Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
+from diffusers import AutoPipelineForText2Image
 import torch
 # from huggingface_hub import notebook_login
 
@@ -14,11 +14,11 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("Shaleen123/kandinsky_2.5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe = AutoPipelineForText2Image.from_pretrained("Shaleen123/kandinsky_2.5", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
 else: 
-    pipe = DiffusionPipeline.from_pretrained("Shaleen123/kandinsky_2.5", use_safetensors=True)
+    pipe = AutoPipelineForText2Image.from_pretrained("Shaleen123/kandinsky_2.5", use_safetensors=True, torch_dtype = torch.float16)
     pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -33,7 +33,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     
     image = pipe(
         prompt = prompt, 
-        negative_prompt = negative_prompt,
+        negative_prompt = 'low quality, bad quality, boring, ugly',
         guidance_scale = guidance_scale, 
         num_inference_steps = num_inference_steps, 
         width = width,