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