57 lines
2.6 KiB
Python
57 lines
2.6 KiB
Python
import torch
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from nodes import MAX_RESOLUTION
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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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class CLIPTextEncodeSDXL:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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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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"crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
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"crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
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"target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
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"target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
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"text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
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"text_l": ("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, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):
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tokens = clip.tokenize(text_g)
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tokens["l"] = clip.tokenize(text_l)["l"]
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if len(tokens["l"]) != len(tokens["g"]):
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empty = clip.tokenize("")
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while len(tokens["l"]) < len(tokens["g"]):
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tokens["l"] += empty["l"]
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while len(tokens["l"]) > len(tokens["g"]):
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tokens["g"] += empty["g"]
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cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
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return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], )
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NODE_CLASS_MAPPINGS = {
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"CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner,
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"CLIPTextEncodeSDXL": CLIPTextEncodeSDXL,
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}
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