Update app.py

app.py

@@ -14,7 +14,7 @@ from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler
 from huggingface_hub import hf_hub_download
 
 DESCRIPTION = """
-# Res-Adapter
+# Res-Adapter :Domain Consistent Resolution Adapter for Diffusion Models
 **Demo by [ameer azam] - [Twitter](https://twitter.com/Ameerazam18) - [GitHub](https://github.com/AMEERAZAM08)) - [Hugging Face](https://huggingface.co/ameerazam08)**
 This is a demo of  https://huggingface.co/jiaxiangc/res-adapter LORAs by ByteDance
 """
@@ -29,9 +29,10 @@ ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 if torch.cuda.is_available():
-    pipe = AutoPipelineForText2Image.from_pretrained('Lykon/dreamshaper-xl-1-0', torch_dtype=torch.float16)
+    #Lykon/dreamshaper-xl-1-0
+    pipe = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',)# torch_dtype=torch.float16, variant="safetensors")
     pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
-    pipe = pipe.to("cuda")
+    pipe = pipe.to(device)
 
 
 pipe.load_lora_weights(
@@ -167,14 +168,14 @@ with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo:
                 minimum=256,
                 maximum=MAX_IMAGE_SIZE,
                 step=32,
-                value=1024,
+                value=512,
             )
             height = gr.Slider(
                 label="Height",
                 minimum=256,
                 maximum=MAX_IMAGE_SIZE,
                 step=32,
-                value=1024,
+                value=512,
             )
         with gr.Row():
             guidance_scale_base = gr.Slider(