diff --git a/example4_multitrans_upscaling.py b/example4_multitrans_upscaling.py
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+# Copyright 2022 Lunar Ring. All rights reserved.
+# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os, sys
+import torch
+torch.backends.cudnn.benchmark = False
+import numpy as np
+import warnings
+warnings.filterwarnings('ignore')
+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, concatenate_movies
+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)
+
+#%% Define vars for low-resoltion pass
+list_prompts = []
+list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow")
+list_prompts.append("statue of a mix between a tree and human, made of marble, incredibly detailed")
+list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city")
+list_prompts.append("statue of a spider that looked like a human")
+list_prompts.append("statue of a bird that looked like a scorpion")
+list_prompts.append("statue of an ancient cybernetic messenger annoucing good news, golden, futuristic")
+
+# You can optionally specify the seeds
+list_seeds = [954375479, 332539350, 956051013, 408831845, 250009012, 675588737]
+
+width = 512
+height = 384
+duration_single_trans = 6
+num_inference_steps_lores = 40
+nmb_max_branches_lores = 10
+depth_strength_lores = 0.5
+
+fp_ckpt_lores = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt" 
+
+#%% Define vars for high-resoltion pass
+fp_ckpt_hires = "../stable_diffusion_models/ckpt/x4-upscaler-ema.ckpt"
+depth_strength_hires = 0.65
+num_inference_steps_hires = 100
+nmb_branches_final_hires = 6
+#%% Run low-res pass
+sdh = StableDiffusionHolder(fp_ckpt_lores)
+t_compute_max_allowed = 12 # per segment
+lb = LatentBlending(sdh)
+
+list_movie_dirs = [] #
+for i in range(len(list_prompts)-1):
+    # For a multi transition we can save some computation time and recycle the latents
+    if i==0:
+        lb.set_prompt1(list_prompts[i])
+        lb.set_prompt2(list_prompts[i+1])
+        recycle_img1 = False
+    else:
+        lb.swap_forward()
+        lb.set_prompt2(list_prompts[i+1])
+        recycle_img1 = True   
+        
+    dp_movie_part = f"tmp_part_{str(i).zfill(3)}"
+    fp_movie_part = os.path.join(dp_movie_part, "movie_lowres.mp4")
+    os.makedirs(dp_movie_part, exist_ok=True)
+    fixed_seeds = list_seeds[i:i+2]
+    
+    # Run latent blending
+    lb.run_transition(
+        depth_strength = depth_strength_lores,
+        nmb_max_branches = nmb_max_branches_lores,
+        fixed_seeds = fixed_seeds
+        )
+    
+    # Save movie and images (needed for upscaling!)
+    lb.write_movie_transition(fp_movie_part, duration_single_trans)
+    lb.write_imgs_transition(dp_movie_part)
+    list_movie_dirs.append(dp_movie_part)
+
+
+    
+#%% Run high-res pass on each segment
+sdh = StableDiffusionHolder(fp_ckpt_hires)
+lb = LatentBlending(sdh) 
+for dp_part in list_movie_dirs:
+    lb.run_upscaling(dp_part, depth_strength_hires, num_inference_steps_hires, nmb_branches_final_hires)
+
+#%% concatenate into one long movie
+list_fp_movies = []
+for dp_part in list_movie_dirs:
+    fp_movie = os.path.join(dp_part, "movie_highres.mp4")
+    assert os.path.isfile(fp_movie)
+    list_fp_movies.append(fp_movie)
+    
+fp_final = "example4.mp4"
+concatenate_movies(fp_final, list_fp_movies)
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