Delete scripts

scripts/loopback_wave.py

@@ -1 +0,0 @@
-{"payload":{"allShortcutsEnabled":true,"fileTree":{"":{"items":[{"name":".github","path":".github","contentType":"directory"},{"name":"ContorlNet_I2I_sequence_toyxyz_V2.py","path":"ContorlNet_I2I_sequence_toyxyz_V2.py","contentType":"file"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"loopback_wave.py","path":"loopback_wave.py","contentType":"file"},{"name":"multi_frame_render.py","path":"multi_frame_render.py","contentType":"file"},{"name":"run_n_times.py","path":"run_n_times.py","contentType":"file"}],"totalCount":7}},"fileTreeProcessingTime":2.108259,"foldersToFetch":[],"reducedMotionEnabled":"system","repo":{"id":589395595,"defaultBranch":"main","name":"stable-diffusion-webui-scripts","ownerLogin":"camenduru","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2023-01-16T09:04:31.000+07:00","ownerAvatar":"https://avatars.githubusercontent.com/u/54370274?v=4","public":true,"private":false,"isOrgOwned":false},"refInfo":{"name":"main","listCacheKey":"v0:1681301905.0","canEdit":true,"refType":"branch","currentOid":"d16ee8b03f5fae8597aa612a99200206d7e0b7a3"},"path":"loopback_wave.py","currentUser":{"id":137641888,"login":"bstungnguyen","userEmail":"[email protected]"},"blob":{"rawLines":["import os\r","import platform\r","import numpy as np\r","from tqdm import trange\r","import math\r","import subprocess as sp\r","import string\r","import random\r","from functools import reduce\r","import re\r","\r","import modules.scripts as scripts\r","import gradio as gr\r","\r","from modules import processing, shared, sd_samplers, images\r","from modules.processing import Processed\r","from modules.sd_samplers import samplers\r","from modules.shared import opts, cmd_opts, state\r","import subprocess\r","\r","\r","wave_completed_regex = r'@wave_completed\\(([\\-]?[0-9]*\\.?[0-9]+), ?([\\-]?[0-9]*\\.?[0-9]+)\\)'\r","wave_remaining_regex = r'@wave_remaining\\(([\\-]?[0-9]*\\.?[0-9]+), ?([\\-]?[0-9]*\\.?[0-9]+)\\)'\r","\r","def run_cmd(cmd):\r","    cmd = list(map(lambda arg: str(arg), cmd))\r","    print(\"Executing %s\" % \" \".join(cmd))\r","    popen_params = {\"stdout\": sp.DEVNULL, \"stderr\": sp.PIPE, \"stdin\": sp.DEVNULL}\r","\r","    if os.name == \"nt\":\r","       popen_params[\"creationflags\"] = 0x08000000\r","\r","    proc = sp.Popen(cmd, **popen_params)\r","    out, err = proc.communicate()  # proc.wait()\r","    proc.stderr.close()\r","\r","    if proc.returncode:\r","        raise IOError(err.decode(\"utf8\"))\r","\r","    del proc\r","\r","def encode_video(input_pattern, starting_number, output_dir, fps, quality, encoding, create_segments, segment_duration, ffmpeg_path):\r","    two_pass = (encoding == \"VP9 (webm)\")\r","    alpha_channel = (\"webm\" in encoding)\r","    suffix = \"webm\" if \"webm\" in encoding else \"mp4\"\r","    output_location = output_dir + f\".{suffix}\"\r","\r","    encoding_lib = {\r","      \"VP9 (webm)\": \"libvpx-vp9\",\r","      \"VP8 (webm)\": \"libvpx\",\r","      \"H.264 (mp4)\": \"libx264\",\r","      \"H.265 (mp4)\": \"libx265\",\r","    }[encoding]\r","\r","    args = [\r","        \"-framerate\", fps,\r","        \"-start_number\", int(starting_number),\r","        \"-i\", input_pattern, \r","        \"-c:v\", encoding_lib, \r","        \"-b:v\",\"0\", \r","        \"-crf\", quality,\r","        ]\r","\r","    if encoding_lib == \"libvpx-vp9\":\r","        args += [\"-pix_fmt\", \"yuva420p\"]\r","        \r","    if(ffmpeg_path == \"\"):\r","        ffmpeg_path = \"ffmpeg\"\r","        if(platform.system == \"Windows\"):\r","            ffmpeg_path += \".exe\"\r","\r","    print(\"\\n\\n\")\r","    if two_pass:\r","        first_pass_args = args + [\r","            \"-pass\", \"1\",\r","            \"-an\", \r","            \"-f\", \"null\",\r","            os.devnull\r","        ]\r","\r","        second_pass_args = args + [\r","            \"-pass\", \"2\",\r","            output_location\r","        ]\r","\r","        print(\"Running first pass ffmpeg encoding\")       \r","\r","        run_cmd([ffmpeg_path] + first_pass_args)\r","        print(\"Running second pass ffmpeg encoding.  