47 lines
1.9 KiB
Python
47 lines
1.9 KiB
Python
import base64
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from typing import Any, Dict, Optional, Union
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from huggingface_hub.hf_api import InferenceProviderMapping
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from huggingface_hub.inference._common import RequestParameters, _as_dict
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from huggingface_hub.inference._providers._common import BaseConversationalTask, TaskProviderHelper, filter_none
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class HyperbolicTextToImageTask(TaskProviderHelper):
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def __init__(self):
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super().__init__(provider="hyperbolic", base_url="https://api.hyperbolic.xyz", task="text-to-image")
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def _prepare_route(self, mapped_model: str, api_key: str) -> str:
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return "/v1/images/generations"
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def _prepare_payload_as_dict(
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self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
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) -> Optional[Dict]:
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mapped_model = provider_mapping_info.provider_id
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parameters = filter_none(parameters)
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if "num_inference_steps" in parameters:
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parameters["steps"] = parameters.pop("num_inference_steps")
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if "guidance_scale" in parameters:
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parameters["cfg_scale"] = parameters.pop("guidance_scale")
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# For Hyperbolic, the width and height are required parameters
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if "width" not in parameters:
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parameters["width"] = 512
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if "height" not in parameters:
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parameters["height"] = 512
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return {"prompt": inputs, "model_name": mapped_model, **parameters}
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def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
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response_dict = _as_dict(response)
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return base64.b64decode(response_dict["images"][0]["image"])
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class HyperbolicTextGenerationTask(BaseConversationalTask):
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"""
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Special case for Hyperbolic, where text-generation task is handled as a conversational task.
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"""
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def __init__(self, task: str):
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super().__init__(
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provider="hyperbolic",
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base_url="https://api.hyperbolic.xyz",
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)
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self.task = task
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