495 lines
25 KiB
Python
495 lines
25 KiB
Python
import nodes
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from ..components.tree import TREE_VISUALS
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from ..components.tree import PRIMERE_ROOT
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import folder_paths
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from ..components import utility
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import os
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from pathlib import Path
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import chardet
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import pandas
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import re
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from ..utils import comfy_dir
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from .modules import networkhandler
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class PrimereVisualCKPT:
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RETURN_TYPES = ("CHECKPOINT_NAME", "STRING")
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RETURN_NAMES = ("MODEL_NAME", "MODEL_VERSION")
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FUNCTION = "load_ckpt_visual_list"
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CATEGORY = TREE_VISUALS
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model_versions = utility.get_category_from_cache('model_version')
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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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"base_model": (folder_paths.get_filename_list("checkpoints"),),
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"show_modal": ("BOOLEAN", {"default": True}),
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"show_hidden": ("BOOLEAN", {"default": True}),
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},
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"hidden": {
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"cached_model": (cls.model_versions,),
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}
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}
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def load_ckpt_visual_list(self, base_model, show_hidden, show_modal):
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modelname_only = Path(base_model).stem
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model_version = utility.get_value_from_cache('model_version', modelname_only)
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if model_version is None:
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LOADED_CHECKPOINT = nodes.CheckpointLoaderSimple.load_checkpoint(self, base_model)
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model_version = utility.getCheckpointVersion(LOADED_CHECKPOINT[0])
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utility.add_value_to_cache('model_version', modelname_only, model_version)
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return (base_model, model_version)
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class PrimereVisualLORA:
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RETURN_TYPES = ("MODEL", "CLIP", "LORA_STACK", "MODEL_KEYWORD")
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RETURN_NAMES = ("MODEL", "CLIP", "LORA_STACK", "LORA_KEYWORD")
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FUNCTION = "visual_lora_stacker"
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CATEGORY = TREE_VISUALS
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LORASCOUNT = 6
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lora_versions = utility.get_category_from_cache('lora_version')
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@classmethod
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def INPUT_TYPES(cls):
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LoraList = folder_paths.get_filename_list("loras")
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return {
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"required": {
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"model": ("MODEL",),
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"clip": ("CLIP",),
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"model_version": ("STRING", {"default": 'BaseModel_1024', "forceInput": True}),
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"stack_version": (["SD", "SDXL", "Flux", "Any"], {"default": "Any"}),
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"show_modal": ("BOOLEAN", {"default": True}),
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"show_hidden": ("BOOLEAN", {"default": True}),
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"use_only_model_weight": ("BOOLEAN", {"default": True}),
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"use_lora_1": ("BOOLEAN", {"default": False}),
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"lora_1": (LoraList,),
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"lora_1_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_1_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lora_2": ("BOOLEAN", {"default": False}),
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"lora_2": (LoraList,),
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"lora_2_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_2_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lora_3": ("BOOLEAN", {"default": False}),
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"lora_3": (LoraList,),
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"lora_3_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_3_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lora_4": ("BOOLEAN", {"default": False}),
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"lora_4": (LoraList,),
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"lora_4_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_4_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lora_5": ("BOOLEAN", {"default": False}),
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"lora_5": (LoraList,),
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"lora_5_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_5_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lora_6": ("BOOLEAN", {"default": False}),
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"lora_6": (LoraList,),
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"lora_6_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lora_6_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lora_keyword": ("BOOLEAN", {"default": False}),
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"lora_keyword_placement": (["First", "Last"], {"default": "Last"}),
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"lora_keyword_selection": (["Select in order", "Random select"], {"default": "Select in order"}),
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"lora_keywords_num": ("INT", {"default": 1, "min": 1, "max": 50, "step": 1}),
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"lora_keyword_weight": ("FLOAT", {"default": 1.0, "min": 0, "max": 10.0, "step": 0.1}),
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},
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"optional": {
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"workflow_tuple": ("TUPLE", {"forceInput": True, "default": []}),
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},
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"hidden": {
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"cached_lora": (cls.lora_versions,),
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}
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}
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def visual_lora_stacker(self, model, clip, use_only_model_weight, use_lora_keyword, lora_keyword_placement, lora_keyword_selection, lora_keywords_num, lora_keyword_weight, workflow_tuple, stack_version = 'Any', model_version = "BaseModel_1024", **kwargs):
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model_keyword = [None, None]
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if 'model_concept' in workflow_tuple and workflow_tuple['model_concept'] != stack_version and workflow_tuple['model_concept'] != 'Normal':
