image_conditioning
Generate images matching the layout and composition of a reference image using text prompts and control modes. Specify attributes to include or exclude for precise output customization.
Instructions
Generate an image that follows the layout and composition of a reference image.
Args:
image_path: File path of the reference image
prompt: Text describing the image to be generated
negative_prompt: Text specifying attributes to exclude from generation
control_mode: Control mode (CANNY_EDGE, etc.)
height: Output image height (pixels)
width: Output image width (pixels)
cfg_scale: Prompt matching degree (1-20)
Returns:
Dict: Dictionary containing the file path of the generated imageInput Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| cfg_scale | No | ||
| control_mode | No | CANNY_EDGE | |
| height | No | ||
| image_path | Yes | ||
| negative_prompt | No | ||
| prompt | Yes | ||
| width | No |
Implementation Reference
- The primary handler function implementing the image_conditioning tool. It processes a reference image, generates a conditioned image using Bedrock API, saves it, and returns the path.
async def image_conditioning( image_path: str, prompt: str, negative_prompt: str = "", control_mode: str = "CANNY_EDGE", height: int = 512, width: int = 512, cfg_scale: float = 8.0, output_path: str = None, ) -> Dict[str, Any]: """ Generate an image that follows the layout and composition of a reference image. Args: image_path: File path of the reference image prompt: Text describing the image to be generated negative_prompt: Text specifying attributes to exclude from generation control_mode: Control mode (CANNY_EDGE, etc.) height: Output image height (pixels) width: Output image width (pixels) cfg_scale: Prompt matching degree (1-20) output_path: Absolute path to save the image Returns: Dict: Dictionary containing the file path of the generated image """ try: # Read image file and encode to base64 with open(image_path, "rb") as image_file: input_image = base64.b64encode(image_file.read()).decode('utf8') body = json.dumps({ "taskType": "TEXT_IMAGE", "textToImageParams": { "text": prompt, "negativeText": negative_prompt, "conditionImage": input_image, "controlMode": control_mode }, "imageGenerationConfig": { "numberOfImages": 1, "height": height, "width": width, "cfgScale": cfg_scale } }) # Generate image image_bytes = generate_image(body) # Save image image_info = save_image(image_bytes, output_path=output_path) # Generate result result = { "image_path": image_info["image_path"], "message": f"Image conditioning completed successfully. Saved location: {image_info['image_path']}" } return result except Exception as e: raise McpError(f"Error occurred while image conditioning: {str(e)}") - aws_nova_canvas_mcp/server.py:35-35 (registration)Registration point for the image_conditioning tool in the MCP server (currently commented out).
# mcp.add_tool(image_conditioning) - aws_nova_canvas_mcp/server.py:9-9 (registration)Import statement bringing the image_conditioning handler into the server module.
from .tools.image_conditioning import image_conditioning