#These nodes were made using code from the Deforum extension for A1111 webui #You can find the project here: https://github.com/deforum-art/sd-webui-deforum import numexpr import torch import torch.nn.functional as F import numpy as np import pandas as pd import re import json #functions used by PromptSchedule nodes #This Settings class is mainly used to reduce clutter and keep things relatively #organized. It is multi-purpose for both regular clip encoding and SDXL encoding #The value schedule doesn't have as many arguments so I didn't bother doing the #same for that. class ScheduleSettings: def __init__( self, text_g: str, pre_text_G: str, app_text_G: str, text_L: str, pre_text_L: str, app_text_L: str, max_frames: int, current_frame: int, print_output: bool, pw_a: float, pw_b: float, pw_c: float, pw_d: float, start_frame: int, end_frame:int, width: int, height: int, crop_w: int, crop_h: int, target_width: int, target_height: int, ): self.text_g=text_g self.pre_text_G=pre_text_G self.app_text_G=app_text_G self.text_l=text_L self.pre_text_L=pre_text_L self.app_text_L=app_text_L self.max_frames=max_frames self.current_frame=current_frame self.print_output=print_output self.pw_a=pw_a self.pw_b=pw_b self.pw_c=pw_c self.pw_d=pw_d self.start_frame=start_frame self.end_frame=end_frame self.width=width self.height=height self.crop_w=crop_w self.crop_h=crop_h self.target_width=target_width self.target_height=target_height def set_sync_option(self, sync_option: bool): self.sync_context_to_pe = sync_option #Addweighted function from Comfyui def addWeighted(conditioning_to, conditioning_from, conditioning_to_strength, max_size=0): out = [] if len(conditioning_from) > 1: print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") cond_from = conditioning_from[0][0] pooled_output_from = conditioning_from[0][1].get("pooled_output", None) for i in range(len(conditioning_to)): t1 = conditioning_to[i][0] pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) if max_size == 0: max_size = max(t1.shape[1], cond_from.shape[1]) t0, max_size = pad_with_zeros(cond_from, max_size) t1, max_size = pad_with_zeros(t1, t0.shape[1]) # Padding t1 to match max_size t0, max_size = pad_with_zeros(t0, t1.shape[1]) tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) t_to = conditioning_to[i][1].copy() t_to["pooled_output"] = pooled_output_from n = [tw, t_to] out.append(n) return out def pad_with_zeros(tensor, target_length): current_length = tensor.shape[1] if current_length < target_length: # Calculate the required padding length pad_length = target_length - current_length # Calculate padding on both sides to maintain the tensor's original shape left_pad = pad_length // 2 right_pad = pad_length - left_pad # Pad the tensor along the second dimension tensor = F.pad(tensor, (0, 0, left_pad, right_pad)) return tensor, target_length def process_input_text(text: str) -> dict: input_text = text.replace('\n', '') input_text = "{" + input_text + "}" input_text = re.sub(r',\s*}', '}', input_text) animation_prompts = json.loads(input_text.strip()) return animation_prompts def check_is_number(value): float_pattern = r'^(?=.)([+-]?([0-9]*)(\.([0-9]+))?)$' return re.match(float_pattern, value) def parse_weight(match, frame=0, max_frames=0) -> float: #calculate weight steps for in-betweens w_raw = match.group("weight") max_f = max_frames # this line has to be left intact as it's in use by numexpr even though it looks like it doesn't if w_raw is None: return 1 if check_is_number(w_raw): return float(w_raw) else: t = frame if len(w_raw) < 3: print('the value inside `-characters cannot represent a math function') return 1 return float(numexpr.evaluate(w_raw[1:-1])) def PoolAnimConditioning(cur_prompt, nxt_prompt, weight, clip): if str(cur_prompt) == str(nxt_prompt): tokens = clip.tokenize(str(cur_prompt)) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return [[cond, {"pooled_output": pooled}]] if weight == 1: tokens = clip.tokenize(str(cur_prompt)) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return [[cond, {"pooled_output": pooled}]] if weight == 0: tokens = clip.tokenize(str(nxt_prompt)) cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return [[cond, {"pooled_output": pooled}]] else: tokens = clip.tokenize(str(nxt_prompt)) cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True) tokens = clip.tokenize(str(cur_prompt)) cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True) return addWeighted([[cond_to, {"pooled_output": pooled_to}]], [[cond_from, {"pooled_output": pooled_from}]], weight) def SDXLencode(g, l, settings:ScheduleSettings, clip): tokens = clip.tokenize(g) tokens["l"] = clip.tokenize(l)["l"] if len(tokens["l"]) != len(tokens["g"]): empty = clip.tokenize("") while len(tokens["l"]) < len(tokens["g"]): tokens["l"] += empty["l"] while len(tokens["l"]) > len(tokens["g"]): tokens["g"] += empty["g"] cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) return [[cond, { "pooled_output": pooled, "width": settings.width, "height": settings.height, "crop_w": settings.crop_w, "crop_h": settings.crop_h, "target_width": settings.target_width, "target_height": settings.target_height }]]