227 lines
8.9 KiB
Python
227 lines
8.9 KiB
Python
import math
|
|
import os
|
|
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
import torch
|
|
import tqdm
|
|
|
|
from modules import processing, shared, images, devices, sd_models
|
|
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
|
|
import gradio as gr
|
|
|
|
|
|
cached_images = {}
|
|
|
|
|
|
def run_extras(extras_mode, resize_mode, image, image_folder, 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):
|
|
devices.torch_gc()
|
|
|
|
imageArr = []
|
|
# Also keep track of original file names
|
|
imageNameArr = []
|
|
|
|
if extras_mode == 1:
|
|
#convert file to pillow image
|
|
for img in image_folder:
|
|
image = Image.open(img)
|
|
imageArr.append(image)
|
|
imageNameArr.append(os.path.splitext(img.orig_name)[0])
|
|
else:
|
|
imageArr.append(image)
|
|
imageNameArr.append(None)
|
|
|
|
outpath = opts.outdir_samples or opts.outdir_extras_samples
|
|
|
|
outputs = []
|
|
for image, image_name in zip(imageArr, imageNameArr):
|
|
if image is None:
|
|
return outputs, "Please select an input image.", ''
|
|
existing_pnginfo = image.info or {}
|
|
|
|
image = image.convert("RGB")
|
|
info = ""
|
|
|
|
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, 2)}\n"
|
|
image = res
|
|
|
|
if resize_mode == 1:
|
|
upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
|
|
crop_info = " (crop)" if upscaling_crop else ""
|
|
info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
|
|
|
|
if upscaling_resize != 1.0:
|
|
def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
|
|
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.scaler.upscale(image, resize, upscaler.data_path)
|
|
if mode == 1 and crop:
|
|
cropped = Image.new("RGB", (resize_w, resize_h))
|
|
cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
|
|
c = cropped
|
|
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, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
|
|
|
|
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
|
|
res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
|
|
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,
|
|
forced_filename=image_name if opts.use_original_name_batch else None)
|
|
|
|
if opts.enable_pnginfo:
|
|
image.info = existing_pnginfo
|
|
image.info["extras"] = info
|
|
|
|
outputs.append(image)
|
|
|
|
devices.torch_gc()
|
|
|
|
return outputs, plaintext_to_html(info), ''
|
|
|
|
|
|
def run_pnginfo(image):
|
|
if image is None:
|
|
return '', '', ''
|
|
|
|
items = image.info
|
|
geninfo = ''
|
|
|
|
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
|
|
geninfo = exif_comment
|
|
|
|
for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
|
|
'loop', 'background', 'timestamp', 'duration']:
|
|
items.pop(field, None)
|
|
|
|
geninfo = items.get('parameters', geninfo)
|
|
|
|
info = ''
|
|
for key, text in items.items():
|
|
info += f"""
|
|
<div>
|
|
<p><b>{plaintext_to_html(str(key))}</b></p>
|
|
<p>{plaintext_to_html(str(text))}</p>
|
|
</div>
|
|
""".strip()+"\n"
|
|
|
|
if len(info) == 0:
|
|
message = "Nothing found in the image."
|
|
info = f"<div><p>{message}<p></div>"
|
|
|
|
return '', geninfo, info
|
|
|
|
|
|
def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
|
|
def weighted_sum(theta0, theta1, theta2, alpha):
|
|
return ((1 - alpha) * theta0) + (alpha * theta1)
|
|
|
|
def add_difference(theta0, theta1, theta2, alpha):
|
|
return theta0 + (theta1 - theta2) * alpha
|
|
|
|
primary_model_info = sd_models.checkpoints_list[primary_model_name]
|
|
secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
|
|
teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
|
|
|
|
print(f"Loading {primary_model_info.filename}...")
|
|
primary_model = torch.load(primary_model_info.filename, map_location='cpu')
|
|
theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
|
|
|
|
print(f"Loading {secondary_model_info.filename}...")
|
|
secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
|
|
theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
|
|
|
|
if teritary_model_info is not None:
|
|
print(f"Loading {teritary_model_info.filename}...")
|
|
teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
|
|
theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
|
|
else:
|
|
theta_2 = None
|
|
|
|
theta_funcs = {
|
|
"Weighted sum": weighted_sum,
|
|
"Add difference": add_difference,
|
|
}
|
|
theta_func = theta_funcs[interp_method]
|
|
|
|
print(f"Merging...")
|
|
|
|
for key in tqdm.tqdm(theta_0.keys()):
|
|
if 'model' in key and key in theta_1:
|
|
t2 = (theta_2 or {}).get(key)
|
|
if t2 is None:
|
|
t2 = torch.zeros_like(theta_0[key])
|
|
|
|
theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
|
|
|
|
if save_as_half:
|
|
theta_0[key] = theta_0[key].half()
|
|
|
|
# I believe this part should be discarded, but I'll leave it for now until I am sure
|
|
for key in theta_1.keys():
|
|
if 'model' in key and key not in theta_0:
|
|
theta_0[key] = theta_1[key]
|
|
if save_as_half:
|
|
theta_0[key] = theta_0[key].half()
|
|
|
|
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
|
|
|
|
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'
|
|
filename = filename if custom_name == '' else (custom_name + '.ckpt')
|
|
output_modelname = os.path.join(ckpt_dir, filename)
|
|
|
|
print(f"Saving to {output_modelname}...")
|
|
torch.save(primary_model, output_modelname)
|
|
|
|
sd_models.list_models()
|
|
|
|
print(f"Checkpoint saved.")
|
|
return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
|