Upload 3 files

minimal complexity

app.py

@@ -23,68 +23,53 @@
 # SOFTWARE.
 #
 #######################################################################################
-
-# This file implements an API endpoint RealESRGAN system.
+#
+#
+# This file implements an API endpoint GFPGAN system.
 # It provides functionality to enhances an image by upscaling it 4 times its original size .
-
-
+#
+#
 # Source code is based on or inspired by several projects.
 # For more details and proper attribution, please refer to the following resources:
 #
-# - [Real-ESRGAN] - [https://github.com/xinntao/Real-ESRGAN]
-
+# - [GFPGAN] - [https://github.com/TencentARC/GFPGAN]
 
-import torch
 import gradio as gr
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from huggingface_hub import hf_hub_download
-from realesrgan import RealESRGANer
+import numpy as np
+from facexlib.utils.face_restoration_helper import FaceRestoreHelper
 
-REALESRGAN_REPO_ID = 'leonelhs/realesrgan'
+from tiny_esrgan import TinyESRGAN
+from tiny_gfpgan import TinyGFPGAN
 
-MODELS = {
-    'RealESRGAN X4+':'RealESRGAN_x4plus.pth',
-    'RealESRNet X4+':'RealESRNet_x4plus.pth',
-    'AI-Forever X4+':'AI-Forever_x4plus.pth'
-}
+face_enhancer = TinyGFPGAN()
+background_enhancer = TinyESRGAN()
+face_helper = FaceRestoreHelper(upscale_factor=4, use_parse=True, model_rootpath='gfpgan/weights')
 
-def download_model(file):
-    return hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename=file)
+def predict(img):
 
-def predict(img, method="RealESRGAN_x4plus.pth"):
-    """
-        Enhances an image by upscaling it 4 times its original size.
-        Parameters:
-            img (string):  Path to the input image file.
-            method (string): Upscaling method to use (RealESRGAN, RealESRNet, AI-Forever).
-        Returns:
-            path: File path to the enhanced, upscaled image.
-    """
+    img = np.asarray(img)
+    face_helper.clean_all()
+    face_helper.read_image(img)
+    # get face landmarks for each face
+    face_helper.get_face_landmarks_5(eye_dist_threshold=5)
+    face_helper.align_warp_face()
 
-    model_path = download_model(method)
+    face_helper.restored_faces = face_enhancer.enhance(face_helper.cropped_faces)
+    bg = background_enhancer.enhance(img)
 
-    # 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)
-    half =  True if torch.cuda.is_available() else False
+    face_helper.get_inverse_affine(None)
+    return face_helper.paste_faces_to_input_image(upsample_img=bg)
 
-    output , _ = RealESRGANer(
-        scale=4,
-        model_path=model_path,
-        model=model,
-        half=half).enhance(img)
 
-    return output
 
 app = gr.Interface(
     predict, [
-        gr.Image(type="numpy", label="Image input"),
-        gr.Dropdown(list(MODELS.items()),
-                type="value", value='RealESRGAN_x4plus.pth', label='Enhancer model'),
+        gr.Image(type="pil", label="Image input"),
     ], [
-        gr.Image(type="numpy", label="Image enhanced")
+        gr.Image(type="numpy", label="Image face enhanced")
     ],
     title="Image enhancer",
-    description="RealESRGAN X4 plus")
+    description="GFPGAN")
 
 app.launch(share=False, debug=True, show_error=True, mcp_server=True)
 app.queue()

tiny_esrgan.py

@@ -0,0 +1,55 @@
+#######################################################################################
+#
+# MIT License
+#
+# Copyright (c) [2025] [leonelhs@gmail.com]
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+#######################################################################################
+
+# This file implements an API endpoint ESRGAN system.
+# It provides functionality to enhances an image by upscaling it 4 times its original size .
+
+
+# Source code is based on or inspired by several projects.
+# For more details and proper attribution, please refer to the following resources:
+#
+# This code aims to study ESRGAN image restoration.
+# It has isolated te minimal functionalities from ESRGAN x4 plus project.
+#
+# [Real-ESRGAN] - [https://github.com/xinntao/Real-ESRGAN]
+#
+
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from huggingface_hub import hf_hub_download
+from realesrgan import RealESRGANer
+
+REPO_ID = 'leonelhs/realesrgan'
+
+class TinyESRGAN:
+    def __init__(self):
+        half = True if torch.cuda.is_available() else False
+        model_path = hf_hub_download(repo_id=REPO_ID, filename="RealESRGAN_x4plus.pth")
+        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+        self.esrgan = RealESRGANer(scale=4, model_path=model_path, model=model, half=half)
+
+    def enhance(self, img):
+        return self.esrgan.enhance(img)[0]

tiny_gfpgan.py

@@ -0,0 +1,92 @@
+#######################################################################################
+#
+# MIT License
+#
+# Copyright (c) [2025] [leonelhs@gmail.com]
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+#######################################################################################
+
+# This file implements an API endpoint GFPGAN system.
+# It provides functionality to enhances an image by upscaling it 4 times its original size .
+
+
+# Source code is based on or inspired by several projects.
+# For more details and proper attribution, please refer to the following resources:
+#
+# - [GFPGAN] - [https://github.com/TencentARC/GFPGAN]
+# - [utils.py] - [https://github.com/TencentARC/GFPGAN/blob/master/gfpgan/utils.py]
+
+
+import torch
+from basicsr.utils import img2tensor, tensor2img
+from gfpgan.archs.gfpganv1_clean_arch import GFPGANv1Clean
+from huggingface_hub import hf_hub_download
+from torchvision.transforms.functional import normalize
+
+GFPGAN_REPO_ID = 'leonelhs/gfpgan'
+
+class TinyGFPGAN:
+    """
+    Minimal functionalities from GFPGAN project.
+
+    Args:
+        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
+    """
+
+    def __init__(self, channel_multiplier=2, device=None):
+
+        # initialize model
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
+        # initialize the GFP-GAN
+        self.gfpgan = GFPGANv1Clean(
+            out_size=512,
+            channel_multiplier=channel_multiplier,
+            fix_decoder=False,
+            input_is_latent=True,
+            different_w=True,
+            sft_half=True)
+
+        model_path = hf_hub_download(repo_id=GFPGAN_REPO_ID, filename="GFPGANv1.4.pth")
+        loadnet = torch.load(model_path)
+        if 'params_ema' in loadnet:
+            keyname = 'params_ema'
+        else:
+            keyname = 'params'
+        self.gfpgan.load_state_dict(loadnet[keyname], strict=True)
+        self.gfpgan.eval()
+        self.gfpgan = self.gfpgan.to(self.device)
+
+    @torch.no_grad()
+    def inference(self, cropped_face, weight=0.5):
+        cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+        normalize(cropped_face_t, [0.5, 0.5, 0.5], [0.5, 0.5, 0.5], inplace=True)
+        cropped_face_t = cropped_face_t.unsqueeze(0).to(self.device)
+
+        try:
+            output = self.gfpgan(cropped_face_t, return_rgb=False, weight=weight)[0]
+            # convert to image
+            restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))
+            return restored_face.astype('uint8')
+        except RuntimeError as error:
+            raise ValueError(f'\tFailed inference for GFPGAN: {error}.')
+
+    def enhance(self, cropped_faces):
+        return [self.inference(face) for face in cropped_faces]