Update app.py

app.py

@@ -1,3 +1,15 @@
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+from IPython.display import display
+
+model_path = "../"             # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive
+
+pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float16).to("cuda")
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+pipe.enable_xformers_memory_efficient_attention()
+g_cuda = None
+
 import gradio as gr
 
 def inference(prompt, negative_prompt, num_samples, height=512, width=512, num_inference_steps=50, guidance_scale=7.5):