44 lines
1.8 KiB
Python
44 lines
1.8 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 ._common import BaseConversationalTask, TaskProviderHelper, filter_none
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class NscaleConversationalTask(BaseConversationalTask):
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def __init__(self):
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super().__init__(provider="nscale", base_url="https://inference.api.nscale.com")
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class NscaleTextToImageTask(TaskProviderHelper):
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def __init__(self):
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super().__init__(provider="nscale", base_url="https://inference.api.nscale.com", 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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# Combine all parameters except inputs and parameters
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parameters = filter_none(parameters)
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if "width" in parameters and "height" in parameters:
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parameters["size"] = f"{parameters.pop('width')}x{parameters.pop('height')}"
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if "num_inference_steps" in parameters:
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parameters.pop("num_inference_steps")
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if "cfg_scale" in parameters:
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parameters.pop("cfg_scale")
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payload = {
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"response_format": "b64_json",
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"prompt": inputs,
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"model": mapped_model,
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**parameters,
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}
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return payload
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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["data"][0]["b64_json"])
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