new gradio interface

Add Dockerfile

.gitignore

@@ -7,6 +7,7 @@ __pycache__/
 *.so
 
 # Distribution / packaging
+*.json
 .Python
 build/
 develop-eggs/

Dockerfile

@@ -0,0 +1,51 @@
+FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04
+
+# Configure environment
+ENV DEBIAN_FRONTEND=noninteractive \
+    PIP_PREFER_BINARY=1 \
+    CUDA_HOME=/usr/local/cuda-12.1 \
+    TORCH_CUDA_ARCH_LIST="8.6"
+
+# Redirect shell
+RUN rm /bin/sh && ln -s /bin/bash /bin/sh
+
+# Install prereqs
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    git-lfs \
+    ffmpeg \ 
+    libgl1-mesa-dev \
+    libglib2.0-0 \
+    git \
+    python3-dev \
+    python3-pip \
+    # Lunar Tools prereqs
+    libasound2-dev \
+    libportaudio2 \
+    && apt clean && rm -rf /var/lib/apt/lists/* \
+    && ln -s /usr/bin/python3 /usr/bin/python
+
+# Set symbolic links
+RUN echo "export PATH=/usr/local/cuda/bin:$PATH" >> /etc/bash.bashrc \
+    && echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH" >> /etc/bash. bashrc \
+    && echo "export CUDA_HOME=/usr/local/cuda-12.1" >> /etc/bash.bashrc
+
+# Install Python packages: Basic, then CUDA-compatible, then custom
+RUN pip3 install \
+    wheel \
+    ninja && \
+    pip3 install \
+    torch==2.1.0 \
+    torchvision==0.16.0 \
+    xformers>=0.0.22 \
+    triton>=2.1.0 \
+    --index-url https://download.pytorch.org/whl/cu121 && \
+    pip3 install git+https://github.com/lunarring/latentblending \
+    git+https://github.com/chengzeyi/stable-fast.git@main#egg=stable-fast
+
+# Optionally store weights in image
+# RUN mkdir -p /root/.cache/torch/hub/checkpoints/ && curl -o /root/.cache/torch/hub/checkpoints//alexnet-owt-7be5be79.pth https://download.pytorch.org/models/alexnet-owt-7be5be79.pth
+# RUN git lfs install && git clone https://huggingface.co/stabilityai/sdxl-turbo /sdxl-turbo
+
+# Clone base repo because why not
+RUN git clone https://github.com/lunarring/latentblending.git

README.md

@@ -2,32 +2,53 @@
 
 Latent blending enables video transitions with incredible smoothness between prompts, computed within seconds. Powered by [stable diffusion XL](https://stability.ai/stable-diffusion), this method involves specific mixing of intermediate latent representations to create a seamless transition – with users having the option to fully customize the transition directly in high-resolution. The new version also supports SDXL Turbo, allowing to generate transitions faster than they are typically played back!
 
-
 ```python
+import torch
+from diffusers import AutoPipelineForText2Image
+from latentblending.blending_engine import BlendingEngine
+from latentblending.diffusers_holder import DiffusersHolder
+
 pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16").to("cuda")
-dh = DiffusersHolder(pipe)
+be = BlendingEngine(pipe)
-lb = LatentBlending(dh)
+be.set_prompt1("photo of underwater landscape, fish, und the sea, incredible detail, high resolution")
-lb.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")
-lb.set_prompt2("rendering of an alien planet, strange plants, strange creatures, surreal")
+be.set_negative_prompt("blurry, ugly, pale")
-lb.set_negative_prompt("blurry, ugly, pale")
 
 # Run latent blending
-lb.run_transition()
+be.run_transition()
 
 # Save movie
-lb.write_movie_transition('movie_example1.mp4', duration_transition=12)
+be.write_movie_transition('movie_example1.mp4', duration_transition=12)
-
 ```
+
+# Installation
+```commandline
+pip install git+https://github.com/lunarring/latentblending
+```
+
+# Extra speedup with stable_fast compile
+Install https://github.com/chengzeyi/stable-fast
+
+Then enable pipe compilation by setting *do_compile=True*
+```python
+be = BlendingEngine(pipe, do_compile=True)
+```
+
 ## Gradio UI
-Coming soon again :)
+We can launch the a user-interface version with:
+```commandline
+python latentblending/gradio_ui.py
+```
+
+With the UI, you can iteratively generate your desired keyframes, and then render the movie with latent blending it at the end.
 
