updated torch, torchvision versions

inference_realesrgan_simplified.py

@@ -0,0 +1,113 @@
+import argparse
+import cv2
+import glob
+import os
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from basicsr.utils.download_util import load_file_from_url
+
+from .realesrgan import RealESRGANer
+from .realesrgan.archs.srvgg_arch import SRVGGNetCompact
+
+import torch
+
+def Upscale(input_file_path, 
+            output_file_path, 
+            model_name='RealESRGAN_x4plus', 
+            denoise_strength=0.5, 
+            outscale=4, 
+            tile=0,
+            tile_pad=10,
+            pre_pad=0,
+            face_enhance=True, 
+            fp32=False,
+            gpu_id=None,
+            ):
+    '''
+    Model_Name Options: RealESRGAN_x4plus | RealESRNet_x4plus | RealESRGAN_x4plus_anime_6B | RealESRGAN_x2plus | realesr-animevideov3 | realesr-general-x4v3
+    AlphaUpsampler Options: realesrgan | bicubic (Not used anywhere tho)
+    fp32 Options = fp32 | fp16(half precision)
+    Denoise Range = 0(weak) to 1(strong)
+    '''
+
+    gpu_id = 0 if torch.cuda.is_available() else None
+
+    # determine models according to model names
+    if model_name == 'RealESRGAN_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
+    elif model_name == 'RealESRNet_x4plus':  # x4 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
+    elif model_name == 'RealESRGAN_x4plus_anime_6B':  # x4 RRDBNet model with 6 blocks
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
+    elif model_name == 'RealESRGAN_x2plus':  # x2 RRDBNet model
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+        netscale = 2
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
+    elif model_name == 'realesr-animevideov3':  # x4 VGG-style model (XS size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
+        netscale = 4
+        file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
+    elif model_name == 'realesr-general-x4v3':  # x4 VGG-style model (S size)
+        model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
+        netscale = 4
+        file_url = [
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
+            'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
+        ]
+
+    model_path = os.path.join('weights', model_name + '.pth')
+    if not os.path.isfile(model_path): #If Model weights file is not in the weights parent dir
+        ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
+        for url in file_url:
+            # model_path will be updated / download from github
+            model_path = load_file_from_url(
+                url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
+
+    # use dni to control the denoise strength
+    dni_weight = None
+    if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
+        wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
+        model_path = [model_path, wdn_model_path]
+        dni_weight = [denoise_strength, 1 - denoise_strength]
+
+    # restorer
+    upsampler = RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        dni_weight=dni_weight,
+        model=model,
+        tile=tile,
+        tile_pad=tile_pad,
+        pre_pad=pre_pad,
+        half=not fp32,
+        gpu_id=gpu_id)
+
+    if face_enhance:  # Use GFPGAN for face enhancement
+        from gfpgan import GFPGANer
+        face_enhancer = GFPGANer(
+            model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
+            upscale=outscale,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=upsampler)
+
+    imgname, extension = os.path.splitext(os.path.basename(input_file_path))
+    print(f'UpSampling Image_FileName: {imgname}')
+
+    img = cv2.imread(input_file_path, cv2.IMREAD_UNCHANGED)
+    try:
+        if face_enhance:
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        else:
+            output, _ = upsampler.enhance(img, outscale=outscale)
+    except RuntimeError as error:
+        print('Error', error)
+        print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
+    else:
+        save_path = os.path.join(output_file_path)
+        cv2.imwrite(save_path, output)

requirements.txt

@@ -4,6 +4,6 @@ gfpgan>=1.3.5
 numpy
 opencv-python
 Pillow
-torch>=1.7
-torchvision
+torch==2.1.0
+torchvision==0.16.0
 tqdm