1580158
/
SD-CN-Animation
mirror of https://github.com/volotat/SD-CN-Animation.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
SD-CN-Animation/scripts/vid2vid.py

479 lines
20 KiB
Python
Raw Blame History

import sys, os
basedirs = [os.getcwd()]
for basedir in basedirs:
paths_to_ensure = [
basedir,
basedir + '/extensions/sd-cn-animation/scripts',
basedir + '/extensions/SD-CN-Animation/scripts'
]
for scripts_path_fix in paths_to_ensure:
if not scripts_path_fix in sys.path:
sys.path.extend([scripts_path_fix])
import math
import os
import sys
import traceback
import numpy as np
from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
from modules import devices, sd_samplers, img2img
from modules import shared, sd_hijack, lowvram
from modules.generation_parameters_copypaste import create_override_settings_dict
from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
from modules.shared import opts, devices, state
import modules.shared as shared
import modules.processing as processing
from modules.ui import plaintext_to_html
import modules.images as images
import modules.scripts
import gc
import cv2
import gradio as gr
import time
import skimage
import datetime
from flow_utils import RAFT_estimate_flow, RAFT_clear_memory, compute_diff_map
from types import SimpleNamespace
class sdcn_anim_tmp:
prepear_counter = 0
process_counter = 0
input_video = None
output_video = None
curr_frame = None
prev_frame = None
prev_frame_styled = None
prev_frame_alpha_mask = None
fps = None
total_frames = None
prepared_frames = None
prepared_next_flows = None
prepared_prev_flows = None
frames_prepared = False
def read_frame_from_video():
# Reading video file
if sdcn_anim_tmp.input_video.isOpened():
ret, cur_frame = sdcn_anim_tmp.input_video.read()
if cur_frame is not None:
cur_frame = cv2.cvtColor(cur_frame, cv2.COLOR_BGR2RGB)
else:
cur_frame = None
sdcn_anim_tmp.input_video.release()
return cur_frame
def get_cur_stat():
stat = f'Frames prepared: {sdcn_anim_tmp.prepear_counter + 1} / {sdcn_anim_tmp.total_frames}; '
stat += f'Frames processed: {sdcn_anim_tmp.process_counter + 1} / {sdcn_anim_tmp.total_frames}; '
return stat
def clear_memory_from_sd():
if shared.sd_model is not None:
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
try:
lowvram.send_everything_to_cpu()
except Exception as e:
...
del shared.sd_model
shared.sd_model = None
gc.collect()
devices.torch_gc()
def get_device():
device=devices.get_optimal_device()
#print('device',device)
return device
def args_to_dict(*args): # converts list of argumets into dictionary for better handling of it
args_list = ['id_task', 'mode', 'prompt', 'negative_prompt', 'prompt_styles', 'init_video', 'sketch', 'init_img_with_mask', 'inpaint_color_sketch', 'inpaint_color_sketch_orig', 'init_img_inpaint', 'init_mask_inpaint', 'steps', 'sampler_index', 'mask_blur', 'mask_alpha', 'inpainting_fill', 'restore_faces', 'tiling', 'n_iter', 'batch_size', 'cfg_scale', 'image_cfg_scale', 'denoising_strength', 'fix_frame_strength', 'seed', 'subseed', 'subseed_strength', 'seed_resize_from_h', 'seed_resize_from_w', 'seed_enable_extras', 'height', 'width', 'resize_mode', 'inpaint_full_res', 'inpaint_full_res_padding', 'inpainting_mask_invert', 'img2img_batch_input_dir', 'img2img_batch_output_dir', 'img2img_batch_inpaint_mask_dir', 'override_settings_texts']
# set default values for params that were not specified
args_dict = {
'mode': 0,
'prompt': '',
'negative_prompt': '',
'prompt_styles': [],
'init_video': None, # Always required
'steps': 15,
'sampler_index': 0, # 'Euler a'
'mask_blur': 0,
'inpainting_fill': 1, # original
'restore_faces': False,
'tiling': False,
'n_iter': 1,
'batch_size': 1,
'cfg_scale': 5.5,
'image_cfg_scale': 1.5,
'denoising_strength': 0.75,
'fix_frame_strength': 0.15,
'seed': -1,
'subseed': -1,
'subseed_strength': 0,
'seed_resize_from_h': 512,
'seed_resize_from_w': 512,
'seed_enable_extras': False,
'height': 512,
'width': 512,
'resize_mode': 1,
