import math, time, os import numpy as np from PIL import Image, ImageDraw, ImageFilter, ImageOps from modules.ui import plaintext_to_html import modules.shared as shared from .helpers import ( fix_env_Path_ffprobe, closest_upper_divisible_by_eight, load_model_from_setting, do_upscaleImg, ) from .sd_helpers import renderImg2Img, renderTxt2Img from .image import shrink_and_paste_on_blank, open_image from .video import write_video def create_zoom( prompts_array, negative_prompt, num_outpainting_steps, guidance_scale, num_inference_steps, custom_init_image, custom_exit_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, seed, outputsizeW, outputsizeH, batchcount, sampler, upscale_do, upscaler_name, upscalerinterpol_name, upscale_by, exitgamma, maskwidth_slider, maskheight_slider, progress=None, ): 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, custom_exit_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, seed, outputsizeW, outputsizeH, sampler, upscale_do, upscaler_name, upscalerinterpol_name, upscale_by, exitgamma, maskwidth_slider, maskheight_slider, progress ) return result def create_zoom_single( prompts_array, negative_prompt, num_outpainting_steps, guidance_scale, num_inference_steps, custom_init_image, custom_exit_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, seed, outputsizeW, outputsizeH, sampler, upscale_do, upscaler_name, upscalerinterpol_name, upscale_by, exitgamma, maskwidth_slider, maskheight_slider, progress=None, ): # try: # if gr.Progress() is not None: # progress = gr.Progress() # progress(0, desc="Preparing Initial Image") # except Exception: # pass fix_env_Path_ffprobe() prompts = {} prompt_images = {} for x in prompts_array: try: key = int(x[0]) value = str(x[1]) file_loc = str(x[2]) prompts[key] = value prompt_images[key] = file_loc except ValueError: pass assert len(prompts_array) > 0, "prompts is empty" print(str(len(prompts)) + " prompts found") print(str(len(prompt_images)) + " prompts Images found") 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") current_seed = seed extra_frames = 0 if custom_init_image: current_image = custom_init_image.resize( (width, height), resample=Image.LANCZOS ) print("using Custom Initial Image") else: if prompt_images[min(k for k in prompt_images.keys() if k >= 0)] == "": load_model_from_setting( "infzoom_txt2img_model", progress, "Loading Model for txt2img: " ) processed, current_seed = renderTxt2Img( prompts[min(k for k in prompts.keys() if k >= 0)], negative_prompt, sampler, num_inference_steps, guidance_scale, current_seed, width, height, ) current_image = processed.images[0] else: current_image = open_image(prompt_images[min(k for k in prompt_images.keys() if k >= 0)]).resize( (width, height), resample=Image.LANCZOS ) mask_width = math.trunc(width / 4) # was initially 512px => 128px mask_height = math.trunc(height / 4) # was initially 512px => 128px num_interpol_frames = round(video_frame_rate * zoom_speed) all_frames = [] if upscale_do and progress: progress(0, desc="upscaling inital image") all_frames.append( do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by) if upscale_do else current_image ) load_model_from_setting("infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: " ) if custom_exit_image: extra_frames += 2 # setup filesystem paths 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) for i in range(num_outpainting_steps + extra_frames): print_out = ( "Outpaint step: " + str(i + 1) + " / " + str(num_outpainting_steps + extra_frames) + " Seed: " + str(current_seed) ) print(print_out) if progress: progress(((i + 1) / num_outpainting_steps), desc=print_out) if custom_exit_image and ((i + 1) == num_outpainting_steps): mask_width=round(width*maskwidth_slider) mask_height=round(height*maskheight_slider) # 30 fps@ maskw 0.25 => 30 # normalize to default speed of 30 fps for 0.25 mask factor num_interpol_frames = round(num_interpol_frames * (1 + (max(maskheight_slider,maskwidth_slider)/0.5) * exitgamma)) prev_image_fix = current_image prev_image = shrink_and_paste_on_blank(current_image, mask_width, mask_height) 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") # Custom and specified images work like keyframes if custom_exit_image and (i + 1) >= (num_outpainting_steps + extra_frames): current_image = custom_exit_image.resize( (width, height), resample=Image.LANCZOS ) print("using Custom Exit Image") else: if prompt_images[max(k for k in prompt_images.keys() if k <= (i + 1))] == "": processed, current_seed = renderImg2Img( prompts[max(k for k in prompts.keys() if k <= (i + 1))], negative_prompt, sampler, num_inference_steps, guidance_scale, current_seed, 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] # only paste previous image when generating a new image current_image.paste(prev_image, mask=prev_image) else: current_image = open_image(prompt_images[max(k for k in prompt_images.keys() if k <= (i + 1))]).resize( (width, height), resample=Image.LANCZOS ) # interpolation steps between 2 inpainted images (=sequential zoom and crop) for j in range(num_interpol_frames - 1): interpol_image = current_image interpol_width = round( ( 1 - (1 - 2 * mask_width / width) ** (1 - (j + 1) / num_interpol_frames) ) * width / 2 ) interpol_height = round( ( 1 - (1 - 2 * mask_height / height) ** (1 - (j + 1) / num_interpol_frames) ) * height / 2 ) interpol_image = interpol_image.crop( ( interpol_width, interpol_height, width - interpol_width, height - interpol_height, ) ) interpol_image = interpol_image.resize((width, height)) # paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming interpol_width2 = round( (1 - (width - 2 * mask_width) / (width - 2 * interpol_width)) / 2 * width ) interpol_height2 = round( (1 - (height - 2 * mask_height) / (height - 2 * interpol_height)) / 2 * height ) if custom_exit_image and ((i + 1) == num_outpainting_steps): opacity = 1 - ((j+1)/num_interpol_frames ) else: opacity = 1 prev_image_fix_crop = shrink_and_paste_on_blank( prev_image_fix, interpol_width2, interpol_height2, opacity=opacity ) interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop) # exit image: from now we see the last prompt on the exit image if custom_exit_image and ((i + 1) == num_outpainting_steps): mask_img = Image.new("L", (width,height), 0) in_center_x = interpol_image.width/2 in_center_y = interpol_image.height/2 # Draw a circular brush on the mask image with 64px diameter and 8px softness draw = ImageDraw.Draw(mask_img) brush_size = 64 brush_softness = 8 brush = Image.new("L", (brush_size, brush_size), 255) draw_brush = ImageDraw.Draw(brush) draw_brush.ellipse((brush_softness, brush_softness, brush_size-brush_softness, brush_size-brush_softness), fill=255, outline=None) brush = brush.filter(ImageFilter.GaussianBlur(radius=brush_softness)) brush_width, brush_height = brush.size # Draw the rectangular frame on the mask image using the circular brush frame_width = width-2*interpol_width2 frame_height = height-2*interpol_height2 frame_left = in_center_x - (frame_width // 2) frame_top = in_center_y - (frame_height // 2) frame_right = frame_left + frame_width frame_bottom = frame_top + frame_height draw.ellipse((frame_left, frame_top, frame_left+brush_width, frame_top+brush_height), fill=255, outline=None) draw.ellipse((frame_right-brush_width, frame_top, frame_right, frame_top+brush_height), fill=255, outline=None) draw.ellipse((frame_left, frame_bottom-brush_height, frame_left+brush_width, frame_bottom), fill=255, outline=None) draw.ellipse((frame_right-brush_width, frame_bottom-brush_height, frame_right, frame_bottom), fill=255, outline=None) draw.rectangle((max(0,frame_left-brush_size/2), max(0,frame_top+brush_size/2), max(0,frame_right-brush_size/2), max(0,frame_bottom-brush_size/2)), fill=255) # inner rect, now we have a bordermask draw.rectangle((max(0,frame_left+brush_size/2), max(0,frame_top-brush_size/2), max(0,frame_right+brush_size/2), max(0,frame_bottom+brush_size/2)), fill=0) # Blur the mask image to soften the edges #mask_img = mask_img.filter(ImageFilter.GaussianBlur(radius=8)) #mask_img = ImageOps.invert(mask_img) #mask_img.save(output_path+os.pathsep+"Mask"+str(int(time.time()))+".png") """processed, newseed = renderImg2Img( prompts[max(k for k in prompts.keys() if k <= i)], negative_prompt, sampler, num_inference_steps, guidance_scale, current_seed, width, height, interpol_image, mask_img, inpainting_denoising_strength, inpainting_mask_blur, inpainting_fill_mode, inpainting_full_res, inpainting_padding, ) #interpol_image = processed.images[0] """ if upscale_do and progress: progress(((i + 1) / num_outpainting_steps), desc="upscaling interpol") all_frames.append( do_upscaleImg(interpol_image, upscale_do, upscalerinterpol_name, upscale_by) if upscale_do else interpol_image ) if upscale_do and progress: progress(((i + 1) / num_outpainting_steps), desc="upscaling current") all_frames.append( do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by) if upscale_do else current_image ) 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") ) print("save to: " + save_path) 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(""), )