chore: initial snapshot for gitea/github upload
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@@ -0,0 +1,29 @@
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from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
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from .dalle2_transformation import DallE2ImageEditConfig
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from .transformation import OpenAIImageEditConfig
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__all__ = [
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"OpenAIImageEditConfig",
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"DallE2ImageEditConfig",
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"get_openai_image_edit_config",
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]
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def get_openai_image_edit_config(model: str) -> BaseImageEditConfig:
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"""
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Get the appropriate OpenAI image edit config based on the model.
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Args:
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model: The model name (e.g., "dall-e-2", "gpt-image-1")
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Returns:
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The appropriate config instance for the model
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"""
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model_normalized = model.lower().replace("-", "").replace("_", "")
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if model_normalized == "dalle2":
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return DallE2ImageEditConfig()
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else:
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# Default to standard OpenAI config for gpt-image-1 and other models
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return OpenAIImageEditConfig()
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from io import BufferedReader
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast
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from httpx._types import RequestFiles
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import litellm
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from litellm.images.utils import ImageEditRequestUtils
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from litellm.types.images.main import ImageEditRequestParams
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from litellm.types.llms.openai import FileTypes
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from litellm.types.router import GenericLiteLLMParams
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from .transformation import OpenAIImageEditConfig
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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class DallE2ImageEditConfig(OpenAIImageEditConfig):
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"""
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DALL-E-2 specific configuration for image edit API.
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DALL-E-2 only supports editing a single image (not an array).
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Uses "image" field name instead of "image[]".
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"""
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def transform_image_edit_request(
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self,
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model: str,
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prompt: Optional[str],
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image: Optional[FileTypes],
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image_edit_optional_request_params: Dict,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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) -> Tuple[Dict, RequestFiles]:
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"""
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Transform image edit request for DALL-E-2.
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DALL-E-2 only accepts a single image with field name "image" (not "image[]").
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"""
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request_params = {
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"model": model,
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**image_edit_optional_request_params,
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}
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if image is not None:
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request_params["image"] = image
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if prompt is not None:
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request_params["prompt"] = prompt
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request = ImageEditRequestParams(**request_params)
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request_dict = cast(Dict, request)
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#########################################################
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# Separate images and masks as `files` and send other parameters as `data`
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#########################################################
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_image_list = request_dict.get("image")
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_mask = request_dict.get("mask")
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data_without_files = {
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k: v for k, v in request_dict.items() if k not in ["image", "mask"]
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}
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files_list: List[Tuple[str, Any]] = []
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# Handle image parameter - DALL-E-2 only supports single image
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if _image_list is not None:
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image_list = (
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[_image_list] if not isinstance(_image_list, list) else _image_list
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)
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# Validate only one image is provided
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if len(image_list) > 1:
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raise litellm.BadRequestError(
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message="DALL-E-2 only supports editing a single image. Please provide one image.",
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model=model,
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llm_provider="openai",
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)
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# Use "image" field name (singular) for DALL-E-2
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for _image in image_list:
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if _image is not None:
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self._add_image_to_files(
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files_list=files_list,
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image=_image,
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field_name="image",
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)
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# Handle mask parameter if provided
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if _mask is not None:
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# Handle case where mask can be a list (extract first mask)
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if isinstance(_mask, list):
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_mask = _mask[0] if _mask else None
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if _mask is not None:
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mask_content_type: str = ImageEditRequestUtils.get_image_content_type(
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_mask
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)
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if isinstance(_mask, BufferedReader):
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files_list.append(("mask", (_mask.name, _mask, mask_content_type)))
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else:
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files_list.append(("mask", ("mask.png", _mask, mask_content_type)))
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return data_without_files, files_list
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@@ -0,0 +1,202 @@
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from io import BufferedReader
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast
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import httpx
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from httpx._types import RequestFiles
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import litellm
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from litellm.images.utils import ImageEditRequestUtils
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from litellm.llms.base_llm.image_edit.transformation import BaseImageEditConfig
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.images.main import (
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ImageEditOptionalRequestParams,
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ImageEditRequestParams,
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)
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from litellm.types.llms.openai import FileTypes
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from litellm.types.router import GenericLiteLLMParams
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from litellm.utils import ImageResponse
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from ..common_utils import OpenAIError
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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class OpenAIImageEditConfig(BaseImageEditConfig):
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"""
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Base configuration for OpenAI image edit API.
