rakan

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+import gradio
 from huggingface_hub import Repository
 import os
 
@@ -18,7 +18,8 @@ token = os.environ['model_fetch']
 
 opt = SwapOptions().parse()
 
-retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/retinaface_resnet50", use_auth_token=token)
+retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/retinaface_resnet50",
+                         private=True, use_auth_token=token, git_user="felixrosberg")
 
 from retina_model.models import *
 
@@ -29,21 +30,25 @@ RetinaFace = load_model("retina_model/retinaface_res50.h5",
                                         "LandmarkHead": LandmarkHead,
                                         "ClassHead": ClassHead})
 
-arc_repo = Repository(local_dir="arcface_model", clone_from="felixrosberg/arcface_tf", use_auth_token=token)
+arc_repo = Repository(local_dir="arcface_model", clone_from="felixrosberg/arcface_tf",
+                      private=True, use_auth_token=token)
 ArcFace = load_model("arcface_model/arc_res50.h5")
 ArcFaceE = load_model("arcface_model/arc_res50e.h5")
 
-g_repo = Repository(local_dir="g_model_c_hq", clone_from="felixrosberg/affa_config_c_hq", use_auth_token=token)
+g_repo = Repository(local_dir="g_model_c_hq", clone_from="felixrosberg/affa_config_c_hq",
+                    private=True, use_auth_token=token)
 G = load_model("g_model_c_hq/generator_t_28.h5", custom_objects={"AdaIN": AdaIN,
                                                                  "AdaptiveAttention": AdaptiveAttention,
                                                                  "InstanceNormalization": InstanceNormalization})
 
-r_repo = Repository(local_dir="reconstruction_attack", clone_from="felixrosberg/reconstruction_attack", use_auth_token=token)
+r_repo = Repository(local_dir="reconstruction_attack", clone_from="felixrosberg/reconstruction_attack",
+                    private=True, use_auth_token=token)
 R = load_model("reconstruction_attack/reconstructor_42.h5", custom_objects={"AdaIN": AdaIN,
                                                                             "AdaptiveAttention": AdaptiveAttention,
                                                                             "InstanceNormalization": InstanceNormalization})
 
-permuter_repo = Repository(local_dir="identity_permuter", clone_from="felixrosberg/identitypermuter", use_auth_token=token, git_user="felixrosberg")
+permuter_repo = Repository(local_dir="identity_permuter", clone_from="felixrosberg/identitypermuter",
+                           private=True, use_auth_token=token, git_user="felixrosberg")
 
 from identity_permuter.id_permuter import identity_permuter
 
@@ -55,65 +60,6 @@ blend_mask_base[80:244, 32:224] = 1
 blend_mask_base = gaussian_filter(blend_mask_base, sigma=7)
 
 
-theme = gr.themes.Monochrome(
-    secondary_hue="emerald",
-    neutral_hue="teal",
-).set(
-    body_background_fill='*primary_950',
-    body_background_fill_dark='*secondary_950',
-    body_text_color='*primary_50',
-    body_text_color_dark='*secondary_100',
-    body_text_color_subdued='*primary_300',
-    body_text_color_subdued_dark='*primary_300',
-    background_fill_primary='*primary_600',
-    background_fill_primary_dark='*primary_400',
-    background_fill_secondary='*primary_950',
-    background_fill_secondary_dark='*primary_950',
-    border_color_accent='*secondary_600',
-    border_color_primary='*secondary_50',
-    border_color_primary_dark='*secondary_50',
-    color_accent='*secondary_50',
-    color_accent_soft='*primary_500',
-    color_accent_soft_dark='*primary_500',
-    link_text_color='*secondary_950',
-    link_text_color_dark='*primary_50',
-    link_text_color_active='*primary_50',
-    link_text_color_active_dark='*primary_50',
-    link_text_color_hover='*primary_50',
-    link_text_color_hover_dark='*primary_50',
-    link_text_color_visited='*primary_50',
-    block_background_fill='*primary_950',
-    block_background_fill_dark='*primary_950',
-    block_border_color='*secondary_500',
-    block_border_color_dark='*secondary_500',
-    block_info_text_color='*primary_50',
-    block_info_text_color_dark='*primary_50',
-    block_label_background_fill='*primary_950',
-    block_label_background_fill_dark='*secondary_950',
-    block_label_border_color='*secondary_500',
-    block_label_border_color_dark='*secondary_500',
-    block_label_text_color='*secondary_500',
-    block_label_text_color_dark='*secondary_500',
-    block_title_background_fill='*primary_950',
-    panel_background_fill='*primary_950',
-    panel_border_color='*primary_950',
-    checkbox_background_color='*primary_950',
-    checkbox_background_color_dark='*primary_950',
-    checkbox_background_color_focus='*primary_950',
-    checkbox_border_color='*secondary_500',
-    input_background_fill='*primary_800',
-    input_background_fill_focus='*primary_950',
-    input_background_fill_hover='*secondary_950',
-    input_placeholder_color='*secondary_950',
-    slider_color='*primary_950',
-    slider_color_dark='*primary_950',
-    table_even_background_fill='*primary_800',
-    table_odd_background_fill='*primary_600',
-    button_primary_background_fill='*primary_800',
-    button_primary_background_fill_dark='*primary_800'
-)
-
-
 def run_inference(target, source, slider, adv_slider, settings):
     try:
         source = np.array(source)
@@ -246,58 +192,26 @@ description = "Performs subject agnostic identity transfer from a source face to
               "-Adversarial defense will add a permutation noise that disrupts the reconstruction attack.\n\n" \
               "NOTE: There is no guarantees with the anonymization process currently.\n\n" \
               "NOTE: source image with too high resolution may not work properly!"
-examples = [["assets/rick.jpg", "assets/musk.jpg", 100, 10, []],
-            ["assets/rick.jpg", "assets/rick.jpg", 100, 10, ["anonymize"]]]
+examples = [["assets/rick.jpg", "assets/musk.jpg", 100, 10, ["compare"]],
+            ["assets/musk.jpg", "assets/musk.jpg", 100, 10, ["anonymize"]]]
 article = """
 Demo is based of recent research from my Ph.D work. Results expects to be published in the coming months.
 """
 
