Merge pull request #3976 from victorca25/esrgan_fea (e359268b) · Commits · github_fork / Stable Diffusion Webui

modules/esrgan_model.py

+13 −4

Original line number	Diff line number	Diff line
		@@ -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 −0

Original line number	Diff line number	Diff line
		@@ -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)

+1 −1

Original line number	Diff line number	Diff line
		@@ -1054,7 +1054,7 @@ 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():

+16 −1

Original line number	Diff line number	Diff line
		@@ -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)]
		No newline at end of file