95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
# Copyright 2022 Lunar Ring. All rights reserved.
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# Written by Johannes Stelzer @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 time
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import subprocess
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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 diffusers import StableDiffusionInpaintPipeline
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from PIL import Image
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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"
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fp_ckpt= "../stable_diffusion_models/ckpt/512-inpainting-ema.ckpt"
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fp_config = '../stablediffusion/configs//stable-diffusion/v2-inpainting-inference.yaml'
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sdh = StableDiffusionHolder(fp_ckpt, fp_config, device)
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#%% Let's first make a source image and mask.
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quality = 'medium'
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depth_strength = 0.65 #Specifies how deep (in terms of diffusion iterations the first branching happens)
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duration_transition = 7 # In seconds
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fps = 30
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seed0 = 190791709
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# Spawn latent blending
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lb = LatentBlending(sdh)
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lb.load_branching_profile(quality=quality, depth_strength=depth_strength)
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prompt1 = "photo of a futuristic alien temple in a desert, mystic, glowing, organic, intricate, sci-fi movie, mesmerizing, scary"
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lb.set_prompt1(prompt1)
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lb.init_inpainting(init_empty=True)
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lb.set_seed(seed0)
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# Run diffusion
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list_latents = lb.run_diffusion([lb.text_embedding1])
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image_source = lb.sdh.latent2image(list_latents[-1])
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mask_image = 255*np.ones([512,512], dtype=np.uint8)
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mask_image[340:420, 170:280] = 0
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mask_image = Image.fromarray(mask_image)
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#%% Now let us compute a transition video with inpainting
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# First inject back the latents that we already computed for our source image.
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lb.inject_latents(list_latents, inject_img1=True)
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# Then setup the seeds. Keep the one from the first image
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fixed_seeds = [seed0, 6579436]
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# Fix the prompts for the target
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prompt2 = "aerial photo of a futuristic alien temple in a blue coastal area, the sun is shining with a bright light"
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lb.set_prompt1(prompt1)
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lb.set_prompt2(prompt2)
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lb.init_inpainting(image_source, mask_image)
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# Run latent blending
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imgs_transition = lb.run_transition(recycle_img1=True, fixed_seeds=fixed_seeds)
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# Let's get more cheap frames via linear interpolation (duration_transition*fps frames)
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imgs_transition_ext = add_frames_linear_interp(imgs_transition, duration_transition, fps)
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# Save as MP4
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fp_movie = "movie_example2.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, shape_hw=[lb.height, lb.width])
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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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