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Used for models like gpt-image-1 that support multiple images.
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"""
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def get_supported_openai_params(self, model: str) -> list:
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"""
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All OpenAI Image Edits params are supported
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"""
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return [
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"image",
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"prompt",
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"background",
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"input_fidelity",
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"mask",
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"model",
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"n",
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"quality",
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"response_format",
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"size",
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"user",
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"extra_headers",
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"extra_query",
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"extra_body",
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"timeout",
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]
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def map_openai_params(
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self,
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image_edit_optional_params: ImageEditOptionalRequestParams,
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model: str,
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drop_params: bool,
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) -> Dict:
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"""No mapping applied since inputs are in OpenAI spec already"""
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return dict(image_edit_optional_params)
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def _add_image_to_files(
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self,
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files_list: List[Tuple[str, Any]],
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image: Any,
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field_name: str,
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) -> None:
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"""Add an image to the files list with appropriate content type"""
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image_content_type = ImageEditRequestUtils.get_image_content_type(image)
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if isinstance(image, BufferedReader):
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files_list.append((field_name, (image.name, image, image_content_type)))
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else:
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files_list.append((field_name, ("image.png", image, image_content_type)))
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def transform_image_edit_request(
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self,
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model: str,
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prompt: Optional[str],
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image: Optional[FileTypes],
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image_edit_optional_request_params: Dict,
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litellm_params: GenericLiteLLMParams,
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headers: dict,
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) -> Tuple[Dict, RequestFiles]:
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"""
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Transform image edit request to OpenAI API format.
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Handles multipart/form-data for images. Uses "image[]" field name
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to support multiple images (e.g., for gpt-image-1).
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"""
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# Build request params, only including non-None values
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request_params = {
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"model": model,
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**image_edit_optional_request_params,
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}
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if image is not None:
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request_params["image"] = image
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if prompt is not None:
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request_params["prompt"] = prompt
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request = ImageEditRequestParams(**request_params)
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request_dict = cast(Dict, request)
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#########################################################
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# Separate images and masks as `files` and send other parameters as `data`
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#########################################################
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_image_list = request_dict.get("image")
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_mask = request_dict.get("mask")
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data_without_files = {
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k: v for k, v in request_dict.items() if k not in ["image", "mask"]
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}
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files_list: List[Tuple[str, Any]] = []
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# Handle image parameter
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if _image_list is not None:
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image_list = (
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[_image_list] if not isinstance(_image_list, list) else _image_list
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)
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for _image in image_list:
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if _image is not None:
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self._add_image_to_files(
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files_list=files_list,
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image=_image,
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field_name="image[]",
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)
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# Handle mask parameter if provided
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if _mask is not None:
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# Handle case where mask can be a list (extract first mask)
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if isinstance(_mask, list):
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_mask = _mask[0] if _mask else None
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if _mask is not None:
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mask_content_type: str = ImageEditRequestUtils.get_image_content_type(
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_mask
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)
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if isinstance(_mask, BufferedReader):
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files_list.append(("mask", (_mask.name, _mask, mask_content_type)))
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else:
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files_list.append(("mask", ("mask.png", _mask, mask_content_type)))
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return data_without_files, files_list
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def transform_image_edit_response(
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self,
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model: str,
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raw_response: httpx.Response,
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logging_obj: LiteLLMLoggingObj,
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) -> ImageResponse:
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"""No transform applied since outputs are in OpenAI spec already"""
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try:
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raw_response_json = raw_response.json()
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except Exception:
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raise OpenAIError(
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message=raw_response.text, status_code=raw_response.status_code
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)
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return ImageResponse(**raw_response_json)
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def validate_environment(
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self,
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headers: dict,
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model: str,
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api_key: Optional[str] = None,
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) -> dict:
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api_key = (
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api_key
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or litellm.api_key
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or litellm.openai_key
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or get_secret_str("OPENAI_API_KEY")
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)
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headers.update(
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{
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"Authorization": f"Bearer {api_key}",
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}
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)
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return headers
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def get_complete_url(
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self,
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model: str,
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api_base: Optional[str],
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litellm_params: dict,
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) -> str:
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"""
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Get the endpoint for OpenAI responses API
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"""
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api_base = (
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api_base
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or litellm.api_base
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or get_secret_str("OPENAI_BASE_URL")
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or get_secret_str("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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# Remove trailing slashes
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api_base = api_base.rstrip("/")
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return f"{api_base}/images/edits"
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