CLIP Vision Encode

Documentation

Class name: CLIPVisionEncode
Category: conditioning
Output node: False

The CLIPVisionEncode node is designed to encode images using a CLIP vision model, transforming visual input into a format suitable for further processing or analysis. This node abstracts the complexity of image encoding, offering a streamlined interface for converting images into encoded representations.

Input types

Required

clip_vision
- The CLIP vision model used for encoding the image. It is crucial for the encoding process, as it determines the method and quality of the encoding.
- Comfy dtype: CLIP_VISION
- Python dtype: torch.nn.Module
image
- The image to be encoded. This input is essential for generating the encoded representation of the visual content.
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor

Output types

clip_vision_output
- Comfy dtype: CLIP_VISION_OUTPUT
- The encoded representation of the input image, produced by the CLIP vision model. This output is suitable for further processing or analysis.
- Python dtype: torch.Tensor

Usage tips

Infra type: GPU
Common nodes:
- unCLIPConditioning

Source code

class CLIPVisionEncode:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "clip_vision": ("CLIP_VISION",),
                              "image": ("IMAGE",)
                             }}
    RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
    FUNCTION = "encode"

    CATEGORY = "conditioning"

    def encode(self, clip_vision, image):
        output = clip_vision.encode_image(image)
        return (output,)

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