411 lines
12 KiB
Python
411 lines
12 KiB
Python
import math, time, os
|
|
import numpy as np
|
|
from PIL import Image
|
|
from modules.ui import plaintext_to_html
|
|
import modules.shared as shared
|
|
|
|
from .helpers import (
|
|
fix_env_Path_ffprobe,
|
|
closest_upper_divisible_by_eight,
|
|
load_model_from_setting,
|
|
do_upscaleImg,
|
|
)
|
|
from .sd_helpers import renderImg2Img, renderTxt2Img
|
|
from .image import shrink_and_paste_on_blank
|
|
from .video import write_video
|
|
|
|
|
|
def create_zoom(
|
|
common_prompt_pre,
|
|
prompts_array,
|
|
common_prompt_suf,
|
|
negative_prompt,
|
|
num_outpainting_steps,
|
|
guidance_scale,
|
|
num_inference_steps,
|
|
custom_init_image,
|
|
custom_exit_image,
|
|
video_frame_rate,
|
|
video_zoom_mode,
|
|
video_start_frame_dupe_amount,
|
|
video_last_frame_dupe_amount,
|
|
inpainting_denoising_strength,
|
|
inpainting_mask_blur,
|
|
inpainting_fill_mode,
|
|
inpainting_full_res,
|
|
inpainting_padding,
|
|
zoom_speed,
|
|
seed,
|
|
outputsizeW,
|
|
outputsizeH,
|
|
batchcount,
|
|
sampler,
|
|
upscale_do,
|
|
upscaler_name,
|
|
upscale_by,
|
|
progress=None,
|
|
):
|
|
for i in range(batchcount):
|
|
print(f"Batch {i+1}/{batchcount}")
|
|
result = create_zoom_single(
|
|
common_prompt_pre,
|
|
prompts_array,
|
|
common_prompt_suf,
|
|
negative_prompt,
|
|
num_outpainting_steps,
|
|
guidance_scale,
|
|
num_inference_steps,
|
|
custom_init_image,
|
|
custom_exit_image,
|
|
video_frame_rate,
|
|
video_zoom_mode,
|
|
video_start_frame_dupe_amount,
|
|
video_last_frame_dupe_amount,
|
|
inpainting_denoising_strength,
|
|
inpainting_mask_blur,
|
|
inpainting_fill_mode,
|
|
inpainting_full_res,
|
|
inpainting_padding,
|
|
zoom_speed,
|
|
seed,
|
|
outputsizeW,
|
|
outputsizeH,
|
|
sampler,
|
|
upscale_do,
|
|
upscaler_name,
|
|
upscale_by,
|
|
progress,
|
|
)
|
|
return result
|
|
|
|
|
|
def prepare_output_path():
|
|
isCollect = shared.opts.data.get("infzoom_collectAllResources", False)
|
|
output_path = shared.opts.data.get("infzoom_outpath", "output")
|
|
|
|
save_path = os.path.join(
|
|
output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms")
|
|
)
|
|
|
|
if isCollect:
|
|
save_path = os.path.join(save_path, "iz_collect" + str(int(time.time())))
|
|
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path)
|
|
|
|
video_filename = os.path.join(
|
|
save_path, "infinite_zoom_" + str(int(time.time())) + ".mp4"
|
|
)
|
|
|
|
return {
|
|
"isCollect": isCollect,
|
|
"save_path": save_path,
|
|
"video_filename": video_filename,
|
|
}
|
|
|
|
|
|
def save2Collect(img, out_config, name):
|
|
if out_config["isCollect"]:
|
|
img.save(f'{out_config["save_path"]}/{name}.png')
|
|
|
|
|
|
def frame2Collect(all_frames, out_config):
|
|
save2Collect(all_frames[-1], out_config, f"frame_{len(all_frames)}.png")
|
|
|
|
|
|
def frames2Collect(all_frames, out_config):
|
|
for i, f in enumerate(all_frames):
|
|
save2Collect(f, out_config, f"frame_{i}.png")
|
|
|
|
|
|
def crop_inner_image(outpainted_img, width_offset, height_offset):
|
|
width, height = outpainted_img.size
|
|
|
|
center_x, center_y = int(width / 2), int(height / 2)
|
|
|
|
# Crop the image to the center
|
|
cropped_img = outpainted_img.crop(
|
|
(
|
|
center_x - width_offset,
|
|
center_y - height_offset,
|
|
center_x + width_offset,
|
|
center_y + height_offset,
|
|
)
|
|
)
|
|
prev_step_img = cropped_img.resize((width, height), resample=Image.LANCZOS)
|
|
# resized_img = resized_img.filter(ImageFilter.SHARPEN)
|
|
|
|
return prev_step_img
|
|
|
|
|
|
def create_zoom_single(
|
|
common_prompt_pre,
|
|
prompts_array,
|
|
common_prompt_suf,
|
|
negative_prompt,
|
|
num_outpainting_steps,
|
|
guidance_scale,
|
|
num_inference_steps,
|
|
custom_init_image,
