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| # 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 numpy as np | |
| import pandas as pd | |
| import re | |
| from .ScheduleFuncs import * | |
| def prepare_batch_prompt(prompt_series, max_frames, frame_idx, prompt_weight_1=0, prompt_weight_2=0, prompt_weight_3=0, | |
| prompt_weight_4=0): # calculate expressions from the text input and return a string | |
| max_f = max_frames - 1 | |
| pattern = r'`.*?`' # set so the expression will be read between two backticks (``) | |
| regex = re.compile(pattern) | |
| prompt_parsed = str(prompt_series) | |
| for match in regex.finditer(prompt_parsed): | |
| matched_string = match.group(0) | |
| parsed_string = matched_string.replace('t', f'{frame_idx}').replace("pw_a", f"{prompt_weight_1}").replace("pw_b", | |
| f"{prompt_weight_2}").replace("pw_c", f"{prompt_weight_3}").replace("pw_d", | |
| f"{prompt_weight_4}").replace("max_f", | |
| f"{max_f}").replace('`', '') # replace t, max_f and `` respectively | |
| parsed_value = numexpr.evaluate(parsed_string) | |
| prompt_parsed = prompt_parsed.replace(matched_string, str(parsed_value)) | |
| return prompt_parsed.strip() | |
| def batch_split_weighted_subprompts(text, pre_text, app_text): | |
| pos = {} | |
| neg = {} | |
| pre_text = str(pre_text) | |
| app_text = str(app_text) | |
| if "--neg" in pre_text: | |
| pre_pos, pre_neg = pre_text.split("--neg") | |
| else: | |
| pre_pos, pre_neg = pre_text, "" | |
| if "--neg" in app_text: | |
| app_pos, app_neg = app_text.split("--neg") | |
| else: | |
| app_pos, app_neg = app_text, "" | |
| for frame, prompt in text.items(): | |
| negative_prompts = "" | |
| positive_prompts = "" | |
| prompt_split = prompt.split("--neg") | |
| if len(prompt_split) > 1: | |
| positive_prompts, negative_prompts = prompt_split[0], prompt_split[1] | |
| else: | |
| positive_prompts = prompt_split[0] | |
| pos[frame] = "" | |
| neg[frame] = "" | |
| pos[frame] += (str(pre_pos) + " " + positive_prompts + " " + str(app_pos)) | |
| neg[frame] += (str(pre_neg) + " " + negative_prompts + " " + str(app_neg)) | |
| if pos[frame].endswith('0'): | |
| pos[frame] = pos[frame][:-1] | |
| if neg[frame].endswith('0'): | |
| neg[frame] = neg[frame][:-1] | |
| return pos, neg | |
| def interpolate_prompt_series(animation_prompts, max_frames, start_frame, pre_text, app_text, prompt_weight_1=[], | |
| prompt_weight_2=[], prompt_weight_3=[], prompt_weight_4=[], Is_print = False): | |
| max_f = max_frames # needed for numexpr even though it doesn't look like it's in use. | |
| parsed_animation_prompts = {} | |
| for key, value in animation_prompts.items(): | |
| if check_is_number(key): # default case 0:(1 + t %5), 30:(5-t%2) | |
| parsed_animation_prompts[key] = value | |
| else: # math on the left hand side case 0:(1 + t %5), maxKeyframes/2:(5-t%2) | |
| parsed_animation_prompts[int(numexpr.evaluate(key))] = value | |
| sorted_prompts = sorted(parsed_animation_prompts.items(), key=lambda item: int(item[0])) | |
| # Automatically set the first keyframe to 0 if it's missing | |
| if sorted_prompts[0][0] != "0": | |
| sorted_prompts.insert(0, ("0", sorted_prompts[0][1])) | |
| # Automatically set the last keyframe to the maximum number of frames | |
| if sorted_prompts[-1][0] != str(max_frames): | |
| sorted_prompts.append((str(max_frames), sorted_prompts[-1][1])) | |
| # Setup containers for interpolated prompts | |
| cur_prompt_series = pd.Series([np.nan for a in range(max_frames)]) | |
| nxt_prompt_series = pd.Series([np.nan for a in range(max_frames)]) | |
| # simple array for strength values | |
| weight_series = [np.nan] * max_frames | |
