diff --git a/example1_standard.py b/example1_standard.py
index ea042e2..c32e4dc 100644
--- a/example1_standard.py
+++ b/example1_standard.py
@@ -40,18 +40,19 @@ sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
     
 #%% Next let's set up all parameters
 quality = 'medium'
-depth_strength = 0.65 # Specifies how deep (in terms of diffusion iterations the first branching happens)
+depth_strength = 0.35 # Specifies how deep (in terms of diffusion iterations the first branching happens)
 fixed_seeds = [69731932, 504430820]
     
-prompt1 = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
-prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph, mystical ambience, incredible detail"
+# prompt1 = "A person in an open filed of grass watching a television, red colors dominate the scene, eerie light, dark clouds on the horizon, artistically rendered by Richter"
+prompt1 = "A person in a bar, people around him, a glass of baer, artistically rendered in the style of Hopper"
+prompt2 = "A person with a sad expression, looking at a painting of an older man, all in the style of Lucien Freud"
 
 duration_transition = 12 # In seconds
 fps = 30
 
 # Spawn latent blending
 lb = LatentBlending(sdh)
-lb.autosetup_branching(quality=quality, depth_strength=depth_strength)
+lb.load_branching_profile(quality=quality, depth_strength=depth_strength)
 lb.set_prompt1(prompt1)
 lb.set_prompt2(prompt2)
 
diff --git a/example2_inpaint.py b/example2_inpaint.py
index 91e8855..2fa4c95 100644
--- a/example2_inpaint.py
+++ b/example2_inpaint.py
@@ -25,7 +25,6 @@ import torch
 from tqdm.auto import tqdm
 from diffusers import StableDiffusionInpaintPipeline
 from PIL import Image
-import matplotlib.pyplot as plt
 import torch
 from movie_util import MovieSaver
 from typing import Callable, List, Optional, Union
@@ -42,21 +41,21 @@ sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
 
 #%% Let's first make a source image and mask.
 quality = 'medium'
-deepth_strength = 0.65 #Specifies how deep (in terms of diffusion iterations the first branching happens)
+depth_strength = 0.65 #Specifies how deep (in terms of diffusion iterations the first branching happens)
 duration_transition = 7 # In seconds
 fps = 30
 seed0 = 190791709
 
 # Spawn latent blending
 lb = LatentBlending(sdh)
-lb.autosetup_branching(quality=quality, deepth_strength=deepth_strength)
+lb.load_branching_profile(quality=quality, depth_strength=depth_strength)
 prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary"
 lb.set_prompt1(prompt1)
 lb.init_inpainting(init_empty=True)
 lb.set_seed(seed0)
 
 # Run diffusion 
-list_latents = lb.run_diffusion(lb.text_embedding1)
+list_latents = lb.run_diffusion([lb.text_embedding1])
 image_source = lb.sdh.latent2image(list_latents[-1])
 
 mask_image = 255*np.ones([512,512], dtype=np.uint8)
diff --git a/example3_multitrans.py b/example3_multitrans.py
index 28499b7..96e2cc4 100644
--- a/example3_multitrans.py
+++ b/example3_multitrans.py
@@ -22,7 +22,6 @@ import warnings
 import torch
 from tqdm.auto import tqdm
 from PIL import Image
-import matplotlib.pyplot as plt
 import torch
 from movie_util import MovieSaver
 from typing import Callable, List, Optional, Union
@@ -32,8 +31,8 @@ torch.set_grad_enabled(False)
 
 #%% First let us spawn a stable diffusion holder
 device = "cuda"
-fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
-fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
+fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
+fp_config = 'configs/v2-inference-v.yaml'
 sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
 
     
@@ -56,16 +55,14 @@ list_prompts.append("statue of an ancient cybernetic messenger annoucing good ne
 list_seeds = [954375479, 332539350, 956051013, 408831845, 250009012, 675588737]
 
 lb = LatentBlending(sdh)
-lb.autosetup_branching(quality=quality, depth_strength=depth_strength)
+lb.load_branching_profile(quality=quality, depth_strength=depth_strength)
 
 fp_movie = "movie_example3.mp4"
 
-ms = MovieSaver(fp_movie, fps=fps)
-
 lb.run_multi_transition(
+        fp_movie, 
         list_prompts, 
         list_seeds, 
-        ms=ms, 
         fps=fps, 
         duration_single_trans=duration_single_trans
     )