328 lines
12 KiB
Python
328 lines
12 KiB
Python
from __future__ import annotations
|
|
import math
|
|
import os
|
|
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
import torch
|
|
import tqdm
|
|
|
|
from typing import Callable, List, OrderedDict, Tuple
|
|
from functools import partial
|
|
from dataclasses import dataclass
|
|
|
|
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
|
|
|
|
|
|
class LruCache(OrderedDict):
|
|
@dataclass(frozen=True)
|
|
class Key:
|
|
image_hash: int
|
|
info_hash: int
|
|
args_hash: int
|
|
|
|
@dataclass
|
|
class Value:
|
|
image: Image.Image
|
|
info: str
|
|
|
|
def __init__(self, max_size: int = 5, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self._max_size = max_size
|
|
|
|
def get(self, key: LruCache.Key) -> LruCache.Value:
|
|
ret = super().get(key)
|
|
if ret is not None:
|
|
self.move_to_end(key) # Move to end of eviction list
|
|
return ret
|
|
|
|
def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
|
|
self[key] = value
|
|
while len(self) > self._max_size:
|
|
self.popitem(last=False)
|
|
|
|
|
|
cached_images: LruCache = LruCache(max_size=5)
|
|
|
|
|
|
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, upscale_first: bool):
|
|
devices.torch_gc()
|
|
|
|
imageArr = []
|
|
# Also keep track of original file names
|
|
imageNameArr = []
|
|
outputs = []
|
|
|
|
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])
|
|
elif extras_mode == 2:
|
|
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
|
|
|
|
if input_dir == '':
|
|
return outputs, "Please select an input directory.", ''
|
|
image_list = shared.listfiles(input_dir)
|
|
for img in image_list:
|
|
try:
|
|
image = Image.open(img)
|
|
except Exception:
|
|
continue
|
|
imageArr.append(image)
|
|
imageNameArr.append(img)
|
|
else:
|
|
imageArr.append(image)
|
|
imageNameArr.append(None)
|
|
|
|
if extras_mode == 2 and output_dir != '':
|
|
outpath = output_dir
|
|
else:
|
|
outpath = opts.outdir_samples or opts.outdir_extras_samples
|
|
|
|
# Extra operation definitions
|
|
|
|
def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
|
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"
|
|
return (res, info)
|
|
|
|
def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
|
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"
|
|
return (res, info)
|
|
|
|
def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
|
|
upscaler = shared.sd_upscalers[scaler_index]
|
|
res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
|
|
if mode == 1 and crop:
|
|
cropped = Image.new("RGB", (resize_w, resize_h))
|
|
cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
|
|
res = cropped
|
|
return res
|
|
|
|
def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
|
# Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
|
|
nonlocal upscaling_resize
|
|
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"
|
|
return (image, info)
|
|
|
|
@dataclass
|
|
class UpscaleParams:
|
|
upscaler_idx: int
|
|
blend_alpha: float
|
|
|
|
def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
|
blended_result: Image.Image = None
|
|
image_hash: str = hash(np.array(image.getdata()).tobytes())
|
|
for upscaler in params:
|
|
upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
|
|
upscaling_resize_w, upscaling_resize_h, upscaling_crop)
|
|
cache_key = LruCache.Key(image_hash=image_hash,
|
|
info_hash=hash(info),
|
|
args_hash=hash(upscale_args))
|
|
cached_entry = cached_images.get(cache_key)
|
|
if cached_entry is None:
|
|
res = upscale(image, *upscale_args)
|
|
info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
|
|
cached_images.put(cache_key, LruCache.Value(image=res, info=info))
|
|
else:
|
|
res, info = cached_entry.image, cached_entry.info
|
|
|
|
if blended_result is None:
|
|
blended_result = res
|
|
else:
|
|
blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
|
|
return (blended_result, info)
|
|
|
|
# Build a list of operations to run
|
|
facefix_ops: List[Callable] = []
|
|
facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
|
|
facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
|
|
|
|
upscale_ops: List[Callable] = []
|
|
upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
|
|
|
|
if upscaling_resize != 0:
|
|
step_params: List[UpscaleParams] = []
|
|
step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
|
|
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
|
|
step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
|
|
|
|
upscale_ops.append(partial(run_upscalers_blend, step_params))
|
|
|
|
extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
|
|
|
|
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 = ""
|
|
# Run each operation on each image
|
|
for op in extras_ops:
|
|
image, info = op(image, info)
|
|
|
|
if opts.use_original_name_batch and image_name != None:
|
|
basename = os.path.splitext(os.path.basename(image_name))[0]
|
|
else:
|
|
basename = ''
|
|
|
|
images.save_image(image, path=outpath, basename=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=None)
|
|
|
|
if opts.enable_pnginfo:
|
|
image.info = existing_pnginfo
|
|
image.info["extras"] = info
|
|
|
|
if extras_mode != 2 or show_extras_results :
|
|
outputs.append(image)
|
|
|
|
devices.torch_gc()
|
|
|
|
return outputs, plaintext_to_html(info), ''
|
|
|
|
def clear_cache():
|
|
cached_images.clear()
|
|
|
|
|
|
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, alpha):
|
|
return ((1 - alpha) * theta0) + (alpha * theta1)
|
|
|
|
def get_difference(theta1, theta2):
|
|
return theta1 - theta2
|
|
|
|
def add_difference(theta0, theta1_2_diff, alpha):
|
|
return theta0 + (alpha * theta1_2_diff)
|
|
|
|
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:
|
|
teritary_model = None
|
|
theta_2 = None
|
|
|
|
theta_funcs = {
|
|
"Weighted sum": (None, weighted_sum),
|
|
"Add difference": (get_difference, add_difference),
|
|
}
|
|
theta_func1, theta_func2 = theta_funcs[interp_method]
|
|
|
|
print(f"Merging...")
|
|
|
|
if theta_func1:
|
|
for key in tqdm.tqdm(theta_1.keys()):
|
|
if 'model' in key:
|
|
if key in theta_2:
|
|
t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
|
|
theta_1[key] = theta_func1(theta_1[key], t2)
|
|
else:
|
|
theta_1[key] = torch.zeros_like(theta_1[key])
|
|
del theta_2, teritary_model
|
|
|
|
for key in tqdm.tqdm(theta_0.keys()):
|
|
if 'model' in key and key in theta_1:
|
|
|
|
theta_0[key] = theta_func2(theta_0[key], theta_1[key], 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)]
|