v1.3.5

* update util

* update predict

* update predict

* update predict

* update predict

* update predict

* update predict

* update predict

* update predict

* merge replicate update

.github/workflows/release.yml

@@ -0,0 +1,41 @@
+name: release
+on:
+  push:
+    tags:
+      - '*'
+
+jobs:
+  build:
+    permissions: write-all
+    name: Create Release
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+      - name: Create Release
+        id: create_release
+        uses: actions/create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          tag_name: ${{ github.ref }}
+          release_name: GFPGAN ${{ github.ref }} Release Note
+          body: |
+            🚀 See you again 😸
+            🚀Have a nice day 😸 and happy everyday 😃
+            🚀 Long time no see ☄️
+
+            ✨ **Highlights**
+            ✅ [Features] Support ...
+
+            🐛 **Bug Fixes**
+
+            🌴 **Improvements**
+
+            📢📢📢
+
+            <p align="center">
+               <img src="https://raw.githubusercontent.com/TencentARC/GFPGAN/master/assets/gfpgan_logo.png" height=150>
+            </p>
+          draft: true
+          prerelease: false

README.md

@@ -4,6 +4,11 @@
 
 ## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div>
 
+<div align="center">
+<!-- <a href="https://twitter.com/_Xintao_" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/17445847/187162058-c764ced6-952f-404b-ac85-ba95cce18e7b.png" width="4%" alt="" />
+</a> -->
+
 [![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
 [![PyPI](https://img.shields.io/pypi/v/gfpgan)](https://pypi.org/project/gfpgan/)
 [![Open issue](https://img.shields.io/github/issues/TencentARC/GFPGAN)](https://github.com/TencentARC/GFPGAN/issues)
@@ -11,12 +16,15 @@
 [![LICENSE](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
 [![python lint](https://github.com/TencentARC/GFPGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)
 [![Publish-pip](https://github.com/TencentARC/GFPGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/publish-pip.yml)
+</div>
 
+1. :boom: **Updated** online demo: [![Replicate](https://img.shields.io/static/v1?label=Demo&message=Replicate&color=blue)](https://replicate.com/tencentarc/gfpgan). Here is the [backup](https://replicate.com/xinntao/gfpgan).
+1. :boom: **Updated** online demo: [![Huggingface Gradio](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/Xintao/GFPGAN)
 1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model)
-2. Online demo: [Huggingface](https://huggingface.co/spaces/akhaliq/GFPGAN) (return only the cropped face)
-3. Online demo: [Replicate.ai](https://replicate.com/xinntao/gfpgan) (may need to sign in, return the whole image)
+
+<!-- 3. Online demo: [Replicate.ai](https://replicate.com/xinntao/gfpgan) (may need to sign in, return the whole image)
 4. Online demo: [Baseten.co](https://app.baseten.co/applications/Q04Lz0d/operator_views/8qZG6Bg) (backed by GPU, returns the whole image)
-5. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**.
+5. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**. -->
 
 > :rocket: **Thanks for your interest in our work. You may also want to check our new updates on the *tiny models* for *anime images and videos* in [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_video_model.md)** :blush:
 

VERSION

@@ -1 +1 @@
-1.3.2
+1.3.5

cog.yaml

@@ -0,0 +1,22 @@
+# This file is used for constructing replicate env
+image: "r8.im/tencentarc/gfpgan"
+
+build:
+  gpu: true
+  python_version: "3.8"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_packages:
+    - "torch==1.7.1"
+    - "torchvision==0.8.2"
+    - "numpy==1.21.1"
+    - "lmdb==1.2.1"
+    - "opencv-python==4.5.3.56"
+    - "PyYAML==5.4.1"
+    - "tqdm==4.62.2"
+    - "yapf==0.31.0"
+    - "basicsr==1.4.2"
+    - "facexlib==0.2.5"
+
+predict: "cog_predict.py:Predictor"