| # in case there is only one keyed promt, set all prompts to that prompt | |
| if len(sorted_prompts) == 1: | |
| for i in range(0, len(cur_prompt_series) - 1): | |
| current_prompt = sorted_prompts[0][1] | |
| cur_prompt_series[i] = str(current_prompt) | |
| nxt_prompt_series[i] = str(current_prompt) | |
| # Initialized outside of loop for nan check | |
| current_key = 0 | |
| next_key = 0 | |
| if type(prompt_weight_1) in {int, float}: | |
| prompt_weight_1 = tuple([prompt_weight_1] * max_frames) | |
| if type(prompt_weight_2) in {int, float}: | |
| prompt_weight_2 = tuple([prompt_weight_2] * max_frames) | |
| if type(prompt_weight_3) in {int, float}: | |
| prompt_weight_3 = tuple([prompt_weight_3] * max_frames) | |
| if type(prompt_weight_4) in {int, float}: | |
| prompt_weight_4 = tuple([prompt_weight_4] * max_frames) | |
| # For every keyframe prompt except the last | |
| for i in range(0, len(sorted_prompts) - 1): | |
| # Get current and next keyframe | |
| current_key = int(sorted_prompts[i][0]) | |
| next_key = int(sorted_prompts[i + 1][0]) | |
| # Ensure there's no weird ordering issues or duplication in the animation prompts | |
| # (unlikely because we sort above, and the json parser will strip dupes) | |
| if current_key >= next_key: | |
| print( | |
| f"WARNING: Sequential prompt keyframes {i}:{current_key} and {i + 1}:{next_key} are not monotonously increasing; skipping interpolation.") | |
| continue | |
| # Get current and next keyframes' positive and negative prompts (if any) | |
| current_prompt = sorted_prompts[i][1] | |
| next_prompt = sorted_prompts[i + 1][1] | |
| # Calculate how much to shift the weight from current to next prompt at each frame. | |
| weight_step = 1 / (next_key - current_key) | |
| for f in range(max(current_key, 0), min(next_key, len(cur_prompt_series))): | |
| next_weight = weight_step * (f - current_key) | |
| current_weight = 1 - next_weight | |
| # add the appropriate prompts and weights to their respective containers. | |
| weight_series[f] = 0.0 | |
| cur_prompt_series[f] = str(current_prompt) | |
| nxt_prompt_series[f] = str(next_prompt) | |
| weight_series[f] += current_weight | |
| current_key = next_key | |
| next_key = max_frames | |
| current_weight = 0.0 | |
| index_offset = 0 | |
| # Evaluate the current and next prompt's expressions | |
| for i in range(start_frame, len(cur_prompt_series)): | |
| cur_prompt_series[i] = prepare_batch_prompt(cur_prompt_series[i], max_frames, i, prompt_weight_1[i], | |
| prompt_weight_2[i], prompt_weight_3[i], prompt_weight_4[i]) | |
| nxt_prompt_series[i] = prepare_batch_prompt(nxt_prompt_series[i], max_frames, i, prompt_weight_1[i], | |
| prompt_weight_2[i], prompt_weight_3[i], prompt_weight_4[i]) | |
| if Is_print == True: | |
| # Show the to/from prompts with evaluated expressions for transparency. | |
| print("\n", "Max Frames: ", max_frames, "\n", "frame index: ", (start_frame + i), "\n", "Current Prompt: ", | |
| cur_prompt_series[i], "\n", "Next Prompt: ", nxt_prompt_series[i], "\n", "Strength : ", | |
| weight_series[i], "\n") | |
| index_offset = index_offset + 1 | |
| # Output methods depending if the prompts are the same or if the current frame is a keyframe. | |
| # if it is an in-between frame and the prompts differ, composable diffusion will be performed. | |
| return (cur_prompt_series, nxt_prompt_series, weight_series) | |
| def BatchPoolAnimConditioning(cur_prompt_series, nxt_prompt_series, weight_series, clip): | |
| pooled_out = [] | |
| cond_out = [] | |
| for i in range(len(cur_prompt_series)): | |
| tokens = clip.tokenize(str(cur_prompt_series[i])) | |
| cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True) | |
| if i < len(nxt_prompt_series): | |
