stable-diffusion-webui/modules/extras.py
Bernard Maltais 4c9c2a6702 -Fix interp_amount rounding in filename
-Sort list of checkpoints in list
2022-09-27 22:16:25 -04:00

194 lines
7.4 KiB
Python

import os
import numpy as np
from PIL import Image
import torch
import tqdm
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(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, 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.fromarray(np.array(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):
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 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,
forced_filename=image_name if opts.use_original_name_batch else None)
outputs.append(image)
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, interp_method, interp_amount):
# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
# Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
def sigmoid(theta0, theta1, alpha):
alpha = alpha * alpha * (3 - (2 * alpha))
return theta0 + ((theta1 - theta0) * alpha)
if os.path.exists(primary_model_name):
primary_model_filename = primary_model_name
primary_model_name = os.path.splitext(os.path.basename(primary_model_name))[0]
else:
primary_model_filename = 'models/' + primary_model_name + '.ckpt'
if os.path.exists(secondary_model_name):
secondary_model_filename = secondary_model_name
secondary_model_name = os.path.splitext(os.path.basename(secondary_model_name))[0]
else:
secondary_model_filename = 'models/' + secondary_model_name + '.ckpt'
print(f"Loading {primary_model_filename}...")
primary_model = torch.load(primary_model_filename, map_location='cpu')
print(f"Loading {secondary_model_filename}...")
secondary_model = torch.load(secondary_model_filename, map_location='cpu')
theta_0 = primary_model['state_dict']
theta_1 = secondary_model['state_dict']
theta_funcs = {
"Weighted Sum": weighted_sum,
"Sigmoid": sigmoid,
}
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:
theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
output_modelname = 'models/' + primary_model_name + '_' + str(round(interp_amount,2)) + '-' + secondary_model_name + '_' + str(round((float(1.0) - interp_amount),2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
print(f"Saving to {output_modelname}...")
torch.save(primary_model, output_modelname)
print(f"Checkpoint saved.")
return "Checkpoint saved to " + output_modelname