Update app.py

app.py

@@ -55,7 +55,6 @@ def generate(
     model = 'SG161222/Realistic_Vision_V6.0_B1_noVAE',
     vaecall = 'stabilityai/sd-vae-ft-mse',
     lora = 'amazonaws-la/juliette',
-    sampler = 'EulerAncestralDiscreteScheduler',
     lora_scale: float = 0.7,
 ) -> PIL.Image.Image:
     if torch.cuda.is_available():
@@ -63,13 +62,12 @@ def generate(
         if not use_vae:  
             pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
             sampler_class = sampler
-            pipe.scheduler = sampler_class.from_config(pipe.scheduler.config)
+            pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
             
         if use_vae:
             vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
             pipe = DiffusionPipeline.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
-            sampler_class = sampler
-            pipe.scheduler = sampler_class.from_config(pipe.scheduler.config)
+            pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
             
         if use_lora:
             pipe.load_lora_weights(lora)
@@ -145,7 +143,6 @@ with gr.Blocks(css="style.css") as demo:
         visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
     )
     with gr.Group():
-        sampler = gr.Text(label='Sampler', value='EulerAncestralDiscreteScheduler')
         model = gr.Text(label='Modelo')
         vaecall = gr.Text(label='VAE')
         lora = gr.Text(label='LoRA')
@@ -334,7 +331,6 @@ with gr.Blocks(css="style.css") as demo:
             model,
             vaecall,
             lora,
-            sampler,
             lora_scale,
         ],
         outputs=result,