55 lines
1.7 KiB
Markdown
55 lines
1.7 KiB
Markdown
---
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tags:
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- CLIP
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- Loader
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- ModelIO
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---
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# CLIP Vision Encode
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## Documentation
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- Class name: `CLIPVisionEncode`
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- Category: `conditioning`
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- Output node: `False`
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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.
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## Input types
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### Required
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- **`clip_vision`**
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- 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.
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- Comfy dtype: `CLIP_VISION`
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- Python dtype: `torch.nn.Module`
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- **`image`**
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- The image to be encoded. This input is essential for generating the encoded representation of the visual content.
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- Comfy dtype: `IMAGE`
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- Python dtype: `torch.Tensor`
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## Output types
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- **`clip_vision_output`**
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- Comfy dtype: `CLIP_VISION_OUTPUT`
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- The encoded representation of the input image, produced by the CLIP vision model. This output is suitable for further processing or analysis.
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- Python dtype: `torch.Tensor`
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## Usage tips
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- Infra type: `GPU`
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- Common nodes:
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- [unCLIPConditioning](../../Comfy/Nodes/unCLIPConditioning.md)
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## Source code
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```python
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class CLIPVisionEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "clip_vision": ("CLIP_VISION",),
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"image": ("IMAGE",)
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}}
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RETURN_TYPES = ("CLIP_VISION_OUTPUT",)
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FUNCTION = "encode"
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CATEGORY = "conditioning"
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def encode(self, clip_vision, image):
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output = clip_vision.encode_image(image)
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return (output,)
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```
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