diff --git a/example1_standard.py b/example1_standard.py
index eac5bfb..b8500d4 100644
--- a/example1_standard.py
+++ b/example1_standard.py
@@ -29,33 +29,14 @@ pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch
 pipe.to("cuda")
 
 dh = DiffusersHolder(pipe)
-# %% Next let's set up all parameters
-depth_strength = 0.55  # Specifies how deep (in terms of diffusion iterations the first branching happens)
-t_compute_max_allowed = 10  # Determines the quality of the transition in terms of compute time you grant it
-num_inference_steps = 4
-size_output = (1024, 1024)
 
-
-prompt1 = "underwater landscape, fish, und the sea, incredible detail, high resolution"
-prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
-negative_prompt = "blurry, ugly, pale"  # Optional
-
-fp_movie = 'movie_example1.mp4'
-duration_transition = 12  # In seconds
-
-# Spawn latent blending
 lb = LatentBlending(dh)
-lb.set_prompt1(prompt1)
-lb.set_prompt2(prompt2)
-lb.set_dimensions(size_output)
-lb.set_negative_prompt(negative_prompt)
-lb.set_guidance_scale(0)
+lb.set_prompt1("photo of underwater landscape, fish, und the sea, incredible detail, high resolution")
+lb.set_prompt2("rendering of an alien planet, strange plants, strange creatures, surreal")
+lb.set_negative_prompt("blurry, ugly, pale")
 
 # Run latent blending
-lb.run_transition(
-    depth_strength=depth_strength,
-    num_inference_steps=num_inference_steps,
-    t_compute_max_allowed=t_compute_max_allowed)
+lb.run_transition()
 
 # Save movie
-lb.write_movie_transition(fp_movie, duration_transition)
+lb.write_movie_transition('movie_example1.mp4', duration_transition=12)