adding "null" to condition tests

app.py

@@ -32,7 +32,7 @@ def models_change(model, scheduler, config):
     trigger_token = ""
     
     # no model selected (because this is UI init run)
-    if type(model) != list and str(model) != 'None':
+    if type(model) != list and str(model) != 'None' and str(model) != 'null':
         
         use_safetensors = str(models[model]['use_safetensors'])
         model_description = models[model]['description']
@@ -137,7 +137,7 @@ def requires_safety_checker_change(requires_safety_checker, config):
 
 def auto_encoders_change(auto_encoder, config):
     
-    if str(auto_encoder) != 'None' and type(auto_encoder) != list:
+    if str(auto_encoder) != 'None' and str(auto_encoder) != 'null' and type(auto_encoder) != list:
         
         auto_encoder_description = auto_encoders[auto_encoder]
         
@@ -150,7 +150,7 @@ def auto_encoders_change(auto_encoder, config):
 
 def schedulers_change(scheduler, config):
     
-    if str(scheduler) != 'None' and type(scheduler) != list:
+    if str(scheduler) != 'None' and str(scheduler) != 'null' and type(scheduler) != list:
         
         scheduler_description = schedulers[scheduler]
         
@@ -163,7 +163,7 @@ def schedulers_change(scheduler, config):
     
 def adapters_textual_inversion_change(adapter_textual_inversion, config):
     
-    if str(adapter_textual_inversion) != 'None' and type(adapter_textual_inversion) != list:
+    if str(adapter_textual_inversion) != 'None' and str(adapter_textual_inversion) != 'null' and type(adapter_textual_inversion) != list:
         
         adapter_textual_inversion_description = adapters['textual_inversion'][adapter_textual_inversion]['description']
         in_adapters_textual_inversion_token = adapters['textual_inversion'][adapter_textual_inversion]['token']
@@ -187,7 +187,7 @@ def run_inference(config, config_history, progress=gr.Progress(track_tqdm=True))
     # str_config = str_config.replace("'", '"').replace('None', 'null').replace('False', 'false')
     # config = json.loads(str_config)
 
-    if str(config["model"]) != 'None' and str(config["scheduler"]) != 'None':
+    if str(config["model"]) != 'None' and str(config["model"]) != 'null' and str(config["scheduler"]) != 'None':
         
         progress((1,3), desc="Preparing pipeline initialization...")
         
@@ -206,14 +206,14 @@ def run_inference(config, config_history, progress=gr.Progress(track_tqdm=True))
             pipeline.enable_model_cpu_offload()
 
         # AUTO ENCODER
-        if str(config["auto_encoder"]).lower() != 'none': 
+        if str(config["auto_encoder"]).lower() != 'none' and str(config["auto_encoder"]).lower() != 'null':
             pipeline.vae = AutoencoderKL.from_pretrained(config["auto_encoder"], torch_dtype=get_data_type(config["data_type"])).to(config["device"])
 
         if str(config["enable_vae_slicing"]).lower() != 'false': pipeline.enable_vae_slicing()
         if str(config["enable_vae_tiling"]).lower() != 'false': pipeline.enable_vae_tiling()
                 
         # INIT REFINER
-        if config['refiner'].lower() != 'none':
+        if str(config['refiner']).lower() != 'none' and str(config['refiner']).lower() != 'null':
             refiner = DiffusionPipeline.from_pretrained(
                     config['refiner'],
                     text_encoder_2=pipeline.text_encoder_2,
@@ -243,7 +243,7 @@ def run_inference(config, config_history, progress=gr.Progress(track_tqdm=True))
         
         # ADAPTERS
         # TEXTUAL INVERSION
-        if str(config["adapter_textual_inversion"]).lower() != 'none':
+        if str(config["adapter_textual_inversion"]).lower() != 'none' and str(config["adapter_textual_inversion"]).lower() != 'null':
             pipeline.load_textual_inversion(config["adapter_textual_inversion"], token=config["adapter_textual_inversion_token"])
         
         progress((3,3), desc="Creating the result...")
@@ -257,7 +257,7 @@ def run_inference(config, config_history, progress=gr.Progress(track_tqdm=True))
             num_inference_steps = int(config["inference_steps"]), 
             guidance_scale = float(config["guidance_scale"])).images
 
-        if config['refiner'].lower() != 'none':
+        if config['refiner'].lower() != 'none' and config['refiner'].lower() != 'null':
             image = refiner(
                 prompt = prompt,
                 num_inference_steps = int(config["inference_steps"]),