from ..components.tree import TREE_VISUALS from ..components.tree import PRIMERE_ROOT import folder_paths from ..components import utility import os from pathlib import Path from ..utils import comfy_dir from .modules import networkhandler import random import datetime from ..Nodes.Inputs import PrimereStyleLoader from ..components import stylehandler class PrimereVisualCKPT: RETURN_TYPES = ("CHECKPOINT_NAME", "STRING") RETURN_NAMES = ("MODEL_NAME", "MODEL_VERSION") FUNCTION = "load_ckpt_visual_list" CATEGORY = TREE_VISUALS @classmethod def INPUT_TYPES(cls): cls.allModels = folder_paths.get_filename_list("checkpoints") return { "required": { "base_model": (cls.allModels,), "show_modal": ("BOOLEAN", {"default": True}), "show_hidden": ("BOOLEAN", {"default": True}), "preview_path": ("BOOLEAN", {"default": True, "label_on": "Primere legacy", "label_off": "Model path"}), "random_model": ("BOOLEAN", {"default": False, "label_on": "From selected path", "label_off": "OFF"}), "aescore_percent_min": ("INT", {"default": 550, "min": 0, "max": 800, "step": 50}), "aescore_percent_max": ("INT", {"default": 800, "min": 200, "max": 1000, "step": 50}) }, "optional": { "random_seed": ("INT", {"default": 0, "min": -1, "max": utility.MAX_SEED}), }, "hidden": { "subdir": ("checkpoints",), "sortbuttons": (['aScore', 'Name', 'Version', 'Path', 'Date', 'Symlink', 'STime'],), "cache_key": ("model",), } } @classmethod def IS_CHANGED(self, **kwargs): if kwargs['random_model'] == True: return float('NaN') def load_ckpt_visual_list(self, base_model, show_hidden, show_modal, preview_path, aescore_percent_min, aescore_percent_max, random_model, random_seed = 0): def new_state_random(): random.seed(datetime.datetime.now().timestamp()) return random.randint(10, utility.MAX_SEED) if random_model == True: fullSource = self.allModels slashIndex = base_model.find('\\') if slashIndex > 0: subdirType = base_model[0: slashIndex] + '\\' models_by_path = list(filter(lambda x: x.startswith(subdirType), fullSource)) if random_seed is None or int(random_seed) <= 0: random_seed = int(new_state_random()) random.seed(random_seed) base_model = random.choice(models_by_path) modelname_only = Path(base_model).stem model_version = utility.get_value_from_cache('model_version', modelname_only) if model_version is None: model_version = utility.getModelType(base_model, 'checkpoints') utility.add_value_to_cache('model_version', modelname_only, model_version) return (base_model, model_version) class PrimereVisualLORA: RETURN_TYPES = ("MODEL", "CLIP", "LORA_STACK", "MODEL_KEYWORD") RETURN_NAMES = ("MODEL", "CLIP", "LORA_STACK", "LORA_KEYWORD") FUNCTION = "visual_lora_stacker" CATEGORY = TREE_VISUALS LORASCOUNT = 6 @classmethod def INPUT_TYPES(cls): LoraList = folder_paths.get_filename_list("loras") return { "required": { "model": ("MODEL",), "clip": ("CLIP",), "model_version": ("STRING", {"default": 'SD1', "forceInput": True}), "stack_version": (["Any", "Auto"] + utility.SUPPORTED_MODELS, {"default": "Auto"}), "show_modal": ("BOOLEAN", {"default": True}), "show_hidden": ("BOOLEAN", {"default": True}), "auto_filter": ("BOOLEAN", {"default": False, "label_on": "Filter by version", "label_off": "OFF"}), "preview_path": ("BOOLEAN", {"default": True, "label_on": "Primere legacy", "label_off": "Model path"}), "randomize": ("BOOLEAN", {"default": False, "label_on": "One random input", "label_off": "OFF"}), "use_only_model_weight": ("BOOLEAN", {"default": True}), "use_lora_1": ("BOOLEAN", {"default": False}), "lora_1": (LoraList,), "lora_1_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora_1_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_lora_2": ("BOOLEAN", {"default": False}), "lora_2": (LoraList,), "lora_2_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora_2_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_lora_3": ("BOOLEAN", {"default": False}), "lora_3": (LoraList,), "lora_3_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora_3_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_lora_4": ("BOOLEAN", {"default": False}), "lora_4": (LoraList,), "lora