62 lines
2.2 KiB
Python
62 lines
2.2 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 diffusers import AutoPipelineForText2Image
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warnings.filterwarnings('ignore')
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torch.set_grad_enabled(False)
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torch.backends.cudnn.benchmark = False
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# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
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pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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dh = DiffusersHolder(pipe)
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# %% Next let's set up all parameters
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depth_strength = 0.55 # Specifies how deep (in terms of diffusion iterations the first branching happens)
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t_compute_max_allowed = 10 # Determines the quality of the transition in terms of compute time you grant it
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num_inference_steps = 4
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size_output = (1024, 1024)
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prompt1 = "underwater landscape, fish, und the sea, incredible detail, high resolution"
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prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
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negative_prompt = "blurry, ugly, pale" # Optional
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fp_movie = 'movie_example1.mp4'
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duration_transition = 12 # In seconds
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# Spawn latent blending
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lb = LatentBlending(dh)
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lb.set_prompt1(prompt1)
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lb.set_prompt2(prompt2)
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lb.set_dimensions(size_output)
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lb.set_negative_prompt(negative_prompt)
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lb.set_guidance_scale(0)
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# Run latent blending
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lb.run_transition(
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depth_strength=depth_strength,
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num_inference_steps=num_inference_steps,
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t_compute_max_allowed=t_compute_max_allowed)
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# Save movie
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lb.write_movie_transition(fp_movie, duration_transition)
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