63 lines
2.5 KiB
Markdown
63 lines
2.5 KiB
Markdown
---
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tags:
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- CLIP
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- CLIPConditioning
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- CLIPTextEncoding
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- Conditioning
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---
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# CLIP Text Encode (Prompt)
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## Documentation
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- Class name: `CLIPTextEncode`
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- Category: `conditioning`
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- Output node: `False`
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The CLIPTextEncode node is designed to encode textual input using a CLIP model, transforming text into a conditioning format suitable for further processing or integration with other models. It focuses on converting text prompts into a structured format that captures the essence of the input for use in conditioned generative tasks.
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## Input types
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### Required
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- **`text`**
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- The 'text' parameter is the primary textual input that the node encodes. It plays a crucial role in determining the output conditioning, as it is directly processed by the CLIP model to generate embeddings.
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- Comfy dtype: `STRING`
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- Python dtype: `str`
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- **`clip`**
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- The 'clip' parameter represents the CLIP model used for text tokenization and encoding. It is essential for transforming the input text into a format that can be utilized for conditioning in generative tasks.
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- Comfy dtype: `CLIP`
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- Python dtype: `object`
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## Output types
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- **`conditioning`**
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- Comfy dtype: `CONDITIONING`
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- The output is a conditioning format that encapsulates the encoded text information, suitable for guiding generative models in producing content that aligns with the input text.
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- Python dtype: `List[Tuple[torch.Tensor, Dict[str, torch.Tensor]]]`
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## Usage tips
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- Infra type: `CPU`
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- Common nodes:
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- [KSampler](../../Comfy/Nodes/KSampler.md)
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- [ControlNetApplyAdvanced](../../Comfy/Nodes/ControlNetApplyAdvanced.md)
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- KSampler //Inspire
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- [SamplerCustom](../../Comfy/Nodes/SamplerCustom.md)
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- Reroute
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- [KSamplerAdvanced](../../Comfy/Nodes/KSamplerAdvanced.md)
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- [ACN_AdvancedControlNetApply](../../ComfyUI-Advanced-ControlNet/Nodes/ACN_AdvancedControlNetApply.md)
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- [ToBasicPipe](../../ComfyUI-Impact-Pack/Nodes/ToBasicPipe.md)
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- [FaceDetailer](../../ComfyUI-Impact-Pack/Nodes/FaceDetailer.md)
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## Source code
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```python
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class CLIPTextEncode:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", )}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "encode"
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CATEGORY = "conditioning"
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def encode(self, clip, text):
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tokens = clip.tokenize(text)
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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return ([[cond, {"pooled_output": pooled}]], )
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```
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