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import copy
import os
import shutil
import cv2
import gradio as gr
import numpy as np
import modules.scripts as scripts
from modules import images, processing
from modules.processing import process_images, Processed
from modules.shared import opts
from PIL import Image, ImageFilter, ImageColor, ImageOps
from pathlib import Path
from typing import List, Tuple, Iterable
#Returns a list of images located in the input path. For ControlNet iamges
def get_all_frames_from_path(path):
if not os.path.isdir(path):
return None
frame_list = []
for filename in sorted(os.listdir(path)):
if filename.endswith(".jpg") or filename.endswith(".png"):
img_path = os.path.join(path, filename)
img = cv2.imread(img_path)
if img is not None:
frame_list.append(img)
frame_list.insert(0, frame_list[0])
return frame_list
#Returns a list of images located in the input path. For Color iamges
def get_images_from_path(path):
if not os.path.isdir(path):
return None
images = []
for filename in os.listdir(path):
if filename.endswith('.jpg') or filename.endswith('.png'):
img_path = os.path.join(path, filename)
img = Image.open(img_path)
images.append(img)
images.append(images[-1])
images.insert(0, images[0])
return images
#Returns the number of the smallest number in the entire image sequence list. For ControlNet
def get_min_frame_num(video_list):
min_frame_num = -1
for video in video_list:
if video is None:
continue
else:
frame_num = len(video)
print(frame_num)
if min_frame_num < 0:
min_frame_num = frame_num
elif frame_num < min_frame_num:
min_frame_num = frame_num
return min_frame_num
#Blende method
def basic(target, blend, opacity):
return target * opacity + blend * (1-opacity)
def blender(func):
def blend(target, blend, opacity=1, *args):
res = func(target, blend, *args)
res = basic(res, blend, opacity)
return np.clip(res, 0, 1)
return blend
class Blend:
@classmethod
def method(cls, name):
return getattr(cls, name)
normal = basic
@staticmethod
@blender
def darken(target, blend, *args):
return np.minimum(target, blend)
@staticmethod
@blender
def multiply(target, blend, *args):
return target * blend
@staticmethod
@blender
def color_burn(target, blend, *args):
return 1 - (1-target)/blend
@staticmethod
@blender
def linear_burn(target, blend, *args):
return target+blend-1
@staticmethod
@blender
def lighten(target, blend, *args):
return np.maximum(target, blend)
@staticmethod
@blender
def screen(target, blend, *args):
return 1 - (1-target) * (1-blend)
@staticmethod
@blender
def color_dodge(target, blend, *args):
return target/(1-blend)
@staticmethod
@blender
def linear_dodge(target, blend, *args):
return target+blend
@staticmethod
@blender
def overlay(target, blend, *args):
return (target>0.5) * (1-(2-2*target)*(1-blend)) +\
(target<=0.5) * (2*target*blend)
@staticmethod
@blender
def soft_light(target, blend, *args):
return (blend>0.5) * (1 - (1-target)*(1-(blend-0.5))) +\
(blend<=0.5) * (target*(blend+0.5))
@staticmethod
@blender
def hard_light(target, blend, *args):
return (blend>0.5) * (1 - (1-target)*(2-2*blend)) +\
(blend<=0.5) * (2*target*blend)
@staticmethod
@blender
def vivid_light(target, blend, *args):
return (blend>0.5) * (1 - (1-target)/(2*blend-1)) +\
(blend<=0.5) * (target/(1-2*blend))
@staticmethod
@blender
def linear_light(target, blend, *args):
return (blend>0.5) * (target + 2*(blend-0.5)) +\
(blend<=0.5) * (target + 2*blend)
@staticmethod
@blender
def pin_light(target, blend, *args):
return (blend>0.5) * np.maximum(target,2*(blend-0.5)) +\
(blend<=0.5) * np.minimum(target,2*blend)
@staticmethod
@blender
def difference(target, blend, *args):
return np.abs(target - blend)
@staticmethod
@blender
def exclusion(target, blend, *args):
return 0.5 - 2*(target-0.5)*(blend-0.5)
blend_methods = [i for i in Blend.__dict__.keys() if i[0]!='_' and i!='method']
def blend_images(base_img, blend_img, blend_method, blend_opacity, do_invert):
img_base = np.array(base_img.convert("RGB")).astype(np.float64)/255
if do_invert:
img_to_blend = ImageOps.invert(blend_img.convert('RGB'))
else:
img_to_blend = blend_img
img_to_blend = img_to_blend.resize((int(base_img.width), int(base_img.height)))
img_to_blend = np.array(img_to_blend.convert("RGB")).astype(np.float64)/255
img_blended = Blend.method(blend_method)(img_to_blend, img_base, blend_opacity)
img_blended *= 255
img_blended = Image.fromarray(img_blended.astype(np.uint8), mode='RGB')
return img_blended
#Define UI and script properties.
