77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
# Copyright 2022 Lunar Ring. All rights reserved.
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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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#%% First let us spawn a stable diffusion holder
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device = "cuda:0"
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num_inference_steps = 20 # Number of diffusion interations
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fp_ckpt = "../stable_diffusion_models/ckpt/768-v-ema.ckpt"
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fp_config = '../stablediffusion/configs/stable-diffusion/v2-inference-v.yaml'
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sdh = StableDiffusionHolder(fp_ckpt, fp_config, device, num_inference_steps=num_inference_steps)
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#%% Next let's set up all parameters
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# FIXME below fix numbers
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# We want 20 diffusion steps in total, begin with 2 branches, have 3 branches at step 12 (=0.6*20)
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# 10 branches at step 16 (=0.8*20) and 24 branches at step 18 (=0.9*20)
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# Furthermore we want seed 993621550 for keyframeA and seed 54878562 for keyframeB ()
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list_nmb_branches = [2, 3, 10, 24] # Branching structure: how many branches
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list_injection_strength = [0.0, 0.6, 0.8, 0.9] # Branching structure: how deep is the blending
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width = 768
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height = 768
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guidance_scale = 5
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fixed_seeds = [993621550, 280335986]
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lb = LatentBlending(sdh, num_inference_steps, guidance_scale)
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prompt1 = "photo of a beautiful forest covered in white flowers, ambient light, very detailed, magic"
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prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph,, mystical ambience, incredible detail"
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lb.set_prompt1(prompt1)
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lb.set_prompt2(prompt2)
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imgs_transition = lb.run_transition(list_nmb_branches, list_injection_strength, fixed_seeds=fixed_seeds)
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# let's get more cheap frames via linear interpolation
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duration_transition = 12
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fps = 60
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imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps)
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# movie saving
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fp_movie = "movie_example1.mp4"
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if os.path.isfile(fp_movie):
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os.remove(fp_movie)
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ms = MovieSaver(fp_movie, fps=fps)
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for img in tqdm(imgs_transition_ext):
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ms.write_frame(img)
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ms.finalize()
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