|
|
custom_exit_image,
|
|
video_frame_rate,
|
|
video_zoom_mode,
|
|
video_start_frame_dupe_amount,
|
|
video_last_frame_dupe_amount,
|
|
inpainting_denoising_strength,
|
|
inpainting_mask_blur,
|
|
inpainting_fill_mode,
|
|
inpainting_full_res,
|
|
inpainting_padding,
|
|
zoom_speed,
|
|
seed,
|
|
outputsizeW,
|
|
outputsizeH,
|
|
sampler,
|
|
upscale_do,
|
|
upscaler_name,
|
|
upscale_by,
|
|
progress=None,
|
|
):
|
|
# try:
|
|
# if gr.Progress() is not None:
|
|
# progress = gr.Progress()
|
|
# progress(0, desc="Preparing Initial Image")
|
|
# except Exception:
|
|
# pass
|
|
fix_env_Path_ffprobe()
|
|
out_config = prepare_output_path()
|
|
|
|
prompts = {}
|
|
|
|
for x in prompts_array:
|
|
try:
|
|
key = int(x[0])
|
|
value = str(x[1])
|
|
prompts[key] = value
|
|
except ValueError:
|
|
pass
|
|
|
|
assert len(prompts_array) > 0, "prompts is empty"
|
|
|
|
width = closest_upper_divisible_by_eight(outputsizeW)
|
|
height = closest_upper_divisible_by_eight(outputsizeH)
|
|
|
|
current_image = Image.new(mode="RGBA", size=(width, height))
|
|
mask_image = np.array(current_image)[:, :, 3]
|
|
mask_image = Image.fromarray(255 - mask_image).convert("RGB")
|
|
current_image = current_image.convert("RGB")
|
|
current_seed = seed
|
|
|
|
if custom_init_image:
|
|
current_image = custom_init_image.resize(
|
|
(width, height), resample=Image.LANCZOS
|
|
)
|
|
save2Collect(current_image, out_config, f"init_img.png")
|
|
print("using Custom Initial Image")
|
|
|
|
else:
|
|
load_model_from_setting(
|
|
"infzoom_txt2img_model", progress, "Loading Model for txt2img: "
|
|
)
|
|
|
|
pr = prompts[min(k for k in prompts.keys() if k >= 0)]
|
|
processed, newseed = renderTxt2Img(
|
|
f"{common_prompt_pre}\n{pr}\n{common_prompt_suf}".strip(),
|
|
negative_prompt,
|
|
sampler,
|
|
num_inference_steps,
|
|
guidance_scale,
|
|
current_seed,
|
|
width,
|
|
height,
|
|
)
|
|
if len(processed.images) > 0:
|
|
current_image = processed.images[0]
|
|
save2Collect(current_image, out_config, f"txt2img.png")
|
|
current_seed = newseed
|
|
|
|
mask_width = math.trunc(width / 4) # was initially 512px => 128px
|
|
mask_height = math.trunc(height / 4) # was initially 512px => 128px
|
|
|
|
num_interpol_frames = round(video_frame_rate * zoom_speed)
|
|
|
|
all_frames = []
|
|
|
|
if upscale_do and progress:
|
|
progress(0, desc="upscaling inital image")
|
|
|
|
load_model_from_setting(
|
|
"infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: "
|
|
)
|
|
|
|
for i in range(num_outpainting_steps):
|
|
print_out = (
|
|
"Outpaint step: "
|
|
+ str(i + 1)
|
|
+ " / "
|
|
+ str(num_outpainting_steps)
|
|
+ " Seed: "
|
|
+ str(current_seed)
|
|
)
|
|
print(print_out)
|
|
if progress:
|
|
progress(((i + 1) / num_outpainting_steps), desc=print_out)
|
|
|
|
prev_image_fix = current_image
|
|
save2Collect(prev_image_fix, out_config, f"prev_image_fix_{i}.png")
|
|
|
|
prev_image = shrink_and_paste_on_blank(current_image, mask_width, mask_height)
|
|
save2Collect(prev_image, out_config, f"prev_image_{i}.png")
|
|
|
|
current_image = prev_image
|
|
|
|
# create mask (black image with white mask_width width edges)
|
|
mask_image = np.array(current_image)[:, :, 3]
|
|
mask_image = Image.fromarray(255 - mask_image).convert("RGB")
|
|
save2Collect(mask_image, out_config, f"mask_image_{i}.png")
|
|
|
|
# inpainting step
|
|
current_image = current_image.convert("RGB")
|
|
|
|
if custom_exit_image and ((i + 1) == num_outpainting_steps):
|
|
current_image = custom_exit_image.resize(
|
|
(width, height), resample=Image.LANCZOS
|
|
)
|
|
print("using Custom Exit Image")
|
|
save2Collect(current_image, out_config, f"exit_img.png")
|
|
else:
|
|
pr = prompts[max(k for k in prompts.keys() if k <= i)]
|
|
