104 lines
3.9 KiB
Python
104 lines
3.9 KiB
Python
# 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
|
|
import torch
|
|
torch.backends.cudnn.benchmark = False
|
|
torch.set_grad_enabled(False)
|
|
import warnings
|
|
warnings.filterwarnings('ignore')
|
|
import warnings
|
|
from latent_blending import LatentBlending
|
|
from stable_diffusion_holder import StableDiffusionHolder
|
|
from movie_util import concatenate_movies
|
|
from huggingface_hub import hf_hub_download
|
|
|
|
# %% 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 = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt")
|
|
|
|
# %% Define vars for high-resoltion pass
|
|
fp_ckpt_hires = hf_hub_download(repo_id="stabilityai/stable-diffusion-x4-upscaler", filename="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(
|
|
recycle_img1=recycle_img1,
|
|
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)
|