ComfyUI Node: MiniCPM VQA

MiniCPM VQA:

MiniCPM_VQA is a versatile node designed to facilitate video and image-based question answering tasks using advanced AI models. This node leverages the MiniCPM-V model to process video frames or images and generate insightful responses based on the provided textual input. It is particularly useful for applications requiring detailed analysis and interpretation of visual content, such as video summarization, content-based video retrieval, and interactive media applications. By integrating video and image data with natural language processing, MiniCPM_VQA offers a powerful tool for creating intelligent and responsive AI-driven solutions.

MiniCPM VQA Input Parameters:

text

This parameter represents the textual input or question that you want the model to answer based on the provided video or images. It is a string that guides the model in generating relevant responses. The quality and specificity of the text input can significantly impact the accuracy and relevance of the output.

model

This parameter specifies the model identifier to be used for inference. It determines which pre-trained MiniCPM-V model will be loaded and utilized for processing the input data. The model identifier should match the available models in the system, and it influences the performance and capabilities of the node.

temperature

This parameter controls the randomness of the model's output. A higher temperature value results in more diverse and creative responses, while a lower value makes the output more focused and deterministic. The temperature value typically ranges from 0.0 to 1.0, with a default value around 0.7.

video_max_num_frames

This parameter sets the maximum number of frames to be sampled from the input video. It helps in managing the computational load and ensures that the model processes a representative subset of the video frames. The value should be chosen based on the video's length and the desired level of detail.

video_max_slice_nums

This parameter defines the maximum number of slices or segments to be considered from the video. It helps in breaking down the video into manageable parts for analysis. The value should be set according to the video's complexity and the specific requirements of the task.

source_image_path_1st

This optional parameter specifies the file path of the first image to be used in the analysis. It is used when the input consists of images rather than a video. The image should be in a format supported by the PIL library.

source_image_path_2nd

This optional parameter specifies the file path of the second image to be used in the analysis. It is used in conjunction with the first image to provide additional visual context. The image should be in a format supported by the PIL library.

source_image_path_3rd

This optional parameter specifies the file path of the third image to be used in the analysis. It is used to provide further visual context when needed. The image should be in a format supported by the PIL library.

source_video_path

This optional parameter specifies the file path of the video to be analyzed. The video should be in a format supported by the Decord library. This parameter is used when the input consists of a video rather than images.

MiniCPM VQA Output Parameters:

result

This parameter contains the output generated by the model, which includes the response to the input text based on the analyzed video frames or images. The result is typically a string or a list of strings that provide the model's interpretation and answer to the given question.

MiniCPM VQA Usage Tips:

• Ensure that the input video or images are of high quality and relevant to the question to improve the accuracy of the model's responses.
• Adjust the temperature parameter to balance between creativity and determinism based on the specific requirements of your task.
• Use the video_max_num_frames and video_max_slice_nums parameters to manage the computational load and focus on the most relevant parts of the video.
• Provide clear and specific text input to guide the model in generating accurate and relevant responses.

MiniCPM VQA Common Errors and Solutions:

FileNotFoundError: [Errno 2] No such file or directory

• Explanation: This error occurs when the specified file path for the video or images does not exist.
• Solution: Verify that the file paths provided in the source_image_path_1st, source_image_path_2nd, source_image_path_3rd, and source_video_path parameters are correct and that the files are accessible.

ValueError: Invalid frame index

• Explanation: This error occurs when the frame indices calculated for sampling exceed the available frames in the video.
• Solution: Ensure that the video_max_num_frames parameter is set to a value that is within the range of the total number of frames in the video.

RuntimeError: Model loading failed

• Explanation: This error occurs when the specified model identifier does not match any available models or the model files are missing.
• Solution: Verify that the model parameter is set to a valid model identifier and that the model files are correctly placed in the expected directory.

TypeError: Input data type mismatch

• Explanation: This error occurs when the input data types for images or video frames do not match the expected formats.
• Solution: Ensure that the input images are in a format supported by the PIL library and that the video is in a format supported by the Decord library.