| tokens = clip.tokenize(str(nxt_prompt_series[i])) | |
| cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True) | |
| else: | |
| cond_from, pooled_from = torch.zeros_like(cond_to), torch.zeros_like(pooled_to) | |
| interpolated_conditioning = addWeighted([[cond_to, {"pooled_output": pooled_to}]], | |
| [[cond_from, {"pooled_output": pooled_from}]], | |
| weight_series[i]) | |
| interpolated_cond = interpolated_conditioning[0][0] | |
| interpolated_pooled = interpolated_conditioning[0][1].get("pooled_output", pooled_from) | |
| cond_out.append(interpolated_cond) | |
| pooled_out.append(interpolated_pooled) | |
| final_pooled_output = torch.cat(pooled_out, dim=0) | |
| final_conditioning = torch.cat(cond_out, dim=0) | |
| return [[final_conditioning, {"pooled_output": final_pooled_output}]] | |
| def BatchGLIGENConditioning(cur_prompt_series, nxt_prompt_series, weight_series, clip): | |
| pooled_out = [] | |
| cond_out = [] | |
| for i in range(len(cur_prompt_series)): | |
| tokens = clip.tokenize(str(cur_prompt_series[i])) | |
| cond_to, pooled_to = clip.encode_from_tokens(tokens, return_pooled=True) | |
| tokens = clip.tokenize(str(nxt_prompt_series[i])) | |
| cond_from, pooled_from = clip.encode_from_tokens(tokens, return_pooled=True) | |
| interpolated_conditioning = addWeighted([[cond_to, {"pooled_output": pooled_to}]], | |
| [[cond_from, {"pooled_output": pooled_from}]], | |
| weight_series[i]) | |
| interpolated_cond = interpolated_conditioning[0][0] | |
| interpolated_pooled = interpolated_conditioning[0][1].get("pooled_output", pooled_from) | |
| pooled_out.append(interpolated_pooled) | |
| cond_out.append(interpolated_cond) | |
| final_pooled_output = torch.cat(pooled_out, dim=0) | |
| final_conditioning = torch.cat(cond_out, dim=0) | |
| return cond_out, pooled_out | |
| def BatchPoolAnimConditioningSDXL(cur_prompt_series, nxt_prompt_series, weight_series, clip): | |
| pooled_out = [] | |
| cond_out = [] | |
| for i in range(len(cur_prompt_series)): | |
| interpolated_conditioning = addWeighted(cur_prompt_series[i], | |
| nxt_prompt_series[i], | |
| weight_series[i]) | |
| interpolated_cond = interpolated_conditioning[0][0] | |
| interpolated_pooled = interpolated_conditioning[0][1].get("pooled_output") | |
| pooled_out.append(interpolated_pooled) | |
| cond_out.append(interpolated_cond) | |
| final_pooled_output = torch.cat(pooled_out, dim=0) | |
| final_conditioning = torch.cat(cond_out, dim=0) | |
| return [[final_conditioning, {"pooled_output": final_pooled_output}]] | |
| def BatchInterpolatePromptsSDXL(animation_promptsG, animation_promptsL, max_frames, clip, app_text_G, | |
| app_text_L, pre_text_G, pre_text_L, pw_a, pw_b, pw_c, pw_d, width, height, crop_w, | |
| crop_h, target_width, target_height, Is_print = False): | |
| # parse the conditioning strength and determine in-betweens. | |
| # Get prompts sorted by keyframe | |
| max_f = max_frames # needed for numexpr even though it doesn't look like it's in use. | |
| parsed_animation_promptsG = {} | |
| parsed_animation_promptsL = {} | |
| for key, value in animation_promptsG.items(): | |
| if check_is_number(key): # default case 0:(1 + t %5), 30:(5-t%2) | |
| parsed_animation_promptsG[key] = value | |
| else: # math on the left hand side case 0:(1 + t %5), maxKeyframes/2:(5-t%2) | |
| parsed_animation_promptsG[int(numexpr.evaluate(key))] = value | |
| sorted_prompts_G = sorted(parsed_animation_promptsG.items(), key=lambda item: int(item[0])) | |
| for key, value in animation_promptsL.items(): | |
| if check_is_number(key): # default case 0:(1 + t %5), 30:(5-t%2) | |
| parsed_animation_promptsL[key] = value | |
