ComfyUI Node: TextEncodeHunyuanVideo_ImageToVideo

TextEncodeHunyuanVideo_ImageToVideo:

The TextEncodeHunyuanVideo_ImageToVideo node is designed to facilitate the transformation of textual prompts into video conditioning data, leveraging both text and image inputs to guide the video generation process. This node is particularly useful for AI artists who wish to create videos that are influenced by both descriptive text and visual elements. By integrating the capabilities of the CLIP model and vision outputs, it allows for a nuanced and dynamic interaction between text prompts and image references, enabling the creation of videos that are richly detailed and contextually relevant. The node's primary function is to encode these inputs into a format that can be used to condition video generation models, ensuring that the resulting videos align closely with the specified themes, styles, and visual cues.

TextEncodeHunyuanVideo_ImageToVideo Input Parameters:

clip

The clip parameter refers to the CLIP model instance used for processing the text and image inputs. It plays a crucial role in tokenizing the text prompt and integrating the visual data from the image, ensuring that both elements are effectively combined to influence the video generation process.

clip_vision_output

The clip_vision_output parameter represents the output from the CLIP model's vision component. This output is used to provide visual context to the text prompt, allowing the node to create a more cohesive and visually informed video conditioning data. It is essential for aligning the visual style and content of the video with the reference image.

prompt

The prompt parameter is a string input that allows you to specify the textual description or theme for the video. This parameter supports multiline and dynamic prompts, enabling you to craft detailed and complex descriptions that guide the video generation process. The prompt serves as the primary narrative or thematic guide for the video.

image_interleave

The image_interleave parameter is an integer that determines the degree of influence the image has over the text prompt in the video generation process. With a default value of 2, it can range from 1 to 512, where a higher value indicates a stronger influence from the text prompt. This parameter allows you to balance the impact of the text and image inputs, tailoring the video output to your creative vision.

TextEncodeHunyuanVideo_ImageToVideo Output Parameters:

CONDITIONING

The CONDITIONING output is the encoded data that results from processing the text and image inputs through the node. This output is crucial for conditioning video generation models, as it encapsulates the combined influence of the text prompt and visual data, ensuring that the generated video aligns with the specified themes and visual cues.

TextEncodeHunyuanVideo_ImageToVideo Usage Tips:

• Experiment with different image_interleave values to find the right balance between text and image influence for your specific project needs.
• Use detailed and descriptive prompts to fully leverage the node's ability to create contextually rich video conditioning data.
• Consider using high-quality and relevant images as input to enhance the visual coherence and style of the generated video.

TextEncodeHunyuanVideo_ImageToVideo Common Errors and Solutions:

Invalid CLIP model instance

• Explanation: This error occurs when the clip parameter is not properly initialized or is incompatible with the node's requirements.
• Solution: Ensure that the clip parameter is correctly set to a valid CLIP model instance that supports both text and vision processing.

Incompatible clip_vision_output

• Explanation: This error arises when the clip_vision_output does not match the expected format or is not derived from a compatible CLIP model.
• Solution: Verify that the clip_vision_output is obtained from the vision component of a compatible CLIP model and is correctly formatted.

Prompt exceeds token limit

• Explanation: The text prompt may be too long, exceeding the tokenization limit of the CLIP model.
• Solution: Shorten the prompt or break it into smaller segments to fit within the tokenization limits of the model.