-with gr.Blocks(theme=theme) as blk_demo:
-    gr.Markdown(value="# Face Dancer \n\n"
-                      "## Paper: [FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping](https://arxiv.org/abs/2210.10473) \n"
-                      "## Check out the code [here](https://github.com/felixrosberg/FaceDancer)")
-    with gr.Row():
-        with gr.Column():
-            with gr.Group():
-                trg_in = gr.Image(type="pil", label='Target')
-                src_in = gr.Image(type="pil", label='Source')
-            with gr.Row():
-                b1 = gr.Button("Face Swap")
-            with gr.Row():
-                with gr.Accordion("Options", open=False):
-                    chk_in = gr.CheckboxGroup(["Compare",
-                                               "Anonymize",
-                                               "Reconstruction Attack",
-                                               "Adversarial Defense"],
-                                              label="Mode",
-                                              info="Anonymize mode? "
-                                                   "Apply reconstruction attack? "
-                                                   "Apply defense against reconstruction attack?")
-                    def_in = gr.Slider(0, 100, value=100,
-                                       label='Anonymization ratio (%)')
-                    mrg_in = gr.Slider(0, 100, value=100,
-                                       label='Adversarial defense ratio (%)')
-            gr.Examples(examples=[["assets/musk.jpg"], ["assets/rick.jpg"]],
-                        inputs=trg_in)
-        with gr.Column():
-            with gr.Group():
-                ano_out = gr.Image(type="pil", label='Output')
-
-    b1.click(run_inference, inputs=[trg_in, src_in, def_in, mrg_in, chk_in], outputs=ano_out)
-"""iface = gradio.Interface(run_inference,
-                         [gradio.Image(shape=None, type="pil", label='Target'),
-                          gradio.Image(shape=None, type="pil", label='Source'),
-                          gradio.Slider(0, 100, value=100, label="Anonymization ratio (%)"),
-                          gradio.Slider(0, 100, value=100, label="Adversarial defense ratio (%)"),
-                          gradio.CheckboxGroup(["compare",
+iface = gradio.Interface(run_inference,
+                         [gradio.inputs.Image(shape=None, label='Target'),
+                          gradio.inputs.Image(shape=None, label='Source'),
+                          gradio.inputs.Slider(0, 100, default=100, label="Anonymization ratio (%)"),
+                          gradio.inputs.Slider(0, 100, default=100, label="Adversarial defense ratio (%)"),
+                          gradio.inputs.CheckboxGroup(["compare",
                                                        "anonymize",
                                                        "reconstruction attack",
                                                        "adversarial defense"],
                                                       label='Options')],
-                         "image",
+                         gradio.outputs.Image(),
                          title="Face Swap",
                          description=description,
                          examples=examples,
                          article=article,
-                         layout="vertical")"""
-blk_demo.launch()
+                         layout="vertical")
+iface.launch()

requirements.txt

@@ -1,5 +1,5 @@
 tensorflow
-tensorflow-addons==0.17.1
+tensorflow-addons
 opencv-python-headless
 scipy
 pillow