2022-10-10 04:26:52 +03:00
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import math
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2022-09-20 01:13:12 +03:00
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import os
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2022-09-11 11:31:16 +03:00
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import numpy as np
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from PIL import Image
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2022-09-26 02:22:12 +03:00
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import torch
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2022-09-27 10:44:00 +03:00
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import tqdm
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2022-09-26 02:22:12 +03:00
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2022-09-29 00:21:54 +03:00
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from modules import processing, shared, images, devices, sd_models
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2022-09-11 11:31:16 +03:00
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from modules.shared import opts
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import modules.gfpgan_model
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from modules.ui import plaintext_to_html
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import modules.codeformer_model
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2022-09-13 19:23:55 +03:00
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import piexif
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2022-09-14 15:20:05 +03:00
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import piexif.helper
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2022-09-29 00:59:44 +03:00
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import gradio as gr
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2022-09-13 19:23:55 +03:00
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2022-09-11 11:31:16 +03:00
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cached_images = {}
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2022-10-16 07:50:55 +03:00
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def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
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2022-09-11 23:24:24 +03:00
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devices.torch_gc()
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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imageArr = []
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2022-09-20 01:13:12 +03:00
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# Also keep track of original file names
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imageNameArr = []
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2022-10-16 07:50:55 +03:00
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outputs = []
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2022-09-22 12:11:48 +03:00
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if extras_mode == 1:
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2022-09-16 06:23:37 +03:00
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#convert file to pillow image
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for img in image_folder:
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2022-10-09 16:14:56 +03:00
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image = Image.open(img)
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2022-09-16 06:23:37 +03:00
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imageArr.append(image)
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2022-09-20 01:13:12 +03:00
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imageNameArr.append(os.path.splitext(img.orig_name)[0])
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2022-10-16 07:50:55 +03:00
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elif extras_mode == 2:
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2022-10-16 09:51:01 +03:00
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assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
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2022-10-16 07:50:55 +03:00
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if input_dir == '':
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return outputs, "Please select an input directory.", ''
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2022-10-21 03:23:13 +03:00
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image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
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2022-10-16 07:50:55 +03:00
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for img in image_list:
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2022-10-20 15:31:29 +03:00
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try:
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image = Image.open(img)
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except Exception:
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continue
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2022-10-16 07:50:55 +03:00
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imageArr.append(image)
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imageNameArr.append(img)
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2022-09-22 12:11:48 +03:00
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else:
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imageArr.append(image)
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imageNameArr.append(None)
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2022-09-11 11:31:16 +03:00
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2022-10-16 07:50:55 +03:00
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if extras_mode == 2 and output_dir != '':
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outpath = output_dir
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else:
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outpath = opts.outdir_samples or opts.outdir_extras_samples
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2022-09-11 11:31:16 +03:00
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2022-10-16 07:50:55 +03:00
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2022-09-20 01:13:12 +03:00
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for image, image_name in zip(imageArr, imageNameArr):
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2022-09-26 17:29:50 +03:00
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if image is None:
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return outputs, "Please select an input image.", ''
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2022-09-16 06:23:37 +03:00
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existing_pnginfo = image.info or {}
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image = image.convert("RGB")
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info = ""
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if gfpgan_visibility > 0:
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restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
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res = Image.fromarray(restored_img)
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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if gfpgan_visibility < 1.0:
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res = Image.blend(image, res, gfpgan_visibility)
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
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image = res
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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if codeformer_visibility > 0:
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restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
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res = Image.fromarray(restored_img)
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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if codeformer_visibility < 1.0:
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res = Image.blend(image, res, codeformer_visibility)
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2022-09-11 11:31:16 +03:00
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2022-09-17 22:02:46 +03:00
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info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
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2022-09-16 06:23:37 +03:00
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image = res
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2022-09-11 11:31:16 +03:00
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2022-10-10 04:26:52 +03:00
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if resize_mode == 1:
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upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
