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import types
from typing import List, Optional
from openai.types.image import Image
from litellm.types.utils import ImageResponse
class AmazonStabilityConfig:
"""
Reference: https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=stability.stable-diffusion-xl-v0
Supported Params for the Amazon / Stable Diffusion models:
- `cfg_scale` (integer): Default `7`. Between [ 0 .. 35 ]. How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)
- `seed` (float): Default: `0`. Between [ 0 .. 4294967295 ]. Random noise seed (omit this option or use 0 for a random seed)
- `steps` (array of strings): Default `30`. Between [ 10 .. 50 ]. Number of diffusion steps to run.
- `width` (integer): Default: `512`. multiple of 64 >= 128. Width of the image to generate, in pixels, in an increment divible by 64.
Engine-specific dimension validation:
- SDXL Beta: must be between 128x128 and 512x896 (or 896x512); only one dimension can be greater than 512.
- SDXL v0.9: must be one of 1024x1024, 1152x896, 1216x832, 1344x768, 1536x640, 640x1536, 768x1344, 832x1216, or 896x1152
- SDXL v1.0: same as SDXL v0.9
- SD v1.6: must be between 320x320 and 1536x1536
- `height` (integer): Default: `512`. multiple of 64 >= 128. Height of the image to generate, in pixels, in an increment divible by 64.
Engine-specific dimension validation:
- SDXL Beta: must be between 128x128 and 512x896 (or 896x512); only one dimension can be greater than 512.
- SDXL v0.9: must be one of 1024x1024, 1152x896, 1216x832, 1344x768, 1536x640, 640x1536, 768x1344, 832x1216, or 896x1152
- SDXL v1.0: same as SDXL v0.9
- SD v1.6: must be between 320x320 and 1536x1536
"""
cfg_scale: Optional[int] = None
seed: Optional[float] = None
steps: Optional[List[str]] = None
width: Optional[int] = None
height: Optional[int] = None
def __init__(
self,
cfg_scale: Optional[int] = None,
seed: Optional[float] = None,
steps: Optional[List[str]] = None,
width: Optional[int] = None,
height: Optional[int] = None,
) -> None:
locals_ = locals()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return {
k: v
for k, v in cls.__dict__.items()
if not k.startswith("__")
and not isinstance(
v,
(
types.FunctionType,
types.BuiltinFunctionType,
classmethod,
staticmethod,
),
)
and v is not None
}
@classmethod
def get_supported_openai_params(cls, model: Optional[str] = None) -> List:
return ["size"]
@classmethod
def map_openai_params(
cls,
non_default_params: dict,
optional_params: dict,
):
_size = non_default_params.get("size")
if _size is not None:
width, height = _size.split("x")
optional_params["width"] = int(width)
optional_params["height"] = int(height)
return optional_params
@classmethod
def transform_response_dict_to_openai_response(
cls, model_response: ImageResponse, response_dict: dict
) -> ImageResponse:
image_list: List[Image] = []
for artifact in response_dict["artifacts"]:
_image = Image(b64_json=artifact["base64"])
image_list.append(_image)
model_response.data = image_list
return model_response
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