Class swarmauri_standard.image_gens.HyperbolicImgGenModel.HyperbolicImgGenModel

swarmauri_standard.image_gens.HyperbolicImgGenModel.HyperbolicImgGenModel

HyperbolicImgGenModel(**kwargs)

Bases: ImageGenBase

A model class for generating images from text prompts using Hyperbolic's image generation API.

ATTRIBUTE	DESCRIPTION
api_key	The API key for authenticating with the Hyperbolic API. TYPE: str
allowed_models	A list of available models for image generation. TYPE: List[str]
asyncio	The asyncio module for handling asynchronous operations. TYPE: ClassVar
name	The name of the model to be used for image generation. TYPE: str
type	The type identifier for the model class. TYPE: Literal['HyperbolicImgGenModel']
height	Height of the generated image. TYPE: int
width	Width of the generated image. TYPE: int
steps	Number of inference steps. TYPE: int
cfg_scale	Classifier-free guidance scale. TYPE: float
enable_refiner	Whether to enable the refiner model. TYPE: bool
backend	Computational backend for the model. TYPE: str

Link to Allowed Models: https://app.hyperbolic.xyz/models Link to API KEYS: https://app.hyperbolic.xyz/settings

Initializes the HyperbolicImgGenModel instance.

This constructor sets up HTTP clients for both synchronous and asynchronous operations and configures request headers with the provided API key.

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

Source code in swarmauri_standard/image_gens/HyperbolicImgGenModel.py

def __init__(self, **kwargs: Dict[str, Any]):
    """
    Initializes the HyperbolicImgGenModel instance.

    This constructor sets up HTTP clients for both synchronous and asynchronous
    operations and configures request headers with the provided API key.

    Args:
        **kwargs (Dict[str, Any]): Additional keyword arguments, which may include api_key and allowed_models.
    """
    super().__init__(**kwargs)
    self._headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {self.api_key.get_secret_value()}",
    }
    self._client = httpx.Client(headers=self._headers, timeout=30)

api_key instance-attribute

api_key

allowed_models class-attribute instance-attribute

allowed_models = [
    "SDXL1.0-base",
    "SD1.5",
    "SSD",
    "SD2",
    "SDXL-turbo",
]

timeout class-attribute instance-attribute

timeout = 600.0

name class-attribute instance-attribute

name = ''

type class-attribute instance-attribute

type = 'HyperbolicImgGenModel'

height class-attribute instance-attribute

height = 1024

width class-attribute instance-attribute

width = 1024

steps class-attribute instance-attribute

steps = 30

cfg_scale class-attribute instance-attribute

cfg_scale = 5.0

enable_refiner class-attribute instance-attribute

enable_refiner = False

backend class-attribute instance-attribute

backend = 'auto'

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)

Generates an image synchronously based on the provided prompt and returns it as a base64-encoded string.

PARAMETER	DESCRIPTION
prompt	The text prompt used for generating the image. TYPE: str

RETURNS	DESCRIPTION
str	The base64-encoded representation of the generated image. TYPE: str

Source code in swarmauri_standard/image_gens/HyperbolicImgGenModel.py

def generate_image(self, prompt: str) -> str:
    """
    Generates an image synchronously based on the provided prompt and returns it as a base64-encoded string.

    Args:
        prompt (str): The text prompt used for generating the image.

    Returns:
        str: The base64-encoded representation of the generated image.
    """
    response_data = self._send_request(prompt)
    return response_data["images"][0]["image"]

agenerate_image async

agenerate_image(prompt)

Generates an image asynchronously based on the provided prompt and returns it as a base64-encoded string.

PARAMETER	DESCRIPTION
prompt	The text prompt used for generating the image. TYPE: str

RETURNS	DESCRIPTION
str	The base64-encoded representation of the generated image. TYPE: str

Source code in swarmauri_standard/image_gens/HyperbolicImgGenModel.py

async def agenerate_image(self, prompt: str) -> str:
    """
    Generates an image asynchronously based on the provided prompt and returns it as a base64-encoded string.

    Args:
        prompt (str): The text prompt used for generating the image.

    Returns:
        str: The base64-encoded representation of the generated image.
    """
    try:
        response_data = await self._async_send_request(prompt)
        return response_data["images"][0]["image"]
    finally:
        await self._close_async_client()

batch_generate

batch_generate(prompts)

Generates images for a batch of prompts synchronously and returns them as a list of base64-encoded strings.

PARAMETER	DESCRIPTION
prompts	A list of text prompts for image generation. TYPE: List[str]

RETURNS	DESCRIPTION
List[str]	List[str]: A list of base64-encoded representations of the generated images.

Source code in swarmauri_standard/image_gens/HyperbolicImgGenModel.py

def batch_generate(self, prompts: List[str]) -> List[str]:
    """
    Generates images for a batch of prompts synchronously and returns them as a list of base64-encoded strings.

    Args:
        prompts (List[str]): A list of text prompts for image generation.

    Returns:
        List[str]: A list of base64-encoded representations of the generated images.
    """
    return [self.generate_image_base64(prompt) for prompt in prompts]

abatch_generate async

abatch_generate(prompts, max_concurrent=5)

Generates images for a batch of prompts asynchronously and returns them as a list of base64-encoded strings.

PARAMETER	DESCRIPTION
prompts	A list of text prompts for image generation. TYPE: List[str]
max_concurrent	The maximum number of concurrent tasks. TYPE: int DEFAULT: 5

RETURNS	DESCRIPTION
List[str]	List[str]: A list of base64-encoded representations of the generated images.

Source code in swarmauri_standard/image_gens/HyperbolicImgGenModel.py

async def abatch_generate(
    self, prompts: List[str], max_concurrent: int = 5
) -> List[str]:
    """
    Generates images for a batch of prompts asynchronously and returns them as a list of base64-encoded strings.

    Args:
        prompts (List[str]): A list of text prompts for image generation.
        max_concurrent (int): The maximum number of concurrent tasks.

    Returns:
        List[str]: A list of base64-encoded representations of the generated images.
    """
    try:
        semaphore = asyncio.Semaphore(max_concurrent)

        async def process_prompt(prompt):
            async with semaphore:
                response_data = await self._async_send_request(prompt)
                return response_data["images"][0]["image"]

        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/HyperbolicImgGenModel.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 [
        "SDXL1.0-base",
        "SD1.5",
        "SSD",
        "SD2",
        "SDXL-turbo",
    ]

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