import sys import os import time basedir = os.getcwd() sys.path.extend(basedir + "/extensions/infinite-zoom-automatic1111-webui/") import numpy as np import gradio as gr from PIL import Image import math import json from iz_helpers import shrink_rotate_and_paste_on_blank, zoom_image, write_video from webui import wrap_gradio_gpu_call from modules import script_callbacks import modules.shared as shared from modules.processing import ( process_images, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, ) from modules.ui import create_output_panel, plaintext_to_html available_samplers = [ "DDIM", "Euler a", "Euler", "LMS", "Heun", "DPM2", "DPM2 a", "DPM++ 2S a", "DPM++ 2M", "DPM++ SDE", "DPM fast", "DPM adaptive", "LMS Karras", "DPM2 Karras", "DPM2 a Karras", "DPM++ 2S a Karras", "DPM++ 2M Karras", "DPM++ SDE Karras", ] default_prompt = "A psychedelic jungle with trees that have glowing, fractal-like patterns, Simon stalenhag poster 1920s style, street level view, hyper futuristic, 8k resolution, hyper realistic" default_negative_prompt = "frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur" def closest_upper_divisible_by_eight(num): if num % 8 == 0: return num else: return math.ceil(num / 8) * 8 def renderTxt2Img(prompt, negative_prompt, sampler, steps, cfg_scale, width, height): processed = None p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=shared.opts.outdir_txt2img_samples, outpath_grids=shared.opts.outdir_txt2img_grids, prompt=prompt, negative_prompt=negative_prompt, # seed=-1, sampler_name=sampler, n_iter=1, steps=steps, cfg_scale=cfg_scale, width=width, height=height, ) processed = process_images(p) return processed def renderImg2Img( prompt, negative_prompt, sampler, steps, cfg_scale, width, height, init_image, mask_image, inpainting_denoising_strength, inpainting_mask_blur, inpainting_fill_mode, inpainting_full_res, inpainting_padding, ): processed = None p = StableDiffusionProcessingImg2Img( sd_model=shared.sd_model, outpath_samples=shared.opts.outdir_img2img_samples, outpath_grids=shared.opts.outdir_img2img_grids, prompt=prompt, negative_prompt=negative_prompt, # seed=-1, sampler_name=sampler, n_iter=1, steps=steps, cfg_scale=cfg_scale, width=width, height=height, init_images=[init_image], denoising_strength=inpainting_denoising_strength, mask_blur=inpainting_mask_blur, inpainting_fill=inpainting_fill_mode, inpaint_full_res=inpainting_full_res, inpaint_full_res_padding=inpainting_padding, mask=mask_image, ) # p.latent_mask = Image.new("RGB", (p.width, p.height), "white") processed = process_images(p) return processed def fix_env_Path_ffprobe(): envpath = os.environ["PATH"] ffppath = shared.opts.data.get("infzoom_ffprobepath", "") if ffppath and not ffppath in envpath: path_sep = ";" if os.name == "nt" else ":" os.environ["PATH"] = envpath + path_sep + ffppath def create_zoom( prompts_array, negative_prompt, num_outpainting_steps, guidance_scale, num_inference_steps, custom_init_image, video_frame_rate, video_zoom_mode, video_start_frame_dupe_amount, video_last_frame_dupe_amount, inpainting_denoising_strength, inpainting_mask_blur, inpainting_fill_mode, inpainting_full_res, inpainting_padding, zoom_speed, outputsizeW, outputsizeH, batchcount, sampler, rotate_angle, progress=gr.Progress(), ): for i in range(batchcount): print(f"Batch {i+1}/{batchcount}") result = create_zoom_single( prompts_array, negative_prompt, num_outpainting_steps, guidance_scale, num_inference_steps, custom_init_image, video_frame_rate, video_zoom_mode, video_start_frame_dupe_amount, video_last_frame_dupe_amount, inpainting_denoising_strength, inpainting_mask_blur, inpainting_fill_mode, inpainting_full_res, inpainting_padding, zoom_speed, outputsizeW, outputsizeH, sampler, rotate_angle, progress, ) return result def create_zoom_single( prompts_array, negative_prompt, num_outpainting_steps, guidance_scale, num_inference_steps, custom_init_image, video_frame_rate, video_zoom_mode, video_start_frame_dupe_amount, video_last_frame_dupe_amount, inpainting_denoising_strength, inpainting_mask_blur, inpainting_fill_mode, inpainting_full_res, inpainting_padding, zoom_speed, outputsizeW, outputsizeH, sampler, rotate_angle, progress=gr.Progress(), ): fix_env_Path_ffprobe() progress(0, desc="Preparing Initial Image") prompts = {} for x in prompts_array: try: key = int(x[0]) value = str(x[1]) prompts[key] = value except ValueError: pass assert len(prompts_array) > 0, "prompts is empty" width = closest_upper_divisible_by_eight(outputsizeW) height = closest_upper_divisible_by_eight(outputsizeH) current_image = Image.new(mode="RGBA", size=(width, height)) mask_image = np.array(current_image)[:, :, 3] mask_image = Image.fromarray(255 - mask_image).convert("RGB") current_image = current_image.convert("RGB") if custom_init_image: current_image = custom_init_image.resize( (width, height), resample=Image.LANCZOS ) else: processed = renderTxt2Img( prompts[min(k for k in prompts.keys() if k >= 0)], negative_prompt, sampler, num_inference_steps, guidance_scale, width, height, ) current_image = processed.images[0] mask_width = math.trunc(width / 4) # was initially 512px => 128px mask_height = math.trunc(height / 4) # was initially 512px => 128px all_images = [] all_images.append(current_image) for i in range(num_outpainting_steps): print_out = "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps) print(print_out) progress( ((i + 1) / num_outpainting_steps), desc=print_out, ) prev_image = shrink_rotate_and_paste_on_blank(current_image, mask_width, mask_height, rotate_angle=rotate_angle) current_image = prev_image # create mask (black image with white mask_width width edges) mask_image = np.array(current_image)[:, :, 3] mask_image = Image.fromarray(255 - mask_image).convert("RGB") # inpainting step current_image = current_image.convert("RGB") processed = renderImg2Img( prompts[max(k for k in prompts.keys() if k <= i)], negative_prompt, sampler, num_inference_steps, guidance_scale, width, height, current_image, mask_image, inpainting_denoising_strength, inpainting_mask_blur, inpainting_fill_mode, inpainting_full_res, inpainting_padding, ) current_image = processed.images[0] current_image.paste(prev_image, mask=prev_image) all_images.append(current_image) # Now create interpolation frames all_frames = [] num_interpol_frames = round(video_frame_rate * zoom_speed) for idx in range(len(all_images)-1): img, prev_image = all_images[-idx-1], all_images[-idx-2] for j in range(num_interpol_frames): # calculate interpolation factor interpol_factor = j / (num_interpol_frames) scaled_img = zoom_image(img, 1+interpol_factor, - (1-interpol_factor)*rotate_angle + rotate_angle) scaled_prev_img = zoom_image(prev_image, 0.5 + interpol_factor/2, interpol_factor*rotate_angle + rotate_angle) if rotate_angle != 0 and idx > 0: # fix black borders when rotating next_image = all_images[-idx] next_image = zoom_image(next_image, 2*(1+interpol_factor), -(1-interpol_factor)*rotate_angle) interpol_image = next_image.copy() interpol_image.paste(scaled_img, mask=scaled_img) else: interpol_image = scaled_img interpol_image.paste(scaled_prev_img, mask=scaled_prev_img) all_frames.append(interpol_image.rotate((idx+1)*2*rotate_angle)) all_frames = all_frames[::-1] video_file_name = "infinite_zoom_" + str(int(time.time())) + ".mp4" output_path = shared.opts.data.get( "infzoom_outpath", shared.opts.data.get("outdir_img2img_samples") ) save_path = os.path.join( output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms") ) if not os.path.exists(save_path): os.makedirs(save_path) out = os.path.join(save_path, video_file_name) write_video( out, all_frames, video_frame_rate, video_zoom_mode, int(video_start_frame_dupe_amount), int(video_last_frame_dupe_amount), ) return ( out, processed.images, processed.js(), plaintext_to_html(processed.info), plaintext_to_html(""), ) def exportPrompts(p, np): print("prompts:" + str(p) + "\n" + str(np)) def putPrompts(files): file_paths = [file.name for file in files] with open(files.name, "r") as f: file_contents = f.read() data = json.loads(file_contents) print(data) return [gr.DataFrame.update(data["prompts"]), gr.Textbox.update(data["negPrompt"])] def on_ui_tabs(): with gr.Blocks(analytics_enabled=False) as infinite_zoom_interface: gr.HTML( """

GitHub Repo Discord server

""" ) generate_btn = gr.Button(value="Generate video", variant="primary") interrupt = gr.Button(value="Interrupt", elem_id="interrupt_training") with gr.Row(): with gr.Column(scale=1, variant="panel"): with gr.Tab("Main"): main_outpaint_steps = gr.Slider( minimum=2, maximum=100, step=1, value=8, label="Total Outpaint Steps", info="The more it is, the longer your videos will be", ) main_rotate_per_step = gr.Slider( minimum=-180, maximum=180, step=1, value=0, label="Rotation (degrees per step)", info="The degrees your image will be rotated before the next outpainting steps.", ) main_prompts = gr.Dataframe( type="array", headers=["outpaint step", "prompt"], datatype=["number", "str"], row_count=1, col_count=(2, "fixed"), value=[[0, default_prompt]], wrap=True, ) main_negative_prompt = gr.Textbox( value=default_negative_prompt, label="Negative Prompt" ) # these button will be moved using JS unde the dataframe view as small ones exportPrompts_button = gr.Button( value="Export prompts", variant="secondary", elem_classes="sm infzoom_tab_butt", elem_id="infzoom_exP_butt", ) importPrompts_button = gr.UploadButton( value="Import prompts", variant="secondary", elem_classes="sm infzoom_tab_butt", elem_id="infzoom_imP_butt", ) exportPrompts_button.click( None, _js="exportPrompts", inputs=[main_prompts, main_negative_prompt], outputs=None, ) importPrompts_button.upload( fn=putPrompts, outputs=[main_prompts, main_negative_prompt], inputs=[importPrompts_button], ) main_sampler = gr.Dropdown( label="Sampler", choices=available_samplers, value="Euler a", type="value", ) with gr.Row(): main_width = gr.Slider( minimum=16, maximum=2048, value=shared.opts.data.get("infzoom_outsizeW", 512), step=16, label="Output Width", ) main_height = gr.Slider( minimum=16, maximum=2048, value=shared.opts.data.get("infzoom_outsizeH", 512), step=16, label="Output Height", ) with gr.Row(): main_guidance_scale = gr.Slider( minimum=0.1, maximum=15, step=0.1, value=7, label="Guidance Scale", ) sampling_step = gr.Slider( minimum=1, maximum=100, step=1, value=50, label="Sampling Steps for each outpaint", ) init_image = gr.Image(type="pil", label="custom initial image") batchcount_slider = gr.Slider( minimum=1, maximum=25, value=shared.opts.data.get("infzoom_batchcount", 1), step=1, label="Batch Count", ) with gr.Tab("Video"): video_frame_rate = gr.Slider( label="Frames per second", value=30, minimum=1, maximum=60, ) video_zoom_mode = gr.Radio( label="Zoom mode", choices=["Zoom-out", "Zoom-in"], value="Zoom-out", type="index", ) video_start_frame_dupe_amount = gr.Slider( label="number of start frame dupe", info="Frames to freeze at the start of the video", value=0, minimum=1, maximum=60, ) video_last_frame_dupe_amount = gr.Slider( label="number of last frame dupe", info="Frames to freeze at the end of the video", value=0, minimum=1, maximum=60, ) video_zoom_speed = gr.Slider( label="Zoom Speed", value=1.0, minimum=0.1, maximum=20.0, step=0.1, info="Zoom speed in seconds (higher values create slower zoom)", ) with gr.Tab("Outpaint"): inpainting_denoising_strength = gr.Slider( label="Denoising Strength", minimum=0.75, maximum=1, value=1 ) inpainting_mask_blur = gr.Slider( label="Mask Blur", minimum=0, maximum=64, value=0 ) inpainting_fill_mode = gr.Radio( label="Masked content", choices=["fill", "original", "latent noise", "latent nothing"], value="latent noise", type="index", ) inpainting_full_res = gr.Checkbox(label="Inpaint Full Resolution") inpainting_padding = gr.Slider( label="masked padding", minimum=0, maximum=256, value=0 ) with gr.Column(scale=1, variant="compact"): output_video = gr.Video(label="Output").style(width=512, height=512) ( out_image, generation_info, html_info, html_log, ) = create_output_panel( "infinit-zoom", shared.opts.outdir_img2img_samples ) generate_btn.click( fn=wrap_gradio_gpu_call(create_zoom, extra_outputs=[None, '', '']), inputs=[ main_prompts, main_negative_prompt, main_outpaint_steps, main_guidance_scale, sampling_step, init_image, video_frame_rate, video_zoom_mode, video_start_frame_dupe_amount, video_last_frame_dupe_amount, inpainting_denoising_strength, inpainting_mask_blur, inpainting_fill_mode, inpainting_full_res, inpainting_padding, video_zoom_speed, main_width, main_height, batchcount_slider, main_sampler, main_rotate_per_step, ], outputs=[output_video, out_image, generation_info, html_info, html_log], ) interrupt.click(fn=lambda: shared.state.interrupt(), inputs=[], outputs=[]) infinite_zoom_interface.queue() return [(infinite_zoom_interface, "Infinite Zoom", "iz_interface")] def on_ui_settings(): section = ("infinite-zoom", "Infinite Zoom") shared.opts.add_option( "infzoom_outpath", shared.OptionInfo( "", "Path where to store your infinite video. Let empty to use img2img-output", gr.Textbox, {"interactive": True}, section=section, ), ) shared.opts.add_option( "infzoom_outSUBpath", shared.OptionInfo( "infinite-zooms", "Which subfolder name to be created in the outpath. Default is 'infinite-zooms'", gr.Textbox, {"interactive": True}, section=section, ), ) shared.opts.add_option( "infzoom_outsizeW", shared.OptionInfo( 512, "Default width of your video", gr.Slider, {"minimum": 16, "maximum": 2048, "step": 16}, section=section, ), ) shared.opts.add_option( "infzoom_outsizeH", shared.OptionInfo( 512, "Default height your video", gr.Slider, {"minimum": 16, "maximum": 2048, "step": 16}, section=section, ), ) shared.opts.add_option( "infzoom_ffprobepath", shared.OptionInfo( "", "Writing videos has dependency to an existing FFPROBE executable on your machine. D/L here (https://github.com/BtbN/FFmpeg-Builds/releases) your OS variant and point to your installation path", gr.Textbox, {"interactive": True}, section=section, ), ) script_callbacks.on_ui_tabs(on_ui_tabs) script_callbacks.on_ui_settings(on_ui_settings)