2023-01-09 09:59:00 +00:00
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# Copyright 2022 Lunar Ring. All rights reserved.
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2023-01-11 10:36:44 +00:00
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# Written by Johannes Stelzer @j_stelzer
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2023-01-09 09:59:00 +00:00
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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
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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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2023-01-11 10:36:44 +00:00
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2023-01-09 09:59:00 +00:00
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#%% Define vars for low-resoltion pass
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dp_img = "upscaling_bleding" # the results will be saved in this folder
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prompt1 = "photo of mount vesuvius erupting a terrifying pyroclastic ash cloud"
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prompt2 = "photo of a inside a building full of ash, fire, death, destruction, explosions"
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fixed_seeds = [5054613, 1168652]
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width = 512
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height = 384
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num_inference_steps_lores = 40
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nmb_branches_final_lores = 10
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depth_strength_lores = 0.5
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device = "cuda"
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fp_ckpt_lores = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
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fp_config_lores = 'configs/v2-inference.yaml'
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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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fp_config_hires = 'configs/x4-upscaling.yaml'
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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, fp_config_lores, device)
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lb = LatentBlending(sdh)
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lb.set_prompt1(prompt1)
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lb.set_prompt2(prompt2)
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lb.set_width(width)
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lb.set_height(height)
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lb.run_upscaling_step1(dp_img, depth_strength_lores, num_inference_steps_lores, nmb_branches_final_lores, fixed_seeds)
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#%% Run high-res pass
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sdh = StableDiffusionHolder(fp_ckpt_hires, fp_config_hires)
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lb = LatentBlending(sdh)
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lb.run_upscaling_step2(dp_img, depth_strength_hires, num_inference_steps_hires, nmb_branches_final_hires)
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