vahid khroasani
GitHub
commit
fe16a68e9f
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@ -1,9 +1,9 @@
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import math, time, os
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import numpy as np
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from PIL import Image
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from PIL import Image, ImageFilter, ImageDraw
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from modules.ui import plaintext_to_html
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import modules.shared as shared
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from modules.paths_internal import script_path
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from .helpers import (
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fix_env_Path_ffprobe,
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closest_upper_divisible_by_eight,
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@ -14,6 +14,157 @@ from .sd_helpers import renderImg2Img, renderTxt2Img
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from .image import shrink_and_paste_on_blank
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from .video import write_video
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def crop_fethear_ellipse(image, feather_margin=30, width_offset=0, height_offset=0):
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# Create a blank mask image with the same size as the original image
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mask = Image.new("L", image.size, 0)
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draw = ImageDraw.Draw(mask)
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# Calculate the ellipse's bounding box
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ellipse_box = (
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width_offset,
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height_offset,
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image.width - width_offset,
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image.height - height_offset,
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)
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# Draw the ellipse on the mask
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draw.ellipse(ellipse_box, fill=255)
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# Apply the mask to the original image
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result = Image.new("RGBA", image.size)
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result.paste(image, mask=mask)
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# Crop the resulting image to the ellipse's bounding box
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cropped_image = result.crop(ellipse_box)
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# Create a new mask image with a black background (0)
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mask = Image.new("L", cropped_image.size, 0)
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draw = ImageDraw.Draw(mask)
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# Draw an ellipse on the mask image
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draw.ellipse(
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(
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0 + feather_margin,
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0 + feather_margin,
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cropped_image.width - feather_margin,
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cropped_image.height - feather_margin,
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),
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fill=255,
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outline=0,
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)
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# Apply a Gaussian blur to the mask image
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mask = mask.filter(ImageFilter.GaussianBlur(radius=feather_margin / 2))
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cropped_image.putalpha(mask)
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res = Image.new(cropped_image.mode, (image.width, image.height))
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paste_pos = (
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int((res.width - cropped_image.width) / 2),
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int((res.height - cropped_image.height) / 2),
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)
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res.paste(cropped_image, paste_pos)
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return res
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def outpaint_steps(
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width,
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height,
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common_prompt_pre,
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common_prompt_suf,
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prompts,
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negative_prompt,
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seed,
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sampler,
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num_inference_steps,
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guidance_scale,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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init_img,
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outpaint_steps,
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out_config,
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mask_width,
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mask_height,
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custom_exit_image,
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frame_correction=True, # TODO: add frame_Correction in UI
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):
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main_frames = [init_img.convert("RGB")]
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for i in range(outpaint_steps):
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print_out = (
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"Outpaint step: "
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+ str(i + 1)
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+ " / "
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+ str(outpaint_steps)
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+ " Seed: "
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+ str(seed)
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)
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print(print_out)
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current_image = main_frames[-1]
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current_image = shrink_and_paste_on_blank(
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current_image, mask_width, mask_height
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)
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255 - mask_image).convert("RGB")
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# create mask (black image with white mask_width width edges)
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if custom_exit_image and ((i + 1) == outpaint_steps):
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current_image = custom_exit_image.resize(
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(width, height), resample=Image.LANCZOS
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)
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main_frames.append(current_image.convert("RGB"))
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# print("using Custom Exit Image")
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save2Collect(current_image, out_config, f"exit_img.png")
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else:
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pr = prompts[max(k for k in prompts.keys() if k <= i)]
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processed, newseed = renderImg2Img(
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f"{common_prompt_pre}\n{pr}\n{common_prompt_suf}".strip(),
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negative_prompt,
