"` - `"audio2txt"` - `"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) - `"private"` - `"public"` - `"unlisted"` - `source: Literal["civitai", "huggingface", "other", "scenario"]` The source of the model - `"civitai"` - `"huggingface"` - `"other"` - `"scenario"` - `status: Literal["copying", "failed", "new", 3 more]` The model status - `"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", 28 more]` The model type (example: "flux.1-lora") - `"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"` - `"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: Starter plan 50: Pro plan 75: Max plan 100: Enterprise plan - `0` - `100` - `25` - `50` - `75` - `author_id: Optional[str]` The author user ID (example: "user_VFhihHKMRZyDDnZAJwLb2Q") - `class_: Optional[Class]` 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. Only available for legacy models. - `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) - `"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[Concept]]` 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. - `model_epoch: Optional[str]` The epoch of the model (example: "000001") Only available for Flux Lora Trained models - `disable_refund: Optional[bool]` When true, credits spent on a generation with this model are not automatically refunded if the generation fails. Absent or false means failed generations are refunded as usual. - `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[Epoch]]` The epochs of the model. Only available for Flux Lora Trained models. - `epoch: str` The epoch hash to identify the epoch - `assets: Optional[List[EpochAsset]]` 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[Input]]` 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 - `"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`. - `"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", 5 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 `file` and `file_array` inputs always expose a kind: when a model does not declare one, `image` is returned, which is the kind used at generation time. - `"3d"` - `"audio"` - `"document"` - `"image"` - `"image-hdr"` - `"json"` - `"text"` - `"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_duration: Optional[float]` The maximum allowed media duration in seconds. Only applies to `file` and `file_array` input types for video and audio assets. Validated against `asset.properties.duration` at job creation time. - `max_length: Optional[float]` The maximum allowed length for `string` and `model` inputs, in characters. For the array types (`string_array`, `model_array`) it bounds the NUMBER OF ITEMS instead — the length of each item is not constrained. - `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` and `model` inputs, in characters. For the array types (`string_array`, `model_array`) it bounds the NUMBER OF ITEMS instead — the length of each item is not constrained. - `model_types: Optional[List[Literal["custom", "elevenlabs-voice", "flux.1", 28 more]]]` The allowed model types for this input. Example: `["flux.1-lora"]`. Only available for `model_array` input type. - `"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"` - `"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[InputRequired]` 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 - `conditional`: Input is required when other inputs have specific values By default, the input is not required. - `always: Optional[bool]` Whether the input is always required - `conditional: Optional[InputRequiredConditional]` Makes this input required when other inputs have one of the listed values - `values: object` The conditions to check: - Key: name of the input to check - Value: values of that input that satisfy the condition - `operator: Optional[Literal["and", "or"]]` How the conditions combine: `and` (all must match) or `or` (any must match) - `"and"` - `"or"` - `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. - `job_id: Optional[str]` The ID of the job that trained the model, returned to the model owner only (example: "job_ocZCnG1Df35XRL1QyCZSRxAG8") - `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[Parameters]` 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 - `class_prompt: Optional[str]` The prompt to specify images in the same class as provided instance images Deprecated legacy training parameter. - `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 Flux LoRA, the default is an empty string. - `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 Flux LoRA, the default is 0.0001. - `learning_rate_text_encoder: Optional[float]` Initial learning rate (after the potential warmup period) for the text encoder Maximum [Flux LoRA: 0.001] Default [Flux LoRA: 0.00001] Minimum [Flux LoRA: 0.000001] - `learning_rate_unet: Optional[float]` Initial learning rate (after the potential warmup period) for the UNet Deprecated legacy training parameter. - `lr_scheduler: Optional[Literal["constant", "constant-with-warmup", "cosine", 3 more]]` The scheduler type to use (default: "constant") Deprecated legacy training parameter. - `"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) Default value varies depending on the model type: - For Flux: number of training images * 100 Maximum value varies depending on the model type: - For Flux: [0, 10000] - `nb_epochs: Optional[float]` The number of epochs to train for Only available for Flux LoRA training - `nb_repeats: