# Beta V0.72
import numpy as np
from tqdm import trange
from PIL import Image, ImageSequence, ImageDraw
import math
import modules.scripts as scripts
import gradio as gr
from modules import processing, shared, sd_samplers, images
from modules.processing import Processed
from modules.sd_samplers import samplers
from modules.shared import opts, cmd_opts, state
from modules import deepbooru
class Script(scripts.Script):
def title(self):
return "(Beta) Multi-frame Video rendering - V0.72"
def show(self, is_img2img):
return is_img2img
def ui(self, is_img2img):
first_denoise = gr.Slider(minimum=0, maximum=1, step=0.05, label='Initial Denoise Strength', value=1, elem_id=self.elem_id("first_denoise"))
append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None")
third_frame_image = gr.Dropdown(label="Third Frame Image", choices=["None", "FirstGen", "GuideImg", "Historical"], value="None")
reference_imgs = gr.UploadButton(label="Upload Guide Frames", file_types = ['.png','.jpg','.jpeg'], live=True, file_count = "multiple")
color_correction_enabled = gr.Checkbox(label="Enable Color Correction", value=False, elem_id=self.elem_id("color_correction_enabled"))
unfreeze_seed = gr.Checkbox(label="Unfreeze Seed", value=False, elem_id=self.elem_id("unfreeze_seed"))
loopback_source = gr.Dropdown(label="Loopback Source", choices=["PreviousFrame", "InputFrame","FirstGen"], value="PreviousFrame")
return [append_interrogation, reference_imgs, first_denoise, third_frame_image, color_correction_enabled, unfreeze_seed, loopback_source]
def run(self, p, append_interrogation, reference_imgs, first_denoise, third_frame_image, color_correction_enabled, unfreeze_seed, loopback_source):
freeze_seed = not unfreeze_seed
loops = len(reference_imgs)
processing.fix_seed(p)
batch_count = p.n_iter
p.batch_size = 1
p.n_iter = 1
output_images, info = None, None
initial_seed = None
initial_info = None
initial_width = p.width
initial_img = p.init_images[0]
grids = []
all_images = []
original_init_image = p.init_images
original_prompt = p.prompt
original_denoise = p.denoising_strength
state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
for n in range(batch_count):
history = []
frames = []
third_image = None
third_image_index = 0
frame_color_correction = None
# Reset to original init image at the start of each batch
p.init_images = original_init_image
p.width = initial_width
for i in range(loops):
p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
p.control_net_input_image = Image.open(reference_imgs[i].name).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS)
if(i > 0):
loopback_image = p.init_images[0]
if loopback_source == "InputFrame":
loopback_image = p.control_net_input_image
elif loopback_source == "FirstGen":
loopback_image = history[0]
if third_frame_image != "None" and i > 1:
p.width = initial_width * 3
img = Image.new("RGB", (initial_width*3, p.height))
img.paste(p.init_images[0], (0, 0))
# img.paste(p.init_images[0], (initial_width, 0))
img.paste(loopback_image, (initial_width, 0))
img.paste(third_image, (initial_width*2, 0))
p.init_images = [img]
if color_correction_enabled:
p.color_corrections = [processing.setup_color_correction(img)]
msk = Image.new("RGB", (initial_width*3, p.height))
msk.paste(Image.open(reference_imgs[i-1].name).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS), (0, 0))
msk.paste(p.control_net_input_image, (initial_width, 0))
msk.paste(Image.open(reference_imgs[third_image_index].name).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS), (initial_width*2, 0))
p.control_net_input_image = msk
latent_mask = Image.new("RGB", (initial_width*3, p.height), "black")
latent_draw = ImageDraw.Draw(latent_mask)
latent_draw.rectangle((initial_width,0,initial_width*2,p.height), fill="white")
p.image_mask = latent_mask
p.denoising_strength = original_denoise
else:
p.width = initial_width * 2
img = Image.new("RGB", (initial_width*2, p.height))
img.paste(p.init_images[0], (0, 0))
# img.paste(p.init_images[0], (initial_width, 0))
img.paste(loopback_image, (initial_width, 0))
p.init_images = [img]
if color_correction_enabled:
p.color_corrections = [processing.setup_color_correction(img)]
msk = Image.new("RGB", (initial_width*2, p.height))
msk.paste(Image.open(reference_imgs[i-1].name).convert("RGB").resize((initial_width, p.height), Image.ANTIALIAS), (0, 0))
msk.paste(p.control_net_input_image, (initial_width, 0))
p.control_net_input_image = msk
frames.append(msk)
# latent_mask = Image.new("RGB", (initial_width*2, p.height), "white")
# latent_draw = ImageDraw.Draw(latent_mask)
# latent_draw.rectangle((0,0,initial_width,p.height), fill="black")
latent_mask = Image.new("RGB", (initial_width*2, p.height), "black")
latent_draw = ImageDraw.Draw(latent_mask)
latent_draw.rectangle((initial_width,0,initial_width*2,p.height), fill="white")
# p.latent_mask = latent_mask
p.image_mask = latent_mask
p.denoising_strength = original_denoise
else:
latent_mask = Image.new("RGB", (initial_width, p.height), "white")
# p.latent_mask = latent_mask
p.image_mask = latent_mask
p.denoising_strength = first_denoise
p.control_net_input_image = p.control_net_input_image.resize((initial_width, p.height))
frames.append(p.control_net_input_image)
if append_interrogation != "None":
p.prompt = original_prompt + ", " if original_prompt != "" else ""
if append_interrogation == "CLIP":
p.prompt += shared.interrogator.interrogate(p.init_images[0])
elif append_interrogation == "DeepBooru":
p.prompt += deepbooru.model.tag(p.init_images[0])
state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"
processed = processing.process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
init_img = processed.images[0]
if(i > 0):
init_img = init_img.crop((initial_width, 0, initial_width*2, p.height))
if third_frame_image != "None":
if third_frame_image == "FirstGen" and i == 0:
third_image = init_img
third_image_index = 0
elif third_frame_image == "GuideImg" and i == 0:
third_image = original_init_image[0]
third_image_index = 0
elif third_frame_image == "Historical":
third_image = processed.images[0].crop((0, 0, initial_width, p.height))
third_image_index = (i-1)
p.init_images = [init_img]
if(freeze_seed):
p.seed = processed.seed
else:
p.seed = processed.seed + 1
history.append(init_img)
if opts.samples_save:
images.save_image(init_img, p.outpath_samples, "Frame", p.seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
frames.append(processed.images[0])
grid = images.image_grid(history, rows=1)
if opts.grid_save:
images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
grids.append(grid)
# all_images += history + frames
all_images += history
p.seed = p.seed+1
if opts.return_grid:
all_images = grids + all_images
processed = Processed(p, all_images, initial_seed, initial_info)
return processed