| # 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 |