_4_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora_4_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_lora_5": ("BOOLEAN", {"default": False}), "lora_5": (LoraList,), "lora_5_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora_5_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_lora_6": ("BOOLEAN", {"default": False}), "lora_6": (LoraList,), "lora_6_model_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "lora_6_clip_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_lora_keyword": ("BOOLEAN", {"default": False}), "lora_keyword_placement": (["First", "Last"], {"default": "Last"}), "lora_keyword_selection": (["Select in order", "Random select"], {"default": "Select in order"}), "lora_keywords_num": ("INT", {"default": 1, "min": 1, "max": 50, "step": 1}), "lora_keyword_weight": ("FLOAT", {"default": 1.0, "min": 0, "max": 10.0, "step": 0.1}), }, "optional": { "random_seed": ("INT", {"default": 0, "min": -1, "max": utility.MAX_SEED}), "workflow_tuple": ("TUPLE", {"forceInput": True, "default": {}}) }, "hidden": { "subdir": ("loras",), "sortbuttons": (['Name', 'Version', 'Path', 'Date'],), "cache_key": ("lora",), "version_filter_input": ("stack_version",), } } 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=None, stack_version ='Any', model_version ="SD1", **kwargs): model_keyword = [None, None] 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': 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 'lora_setup' in workflow_tuple['setup_states'] and 'exif_status' in workflow_tuple and workflow_tuple['exif_status'] == 'SUCCEED': if workflow_tuple['setup_states']['lora_setup'] == True: if 'network_data' in workflow_tuple: loader = networkhandler.getNetworkLoader(workflow_tuple, 'lora', self.LORASCOUNT, True, stack_version) if len(loader) > 0: 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) else: return (model, clip, [], model_keyword) else: return (model, clip, [], model_keyword) else: return (model, clip, [], model_keyword) 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) class PrimereVisualEmbedding: RETURN_TYPES = ("EMBEDDING", "EMBEDDING", "EMBEDDING_STACK") RETURN_NAMES = ("EMBEDDING+", "EMBEDDING-", "EMBEDDING_STACK") FUNCTION = "primere_visual_embedding" CATEGORY = TREE_VISUALS EMBCOUNT = 6 @classmethod def INPUT_TYPES(cls): EmbeddingList = folder_paths.get_filename_list("embeddings") return { "required": { "model_version": ("STRING", {"default": 'SD1', "forceInput": True}), "stack_version": (["Any", "Auto"] + utility.SUPPORTED_MODELS, {"default": "Auto"}), "show_modal": ("BOOLEAN", {"default": True}), "show_hidden": ("BOOLEAN", {"default": True}), "auto_filter": ("BOOLEAN", {"default": False, "label_on": "Filter by version", "label_off": "OFF"}), "preview_path": ("BOOLEAN", {"default": True, "label_on": "Primere legacy", "label_off": "Model path"}), "randomize": ("BOOLEAN", {"default": False, "label_on": "One random input", "label_off": "OFF"}), "use_embedding_1": ("BOOLEAN", {"default": False}), "embedding_1": (EmbeddingList,), "embedding_1_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },), "is_negative_1": ("BOOLEAN", {"default": False}), "use_embedding_2": ("BOOLEAN", {"default": False}), "embedding_2": (EmbeddingList,), "embedding_2_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },), "is_negative_2": ("BOOLEAN", {"default": False}), "use_embedding_3": ("BOOLEAN", {"default": False}), "embedding_3": (EmbeddingList,), "embedding_3_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },), "is_negative_3": ("BOOLEAN", {"default": False}), "use_embedding_4": ("BOOLEAN", {"default": False}), "embedding_4": (EmbeddingList,), "embedding_4_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },), "is_negative_4": ("BOOLEAN", {"default": False}), "use_embedding_5": ("BOOLEAN", {"default": False}), "embedding_5": (EmbeddingList,), "embedding_5_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },), "is_negative_5": ("BOOLEAN", {"default": False}), "use_embedding_6": ("BOOLEAN", {"default": False}), "embedding_6": (EmbeddingList,), "embedding_6_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, },), "is_negative_6": ("BOOLEAN", {"default": False}), "embedding_placement_pos": (["First", "Last"], {"default": "Last"}), "embedding_placement_neg": (["First", "Last"], {"default": "Last"}), }, "optional": { "random_seed": ("INT", {"default": 