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Holy $hit.

Browse Source 
Yep.

Fix gfpgan_model_arch requirement(s).
Add Upscaler base class, move from images.
Add a lot of methods to Upscaler.
Re-work all the child upscalers to be proper classes.
Add BSRGAN scaler.
Add ldsr_model_arch class, removing the dependency for another repo that just uses regular latent-diffusion stuff.
Add one universal method that will always find and load new upscaler models without having to add new "setup_model" calls. Still need to add command line params, but that could probably be automated.
Add a "self.scale" property to all Upscalers so the scalers themselves can do "things" in response to the requested upscaling size.
Ensure LDSR doesn't get stuck in a longer loop of "upscale/downscale/upscale" as we try to reach the target upscale size.
Add typehints for IDE sanity.
PEP-8 improvements.
Moar.
This commit is contained in:
d8ahazard
2022-09-29 17:46:23 -05:00
parent 31ad536c33
commit 0dce0df1ee
18 changed files with 1009 additions and 641 deletions
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223
modules/esrgan_model.py
View File

@@ -1,6 +1,4 @@
import os
import sys
import traceback
import numpy as np
import torch
@@ -8,93 +6,119 @@ from PIL import Image
from basicsr.utils.download_util import load_file_from_url
import modules.esrgam_model_arch as arch
import modules.images
from modules import shared
from modules import shared, modelloader
from modules import shared, modelloader, images
from modules.devices import has_mps
from modules.paths import models_path
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import opts
model_dir = "ESRGAN"
model_path = os.path.join(models_path, model_dir)
model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
model_name = "ESRGAN_x4"
class UpscalerESRGAN(Upscaler):
def __init__(self, dirname):
self.name = "ESRGAN"
self.model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
self.model_name = "ESRGAN 4x"
self.scalers = []
self.user_path = dirname
self.model_path = os.path.join(models_path, self.name)
super().__init__()
model_paths = self.find_models(ext_filter=[".pt", ".pth"])
scalers = []
if len(model_paths) == 0:
scaler_data = UpscalerData(self.model_name, self.model_url, self, 4)
scalers.append(scaler_data)
for file in model_paths:
print(f"File: {file}")
if "http" in file:
name = self.model_name
else:
name = modelloader.friendly_name(file)
def load_model(path: str, name: str):
global model_path
global model_url
global model_dir
global model_name
if "http" in path:
filename = load_file_from_url(url=model_url, model_dir=model_path, file_name="%s.pth" % model_name, progress=True)
else:
filename = path
if not os.path.exists(filename) or filename is None:
print("Unable to load %s from %s" % (model_dir, filename))
return None
print("Loading %s from %s" % (model_dir, filename))
# this code is adapted from https://github.com/xinntao/ESRGAN
pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
scaler_data = UpscalerData(name, file, self, 4)
print(f"ESRGAN: Adding scaler {name}")
self.scalers.append(scaler_data)
if 'conv_first.weight' in pretrained_net:
crt_model.load_state_dict(pretrained_net)
def do_upscale(self, img, selected_model):
model = self.load_model(selected_model)
if model is None:
return img
model.to(shared.device)
img = esrgan_upscale(model, img)
return img
def load_model(self, path: str):
if "http" in path:
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
file_name="%s.pth" % self.model_name,
progress=True)
else:
filename = path
if not os.path.exists(filename) or filename is None:
print("Unable to load %s from %s" % (self.model_path, filename))
return None
# this code is adapted from https://github.com/xinntao/ESRGAN
pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
if 'conv_first.weight' in pretrained_net:
crt_model.load_state_dict(pretrained_net)
return crt_model
if 'model.0.weight' not in pretrained_net:
is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net[
"params_ema"]
if is_realesrgan:
raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
else:
raise Exception("The file is not a ESRGAN model.")
crt_net = crt_model.state_dict()
load_net_clean = {}
for k, v in pretrained_net.items():
if k.startswith('module.'):
load_net_clean[k[7:]] = v
else:
load_net_clean[k] = v
pretrained_net = load_net_clean
tbd = []
for k, v in crt_net.items():
tbd.append(k)
# directly copy
for k, v in crt_net.items():
if k in pretrained_net and pretrained_net[k].size() == v.size():
crt_net[k] = pretrained_net[k]
tbd.remove(k)
crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
for k in tbd.copy():
if 'RDB' in k:
ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[k] = pretrained_net[ori_k]
tbd.remove(k)
crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
crt_model.load_state_dict(crt_net)
crt_model.eval()
return crt_model
if 'model.0.weight' not in pretrained_net:
is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
if is_realesrgan:
raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
else:
raise Exception("The file is not a ESRGAN model.")
crt_net = crt_model.state_dict()
load_net_clean = {}
for k, v in pretrained_net.items():
if k.startswith('module.'):
load_net_clean[k[7:]] = v
else:
load_net_clean[k] = v
pretrained_net = load_net_clean
tbd = []
for k, v in crt_net.items():
tbd.append(k)
# directly copy
for k, v in crt_net.items():
if k in pretrained_net and pretrained_net[k].size() == v.size():
crt_net[k] = pretrained_net[k]
tbd.remove(k)
crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
crt_net['conv_first.bias'] = pretrained_net['model.0.bias']
for k in tbd.copy():
if 'RDB' in k:
ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
if '.weight' in k:
ori_k = ori_k.replace('.weight', '.0.weight')
elif '.bias' in k:
ori_k = ori_k.replace('.bias', '.0.bias')
crt_net[k] = pretrained_net[ori_k]
tbd.remove(k)
crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
crt_net['conv_last.bias'] = pretrained_net['model.10.bias']
crt_model.load_state_dict(crt_net)
crt_model.eval()
return crt_model
def upscale_without_tiling(model, img):
img = np.array(img)
@@ -115,7 +139,7 @@ def esrgan_upscale(model, img):
if opts.ESRGAN_tile == 0:
return upscale_without_tiling(model, img)
grid = modules.images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
grid = images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
newtiles = []
scale_factor = 1
@@ -130,38 +154,7 @@ def esrgan_upscale(model, img):
newrow.append([x * scale_factor, w * scale_factor, output])
newtiles.append([y * scale_factor, h * scale_factor, newrow])
newgrid = modules.images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
output = modules.images.combine_grid(newgrid)
newgrid = images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor,
grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
output = images.combine_grid(newgrid)
return output
class UpscalerESRGAN(modules.images.Upscaler):
def __init__(self, filename, title):
self.name = title
self.filename = filename
def do_upscale(self, img):
model = load_model(self.filename, self.name)
if model is None:
return img
model.to(shared.device)
img = esrgan_upscale(model, img)
return img
def setup_model(dirname):
global model_path
global model_name
if not os.path.exists(model_path):
os.makedirs(model_path)
model_paths = modelloader.load_models(model_path, command_path=dirname, ext_filter=[".pt", ".pth"])
if len(model_paths) == 0:
modules.shared.sd_upscalers.append(UpscalerESRGAN(model_url, model_name))
for file in model_paths:
name = modelloader.friendly_name(file)
try:
modules.shared.sd_upscalers.append(UpscalerESRGAN(file, name))
except Exception:
print(f"Error loading ESRGAN model: {file}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)