Seedance 2.0 Prompt Enhancer (Cloud / Local GGUF):
The Seedance20PromptEnhancerT8 is a sophisticated node designed to enhance prompts by leveraging the Seedance 2.0 framework, which integrates cloud or local visual LLM channels to apply official task phrasing and shot-order guidance. This node is particularly beneficial for AI artists looking to refine their creative prompts with structured guidance, ensuring that the final output aligns with the Seedance 2.0 standards. By utilizing either cloud-based channels for complete video analysis or local Qwen for timestamped visual analysis, the node optimizes the user's intent into a coherent and actionable prompt. This process excludes any extraneous elements such as Markdown fences or explanations, focusing solely on delivering a refined and usable prompt. The Seedance20PromptEnhancerT8 is essential for those seeking to enhance their creative workflows with precise and structured prompt enhancements.
Seedance 2.0 Prompt Enhancer (Cloud / Local GGUF) Input Parameters:
prompt
The prompt parameter is the initial text input that you wish to enhance using the Seedance 2.0 framework. It serves as the foundation for the enhancement process, where the node applies structured guidance to refine and optimize the content. There are no specific minimum or maximum values, but the clarity and relevance of the prompt will significantly impact the quality of the output.
task_intent
The task_intent parameter defines the primary objective or purpose of the prompt enhancement. It influences how the node interprets and restructures the prompt to align with the intended task. Options include predefined intents such as "AUTO," which automatically determines the best approach based on the input.
complexity_mode
The complexity_mode parameter determines the level of complexity applied during the enhancement process. It affects the depth and intricacy of the resulting prompt, with options ranging from simple to complex modes. This parameter allows you to tailor the output to match the desired level of detail and sophistication.
duration_seconds
The duration_seconds parameter specifies the intended duration of the visual content associated with the prompt. It guides the node in structuring the prompt to fit within the specified timeframe, ensuring that the output is concise and time-appropriate.
shot_count
The shot_count parameter indicates the number of visual shots or segments to be considered during the enhancement process. It helps the node organize the prompt into distinct sections, each corresponding to a specific visual shot, thereby enhancing the narrative flow.
rewrite_mode
The rewrite_mode parameter controls the extent to which the original prompt is restructured. Options such as "strict" dictate how closely the enhanced prompt adheres to the original content, balancing between maintaining the initial intent and introducing new elements.
output_detail
The output_detail parameter specifies the level of detail included in the enhanced prompt. It influences the richness and granularity of the output, with options like "detailed" providing more comprehensive descriptions and insights.
output_language
The output_language parameter determines the language in which the enhanced prompt is delivered. This allows for multilingual support, enabling you to receive the output in your preferred language, such as English.
reference_images
The reference_images parameter allows you to provide visual references that the node can use to inform the enhancement process. These images serve as visual cues that guide the structuring and content of the enhanced prompt.
reference_videos
The reference_videos parameter enables you to include video references that the node can analyze to enhance the prompt. These videos provide temporal and contextual information that enriches the output.
reference_roles
The reference_roles parameter defines the roles of the provided references, specifying how each image or video contributes to the enhancement process. This parameter helps the node integrate visual elements into the prompt effectively.
constraints
The constraints parameter outlines any specific limitations or rules that the node must adhere to during the enhancement process. It ensures that the output complies with predefined guidelines, such as not altering visible fixture codes.
api_mode
The api_mode parameter selects the mode of API interaction, determining whether the enhancement process utilizes cloud-based or local resources. This choice affects the processing capabilities and speed of the node.
ai_workshop_model
The ai_workshop_model parameter specifies the AI model used for the enhancement process. It allows you to choose between different models, each offering unique capabilities and strengths, to best suit your enhancement needs.
custom_model
The custom_model parameter provides the option to use a custom AI model for the enhancement process. This flexibility enables you to tailor the node's performance to specific requirements or preferences.
case_template
The case_template parameter defines the template used for structuring the enhanced prompt. It influences the format and organization of the output, ensuring consistency with the Seedance 2.0 standards.
Seedance 2.0 Prompt Enhancer (Cloud / Local GGUF) Output Parameters:
result
The result parameter is the final enhanced prompt generated by the node. It represents the culmination of the enhancement process, incorporating structured guidance and visual analysis to deliver a refined and actionable prompt. The output is designed to be directly usable, free from any extraneous elements, and aligned with the Seedance 2.0 framework.
Seedance 2.0 Prompt Enhancer (Cloud / Local GGUF) Usage Tips:
- To achieve the best results, ensure that your initial prompt is clear and concise, as this will significantly influence the quality of the enhanced output.
- Experiment with different
complexity_modesettings to find the right balance between simplicity and detail that suits your creative needs.
Seedance 2.0 Prompt Enhancer (Cloud / Local GGUF) Common Errors and Solutions:
"failed"
- Explanation: This error occurs when the enhancement process encounters an issue, such as invalid input parameters or a problem with the API interaction.
- Solution: Verify that all input parameters are correctly specified and that the API mode is properly configured. Check for any network connectivity issues if using cloud-based resources.