This could take awhile...\")\r","        run_cmd([ffmpeg_path] + second_pass_args)\r","    else:\r","        print(\"Running ffmpeg encoding.  This could take awhile...\")\r","        run_cmd([ffmpeg_path] + args + [output_location])\r","\r","    if(create_segments):\r","      print(\"Segmenting video\")\r","      run_cmd([ffmpeg_path] + [\r","          \"-i\", output_location,\r","          \"-f\", \"segment\",\r","          \"-segment_time\", segment_duration,\r","          \"-vcodec\", \"copy\",\r","          \"-acodec\", \"copy\",\r","          f\"{output_dir}.%d.{suffix}\"\r","      ])\r","      \r","def set_weights(match_obj, wave_progress):\r","  weight_0 = 0\r","  weight_1 = 0\r","  if match_obj.group(1) is not None:\r","    weight_0 = float(match_obj.group(1))\r","  if match_obj.group(2) is not None:\r","    weight_1 = float(match_obj.group(2))\r","    \r","  max_weight = max(weight_0, weight_1)\r","  min_weight = min(weight_0, weight_1)\r","  \r","  weight_range = max_weight - min_weight\r","  weight = min_weight + weight_range * wave_progress\r","  return str(weight)\r","\r","\r","class Script(scripts.Script):\r","    def title(self):\r","        return \"Loopback Wave V1.4.1\"\r","\r","    def show(self, is_img2img):\r","        return is_img2img\r","\r","    def ui(self, is_img2img):\r","        frames = gr.Slider(minimum=1, maximum=2048, step=1, label='Frames', value=100)\r","        frames_per_wave = gr.Slider(minimum=0, maximum=120, step=1, label='Frames Per Wave', value=20)\r","        denoising_strength_change_amplitude = gr.Slider(minimum=0, maximum=1, step=0.01, label='Max additional denoise', value=0.6)\r","        denoising_strength_change_offset = gr.Number(minimum=0, maximum=180, step=1, label='Wave offset (ignore this if you don\\'t know what it means)', value=0)\r","        initial_image_number = gr.Number(minimum=0, label='Initial generated image number', value=0)\r","\r","        save_prompts = gr.Checkbox(label='Save prompts as text file', value=True)\r","        prompts = gr.Textbox(label=\"Prompt Changes\", lines=5, value=\"\")\r","\r","        save_video = gr.Checkbox(label='Save results as video', value=True)\r","        output_dir = gr.Textbox(label=\"Video Name\", lines=1, value=\"\")\r","        video_fps = gr.Slider(minimum=1, maximum=120, step=1, label='Frames per second', value=10)\r","        video_quality = gr.Slider(minimum=0, maximum=60, step=1, label='Video Quality (crf)', value=40)\r","        video_encoding = gr.Dropdown(label='Video encoding', value=\"VP9 (webm)\", choices=[\"VP9 (webm)\", \"VP8 (webm)\", \"H.265 (mp4)\", \"H.264 (mp4)\"])\r","        ffmpeg_path = gr.Textbox(label=\"ffmpeg binary.  Only set this if it fails otherwise.\", lines=1, value=\"\")\r","\r","        segment_video = gr.Checkbox(label='Cut video in to segments', value=True)\r","        video_segment_duration = gr.Slider(minimum=10, maximum=60, step=1, label='Video Segment Duration (seconds)', value=20)\r","\r","\r","        return [frames, denoising_strength_change_amplitude, frames_per_wave, denoising_strength_change_offset,initial_image_number, prompts, save_prompts, save_video, output_dir, video_fps, video_quality, video_encoding, ffmpeg_path, segment_video, video_segment_duration]\r","\r","    def run(self, p, frames, denoising_strength_change_amplitude, frames_per_wave, denoising_strength_change_offset, initial_image_number, prompts: str,save_prompts, save_video, output_dir, video_fps, video_quality, video_encoding, ffmpeg_path, segment_video, video_segment_duration):\r","        processing.fix_seed(p)\r","        batch_count = p.n_iter\r","        p.extra_generation_params = {\r","            \"Max Additional Denoise\": denoising_strength_change_amplitude,\r","            \"Frames per wave\": frames_per_wave,\r","            \"Wave Offset\": denoising_strength_change_offset,\r","        }\r","\r","        # We save them ourselves for the sake of ffmpeg\r","        p.do_not_save_samples = True\r","\r","        changes_dict = {}\r","\r","\r","        p.batch_size = 1\r","        p.n_iter = 1\r","\r","        output_images, info = None, None\r","        initial_seed = None\r","        initial_info = None\r","\r","        grids = []\r","        all_images = []\r","        original_init_image = p.init_images\r","        state.job_count = frames * batch_count\r","\r","        initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]\r","        initial_denoising_strength = p.denoising_strength\r","\r","        if(output_dir==\"\"):\r","            output_dir = str(p.seed)\r","        else:\r","            output_dir = output_dir + \"-\" + str(p.seed)\r","\r","        loopback_wave_path = os.path.join(p.outpath_samples, \"loopback-wave\")\r","        loopback_wave_images_path = os.path.join(loopback_wave_path, output_dir)\r","\r","        os.makedirs(loopback_wave_images_path, exist_ok=True)\r","\r","        p.outpath_samples = loopback_wave_images_path\r","        \r","        prompts = prompts.strip()\r","        \r","        if save_prompts:\r","            with open(loopback_wave_images_path + \"-prompts.txt\", \"w\") as f:\r","                generation_settings = [\r","                  \"Generation Settings\",\r","                  f\"Total Frames: {frames}\",\r","                  f\"Frames Per Wave: {frames_per_wave}\",\r","                  f\"Wave Offset: {denoising_strength_change_offset}\",\r","                  f\"Base Denoising Strength: {initial_denoising_strength}\",\r","                  f\"Max Additional Denoise: {denoising_strength_change_amplitude}\",\r","                  f\"Initial Image Number: {initial_image_number}\",\r","                  \"\",\r","                  \"Video Encoding Settings\",\r","                  f\"Save Video: {save_video}\"\r","                ]\r","                \r","                if save_video:\r","                  generation_settings = generation_settings + [\r","                    f\"Framerate: {video_fps}\",\r","                    f\"Quality: {video_quality}\",\r","                    f\"Encoding: {video_encoding}\",\r","                    f\"Create Segmented Video: {segment_video}\"\r","                  ]\r","                  \r","                  if segment_video:\r","                    generation_settings = generation_settings + [f\"Segment Duration: {video_segment_duration}\"]\r","                 \r","                generation_settings = generation_settings + [\r","                  \"\",\r","                  \"Prompt Details\",\r","                  \"Initial Prompt:\" + p.prompt,\r","                  \"\",\r","                  \"Negative Prompt:\" + p.negative_prompt,\r","                  \"\",\r","                  \"Frame change prompts:\",\r","                  prompts\r","                ]\r","                  \r","\r","\r","                f.write('\\n'.join(generation_settings))\r","\r","        if prompts:\r","            lines = prompts.split(\"\\n\")\r","            for prompt_line in lines:\r","              params = prompt_line.split(\"::\")\r","              if len(params) == 2:\r","                changes_dict[params[0]] = { \"prompt\": params[1] }\r","              elif len(params) == 3:\r","                changes_dict[params[0]] = { \"seed\": params[1], \"prompt\": params[2] }\r","              else:\r","                raise IOError(f\"Invalid input in prompt line: {prompt_line}\")\r","        \r","        raw_prompt = p.prompt\r","                \r","        for