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example1_standard.py
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# Copyright 2022 Lunar Ring. All rights reserved.
# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import warnings
from latent_blending import LatentBlending
from diffusers_holder import DiffusersHolder
from diffusers import AutoPipelineForText2Image
warnings.filterwarnings('ignore')
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = False
# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
dh = DiffusersHolder(pipe)
lb = LatentBlending(dh)
lb.set_prompt1("photo of underwater landscape, fish, und the sea, incredible detail, high resolution")
lb.set_prompt2("rendering of an alien planet, strange plants, strange creatures, surreal")
lb.set_negative_prompt("blurry, ugly, pale")
# Run latent blending
lb.run_transition()
# Save movie
lb.write_movie_transition('movie_example1.mp4', duration_transition=12)

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latentblending/__init__.py Normal file
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from .blending_engine import BlendingEngine
from .diffusers_holder import DiffusersHolder
from .movie_util import MovieSaver
from .utils import interpolate_spherical, add_frames_linear_interp, interpolate_linear, get_spacing, get_time, yml_load, yml_save

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latent_blending.py → latentblending/blending_engine.py
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@ -1,18 +1,3 @@
# Copyright 2022 Lunar Ring. All rights reserved.
# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os import os
import torch import torch
import numpy as np import numpy as np
@ -20,16 +5,16 @@ import warnings
import time import time
from tqdm.auto import tqdm from tqdm.auto import tqdm
from PIL import Image from PIL import Image
from movie_util import MovieSaver from latentblending.movie_util import MovieSaver
from typing import List, Optional from typing import List, Optional
import lpips import lpips
from utils import interpolate_spherical, interpolate_linear, add_frames_linear_interp, yml_load, yml_save from latentblending.utils import interpolate_spherical, interpolate_linear, add_frames_linear_interp, yml_load, yml_save
warnings.filterwarnings('ignore') warnings.filterwarnings('ignore')
torch.backends.cudnn.benchmark = False torch.backends.cudnn.benchmark = False
torch.set_grad_enabled(False) torch.set_grad_enabled(False)
class LatentBlending(): class BlendingEngine():
def __init__( def __init__(
self, self,
dh: None, dh: None,

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diffusers_holder.py → latentblending/diffusers_holder.py
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@ -1,24 +1,9 @@
# Copyright 2022 Lunar Ring. All rights reserved.
# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch import torch
import numpy as np import numpy as np
import warnings import warnings
from typing import Any, Callable, Dict, List, Optional, Tuple, Union from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from utils import interpolate_spherical from latentblending.utils import interpolate_spherical
from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.models.attention_processor import ( from diffusers.models.attention_processor import (
AttnProcessor2_0, AttnProcessor2_0,

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latentblending/example1_standard.py Normal file
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import torch
import warnings
from blending_engine import BlendingEngine
from diffusers_holder import DiffusersHolder
from diffusers import AutoPipelineForText2Image
warnings.filterwarnings('ignore')
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = False
# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.to("cuda")
dh = DiffusersHolder(pipe)
be = BlendingEngine(dh)
be.set_prompt1("photo of underwater landscape, fish, und the sea, incredible detail, high resolution")
be.set_prompt2("rendering of an alien planet, strange plants, strange creatures, surreal")
be.set_negative_prompt("blurry, ugly, pale")
# Run latent blending
be.run_transition()
# Save movie
be.write_movie_transition('movie_example1.mp4', duration_transition=12)

