reorganization

latentblending/__init__.py

@@ -0,0 +1,4 @@
+from .blending_engine import BlendingEngine
+from .diffusers_holder import DiffusersHolder
+from .movie_utils import MovieSaver
+from .utils import interpolate_spherical, add_frames_linear_interp, interpolate_linear, get_spacing, get_time, yml_load, yml_save

latentblending/blending_engine.py

@@ -5,16 +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,

latentblending/diffusers_holder.py

@@ -3,7 +3,7 @@ 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,

latentblending/example1_standard.py

@@ -1,6 +1,6 @@
 import torch
 import warnings
-from latent_blending import LatentBlending
+from blending_engine import BlendingEngine
 from diffusers_holder import DiffusersHolder
 from diffusers import AutoPipelineForText2Image
 

latentblending/example2_multitrans.py

@@ -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
@@ -42,7 +27,7 @@ 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)
+lb = BlendingEngine(dh)
 
 list_movie_parts = []
 for i in range(len(list_prompts) - 1):

requirements.txt

@@ -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
+diffusers==0.25.0
+transformers
 pytest

setup.py

@@ -0,0 +1,19 @@
+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,
+)
+

test_latentblending.py

@@ -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()