Optional[float]` The number of times to repeat the training Only available for Flux LoRA training - `num_text_train_steps: Optional[float]` The number of training steps for the text encoder Deprecated legacy training parameter. - `num_u_net_train_steps: Optional[float]` The number of training steps for the UNet Deprecated legacy training parameter. - `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 - `"likeness"` - `prior_loss_weight: Optional[float]` The weight of prior preservation loss Deprecated legacy training parameter. - `random_crop: Optional[bool]` Whether to random crop or center crop images before resizing to the working resolution Deprecated legacy training parameter. - `random_crop_ratio: Optional[float]` Ratio of random crops Deprecated legacy training parameter. - `random_crop_scale: Optional[float]` Scale of random crops Deprecated legacy training parameter. - `rank: Optional[float]` The dimension of the LoRA update matrices Only available for Flux LoRA and Musubi training Default value varies depending on the model type: - For Flux: 16 - For Musubi: 64 Each trainer enforces its own tighter limit (Flux LoRA: [2; 64], Musubi: [2; 128]) - `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 Deprecated legacy training parameter. - `seed: Optional[float]` Used to reproduce previous results. Default: randomly generated number. Deprecated legacy training parameter. - `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` Deprecated legacy training parameter. - `validation_frequency: Optional[float]` Validation frequency. Cannot be greater than maxTrainSteps value Deprecated legacy training parameter. - `validation_prompt: Optional[str]` Validation prompt Deprecated legacy training parameter. - `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[PerformanceStats]` Aggregated performance stats - `variants: List[PerformanceStatsVariant]` 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[PerformanceStatsVariantArenaScore]` 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[Thumbnail]` 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[TrainingImagePair]]` 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[TrainingImage]]` 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[TrainingProgress]` Additional information about the training progress of the model - `stage: Literal["pending", "pending-captcha", "queued-for-train", 3 more]` The stage of the request - `"pending"` - `"pending-captcha"` - `"queued-for-train"` - `"ready-for-captcha"` - `"running-train"` - `"starting-train"` - `updated_at: float` Timestamp in milliseconds of the last time the training progress was updated - `captcha_image_url: Optional[str]` Signed URL of the captcha image to read aloud during PVC voice cloning. Only present when stage === 'pending-captcha'. Overwritten on each retry. - `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 - `remaining_time_ms: Optional[float]` The remaining time in milliseconds - `retryable_error: Optional[str]` Last recoverable failure message during PVC. Present when the model has bounced back to stage === 'ready-for-captcha' after a step 2 or step 3 failure (e.g. captcha rejected, time limit exceeded). Surface above the retry button so the user understands why they're back here. - `retry_count: Optional[float]` Number of consecutive PVC step 2 / step 3 failures on this voice model. The model is marked Failed when this reaches the platform's max retries. - `started_at: Optional[float]` The timestamp in millisecond marking the start of the process - `training_stats: Optional[TrainingStats]` 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[UiConfig]` The UI configuration for the model - `input_properties: Optional[Dict[str, UiConfigInputProperties]]` Configuration for the input properties - `collapsed: Optional[bool]` - `loras_component: Optional[UiConfigLorasComponent]` 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 - `output_properties: Optional[Dict[str, UiConfigOutputProperties]]` Configuration for the provider outputs exposed by `exposeOutput` - `label: str` - `presets: Optional[List[UiConfigPreset]]` Configuration for the presets - `fields: List[str]` - `presets: object` - `resolution_component: Optional[UiConfigResolutionComponent]` Configuration for the resolution component - `height_input: str` The input name of the height - `label: str` The label of the component - `presets: List[UiConfigResolutionComponentPreset]` 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[UiConfigTriggerGenerate]` 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. - `"bottom"` - `"top"` - `user_id: Optional[str]` (Deprecated) The user ID (example: "user_VFhihHKMRZyDDnZAJwLb2Q") ### Example ```python 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 ) page = client.recommendations.list_models() page = page.models[0] print(page.id) ``` #### Response ```json { "models": [ { "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" } ], "disableRefund": true, "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, "maxDuration": 0, "maxLength": 0, "maxSize": 0, "min": 0, "minLength": 0, "modelTypes": [ "custom" ], "parent": true, "placeholder": "placeholder", "prompt": true, "promptSpark": true, "required": { "always": true, "conditional": { "values": {}, "operator": "and" }, "ifDefined": {}, "ifNotDefined": {} }, "step": 1 } ], "jobId": "jobId", "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, "captchaImageUrl": "captchaImageUrl", "position": 0, "progress": 0, "remainingTimeMs": 0, "retryableError": "retryableError", "retryCount": 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" }, "outputProperties": { "foo": { "label": "label" } }, "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" } ], "nextToken": "nextToken" } ``` ## Domain Types ### Recommendation List Models Response - `class RecommendationListModelsResponse: …` - `id: str` The model ID (example: "model_eyVcnFJcR92BxBkz7N6g5w") - `capabilities: List[Literal["3d23d", "audio2audio", "audio2txt", 30 more]]` List of model capabilities (example: ["txt2img", "img2img", "txt2img_ip_adapter", ...]) - `"3d23d"` - `"audio2audio"` - `"audio2txt"` - `"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) - `"private"` - `"public"` - `"unlisted"` - `source: Literal["civitai", "huggingface", "other", "scenario"]` The source of the model - `"civitai"` - `"huggingface"` - `"other"` - `"scenario"` - `status: Literal["copying", "failed", "new", 3 more]` The model status - `"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", 28 more]` The model type (example: "flux.1-lora") - `"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"` - `"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: Starter plan 50: Pro plan 75: Max plan 100: Enterprise plan - `0` - `100` - `25` - `50` - `75` - `author_id: Optional[str]` The author user ID (example: "user_VFhihHKMRZyDDnZAJwLb2Q") - `class_: Optional[Class]` 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. Only available for legacy models. - `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) - `"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[Concept]]` 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. - `model_epoch: Optional[str]` The epoch of the model (example: "000001") Only available for Flux Lora Trained models - `disable_refund: Optional[bool]` When true, credits spent on a generation with this model are not automatically refunded if the generation fails. Absent or false means failed generations are refunded as usual. - `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[Epoch]]` The epochs of the model. Only available for Flux Lora Trained models. - `epoch: str` The epoch hash to identify the epoch - `assets: Optional[List[EpochAsset]]` 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[Input]]` 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 - `"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`. - `"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", 5 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 `file` and `file_array` inputs always expose a kind: when a model does not declare one, `image` is returned, which is the kind used at generation time. - `"3d"` - `"audio"` - `"document"` - `"image"` - `"image-hdr"` - `"json"` - `"text"` - `"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_duration: Optional[float]` The maximum allowed media duration in seconds. Only applies to `file` and `file_array` input types for video and audio assets. Validated against `asset.properties.duration` at job creation time. - `max_length: Optional[float]` The maximum allowed length for `string` and `model` inputs, in characters. For the array types (`string_array`, `model_array`) it bounds the NUMBER OF ITEMS instead — the length of each item is not constrained. - `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` and `model` inputs, in characters. For the array types (`string_array`, `model_array`) it bounds the NUMBER OF ITEMS instead — the length of each item is not constrained. - `model_types: Optional[List[Literal["custom", "elevenlabs-voice", "flux.1", 28 more]]]` The allowed model types for this input. Example: `["flux.1-lora"]`. Only available for `model_array` input type. - `"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"` - `"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[InputRequired]` 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 - `conditional`: Input is required when other inputs have specific values By default, the input is not required. - `always: Optional[bool]` Whether the input is always required - `conditional: Optional[InputRequiredConditional]` Makes this input required when other inputs have one of the listed values - `values: object` The conditions to check: - Key: name of the input to check - Value: values of that input that satisfy the condition - `operator: Optional[Literal["and", "or"]]` How the conditions combine: `and` (all must match) or `or` (any must match) - `"and"` - `"or"` - `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. - `job_id: Optional[str]` The ID of the job that trained the model, returned to the model owner only (example: "job_ocZCnG1Df35XRL1QyCZSRxAG8") - `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[Parameters]` 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 - `class_prompt: Optional[str]` The prompt to specify images in the same class as provided instance images Deprecated legacy training parameter. - `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 Flux LoRA, the default is an empty string. - `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 Flux LoRA, the default is 0.0001. - `learning_rate_text_encoder: Optional[float]` Initial learning rate (after the potential warmup period) for the text encoder Maximum [Flux LoRA: 0.001] Default [Flux