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crop_info = " (crop)" if upscaling_crop else ""
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info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
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2022-09-16 06:23:37 +03:00
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if upscaling_resize != 1.0:
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2022-10-10 04:26:52 +03:00
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def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
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2022-09-16 06:23:37 +03:00
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small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
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pixels = tuple(np.array(small).flatten().tolist())
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2022-10-18 00:46:56 +03:00
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key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight,
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resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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c = cached_images.get(key)
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if c is None:
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upscaler = shared.sd_upscalers[scaler_index]
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2022-09-30 11:42:40 +03:00
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c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
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2022-10-10 04:26:52 +03:00
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if mode == 1 and crop:
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2022-10-10 21:04:21 +03:00
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cropped = Image.new("RGB", (resize_w, resize_h))
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cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
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c = cropped
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2022-09-16 06:23:37 +03:00
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cached_images[key] = c
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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return c
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
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2022-10-10 04:26:52 +03:00
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res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
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2022-10-10 04:26:52 +03:00
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res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
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2022-09-16 06:23:37 +03:00
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info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
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res = Image.blend(res, res2, extras_upscaler_2_visibility)
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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image = res
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2022-09-11 11:31:16 +03:00
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2022-09-16 06:23:37 +03:00
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while len(cached_images) > 2:
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del cached_images[next(iter(cached_images.keys()))]
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2022-10-20 13:10:32 +03:00
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if opts.use_original_name_batch and image_name != None:
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basename = os.path.splitext(os.path.basename(image_name))[0]
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else:
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2022-10-20 15:43:27 +03:00
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basename = ''
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2022-10-20 13:10:32 +03:00
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2022-10-20 15:43:27 +03:00
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images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
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no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
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2022-09-11 11:31:16 +03:00
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2022-10-09 13:10:15 +03:00
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if opts.enable_pnginfo:
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image.info = existing_pnginfo
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image.info["extras"] = info
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2022-10-16 07:50:55 +03:00
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if extras_mode != 2 or show_extras_results :
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outputs.append(image)
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2022-09-16 12:43:24 +03:00
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2022-09-29 04:14:13 +03:00
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devices.torch_gc()
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2022-09-16 12:43:24 +03:00
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return outputs, plaintext_to_html(info), ''
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2022-09-11 11:31:16 +03:00
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2022-09-17 09:07:07 +03:00
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def run_pnginfo(image):
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2022-09-19 20:18:16 +03:00
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if image is None:
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return '', '', ''
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2022-09-13 19:23:55 +03:00
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items = image.info
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2022-09-23 22:49:21 +03:00
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geninfo = ''
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2022-09-13 19:23:55 +03:00
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if "exif" in image.info:
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exif = piexif.load(image.info["exif"])
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exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
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2022-09-14 15:20:05 +03:00
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try:
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exif_comment = piexif.helper.UserComment.load(exif_comment)
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except ValueError:
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exif_comment = exif_comment.decode('utf8', errors="ignore")
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2022-09-13 19:23:55 +03:00
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items['exif comment'] = exif_comment
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2022-09-23 22:49:21 +03:00
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geninfo = exif_comment
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2022-09-13 19:23:55 +03:00
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2022-09-16 23:48:22 +03:00
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for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
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'loop', 'background', 'timestamp', 'duration']:
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items.pop(field, None)
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2022-09-13 19:23:55 +03:00
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2022-09-23 22:49:21 +03:00
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geninfo = items.get('parameters', geninfo)
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2022-09-13 19:23:55 +03:00
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2022-09-11 11:31:16 +03:00
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info = ''
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2022-09-13 19:23:55 +03:00
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for key, text in items.items():
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2022-09-11 11:31:16 +03:00
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info += f"""
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<div>
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<p><b>{plaintext_to_html(str(key))}</b></p>
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<p>{plaintext_to_html(str(text))}</p>
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</div>
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""".strip()+"\n"
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if len(info) == 0:
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message = "Nothing found in the image."
