Class swarmauri_standard.image_gens.FalAIImgGenModel.FalAIImgGenModel

swarmauri_standard.image_gens.FalAIImgGenModel.FalAIImgGenModel

FalAIImgGenModel(**kwargs)

Bases: ImageGenBase

A model class for generating images from text using FluxPro's image generation model, provided by FalAI. This class uses a queue-based API to handle image generation requests.

ATTRIBUTE	DESCRIPTION
allowed_models	List of valid model names for image generation. TYPE: List[str]
api_key	The API key for authenticating requests with the FalAI service. TYPE: str
model_name	The name of the model used for image generation. TYPE: str
type	The model type, fixed as "FalAIImgGenModel". TYPE: Literal
max_retries	The maximum number of retries for polling request status. TYPE: int
retry_delay	Delay in seconds between status check retries. TYPE: float

Initializes the model with the specified API key and model name.

PARAMETER	DESCRIPTION
**kwargs	Additional keyword arguments, which may includes api_key and allowed_models. TYPE: Dict[str, Any] DEFAULT: {}

RAISES	DESCRIPTION
ValueError	If an invalid model name is provided.

Source code in swarmauri_standard/image_gens/FalAIImgGenModel.py

def __init__(self, **kwargs: Dict[str, Any]):
    """
    Initializes the model with the specified API key and model name.

    Args:
        **kwargs (Dict[str, Any]): Additional keyword arguments, which may includes api_key and allowed_models.

    Raises:
        ValueError: If an invalid model name is provided.
    """
    super().__init__(**kwargs)
    self._headers = {
        "Content-Type": "application/json",
        "Authorization": f"Key {self.api_key.get_secret_value()}",
    }
    self._client = httpx.Client(headers=self._headers, timeout=self.timeout)

allowed_models class-attribute instance-attribute

allowed_models = [
    "fal-ai/flux-pro/v1.1-ultra-finetuned",
    "fal-ai/minimax-image",
    "fal-ai/aura-flow",
    "fal-ai/flux/dev",
    "fal-ai/flux-lora",
    "fal-ai/flux-lora/inpainting",
    "fal-ai/flux/schnell",
    "fal-ai/flux-pro/v1.1",
    "fal-ai/flux-pro/new",
    "fal-ai/sana",
    "fal-ai/omnigen-v1",
    "fal-ai/lumina-image/v2",
    "fal-ai/stable-diffusion-v35-large",
    "fal-ai/switti",
    "fal-ai/switti/512",
    "fal-ai/recraft-20b",
    "fal-ai/ideogram/v2/turbo",
    "fal-ai/ideogram/v2a",
    "fal-ai/ideogram/v2a/turbo",
    "fal-ai/bria/text-to-image/base",
    "fal-ai/bria/text-to-image/fast",
    "fal-ai/bria/text-to-image/hd",
    "fal-ai/flux-control-lora-canny",
    "fal-ai/flux-control-lora-depth",
    "fal-ai/flux-general",
    "rundiffusion-fal/juggernaut-flux/base",
    "rundiffusion-fal/juggernaut-flux/lightning",
    "rundiffusion-fal/juggernaut-flux/pro",
    "rundiffusion-fal/juggernaut-flux-lora",
    "rundiffusion-fal/rundiffusion-photo-flux",
    "fal-ai/stable-diffusion-v3-medium",
    "fal-ai/fast-sdxl",
    "fal-ai/lora",
    "fal-ai/imagen3/fast",
    "fal-ai/imagen3",
    "fal-ai/janus",
    "fal-ai/sdxl-controlnet-union",
    "fal-ai/kolors",
    "fal-ai/stable-cascade/sote-diffusion",
    "fal-ai/lightning-models",
    "fal-ai/realistic-vision",
    "fal-ai/dreamshaper",
    "fal-ai/pixart-sigma",
    "fal-ai/stable-diffusion-v15",
    "fal-ai/layer-diffusion",
    "fal-ai/fast-fooocus-sdxl/image-to-image",
    "fal-ai/fast-fooocus-sdxl",
    "fal-ai/fooocus/inpaint",
    "fal-ai/fooocus/image-prompt",
    "fal-ai/fooocus/upscale-or-vary",
    "fal-ai/diffusion-edge",
    "fal-ai/fast-sdxl-controlnet-canny",
    "fal-ai/illusion-diffusion",
    "fal-ai/fooocus",
    "fal-ai/lcm",
    "fal-ai/playground-v25",
    "fal-ai/hyper-sdxl",
    "fal-ai/fast-lightning-sdxl",
    "fal-ai/fast-lcm-diffusion",
    "fal-ai/flowedit",
    "fal-ai/stable-cascade",
    "fal-ai/luma-photon",
    "fal-ai/luma-photon/flash",
    "fal-ai/cogview4",
    "fal-ai/fast-turbo-diffusion",
]

