Add application file

app.py

@@ -48,7 +48,7 @@ def load_model(model_name):
         gc.collect()  # Force garbage collection
     
     if "wan-t2v" in model_name:
-        vae = AutoencoderKLWan.from_pretrained(model_paths[model_name], subfolder="vae", torch_dtype=torch.float32)
+        vae = AutoencoderKLWan.from_pretrained(model_paths[model_name], subfolder="vae", torch_dtype=torch.bfloat16)
         scheduler = UniPCMultistepScheduler(prediction_type='flow_prediction', use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=8.0)
         current_model = WanPipeline.from_pretrained(model_paths[model_name], vae=vae, torch_dtype=torch.float16).to("cuda")
         current_model.scheduler = scheduler
@@ -146,7 +146,7 @@ def generate_content(prompt, model_name, guidance_scale=7.5, num_inference_steps
 demo = gr.Interface(
     fn=generate_content,
     inputs=[
-        gr.Textbox(value="A capybara holding a sign that reads Hello World", label="Enter your prompt"),
+        gr.Textbox(value="A cosmic whale swimming throught a glaxy with stars and swirling cosmic dusts.", label="Enter your prompt"),
         gr.Dropdown(choices=list(model_paths.keys()), label="Choose Model"),
         gr.Slider(1, 20, value=4.0, step=0.5, label="Guidance Scale"),
         gr.Slider(10, 100, value=28, step=5, label="Inference Steps"),