 ## Example 1: Simple transition
 ![](example1.jpg)
-To run a simple transition between two prompts, run `example1_standard.py`
+To run a simple transition between two prompts, see `examples/single_trans.py`, or [check this volcano eruption ](https://youtu.be/O_2fpWHdnm4).
 
 ## Example 2: Multi transition
-To run multiple transition between K prompts, resulting in a stitched video, run `example2_multitrans.py`.
+To run multiple transition between K prompts, resulting in a stitched video, see `examples/multi_trans.py`.
-[View a longer example video here.](https://vimeo.com/789052336/80dcb545b2)
+[View a longer example video here.](https://youtu.be/RLF-yW5dR_Q) 
 
 
 # Customization
@@ -35,19 +56,19 @@ To run multiple transition between K prompts, resulting in a stitched video, run
 ### Change the height/width
 ```python 
 size_output = (1024, 768)
-lb.set_dimensions(size_output)
+be.set_dimensions(size_output)
 ```
 
 ### Change the number of diffusion steps (set_num_inference_steps)
 ```python
-lb.set_num_inference_steps(50)
+be.set_num_inference_steps(50)
 ```
 For SDXL this is set as default=30, for SDXL Turbo a value of 4 is taken.
 
 
 ### Change the guidance scale
 ```python
-lb.set_guidance_scale(3.0)
+be.set_guidance_scale(3.0)
 ```
 For SDXL this is set as default=4.0, for SDXL Turbo a value of 0 is taken.
 
@@ -55,7 +76,7 @@ For SDXL this is set as default=4.0, for SDXL Turbo a value of 0 is taken.
 ```python
 depth_strength = 0.5
 nmb_max_branches = 15
-lb.set_branching(depth_strength=depth_strength, t_compute_max_allowed=None, nmb_max_branches=None)
+be.set_branching(depth_strength=depth_strength, t_compute_max_allowed=None, nmb_max_branches=None)
 ```
 * depth_strength: The strength of the diffusion iterations determines when the blending process will begin. A value close to zero results in more creative and intricate outcomes, while a value closer to one indicates a simpler alpha blending. However, low values may also bring about the introduction of additional objects and motion.
 * t_compute_max_allowed:  maximum time allowed for computation. Higher values give better results but take longer. Either provide t_compute_max_allowed or nmb_max_branches. Does not work for SDXL Turbo.
@@ -66,7 +87,7 @@ You can find the [most relevant parameters here.](parameters.md)
 
 ### Change guidance scale
 ```python 
-lb.set_guidance_scale(5.0)
+be.set_guidance_scale(5.0)
 ```
 
 ### Crossfeeding to the last image.
@@ -76,7 +97,7 @@ Cross-feeding latents is a key feature of latent blending. Here, you can set how
 crossfeed_power = 0.5 # 50% of the latents in the last branch are copied from branch1
 crossfeed_range = 0.7 # The crossfeed is active until 70% of num_iteration, then switched off
 crossfeed_decay = 0.2 # The power of the crossfeed decreases over diffusion iterations, here it would be 0.5*0.2=0.1 in the end of the range.
-lb.set_branch1_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay)
+be.set_branch1_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay)
 ```
 
 ### Crossfeeding to all transition images
@@ -86,16 +107,10 @@ Here, you can set how much the parent branches influence the mixed one. In the a
 crossfeed_power = 0.5 # 50% of the latents in the last branch are copied from the parents
 crossfeed_range = 0.7 # The crossfeed is active until 70% of num_iteration, then switched off
 crossfeed_decay = 0.2 # The power of the crossfeed decreases over diffusion iterations, here it would be 0.5*0.2=0.1 in the end of the range.
-lb.set_parental_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay)
+be.set_parental_crossfeed(crossfeed_power, crossfeed_range, crossfeed_decay)
 ```
 
 
-# Installation
-#### Packages
-```commandline
-pip install -r requirements.txt
-```
-
 # How does latent blending work?
 ## Method
 ![](animation.gif)
@@ -104,9 +119,9 @@ In the figure above, a diffusion tree is illustrated. The diffusion steps are re
 
