stable-diffusion-webui/modules/extras.py

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)]