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return (model, clip, [], model_keyword)
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if model_version == 'SDXL_2048' and stack_version == 'SD':
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return (model, clip, [], model_keyword)
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if model_version != 'SDXL_2048' and stack_version == 'SDXL':
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return (model, clip, [], model_keyword)
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if workflow_tuple is not None and len(workflow_tuple) > 0 and 'setup_states' in workflow_tuple and 'lora_setup' in workflow_tuple['setup_states'] and 'exif_status' in workflow_tuple and workflow_tuple['exif_status'] == 'SUCCEED':
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if workflow_tuple['setup_states']['lora_setup'] == True:
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if 'network_data' in workflow_tuple:
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loader = networkhandler.getNetworkLoader(workflow_tuple, 'lora', self.LORASCOUNT, True, stack_version)
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if len(loader) > 0:
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return networkhandler.LoraHandler(self, loader, model, clip, model_keyword, use_only_model_weight, lora_keywords_num, use_lora_keyword, lora_keyword_selection, lora_keyword_weight, lora_keyword_placement)
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else:
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return (model, clip, [], model_keyword)
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else:
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return (model, clip, [], model_keyword)
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else:
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return (model, clip, [], model_keyword)
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return networkhandler.LoraHandler(self, kwargs, model, clip, model_keyword, use_only_model_weight, lora_keywords_num, use_lora_keyword, lora_keyword_selection, lora_keyword_weight, lora_keyword_placement)
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class PrimereVisualEmbedding:
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RETURN_TYPES = ("EMBEDDING", "EMBEDDING", "EMBEDDING_STACK")
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RETURN_NAMES = ("EMBEDDING+", "EMBEDDING-", "EMBEDDING_STACK")
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FUNCTION = "primere_visual_embedding"
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CATEGORY = TREE_VISUALS
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EMBCOUNT = 6
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embedding_versions = utility.get_category_from_cache('embedding_version')
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@classmethod
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def INPUT_TYPES(cls):
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EmbeddingList = folder_paths.get_filename_list("embeddings")
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return {
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"required": {
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"model_version": ("STRING", {"default": 'BaseModel_1024', "forceInput": True}),
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"stack_version": (["SD", "SDXL", "Flux", "Any"], {"default": "Any"}),
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"show_modal": ("BOOLEAN", {"default": True}),
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"show_hidden": ("BOOLEAN", {"default": True}),
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"use_embedding_1": ("BOOLEAN", {"default": False}),
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"embedding_1": (EmbeddingList,),
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"embedding_1_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },),
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"is_negative_1": ("BOOLEAN", {"default": False}),
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"use_embedding_2": ("BOOLEAN", {"default": False}),
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"embedding_2": (EmbeddingList,),
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"embedding_2_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },),
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"is_negative_2": ("BOOLEAN", {"default": False}),
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"use_embedding_3": ("BOOLEAN", {"default": False}),
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"embedding_3": (EmbeddingList,),
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"embedding_3_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },),
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"is_negative_3": ("BOOLEAN", {"default": False}),
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"use_embedding_4": ("BOOLEAN", {"default": False}),
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"embedding_4": (EmbeddingList,),
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"embedding_4_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },),
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"is_negative_4": ("BOOLEAN", {"default": False}),
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"use_embedding_5": ("BOOLEAN", {"default": False}),
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"embedding_5": (EmbeddingList,),
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"embedding_5_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },),
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"is_negative_5": ("BOOLEAN", {"default": False}),
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"use_embedding_6": ("BOOLEAN", {"default": False}),
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"embedding_6": (EmbeddingList,),
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"embedding_6_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },),
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"is_negative_6": ("BOOLEAN", {"default": False}),
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"embedding_placement_pos": (["First", "Last"], {"default": "Last"}),
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"embedding_placement_neg": (["First", "Last"], {"default": "Last"}),
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},
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"optional": {
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"workflow_tuple": ("TUPLE", {"forceInput": True, "default": []}),
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},
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"hidden": {
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"cached_embedding": (cls.embedding_versions,),
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}
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}
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def primere_visual_embedding(self, embedding_placement_pos, embedding_placement_neg, workflow_tuple, stack_version='Any', model_version="BaseModel_1024", **kwargs):
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if 'model_concept' in workflow_tuple and workflow_tuple['model_concept'] != stack_version and workflow_tuple['model_concept'] != 'Normal':
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return ([None, None], [None, None], [])
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if model_version == 'SDXL_2048' and stack_version == 'SD':
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return ([None, None], [None, None], [])
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if model_version != 'SDXL_2048' and stack_version == 'SDXL':
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return ([None, None], [None, None], [])
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if workflow_tuple is not None and len(workflow_tuple) > 0 and 'setup_states' in workflow_tuple and 'embedding_setup' in workflow_tuple['setup_states'] and 'exif_status' in workflow_tuple and workflow_tuple['exif_status'] == 'SUCCEED':
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if workflow_tuple['setup_states']['embedding_setup'] == True:
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if 'network_data' in workflow_tuple:
