110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
# Copyright 2022 Lunar Ring. All rights reserved.
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# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os, sys
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import torch
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torch.backends.cudnn.benchmark = False
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import numpy as np
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import warnings
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warnings.filterwarnings('ignore')
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import warnings
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import torch
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from tqdm.auto import tqdm
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from PIL import Image
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# import matplotlib.pyplot as plt
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import torch
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from movie_util import MovieSaver, concatenate_movies
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from typing import Callable, List, Optional, Union
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from latent_blending import LatentBlending, add_frames_linear_interp
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from stable_diffusion_holder import StableDiffusionHolder
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torch.set_grad_enabled(False)
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#%% Define vars for low-resoltion pass
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list_prompts = []
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list_prompts.append("surrealistic statue made of glitter and dirt, standing in a lake, atmospheric light, strange glow")
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list_prompts.append("statue of a mix between a tree and human, made of marble, incredibly detailed")
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list_prompts.append("weird statue of a frog monkey, many colors, standing next to the ruins of an ancient city")
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list_prompts.append("statue of a spider that looked like a human")
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list_prompts.append("statue of a bird that looked like a scorpion")
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list_prompts.append("statue of an ancient cybernetic messenger annoucing good news, golden, futuristic")
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# You can optionally specify the seeds
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list_seeds = [954375479, 332539350, 956051013, 408831845, 250009012, 675588737]
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width = 512
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height = 384
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duration_single_trans = 6
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num_inference_steps_lores = 40
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nmb_max_branches_lores = 10
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depth_strength_lores = 0.5
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fp_ckpt_lores = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
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#%% Define vars for high-resoltion pass
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fp_ckpt_hires = "../stable_diffusion_models/ckpt/x4-upscaler-ema.ckpt"
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depth_strength_hires = 0.65
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num_inference_steps_hires = 100
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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 = [] #
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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
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else:
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lb.swap_forward()
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lb.set_prompt2(list_prompts[i+1])
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recycle_img1 = True
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dp_movie_part = f"tmp_part_{str(i).zfill(3)}"
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fp_movie_part = os.path.join(dp_movie_part, "movie_lowres.mp4")
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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
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lb.run_transition(
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depth_strength = depth_strength_lores,
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nmb_max_branches = nmb_max_branches_lores,
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fixed_seeds = fixed_seeds
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)
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# Save movie and images (needed for upscaling!)
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lb.write_movie_transition(fp_movie_part, duration_single_trans)
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lb.write_imgs_transition(dp_movie_part)
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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:
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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 = []
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for dp_part in list_movie_dirs:
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fp_movie = os.path.join(dp_part, "movie_highres.mp4")
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assert os.path.isfile(fp_movie)
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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) |