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):

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 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.scaler.upscale(image, resize, upscaler.data_path)

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)

devices.torch_gc()

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"""

""".strip()+"\n"

if len(info) == 0:

message = "Nothing found in the image."

info = f"<div><p>{message}<p></div>"

# 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)

# Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)

def inv_sigmoid(theta0, theta1, alpha):

import math

alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)

return theta0 + ((theta1 - theta0) * alpha)

primary_model_info = sd_models.checkpoints_list[primary_model_name]

secondary_model_info = sd_models.checkpoints_list[secondary_model_name]

print(f"Loading {primary_model_info.filename}...")

primary_model = torch.load(primary_model_info.filename, map_location='cpu')

print(f"Loading {secondary_model_info.filename}...")

secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')

theta_0 = primary_model['state_dict']

theta_1 = secondary_model['state_dict']

theta_funcs = {

"Weighted Sum": weighted_sum,

"Sigmoid": sigmoid,

"Inverse Sigmoid": inv_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

if save_as_half:

theta_0[key] = theta_0[key].half()

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(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 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.")