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loader = networkhandler.getNetworkLoader(workflow_tuple, 'embedding', self.EMBCOUNT, False, stack_version)
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if len(loader) > 0:
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return networkhandler.EmbeddingHandler(self, loader, embedding_placement_pos, embedding_placement_neg)
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else:
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return ([None, None], [None, None], [])
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else:
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return ([None, None], [None, None], [])
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else:
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return ([None, None], [None, None], [])
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return networkhandler.EmbeddingHandler(self, kwargs, embedding_placement_pos, embedding_placement_neg)
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class PrimereVisualHypernetwork:
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RETURN_TYPES = ("MODEL", "HYPERNETWORK_STACK")
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RETURN_NAMES = ("MODEL", "HYPERNETWORK_STACK")
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FUNCTION = "visual_hypernetwork"
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CATEGORY = TREE_VISUALS
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EMBCOUNT = 6
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@classmethod
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def INPUT_TYPES(s):
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HypernetworkList = folder_paths.get_filename_list("hypernetworks")
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return {
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"required": {
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"model": ("MODEL",),
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"model_version": ("STRING", {"default": 'BaseModel_1024', "forceInput": True}),
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"safe_load": ("BOOLEAN", {"default": True}),
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"stack_version": (["SD", "SDXL", "Flux", "Any"], {"default": "Any"}),
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"show_modal": ("BOOLEAN", {"default": True}),
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"show_hidden": ("BOOLEAN", {"default": True}),
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"use_hypernetwork_1": ("BOOLEAN", {"default": False}),
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"hypernetwork_1": (HypernetworkList, ),
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"hypernetwork_1_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_hypernetwork_2": ("BOOLEAN", {"default": False}),
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"hypernetwork_2": (HypernetworkList,),
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"hypernetwork_2_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_hypernetwork_3": ("BOOLEAN", {"default": False}),
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"hypernetwork_3": (HypernetworkList,),
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"hypernetwork_3_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_hypernetwork_4": ("BOOLEAN", {"default": False}),
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"hypernetwork_4": (HypernetworkList,),
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"hypernetwork_4_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_hypernetwork_5": ("BOOLEAN", {"default": False}),
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"hypernetwork_5": (HypernetworkList,),
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"hypernetwork_5_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_hypernetwork_6": ("BOOLEAN", {"default": False}),
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"hypernetwork_6": (HypernetworkList,),
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"hypernetwork_6_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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},
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"optional": {
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"workflow_tuple": ("TUPLE", {"forceInput": True, "default": []}),
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},
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}
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def visual_hypernetwork(self, model, model_version, workflow_tuple, stack_version = "Any", safe_load = True, **kwargs):
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if 'model_concept' in workflow_tuple and workflow_tuple['model_concept'] != stack_version and workflow_tuple['model_concept'] != 'Normal':
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return (model, [],)
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if model_version == 'SDXL_2048' and stack_version == 'SD':
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return (model, [],)
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if model_version != 'SDXL_2048' and stack_version == 'SDXL':
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return (model, [],)
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if workflow_tuple is not None and len(workflow_tuple) > 0 and 'setup_states' in workflow_tuple and 'hypernetwork_setup' in workflow_tuple['setup_states'] and 'exif_status' in workflow_tuple and workflow_tuple['exif_status'] == 'SUCCEED':
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if workflow_tuple['setup_states']['hypernetwork_setup'] == True:
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loader = networkhandler.getNetworkLoader(workflow_tuple, 'hypernetwork', self.EMBCOUNT, False, stack_version)
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if len(loader) > 0:
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return networkhandler.HypernetworkHandler(self, loader, model, safe_load)
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else:
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return (model, [],)
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else:
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return (model, [],)
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return networkhandler.HypernetworkHandler(self, kwargs, model, safe_load)
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class PrimereVisualStyle:
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RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING")
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RETURN_NAMES = ("PROMPT+", "PROMPT-", "SUBPATH", "MODEL", "ORIENTATION", "PREFERRED")
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FUNCTION = "load_visual_csv"
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CATEGORY = TREE_VISUALS
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@staticmethod
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def load_styles_csv(styles_path: str):
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fileTest = open(styles_path, 'rb').readline()
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result = chardet.detect(fileTest)
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ENCODING = result['encoding']
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if ENCODING == 'ascii':
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ENCODING = 'UTF-8'
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with open(styles_path, "r", newline = '', encoding = ENCODING) as csv_file:
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try:
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return pandas.read_csv(csv_file)
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except pandas.errors.ParserError as e:
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errorstring = repr(e)
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matchre = re.compile('Expected (\d+) fields in line (\d+), saw (\d+)')
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(expected, line, saw) = map(int, matchre.search(errorstring).groups())
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print(f'Error at line {line}. Fields added : {saw - expected}.')