0, "min": -1, "max": utility.MAX_SEED}), "workflow_tuple": ("TUPLE", {"forceInput": True, "default": {}}) }, "hidden": { "subdir": ("embeddings",), "sortbuttons": (['Name', 'Version', 'Path', 'Date'],), "cache_key": ("embedding",), "version_filter_input": ("stack_version",), } } def primere_visual_embedding(self, embedding_placement_pos, embedding_placement_neg, workflow_tuple=None, stack_version='Any', model_version="SD1", **kwargs): 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': return ([None, None], [None, None], []) if model_version == 'SDXL_2048' and stack_version == 'SD': return ([None, None], [None, None], []) if model_version != 'SDXL_2048' and stack_version == 'SDXL': return ([None, None], [None, None], []) 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': if workflow_tuple['setup_states']['embedding_setup'] == True: if 'network_data' in workflow_tuple: loader = networkhandler.getNetworkLoader(workflow_tuple, 'embedding', self.EMBCOUNT, False, stack_version) if len(loader) > 0: return networkhandler.EmbeddingHandler(self, loader, embedding_placement_pos, embedding_placement_neg) else: return ([None, None], [None, None], []) else: return ([None, None], [None, None], []) else: return ([None, None], [None, None], []) return networkhandler.EmbeddingHandler(self, kwargs, embedding_placement_pos, embedding_placement_neg) class PrimereVisualHypernetwork: RETURN_TYPES = ("MODEL", "HYPERNETWORK_STACK") RETURN_NAMES = ("MODEL", "HYPERNETWORK_STACK") FUNCTION = "visual_hypernetwork" CATEGORY = TREE_VISUALS HNCOUNT = 6 @classmethod def INPUT_TYPES(s): HypernetworkList = folder_paths.get_filename_list("hypernetworks") return { "required": { "model": ("MODEL",), "safe_load": ("BOOLEAN", {"default": True}), "show_modal": ("BOOLEAN", {"default": True}), "show_hidden": ("BOOLEAN", {"default": True}), "preview_path": ("BOOLEAN", {"default": True, "label_on": "Primere legacy", "label_off": "Model path"}), "randomize": ("BOOLEAN", {"default": False, "label_on": "One random input", "label_off": "OFF"}), "use_hypernetwork_1": ("BOOLEAN", {"default": False}), "hypernetwork_1": (HypernetworkList, ), "hypernetwork_1_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_hypernetwork_2": ("BOOLEAN", {"default": False}), "hypernetwork_2": (HypernetworkList,), "hypernetwork_2_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_hypernetwork_3": ("BOOLEAN", {"default": False}), "hypernetwork_3": (HypernetworkList,), "hypernetwork_3_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_hypernetwork_4": ("BOOLEAN", {"default": False}), "hypernetwork_4": (HypernetworkList,), "hypernetwork_4_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_hypernetwork_5": ("BOOLEAN", {"default": False}), "hypernetwork_5": (HypernetworkList,), "hypernetwork_5_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "use_hypernetwork_6": ("BOOLEAN", {"default": False}), "hypernetwork_6": (HypernetworkList,), "hypernetwork_6_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), }, "optional": { "random_seed": ("INT", {"default": 0, "min": -1, "max": utility.MAX_SEED}), "workflow_tuple": ("TUPLE", {"forceInput": True, "default": {}}) }, "hidden": { "subdir": ("hypernetworks",), "sortbuttons": (['Name', 'Path', 'Date'],), "cache_key": ("embedding",), } } def visual_hypernetwork(self, model, model_version, workflow_tuple = None, stack_version = "Any", safe_load = True, **kwargs): 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': return (model, [],) if model_version == 'SDXL_2048' and stack_version == 'SD': return (model, [],) if model_version != 'SDXL_2048' and stack_version == 'SDXL': return (model, [],) 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': if workflow_tuple['setup_states']['hypernetwork_setup'] == True: loader = networkhandler.getNetworkLoader(workflow_tuple, 