add upsampler

app.py

@@ -2,6 +2,7 @@ import os
 
 import gradio as gr
 import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
 from basicsr.archs.srvgg_arch import SRVGGNetCompact
 from gfpgan.utils import GFPGANer
 from huggingface_hub import hf_hub_download
@@ -12,46 +13,115 @@ GFPGAN_REPO_ID = 'leonelhs/gfpgan'
 
 os.system("pip freeze")
 
-# background enhancer with RealESRGAN
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename='realesr-general-x4v3.pth')
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
 
+def showGPU():
+    if torch.cuda.is_available():
+        devices = torch.cuda.device_count()
+        current = torch.cuda.current_device()
+        return f"Running on GPU:{current} of {devices} total devices"
+    return "Running on CPU"
 
-def download_model(file):
+
+def download_model_gfpgan(file):
     return hf_hub_download(repo_id=GFPGAN_REPO_ID, filename=file)
 
 
-def predict(image, version, scale):
+def download_model_realesrgan(file):
+    return hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename=file)
+
+
+def select_upsampler(version, netscale=4):
+    model = None
+    dni_weight = None
+
+    version = version + ".pth"
+    model_path = download_model_realesrgan(version)
+
+    if version == 'RealESRGAN_x4plus.pth':  # 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)
+
+    if version == 'RealESRNet_x4plus.pth':  # 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)
+
+    if version == 'AI-Forever_x4plus.pth':  # 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)
+
+    if version == 'RealESRGAN_x4plus_anime_6B.pth':  # 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)
+
+    if version == 'RealESRGAN_x2plus.pth':  # 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  # This is
+
+    if version == 'AI-Forever_x2plus.pth':  # 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  # This is
+
+    if version == 'realesr-animevideov3.pth':  # 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')
+
+    if version == 'realesr-general-x4v3.pth':  # 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')
+        model_path = [
+            download_model_realesrgan("realesr-general-x4v3.pth"),
+            download_model_realesrgan("realesr-general-wdn-x4v3.pth")
+        ]
+        dni_weight = [0.2, 0.8]
+
+    half = True if torch.cuda.is_available() else False
+
+    return RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        dni_weight=dni_weight,
+        model=model,
+        tile=0,
+        tile_pad=10,
+        pre_pad=0,
+        half=half,
+        gpu_id=0)
+
+
+def select_face_enhancer(version, scale, upsampler):
+    if 'v1.2' in version:
+        model_path = download_model_gfpgan('GFPGANv1.2.pth')
+        return GFPGANer(
+            model_path=model_path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+    elif 'v1.3' in version:
+        model_path = download_model_gfpgan('GFPGANv1.3.pth')
+        return GFPGANer(
+            model_path=model_path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+    elif 'v1.4' in version:
+        model_path = download_model_gfpgan('GFPGANv1.4.pth')
+        return GFPGANer(
+            model_path=model_path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+    elif 'RestoreFormer' in version:
+        model_path = download_model_gfpgan('RestoreFormer.pth')
+        return GFPGANer(
+            model_path=model_path, upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
+
+
+def predict(image, version_upsampler, version_enhancer, scale):
     scale = int(scale)
-    face_enhancer = None
-
-    if version == 'v1.2':
-        path = download_model('GFPGANv1.2.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif version == 'v1.3':
-        path = download_model('GFPGANv1.3.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif version == 'v1.4':
-        path = download_model('GFPGANv1.4.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-    elif version == 'RestoreFormer':
-        path = download_model('RestoreFormer.pth')
-        face_enhancer = GFPGANer(
-            model_path=path, upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-
-    _, _, output = face_enhancer.enhance(image, has_aligned=False, only_center_face=False, paste_back=True)
-
-    return output
-
-
-title = "GFPGAN"
+
+    upsampler = select_upsampler(version_upsampler)
+
+    if "No additional" not in version_enhancer:
+        face_enhancer = select_face_enhancer(version_enhancer, scale, upsampler)
+        _, _, output = face_enhancer.enhance(image, has_aligned=False, only_center_face=False, paste_back=True)
+    else:
+        output, _ = upsampler.enhance(image, outscale=scale)
+
+    log = f"General enhance version: {version_upsampler}\n " \
+          f"Face enhance version: {version_enhancer} \n " \
+          f"Scale:{scale} \n {showGPU()}"
+
+    return output, log
+
+
+title = "Super Face"
 description = r"""
-<b>Practical Face Restoration Algorithm</b>
+<b>Practical Image Restoration Algorithm based on Real-ESRGAN, GFPGAN</b>
 """
 article = r"""
 <center><span>[email protected] or [email protected]</span></center>
@@ -62,13 +132,31 @@ article = r"""
 demo = gr.Interface(
     predict, [
         gr.Image(type="numpy", label="Input"),
-        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
+        gr.Dropdown([
+            'RealESRGAN_x2plus',
+            'RealESRGAN_x4plus',
+            'RealESRNet_x4plus',
+            'AI-Forever_x2plus',
+            'AI-Forever_x4plus',
+            'RealESRGAN_x4plus_anime_6B',
+            'realesr-animevideov3',
+            'realesr-general-x4v3'],
+            type="value", value='RealESRGAN_x4plus', label='General restoration algorithm', info="version"),
+        gr.Dropdown([
+            'No additional face process',
+            'GFPGANv1.2',
+            'GFPGANv1.3',
+            'GFPGANv1.4',
+            'RestoreFormer'],
+            type="value", value='No additional face process', label='Special face restoration algorithm',
+            info="version"),
         gr.Dropdown(["1", "2", "3", "4"], value="2", label="Rescaling factor")
     ], [
-        gr.Image(type="numpy", label="Output", interactive=False)
+        gr.Image(type="numpy", label="Output", interactive=False),
+        gr.Textbox(label="log info")
     ],
     title=title,
     description=description,
     article=article)
 
-demo.queue().launch()
+demo.queue().launch(share=True, debug=True)