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import copy
import os
import shutil
import cv2
import gradio as gr
import modules.scripts as scripts
from modules import images
from modules.processing import process_images
from modules.shared import opts
from PIL import Image
import numpy as np
_BASEDIR = "/controlnet-m2m"
_BASEFILE = "animation"
def get_all_frames(video_path):
if video_path is None:
return None
cap = cv2.VideoCapture(video_path)
frame_list = []
if not cap.isOpened():
return
while True:
ret, frame = cap.read()
if ret:
frame_list.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
else:
return frame_list
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
def pil2cv(image):
new_image = np.array(image, dtype=np.uint8)
if new_image.ndim == 2:
pass
elif new_image.shape[2] == 3:
new_image = new_image[:, :, ::-1]
elif new_image.shape[2] == 4:
new_image = new_image[:, :, [2, 1, 0, 3]]
return new_image
def save_gif(path, image_list, name, duration):
tmp_dir = path + "/tmp/"
if os.path.isdir(tmp_dir):
shutil.rmtree(tmp_dir)
os.mkdir(tmp_dir)
for i, image in enumerate(image_list):
images.save_image(image, tmp_dir, f"output_{i}")
os.makedirs(f"{path}{_BASEDIR}", exist_ok=True)
image_list[0].save(f"{path}{_BASEDIR}/{name}.gif", save_all=True, append_images=image_list[1:], optimize=False, duration=duration, loop=0)
class Script(scripts.Script):
def title(self):
return "controlnet m2m"
def show(self, is_img2img):
return True
def ui(self, is_img2img):
# How the script's is displayed in the UI. See https://gradio.app/docs/#components
# for the different UI components you can use and how to create them.
# Most UI components can return a value, such as a boolean for a checkbox.
# The returned values are passed to the run method as parameters.
ctrls_group = ()
max_models = opts.data.get("control_net_unit_count", 3)
with gr.Group():
with gr.Accordion("ControlNet-M2M", open = False):
duration = gr.Slider(label=f"Duration", value=50.0, minimum=10.0, maximum=200.0, step=10, interactive=True, elem_id='controlnet_movie2movie_duration_slider')
with gr.Tabs():
for i in range(max_models):
with gr.Tab(f"ControlNet-{i}"):
with gr.TabItem("Movie Input"):
ctrls_group += (gr.Video(format='mp4', source='upload', elem_id = f"video_{i}"), )
with gr.TabItem("Image Input"):
ctrls_group += (gr.Image(source='upload', brush_radius=20, mirror_webcam=False, type='numpy', tool='sketch', elem_id=f'image_{i}'), )
ctrls_group += (gr.Checkbox(label=f"Save preprocessed", value=False, elem_id = f"save_pre_{i}"),)
ctrls_group += (duration,)
return ctrls_group
def run(self, p, *args):
# This is where the additional processing is implemented. The parameters include
# self, the model object "p" (a StableDiffusionProcessing class, see
# processing.py), and the parameters returned by the ui method.
# Custom functions can be defined here, and additional libraries can be imported
# to be used in processing. The return value should be a Processed object, which is
# what is returned by the process_images method.
contents_num = opts.data.get("control_net_unit_count", 3)
arg_num = 3
item_list = []
video_list = []
for input_set in [tuple(args[:contents_num * arg_num][i:i+3]) for i in range(0, len(args[:contents_num * arg_num]), arg_num)]:
if input_set[0] is not None:
item_list.append([get_all_frames(input_set[0]), "video"])
video_list.append(get_all_frames(input_set[0]))
if input_set[1] is not None:
item_list.append([cv2.cvtColor(pil2cv(input_set[1]["image"]), cv2.COLOR_BGRA2RGB), "image"])
save_pre = list(args[2:contents_num * arg_num:3])
item_num = len(item_list)
video_num = len(video_list)
duration, = args[contents_num * arg_num:]
frame_num = get_min_frame_num(video_list)
if frame_num > 0:
output_image_list = []
pre_output_image_list = []
for i in range(item_num):
pre_output_image_list.append([])
for frame in range(frame_num):
copy_p = copy.copy(p)
copy_p.control_net_input_image = []
for item in item_list:
if item[1] == "video":
copy_p.control_net_input_image.append(item[0][frame])
elif item[1] == "image":
copy_p.control_net_input_image.append(item[0])
else:
continue
proc = process_images(copy_p)
img = proc.images[0]
output_image_list.append(img)
for i in range(len(save_pre)):
if save_pre[i]:
try:
pre_output_image_list[i].append(proc.images[i + 1])
except:
print(f"proc.images[{i} failed")
copy_p.close()
# filename format is seq-seed-animation.gif seq is 5 places left filled with 0
seq = images.get_next_sequence_number(f"{p.outpath_samples}{_BASEDIR}", "")
filename = f"{seq:05}-{proc.seed}-{_BASEFILE}"
save_gif(p.outpath_samples, output_image_list, filename, duration)
proc.images = [f"{p.outpath_samples}{_BASEDIR}/{filename}.gif"]
for i in range(len(save_pre)):
if save_pre[i]:
# control files add -controlX.gif where X is the controlnet number
save_gif(p.outpath_samples, pre_output_image_list[i], f"{filename}-control{i}", duration)
proc.images.append(f"{p.outpath_samples}{_BASEDIR}/{filename}-control{i}.gif")
else:
proc = process_images(p)
return proc
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