Update app.py

app.py

@@ -34,19 +34,10 @@ with open('loras.json', 'r') as f:
 # Initialize the base model with authentication and specify the device
 pipe = DiffusionPipeline.from_pretrained("sayakpaul/FLUX.1-merged", torch_dtype=dtype, token=hf_token).to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
+MAX_SEED = 2**32 - 1
 MAX_IMAGE_SIZE = 2048
 
 
-device_map = infer_auto_device_map(
-    model,
-    max_memory=max_memory,
-    no_split_module_classes=["DecoderLayer", "Attention", "MLP", "LayerNorm", "Linear"],
-    dtype='float16'
-)
-
-model = dispatch_model(model, device_map='torch.cuda:0')
-
 class calculateDuration:
     def __init__(self, activity_name=""):
         self.activity_name = activity_name
@@ -63,7 +54,7 @@ class calculateDuration:
         else:
             print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
 
-@spaces.GPU(duration=200)
+@spaces.GPU(duration=90)
 def generate_images(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, num_images, progress):
     generator = torch.Generator(device=device).manual_seed(seed)
     images = []