'hypernetwork', self.HNCOUNT, False, stack_version) if len(loader) > 0: return networkhandler.HypernetworkHandler(self, loader, model, safe_load) else: return (model, [],) else: return (model, [],) return networkhandler.HypernetworkHandler(self, kwargs, model, safe_load) class PrimereVisualStyle: RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING") RETURN_NAMES = ("PROMPT+", "PROMPT-", "SUBPATH", "MODEL", "ORIENTATION", "PREFERRED") FUNCTION = "load_visual_csv" CATEGORY = TREE_VISUALS STYLE_DIR = os.path.join(PRIMERE_ROOT, 'stylecsv') STYLE_FILE = os.path.join(STYLE_DIR, "styles.csv") STYLE_FILE_EXAMPLE = os.path.join(STYLE_DIR, "styles.example.csv") if Path(STYLE_FILE).is_file() == True: STYLE_SOURCE = STYLE_FILE else: STYLE_SOURCE = STYLE_FILE_EXAMPLE styles_csv = PrimereStyleLoader.load_styles_csv(STYLE_SOURCE) @classmethod def INPUT_TYPES(cls): return { "required": { "styles": (sorted(list(cls.styles_csv['name'])),), "show_modal": ("BOOLEAN", {"default": True}), "show_hidden": ("BOOLEAN", {"default": True}), "use_subpath": ("BOOLEAN", {"default": False}), "use_model": ("BOOLEAN", {"default": False}), "use_orientation": ("BOOLEAN", {"default": False}), "random_prompt": ("BOOLEAN", {"default": False, "label_on": "From preferred path", "label_off": "OFF"}), "aescore_percent_min": ("INT", {"default": 550, "min": 0, "max": 800, "step": 50}), "aescore_percent_max": ("INT", {"default": 800, "min": 200, "max": 1000, "step": 50}) }, "optional": { "random_seed": ("INT", {"default": 0, "min": 0, "max": utility.MAX_SEED}), }, "hidden": { "subdir": ("styles",), "sortbuttons": (['aScore', 'Name', 'Path'],), "cache_key": ("styles",), } } @classmethod def IS_CHANGED(self, **kwargs): if kwargs['random_prompt'] == True: return float('NaN') def load_visual_csv(self, styles, show_modal, show_hidden, use_subpath, use_model, use_orientation, aescore_percent_min, aescore_percent_max, random_prompt, random_seed = 0): def new_state_random(): random.seed(datetime.datetime.now().timestamp()) return random.randint(10, utility.MAX_SEED) styleKey = self.styles_csv['name'] == styles try: preferred_subpath = self.styles_csv[styleKey]['preferred_subpath'].values[0] except Exception: preferred_subpath = '' if random_prompt == True: if str(preferred_subpath) == "nan": resultsBySubpath = self.styles_csv[self.styles_csv['preferred_subpath'].isnull()] else: resultsBySubpath = self.styles_csv[self.styles_csv['preferred_subpath'] == preferred_subpath] if random_seed is None or int(random_seed) <= 0: random_seed = int(new_state_random()) random.seed(random_seed) random_stylename = random.choice(list(resultsBySubpath['name'])) styleKey = self.styles_csv['name'] == random_stylename else: styleKey = self.styles_csv['name'] == styles try: positive_prompt = self.styles_csv[styleKey]['prompt'].values[0] except Exception: positive_prompt = '' try: negative_prompt = self.styles_csv[styleKey]['negative_prompt'].values[0] except Exception: negative_prompt = '' try: preferred_model = self.styles_csv[styleKey]['preferred_model'].values[0] except Exception: preferred_model = '' try: preferred_orientation = self.styles_csv[styleKey]['preferred_orientation'].values[0] except Exception: preferred_orientation = '' pos_type = type(positive_prompt).__name__ neg_type = type(negative_prompt).__name__ subp_type = type(preferred_subpath).__name__ model_type = type(preferred_model).__name__ orientation_type = type(preferred_orientation).__name__ if (pos_type != 'str'): positive_prompt = '' if (neg_type != 'str'): negative_prompt = '' if (subp_type != 'str'): preferred_subpath = '' if (model_type != 'str'): preferred_model = '' if (orientation_type != 'str'): preferred_orientation = '' if len(preferred_subpath.strip()) < 1: preferred_subpath = None if len(preferred_model.strip()) < 1: preferred_model = None if len(preferred_orientation.strip()) < 1: preferred_orientation = None if use_subpath == False: preferred_subpath = None if use_model == False: preferred_model = None if use_orientation == False: preferred_orientation = None preferred = {'subpath': preferred_subpath, 'model': preferred_model, 'orientation': preferred_orientation} return (positive_prompt, negative_prompt, preferred_subpath, preferred_model, preferred_orientation, preferred) class PrimereVisualPromptOrganizerCSV: RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING", "STRING", "STRING") RETURN_NAMES = ("PROMPT+", "PROMPT-", "SUBPATH", "MODEL", "ORIENTATION", "PREFERRED") FUNCTION = "prompt_visual_organizer_csv" CATEGORY = TREE_VISUALS @ classmethod def INPUT_TYPES(cls): STYLE_DIR = os.path.join(PRIMERE_ROOT, 'stylecsv') STYLE_FILE = os.path.join(STYLE_DIR, "styles.csv") STYLE_FILE_EXAMPLE = os.path.join(STYLE_DIR, "styles.example.csv") if Path(STYLE_FILE).is_file() == True: STYLE_SOURCE = STYLE_FILE else: STYLE_SOURCE = STYLE_FILE_EXAMPLE cls.styles_csv = PrimereStyleLoader.load_styles_csv(STYLE_SOURCE) STYLE_RESULT = stylehandler.csv2node(cls.styles_csv) additionalDict = { "show_modal": ("BOOLEAN", {"default": True}), "show_hidden": ("BOOLEAN", {"default": True}), "use_subpath": ("BOOLEAN", {"default": False}), "use_model": ("BOOLEAN", {"default": False}), "use_orientation": ("BOOLEAN", {"default": False}), "random_prompt": ("BOOLEAN", {"default": False, "label_on": "From preferred path", "label_off": "OFF"}) } hiddenDict = { "subdir": ("styles",), "sortbuttons": (['aScore', 'Name'],), "cache_key": ("styles",) } optionalDict = { "random_seed": ("INT", {"default": 0, "min": 0, "max": utility.MAX_SEED}) } MERGED_REQ = utility.merge_dict(additionalDict, STYLE_RESULT) INPUT_DICT_FINAL = {'required': MERGED_REQ, 'optional': optionalDict, 'hidden': hiddenDict} return INPUT_DICT_FINAL def prompt_visual_organizer_csv(self, show_modal, show_hidden, random_prompt, use_subpath = False, use_model = False, use_orientation = False, random_seed = 0, **kwargs): input_data = kwargs styleResult = {} styleResult[0] = None styleResult[1] = None styleResult[2] = None styleResult[3] = None styleResult[4] = None styleResult[5] = None for inputKey, inputValue in input_data.items(): if inputValue != 'None': styleResult = PrimereStyleLoader.load_csv(self, inputValue, use_subpath, use_model, use_orientation) break return (styleResult[0], styleResult[1], styleResult[2], styleResult[3], styleResult[4], styleResult[5]) class PrimereVisualLYCORIS: RETURN_TYPES = ("MODEL", "CLIP", "LYCORIS_STACK", "MODEL_KEYWORD") RETURN_NAMES = ("MODEL", "CLIP", "LYCORIS_STACK", "LYCORIS_KEYWORD") FUNCTION = "primere_visual_lycoris_stacker" CATEGORY = TREE_VISUALS LYCOSCOUNT = 6 @classmethod def INPUT_TYPES(cls): LYCO_DIR = os.path.join(folder_paths.models_dir, 'lycoris') folder_paths.add_model_folder_path("lycoris", LYCO_DIR) LyCORIS = folder_paths.get_filename_list("lycoris") LyCORISList = folder_paths.filter_files_extensions(LyCORIS, ['.ckpt', '.safetensors']) return { "required": { "model": ("MODEL",), "clip": ("CLIP",), "model_version": ("STRING", {"default": 'SD1', "forceInput": True}), "stack_version": (["Any", "Auto"] + utility.SUPPORTED_MODELS, {"default": "Auto"}), "show_modal": ("BOOLEAN", {"default": True}), "show_hidden": ("BOOLEAN", {"default": True}), "auto_filter": ("BOOLEAN", {"default": False, "label_on": "Filter by version", "label_off": "OFF"}), "preview_path": ("BOOLEAN", {"default": True, "label_on": "Primere legacy", "label_off": "Model path"}), "randomize": ("BOOLEAN", {"default": False, "label_on": "One random input", "label_off": "OFF"}), "use_only_model_weight": ("BOOLEAN", {"default": True}), "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": { "random_seed": ("INT", {"default": 0, "min": -1, "max": utility.MAX_SEED}), "workflow_tuple": ("TUPLE", {"forceInput": True, "default": {}}) }, "hidden": { "subdir": ("lycoris",), "sortbuttons": (['Name', 'Version', 'Path', 'Date'],), "cache_key": ("lycoris",), "version_filter_input": ("stack_version",), } } 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=None, stack_version = 'Any', model_version = "SD1", **kwargs): model_keyword = [None, None] 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': 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)