generate_hyper3d_model_via_images
Create 3D models with built-in materials from input images and import them directly into Blender for 3D modeling workflows.
Instructions
Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender. The 3D asset has built-in materials. The generated model has a normalized size, so re-scaling after generation can be useful.
Parameters:
input_image_paths: The absolute paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode.
input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode.
bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model.
Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode. Returns a message indicating success or failure.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| bbox_condition | No | ||
| input_image_paths | No | ||
| input_image_urls | No |
Implementation Reference
- src/blender_mcp/server.py:747-802 (handler)The handler function for the 'generate_hyper3d_model_via_images' tool. It validates input images from paths or URLs, encodes path images to base64, sends a 'create_rodin_job' command to Blender via socket, and returns the job UUID and subscription key or error details. Includes input schema in docstring and type hints.@mcp.tool() def generate_hyper3d_model_via_images( ctx: Context, input_image_paths: list[str]=None, input_image_urls: list[str]=None, bbox_condition: list[float]=None ) -> str: """ Generate 3D asset using Hyper3D by giving images of the wanted asset, and import the generated asset into Blender. The 3D asset has built-in materials. The generated model has a normalized size, so re-scaling after generation can be useful. Parameters: - input_image_paths: The **absolute** paths of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in MAIN_SITE mode. - input_image_urls: The URLs of input images. Even if only one image is provided, wrap it into a list. Required if Hyper3D Rodin in FAL_AI mode. - bbox_condition: Optional. If given, it has to be a list of ints of length 3. Controls the ratio between [Length, Width, Height] of the model. Only one of {input_image_paths, input_image_urls} should be given at a time, depending on the Hyper3D Rodin's current mode. Returns a message indicating success or failure. """ if input_image_paths is not None and input_image_urls is not None: return f"Error: Conflict parameters given!" if input_image_paths is None and input_image_urls is None: return f"Error: No image given!" if input_image_paths is not None: if not all(os.path.exists(i) for i in input_image_paths): return "Error: not all image paths are valid!" images = [] for path in input_image_paths: with open(path, "rb") as f: images.append( (Path(path).suffix, base64.b64encode(f.read()).decode("ascii")) ) elif input_image_urls is not None: if not all(urlparse(i) for i in input_image_paths): return "Error: not all image URLs are valid!" images = input_image_urls.copy() try: blender = get_blender_connection() result = blender.send_command("create_rodin_job", { "text_prompt": None, "images": images, "bbox_condition": _process_bbox(bbox_condition), }) succeed = result.get("submit_time", False) if succeed: return json.dumps({ "task_uuid": result["uuid"], "subscription_key": result["jobs"]["subscription_key"], }) else: return json.dumps(result) except Exception as e: logger.error(f"Error generating Hyper3D task: {str(e)}") return f"Error generating Hyper3D task: {str(e)}"
- src/blender_mcp/server.py:702-710 (helper)Helper function to process the bbox_condition parameter, normalizing it to integers 0-100 based on relative proportions.def _process_bbox(original_bbox: list[float] | list[int] | None) -> list[int] | None: if original_bbox is None: return None if all(isinstance(i, int) for i in original_bbox): return original_bbox if any(i<=0 for i in original_bbox): raise ValueError("Incorrect number range: bbox must be bigger than zero!") return [int(float(i) / max(original_bbox) * 100) for i in original_bbox] if original_bbox else None