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@classmethod
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def INPUT_TYPES(cls):
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STYLE_DIR = os.path.join(PRIMERE_ROOT, 'stylecsv')
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try:
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cls.styles_csv = cls.load_styles_csv(os.path.join(STYLE_DIR, "styles.csv"))
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except Exception:
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cls.styles_csv = cls.load_styles_csv(os.path.join(STYLE_DIR, "styles.example.csv"))
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return {
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"required": {
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"styles": (sorted(list(cls.styles_csv['name'])),),
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"show_modal": ("BOOLEAN", {"default": True}),
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"show_hidden": ("BOOLEAN", {"default": True}),
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"use_subpath": ("BOOLEAN", {"default": False}),
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"use_model": ("BOOLEAN", {"default": False}),
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"use_orientation": ("BOOLEAN", {"default": False}),
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},
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}
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def load_visual_csv(self, styles, show_modal, show_hidden, use_subpath, use_model, use_orientation):
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try:
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positive_prompt = self.styles_csv[self.styles_csv['name'] == styles]['prompt'].values[0]
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except Exception:
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positive_prompt = ''
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try:
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negative_prompt = self.styles_csv[self.styles_csv['name'] == styles]['negative_prompt'].values[0]
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except Exception:
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negative_prompt = ''
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try:
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preferred_subpath = self.styles_csv[self.styles_csv['name'] == styles]['preferred_subpath'].values[0]
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except Exception:
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preferred_subpath = ''
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try:
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preferred_model = self.styles_csv[self.styles_csv['name'] == styles]['preferred_model'].values[0]
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except Exception:
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preferred_model = ''
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try:
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preferred_orientation = self.styles_csv[self.styles_csv['name'] == styles]['preferred_orientation'].values[0]
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except Exception:
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preferred_orientation = ''
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pos_type = type(positive_prompt).__name__
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neg_type = type(negative_prompt).__name__
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subp_type = type(preferred_subpath).__name__
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model_type = type(preferred_model).__name__
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orientation_type = type(preferred_orientation).__name__
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if (pos_type != 'str'):
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positive_prompt = ''
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if (neg_type != 'str'):
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negative_prompt = ''
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if (subp_type != 'str'):
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preferred_subpath = ''
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if (model_type != 'str'):
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preferred_model = ''
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if (orientation_type != 'str'):
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preferred_orientation = ''
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if len(preferred_subpath.strip()) < 1:
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preferred_subpath = None
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if len(preferred_model.strip()) < 1:
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preferred_model = None
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if len(preferred_orientation.strip()) < 1:
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preferred_orientation = None
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if use_subpath == False:
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preferred_subpath = None
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if use_model == False:
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preferred_model = None
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if use_orientation == False:
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preferred_orientation = None
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|
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preferred = {'subpath': preferred_subpath, 'model': preferred_model, 'orientation': preferred_orientation}
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return (positive_prompt, negative_prompt, preferred_subpath, preferred_model, preferred_orientation, preferred)
