remove codeformer

README.md

@@ -35,7 +35,7 @@ It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g
 
 :triangular_flag_on_post: **Updates**
 
-- :white_check_mark: Add CodeFormer ([CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/)) and RestoreFormer.
+- :white_check_mark: Add [RestoreFormer](https://github.com/wzhouxiff/RestoreFormer) inference codes.
 - :white_check_mark: Add [V1.4 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth), which produces slightly more details and better identity than V1.3.
 - :white_check_mark: Add **[V1.3 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth)**, which produces **more natural** restoration results, and better results on *very low-quality* / *high-quality* inputs. See more in [Model zoo](#european_castle-model-zoo), [Comparisons.md](Comparisons.md)
 - :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/GFPGAN).

cog_predict.py

@@ -46,9 +46,6 @@ def setup(self):
             os.system(
                 'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P ./gfpgan/weights'
             )
-        if not os.path.exists('gfpgan/weights/CodeFormer.pth'):
-            os.system(
-                'wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.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')
@@ -71,17 +68,11 @@ def predict(
         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', 'RestoreFormer', 'CodeFormer'],
+            choices=['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'],
             default='v1.4'),
         scale: float = Input(description='Rescaling factor', default=2),
-        weight: float = Input(
-            description='Weight, only for CodeFormer. 0 for better quality, 1 for better identity',
-            default=0.5,
-            ge=0,
-            le=1.0)
     ) -> Path:
-        if not isinstance(weight, (int, float)):
-            weight = 0.5
+        weight = 0.5
         print(img, version, scale, weight)
         try:
             extension = os.path.splitext(os.path.basename(str(img)))[1]
@@ -130,13 +121,6 @@ def predict(
                         arch='RestoreFormer',
                         channel_multiplier=2,
                         bg_upsampler=self.upsampler)
-                elif version == 'CodeFormer':
-                    self.face_enhancer = GFPGANer(
-                        model_path='gfpgan/weights/CodeFormer.pth',
-                        upscale=2,
-                        arch='CodeFormer',
-                        channel_multiplier=2,
-                        bg_upsampler=self.upsampler)
 
             try:
                 _, _, output = self.face_enhancer.enhance(