Update app.py

app.py

@@ -474,7 +474,7 @@ samplers_diffusers = [
 # ]
 
 start_time = time.time()
-timeout = 360
+timeout = 1800
 
 scheduler = DDIMScheduler.from_pretrained(
     base_model,
@@ -567,14 +567,18 @@ current_model = base_name
 def setup_controlnet(name_control,device):
     global controlnet_type,controlnetmodel_cache
     if name_control not in controlnetmodel_cache:
-        model_control = ControlNetModel.from_pretrained(name_control, torch_dtype=torch.float16).to(device)
+        model_control = ControlNetModel.from_pretrained(name_control, 
+                                                        #torch_dtype=torch.float16
+                                                       ).to(device)
         controlnetmodel_cache[name_control] = model_control
     return controlnetmodel_cache[name_control]
 
 def setup_adapter(adapter_sp,device):
     global model_ip_adapter_type,adapter_cache
     if adapter_sp not in adapter_cache:
-        model_control = T2IAdapter.from_pretrained(adapter_sp, torch_dtype=torch.float16).to(device)
+        model_control = T2IAdapter.from_pretrained(adapter_sp, 
+                                                   #torch_dtype=torch.float16
+                                                  ).to(device)
         adapter_cache[adapter_sp] = model_control
     return adapter_cache[adapter_sp]
 
@@ -582,16 +586,26 @@ def setup_vae(model,vae_used = "Default"):
     global vae_link,vae_single_file
     vae_model = None
     if vae_used == "Default":
-        vae_model = AutoencoderKL.from_pretrained(model,subfolder="vae",torch_dtype=torch.float16)
+        vae_model = AutoencoderKL.from_pretrained(model,subfolder="vae",
+                                                  #torch_dtype=torch.float16
+                                                 )
     elif vae_used == "Consistency Decoder":
-        vae_model = ConsistencyDecoderVAE.from_pretrained(vae_link[vae_used], torch_dtype=torch.float16)
+        vae_model = ConsistencyDecoderVAE.from_pretrained(vae_link[vae_used], 
+                                                          #torch_dtype=torch.float16
+                                                         )
     else:
         if vae_single_file[vae_used]:
-            vae_model = AutoencoderKL.from_single_file(vae_link[vae_used],torch_dtype=torch.float16)
+            vae_model = AutoencoderKL.from_single_file(vae_link[vae_used],
+                                                       #torch_dtype=torch.float16
+                                                      )
         else:
-            vae_model = AutoencoderKL.from_pretrained(vae_link[vae_used],torch_dtype=torch.float16)
+            vae_model = AutoencoderKL.from_pretrained(vae_link[vae_used],
+                                                      #torch_dtype=torch.float16
+                                                     )
     if vae_model is None:
-        vae_model = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
+        vae_model = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", 
+                                                  #torch_dtype=torch.float16
+                                                 )
     return vae_model