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sampler,
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num_inference_steps,
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guidance_scale,
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seed,
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width,
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height,
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current_image,
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mask_image,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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)
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if len(processed.images) > 0:
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main_frames.append(processed.images[0].convert("RGB"))
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save2Collect(processed.images[0], out_config, f"outpain_step_{i}.png")
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seed = newseed
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# TODO: seed behavior
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if frame_correction and inpainting_mask_blur > 0:
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corrected_frame = crop_inner_image(
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main_frames[i + 1], mask_width, mask_height
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)
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enhanced_img = crop_fethear_ellipse(
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main_frames[i],
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30,
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inpainting_mask_blur / 3 // 2,
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inpainting_mask_blur / 3 // 2,
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)
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save2Collect(main_frames[i], out_config, f"main_frame_{i}")
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save2Collect(enhanced_img, out_config, f"main_frame_enhanced_{i}")
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corrected_frame.paste(enhanced_img, mask=enhanced_img)
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main_frames[i] = corrected_frame
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# else :TEST
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# current_image.paste(prev_image, mask=prev_image)
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return main_frames, processed
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def create_zoom(
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common_prompt_pre,
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prompts_array,
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@ -78,40 +229,65 @@ def create_zoom(
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return result
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def prepare_output_path():
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isCollect = shared.opts.data.get("infzoom_collectAllResources", False)
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output_path = shared.opts.data.get("infzoom_outpath", "outputs")
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isCollect = shared.opts.data.get("infzoom_collectAllResources",False)
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output_path = shared.opts.data.get(
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"infzoom_outpath", "output"
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)
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save_path = os.path.join(
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output_path, shared.opts.data.get("infzoom_outSUBpath", "infinite-zooms")
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)
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if isCollect:
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save_path = os.path.join(save_path,"iz_collect" + str(int(time.time())))
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save_path = os.path.join(save_path, "iz_collect" + str(int(time.time())))
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if not os.path.exists(save_path):
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os.makedirs(save_path)
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video_filename = os.path.join(save_path,"infinite_zoom_" + str(int(time.time())) + ".mp4")
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video_filename = os.path.join(
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save_path, "infinite_zoom_" + str(int(time.time())) + ".mp4"
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)
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return {"isCollect":isCollect,"save_path":save_path,"video_filename":video_filename}
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return {
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"isCollect": isCollect,
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"save_path": save_path,
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"video_filename": video_filename,
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}
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def save2Collect(img, out_config, name):
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def save2Collect(img, out_config, name):
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if out_config["isCollect"]:
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img.save(f'{out_config["save_path"]}/{name}.png')
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def frame2Collect(all_frames, out_config):
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save2Collect(all_frames[-1], out_config, f"frame_{len(all_frames)}.png")
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save2Collect(all_frames[-1], out_config, f"frame_{len(all_frames)}")
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def frames2Collect(all_frames, out_config):
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for i,f in enumerate(all_frames):
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save2Collect(f, out_config, f"frame_{i}.png")
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for i, f in enumerate(all_frames):
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save2Collect(f, out_config, f"frame_{i}")
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def crop_inner_image(outpainted_img, width_offset, height_offset):
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width, height = outpainted_img.size
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center_x, center_y = int(width / 2), int(height / 2)
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# Crop the image to the center
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cropped_img = outpainted_img.crop(
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(
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center_x - width_offset,
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center_y - height_offset,
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center_x + width_offset,
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center_y + height_offset,
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)
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)
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prev_step_img = cropped_img.resize((width, height), resample=Image.LANCZOS)
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# resized_img = resized_img.filter(ImageFilter.SHARPEN)
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return prev_step_img
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def create_zoom_single(
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common_prompt_pre,
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prompts_array,
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@ -148,11 +324,10 @@ def create_zoom_single(
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# except Exception:
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# pass
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fix_env_Path_ffprobe()
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out_config = prepare_output_path()
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prompts = {}
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for x in prompts_array:
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try:
