diff --git a/example3_multitrans.py b/example3_multitrans.py
index 5daa5c4..479ab5a 100644
--- a/example3_multitrans.py
+++ b/example3_multitrans.py
@@ -21,34 +21,23 @@ warnings.filterwarnings('ignore')
 import warnings
 import torch
 from tqdm.auto import tqdm
-from diffusers import StableDiffusionPipeline
-from diffusers.schedulers import DDIMScheduler
 from PIL import Image
 import matplotlib.pyplot as plt
 import torch
 from movie_util import MovieSaver
 from typing import Callable, List, Optional, Union
 from latent_blending import LatentBlending, add_frames_linear_interp
+from stable_diffusion_holder import StableDiffusionHolder
 torch.set_grad_enabled(False)
 
-#%% First let us spawn a diffusers pipe using DDIMScheduler
+#%% First let us spawn a stable diffusion holder
 device = "cuda:0"
-model_path = "../stable_diffusion_models/stable-diffusion-v1-5"
+num_inference_steps = 20 # Number of diffusion interations
+fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
+fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
+
+sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, num_inference_steps=num_inference_steps)
 
-scheduler = DDIMScheduler(beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="scaled_linear",
-            clip_sample=False,
-            set_alpha_to_one=False)
-            
-pipe = StableDiffusionPipeline.from_pretrained(
-    model_path,
-    revision="fp16", 
-    torch_dtype=torch.float16,
-    scheduler=scheduler,
-    use_auth_token=True
-)
-pipe = pipe.to(device)
     
 #%% MULTITRANS
 
@@ -57,18 +46,21 @@ list_nmb_branches = [2, 10, 50, 100, 200] #
 list_injection_strength = list(np.linspace(0.5, 0.95, 4)) # Branching structure: how deep is the blending
 list_injection_strength.insert(0, 0.0)
 
-width = 512
-height = 512
+
+
+
+
 guidance_scale = 5
 fps = 30
 duration_single_trans = 20
-width = 512
-height = 512
+width = 768
+height = 768
 
-lb = LatentBlending(pipe, device, height, width, num_inference_steps, guidance_scale)
+lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
+
+# deepth_strength = 0.5
+# num_inference_steps, list_injection_idx, list_nmb_branches = lb.get_branching('medium', deepth_strength, fps*duration_single_trans)
 
-#list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches
-#list_injection_strength = [0.0, 0.6, 0.8, 0.9] # 
 
 list_prompts = []
 list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow")
@@ -82,13 +74,14 @@ list_prompts.append("statue of an ancient cybernetic messenger annoucing good ne
 
 list_seeds = [234187386, 422209351, 241845736, 28652396, 783279867, 831049796, 234903931]
 
-fp_movie = "/home/lugo/tmp/latentblending/bubua.mp4"
+fp_movie = "movie_example3.mp4"
 ms = MovieSaver(fp_movie, fps=fps)
 
 lb.run_multi_transition(
         list_prompts, 
         list_seeds, 
         list_nmb_branches, 
+        # list_injection_idx=list_injection_idx, 
         list_injection_strength=list_injection_strength, 
         ms=ms, 
         fps=fps, 
diff --git a/latent_blending.py b/latent_blending.py
index 73e528e..6661331 100644
--- a/latent_blending.py
+++ b/latent_blending.py
@@ -764,12 +764,15 @@ def get_branching(
             idx_last_check +=1 
     list_injection_idx_clean = [int(l) for l in list_injection_idx_clean]
     list_nmb_branches_clean = [int(l) for l in list_nmb_branches_clean]
+    
+    list_injection_idx = list_injection_idx_clean
+    list_nmb_branches = list_nmb_branches_clean
 
     print(f"num_inference_steps: {num_inference_steps}")
-    print(f"list_injection_idx: {list_injection_idx_clean}")
-    print(f"list_nmb_branches: {list_nmb_branches_clean}")
+    print(f"list_injection_idx: {list_injection_idx}")
+    print(f"list_nmb_branches: {list_nmb_branches}")
     
-    return num_inference_steps, list_injection_idx_clean, list_nmb_branches_clean
+    return num_inference_steps, list_injection_idx, list_nmb_branches