api_key class-attribute instance-attribute

api_key = Field(default=None)

name class-attribute instance-attribute

name = 'fal-ai/flux-pro/v1.1-ultra-finetuned'

timeout class-attribute instance-attribute

timeout = 600.0

type class-attribute instance-attribute

type = 'FalAIImgGenModel'

max_retries class-attribute instance-attribute

max_retries = Field(default=60)

retry_delay class-attribute instance-attribute

retry_delay = Field(default=1.0)

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="forbid", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

resource class-attribute instance-attribute

resource = Field(default=IMAGE_GEN.value, frozen=True)

version class-attribute instance-attribute

version = '0.1.0'

generate_image

generate_image(prompt, **kwargs)

Generates an image based on the prompt and returns the image URL.

PARAMETER	DESCRIPTION
prompt	The text prompt for image generation. TYPE: str
**kwargs	Additional parameters for the request. TYPE: Dict[str, Any] DEFAULT: {}

RETURNS	DESCRIPTION
str	The URL of the generated image. TYPE: str

Source code in swarmauri_standard/image_gens/FalAIImgGenModel.py

def generate_image(self, prompt: str, **kwargs: Dict[str, Any]) -> str:
    """
    Generates an image based on the prompt and returns the image URL.

    Args:
        prompt (str): The text prompt for image generation.
        **kwargs (Dict[str, Any]): Additional parameters for the request.

    Returns:
        str: The URL of the generated image.
    """
    initial_response = self._send_request(prompt, **kwargs)
    request_id = initial_response["request_id"]
    final_response = self._wait_for_completion(request_id)
    return final_response["images"][0]["url"]

agenerate_image async

agenerate_image(prompt, **kwargs)

Asynchronously generates an image based on the prompt and returns the image URL.

PARAMETER	DESCRIPTION
prompt	The text prompt for image generation TYPE: str
**kwargs	Additional parameters to pass to the API TYPE: Dict[str, Any] DEFAULT: {}

RETURNS	DESCRIPTION
str	The URL of the generated image TYPE: str

Source code in swarmauri_standard/image_gens/FalAIImgGenModel.py

async def agenerate_image(self, prompt: str, **kwargs: Dict[str, Any]) -> str:
    """
    Asynchronously generates an image based on the prompt and returns the image URL.

    Args:
        prompt (str): The text prompt for image generation
        **kwargs (Dict[str, Any]): Additional parameters to pass to the API

    Returns:
        str: The URL of the generated image
    """
    try:
        initial_response = await self._async_send_request(prompt, **kwargs)
        request_id = initial_response["request_id"]
        final_response = await self._async_wait_for_completion(request_id)
        return final_response["images"][0]["url"]
    finally:
        await self._close_async_client()

batch_generate

batch_generate(prompts, **kwargs)

Generates images for a batch of prompts.

PARAMETER	DESCRIPTION
prompts	List of text prompts TYPE: List[str]
**kwargs	Additional parameters to pass to the API TYPE: Dict[str, Any] DEFAULT: {}

RETURNS	DESCRIPTION
List[str]	List[str]: List of image URLs

Source code in swarmauri_standard/image_gens/FalAIImgGenModel.py

def batch_generate(self, prompts: List[str], **kwargs: Dict[str, Any]) -> List[str]:
    """
    Generates images for a batch of prompts.

    Args:
        prompts (List[str]): List of text prompts
        **kwargs (Dict[str, Any]): Additional parameters to pass to the API

    Returns:
        List[str]: List of image URLs
    """
    return [self.generate_image(prompt, **kwargs) for prompt in prompts]

abatch_generate async

abatch_generate(prompts, max_concurrent=5, **kwargs)

Asynchronously generates images for a batch of prompts.

PARAMETER	DESCRIPTION
prompts	List of text prompts TYPE: List[str]
max_concurrent	Maximum number of concurrent requests TYPE: int DEFAULT: 5
**kwargs	Additional parameters to pass to the API TYPE: Dict[str, Any] DEFAULT: {}

RETURNS	DESCRIPTION
List[str]	List[str]: List of image URLs

Source code in swarmauri_standard/image_gens/FalAIImgGenModel.py

async def abatch_generate(
    self, prompts: List[str], max_concurrent: int = 5, **kwargs: Dict[str, Any]
) -> List[str]:
    """
    Asynchronously generates images for a batch of prompts.