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key = int(x[0])
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@ -160,7 +335,7 @@ def create_zoom_single(
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prompts[key] = value
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except ValueError:
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pass
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assert len(prompts_array) > 0, "prompts is empty"
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width = closest_upper_divisible_by_eight(outputsizeW)
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@ -176,8 +351,7 @@ def create_zoom_single(
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current_image = custom_init_image.resize(
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(width, height), resample=Image.LANCZOS
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)
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save2Collect(current_image, out_config, f"init_img.png")
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print("using Custom Initial Image")
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save2Collect(current_image, out_config, f"init_custom.png")
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else:
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load_model_from_setting(
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@ -195,10 +369,9 @@ def create_zoom_single(
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width,
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height,
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)
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if(len(processed.images) > 0):
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if len(processed.images) > 0:
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current_image = processed.images[0]
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save2Collect(current_image, out_config, f"txt2img.png")
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save2Collect(current_image, out_config, f"init_txt2img.png")
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current_seed = newseed
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mask_width = math.trunc(width / 4) # was initially 512px => 128px
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@ -211,84 +384,45 @@ def create_zoom_single(
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if upscale_do and progress:
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progress(0, desc="upscaling inital image")
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all_frames.append(
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do_upscaleImg(current_image, upscale_do, upscaler_name, upscale_by)
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if upscale_do
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else current_image
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)
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load_model_from_setting(
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"infzoom_inpainting_model", progress, "Loading Model for inpainting/img2img: "
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)
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for i in range(num_outpainting_steps):
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print_out = (
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"Outpaint step: "
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+ str(i + 1)
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+ " / "
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+ str(num_outpainting_steps)
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+ " Seed: "
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+ str(current_seed)
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)
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print(print_out)
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if progress:
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progress(((i + 1) / num_outpainting_steps), desc=print_out)
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prev_image_fix = current_image
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save2Collect(prev_image_fix, out_config, f"prev_image_fix_{i}.png")
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prev_image = shrink_and_paste_on_blank(current_image, mask_width, mask_height)
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save2Collect(prev_image, out_config, f"prev_image_{1}.png")
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current_image = prev_image
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# create mask (black image with white mask_width width edges)
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mask_image = np.array(current_image)[:, :, 3]
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mask_image = Image.fromarray(255 - mask_image).convert("RGB")
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save2Collect(mask_image, out_config, f"mask_image_{i}.png")
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# inpainting step
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current_image = current_image.convert("RGB")
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if custom_exit_image and ((i + 1) == num_outpainting_steps):
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current_image = custom_exit_image.resize(
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(width, height), resample=Image.LANCZOS
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)
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print("using Custom Exit Image")
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save2Collect(current_image, out_config, f"exit_img.png")
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else:
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pr = prompts[max(k for k in prompts.keys() if k <= i)]
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processed, newseed = renderImg2Img(
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f"{common_prompt_pre}\n{pr}\n{common_prompt_suf}".strip(),
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negative_prompt,
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sampler,
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num_inference_steps,
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guidance_scale,
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current_seed,
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width,
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height,
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current_image,
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mask_image,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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)
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if(len(processed.images) > 0):
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current_image = processed.images[0]
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current_seed = newseed
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if(len(processed.images) > 0):
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current_image.paste(prev_image, mask=prev_image)
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save2Collect(current_image, out_config, f"curr_prev_paste_{i}.png")
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main_frames, processed = outpaint_steps(
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width,
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height,
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common_prompt_pre,
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common_prompt_suf,
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prompts,
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negative_prompt,
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seed,
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sampler,
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num_inference_steps,
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guidance_scale,
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inpainting_denoising_strength,
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inpainting_mask_blur,
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inpainting_fill_mode,
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inpainting_full_res,
