# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import base64
import logging
from typing import Any, Dict, Literal, Optional
import httpx
from pyrit.exceptions import (
EmptyResponseException,
pyrit_target_retry,
)
from pyrit.models import (
Message,
construct_response_from_request,
data_serializer_factory,
)
from pyrit.prompt_target.common.utils import limit_requests_per_minute
from pyrit.prompt_target.openai.openai_target import OpenAITarget
logger = logging.getLogger(__name__)
[docs]
class OpenAIImageTarget(OpenAITarget):
"""A target for image generation using OpenAI's image models."""
[docs]
def __init__(
self,
image_size: Literal["256x256", "512x512", "1024x1024", "1536x1024", "1024x1536"] = "1024x1024",
quality: Optional[Literal["standard", "hd", "low", "medium", "high"]] = None,
style: Optional[Literal["natural", "vivid"]] = None,
*args: Any,
**kwargs: Any,
) -> None:
"""
Initialize the image target with specified parameters.
Args:
model_name (str, Optional): The name of the model (or deployment name in Azure).
If no value is provided, the OPENAI_IMAGE_MODEL environment variable will be used.
endpoint (str, Optional): The target URL for the OpenAI service.
api_key (str | Callable[[], str], Optional): The API key for accessing the OpenAI service,
or a callable that returns an access token. For Azure endpoints with Entra authentication,
pass a token provider from pyrit.auth (e.g., get_azure_openai_auth(endpoint)).
Defaults to the `OPENAI_IMAGE_API_KEY` environment variable.
headers (str, Optional): Headers of the endpoint (JSON).
max_requests_per_minute (int, Optional): Number of requests the target can handle per
minute before hitting a rate limit. The number of requests sent to the target
will be capped at the value provided.
image_size (Literal["256x256", "512x512", "1024x1024"], Optional): The size of the generated images.
Defaults to "1024x1024".
quality (Literal["standard", "hd", "low", "medium", "high"], Optional): The quality of the generated images.
Different models support different quality settings.
For DALL-E-3, there's "standard" and "hd".
For newer models, there are "low", "medium", and "high".
Default is to not specify.
style (Literal["natural", "vivid"], Optional): The style of the generated images.
Default is to not specify.
*args: Additional positional arguments to be passed to AzureOpenAITarget.
**kwargs: Additional keyword arguments to be passed to AzureOpenAITarget.
httpx_client_kwargs (dict, Optional): Additional kwargs to be passed to the
`httpx.AsyncClient()` constructor.
For example, to specify a 3 minutes timeout: httpx_client_kwargs={"timeout": 180}
"""
self.quality = quality
self.style = style
self.image_size = image_size
super().__init__(*args, **kwargs)
def _set_openai_env_configuration_vars(self) -> None:
self.model_name_environment_variable = "OPENAI_IMAGE_MODEL"
self.endpoint_environment_variable = "OPENAI_IMAGE_ENDPOINT"
self.api_key_environment_variable = "OPENAI_IMAGE_API_KEY"
self.underlying_model_environment_variable = "OPENAI_IMAGE_UNDERLYING_MODEL"
def _get_target_api_paths(self) -> list[str]:
"""Return API paths that should not be in the URL."""
return ["/images/generations", "/v1/images/generations"]
def _get_provider_examples(self) -> dict[str, str]:
"""Return provider-specific example URLs."""
return {
".openai.azure.com": "https://{resource}.openai.azure.com/openai/v1",
"api.openai.com": "https://api.openai.com/v1",
}
@limit_requests_per_minute
@pyrit_target_retry
async def send_prompt_async(
self,
*,
message: Message,
) -> list[Message]:
"""
Send a prompt to the DALL-E target and return the response.
Args:
message (Message): The message to send.
Returns:
list[Message]: A list containing the response from the image target.
"""
self._validate_request(message=message)
message_piece = message.message_pieces[0]
logger.info(f"Sending the following prompt to the prompt target: {message_piece}")
# Construct request parameters
image_generation_args: Dict[str, Any] = {
"model": self._model_name,
"prompt": message_piece.converted_value,
"size": self.image_size,
}
if self.quality:
image_generation_args["quality"] = self.quality
if self.style:
image_generation_args["style"] = self.style
# Use unified error handler for consistent error handling
response = await self._handle_openai_request(
api_call=lambda: self._async_client.images.generate(**image_generation_args),
request=message,
)
return [response]
async def _construct_message_from_response(self, response: Any, request: Any) -> Message:
"""
Construct a Message from an ImagesResponse.
Args:
response: The ImagesResponse from OpenAI SDK.
request: The original request MessagePiece.
Returns:
Message: Constructed message with image path.
Raises:
EmptyResponseException: If the image generation returned an empty response.
"""
image_data = response.data[0]
image_bytes = await self._get_image_bytes(image_data)
data = data_serializer_factory(category="prompt-memory-entries", data_type="image_path")
await data.save_data(data=image_bytes)
return construct_response_from_request(
request=request, response_text_pieces=[data.value], response_type="image_path"
)
async def _get_image_bytes(self, image_data: Any) -> bytes:
"""
Extract image bytes from the API response.
Handles both base64-encoded data and URL responses. Some models (like gpt-image-1)
return base64 directly, while others (like dall-e) may return URLs.
Args:
image_data: The image data object from the API response.
Returns:
bytes: The raw image bytes.
Raises:
EmptyResponseException: If neither base64 data nor URL is available.
"""
# Try base64 first (preferred format)
b64_data = getattr(image_data, "b64_json", None)
if b64_data:
return base64.b64decode(b64_data)
# Fall back to URL download
image_url = getattr(image_data, "url", None)
if image_url:
logger.info("Image model returned URL. Downloading image.")
async with httpx.AsyncClient() as http_client:
image_response = await http_client.get(image_url)
image_response.raise_for_status()
return image_response.content
raise EmptyResponseException(message="The image generation returned an empty response.")
def _validate_request(self, *, message: Message) -> None:
n_pieces = len(message.message_pieces)
if n_pieces != 1:
raise ValueError(f"This target only supports a single message piece. Received: {n_pieces} pieces.")
piece_type = message.message_pieces[0].converted_value_data_type
if piece_type != "text":
raise ValueError(f"This target only supports text prompt input. Received: {piece_type}.")
[docs]
def is_json_response_supported(self) -> bool:
"""
Check if the target supports JSON as a response format.
Returns:
bool: True if JSON response is supported, False otherwise.
"""
return False
