diff --git a/CHANGELOG.md b/CHANGELOG.md
index 6609b35df..3cef36f05 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -16,9 +16,9 @@
   write your prompts forin ~110 auto-detected languages!  
   compatible with SD15 and SDXL  
   enable in scripts -> MuLan and set encoder to `InternVL-14B-224px` encoder  
-  *Note*: right now this is more of a proof-of-concept before smaller and/or quantized models are released  
+  *note*: right now this is more of a proof-of-concept before smaller and/or quantized models are released  
   model will be auto-downloaded on first use: note its huge size of 27GB  
-  even executing it in FP16 context will require ~16GB of VRAM for text encoder alone  
+  even executing it in FP16 will require ~16GB of VRAM for text encoder alone  
   examples:  
   - English: photo of a beautiful woman wearing a white bikini on a beach with a city skyline in the background
   - Croatian: fotografija lijepe žene u bijelom bikiniju na plaži s gradskim obzorom u pozadini
@@ -35,8 +35,8 @@
   download from <https://huggingface.co/Kijai/converted_pcm_loras_fp16/tree/main>
 - **Kohya HiRes Fix** allows for higher resolution generation using standard sd15 models  
   enable via scripts -> kohya-hires-fix  
-  *note*: this alternative to regular hidiffusion method, but with different approach to scaling  
-- additional built-in controlnet models: TODO
+  *note*: alternative to regular hidiffusion method, but with different approach to scaling  
+- additional built-in **ControlNet** models: TODO
 - lower overhead on generate calls
 - cumulative fixes since the last release
 
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 66f8b1b3f..8b8326aca 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -116,7 +116,8 @@ def process_diffusers(p: processing.StableDiffusionProcessing):
         hidiffusion.apply_hidiffusion(p, shared.sd_model_type)
         # if 'image' in base_args:
         #    base_args['image'] = set_latents(p)
-        if hasattr(shared.sd_model, 'tgate'):
+        if hasattr(shared.sd_model, 'tgate') and getattr(p, 'gate_step', -1) > 0:
+            base_args['gate_step'] = p.gate_step
             output = shared.sd_model.tgate(**base_args) # pylint: disable=not-callable
         else:
             output = shared.sd_model(**base_args)
diff --git a/scripts/t_gate.py b/scripts/t_gate.py
index 72a1b5375..7c55f998d 100644
--- a/scripts/t_gate.py
+++ b/scripts/t_gate.py
@@ -21,6 +21,7 @@ class Script(scripts.Script):
         return [enabled, gate_step]
 
     def run(self, p: processing.StableDiffusionProcessing, enabled, gate_step): # pylint: disable=arguments-differ
+        p.gate_step = min(gate_step, p.steps) if enabled else -1
         if not enabled:
             return None
         install('tgate')
@@ -33,11 +34,12 @@ class Script(scripts.Script):
             shared.log.warning(f'T-Gate: pipeline={shared.sd_model_type} required=sd or sdxl')
             return None
         old_pipe = shared.sd_model
-        shared.sd_model = cls(shared.sd_model, gate_step=min(gate_step, p.steps))
+        shared.sd_model = cls(shared.sd_model, gate_step=p.gate_step)
         sd_models.copy_diffuser_options(shared.sd_model, old_pipe)
         sd_models.move_model(shared.sd_model, devices.device) # move pipeline to device
         sd_models.set_diffuser_options(shared.sd_model, vae=None, op='model')
-        shared.log.debug(f'T-Gate: pipeline={shared.sd_model.__class__.__name__} steps={gate_step}')
+        shared.log.debug(f'T-Gate: pipeline={shared.sd_model.__class__.__name__} steps={p.gate_step}')
         processed = processing.process_images(p)
         shared.sd_model = old_pipe
+        del shared.sd_model.tgate
         return processed