Update app.py

app.py

@@ -1,8 +1,3 @@
-"""
-This file is used for deploying hugging face demo:
-https://huggingface.co/spaces/sczhou/CodeFormer
-"""
-
 import sys
 sys.path.append('CodeFormer')
 import os
@@ -12,90 +7,49 @@ import torch.nn.functional as F
 import gradio as gr
 
 from torchvision.transforms.functional import normalize
-
 from basicsr.utils import imwrite, img2tensor, tensor2img
 from basicsr.utils.download_util import load_file_from_url
 from facelib.utils.face_restoration_helper import FaceRestoreHelper
 from basicsr.archs.rrdbnet_arch import RRDBNet
 from basicsr.utils.realesrgan_utils import RealESRGANer
 from facelib.utils.misc import is_gray
-
 from basicsr.utils.registry import ARCH_REGISTRY
 
-
-os.system("pip freeze")
-
+# Model weight URLs
 pretrain_model_url = {
     'codeformer': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth',
     'detection': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
     'parsing': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth',
     'realesrgan': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth'
 }
-# download weights
-if not os.path.exists('CodeFormer/weights/CodeFormer/codeformer.pth'):
-    load_file_from_url(url=pretrain_model_url['codeformer'], model_dir='CodeFormer/weights/CodeFormer', progress=True, file_name=None)
-if not os.path.exists('CodeFormer/weights/facelib/detection_Resnet50_Final.pth'):
-    load_file_from_url(url=pretrain_model_url['detection'], model_dir='CodeFormer/weights/facelib', progress=True, file_name=None)
-if not os.path.exists('CodeFormer/weights/facelib/parsing_parsenet.pth'):
-    load_file_from_url(url=pretrain_model_url['parsing'], model_dir='CodeFormer/weights/facelib', progress=True, file_name=None)
-if not os.path.exists('CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth'):
-    load_file_from_url(url=pretrain_model_url['realesrgan'], model_dir='CodeFormer/weights/realesrgan', progress=True, file_name=None)
 
-# download images
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/fa3fe3d1-76b0-4ca8-ac0d-0a925cb0ff54/06.png',
-    '01.png')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/a1daba8e-af14-4b00-86a4-69cec9619b53/04.jpg',
-    '02.jpg')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/542d64f9-1712-4de7-85f7-3863009a7c3d/03.jpg',
-    '03.jpg')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/a11098b0-a18a-4c02-a19a-9a7045d68426/010.jpg',
-    '04.jpg')
-torch.hub.download_url_to_file(
-    'https://replicate.com/api/models/sczhou/codeformer/files/7cf19c2c-e0cf-4712-9af8-cf5bdbb8d0ee/012.jpg',
-    '05.jpg')
-torch.hub.download_url_to_file(
-    'https://raw.githubusercontent.com/sczhou/CodeFormer/master/inputs/cropped_faces/0729.png',
-    '06.png')
+# Download weights if not already present
+for key, url in pretrain_model_url.items():
+    file_path = f"CodeFormer/weights/{key}/{url.split('/')[-1]}"
+    if not os.path.exists(file_path):
+        load_file_from_url(url=url, model_dir=os.path.dirname(file_path), progress=True)
 
+# Helper functions
 def imread(img_path):
     img = cv2.imread(img_path)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     return img
 
-# set enhancer with RealESRGAN
 def set_realesrgan():
-    half = True if torch.cuda.is_available() else False
-    model = RRDBNet(
-        num_in_ch=3,
-        num_out_ch=3,
-        num_feat=64,
-        num_block=23,
-        num_grow_ch=32,
-        scale=2,
-    )
+    half = torch.cuda.is_available()
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
     upsampler = RealESRGANer(
-        scale=2,
-        model_path="CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth",
-        model=model,
-        tile=400,
-        tile_pad=40,
-        pre_pad=0,
-        half=half,
+        scale=2, model_path="CodeFormer/weights/realesrgan/RealESRGAN_x2plus.pth",
+        model=model, tile=400, tile_pad=40, pre_pad=0, half=half
     )
     return upsampler
 
+# Model setup
 upsampler = set_realesrgan()
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 codeformer_net = ARCH_REGISTRY.get("CodeFormer")(
-    dim_embd=512,
-    codebook_size=1024,
-    n_head=8,
-    n_layers=9,
-    connect_list=["32", "64", "128", "256"],
+    dim_embd=512, codebook_size=1024, n_head=8, n_layers=9,
+    connect_list=["32", "64", "128", "256"]
 ).to(device)
 ckpt_path = "CodeFormer/weights/CodeFormer/codeformer.pth"
 checkpoint = torch.load(ckpt_path)["params_ema"]
@@ -104,205 +58,69 @@ codeformer_net.eval()
 
 os.makedirs('output', exist_ok=True)
 
