import numpy as np from PIL import Image from modules import processing, shared, images, devices from modules.shared import opts import modules.gfpgan_model from modules.ui import plaintext_to_html import modules.codeformer_model import piexif import piexif.helper cached_images = {} def run_extras(image, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility): devices.torch_gc() existing_pnginfo = image.info or {} image = image.convert("RGB") info = "" outpath = opts.outdir_samples or opts.outdir_extras_samples if gfpgan_visibility > 0: restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8)) res = Image.fromarray(restored_img) if gfpgan_visibility < 1.0: res = Image.blend(image, res, gfpgan_visibility) info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n" image = res if codeformer_visibility > 0: restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight) res = Image.fromarray(restored_img) if codeformer_visibility < 1.0: res = Image.blend(image, res, codeformer_visibility) info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility)}\n" image = res if upscaling_resize != 1.0: def upscale(image, scaler_index, resize): small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10)) pixels = tuple(np.array(small).flatten().tolist()) key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels c = cached_images.get(key) if c is None: upscaler = shared.sd_upscalers[scaler_index] c = upscaler.upscale(image, image.width * resize, image.height * resize) cached_images[key] = c return c info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n" res = upscale(image, extras_upscaler_1, upscaling_resize) if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: res2 = upscale(image, extras_upscaler_2, upscaling_resize) info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n" res = Image.blend(res, res2, extras_upscaler_2_visibility) image = res while len(cached_images) > 2: del cached_images[next(iter(cached_images.keys()))] images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo) return image, plaintext_to_html(info), '' def run_pnginfo(image): items = image.info if "exif" in image.info: exif = piexif.load(image.info["exif"]) exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'') try: exif_comment = piexif.helper.UserComment.load(exif_comment) except ValueError: exif_comment = exif_comment.decode('utf8', errors="ignore") items['exif comment'] = exif_comment for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif']: del items[field] info = '' for key, text in items.items(): info += f"""

{plaintext_to_html(str(key))}

{plaintext_to_html(str(text))}

""".strip()+"\n" if len(info) == 0: message = "Nothing found in the image." info = f"

{message}

" return '', '', info