'inpaint_full_res': True,
'inpaint_full_res_padding': 0,
}
args = list(args)
for i in range(len(args_list)):
if (args[i] is None) and (args_list[i] in args_dict):
args[i] = args_dict[args_list[i]]
else:
args_dict[args_list[i]] = args[i]
args_dict['script_inputs'] = args[len(args_list):]
return args_dict, args
# TODO: Refactor all the code below
def start_process(*args):
args_dict, args_list = args_to_dict(*args)
sdcn_anim_tmp.process_counter = 0
sdcn_anim_tmp.prepear_counter = 0
# Open the input video file
sdcn_anim_tmp.input_video = cv2.VideoCapture(args_dict['init_video'].name)
# Get useful info from the source video
sdcn_anim_tmp.fps = int(sdcn_anim_tmp.input_video.get(cv2.CAP_PROP_FPS))
sdcn_anim_tmp.total_frames = int(sdcn_anim_tmp.input_video.get(cv2.CAP_PROP_FRAME_COUNT))
# Create an output video file with the same fps, width, and height as the input video
output_video_name = f'outputs/sd-cn-animation/vid2vid/{datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S")}.mp4'
os.makedirs(os.path.dirname(output_video_name), exist_ok=True)
sdcn_anim_tmp.output_video = cv2.VideoWriter(output_video_name, cv2.VideoWriter_fourcc(*'mp4v'), sdcn_anim_tmp.fps, (args_dict['width'], args_dict['height']))
curr_frame = read_frame_from_video()
curr_frame = cv2.resize(curr_frame, (args_dict['width'], args_dict['height']))
sdcn_anim_tmp.prepared_frames = np.zeros((11, args_dict['height'], args_dict['width'], 3), dtype=np.uint8)
sdcn_anim_tmp.prepared_next_flows = np.zeros((10, args_dict['height'], args_dict['width'], 2))
sdcn_anim_tmp.prepared_prev_flows = np.zeros((10, args_dict['height'], args_dict['width'], 2))
sdcn_anim_tmp.prepared_frames[0] = curr_frame
args_dict['init_img'] = Image.fromarray(curr_frame)
#args_list[5] = Image.fromarray(curr_frame)
processed_frames, _, _, _ = img2img(args_dict)
processed_frame = np.array(processed_frames[0])
processed_frame = skimage.exposure.match_histograms(processed_frame, curr_frame, multichannel=False, channel_axis=-1)
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
#print('Processed frame ', 0)
sdcn_anim_tmp.curr_frame = curr_frame
sdcn_anim_tmp.prev_frame = curr_frame.copy()
sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
yield get_cur_stat(), sdcn_anim_tmp.curr_frame, None, None, processed_frame, ''
for step in range((sdcn_anim_tmp.total_frames-1) * 2):
args_dict, args_list = args_to_dict(*args)
occlusion_mask = None
prev_frame = None
curr_frame = sdcn_anim_tmp.curr_frame
warped_styled_frame = gr.Image.update()
processed_frame = gr.Image.update()
prepare_steps = 10
if sdcn_anim_tmp.process_counter % prepare_steps == 0 and not sdcn_anim_tmp.frames_prepared: # prepare next 10 frames for processing
#clear_memory_from_sd()
device = get_device()
curr_frame = read_frame_from_video()
if curr_frame is not None:
curr_frame = cv2.resize(curr_frame, (args_dict['width'], args_dict['height']))
prev_frame = sdcn_anim_tmp.prev_frame.copy()
next_flow, prev_flow, occlusion_mask, frame1_bg_removed, frame2_bg_removed = RAFT_estimate_flow(prev_frame, curr_frame, subtract_background=False, device=device)
occlusion_mask = np.clip(occlusion_mask * 0.1 * 255, 0, 255).astype(np.uint8)
cn = sdcn_anim_tmp.prepear_counter % 10
if sdcn_anim_tmp.prepear_counter % 10 == 0:
sdcn_anim_tmp.prepared_frames[cn] = sdcn_anim_tmp.prev_frame
sdcn_anim_tmp.prepared_frames[cn + 1] = curr_frame.copy()
sdcn_anim_tmp.prepared_next_flows[cn] = next_flow.copy()
sdcn_anim_tmp.prepared_prev_flows[cn] = prev_flow.copy()
#print('Prepared frame ', cn+1)
sdcn_anim_tmp.prev_frame = curr_frame.copy()
sdcn_anim_tmp.prepear_counter += 1
if sdcn_anim_tmp.prepear_counter % prepare_steps == 0 or \
sdcn_anim_tmp.prepear_counter >= sdcn_anim_tmp.total_frames - 1 or \
curr_frame is None:
# Remove RAFT from memory
RAFT_clear_memory()
sdcn_anim_tmp.frames_prepared = True
else:
# process frame
sdcn_anim_tmp.frames_prepared = False
cn = sdcn_anim_tmp.process_counter % 10
curr_frame = sdcn_anim_tmp.prepared_frames[cn+1]
