82 lines
3.0 KiB
Python
82 lines
3.0 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 torch
|
|
import warnings
|
|
from latent_blending import LatentBlending
|
|
from diffusers_holder import DiffusersHolder
|
|
from diffusers import DiffusionPipeline
|
|
from movie_util import concatenate_movies
|
|
torch.set_grad_enabled(False)
|
|
torch.backends.cudnn.benchmark = False
|
|
warnings.filterwarnings('ignore')
|
|
|
|
# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
|
|
pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
|
|
pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
|
|
pipe.to('cuda')
|
|
dh = DiffusersHolder(pipe)
|
|
|
|
# %% Let's setup the multi transition
|
|
fps = 30
|
|
duration_single_trans = 20
|
|
depth_strength = 0.25 # Specifies how deep (in terms of diffusion iterations the first branching happens)
|
|
size_output = (1280, 768)
|
|
num_inference_steps = 30
|
|
|
|
# Specify a list of prompts below
|
|
list_prompts = []
|
|
list_prompts.append("A beautiful astronomic photo of a nebula, with intricate microscopic structures, mitochondria")
|
|
list_prompts.append("Microscope fluorescence photo, cell filaments, intricate galaxy, astronomic nebula")
|
|
list_prompts.append("telescope photo starry sky, nebula, cell core, dna, stunning")
|
|
|
|
|
|
# You can optionally specify the seeds
|
|
list_seeds = [95437579, 33259350, 956051013]
|
|
t_compute_max_allowed = 20 # per segment
|
|
fp_movie = 'movie_example2.mp4'
|
|
lb = LatentBlending(dh)
|
|
lb.set_dimensions(size_output)
|
|
lb.dh.set_num_inference_steps(num_inference_steps)
|
|
|
|
|
|
list_movie_parts = []
|
|
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
|
|
|
|
fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
|
|
fixed_seeds = list_seeds[i:i + 2]
|
|
# Run latent blending
|
|
lb.run_transition(
|
|
recycle_img1=recycle_img1,
|
|
depth_strength=depth_strength,
|
|
t_compute_max_allowed=t_compute_max_allowed,
|
|
fixed_seeds=fixed_seeds)
|
|
|
|
# Save movie
|
|
lb.write_movie_transition(fp_movie_part, duration_single_trans)
|
|
list_movie_parts.append(fp_movie_part)
|
|
|
|
# Finally, concatente the result
|
|
concatenate_movies(fp_movie, list_movie_parts)
|