| else: # math on the left hand side case 0:(1 + t %5), maxKeyframes/2:(5-t%2) | |
| parsed_animation_promptsL[int(numexpr.evaluate(key))] = value | |
| sorted_prompts_L = sorted(parsed_animation_promptsL.items(), key=lambda item: int(item[0])) | |
| # Setup containers for interpolated prompts | |
| cur_prompt_series_G = pd.Series([np.nan for a in range(max_frames)]) | |
| nxt_prompt_series_G = pd.Series([np.nan for a in range(max_frames)]) | |
| cur_prompt_series_L = pd.Series([np.nan for a in range(max_frames)]) | |
| nxt_prompt_series_L = pd.Series([np.nan for a in range(max_frames)]) | |
| # simple array for strength values | |
| weight_series = [np.nan] * max_frames | |
| # in case there is only one keyed promt, set all prompts to that prompt | |
| if len(sorted_prompts_G) - 1 == 0: | |
| for i in range(0, len(cur_prompt_series_G) - 1): | |
| current_prompt_G = sorted_prompts_G[0][1] | |
| cur_prompt_series_G[i] = str(pre_text_G) + " " + str(current_prompt_G) + " " + str(app_text_G) | |
| nxt_prompt_series_G[i] = str(pre_text_G) + " " + str(current_prompt_G) + " " + str(app_text_G) | |
| if len(sorted_prompts_L) - 1 == 0: | |
| for i in range(0, len(cur_prompt_series_L) - 1): | |
| current_prompt_L = sorted_prompts_L[0][1] | |
| cur_prompt_series_L[i] = str(pre_text_L) + " " + str(current_prompt_L) + " " + str(app_text_L) | |
| nxt_prompt_series_L[i] = str(pre_text_L) + " " + str(current_prompt_L) + " " + str(app_text_L) | |
| # Initialized outside of loop for nan check | |
| current_key = 0 | |
| next_key = 0 | |
| # For every keyframe prompt except the last | |
| for i in range(0, len(sorted_prompts_G) - 1): | |
| # Get current and next keyframe | |
| current_key = int(sorted_prompts_G[i][0]) | |
| next_key = int(sorted_prompts_G[i + 1][0]) | |
| # Ensure there's no weird ordering issues or duplication in the animation prompts | |
| # (unlikely because we sort above, and the json parser will strip dupes) | |
| if current_key >= next_key: | |
| print( | |
| f"WARNING: Sequential prompt keyframes {i}:{current_key} and {i + 1}:{next_key} are not monotonously increasing; skipping interpolation.") | |
| continue | |
| # Get current and next keyframes' positive and negative prompts (if any) | |
| current_prompt_G = sorted_prompts_G[i][1] | |
| next_prompt_G = sorted_prompts_G[i + 1][1] | |
| # Calculate how much to shift the weight from current to next prompt at each frame. | |
| weight_step = 1 / (next_key - current_key) | |
| for f in range(current_key, next_key): | |
| next_weight = weight_step * (f - current_key) | |
| current_weight = 1 - next_weight | |
| # add the appropriate prompts and weights to their respective containers. | |
| if f < max_frames: | |
| cur_prompt_series_G[f] = '' | |
| nxt_prompt_series_G[f] = '' | |
| weight_series[f] = 0.0 | |
| cur_prompt_series_G[f] += (str(pre_text_G) + " " + str(current_prompt_G) + " " + str(app_text_G)) | |
| nxt_prompt_series_G[f] += (str(pre_text_G) + " " + str(next_prompt_G) + " " + str(app_text_G)) | |
| weight_series[f] += current_weight | |
| current_key = next_key | |
| next_key = max_frames | |
| current_weight = 0.0 | |
| # second loop to catch any nan runoff | |
| for f in range(current_key, next_key): | |
| next_weight = weight_step * (f - current_key) | |
| # add the appropriate prompts and weights to their respective containers. | |
| cur_prompt_series_G[f] = '' | |
| nxt_prompt_series_G[f] = '' | |
| weight_series[f] = current_weight | |
| cur_prompt_series_G[f] += (str(pre_text_G) + " " + str(current_prompt_G) + " " + str(app_text_G)) | |
| nxt_prompt_series_G[f] += (str(pre_text_G) + " " + str(next_prompt_G) + " " + str(app_text_G)) | |
| # Reset outside of loop for nan check | |
