Update to V2 version

app.py

@@ -15,17 +15,14 @@ logging.basicConfig(
     level=logging.INFO,
     datefmt='%Y-%m-%d %H:%M:%S')
 
-MODEL_IMG_SIZE = 256
-usage_count = 35  # Based on hugging face logs
+MODEL_IMG_SIZE = 512
+usage_count = 0  # Based on hugging face logs
 def load_model():
-    REPO_ID = "Podtekatel/ARCNEGAN"
-    FILENAME_OLD = "arcane_exp_203_ep_399.onnx"
-    FILENAME_NEW = "arcane_exp_206_ep_138.onnx"
+    REPO_ID = "Podtekatel/ArcaneVSK2"
+    FILENAME_OLD = "arcane_exp_228_ep_159_512_res_V2.onnx"
 
     global model_old
-    global model_new
     global pipeline_old
-    global pipeline_new
 
     # Old model
     model_path = cached_download(
@@ -35,24 +32,12 @@ def load_model():
 
     pipeline_old = VSNetModelPipeline(model_old, StatRetinaFaceDetector(MODEL_IMG_SIZE), background_resize=1024, no_detected_resize=1024)
 
-    # New model
-    model_path = cached_download(
-        hf_hub_url(REPO_ID, FILENAME_NEW), use_auth_token=os.getenv('HF_TOKEN')
-    )
-    model_new = ONNXModel(model_path)
-
-    pipeline_new = VSNetModelPipeline(model_new, StatRetinaFaceDetector(MODEL_IMG_SIZE), background_resize=1024,
-                                      no_detected_resize=1024)
-
-    return model_old, model_new
+    return model_old
 load_model()
 
-def inference(img, ver):
+def inference(img):
     img = np.array(img)
-    if ver == 'version 2':
-        out_img = pipeline_new(img)
-    else:
-        out_img = pipeline_old(img)
+    out_img = pipeline_old(img)
 
     out_img = Image.fromarray(out_img)
     global usage_count
@@ -61,23 +46,23 @@ def inference(img, ver):
     return out_img
 
 
-title = "ARCNStyleTransfer"
+title = "ARCNStyleTransferV2"
 description = "Gradio Demo for Arcane Season 1 style transfer. To use it, simply upload your image, or click one of the examples to load them. Press ❤️ if you like this space!"
 article = "This is one of my successful experiments on style transfer. I've built my own pipeline, generator model and private dataset to train this model<br>" \
           "" \
           "" \
           "" \
           "Model pipeline which used in project is improved CartoonGAN.<br>" \
-          "This model was trained on RTX 2080 Ti 1.5 days with batch size 7.<br>" \
-          "Model weights 64 MB in ONNX fp32 format, infers 25 ms on GPU and 150 ms on CPU at 256x256 resolution.<br>" \
+          "This model was trained on RTX 2080 Ti 3 days with batch size 7.<br>" \
+          "Model weights 80 MB in ONNX fp32 format, infers 100 ms on GPU and 600 ms on CPU at 512x512 resolution.<br>" \
           "If you want to use this app or integrate this model into yours, please contact me at email 'neuromancer.ai.lover@gmail.com'."
 
 imgs_folder = 'demo'
-examples = [[os.path.join(imgs_folder, img_filename), version] for img_filename in sorted(os.listdir(imgs_folder)) for version in ['version 2']]
+examples = [[os.path.join(imgs_folder, img_filename)] for img_filename in sorted(os.listdir(imgs_folder))]
 
 demo = gr.Interface(
     fn=inference,
-    inputs=[gr.inputs.Image(type="pil"), gr.inputs.Radio(['version 1', 'version 2'], type="value", default='version 2', label='version')],
+    inputs=[gr.inputs.Image(type="pil")],
     outputs=gr.outputs.Image(type="pil"),
     title=title,
     description=description,