cog_predict.py

@@ -0,0 +1,147 @@
+# flake8: noqa
+# This file is used for deploying replicate models
+# running: cog predict -i img=@inputs/whole_imgs/10045.png -i version='v1.4' -i scale=2
+# push: cog push r8.im/tencentarc/gfpgan
+# push (backup): cog push r8.im/xinntao/gfpgan
+
+import os
+
+os.system('python setup.py develop')
+os.system('pip install realesrgan')
+
+import cv2
+import shutil
+import tempfile
+import torch
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+
+from gfpgan import GFPGANer
+
+try:
+    from cog import BasePredictor, Input, Path
+    from realesrgan.utils import RealESRGANer
+except Exception:
+    print('please install cog and realesrgan package')
+
+
+class Predictor(BasePredictor):
+
+    def setup(self):
+        # download weights
+        if not os.path.exists('gfpgan/weights/realesr-general-x4v3.pth'):
+            os.system(
+                'wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P ./gfpgan/weights'
+            )
+        if not os.path.exists('gfpgan/weights/GFPGANv1.2.pth'):
+            os.system(
+                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P ./gfpgan/weights')
+        if not os.path.exists('gfpgan/weights/GFPGANv1.3.pth'):
+            os.system(
+                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P ./gfpgan/weights')
+        if not os.path.exists('gfpgan/weights/GFPGANv1.4.pth'):
+            os.system(
+                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P ./gfpgan/weights')
+
+        # 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 = 'gfpgan/weights/realesr-general-x4v3.pth'
+        half = True if torch.cuda.is_available() else False
+        self.upsampler = RealESRGANer(
+            scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
+
+        # Use GFPGAN for face enhancement
+        self.face_enhancer = GFPGANer(
+            model_path='gfpgan/weights/GFPGANv1.4.pth',
+            upscale=2,
+            arch='clean',
+            channel_multiplier=2,
+            bg_upsampler=self.upsampler)
+        self.current_version = 'v1.4'
+
+    def predict(
+        self,
+        img: Path = Input(description='Input'),
+        version: str = Input(
+            description='GFPGAN version. v1.3: better quality. v1.4: more details and better identity.',
+            choices=['v1.2', 'v1.3', 'v1.4'],
+            default='v1.4'),
+        scale: float = Input(description='Rescaling factor', default=2)
+    ) -> Path:
+        print(img, version, scale)
+        try:
+            img = cv2.imread(str(img), cv2.IMREAD_UNCHANGED)
+            if len(img.shape) == 3 and img.shape[2] == 4:
+                img_mode = 'RGBA'
+            else:
+                img_mode = None
+
+            h, w = img.shape[0:2]
+            if h < 300:
+                img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+
+            if self.current_version != version:
+                if version == 'v1.2':
+                    self.face_enhancer = GFPGANer(
+                        model_path='gfpgan/weights/GFPGANv1.2.pth',
+                        upscale=2,
+                        arch='clean',
+                        channel_multiplier=2,
+                        bg_upsampler=self.upsampler)
+                    self.current_version = 'v1.2'
+                elif version == 'v1.3':
+                    self.face_enhancer = GFPGANer(
+                        model_path='gfpgan/weights/GFPGANv1.3.pth',
+                        upscale=2,
+                        arch='clean',
+                        channel_multiplier=2,
+                        bg_upsampler=self.upsampler)
+                    self.current_version = 'v1.3'
+                elif version == 'v1.4':
+                    self.face_enhancer = GFPGANer(
+                        model_path='gfpgan/weights/GFPGANv1.4.pth',
+                        upscale=2,
+                        arch='clean',
+                        channel_multiplier=2,
+                        bg_upsampler=self.upsampler)
+                    self.current_version = 'v1.4'
+
+            try:
+                _, _, output = self.face_enhancer.enhance(
+                    img, has_aligned=False, only_center_face=False, paste_back=True)
+            except RuntimeError as error:
+                print('Error', error)
+            else:
+                extension = 'png'
+
+            try:
+                if scale != 2:
+                    interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
+                    h, w = img.shape[0:2]
+                    output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
+            except Exception as error:
+                print('wrong scale input.', error)
+            if img_mode == 'RGBA':  # RGBA images should be saved in png format
+                extension = 'png'
+            else:
+                extension = 'jpg'
+            save_path = f'output/out.{extension}'
+            cv2.imwrite(save_path, output)
+            out_path = Path(tempfile.mkdtemp()) / 'output.png'
+            cv2.imwrite(str(out_path), output)
+        except Exception as error:
+            print('global exception', error)
+        finally:
+            clean_folder('output')
+        return out_path
+
+
+def clean_folder(folder):
+    for filename in os.listdir(folder):
+        file_path = os.path.join(folder, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)
+        except Exception as e:
+            print(f'Failed to delete {file_path}. Reason: {e}')

gfpgan/models/gfpgan_model.py

@@ -3,7 +3,7 @@ import os.path as osp
 import torch
 from basicsr.archs import build_network
 from basicsr.losses import build_loss
-from basicsr.losses.losses import r1_penalty
+from basicsr.losses.gan_loss import r1_penalty
 from basicsr.metrics import calculate_metric
 from basicsr.models.base_model import BaseModel
 from basicsr.utils import get_root_logger, imwrite, tensor2img

gfpgan/utils.py

@@ -29,12 +29,12 @@ class GFPGANer():
         bg_upsampler (nn.Module): The upsampler for the background. Default: None.
     """
 
-    def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None):
+    def __init__(self, model_path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=None, device=None):
         self.upscale = upscale
         self.bg_upsampler = bg_upsampler
 
         # initialize model
-        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
         # initialize the GFP-GAN
         if arch == 'clean':
             self.gfpgan = GFPGANv1Clean(
@@ -79,7 +79,9 @@ class GFPGANer():
             crop_ratio=(1, 1),
             det_model='retinaface_resnet50',
             save_ext='png',
-            device=self.device)
+            use_parse=True,
+            device=self.device,
+            model_rootpath='gfpgan/weights')
 
         if model_path.startswith('https://'):
             model_path = load_file_from_url(

requirements.txt

@@ -1,12 +1,12 @@
-torch>=1.7
-numpy<1.21  # numba requires numpy<1.21,>=1.17
-opencv-python
-torchvision
-scipy
-tqdm
-basicsr>=1.3.4.0
-facexlib>=0.2.0.3
+basicsr>=1.4.2
+facexlib>=0.2.5
 lmdb
+numpy
+opencv-python
 pyyaml
+scipy
 tb-nightly
+torch>=1.7
+torchvision
+tqdm
 yapf