66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
class embeddingSearch:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"Embedding Search": ("STRING", {
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"tooltip": "Searchg For A Specific Embedding By Name Or Civit AIR Code (eg: EasyNegative).",
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}),
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"Model Architecture": ([
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"All",
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"FLUX.1-Schnell",
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"FLUX.1-Dev",
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"Pony",
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"SD 1.5",
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"SD 1.5 Hyper",
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"SD 1.5 LCM",
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"SD 3",
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"SDXL 1.0",
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"SDXL 1.0 LCM",
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"SDXL Distilled",
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"SDXL Hyper",
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"SDXL Lightning",
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"SDXL Turbo",
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], {
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"tooltip": "Choose Embedding Model Architecture To Filter Out The Results.",
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"default": "SD 1.5",
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}),
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"EmbeddingList": ([
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"civitai:7808@9208 (EasyNegative)",
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"civitai:4629@5637 (Deep Negative V1.x)",
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"civitai:56519@60938 (negative_hand Negative Embedding)",
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"civitai:72437@77169 (BadDream + UnrealisticDream (Negative Embeddings))",
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"civitai:11772@25820 (veryBadImageNegative)",
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"civitai:71961@94057 (Fast Negative Embedding (+ FastNegativeV2))",
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], {
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"tooltip": "Embedding Results Will Show UP Here So You Could Choose From.",
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"default": "civitai:7808@9208 (EasyNegative)",
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}),
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"weight": ("FLOAT", {
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"tooltip": "Defines the strength or influence of the Embedding on the generation process.",
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"default": 1.0,
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"min": 0,
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"max": 1,
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"step": 0.1,
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}),
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},
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}
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DESCRIPTION = "Directly Search and Connect Embeddings to Runware Inference Nodes In ComfyUI."
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FUNCTION = "embeddingSearch"
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RETURN_TYPES = ("RUNWAREEMBEDDING",)
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RETURN_NAMES = ("Runware Embedding",)
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CATEGORY = "Runware"
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@classmethod
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def VALIDATE_INPUTS(cls, EmbeddingList):
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return True
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def embeddingSearch(self, **kwargs):
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currentModel = kwargs.get("EmbeddingList")
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embeddingWeight = kwargs.get("weight")
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embeddingAIRCode = currentModel.split(" ")[0]
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return ({
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"model": embeddingAIRCode,
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"weight": round(embeddingWeight, 2),
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},) |