class Script(scripts.Script):
def title(self):
return "controlnet I2I sequence_toyxyz_v2"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
ctrls_group = ()
max_models = opts.data.get("control_net_max_models_num", 1)
input_list = []
with gr.Group():
with gr.Accordion("ControlNet-I2I-sequence-toyxyz", open = True):
with gr.Column():
feed_prev_frame = gr.Checkbox(value=False, label="Feed previous frame / Reduce flickering by feeding the previous frame image generated by Img2Img")
use_init_img = gr.Checkbox(value=False, label="Blend color image / Blend the color image sequence with the initial Img2Img image or previous frame")
use_TemporalNet = gr.Checkbox(value=False, label="Use TemporalNet / Using TemporalNet to reduce flicker between image sequences. Add TemporalNet in addition to the multi-controlnet you need. It should be placed at the end of the controlnet list.")
blendmode = gr.Dropdown(blend_methods, value='normal', label='Blend mode / Choose how to blend the color image with the Previous frame or Img2Img initial image')
opacityvalue = gr.Slider(0, 1, value=0, label="Opacity / Previous frame or Img2Img initial image + (color image * opacity)", info="Choose betwen 0 and 1")
for i in range(max_models):
input_path = gr.Textbox(label=f"ControlNet-{i}", placeholder="image sequence path")
input_list.append(input_path)
tone_image_path = gr.Textbox(label=f"Color_Image / Color images to be used for Img2Img in sequence", placeholder="image sequence path")
output_path = gr.Textbox(label=f"Output_path / Deletes the contents located in the path, and creates a new path if it does not exist", placeholder="Output path")
ctrls_group += tuple(input_list) + (use_TemporalNet, use_init_img, opacityvalue, blendmode, feed_prev_frame, tone_image_path, output_path)
return ctrls_group
#Image Generate Definition
def run(self, p, *args):
path = p.outpath_samples
output_path = args[-1] # get the last argument, which is the output path
feedprev = args[-3]
blendm = args[-4]
opacityval = args[-5]
useinit = args[-6]
usetempo = args[-7]
# Check whether the output path exists, if it does, delete it and create a new one.
if os.path.isdir(output_path):
for file in os.scandir(output_path):
os.remove(file.path)
else :
os.mkdir(output_path)
#Get the number of controlnet models.
video_num = opts.data.get("control_net_max_models_num", 1)
# Get the ControlNet image sequence list.
image_list = [get_all_frames_from_path(image) for image in args[:video_num]]
# Get a list of color image sequences.
color_image_list = get_images_from_path(args[-2])
# Get the first frame
previmg = p.init_images
tempoimg = p.init_images[0]
#If img2img color correction is enabled in webui settings, color correction is performed based on the first frame.
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
#Save initial img2img image
initial_image = p.init_images[0]
# Get the total number of frames.
frame_num = get_min_frame_num(image_list)
# image processing
if frame_num > 0:
output_image_list = []
for frame in range(frame_num):
copy_p = copy.copy(p)
copy_p.control_net_input_image = []
for video in image_list:
if video is None:
continue
copy_p.control_net_input_image.append(video[frame])
if usetempo == True :
copy_p.control_net_input_image.append(tempoimg)
if color_image_list and feedprev == False:
if frame<len(color_image_list):
tone_image = color_image_list[frame+1]
if useinit:
tone_image = blend_images(initial_image, tone_image, blendm, opacityval, False)
p.init_images = [tone_image.convert("RGB")]
proc = process_images(copy_p)
if feedprev == True and useinit == False:
if previmg is None:
continue
else:
previmg = proc.images[0]
if frame == 0:
previmg = initial_image
p.init_images = [previmg]
if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
if feedprev == True and color_image_list and useinit:
if previmg is None:
continue
else:
previmg = proc.images[0]
if frame == 0:
previmg = initial_image
previmg = blend_images(previmg, color_image_list[frame+1], blendm, opacityval, False)
p.init_images = [previmg]
if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
img = proc.images[0]
if usetempo == True :
if frame > 0 :
tempoimg = proc.images[0]
#Save image
if(frame>0):
images.save_image(img, output_path, f"Frame_{frame}")
copy_p.close()
else:
proc = process_images(p)
return proc |