99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
# 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()
|
|
|