73 lines
2.7 KiB
Markdown
73 lines
2.7 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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# CLIPTextEncodeSDXLRefiner
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## Documentation
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- Class name: `CLIPTextEncodeSDXLRefiner`
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- Category: `advanced/conditioning`
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- Output node: `False`
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The CLIPTextEncodeSDXLRefiner node specializes in refining text inputs for image generation by encoding them using a CLIP model, incorporating aesthetic scores and dimensions to enhance the conditioning for image synthesis.
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## Input types
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### Required
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- **`ascore`**
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- The aesthetic score parameter influences the aesthetic quality of the generated image, allowing for fine-tuning based on aesthetic preferences.
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- Comfy dtype: `FLOAT`
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- Python dtype: `float`
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- **`width`**
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- Specifies the desired width of the output image, impacting the aspect ratio and detail level of the generated image.
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- Comfy dtype: `INT`
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- Python dtype: `int`
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- **`height`**
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- Determines the height of the output image, affecting its aspect ratio and the level of detail in the vertical dimension.
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- Comfy dtype: `INT`
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- Python dtype: `int`
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- **`text`**
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- The text input to be encoded, serving as the primary source of semantic information for image generation.
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- Comfy dtype: `STRING`
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- Python dtype: `str`
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- **`clip`**
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- A CLIP model instance used for encoding the text input, crucial for interpreting the semantic content.
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- Comfy dtype: `CLIP`
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- Python dtype: `torch.nn.Module`
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## Output types
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- **`conditioning`**
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- Comfy dtype: `CONDITIONING`
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- Provides the encoded text along with aesthetic scores and dimensions, ready for use in further image synthesis processes.
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- Python dtype: `list`
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## Usage tips
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- Infra type: `GPU`
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- Common nodes:
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- [KSamplerAdvanced](../../Comfy/Nodes/KSamplerAdvanced.md)
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- [UltimateSDUpscale](../../ComfyUI_UltimateSDUpscale/Nodes/UltimateSDUpscale.md)
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## Source code
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```python
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class CLIPTextEncodeSDXLRefiner:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
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"width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
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"height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
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"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "encode"
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CATEGORY = "advanced/conditioning"
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def encode(self, clip, ascore, width, height, 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, "aesthetic_score": ascore, "width": width,"height": height}]], )
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```
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