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class PrimereVisualLYCORIS:
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RETURN_TYPES = ("MODEL", "CLIP", "LYCORIS_STACK", "MODEL_KEYWORD")
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RETURN_NAMES = ("MODEL", "CLIP", "LYCORIS_STACK", "LYCORIS_KEYWORD")
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FUNCTION = "primere_visual_lycoris_stacker"
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CATEGORY = TREE_VISUALS
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LYCOSCOUNT = 6
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|
|
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lyco_versions = utility.get_category_from_cache('lycoris_version')
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|
|
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@classmethod
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def INPUT_TYPES(cls):
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LYCO_DIR = os.path.join(comfy_dir, 'models', 'lycoris')
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folder_paths.add_model_folder_path("lycoris", LYCO_DIR)
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LyCORIS = folder_paths.get_filename_list("lycoris")
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LyCORISList = folder_paths.filter_files_extensions(LyCORIS, ['.ckpt', '.safetensors'])
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|
|
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return {
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|
"required": {
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|
"model": ("MODEL",),
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|
"clip": ("CLIP",),
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|
"model_version": ("STRING", {"default": 'BaseModel_1024', "forceInput": True}),
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|
|
|
"stack_version": (["SD", "SDXL", "Flux", "Any"], {"default": "Any"}),
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|
"show_modal": ("BOOLEAN", {"default": True}),
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|
"show_hidden": ("BOOLEAN", {"default": True}),
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|
"use_only_model_weight": ("BOOLEAN", {"default": True}),
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|
|
|
"use_lycoris_1": ("BOOLEAN", {"default": False}),
|
|
"lycoris_1": (LyCORISList,),
|
|
"lycoris_1_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
"lycoris_1_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
|
|
"use_lycoris_2": ("BOOLEAN", {"default": False}),
|
|
"lycoris_2": (LyCORISList,),
|
|
"lycoris_2_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
"lycoris_2_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
|
|
"use_lycoris_3": ("BOOLEAN", {"default": False}),
|
|
"lycoris_3": (LyCORISList,),
|
|
"lycoris_3_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
"lycoris_3_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
|
|
"use_lycoris_4": ("BOOLEAN", {"default": False}),
|
|
"lycoris_4": (LyCORISList,),
|
|
"lycoris_4_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
"lycoris_4_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
|
|
"use_lycoris_5": ("BOOLEAN", {"default": False}),
|
|
"lycoris_5": (LyCORISList,),
|
|
"lycoris_5_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
"lycoris_5_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
|
|
"use_lycoris_6": ("BOOLEAN", {"default": False}),
|
|
"lycoris_6": (LyCORISList,),
|
|
"lycoris_6_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
"lycoris_6_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
|
|
|
|
"use_lycoris_keyword": ("BOOLEAN", {"default": False}),
|
|
"lycoris_keyword_placement": (["First", "Last"], {"default": "Last"}),
|
|
"lycoris_keyword_selection": (["Select in order", "Random select"], {"default": "Select in order"}),
|
|
"lycoris_keywords_num": ("INT", {"default": 1, "min": 1, "max": 50, "step": 1}),
|
|
"lycoris_keyword_weight": ("FLOAT", {"default": 1.0, "min": 0, "max": 10.0, "step": 0.1}),
|
|
},
|
|
"optional": {
|
|
"workflow_tuple": ("TUPLE", {"forceInput": True, "default": []}),
|
|
},
|
|
"hidden": {
|
|
"cached_lyco": (cls.lyco_versions,),
|
|
}
|
|
}
|
|
|
|
def primere_visual_lycoris_stacker(self, model, clip, use_only_model_weight, use_lycoris_keyword, lycoris_keyword_placement, lycoris_keyword_selection, lycoris_keywords_num, lycoris_keyword_weight, workflow_tuple, stack_version = 'Any', model_version = "BaseModel_1024", **kwargs):
|
|
model_keyword = [None, None]
|
|
|
|
if 'model_concept' in workflow_tuple and workflow_tuple['model_concept'] != stack_version and workflow_tuple['model_concept'] != 'Normal':
|
|
return (model, clip, [], model_keyword)
|
|
|
|
if model_version == 'SDXL_2048' and stack_version == 'SD':
|
|
return (model, clip, [], model_keyword)
|
|
|
|
if model_version != 'SDXL_2048' and stack_version == 'SDXL':
|
|
return (model, clip, [], model_keyword)
|
|
|
|
if workflow_tuple is not None and len(workflow_tuple) > 0 and 'setup_states' in workflow_tuple and 'lycoris_setup' in workflow_tuple['setup_states'] and 'exif_status' in workflow_tuple and workflow_tuple['exif_status'] == 'SUCCEED':
|
|
if workflow_tuple['setup_states']['lycoris_setup'] == True:
|
|
if 'network_data' in workflow_tuple:
|
|
loader = networkhandler.getNetworkLoader(workflow_tuple, 'lycoris', self.LYCOSCOUNT, True, stack_version)
|
|
if len(loader) > 0:
|
|
return networkhandler.LycorisHandler(self, loader, model, clip, model_keyword, use_only_model_weight, lycoris_keywords_num, use_lycoris_keyword, lycoris_keyword_selection, lycoris_keyword_weight, lycoris_keyword_placement)
|
|
else:
|
|
return (model, clip, [], model_keyword)
|
|
else:
|
|
return (model, clip, [], model_keyword)
|
|
else:
|
|
return (model, clip, [], model_keyword)
|
|
|
|
return networkhandler.LycorisHandler(self, kwargs, model, clip, model_keyword, use_only_model_weight, lycoris_keywords_num, use_lycoris_keyword, lycoris_keyword_selection, lycoris_keyword_weight, lycoris_keyword_placement) |