regular json module implemented
This commit is contained in:
FizzleDorf
+1
-155
@@ -6,7 +6,7 @@ import torch
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import re
|
||||
|
||||
import json
|
||||
|
||||
#functions used by PromptSchedule nodes
|
||||
|
||||
@@ -71,21 +71,6 @@ def prepare_prompt(prompt_series, max_frames, frame_idx, prompt_weight_1 = 0, pr
|
||||
prompt_parsed = prompt_parsed.replace(matched_string, str(parsed_value))
|
||||
return prompt_parsed.strip()
|
||||
|
||||
|
||||
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 interpolate_string(animation_prompts, max_frames, current_frame, pre_text, app_text, prompt_weight_1,
|
||||
prompt_weight_2, prompt_weight_3,
|
||||
prompt_weight_4): # parse the conditioning strength and determine in-betweens.
|
||||
@@ -272,145 +257,6 @@ def PoolAnimConditioning(cur_prompt, nxt_prompt, weight, clip):
|
||||
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 interpolate_prompt_series(animation_prompts, max_frames, pre_text, app_text, prompt_weight_1 = [],
|
||||
prompt_weight_2 = [], prompt_weight_3 = [],
|
||||
prompt_weight_4 = []): # 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_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]))
|
||||
|
||||
# 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 == 0:
|
||||
for i in range(0, len(cur_prompt_series) - 1):
|
||||
current_prompt = sorted_prompts[0][1]
|
||||
cur_prompt_series[i] = str(pre_text) + " " + str(current_prompt) + " " + str(app_text)
|
||||
nxt_prompt_series[i] = str(pre_text) + " " + str(current_prompt) + " " + str(app_text)
|
||||
|
||||
# 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) - 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.
|
||||
# print(weight_series)
|
||||
# print(weight_series[f])
|
||||
cur_prompt_series[f] = ''
|
||||
nxt_prompt_series[f] = ''
|
||||
weight_series[f] = 0.0
|
||||
|
||||
cur_prompt_series[f] += (str(pre_text) + " " + str(current_prompt) + " " + str(app_text))
|
||||
nxt_prompt_series[f] += (str(pre_text) + " " + str(next_prompt) + " " + str(app_text))
|
||||
|
||||
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[f] = ''
|
||||
nxt_prompt_series[f] = ''
|
||||
weight_series[f] = current_weight
|
||||
|
||||
cur_prompt_series[f] += (str(pre_text) + " " + str(current_prompt) + " " + str(app_text))
|
||||
nxt_prompt_series[f] += (str(pre_text) + " " + str(next_prompt) + " " + str(app_text))
|
||||
|
||||
if isinstance(prompt_weight_1, int):
|
||||
prompt_weight_1 = tuple([prompt_weight_1] * max_frames)
|
||||
|
||||
if isinstance(prompt_weight_2, int):
|
||||
prompt_weight_2 = tuple([prompt_weight_2] * max_frames)
|
||||
|
||||
if isinstance(prompt_weight_3, int):
|
||||
prompt_weight_3 = tuple([prompt_weight_3] * max_frames)
|
||||
|
||||
if isinstance(prompt_weight_4, int):
|
||||
prompt_weight_4 = tuple([prompt_weight_4] * max_frames)
|
||||
|
||||
# Evaluate the current and next prompt's expressions
|
||||
for i in range(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])
|
||||
|
||||
# Show the to/from prompts with evaluated expressions for transparency.
|
||||
for i in range(len(cur_prompt_series)):
|
||||
print("\n", "Max Frames: ", max_frames, "\n", "Current Prompt: ", cur_prompt_series[i], "\n",
|
||||
"Next Prompt: ", nxt_prompt_series[i], "\n", "Strength : ", weight_series[i], "\n")
|
||||
|
||||
# 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)
|
||||
|
||||
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 [[final_conditioning, {"pooled_output": final_pooled_output}]]
|
||||
|
||||
|
||||
def SDXLencode(clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):
|
||||
tokens = clip.tokenize(text_g)
|
||||
tokens["l"] = clip.tokenize(text_l)["l"]
|
||||
|
||||
+3
-2
@@ -6,10 +6,11 @@ import torch
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import re
|
||||
import json
|
||||
|
||||
|
||||
from .ScheduleFuncs import check_is_number, interpolate_prompts, interpolate_prompts_SDXL, PoolAnimConditioning, interpolate_string, interpolate_prompt_series, BatchPoolAnimConditioning
|
||||
|
||||
from .ScheduleFuncs import check_is_number, interpolate_prompts, interpolate_prompts_SDXL, PoolAnimConditioning, interpolate_string
|
||||
from .BatchFuncs import interpolate_prompt_series, BatchPoolAnimConditioning
|
||||
#Max resolution value for Gligen area calculation.
|
||||
MAX_RESOLUTION=8192
|
||||
|
||||
|
||||
Reference in New Issue
Block a user