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104 lines
2.8 KiB
Python
104 lines
2.8 KiB
Python
"""Hugging Face client."""
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import logging
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from typing import Any, Dict, Optional
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import numpy as np
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import requests
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from manifest.clients.client import Client
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from manifest.request import DiffusionRequest
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logger = logging.getLogger(__name__)
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class DiffuserClient(Client):
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"""Diffuser client."""
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# User param -> (client param, default value)
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PARAMS = {
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"num_inference_steps": ("num_inference_steps", 50),
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"height": ("height", 512),
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"width": ("width", 512),
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"n": ("num_images_per_prompt", 1),
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"guidance_scale": ("guidance_scale", 7.5),
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"eta": ("eta", 0.0),
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}
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REQUEST_CLS = DiffusionRequest
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NAME = "diffuser"
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def connect(
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self,
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connection_str: Optional[str] = None,
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client_args: Dict[str, Any] = {},
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) -> None:
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"""
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Connect to the Diffuser url.
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Arsg:
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connection_str: connection string.
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client_args: client arguments.
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"""
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self.host = connection_str.rstrip("/")
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for key in self.PARAMS:
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setattr(self, key, client_args.pop(key, self.PARAMS[key][1]))
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self.model_params = self.get_model_params()
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def to_numpy(self, image: np.ndarray) -> np.ndarray:
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"""Convert a numpy image to a PIL image.
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Adapted from https://github.com/huggingface/diffusers/blob/src/diffusers/pipelines/pipeline_utils.py#L808 # noqa: E501
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"""
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image = (image * 255).round().astype("uint8")
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return image
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def close(self) -> None:
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"""Close the client."""
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pass
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def get_generation_url(self) -> str:
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"""Get generation URL."""
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return self.host + "/completions"
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def get_generation_header(self) -> Dict[str, str]:
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"""
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Get generation header.
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Returns:
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header.
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"""
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return {}
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def supports_batch_inference(self) -> bool:
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"""Return whether the client supports batch inference."""
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return True
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def get_model_params(self) -> Dict:
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"""
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Get model params.
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By getting model params from the server, we can add to request
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and make sure cache keys are unique to model.
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Returns:
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model params.
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"""
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res = requests.post(self.host + "/params").json()
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res["client_name"] = self.NAME
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return res
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def validate_response(self, response: Dict, request: Dict) -> Dict[str, Any]:
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"""
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Format response to dict.
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Args:
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response: response
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request: request
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Return:
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response as dict
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"""
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# Convert array to np.array
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for choice in response["choices"]:
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choice["array"] = self.to_numpy(np.array(choice["array"]))
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return response
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