latentblending/example4_multitrans_upscali...

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# 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.
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import os
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import torch
torch.backends.cudnn.benchmark = False
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torch.set_grad_enabled(False)
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import warnings
warnings.filterwarnings('ignore')
import warnings
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from latent_blending import LatentBlending
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from stable_diffusion_holder import StableDiffusionHolder
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from movie_util import concatenate_movies
from huggingface_hub import hf_hub_download
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# %% Define vars for low-resoltion pass
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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
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fp_ckpt_lores = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt")
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# %% Define vars for high-resoltion pass
fp_ckpt_hires = hf_hub_download(repo_id="stabilityai/stable-diffusion-x4-upscaler", filename="x4-upscaler-ema.ckpt")
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depth_strength_hires = 0.65
num_inference_steps_hires = 100
nmb_branches_final_hires = 6
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# %% Run low-res pass
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sdh = StableDiffusionHolder(fp_ckpt_lores)
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t_compute_max_allowed = 12 # Per segment
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lb = LatentBlending(sdh)
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list_movie_dirs = []
for i in range(len(list_prompts) - 1):
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# For a multi transition we can save some computation time and recycle the latents
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if i == 0:
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lb.set_prompt1(list_prompts[i])
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lb.set_prompt2(list_prompts[i + 1])
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recycle_img1 = False
else:
lb.swap_forward()
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lb.set_prompt2(list_prompts[i + 1])
recycle_img1 = True
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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)
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fixed_seeds = list_seeds[i:i + 2]
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# Run latent blending
lb.run_transition(
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depth_strength=depth_strength_lores,
nmb_max_branches=nmb_max_branches_lores,
fixed_seeds=fixed_seeds)
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# 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)
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# %% Run high-res pass on each segment
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sdh = StableDiffusionHolder(fp_ckpt_hires)
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lb = LatentBlending(sdh)
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for dp_part in list_movie_dirs:
lb.run_upscaling(dp_part, depth_strength_hires, num_inference_steps_hires, nmb_branches_final_hires)
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# %% concatenate into one long movie
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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)
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fp_final = "example4.mp4"
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concatenate_movies(fp_final, list_fp_movies)