Update

README.md

@@ -4,7 +4,7 @@ emoji: 👁
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 3.19.1
+sdk_version: 3.34.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -7,10 +7,7 @@ import numpy as np
 
 from model import Model
 
-DESCRIPTION = '''# StyleSwin
-
-This is an unofficial demo for [https://github.com/microsoft/StyleSwin](https://github.com/microsoft/StyleSwin).
-'''
+DESCRIPTION = '# [StyleSwin](https://github.com/microsoft/StyleSwin)'
 
 
 def get_sample_image_url(name: str) -> str:
@@ -69,15 +66,12 @@ with gr.Blocks(css='style.css') as demo:
                 text = get_sample_image_markdown(model_name2.value)
                 sample_images = gr.Markdown(text)
 
-    model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
     run_button.click(fn=model.set_model_and_generate_image,
-                     inputs=[
-                         model_name,
-                         seed,
-                     ],
-                     outputs=result)
+                     inputs=[model_name, seed],
+                     outputs=result,
+                     api_name='run')
     model_name2.change(fn=get_sample_image_markdown,
                        inputs=model_name2,
                        outputs=sample_images)
 
-demo.queue().launch(show_api=False)
+demo.queue(max_size=15).launch()

model.py

@@ -19,8 +19,6 @@ sys.path.insert(0, submodule_dir.as_posix())
 
 from models.generator import Generator
 
-HF_TOKEN = os.getenv('HF_TOKEN')
-
 
 class Model:
     MODEL_NAMES = [
@@ -52,9 +50,8 @@ class Model:
                           style_dim=512,
                           n_mlp=8,
                           channel_multiplier=channel_multiplier)
-        ckpt_path = huggingface_hub.hf_hub_download('hysts/StyleSwin',
-                                                    f'models/{model_name}.pt',
-                                                    use_auth_token=HF_TOKEN)
+        ckpt_path = huggingface_hub.hf_hub_download('public-data/StyleSwin',
+                                                    f'models/{model_name}.pt')
         ckpt = torch.load(ckpt_path)
         model.load_state_dict(ckpt['g_ema'])
         model.to(self.device)