prev_frame = sdcn_anim_tmp.prepared_frames[cn]
next_flow = sdcn_anim_tmp.prepared_next_flows[cn]
prev_flow = sdcn_anim_tmp.prepared_prev_flows[cn]
# process current frame
args_dict['init_img'] = Image.fromarray(curr_frame)
args_dict['seed'] = -1
#args_list[5] = Image.fromarray(curr_frame)
#args_list[24] = -1
processed_frames, _, _, _ = img2img(args_dict)
processed_frame = np.array(processed_frames[0])
alpha_mask, warped_styled_frame = compute_diff_map(next_flow, prev_flow, prev_frame, curr_frame, sdcn_anim_tmp.prev_frame_styled)
if sdcn_anim_tmp.process_counter > 0:
alpha_mask = alpha_mask + sdcn_anim_tmp.prev_frame_alpha_mask * 0.5
sdcn_anim_tmp.prev_frame_alpha_mask = alpha_mask
# alpha_mask = np.clip(alpha_mask + 0.05, 0.05, 0.95)
alpha_mask = np.clip(alpha_mask, 0, 1)
fl_w, fl_h = prev_flow.shape[:2]
prev_flow_n = prev_flow / np.array([fl_h,fl_w])
flow_mask = np.clip(1 - np.linalg.norm(prev_flow_n, axis=-1)[...,None], 0, 1)
# fix warped styled frame from duplicated that occures on the places where flow is zero, but only because there is no place to get the color from
warped_styled_frame = curr_frame.astype(float) * alpha_mask * flow_mask + warped_styled_frame.astype(float) * (1 - alpha_mask * flow_mask)
# This clipping at lower side required to fix small trailing issues that for some reason left outside of the bright part of the mask,
# and at the higher part it making parts changed strongly to do it with less flickering.
occlusion_mask = np.clip(alpha_mask * 255, 0, 255).astype(np.uint8)
# normalizing the colors
processed_frame = skimage.exposure.match_histograms(processed_frame, curr_frame, multichannel=False, channel_axis=-1)
processed_frame = processed_frame.astype(float) * alpha_mask + warped_styled_frame.astype(float) * (1 - alpha_mask)
processed_frame = processed_frame * 0.9 + curr_frame * 0.1
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
sdcn_anim_tmp.prev_frame_styled = processed_frame.copy()
args_dict['init_img'] = Image.fromarray(processed_frame)
args_dict['denoising_strength'] = args_dict['fix_frame_strength']
args_dict['seed'] = 8888
#args_list[5] = Image.fromarray(processed_frame)
#args_list[23] = 0.15
#args_list[24] = 8888
processed_frames, _, _, _ = img2img(args_dict)
processed_frame = np.array(processed_frames[0])
processed_frame = np.clip(processed_frame, 0, 255).astype(np.uint8)
warped_styled_frame = np.clip(warped_styled_frame, 0, 255).astype(np.uint8)
# Write the frame to the output video
frame_out = np.clip(processed_frame, 0, 255).astype(np.uint8)
frame_out = cv2.cvtColor(frame_out, cv2.COLOR_RGB2BGR)
sdcn_anim_tmp.output_video.write(frame_out)
sdcn_anim_tmp.process_counter += 1
if sdcn_anim_tmp.process_counter >= sdcn_anim_tmp.total_frames - 1:
sdcn_anim_tmp.input_video.release()
sdcn_anim_tmp.output_video.release()
sdcn_anim_tmp.prev_frame = None
#print(f'\nEND OF STEP {step}, {sdcn_anim_tmp.prepear_counter}, {sdcn_anim_tmp.process_counter}')
yield get_cur_stat(), curr_frame, occlusion_mask, warped_styled_frame, processed_frame, ''
#sdcn_anim_tmp.input_video.release()
#sdcn_anim_tmp.output_video.release()
return get_cur_stat(), curr_frame, occlusion_mask, warped_styled_frame, processed_frame, ''
def process_img(p, input_img, output_dir, inpaint_mask_dir, args):
processing.fix_seed(p)
#images = shared.listfiles(input_dir)
images = [input_img]
is_inpaint_batch = False
#if inpaint_mask_dir:
# inpaint_masks = shared.listfiles(inpaint_mask_dir)
# is_inpaint_batch = len(inpaint_masks) > 0
#if is_inpaint_batch:
# print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
#print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
save_normally = output_dir == ''
p.do_not_save_grid = True
p.do_not_save_samples = not save_normally
state.job_count = len(images) * p.n_iter
generated_images = []
for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
if state.skipped:
state.skipped = False
if state.interrupted:
break
img = image #Image.open(image)