n in range(batch_count):\r","            history = []\r","\r","            # Reset to original init image at the start of each batch\r","            p.init_images = original_init_image\r","            \r","            seed_state = \"adding\"\r","            current_seed = p.seed\r","\r","            for i in range(frames):\r","                current_seed = p.seed\r","                state.job = \"\"\r","                \r","                if str(i) in changes_dict:\r","                  raw_prompt = changes_dict[str(i)][\"prompt\"]\r","                  state.job = \"New prompt: %s\\n\" % raw_prompt\r","                                    \r","                  if \"seed\" in changes_dict[str(i)]:\r","                    current_seed = changes_dict[str(i)][\"seed\"]\r","                    \r","                    if current_seed.startswith(\"+\"):\r","                      seed_state = \"adding\"\r","                      current_seed = current_seed.strip(\"+\")\r","                    elif current_seed.startswith(\"-\"):\r","                      seed_state = \"subtracting\"\r","                      current_seed = current_seed.strip(\"-\")\r","                    else:\r","                      seed_state = \"constant\"\r","                      \r","                    current_seed = int(current_seed)\r","                    p.seed = current_seed\r","                      \r","                      \r","                  \r","                p.n_iter = 1\r","                p.batch_size = 1\r","                p.do_not_save_grid = True\r","\r","                if opts.img2img_color_correction:\r","                    p.color_corrections = initial_color_corrections\r","                    \r","                    \r","                wave_progress = float(1)/(float(frames_per_wave - 1))*float(((float(i)%float(frames_per_wave)) + ((float(1)/float(180))*denoising_strength_change_offset)))\r","                print(wave_progress)\r","                new_prompt = re.sub(wave_completed_regex, lambda x: set_weights(x, wave_progress), raw_prompt)\r","                new_prompt = re.sub(wave_remaining_regex, lambda x: set_weights(x, 1 - wave_progress), new_prompt)\r","                p.prompt = new_prompt\r","                \r","                print(new_prompt)\r","\r","                denoising_strength_change_rate = 180/frames_per_wave\r","\r","                cos = abs(math.cos(math.radians(i*denoising_strength_change_rate + denoising_strength_change_offset)))\r","                p.denoising_strength = initial_denoising_strength + denoising_strength_change_amplitude - (cos * denoising_strength_change_amplitude)\r","\r","                state.job += f\"Iteration {i + 1}/{frames}, batch {n + 1}/{batch_count}. Denoising Strength: {p.denoising_strength}\"\r","\r","                processed = processing.process_images(p)\r","\r","                if initial_seed is None:\r","                    initial_seed = processed.seed\r","                    initial_info = processed.info\r","\r","                init_img = processed.images[0]\r","\r","                p.init_images = [init_img]\r","                \r","                if seed_state == \"adding\":\r","                  p.seed = processed.seed + 1\r","                elif seed_state == \"subtracting\":\r","                  p.seed = processed.seed - 1\r","                  \r","                image_number = int(initial_image_number + i)\r","                images.save_image(init_img, p.outpath_samples, \"\", processed.seed, processed.prompt, forced_filename=str(image_number))\r","\r","                history.append(init_img)\r","\r","            grid = images.image_grid(history, rows=1)\r","            if opts.grid_save:\r","                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scripts/multi_frame_render.py

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