82 lines
2.8 KiB
Python
82 lines
2.8 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 torch
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import warnings
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from latent_blending import LatentBlending
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from diffusers_holder import DiffusersHolder
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from diffusers import DiffusionPipeline
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from movie_util import concatenate_movies
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torch.set_grad_enabled(False)
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torch.backends.cudnn.benchmark = False
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warnings.filterwarnings('ignore')
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# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe.to('cuda')
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dh = DiffusersHolder(pipe)
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# %% Let's setup the multi transition
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fps = 30
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duration_single_trans = 20
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depth_strength = 0.25 # Specifies how deep (in terms of diffusion iterations the first branching happens)
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size_output = (1280, 768)
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num_inference_steps = 30
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# Specify a list of prompts below
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list_prompts = []
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list_prompts.append("Photo of a house, high detail")
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list_prompts.append("Photo of an elephant in african savannah")
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list_prompts.append("photo of a house, high detail")
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# You can optionally specify the seeds
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list_seeds = [95437579, 33259350, 956051013]
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t_compute_max_allowed = 20 # per segment
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fp_movie = 'movie_example2.mp4'
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lb = LatentBlending(dh)
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lb.set_dimensions(size_output)
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lb.dh.set_num_inference_steps(num_inference_steps)
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list_movie_parts = []
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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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fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
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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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recycle_img1=recycle_img1,
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depth_strength=depth_strength,
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t_compute_max_allowed=t_compute_max_allowed,
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fixed_seeds=fixed_seeds)
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# Save movie
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lb.write_movie_transition(fp_movie_part, duration_single_trans)
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list_movie_parts.append(fp_movie_part)
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# Finally, concatente the result
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concatenate_movies(fp_movie, list_movie_parts)
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