# Use the EXIF orientation of photos taken by smartphones.
img = ImageOps.exif_transpose(img)
p.init_images = [img] * p.batch_size
#if is_inpaint_batch:
# # try to find corresponding mask for an image using simple filename matching
# mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
# # if not found use first one ("same mask for all images" use-case)
# if not mask_image_path in inpaint_masks:
# mask_image_path = inpaint_masks[0]
# mask_image = Image.open(mask_image_path)
# p.image_mask = mask_image
proc = modules.scripts.scripts_img2img.run(p, *args)
if proc is None:
proc = process_images(p)
generated_images.append(proc.images[0])
#for n, processed_image in enumerate(proc.images):
# filename = os.path.basename(image)
# if n > 0:
# left, right = os.path.splitext(filename)
# filename = f"{left}-{n}{right}"
# if not save_normally:
# os.makedirs(output_dir, exist_ok=True)
# if processed_image.mode == 'RGBA':
# processed_image = processed_image.convert("RGB")
# processed_image.save(os.path.join(output_dir, filename))
return generated_images
# id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles: list, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args
def img2img(args_dict):
args = SimpleNamespace(**args_dict)
print('override_settings:', args.override_settings_texts)
override_settings = create_override_settings_dict(args.override_settings_texts)
is_batch = args.mode == 5
if args.mode == 0: # img2img
image = args.init_img.convert("RGB")
mask = None
elif args.mode == 1: # img2img sketch
image = args.sketch.convert("RGB")
mask = None
elif args.mode == 2: # inpaint
image, mask = args.init_img_with_mask["image"], args.init_img_with_mask["mask"]
alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
image = image.convert("RGB")
elif args.mode == 3: # inpaint sketch
image = args.inpaint_color_sketch
orig = args.inpaint_color_sketch_orig or args.inpaint_color_sketch
pred = np.any(np.array(image) != np.array(orig), axis=-1)
mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
mask = ImageEnhance.Brightness(mask).enhance(1 - args.mask_alpha / 100)
blur = ImageFilter.GaussianBlur(args.mask_blur)
image = Image.composite(image.filter(blur), orig, mask.filter(blur))
image = image.convert("RGB")
elif args.mode == 4: # inpaint upload mask
image = args.init_img_inpaint
mask = args.init_mask_inpaint
else:
image = None
mask = None
# Use the EXIF orientation of photos taken by smartphones.
if image is not None:
image = ImageOps.exif_transpose(image)
assert 0. <= args.denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
p = StableDiffusionProcessingImg2Img(
sd_model=shared.sd_model,
outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples,
outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids,
prompt=args.prompt,
negative_prompt=args.negative_prompt,
styles=args.prompt_styles,
seed=args.seed,
subseed=args.subseed,
subseed_strength=args.subseed_strength,
seed_resize_from_h=args.seed_resize_from_h,
seed_resize_from_w=args.seed_resize_from_w,
seed_enable_extras=args.seed_enable_extras,
sampler_name=sd_samplers.samplers_for_img2img[args.sampler_index].name,
batch_size=args.batch_size,
n_iter=args.n_iter,
steps=args.steps,
cfg_scale=args.cfg_scale,
width=args.width,
height=args.height,
restore_faces=args.restore_faces,
tiling=args.tiling,
init_images=[image],
mask=mask,
mask_blur=args.mask_blur,
inpainting_fill=args.inpainting_fill,
resize_mode=args.resize_mode,
denoising_strength=args.denoising_strength,
image_cfg_scale=args.image_cfg_scale,
inpaint_full_res=args.inpaint_full_res,
inpaint_full_res_padding=args.inpaint_full_res_padding,
inpainting_mask_invert=args.inpainting_mask_invert,
override_settings=override_settings,
)
p.scripts = modules.scripts.scripts_txt2img
p.script_args = args.script_inputs
print('script_inputs 1:', args.script_inputs[1].__dict__)
#print('script_inputs 2:', args.script_inputs[1])
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {args.prompt}", file=shared.progress_print_out)
if mask:
p.extra_generation_params["Mask blur"] = args.mask_blur
'''
if is_batch:
...
# assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
# process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args.script_inputs)
# processed = Processed(p, [], p.seed, "")
else:
processed = modules.scripts.scripts_img2img.run(p, *args.script_inputs)
if processed is None:
processed = process_images(p)
'''
generated_images = process_img(p, image, None, '', args.script_inputs)
processed = Processed(p, [], p.seed, "")
p.close()
shared.total_tqdm.clear()
generation_info_js = processed.js()
if opts.samples_log_stdout:
print(generation_info_js)
if opts.do_not_show_images:
processed.images = []
return generated_images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)
Reference in New Issue View Git Blame Copy Permalink