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inpainting_padding,
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current_image,
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num_outpainting_steps,
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out_config,
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mask_width,
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mask_height,
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custom_exit_image,
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)
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all_frames.append(
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do_upscaleImg(main_frames[0], upscale_do, upscaler_name, upscale_by)
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if upscale_do
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else main_frames[0]
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)
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for i in range(len(main_frames) - 1):
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# interpolation steps between 2 inpainted images (=sequential zoom and crop)
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for j in range(num_interpol_frames - 1):
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current_image = main_frames[i + 1]
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interpol_image = current_image
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save2Collect(interpol_image, out_config, f"interpol_img_{i}_{j}].png")
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interpol_width = round(
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interpol_width = math.ceil(
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(
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1
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- (1 - 2 * mask_width / width)
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@ -298,7 +432,7 @@ def create_zoom_single(
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/ 2
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)
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interpol_height = round(
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interpol_height = math.ceil(
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(
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1
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- (1 - 2 * mask_height / height)
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@ -316,28 +450,26 @@ def create_zoom_single(
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height - interpol_height,
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)
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)
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save2Collect(interpol_image, out_config, f"interpol_crop_{i}_{j}.png")
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interpol_image = interpol_image.resize((width, height))
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save2Collect(interpol_image, out_config, f"interpol_resize_{i}_{j}.png")
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# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
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interpol_width2 = round(
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interpol_width2 = math.ceil(
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(1 - (width - 2 * mask_width) / (width - 2 * interpol_width))
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/ 2
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* width
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)
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interpol_height2 = round(
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interpol_height2 = math.ceil(
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(1 - (height - 2 * mask_height) / (height - 2 * interpol_height))
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/ 2
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* height
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)
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prev_image_fix_crop = shrink_and_paste_on_blank(
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prev_image_fix, interpol_width2, interpol_height2
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main_frames[i], interpol_width2, interpol_height2
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)
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save2Collect(prev_image_fix, out_config, f"prev_image_fix_crop_{i}_{j}.png")
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interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
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save2Collect(interpol_image, out_config, f"interpol_prevcrop_{i}_{j}.png")
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@ -370,10 +502,10 @@ def create_zoom_single(
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int(video_start_frame_dupe_amount),
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int(video_last_frame_dupe_amount),
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)
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print("Video saved in: " + os.path.join(script_path, out_config["video_filename"]))
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return (
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out_config["video_filename"],
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processed.images,
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main_frames,
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processed.js(),
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plaintext_to_html(processed.info),
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plaintext_to_html(""),
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|
|
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|
|
@ -4,21 +4,22 @@ import modules.sd_samplers
|
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|
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default_prompt = """
|
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{
|
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"prePrompt":"<lora:epiNoiseoffset_v2:0.6> ",
|
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"prompts":{
|
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"headers":["outpaint steps","prompt","img"],
|
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"data":[
|
||||
[0,"Huge spectacular Waterfall in a dense tropical forest,epic perspective,(vegetation overgrowth:1.3)(intricate, ornamentation:1.1),(baroque:1.1), fantasy, (realistic:1) digital painting , (magical,mystical:1.2) , (wide angle shot:1.4), (landscape composed:1.2)(medieval:1.1), divine,cinematic,(tropical forest:1.4),(river:1.3)mythology,india, volumetric lighting, Hindu ,epic"]
|
||||
]
|
||||
},
|
||||
"postPrompt":"style by Alex Horley Wenjun Lin greg rutkowski Ruan Jia (Wayne Barlowe:1.2)",
|
||||
"negPrompt":"frames, border, edges, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur, bad-artist"
|
||||
"commonPromptPrefix": "Huge spectacular Waterfall in ",
|
||||
"prompts": {
|
||||
"headers": ["outpaint steps", "prompt"],
|
||||
"data": [
|
||||
[0, "a dense tropical forest"],
|
||||
[2, "a Lush jungle"],
|
||||
[3, "a Thick rainforest"],
|
||||
[5, "a Verdant canopy"]
|
||||
]
|
||||
},
|
||||
"commonPromptSuffix": "epic perspective,(vegetation overgrowth:1.3)(intricate, ornamentation:1.1),(baroque:1.1), fantasy, (realistic:1) digital painting , (magical,mystical:1.2) , (wide angle shot:1.4), (landscape composed:1.2)(medieval:1.1),(tropical forest:1.4),(river:1.3) volumetric lighting ,epic, style by Alex Horley Wenjun Lin greg rutkowski Ruan Jia (Wayne Barlowe:1.2)",
|
||||
"negPrompt": "frames, border, edges, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur, bad-artist"
|
||||
}
|
||||
"""
|
||||
|
||||
empty_prompt = (
|
||||
'{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":"", prePrompt:"", postPrompt:""}'
|
||||
)
|
||||
empty_prompt = '{"prompts":{"data":[],"headers":["outpaint steps","prompt"]},"negPrompt":"", commonPromptPrefix:"", commonPromptSuffix}'
|
||||
|
||||
invalid_prompt = {
|
||||
"prompts": {
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ def on_ui_tabs():
|
|||
)
|
||||
|
||||
main_outpaint_steps = gr.Slider(
|
||||
minimum=2,
|
||||
minimum=1,
|
||||
maximum=100,
|
||||
step=1,
|
||||
value=8,
|
||||
|
|
|
|||
Loading…
Reference in New Issue