-def inference(image, face_align, background_enhance, face_upsample, upscale, codeformer_fidelity):
-    """Run a single prediction on the model"""
-    try: # global try
-        # take the default setting for the demo
+# Inference function
+def inference(image, face_align=True, background_enhance=True, face_upsample=True, upscale=2, codeformer_fidelity=0.5):
+    try:
         only_center_face = False
-        draw_box = False
         detection_model = "retinaface_resnet50"
-
-        print('Inp:', image, background_enhance, face_upsample, upscale, codeformer_fidelity)
-        face_align = face_align if face_align is not None else True
-        background_enhance = background_enhance if background_enhance is not None else True
-        face_upsample = face_upsample if face_upsample is not None else True
-        upscale = upscale if (upscale is not None and upscale > 0) else 2
-
-        has_aligned = not face_align
-        upscale = 1 if has_aligned else upscale
-
+        
+        # Load image and set parameters
         img = cv2.imread(str(image), cv2.IMREAD_COLOR)
-        print('\timage size:', img.shape)
-
-        upscale = int(upscale) # convert type to int
-        if upscale > 4: # avoid memory exceeded due to too large upscale
-            upscale = 4 
-        if upscale > 2 and max(img.shape[:2])>1000: # avoid memory exceeded due to too large img resolution
-            upscale = 2 
-        if max(img.shape[:2]) > 1500: # avoid memory exceeded due to too large img resolution
-            upscale = 1
-            background_enhance = False
-            face_upsample = False
+        has_aligned = not face_align
+        upscale = min(max(1, int(upscale)), 4)
 
         face_helper = FaceRestoreHelper(
-            upscale,
-            face_size=512,
-            crop_ratio=(1, 1),
-            det_model=detection_model,
-            save_ext="png",
-            use_parse=True,
-            device=device,
+            upscale, face_size=512, crop_ratio=(1, 1), det_model=detection_model,
+            save_ext="png", use_parse=True, device=device
         )
+        
         bg_upsampler = upsampler if background_enhance else None
         face_upsampler = upsampler if face_upsample else None
 
         if has_aligned:
-            # the input faces are already cropped and aligned
             img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)
             face_helper.is_gray = is_gray(img, threshold=5)
-            if face_helper.is_gray:
-                print('\tgrayscale input: True')
             face_helper.cropped_faces = [img]
         else:
             face_helper.read_image(img)
-            # get face landmarks for each face
-            num_det_faces = face_helper.get_face_landmarks_5(
-            only_center_face=only_center_face, resize=640, eye_dist_threshold=5
-            )
-            print(f'\tdetect {num_det_faces} faces')
-            # align and warp each face
+            num_det_faces = face_helper.get_face_landmarks_5(only_center_face=only_center_face, resize=640, eye_dist_threshold=5)
             face_helper.align_warp_face()
 
-        # face restoration for each cropped face
-        for idx, cropped_face in enumerate(face_helper.cropped_faces):
-            # prepare data
+        for cropped_face in face_helper.cropped_faces:
             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(device)
 
-            try:
-                with torch.no_grad():
-                    output = codeformer_net(
-                        cropped_face_t, w=codeformer_fidelity, adain=True
-                    )[0]
-                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                del output
-                torch.cuda.empty_cache()
-            except RuntimeError as error:
-                print(f"Failed inference for CodeFormer: {error}")
-                restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+            with torch.no_grad():
+                output = codeformer_net(cropped_face_t, w=codeformer_fidelity, adain=True)[0]
+                restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+            face_helper.add_restored_face(restored_face.astype("uint8"), cropped_face)
 
-            restored_face = restored_face.astype("uint8")
-            face_helper.add_restored_face(restored_face, cropped_face)
-
-        # paste_back
-        if not has_aligned:
-            # upsample the background
-            if bg_upsampler is not None:
-                # Now only support RealESRGAN for upsampling background
-                bg_img = bg_upsampler.enhance(img, outscale=upscale)[0]
-            else:
-                bg_img = None
-            face_helper.get_inverse_affine(None)
-            # paste each restored face to the input image
-            if face_upsample and face_upsampler is not None:
-                restored_img = face_helper.paste_faces_to_input_image(
-                    upsample_img=bg_img,
-                    draw_box=draw_box,
-                    face_upsampler=face_upsampler,
-                )
-            else:
-                restored_img = face_helper.paste_faces_to_input_image(
-                    upsample_img=bg_img, draw_box=draw_box
-                )
-        else:
-            restored_img = restored_face
-
-        # save restored img
-        save_path = f'output/out.png'
-        imwrite(restored_img, str(save_path))
+        restored_img = face_helper.paste_faces_to_input_image(
+            upsample_img=bg_upsampler.enhance(img, outscale=upscale)[0] if bg_upsampler else None,
+            face_upsampler=face_upsampler
+        )
 