processed, newseed = renderImg2Img(
|
|
f"{common_prompt_pre}\n{pr}\n{common_prompt_suf}".strip(),
|
|
negative_prompt,
|
|
sampler,
|
|
num_inference_steps,
|
|
guidance_scale,
|
|
current_seed,
|
|
width,
|
|
height,
|
|
current_image,
|
|
mask_image,
|
|
inpainting_denoising_strength,
|
|
inpainting_mask_blur,
|
|
inpainting_fill_mode,
|
|
inpainting_full_res,
|
|
inpainting_padding,
|
|
)
|
|
if len(processed.images) > 0:
|
|
current_image = processed.images[0]
|
|
current_seed = newseed
|
|
if len(processed.images) > 0:
|
|
# current_image.paste(prev_image, mask=prev_image)
|
|
save2Collect(current_image, out_config, f"curr_prev_paste_{i}.png")
|
|
if True or i > 0:
|
|
correction_crop = crop_inner_image(current_image, mask_width, mask_height)
|
|
prev_image_fix = correction_crop
|
|
# paste_x = (current_image.width - prev_image.width) // 2
|
|
# paste_y = (current_image.height - prev_image.height) // 2
|
|
# current_image.paste(prev_image, (paste_x, paste_y), mask=prev_image)
|
|
# replace the prev frame with current croped
|
|
all_frames.append(
|
|
do_upscaleImg(
|
|
prev_image_fix.convert("RGB"), upscale_do, upscaler_name, upscale_by
|
|
)
|
|
if upscale_do
|
|
else prev_image_fix.convert("RGB")
|
|
)
|
|
# interpolation steps between 2 inpainted images (=sequential zoom and crop)
|
|
for j in range(num_interpol_frames - 1):
|
|
interpol_image = current_image
|
|
save2Collect(interpol_image, out_config, f"interpol_img_{i}_{j}].png")
|
|
|
|
interpol_width = round(
|
|
(
|
|
1
|
|
- (1 - 2 * mask_width / width)
|
|
** (1 - (j + 1) / num_interpol_frames)
|
|
)
|
|
* width
|
|
/ 2
|
|
)
|
|
|
|
interpol_height = round(
|
|
(
|
|
1
|
|
- (1 - 2 * mask_height / height)
|
|
** (1 - (j + 1) / num_interpol_frames)
|
|
)
|
|
* height
|
|
/ 2
|
|
)
|
|
|
|
interpol_image = interpol_image.crop(
|
|
(
|
|
interpol_width,
|
|
interpol_height,
|
|
width - interpol_width,
|
|
height - interpol_height,
|
|
)
|
|
)
|
|
# save2Collect(interpol_image, out_config, f"interpol_crop_{i}_{j}.png")
|
|
|
|
interpol_image = interpol_image.resize((width, height))
|
|
# save2Collect(interpol_image, out_config, f"interpol_resize_{i}_{j}.png")
|
|
|
|
# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
|
|
interpol_width2 = round(
|
|
(1 - (width - 2 * mask_width) / (width - 2 * interpol_width))
|
|
/ 2
|
|
* width
|
|
)
|
|
|
|
interpol_height2 = round(
|
|
(1 - (height - 2 * mask_height) / (height - 2 * interpol_height))
|
|
/ 2
|
|
* height
|
|
)
|
|
|
|
prev_image_fix_crop = shrink_and_paste_on_blank(
|
|
prev_image_fix, interpol_width2, interpol_height2
|
|
)
|
|
# save2Collect(prev_image_fix, out_config, f"prev_image_fix_crop_{i}_{j}.png")
|
|
|
|
interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
|
|
# save2Collect(interpol_image, out_config, f"interpol_prevcrop_{i}_{j}.png")
|
|
|
|
if upscale_do and progress:
|
|
progress(((i + 1) / num_outpainting_steps), desc="upscaling interpol")
|
|
|
|
all_frames.append(
|
|
do_upscaleImg(interpol_image, upscale_do, upscaler_name, upscale_by)
|
|
if upscale_do
|
|
else interpol_image
|
|
)
|
|
|
|
if upscale_do and progress:
|
|
progress(((i + 1) / num_outpainting_steps), desc="upscaling current")
|
|
|
|
all_frames.append(
|
|
do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
|
|
if upscale_do
|
|
else current_image
|
|
)
|
|
|
|
frames2Collect(all_frames, out_config)
|
|
|
|
write_video(
|
|
out_config["video_filename"],
|
|
all_frames,
|
|
video_frame_rate,
|
|
video_zoom_mode,
|
|
int(video_start_frame_dupe_amount),
|
|
int(video_last_frame_dupe_amount),
|
|
)
|
|
|
|
return (
|
|
out_config["video_filename"],
|
|
processed.images,
|
|
processed.js(),
|
|
plaintext_to_html(processed.info),
|
|
plaintext_to_html(""),
|
|
)
|