1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
|
import base64
from typing import Any, Dict, Optional, Union
from huggingface_hub.inference._common import _as_dict
from huggingface_hub.inference._providers._common import BaseConversationalTask, TaskProviderHelper, filter_none
class HyperbolicTextToImageTask(TaskProviderHelper):
def __init__(self):
super().__init__(provider="hyperbolic", base_url="https://api.hyperbolic.xyz", task="text-to-image")
def _prepare_route(self, mapped_model: str) -> str:
return "/v1/images/generations"
def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
parameters = filter_none(parameters)
if "num_inference_steps" in parameters:
parameters["steps"] = parameters.pop("num_inference_steps")
if "guidance_scale" in parameters:
parameters["cfg_scale"] = parameters.pop("guidance_scale")
# For Hyperbolic, the width and height are required parameters
if "width" not in parameters:
parameters["width"] = 512
if "height" not in parameters:
parameters["height"] = 512
return {"prompt": inputs, "model_name": mapped_model, **parameters}
def get_response(self, response: Union[bytes, Dict]) -> Any:
response_dict = _as_dict(response)
return base64.b64decode(response_dict["images"][0]["image"])
class HyperbolicTextGenerationTask(BaseConversationalTask):
"""
Special case for Hyperbolic, where text-generation task is handled as a conversational task.
"""
def __init__(self, task: str):
super().__init__(
provider="hyperbolic",
base_url="https://api.hyperbolic.xyz",
)
self.task = task
|
