autogen.agentchat.contrib.capabilities.generate_images.ImageGeneration

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ImageGeneration

ImageGeneration(
    image_generator: ImageGenerator,
    cache: AbstractCache | None = None,
    text_analyzer_llm_config: LLMConfig | dict[str, Any] | None = None,
    text_analyzer_instructions: str = 'In detail, please summarize the provided prompt to generate the image described in the TEXT.\nDO NOT include any advice. RESPOND like the following example:\nEXAMPLE: Blue background, 3D shapes, ...\n',
    verbosity: int = 0,
    register_reply_position: int = 2
)

This capability allows a ConversableAgent to generate images based on the message received from other Agents.
1. Utilizes a TextAnalyzerAgent to analyze incoming messages to identify requests for image generation and extract relevant details.
2. Leverages the provided ImageGenerator (e.g., DalleImageGenerator) to create the image.
3. Optionally caches generated images for faster retrieval in future conversations.
NOTE: This capability increases the token usage of the agent, as it uses TextAnalyzerAgent to analyze every message received by the agent.
Example:
```python import autogen from autogen.agentchat.contrib.capabilities.image_generation import ImageGeneration

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Assuming you have llm configs configured for the LLMs you want to use and Dalle.
# Create the agent

agent = autogen.ConversableAgent( name=“dalle”, llm_config={…}, max_consecutive_auto_reply=3, human_input_mode=“NEVER” )

​

Create an ImageGenerator with desired settings

dalle_gen = generate_images.DalleImageGenerator(llm_config={…})

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Add the ImageGeneration capability to the agent

agent.add_capability(ImageGeneration(image_generator=dalle_gen))

<b>Parameters:</b>
| Name | Description |
|--|--|
| `image_generator` | **Type:** [ImageGenerator](/docs/api-reference/autogen/agentchat/contrib/capabilities/generate_images/ImageGenerator) |
| `cache` | **Type:** [AbstractCache](/docs/api-reference/autogen/cache/AbstractCache) \| None<br/><br/>**Default:** None |
| `text_analyzer_llm_config` | **Type:** [LLMConfig](/docs/api-reference/autogen/llm_config/LLMConfig) \| dict[str, typing.Any] \| None<br/><br/>**Default:** None |
| `text_analyzer_instructions` | **Type:** str<br/><br/>**Default:** 'In detail, please summarize the provided prompt to generate the image described in the TEXT.\nDO NOT include any advice. RESPOND like the following example |
| `verbosity` | The verbosity level.<br/><br/>Defaults to 0 and must be greater than or equal to 0. The text analyzer llm calls will be silent if verbosity is less than 2.br/>register_reply_position (int): The position of the reply function in the agent's list of reply functions.br/> This capability registers a new reply function to handle messages with image generation requests.br/> Defaults to 2 to place it after the check termination and human reply for a ConversableAgent.<br/><br/>**Type:** int<br/><br/>**Default:** 0 |
| `register_reply_position` | **Type:** int<br/><br/>**Default:** 2 |

### Instance Methods

<code class="doc-symbol doc-symbol-heading doc-symbol-method"></code>
#### add_to_agent

```python
add_to_agent(self, agent: ConversableAgent) -> 

Adds the Image Generation capability to the specified ConversableAgent.
This function performs the following modifications to the agent:
1. Registers a reply function: A new reply function is registered with the agent to handle messages that potentially request image generation. This function analyzes the message and triggers image generation if necessary.
2. Creates an Agent (TextAnalyzerAgent): This is used to analyze messages for image generation requirements.
3. Updates System Message: The agent’s system message is updated to include a message indicating the capability to generate images has been added.
4. Updates Description: The agent’s description is updated to reflect the addition of the Image Generation capability. This might be helpful in certain use cases, like group chats.

Parameters:

Name	Description
agent	The ConversableAgent to add the capability to. Type: ConversableAgent