openvinotoolkit/stable-diffusion-webui
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add resrgan 8x, allow use 1x and up to 8x extra models, move BSRGAN model, add nearest

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victorca25 2022-10-30 12:52:50 +01:00 committed by victorca25
parent 17a2076f72
commit c9bb33dd43
4 changed files with 33 additions and 6 deletions
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17
modules/esrgan_model.py
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@ -50,6 +50,7 @@ def mod2normal(state_dict):
def resrgan2normal(state_dict, nb=23):
# this code is copied from https://github.com/victorca25/iNNfer
if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
re8x = 0
crt_net = {}
items = []
for k, v in state_dict.items():
@ -75,10 +76,18 @@ def resrgan2normal(state_dict, nb=23):
crt_net['model.3.bias'] = state_dict['conv_up1.bias']
crt_net['model.6.weight'] = state_dict['conv_up2.weight']
crt_net['model.6.bias'] = state_dict['conv_up2.bias']
crt_net['model.8.weight'] = state_dict['conv_hr.weight']
crt_net['model.8.bias'] = state_dict['conv_hr.bias']
crt_net['model.10.weight'] = state_dict['conv_last.weight']
crt_net['model.10.bias'] = state_dict['conv_last.bias']
if 'conv_up3.weight' in state_dict:
# modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
re8x = 3
crt_net['model.9.weight'] = state_dict['conv_up3.weight']
crt_net['model.9.bias'] = state_dict['conv_up3.bias']
crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
state_dict = crt_net
return state_dict

3
modules/modelloader.py
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@ -85,6 +85,9 @@ def cleanup_models():
src_path = os.path.join(root_path, "ESRGAN")
dest_path = os.path.join(models_path, "ESRGAN")
move_files(src_path, dest_path)
src_path = os.path.join(models_path, "BSRGAN")
dest_path = os.path.join(models_path, "ESRGAN")
move_files(src_path, dest_path, ".pth")
src_path = os.path.join(root_path, "gfpgan")
dest_path = os.path.join(models_path, "GFPGAN")
move_files(src_path, dest_path)

2
modules/ui.py
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@ -1059,7 +1059,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Tabs(elem_id="extras_resize_mode"):
with gr.TabItem('Scale by'):
upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
with gr.TabItem('Scale to'):
with gr.Group():
with gr.Row():

17
modules/upscaler.py
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@ -10,6 +10,7 @@ import modules.shared
from modules import modelloader, shared
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
NEAREST = (Image.Resampling.NEAREST if hasattr(Image, 'Resampling') else Image.NEAREST)
from modules.paths import models_path
@ -57,7 +58,7 @@ class Upscaler:
dest_w = img.width * scale
dest_h = img.height * scale
for i in range(3):
if img.width >= dest_w and img.height >= dest_h:
if img.width > dest_w and img.height > dest_h:
break
img = self.do_upscale(img, selected_model)
if img.width != dest_w or img.height != dest_h:
@ -120,3 +121,17 @@ class UpscalerLanczos(Upscaler):
self.name = "Lanczos"
self.scalers = [UpscalerData("Lanczos", None, self)]
class UpscalerNearest(Upscaler):
scalers = []
def do_upscale(self, img, selected_model=None):
return img.resize((int(img.width * self.scale), int(img.height * self.scale)), resample=NEAREST)
def load_model(self, _):
pass
def __init__(self, dirname=None):
super().__init__(False)
self.name = "Nearest"
self.scalers = [UpscalerData("Nearest", None, self)]