331 lines
18 KiB
Python
331 lines
18 KiB
Python
from ..components.tree import TREE_NETWORKS
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import folder_paths
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import os
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from ..utils import comfy_dir
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from .modules import networkhandler
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from ..components import utility
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class PrimereLORA:
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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 = "primere_lora_stacker"
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CATEGORY = TREE_NETWORKS
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LORASCOUNT = 6
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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": 'SD1', "forceInput": True}),
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"stack_version": (["Any", "Auto"] + utility.SUPPORTED_MODELS, {"default": "Auto"}),
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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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}
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def primere_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 = None, stack_version = 'Any', model_version = "SD1", **kwargs):
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model_keyword = [None, None]
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if workflow_tuple is not None and '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 PrimereEmbedding:
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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_embedding"
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CATEGORY = TREE_NETWORKS
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EMBCOUNT = 6
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@classmethod
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def INPUT_TYPES(self):
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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": 'SD1', "forceInput": True}),
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"stack_version": (["Any", "Auto"] + utility.SUPPORTED_MODELS, {"default": "Auto"}),
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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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}
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def primere_embedding(self, embedding_placement_pos, embedding_placement_neg, workflow_tuple = None, stack_version = 'Any', model_version = "SD1", **kwargs):
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if workflow_tuple is not None and '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 PrimereHypernetwork:
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RETURN_TYPES = ("MODEL", "HYPERNETWORK_STACK")
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RETURN_NAMES = ("MODEL", "HYPERNETWORK_STACK")
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FUNCTION = "primere_hypernetwork"
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CATEGORY = TREE_NETWORKS
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HNCOUNT = 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": 'SD1', "forceInput": True}),
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"safe_load": ("BOOLEAN", {"default": True}),
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"stack_version": (["Any", "Auto"] + utility.SUPPORTED_MODELS, {"default": "Auto"}),
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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 primere_hypernetwork(self, model, model_version, workflow_tuple = None, stack_version = 'Any', safe_load = True, **kwargs):
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if workflow_tuple is not None and '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.HNCOUNT, 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 PrimereLYCORIS:
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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_lycoris_stacker"
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CATEGORY = TREE_NETWORKS
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LYCOSCOUNT = 6
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@classmethod
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def INPUT_TYPES(cls):
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LYCO_DIR = os.path.join(folder_paths.models_dir, '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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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": 'SD1', "forceInput": True}),
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"stack_version": (["Any", "Auto"] + utility.SUPPORTED_MODELS, {"default": "Auto"}),
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"use_only_model_weight": ("BOOLEAN", {"default": True}),
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"use_lycoris_1": ("BOOLEAN", {"default": False}),
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"lycoris_1": (LyCORISList,),
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"lycoris_1_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lycoris_1_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lycoris_2": ("BOOLEAN", {"default": False}),
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"lycoris_2": (LyCORISList,),
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"lycoris_2_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lycoris_2_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lycoris_3": ("BOOLEAN", {"default": False}),
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"lycoris_3": (LyCORISList,),
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"lycoris_3_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lycoris_3_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lycoris_4": ("BOOLEAN", {"default": False}),
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"lycoris_4": (LyCORISList,),
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"lycoris_4_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lycoris_4_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lycoris_5": ("BOOLEAN", {"default": False}),
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"lycoris_5": (LyCORISList,),
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"lycoris_5_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lycoris_5_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lycoris_6": ("BOOLEAN", {"default": False}),
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"lycoris_6": (LyCORISList,),
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"lycoris_6_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"lycoris_6_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"use_lycoris_keyword": ("BOOLEAN", {"default": False}),
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"lycoris_keyword_placement": (["First", "Last"], {"default": "Last"}),
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"lycoris_keyword_selection": (["Select in order", "Random select"], {"default": "Select in order"}),
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"lycoris_keywords_num": ("INT", {"default": 1, "min": 1, "max": 50, "step": 1}),
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"lycoris_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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}
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def primere_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 = None, stack_version = 'Any', model_version = "SD1", **kwargs):
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model_keyword = [None, None]
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if workflow_tuple is not None and '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 'lycoris_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']['lycoris_setup'] == True:
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if 'network_data' in workflow_tuple:
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loader = networkhandler.getNetworkLoader(workflow_tuple, 'lycoris', self.LYCOSCOUNT, True, stack_version)
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if len(loader) > 0:
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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)
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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.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) |