mirror of
https://github.com/comfyanonymous/ComfyUI.git
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80 lines
3.0 KiB
Python
80 lines
3.0 KiB
Python
from __future__ import annotations
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import nodes
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from comfy_api.v3 import io
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class CLIPTextEncodeSDXL(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="CLIPTextEncodeSDXL_V3",
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category="advanced/conditioning",
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inputs=[
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io.Clip.Input("clip"),
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io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.String.Input("text_g", multiline=True, dynamic_prompts=True),
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io.String.Input("text_l", multiline=True, dynamic_prompts=True),
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],
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outputs=[io.Conditioning.Output()],
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)
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@classmethod
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def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput:
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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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conditioning = clip.encode_from_tokens_scheduled(
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tokens,
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add_dict={
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"width": width,
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"height": height,
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"crop_w": crop_w,
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"crop_h": crop_h,
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"target_width": target_width,
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"target_height": target_height,
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},
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)
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return io.NodeOutput(conditioning)
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class CLIPTextEncodeSDXLRefiner(io.ComfyNodeV3):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="CLIPTextEncodeSDXLRefiner_V3",
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category="advanced/conditioning",
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inputs=[
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io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
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io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
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io.String.Input("text", multiline=True, dynamic_prompts=True),
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io.Clip.Input("clip"),
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],
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outputs=[io.Conditioning.Output()],
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)
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@classmethod
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def execute(cls, ascore, width, height, text, clip) -> io.NodeOutput:
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tokens = clip.tokenize(text)
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conditioning = clip.encode_from_tokens_scheduled(
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tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}
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)
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return io.NodeOutput(conditioning)
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NODES_LIST = [
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CLIPTextEncodeSDXL,
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CLIPTextEncodeSDXLRefiner,
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]
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