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info = f"<div><p>{message}<p></div>"
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2022-09-23 22:49:21 +03:00
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return '', geninfo, info
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2022-09-26 02:22:12 +03:00
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2022-10-14 22:01:49 +03:00
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def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
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2022-10-17 01:44:39 +03:00
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def weighted_sum(theta0, theta1, alpha):
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2022-09-26 17:50:21 +03:00
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return ((1 - alpha) * theta0) + (alpha * theta1)
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2022-10-17 01:44:39 +03:00
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def get_difference(theta1, theta2):
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return theta1 - theta2
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def add_difference(theta0, theta1_2_diff, alpha):
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return theta0 + (alpha * theta1_2_diff)
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2022-10-14 09:05:06 +03:00
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2022-09-29 00:59:44 +03:00
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primary_model_info = sd_models.checkpoints_list[primary_model_name]
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secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
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2022-10-14 09:05:06 +03:00
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teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
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2022-09-27 10:44:00 +03:00
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2022-09-29 00:59:44 +03:00
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print(f"Loading {primary_model_info.filename}...")
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primary_model = torch.load(primary_model_info.filename, map_location='cpu')
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2022-10-14 09:05:06 +03:00
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theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
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2022-09-28 04:34:24 +03:00
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2022-09-29 00:59:44 +03:00
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print(f"Loading {secondary_model_info.filename}...")
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secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
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2022-10-09 10:23:31 +03:00
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theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
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2022-09-27 10:44:00 +03:00
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2022-10-14 09:05:06 +03:00
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if teritary_model_info is not None:
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print(f"Loading {teritary_model_info.filename}...")
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teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
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theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
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else:
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2022-10-17 08:28:18 +03:00
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teritary_model = None
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2022-10-14 09:05:06 +03:00
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theta_2 = None
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2022-09-27 10:44:00 +03:00
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theta_funcs = {
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2022-10-17 01:44:39 +03:00
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"Weighted sum": (None, weighted_sum),
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"Add difference": (get_difference, add_difference),
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2022-09-27 10:44:00 +03:00
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}
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2022-10-17 01:44:39 +03:00
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theta_func1, theta_func2 = theta_funcs[interp_method]
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2022-09-27 10:44:00 +03:00
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print(f"Merging...")
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2022-10-14 09:05:06 +03:00
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2022-10-17 01:44:39 +03:00
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if theta_func1:
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for key in tqdm.tqdm(theta_1.keys()):
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if 'model' in key:
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2022-10-18 15:33:24 +03:00
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if key in theta_2:
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t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
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theta_1[key] = theta_func1(theta_1[key], t2)
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else:
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2022-10-18 16:05:52 +03:00
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theta_1[key] = torch.zeros_like(theta_1[key])
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2022-10-17 01:44:39 +03:00
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del theta_2, teritary_model
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2022-09-27 10:44:00 +03:00
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for key in tqdm.tqdm(theta_0.keys()):
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2022-09-26 02:22:12 +03:00
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if 'model' in key and key in theta_1:
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2022-10-14 21:20:28 +03:00
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2022-10-17 01:44:39 +03:00
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theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier)
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2022-10-14 21:20:28 +03:00
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2022-09-29 00:59:44 +03:00
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if save_as_half:
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theta_0[key] = theta_0[key].half()
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2022-10-10 04:26:52 +03:00
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2022-10-14 09:05:06 +03:00
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# I believe this part should be discarded, but I'll leave it for now until I am sure
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2022-09-26 02:22:12 +03:00
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for key in theta_1.keys():
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if 'model' in key and key not in theta_0:
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theta_0[key] = theta_1[key]
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2022-09-29 00:59:44 +03:00
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if save_as_half:
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theta_0[key] = theta_0[key].half()
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2022-09-27 10:44:00 +03:00
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2022-09-30 22:57:25 +03:00
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ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
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2022-10-14 22:01:49 +03:00
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filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
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2022-09-29 02:50:34 +03:00
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filename = filename if custom_name == '' else (custom_name + '.ckpt')
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2022-09-30 22:57:25 +03:00
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output_modelname = os.path.join(ckpt_dir, filename)
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2022-09-29 00:21:54 +03:00
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2022-09-27 10:44:00 +03:00
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print(f"Saving to {output_modelname}...")
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2022-09-28 04:34:24 +03:00
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torch.save(primary_model, output_modelname)
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2022-09-27 10:44:00 +03:00
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2022-09-29 00:59:44 +03:00
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sd_models.list_models()
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2022-09-27 10:44:00 +03:00
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print(f"Checkpoint saved.")
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2022-10-14 09:05:06 +03:00
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return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
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