Update app.py

app.py

@@ -12,7 +12,7 @@ import spaces
 import torch
 from PIL import Image
 from io import BytesIO
-from diffusers import AutoencoderKL, DiffusionPipeline, AutoPipelineForImage2Image, EulerAncestralDiscreteScheduler
+from diffusers import AutoencoderKL, DiffusionPipeline, AutoPipelineForImage2Image
 
 DESCRIPTION = "# Run any LoRA or SD Model"
 if not torch.cuda.is_available():
@@ -61,24 +61,20 @@ def generate(
         if not use_img2img:
             pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
             pipe.enable_model_cpu_offload()
-            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)
                 pipe.enable_model_cpu_offload()
-                pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 
         if use_img2img:
             pipe = AutoPipelineForImage2Image.from_pretrained(model, torch_dtype=torch.float16)
             pipe.enable_model_cpu_offload()
-            pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
             
             if use_vae:  
                 vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
                 pipe = AutoPipelineForImage2Image.from_pretrained(model, vae=vae, torch_dtype=torch.float16)
                 pipe.enable_model_cpu_offload()
-                pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 
             response = requests.get(url)
             init_image = Image.open(BytesIO(response.content)).convert("RGB")