ComfyUI Node: General Prompt Rewriter

General Prompt Rewriter:

The GeneralPromptRewriter is a versatile node designed to enhance and refine text prompts for AI-driven creative processes. Its primary function is to take an initial user-provided prompt and transform it into a more polished and effective version, suitable for generating high-quality AI art or other creative outputs. This node leverages advanced language models to rewrite prompts, ensuring they are clear, engaging, and aligned with the desired artistic style or theme. By doing so, it helps users achieve more consistent and aesthetically pleasing results in their AI-generated artworks. The GeneralPromptRewriter is particularly beneficial for artists who wish to explore different creative directions or improve the coherence and impact of their prompts without needing extensive technical knowledge.

General Prompt Rewriter Input Parameters:

image

This parameter accepts an image input, which serves as a reference or context for the prompt rewriting process. The image can influence the style or content of the rewritten prompt, ensuring it aligns with the visual elements present in the image. This parameter is crucial for tasks where the prompt needs to be closely tied to specific visual characteristics.

prompt

The prompt parameter is a multiline string input that contains the initial text prompt provided by the user. This is the core content that the node will process and rewrite. The quality and clarity of the initial prompt can significantly impact the effectiveness of the rewritten output, making it essential for users to provide a well-thought-out starting point.

prompt_style

This parameter allows users to select the style of prompt rewriting, with options such as "Qwen-Image-Edit" and "Qwen-Image." The choice of style determines the approach and techniques used in the rewriting process, enabling users to tailor the output to specific artistic or thematic preferences. The default option is "Qwen-Image-Edit," which is suitable for most general use cases.

llm_model

This parameter specifies the language model to be used for the prompt rewriting process. Different models may offer varying levels of creativity, coherence, and style, allowing users to select the one that best fits their needs. The choice of model can influence the tone and complexity of the rewritten prompt.

General Prompt Rewriter Output Parameters:

conditioning

The conditioning output provides a set of encoded tokens that represent the rewritten prompt. These tokens are used to guide the AI model in generating outputs that are consistent with the refined prompt. This output is essential for ensuring that the generated art aligns with the user's creative vision.

conditioningN

Similar to the conditioning output, conditioningN provides encoded tokens for a negative prompt, if applicable. This allows users to specify elements or styles they wish to avoid in the generated output, offering greater control over the final artistic result.

General Prompt Rewriter Usage Tips:

• Experiment with different prompt_style options to see how they affect the tone and style of the rewritten prompt. This can help you find the best fit for your creative project.
• Ensure your initial prompt is clear and specific to get the most effective rewritten output. A well-defined starting point can lead to more coherent and impactful results.

General Prompt Rewriter Common Errors and Solutions:

API_KEY is not set! Enter your Aliyun api_key in "API_KEY".

• Explanation: This error occurs when the API key required for accessing the language model is not provided or is invalid.
• Solution: Ensure that you have entered a valid API key in the "API_KEY" field or saved it to the specified key path.

Error during API call: <error>

• Explanation: This error indicates that there was an issue during the API call to the language model, which could be due to network issues or incorrect parameters.
• Solution: Check your network connection and verify that all input parameters are correctly set. If the problem persists, consider increasing the max_retries parameter to allow for additional attempts.