    Args:
        prompts (List[str]): List of text prompts
        max_concurrent (int): Maximum number of concurrent requests
        **kwargs (Dict[str, Any]): Additional parameters to pass to the API

    Returns:
        List[str]: List of image URLs
    """
    try:
        semaphore = asyncio.Semaphore(max_concurrent)

        async def process_prompt(prompt):
            async with semaphore:
                initial_response = await self._async_send_request(prompt, **kwargs)
                request_id = initial_response["request_id"]
                final_response = await self._async_wait_for_completion(request_id)
                return final_response["response"]["images"][0]["url"]

        tasks = [process_prompt(prompt) for prompt in prompts]
        return await asyncio.gather(*tasks)
    finally:
        await self._close_async_client()

get_allowed_models

get_allowed_models()

Queries the LLMProvider API endpoint to get the list of allowed models.

RETURNS	DESCRIPTION
List[str]	List[str]: List of allowed model names.

Source code in swarmauri_standard/image_gens/FalAIImgGenModel.py

def get_allowed_models(self) -> List[str]:
    """
    Queries the LLMProvider API endpoint to get the list of allowed models.

    Returns:
        List[str]: List of allowed model names.
    """
    return [
        "fal-ai/flux-pro/v1.1-ultra-finetuned",
        "fal-ai/minimax-image",
        "fal-ai/aura-flow",
        "fal-ai/flux/dev",
        "fal-ai/flux-lora",
        "fal-ai/flux-lora/inpainting",
        "fal-ai/flux/schnell",
        "fal-ai/flux-pro/v1.1",
        "fal-ai/flux-pro/new",
        "fal-ai/sana",
        "fal-ai/omnigen-v1",
        "fal-ai/lumina-image/v2",
        "fal-ai/stable-diffusion-v35-large",
        "fal-ai/switti",
        "fal-ai/switti/512",
        "fal-ai/recraft-20b",
        "fal-ai/ideogram/v2/turbo",
        "fal-ai/ideogram/v2a",
        "fal-ai/ideogram/v2a/turbo",
        "fal-ai/bria/text-to-image/base",
        "fal-ai/bria/text-to-image/fast",
        "fal-ai/bria/text-to-image/hd",
        "fal-ai/flux-control-lora-canny",
        "fal-ai/flux-control-lora-depth",
        "fal-ai/flux-general",
        "rundiffusion-fal/juggernaut-flux/base",
        "rundiffusion-fal/juggernaut-flux/lightning",
        "rundiffusion-fal/juggernaut-flux/pro",
        "rundiffusion-fal/juggernaut-flux-lora",
        "rundiffusion-fal/rundiffusion-photo-flux",
        "fal-ai/stable-diffusion-v3-medium",
        "fal-ai/fast-sdxl",
        "fal-ai/lora",
        "fal-ai/imagen3/fast",
        "fal-ai/imagen3",
        "fal-ai/janus",
        "fal-ai/sdxl-controlnet-union",
        "fal-ai/kolors",
        "fal-ai/stable-cascade/sote-diffusion",
        "fal-ai/lightning-models",
        "fal-ai/realistic-vision",
        "fal-ai/dreamshaper",
        "fal-ai/pixart-sigma",
        "fal-ai/stable-diffusion-v15",
        "fal-ai/layer-diffusion",
        "fal-ai/fast-fooocus-sdxl/image-to-image",
        "fal-ai/fast-fooocus-sdxl",
        "fal-ai/fooocus/inpaint",
        "fal-ai/fooocus/image-prompt",
        "fal-ai/fooocus/upscale-or-vary",
        "fal-ai/diffusion-edge",
        "fal-ai/fast-sdxl-controlnet-canny",
        "fal-ai/illusion-diffusion",
        "fal-ai/fooocus",
        "fal-ai/lcm",
        "fal-ai/playground-v25",
        "fal-ai/hyper-sdxl",
        "fal-ai/fast-lightning-sdxl",
        "fal-ai/fast-lcm-diffusion",
        "fal-ai/flowedit",
        "fal-ai/stable-cascade",
        "fal-ai/luma-photon",
        "fal-ai/luma-photon/flash",
        "fal-ai/cogview4",
        "fal-ai/fast-turbo-diffusion",
    ]

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger