Datasets:
spinosorus
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html.stablediffusion

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p.n_iter = 1
p.batch_size = 1
p.do_not_save_grid = True
if opts.img2img_color_correction:
p.color_corrections = initial_color_corrections
wave_progress = float(1)/(float(frames_per_wave - 1))*float(((float(i)%float(frames_per_wave)) + ((float(1)/float(180))*denoising_strength_change_offset)))
print(wave_progress)
new_prompt = re.sub(wave_completed_regex, lambda x: set_weights(x, wave_progress), raw_prompt)
new_prompt = re.sub(wave_remaining_regex, lambda x: set_weights(x, 1 - wave_progress), new_prompt)
p.prompt = new_prompt
print(new_prompt)
denoising_strength_change_rate = 180/frames_per_wave
cos = abs(math.cos(math.radians(i*denoising_strength_change_rate + denoising_strength_change_offset)))
p.denoising_strength = initial_denoising_strength + denoising_strength_change_amplitude - (cos * denoising_strength_change_amplitude)
state.job += f"Iteration {i + 1}/{frames}, batch {n + 1}/{batch_count}. Denoising Strength: {p.denoising_strength}"
processed = processing.process_images(p)
if initial_seed is None:
initial_seed = processed.seed
initial_info = processed.info
init_img = processed.images[0]
p.init_images = [init_img]
if seed_state == "adding":
p.seed = processed.seed + 1
elif seed_state == "subtracting":
p.seed = processed.seed - 1
image_number = int(initial_image_number + i)
images.save_image(init_img, p.outpath_samples, "", processed.seed, processed.prompt, forced_filename=str(image_number))
history.append(init_img)
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
if opts.return_grid:
all_images = grids + all_images
if save_video:
input_pattern = os.path.join(loopback_wave_images_path, "%d.png")
encode_video(input_pattern, initial_image_number, loopback_wave_images_path, video_fps, video_quality, video_encoding, segment_video, video_segment_duration, ffmpeg_path)
processed = Processed(p, all_images, initial_seed, initial_info)
return processed
# 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)