| current_key = 0 | |
| next_key = 0 | |
| # For every keyframe prompt except the last | |
| for i in range(0, len(sorted_prompts_L) - 1): | |
| # Get current and next keyframe | |
| current_key = int(sorted_prompts_L[i][0]) | |
| next_key = int(sorted_prompts_L[i + 1][0]) | |
| # Ensure there's no weird ordering issues or duplication in the animation prompts | |
| # (unlikely because we sort above, and the json parser will strip dupes) | |
| if current_key >= next_key: | |
| print( | |
| f"WARNING: Sequential prompt keyframes {i}:{current_key} and {i + 1}:{next_key} are not monotonously increasing; skipping interpolation.") | |
| continue | |
| # Get current and next keyframes' positive and negative prompts (if any) | |
| current_prompt_L = sorted_prompts_L[i][1] | |
| next_prompt_L = sorted_prompts_L[i + 1][1] | |
| # Calculate how much to shift the weight from current to next prompt at each frame. | |
| weight_step = 1 / (next_key - current_key) | |
| for f in range(current_key, next_key): | |
| next_weight = weight_step * (f - current_key) | |
| current_weight = 1 - next_weight | |
| # add the appropriate prompts and weights to their respective containers. | |
| if f < max_frames: | |
| cur_prompt_series_L[f] = '' | |
| nxt_prompt_series_L[f] = '' | |
| weight_series[f] = 0.0 | |
| cur_prompt_series_L[f] += (str(pre_text_L) + " " + str(current_prompt_L) + " " + str(app_text_L)) | |
| nxt_prompt_series_L[f] += (str(pre_text_L) + " " + str(next_prompt_L) + " " + str(app_text_L)) | |
| weight_series[f] += current_weight | |
| current_key = next_key | |
| next_key = max_frames | |
| current_weight = 0.0 | |
| # second loop to catch any nan runoff | |
| for f in range(current_key, next_key): | |
| next_weight = weight_step * (f - current_key) | |
| # add the appropriate prompts and weights to their respective containers. | |
| cur_prompt_series_L[f] = '' | |
| nxt_prompt_series_L[f] = '' | |
| weight_series[f] = current_weight | |
| cur_prompt_series_L[f] += (str(pre_text_L) + " " + str(current_prompt_L) + " " + str(app_text_L)) | |
| nxt_prompt_series_L[f] += (str(pre_text_L) + " " + str(next_prompt_L) + " " + str(app_text_L)) | |
| # Evaluate the current and next prompt's expressions | |
| for i in range(0, max_frames): | |
| cur_prompt_series_G[i] = prepare_batch_prompt(cur_prompt_series_G[i], max_frames, i, | |
| pw_a, pw_b, pw_c, pw_d) | |
| nxt_prompt_series_G[i] = prepare_batch_prompt(nxt_prompt_series_G[i], max_frames, i, | |
| pw_a, pw_b, pw_c, pw_d) | |
| cur_prompt_series_L[i] = prepare_batch_prompt(cur_prompt_series_L[i], max_frames, i, | |
| pw_a, pw_b, pw_c, pw_d) | |
| nxt_prompt_series_L[i] = prepare_batch_prompt(nxt_prompt_series_L[i], max_frames, i, | |
| pw_a, pw_b, pw_c, pw_d) | |
| current_conds = [] | |
| next_conds = [] | |
| for i in range(0, max_frames): | |
| current_conds.append(SDXLencode(clip, width, height, crop_w, crop_h, target_width, target_height, | |
| cur_prompt_series_G[i], cur_prompt_series_L[i])) | |
| next_conds.append(SDXLencode(clip, width, height, crop_w, crop_h, target_width, target_height, | |
| nxt_prompt_series_G[i], nxt_prompt_series_L[i])) | |
| if Is_print == True: | |
| # Show the to/from prompts with evaluated expressions for transparency. | |
| for i in range(0, max_frames): | |
| print("\n", "Max Frames: ", max_frames, "\n", "Current Prompt G: ", cur_prompt_series_G[i], | |
| "\n", "Current Prompt L: ", cur_prompt_series_L[i], "\n", "Next Prompt G: ", nxt_prompt_series_G[i], | |
| "\n", "Next Prompt L : ", nxt_prompt_series_L[i], "\n"), "\n", "Current weight: ", weight_series[i] | |
| return BatchPoolAnimConditioningSDXL(current_conds, next_conds, weight_series, clip) | |