-        restored_img = cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
-        return restored_img
+        save_path = 'output/out.png'
+        imwrite(restored_img, save_path)
+        return cv2.cvtColor(restored_img, cv2.COLOR_BGR2RGB)
     except Exception as error:
-        print('Global exception', error)
-        return None, None
-
-
-title = "CodeFormer: Robust Face Restoration and Enhancement Network"
-
-description = r"""<center><img src='https://user-images.githubusercontent.com/14334509/189166076-94bb2cac-4f4e-40fb-a69f-66709e3d98f5.png' alt='CodeFormer logo'></center>
-<br>
-<b>Official Gradio demo</b> for <a href='https://github.com/sczhou/CodeFormer' target='_blank'><b>Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022)</b></a><br>
-🔥 CodeFormer is a robust face restoration algorithm for old photos or AI-generated faces.<br>
-🤗 Try CodeFormer for improved stable-diffusion generation!<br>
-"""
-
-article = r"""
-If CodeFormer is helpful, please help to ⭐ the <a href='https://github.com/sczhou/CodeFormer' target='_blank'>Github Repo</a>. Thanks! 
-[![GitHub Stars](https://img.shields.io/github/stars/sczhou/CodeFormer?style=social)](https://github.com/sczhou/CodeFormer)
-
----
-
-📝 **Citation**
-
-If our work is useful for your research, please consider citing:
-```bibtex
-@inproceedings{zhou2022codeformer,
-    author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
-    title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
-    booktitle = {NeurIPS},
-    year = {2022}
-}
-```
-
-📋 **License**
-
-This project is licensed under <a rel="license" href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">S-Lab License 1.0</a>. 
-Redistribution and use for non-commercial purposes should follow this license.
-
-📧 **Contact**
-
-If you have any questions, please feel free to reach me out at <b>[email protected]</b>.
-
-🤗 **Find Me:**
-<style type="text/css">
-td {
-    padding-right: 0px !important;
-}
-
-.gradio-container-4-37-2 .prose table, .gradio-container-4-37-2 .prose tr, .gradio-container-4-37-2 .prose td, .gradio-container-4-37-2 .prose th {
-    border: 0px solid #ffffff;
-    border-bottom: 0px solid #ffffff;
-}
-
-</style>
-
-<table>
-<tr>
-    <td><a href="https://github.com/sczhou"><img style="margin:-0.8em 0 2em 0" src="https://img.shields.io/github/followers/sczhou?style=social" alt="Github Follow"></a></td>
-    <td><a href="https://twitter.com/ShangchenZhou"><img style="margin:-0.8em 0 2em 0" src="https://img.shields.io/twitter/follow/ShangchenZhou?label=%40ShangchenZhou&style=social" alt="Twitter Follow"></a></td>
-</tr>
-</table>
-
-<center><img src='https://api.infinitescript.com/badgen/count?name=sczhou/CodeFormer&ltext=Visitors&color=6dc9aa' alt='visitors'></center>
-"""
+        print('Error during inference:', error)
+        return None
 
+# Gradio Interface
 demo = gr.Interface(
-    inference, [
+    fn=inference, 
+    inputs=[
         gr.Image(type="filepath", label="Input"),
         gr.Checkbox(value=True, label="Pre_Face_Align"),
         gr.Checkbox(value=True, label="Background_Enhance"),
         gr.Checkbox(value=True, label="Face_Upsample"),
         gr.Number(value=2, label="Rescaling_Factor (up to 4)"),
-        gr.Slider(0, 1, value=0.5, step=0.01, label='Codeformer_Fidelity (0 for better quality, 1 for better identity)')
-    ], [
-        gr.Image(type="numpy", label="Output")
-    ],
-    title=title,
-    description=description,
-    article=article,       
-    examples=[
-        ['01.png', True, True, True, 2, 0.7],
-        ['02.jpg', True, True, True, 2, 0.7],
-        ['03.jpg', True, True, True, 2, 0.7],
-        ['04.jpg', True, True, True, 2, 0.1],
-        ['05.jpg', True, True, True, 2, 0.1],
-        ['06.png', False, True, True, 1, 0.5]
-      ],
-    concurrency_limit=2
-    )
+        gr.Slider(0, 1, value=0.5, step=0.01, label='Codeformer_Fidelity')
+    ], 
+    outputs=gr.Image(type="numpy", label="Output"),
+    title="CodeFormer: Robust Face Restoration and Enhancement Network"
+)
 
-DEBUG = os.getenv('DEBUG') == '1'
-# demo.launch(debug=DEBUG)
-demo.launch(debug=DEBUG, share=True)
+demo.launch(debug=os.getenv('DEBUG') == '1', share=True)