init mirror

README.md

@@ -1,13 +1,13 @@
 ---
 title: GFPGAN
-emoji: 📊
-colorFrom: pink
-colorTo: blue
+emoji: 😁
+colorFrom: yellow
+colorTo: green
 sdk: gradio
-sdk_version: 3.34.0
+sdk_version: 3.26.0
 app_file: app.py
 pinned: false
-license: mit
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+
+import cv2
+import gradio as gr
+import torch
+from basicsr.archs.srvgg_arch import SRVGGNetCompact
+from gfpgan.utils import GFPGANer
+from huggingface_hub import snapshot_download, hf_hub_download
+from realesrgan.utils import RealESRGANer
+
+REALESRGAN_REPO_ID = 'leonelhs/realesrgan'
+GFPGAN_REPO_ID = 'leonelhs/gfpgan'
+
+os.system("pip freeze")
+
+torch.hub.download_url_to_file(
+    'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
+    'lincoln.jpg')
+torch.hub.download_url_to_file(
+    'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
+    'AI-generate.jpg')
+torch.hub.download_url_to_file(
+    'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
+    'Blake_Lively.jpg')
+torch.hub.download_url_to_file(
+    'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
+    '10045.png')
+
+# 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)
+
+os.makedirs('output', exist_ok=True)
+
+
+# def inference(img, version, scale, weight):
+def inference(img, version, scale):
+    # weight /= 100
+    print(img, version, scale)
+    if scale > 4:
+        scale = 4  # avoid too large scale value
+    try:
+        extension = os.path.splitext(os.path.basename(str(img)))[1]
+        img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
+        if len(img.shape) == 3 and img.shape[2] == 4:
+            img_mode = 'RGBA'
+        elif len(img.shape) == 2:  # for gray inputs
+            img_mode = None
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        else:
+            img_mode = None
+
+        h, w = img.shape[0:2]
+        if h > 3500 or w > 3500:
+            print('too large size')
+            return None, None
+
+        if h < 300:
+            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
+
+        face_enhancer = None
+        snapshot_folder = snapshot_download(repo_id=GFPGAN_REPO_ID)
+
+        if version == 'v1.2':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.2.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.3':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.3.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'v1.4':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.4.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'RestoreFormer':
+            path = os.path.join(snapshot_folder, 'RestoreFormer.pth')
+            face_enhancer = GFPGANer(
+                model_path=path, upscale=2, arch='RestoreFormer', channel_multiplier=2,
+                bg_upsampler=upsampler)
+
+        try:
+            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
+        except RuntimeError as error:
+            print('Error', error)
+
+        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)
+
+        output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
+        return output, save_path
+    except Exception as error:
+        print('global exception', error)
+        return None, None
+
+
+title = "GFPGAN: Practical Face Restoration Algorithm"
+description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
+It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
+To use it, simply upload your image.<br>
+If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
+"""
+article = r"""
+
+[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
+[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
+
+If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
+
+<center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
+<center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center>
+"""
+demo = gr.Interface(
+    inference, [
+        gr.Image(type="filepath", label="Input"),
+        # gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
+        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
+        gr.Number(label="Rescaling factor", value=2),
+        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
+    ], [
+        gr.Image(type="numpy", label="Output (The whole image)"),
+        gr.File(label="Download the output image")
+    ],
+    title=title,
+    description=description,
+    article=article,
+    examples=[['AI-generate.jpg', 'v1.4', 2],
+              ['lincoln.jpg', 'v1.4', 2],
+              ['Blake_Lively.jpg', 'v1.4', 2],
+              ['10045.png', 'v1.4', 2]])
+demo.queue().launch()

packages.txt

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+ffmpeg
+libsm6
+libxext6

requirements.txt

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+torch>=2.0.1
+basicsr>=1.4.2
+facexlib>=0.3.0
+gfpgan>=1.3.8
+realesrgan>=0.3.0
+numpy
+opencv-python
+torchvision
+scipy
+tqdm
+lmdb
+pyyaml
+yapf