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| import os | |
| import re | |
| from nodes import MAX_RESOLUTION | |
| from comfy_extras.nodes_clip_sdxl import CLIPTextEncodeSDXL | |
| from .log import log_node_warn, log_node_info, log_node_success | |
| from .constants import get_category, get_name | |
| from .power_prompt_utils import get_and_strip_loras | |
| from nodes import LoraLoader, CLIPTextEncode | |
| import folder_paths | |
| NODE_NAME = get_name('SDXL Power Prompt - Positive') | |
| class RgthreeSDXLPowerPromptPositive: | |
| """The Power Prompt for positive conditioning.""" | |
| NAME = NODE_NAME | |
| CATEGORY = get_category() | |
| def INPUT_TYPES(cls): # pylint: disable = invalid-name, missing-function-docstring | |
| SAVED_PROMPTS_FILES = folder_paths.get_filename_list('saved_prompts') | |
| SAVED_PROMPTS_CONTENT = [] | |
| for filename in SAVED_PROMPTS_FILES: | |
| with open(folder_paths.get_full_path('saved_prompts', filename), 'r') as f: | |
| SAVED_PROMPTS_CONTENT.append(f.read()) | |
| return { | |
| 'required': { | |
| 'prompt_g': ('STRING', { | |
| 'multiline': True | |
| }), | |
| 'prompt_l': ('STRING', { | |
| 'multiline': True | |
| }), | |
| }, | |
| 'optional': { | |
| "opt_model": ("MODEL",), | |
| "opt_clip": ("CLIP",), | |
| "opt_clip_width": ("INT", { | |
| "forceInput": True, | |
| "default": 1024.0, | |
| "min": 0, | |
| "max": MAX_RESOLUTION | |
| }), | |
| "opt_clip_height": ("INT", { | |
| "forceInput": True, | |
| "default": 1024.0, | |
| "min": 0, | |
| "max": MAX_RESOLUTION | |
| }), | |
| 'insert_lora': (['CHOOSE', 'DISABLE LORAS'] + | |
| [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('loras')],), | |
| 'insert_embedding': ([ | |
| 'CHOOSE', | |
| ] + [os.path.splitext(x)[0] for x in folder_paths.get_filename_list('embeddings')],), | |
| 'insert_saved': ([ | |
| 'CHOOSE', | |
| ] + SAVED_PROMPTS_FILES,), | |
| # We'll hide these in the UI for now. | |
| "target_width": ("INT", { | |
| "default": -1, | |
| "min": -1, | |
| "max": MAX_RESOLUTION | |
| }), | |
| "target_height": ("INT", { | |
| "default": -1, | |
| "min": -1, | |
| "max": MAX_RESOLUTION | |
| }), | |
| "crop_width": ("INT", { | |
| "default": -1, | |
| "min": -1, | |
| "max": MAX_RESOLUTION | |
| }), | |
| "crop_height": ("INT", { | |
| "default": -1, | |
| "min": -1, | |
| "max": MAX_RESOLUTION | |
| }), | |
| }, | |
| 'hidden': { | |
| 'values_insert_saved': (['CHOOSE'] + SAVED_PROMPTS_CONTENT,), | |
| } | |
| } | |
| RETURN_TYPES = ('CONDITIONING', 'MODEL', 'CLIP', 'STRING', 'STRING') | |
| RETURN_NAMES = ('CONDITIONING', 'MODEL', 'CLIP', 'TEXT_G', 'TEXT_L') | |
| FUNCTION = 'main' | |
| def main(self, | |
| prompt_g, | |
| prompt_l, | |
| opt_model=None, | |
| opt_clip=None, | |
| opt_clip_width=None, | |
| opt_clip_height=None, | |
| insert_lora=None, | |
| insert_embedding=None, | |
| insert_saved=None, | |
| target_width=-1, | |
| target_height=-1, | |
| crop_width=-1, | |
| crop_height=-1, | |
| values_insert_saved=None): | |
| if insert_lora == 'DISABLE LORAS': | |
| prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g, | |
| True, | |
| log_node=self.NAME) | |
| prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l, | |
| True, | |
| log_node=self.NAME) | |
| loras = loras_g + loras_l | |
| log_node_info( | |
| NODE_NAME, | |
| f'Disabling all found loras ({len(loras)}) and stripping lora tags for TEXT output.') | |
| elif opt_model is not None and opt_clip is not None: | |
| prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g, log_node=self.NAME) | |
| prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l, log_node=self.NAME) | |
| loras = loras_g + loras_l | |
| if len(loras) > 0: | |
| for lora in loras: | |
| opt_model, opt_clip = LoraLoader().load_lora(opt_model, opt_clip, lora['lora'], | |
| lora['strength'], lora['strength']) | |
| log_node_success(NODE_NAME, f'Loaded "{lora["lora"]}" from prompt') | |
| log_node_info(NODE_NAME, f'{len(loras)} Loras processed; stripping tags for TEXT output.') | |
| elif '<lora:' in prompt_g or '<lora:' in prompt_l: | |
| _prompt_g, loras_g, _skipped, _unfound = get_and_strip_loras(prompt_g, | |
| True, | |
| log_node=self.NAME) | |
| _prompt_l, loras_l, _skipped, _unfound = get_and_strip_loras(prompt_l, | |
| True, | |
| log_node=self.NAME) | |
| loras = loras_g + loras_l | |
| if len(loras): | |
| log_node_warn( | |
| NODE_NAME, f'Found {len(loras)} lora tags in prompt but model & clip were not supplied!') | |
| log_node_info(NODE_NAME, 'Loras not processed, keeping for TEXT output.') | |
| conditioning = self.get_conditioning(prompt_g, prompt_l, opt_clip, opt_clip_width, | |
| opt_clip_height, target_width, target_height, crop_width, | |
| crop_height) | |
| return (conditioning, opt_model, opt_clip, prompt_g, prompt_l) | |
| def get_conditioning(self, prompt_g, prompt_l, opt_clip, opt_clip_width, opt_clip_height, | |
| target_width, target_height, crop_width, crop_height): | |
| """Checks the inputs and gets the conditioning.""" | |
| conditioning = None | |
| if opt_clip is not None: | |
| do_regular_clip_text_encode = opt_clip_width and opt_clip_height | |
| if do_regular_clip_text_encode: | |
| target_width = target_width if target_width and target_width > 0 else opt_clip_width | |
| target_height = target_height if target_height and target_height > 0 else opt_clip_height | |
| crop_width = crop_width if crop_width and crop_width > 0 else 0 | |
| crop_height = crop_height if crop_height and crop_height > 0 else 0 | |
| try: | |
| conditioning = CLIPTextEncodeSDXL().encode(opt_clip, opt_clip_width, opt_clip_height, | |
| crop_width, crop_height, target_width, | |
| target_height, prompt_g, prompt_l)[0] | |
| except Exception: | |
| do_regular_clip_text_encode = True | |
| log_node_info( | |
| self.NAME, | |
| 'Exception while attempting to CLIPTextEncodeSDXL, will fall back to standard encoding.' | |
| ) | |
| else: | |
| log_node_info( | |
| self.NAME, | |
| 'CLIP supplied, but not CLIP_WIDTH and CLIP_HEIGHT. Text encoding will use standard ' + | |
| 'encoding with prompt_g and prompt_l concatenated.') | |
| if not do_regular_clip_text_encode: | |
| conditioning = CLIPTextEncode().encode( | |
| opt_clip, f'{prompt_g if prompt_g else ""}\n{prompt_l if prompt_l else ""}')[0] | |
| return conditioning | |