Update app.py

app.py

@@ -98,7 +98,6 @@ local_repo_path = snapshot_download(repo_id=repo_id, repo_type="model")
 local_folder = os.path.join(local_repo_path, subfolder)
 local_folder2 = os.path.join(local_repo_path, subfolder2) # Path to the ip_adapter dir
 ip_ckpt = os.path.join(local_folder2, "ip-adapter_sdxl_vit-h.bin") # Correct path
-ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
 
 #sampling_schedule = AysSchedules["StableDiffusionXLTimesteps"]
 upscaler = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device("cuda:0"))
@@ -284,6 +283,7 @@ def generate_30(
     #gc.collect()
     global models
     pipe = models
+    ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device='cuda').manual_seed(seed)
     if latent_file:  # Check if a latent file is provided
@@ -339,6 +339,7 @@ def generate_60(
     #gc.collect()
     global models
     pipe = models
+    ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device='cuda').manual_seed(seed)
     if latent_file:  # Check if a latent file is provided
@@ -394,6 +395,7 @@ def generate_90(
     #gc.collect()
     global models
     pipe = models
+    ip_model = IPAdapterXL(pipe, local_folder, ip_ckpt, device)
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator(device='cuda').manual_seed(seed)
     if latent_file:  # Check if a latent file is provided