LoRA: 0.00001] Minimum [Flux LoRA: 0.000001] - `learning_rate_unet: Optional[float]` Initial learning rate (after the potential warmup period) for the UNet Deprecated legacy training parameter. - `lr_scheduler: Optional[Literal["constant", "constant-with-warmup", "cosine", 3 more]]` The scheduler type to use (default: "constant") Deprecated legacy training parameter. - `"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) Default value varies depending on the model type: - For Flux: number of training images * 100 Maximum value varies depending on the model type: - For Flux: [0, 10000] - `nb_epochs: Optional[float]` The number of epochs to train for Only available for Flux LoRA training - `nb_repeats: Optional[float]` The number of times to repeat the training Only available for Flux LoRA training - `num_text_train_steps: Optional[float]` The number of training steps for the text encoder Deprecated legacy training parameter. - `num_u_net_train_steps: Optional[float]` The number of training steps for the UNet Deprecated legacy training parameter. - `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 - `"likeness"` - `prior_loss_weight: Optional[float]` The weight of prior preservation loss Deprecated legacy training parameter. - `random_crop: Optional[bool]` Whether to random crop or center crop images before resizing to the working resolution Deprecated legacy training parameter. - `random_crop_ratio: Optional[float]` Ratio of random crops Deprecated legacy training parameter. - `random_crop_scale: Optional[float]` Scale of random crops Deprecated legacy training parameter. - `rank: Optional[float]` The dimension of the LoRA update matrices Only available for Flux LoRA and Musubi training Default value varies depending on the model type: - For Flux: 16 - For Musubi: 64 Each trainer enforces its own tighter limit (Flux LoRA: [2; 64], Musubi: [2; 128]) - `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 Deprecated legacy training parameter. - `seed: Optional[float]` Used to reproduce previous results. Default: randomly generated number. Deprecated legacy training parameter. - `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` Deprecated legacy training parameter. - `validation_frequency: Optional[float]` Validation frequency. Cannot be greater than maxTrainSteps value Deprecated legacy training parameter. - `validation_prompt: Optional[str]` Validation prompt Deprecated legacy training parameter. - `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[PerformanceStats]` Aggregated performance stats - `variants: List[PerformanceStatsVariant]` 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[PerformanceStatsVariantArenaScore]` 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[Thumbnail]` 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[TrainingImagePair]]` 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[TrainingImage]]` 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[TrainingProgress]` Additional information about the training progress of the model - `stage: Literal["pending", "pending-captcha", "queued-for-train", 3 more]` The stage of the request - `"pending"` - `"pending-captcha"` - `"queued-for-train"` - `"ready-for-captcha"` - `"running-train"` - `"starting-train"` - `updated_at: float` Timestamp in milliseconds of the last time the training progress was updated - `captcha_image_url: Optional[str]` Signed URL of the captcha image to read aloud during PVC voice cloning. Only present when stage === 'pending-captcha'. Overwritten on each retry. - `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 - `remaining_time_ms: Optional[float]` The remaining time in milliseconds - `retryable_error: Optional[str]` Last recoverable failure message during PVC. Present when the model has bounced back to stage === 'ready-for-captcha' after a step 2 or step 3 failure (e.g. captcha rejected, time limit exceeded). Surface above the retry button so the user understands why they're back here. - `retry_count: Optional[float]` Number of consecutive PVC step 2 / step 3 failures on this voice model. The model is marked Failed when this reaches the platform's max retries. - `started_at: Optional[float]` The timestamp in millisecond marking the start of the process - `training_stats: Optional[TrainingStats]` 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[UiConfig]` The UI configuration for the model - `input_properties: Optional[Dict[str, UiConfigInputProperties]]` Configuration for the input properties - `collapsed: Optional[bool]` - `loras_component: Optional[UiConfigLorasComponent]` 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 - `output_properties: Optional[Dict[str, UiConfigOutputProperties]]` Configuration for the provider outputs exposed by `exposeOutput` - `label: str` - `presets: Optional[List[UiConfigPreset]]` Configuration for the presets - `fields: List[str]` - `presets: object` - `resolution_component: Optional[UiConfigResolutionComponent]` Configuration for the resolution component - `height_input: str` The input name of the height - `label: str` The label of the component - `presets: List[UiConfigResolutionComponentPreset]` 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[UiConfigTriggerGenerate]` 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. - `"bottom"` - `"top"` - `user_id: Optional[str]` (Deprecated) The user ID (example: "user_VFhihHKMRZyDDnZAJwLb2Q")