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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 torch
import numpy as np
@ -20,16 +5,16 @@ import warnings
import time
from tqdm.auto import tqdm
from PIL import Image
from movie_util import MovieSaver
from latentblending.movie_util import MovieSaver
from typing import List, Optional
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')
torch.backends.cudnn.benchmark = False
torch.set_grad_enabled(False)
class LatentBlending():
class BlendingEngine():
def __init__(
self,
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 numpy as np
import warnings
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.models.attention_processor import (
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 warnings
from latent_blending import LatentBlending
from blending_engine import BlendingEngine
from diffusers_holder import DiffusersHolder
from diffusers import AutoPipelineForText2Image
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
list_seeds = [95437579, 33259350, 956051013]
fp_movie = 'movie_example2.mp4'
lb = LatentBlending(dh)
be = BlendingEngine(dh)
list_movie_parts = []
for i in range(len(list_prompts) - 1):
# For a multi transition we can save some computation time and recycle the latents
if i == 0:
lb.set_prompt1(list_prompts[i])
lb.set_prompt2(list_prompts[i + 1])
be.set_prompt1(list_prompts[i])
be.set_prompt2(list_prompts[i + 1])
recycle_img1 = False
else:
lb.swap_forward()
lb.set_prompt2(list_prompts[i + 1])
be.swap_forward()
be.set_prompt2(list_prompts[i + 1])
recycle_img1 = True
fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
fixed_seeds = list_seeds[i:i + 2]
# Run latent blending
lb.run_transition(
be.run_transition(
recycle_img1=recycle_img1,
fixed_seeds=fixed_seeds)
# 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)
# 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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@ -1,6 +1,6 @@
lpips==0.1.4
opencv-python==4.7.0.68
opencv-python
ffmpeg-python
diffusers["torch"]==0.23.0
transformers==4.35.2
pytest
diffusers==0.25.0
transformers
pytest

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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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@ -1,54 +0,0 @@
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()