Update app.py

app.py

@@ -8,7 +8,7 @@ import random
 import gradio as gr
 import numpy as np
 import PIL.Image
-import spaces
+# import spaces
 import torch
 from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler
 from huggingface_hub import hf_hub_download
@@ -28,12 +28,9 @@ USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
 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():
-    #Lykon/dreamshaper-xl-1-0
-    # pipe = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',)# torch_dtype=torch.float16, variant="safetensors")
-    pipe = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',use_safetensors=True)
-    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
-    pipe = pipe.to(device)
+pipe = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0',use_safetensors=True)# torch_dtype=torch.float16, variant="safetensors")
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
+
 
 
 pipe.load_lora_weights(
@@ -45,6 +42,7 @@ pipe.load_lora_weights(
     adapter_name="res_adapter",
 )
 pipe.set_adapters(["res_adapter"], adapter_weights=[1.0])
+pipe = pipe.to(device)
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
@@ -52,7 +50,7 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     return seed
 
 
-@spaces.GPU(enable_queue=True)
+# @spaces.GPU(enable_queue=True)
 def generate(
     prompt: str,
     negative_prompt: str = "",
@@ -89,6 +87,7 @@ def generate(
         num_inference_steps=num_inference_steps_base,
         generator=generator,
         output_type="pil").images[0]
+
     
     
     res_adapt=pipe(