latentblending/example1_standard.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)

    
#%% Next let's set up all parameters
# FIXME below fix numbers
# We want 20 diffusion steps in total, begin with 2 branches, have 3 branches at step 12 (=0.6*20)
# 10 branches at step 16 (=0.8*20) and 24 branches at step 18 (=0.9*20)
# Furthermore we want seed 993621550 for keyframeA and seed 54878562 for keyframeB ()
list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches
list_injection_strength = [0.0, 0.6, 0.8, 0.9] # Branching structure: how deep is the blending
width = 768 
height = 768
guidance_scale = 5
fixed_seeds = [993621550, 280335986]
    
lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
prompt1 = "photo of a beautiful 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"
lb.set_prompt1(prompt1)
lb.set_prompt2(prompt2)

imgs_transition = lb.run_transition(list_nmb_branches, list_injection_strength, fixed_seeds=fixed_seeds)

# let's get more cheap frames via linear interpolation
duration_transition = 12
fps = 60
imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps)

# movie saving
fp_movie = "movie_example1.mp4"
if os.path.isfile(fp_movie):
    os.remove(fp_movie)
ms = MovieSaver(fp_movie, fps=fps)
for img in tqdm(imgs_transition_ext):
    ms.write_frame(img)
ms.finalize()