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example2_multitrans.py → latentblending/example2_multitrans.py
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@ -1,21 +1,6 @@
# Copyright 2022 Lunar Ring. All rights reserved.
# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch import torch
import warnings import warnings
from latent_blending import LatentBlending from blending_engine import BlendingEngine
from diffusers_holder import DiffusersHolder from diffusers_holder import DiffusersHolder
from diffusers import AutoPipelineForText2Image from diffusers import AutoPipelineForText2Image
from movie_util import concatenate_movies from movie_util import concatenate_movies
@ -42,29 +27,29 @@ list_prompts.append("photo of a house, high detail")
# You can optionally specify the seeds # You can optionally specify the seeds
list_seeds = [95437579, 33259350, 956051013] list_seeds = [95437579, 33259350, 956051013]
fp_movie = 'movie_example2.mp4' fp_movie = 'movie_example2.mp4'
lb = LatentBlending(dh) be = BlendingEngine(dh)
list_movie_parts = [] list_movie_parts = []
for i in range(len(list_prompts) - 1): for i in range(len(list_prompts) - 1):
# For a multi transition we can save some computation time and recycle the latents # For a multi transition we can save some computation time and recycle the latents
if i == 0: if i == 0:
lb.set_prompt1(list_prompts[i]) be.set_prompt1(list_prompts[i])
lb.set_prompt2(list_prompts[i + 1]) be.set_prompt2(list_prompts[i + 1])
recycle_img1 = False recycle_img1 = False
else: else:
lb.swap_forward() be.swap_forward()
lb.set_prompt2(list_prompts[i + 1]) be.set_prompt2(list_prompts[i + 1])
recycle_img1 = True recycle_img1 = True
fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4" fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
fixed_seeds = list_seeds[i:i + 2] fixed_seeds = list_seeds[i:i + 2]
# Run latent blending # Run latent blending
lb.run_transition( be.run_transition(
recycle_img1=recycle_img1, recycle_img1=recycle_img1,
fixed_seeds=fixed_seeds) fixed_seeds=fixed_seeds)
# Save movie # Save movie
lb.write_movie_transition(fp_movie_part, duration_single_trans) be.write_movie_transition(fp_movie_part, duration_single_trans)
list_movie_parts.append(fp_movie_part) list_movie_parts.append(fp_movie_part)
# Finally, concatente the result # Finally, concatente the result

0
gradio_ui.py → latentblending/gradio_ui.py
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0
movie_util.py → latentblending/movie_util.py
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0
utils.py → latentblending/utils.py
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requirements.txt
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@ -1,7 +1,6 @@
lpips==0.1.4 lpips==0.1.4
opencv-python==4.7.0.68 opencv-python
ffmpeg-python ffmpeg-python
diffusers["torch"]==0.23.0 diffusers==0.25.0
transformers==4.35.2 transformers
pytest pytest
accelerate

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setup.py Normal file
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from setuptools import setup, find_packages
# Read requirements.txt and store its contents in a list
with open('requirements.txt') as f:
required = f.read().splitlines()
setup(
name='latentblending',
version='0.2',
url='https://github.com/lunarring/latentblending',
description='Butter-smooth video transitions',
long_description=open('README.md').read(),
install_requires=required,
dependency_links=[
'git+https://github.com/lunarring/lunar_tools#egg=lunar_tools'
],
include_package_data=False,
)

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test_latentblending.py
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import unittest
from latent_blending import LatentBlending
from diffusers_holder import DiffusersHolder
from diffusers import DiffusionPipeline
import torch
default_pipe = "stabilityai/stable-diffusion-xl-base-1.0"
class TestDiffusersHolder(unittest.TestCase):
def test_load_diffusers_holder(self):
pipe = DiffusionPipeline.from_pretrained(default_pipe, torch_dtype=torch.float16).to('cuda')
dh = DiffusersHolder(pipe)
self.assertIsNotNone(dh, "Failed to load DiffusersHolder")
class TestSingleImageGeneration(unittest.TestCase):
def test_single_image_generation(self):
pipe = DiffusionPipeline.from_pretrained(default_pipe, torch_dtype=torch.float16).to('cuda')
dh = DiffusersHolder(pipe)
dh.set_dimensions((1024, 704))
dh.set_num_inference_steps(40)
prompt = "Your prompt here"
text_embeddings = dh.get_text_embedding(prompt)
generator = torch.Generator(device=dh.device).manual_seed(int(420))
latents_start = dh.get_noise()
list_latents_1 = dh.run_diffusion(text_embeddings, latents_start)
img_orig = dh.latent2image(list_latents_1[-1])
self.assertIsNotNone(img_orig, "Failed to generate an image")
class TestImageTransition(unittest.TestCase):
def test_image_transition(self):
pipe = DiffusionPipeline.from_pretrained(default_pipe, torch_dtype=torch.float16).to('cuda')
dh = DiffusersHolder(pipe)
lb = LatentBlending(dh)
lb.set_prompt1('photo of my first prompt1')
lb.set_prompt2('photo of my second prompt')
depth_strength = 0.6
t_compute_max_allowed = 10
num_inference_steps = 30
imgs_transition = lb.run_transition(
depth_strength=depth_strength,
num_inference_steps=num_inference_steps,
t_compute_max_allowed=t_compute_max_allowed)
self.assertTrue(len(imgs_transition) > 0, "No transition images generated")
if __name__ == '__main__':
unittest.main()