latentblending/example3_multitrans.py

# Copyright 2022 Lunar Ring. All rights reserved.
#
# 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
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 stable diffusion holder
device = "cuda:0"
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)

    
#%% MULTITRANS

num_inference_steps = 30 # Number of diffusion interations
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)





guidance_scale = 5
fps = 30
duration_single_trans = 20
width = 768
height = 768

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_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 made of hot metal, bizzarre, dark clouds in the sky")
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")


list_seeds = [234187386, 422209351, 241845736, 28652396, 783279867, 831049796, 234903931]

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, 
        duration_single_trans=duration_single_trans
    )


#%%
#for img in lb.tree_final_imgs:
#    if img is not None:
#        ms.write_frame(img)
#        
#ms.finalize()