import numpy as np from tqdm import trange from PIL import Image, ImageEnhance 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 copy import deepcopy from math import sin, pi class Script(scripts.Script): def title(self): return "Advanced loopback blend" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): loops = gr.Number(minimum=1, step=1, label='Loops', value=4) use_first_image_colors = gr.Checkbox(label='Use first image colors (custom color correction) ', value=False) denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor (overridden if proportional used)', value=1) with gr.Row(): zoom_level = gr.Slider(minimum=0, maximum=50, step=1, label='Zoom level ', value=0) zoom_blend = gr.Checkbox(label='Blend 50/50 with original when zoomed. Doesn\'t work with sine variation.', value=False) # zoom_refresh = gr.Slider(minimum=0, maximum=50, step=1, label='Refresh base image for blend every n iterations', value=0) # direction_x = gr.Slider(minimum=-0.1, maximum=0.1, step=0.01, label='Direction X', value=0) # direction_y = gr.Slider(minimum=-0.1, maximum=0.1, step=0.01, label='Direction Y', value=0) with gr.Row(): denoising_strength_first_image = gr.Number(minimum=0, step=1, label='Denoising strength start ', value=0) denoising_strength_last_image = gr.Number(minimum=0, step=1, label='Denoising strength end ', value=4) denoising_strength_min = gr.Slider(minimum=0.1, maximum=1, step=0.01, label='Denoising strength proportional change starting value ', value=0.1) denoising_strength_max = gr.Slider(minimum=0.1, maximum=1, step=0.01, label='Denoising strength proportional change ending value (0.1 = disabled) ', value=0.1) cfg_scale_min = gr.Slider(minimum=0.1, maximum=30, step=0.1, label='CFG scale proportional change starting value ', value=0.1) cfg_scale_max = gr.Slider(minimum=0.1, maximum=30, step=0.1, label='CFG scale proportional change ending value (0.1 = disabled) ', value=0.1) saturation_per_image = gr.Slider(minimum=0.99, maximum=1.01, step=0.001, label='Saturation enhancement per image ', value=1) with gr.Row(): use_sine_variation_dns = gr.Checkbox(label='Use sine denoising strength variation (CFG will be scaled with it if the slider is > 0.1)', value=True) phase_diff_denoising = gr.Slider(minimum=0, maximum=1, step=0.05, label='Phase difference', value=0) amplify_sine_variation_denoise = gr.Slider(minimum=1, maximum=10, step=1, label='Denoising strength exponentiation ', value=1) with gr.Row(): use_sine_variation_zoom = gr.Checkbox(label='Use sine zoom variation', value=False) phase_diff_zoom = gr.Slider(minimum=0, maximum=1, step=0.05, label='Phase difference', value=0) amplify_sine_variation_zoom = gr.Slider(minimum=1, maximum=10, step=1, label='Zoom exponentiation ', value=1) with gr.Row(): use_multi_prompts = gr.Checkbox(label='Use multiple prompts', value=False) same_seed_per_prompt = gr.Checkbox(label='Same seed per prompt', value=False) same_seed_always = gr.Checkbox(label='Same seed for everything', value=False) same_init_image = gr.Checkbox(label='Original init image for everything', value=False) multi_prompts = gr.Textbox(label="Multiple prompts : 1 line positive, 1 line negative, leave a blank line for no negative", lines=2, max_lines=2000) return [ loops, denoising_strength_change_factor, zoom_level, zoom_blend, # zoom_refresh, # direction_x, # direction_y, denoising_strength_first_image, denoising_strength_last_image, denoising_strength_min, denoising_strength_max, cfg_scale_min, cfg_scale_max, saturation_per_image, use_first_image_colors, use_sine_variation_dns, use_sine_variation_zoom, phase_diff_zoom, use_multi_prompts, multi_prompts, amplify_sine_variation_zoom, same_seed_per_prompt, phase_diff_denoising, amplify_sine_variation_denoise, same_seed_always, same_init_image ] def zoom_into(self, img, zoom): w, h = img.size img = img.crop((zoom,zoom,w-zoom,h-zoom)) return img.resize((w, h), Image.LANCZOS) def run(self, p, loops, denoising_strength_change_factor, zoom_level, zoom_blend, # zoom_refresh, # direction_x, # direction_y, denoising_strength_first_image, denoising_strength_last_image, denoising_strength_min, denoising_strength_max, cfg_scale_min, cfg_scale_max, saturation_per_image, use_first_image_colors, use_sine_variation_dns, use_sine_variation_zoom, phase_diff_zoom, use_multi_prompts, multi_prompts, amplify_sine_variation_zoom, same_seed_per_prompt, phase_diff_denoising, amplify_sine_variation_denoise, same_seed_always, same_init_image ): ppos = [] pneg = [] if use_multi_prompts : prompts_list = multi_prompts.splitlines() oddeven = lambda x: 1 if x%2==0 else 0 for x in range(len(prompts_list)) : if oddeven(x): ppos.append(prompts_list[x]) else: pneg.append(prompts_list[x]) if len(pneg) < len(ppos) : pneg.append("") def remap_range(value, minIn, MaxIn, minOut, maxOut): if value > MaxIn: value = MaxIn; if value < minIn: value = minIn; finalValue = ((value - minIn) / (MaxIn - minIn)) * (maxOut - minOut) + minOut; return finalValue; def get_sin_steps(i,amplify,phase_diff=0): i -= denoising_strength_first_image range = (denoising_strength_last_image - denoising_strength_first_image) x = i % (range) y = remap_range(x,0,range,0,1) y = y ** amplify z = sin((y+phase_diff/2)*pi) return z processing.fix_seed(p) batch_count = p.n_iter p.extra_generation_params = { "Denoising strength change factor": denoising_strength_change_factor, 'Denoising strength proportional change start image':denoising_strength_first_image, 'Denoising strength proportional change end image':denoising_strength_last_image, 'Denoising strength proportional change starting value':denoising_strength_min, 'Denoising strength proportional change ending value':denoising_strength_max, 'CFG min':cfg_scale_min, 'CFG max':cfg_scale_max, 'use first image colors': use_first_image_colors, 'Saturation enhancement per image':saturation_per_image, 'Zoom level':zoom_level, } p.batch_size = 1 p.n_iter = 1 output_images, info = None, None initial_seed = None initial_info = None grids = [] all_images = [] state.job_count = loops * batch_count original_image = p.init_images[0].copy() original_image_for_zoom = p.init_images[0].copy() if opts.img2img_color_correction: p.color_corrections = [processing.setup_color_correction(p.init_images[0])] for n in range(batch_count): history = [] multi_prompts_index = 0 loops = round(loops) for i in range(loops): p.n_iter = 1 p.batch_size = 1 p.do_not_save_grid = True if use_multi_prompts : image_range = (denoising_strength_last_image - denoising_strength_first_image) il = i % (image_range) if i == 0: p.prompt = ppos[multi_prompts_index] p.negative_prompt = pneg[multi_prompts_index] print("Prompt :",p.prompt) print("Negative prompt :",p.negative_prompt) if il == 0 and i > 0: multi_prompts_index+=1 try: if same_seed_per_prompt: if not same_seed_always: p.subseed = p.subseed + 1 if p.subseed_strength > 0 else p.subseed p.seed = p.seed + 1 if p.subseed_strength == 0 else p.seed p.prompt = ppos[multi_prompts_index] p.negative_prompt = pneg[multi_prompts_index] except Exception as e: multi_prompts_index = 0 if same_seed_per_prompt: if not same_seed_always: p.subseed = p.subseed + 1 if p.subseed_strength > 0 else p.subseed p.seed = p.seed + 1 if p.subseed_strength == 0 else p.seed p.prompt = ppos[multi_prompts_index] p.negative_prompt = pneg[multi_prompts_index] # print("Prompt :",p.prompt) # print("Negative prompt :",p.negative_prompt) if use_first_image_colors: p.color_corrections = [processing.setup_color_correction(original_image)] state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}" if denoising_strength_max > 0.1 : if use_sine_variation_dns : ds = remap_range(get_sin_steps(i,amplify_sine_variation_denoise,phase_diff_denoising),0,1,denoising_strength_min,denoising_strength_max) else: ds = remap_range(i+1,denoising_strength_first_image,denoising_strength_last_image,denoising_strength_min,denoising_strength_max) p.denoising_strength = round(ds,3) print("Denoising strength : "+str(p.denoising_strength)) if cfg_scale_max > 0.1 : if use_sine_variation_dns : cfgs = remap_range(get_sin_steps(i,amplify_sine_variation_denoise,phase_diff_denoising),0,1,cfg_scale_min,cfg_scale_max) else: cfgs = remap_range(i+1,denoising_strength_first_image,denoising_strength_last_image,cfg_scale_min,cfg_scale_max) p.cfg_scale = round(cfgs,2) print("CFG scale : "+str(p.cfg_scale)) processed = processing.process_images(p) if zoom_level > 0: if use_sine_variation_zoom : if loops >= denoising_strength_first_image : z = remap_range(get_sin_steps(i,amplify_sine_variation_zoom,phase_diff_zoom),0,1,1,zoom_level) processed.images[0] = self.zoom_into(processed.images[0], z) print("Zoom level :",z) else: processed.images[0] = self.zoom_into(processed.images[0], zoom_level) if zoom_blend: # if zoom_refresh > 0 and i%zoom_refresh == 0: # original_image_for_zoom = processed.images[0].copy() original_image_zoomed = self.zoom_into(original_image_for_zoom.copy(), zoom_level*(i+1)) processed.images[0] = Image.blend(original_image_zoomed.copy().convert('RGB').resize(processed.images[0].size, Image.LANCZOS), processed.images[0].copy().convert('RGB'), alpha=0.5) if initial_seed is None: initial_seed = processed.seed initial_info = processed.info if not same_init_image : init_img = processed.images[0] else: init_img = original_image if saturation_per_image != 1 : init_img = ImageEnhance.Color(init_img).enhance(saturation_per_image) p.init_images = [init_img] if not same_seed_per_prompt: if not same_seed_always: p.subseed = p.subseed + 1 if p.subseed_strength > 0 else p.subseed p.seed = p.seed + 1 if p.subseed_strength == 0 else p.seed p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1) history.append(processed.images[0]) if state.interrupted: break 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 processed = Processed(p, all_images, initial_seed, initial_info) return processed