 The concrete parameters for the transition above would be:
 ```
-lb.set_branch1_crossfeed(crossfeed_power=0.8, crossfeed_range=0.6, crossfeed_decay=0.4)
+be.set_branch1_crossfeed(crossfeed_power=0.8, crossfeed_range=0.6, crossfeed_decay=0.4)
-lb.set_parental_crossfeed(crossfeed_power=0.8, crossfeed_range=0.8, crossfeed_decay=0.2)
+be.set_parental_crossfeed(crossfeed_power=0.8, crossfeed_range=0.8, crossfeed_decay=0.2)
-imgs_transition = lb.run_transition(num_inference_steps=10, depth_strength=0.2, nmb_max_branches=7)
+imgs_transition = be.run_transition(num_inference_steps=10, depth_strength=0.2, nmb_max_branches=7)
 ```
 
 ## Perceptual aspects
@@ -124,7 +139,7 @@ With latent blending, we can create transitions that appear to defy the laws of
 * Inpaint support dropped (as it only makes sense for a single transition)
 
 # Coming soon...
-- [ ] Gradio interface
+- [ ] MacOS support
 - [ ] Huggingface Space
 - [ ] Controlnet
 - [ ] IP-Adapter

example_multi_trans.py

@@ -1,33 +1,39 @@
 import torch
 import warnings
-from blending_engine import BlendingEngine
-from diffusers_holder import DiffusersHolder
 from diffusers import AutoPipelineForText2Image
-from movie_util import concatenate_movies
+from lunar_tools import concatenate_movies
+from latentblending.blending_engine import BlendingEngine
+import numpy as np
 torch.set_grad_enabled(False)
 torch.backends.cudnn.benchmark = False
 warnings.filterwarnings('ignore')
 
 # %% 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")
+pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
-pipe.to('cuda')
+# pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
-dh = DiffusersHolder(pipe)
 
+pipe = AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
+pipe.to('cuda')
+be = BlendingEngine(pipe, do_compile=True)
+be.set_negative_prompt("blurry, pale, low-res, lofi")
 # %% Let's setup the multi transition
 fps = 30
 duration_single_trans = 10
+be.set_dimensions((1024, 1024))
+nmb_prompts = 20
+
 
 # Specify a list of prompts below
+#%%
+
 list_prompts = []
-list_prompts.append("Photo of a house, high detail")
+list_prompts.append("high resolution ultra 8K image with lake and forest")
-list_prompts.append("Photo of an elephant in african savannah")
+list_prompts.append("strange and alien desolate lanscapes 8K")
-list_prompts.append("photo of a house, high detail")
+list_prompts.append("ultra high res psychedelic skyscraper city landscape 8K unreal engine")
-
+#%%
-
+fp_movie = f'surreal_nmb{len(list_prompts)}.mp4'
-# You can optionally specify the seeds
+# Specify the seeds
-list_seeds = [95437579, 33259350, 956051013]
+list_seeds = np.random.randint(0, np.iinfo(np.int32).max, len(list_prompts))
-fp_movie = 'movie_example2.mp4'
-be = BlendingEngine(dh)
 
 list_movie_parts = []
 for i in range(len(list_prompts) - 1):

example_multi_trans_json.py

@@ -0,0 +1,75 @@
+import torch
+import warnings
+from diffusers import AutoPipelineForText2Image
+from latentblending.blending_engine import BlendingEngine
+from lunar_tools import concatenate_movies
+import numpy as np
+torch.set_grad_enabled(False)
+torch.backends.cudnn.benchmark = False
+warnings.filterwarnings('ignore')
+
+import json
+# %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
+# pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
+pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
+
+pipe = AutoPipelineForText2Image.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
+pipe.to('cuda')
+be = BlendingEngine(pipe, do_compile=False)
+
+fp_movie = f'test.mp4'
+fp_json = "movie_240221_1520.json"
+duration_single_trans = 10
+
+# Load the JSON data from the file
+with open(fp_json, 'r') as file:
+    data = json.load(file)
+
+# Set up width, height, num_inference steps
+width = data[0]["width"]
+height = data[0]["height"]
+num_inference_steps = data[0]["num_inference_steps"]
+
+be.set_dimensions((width, height))
+be.set_num_inference_steps(num_inference_steps)
+
+# Initialize lists for prompts, negative prompts, and seeds
+list_prompts = []
+list_negative_prompts = []
+list_seeds = []
+
+# Extract prompts, negative prompts, and seeds from the data
+for item in data[1:]:  # Skip the first item as it contains settings
+    list_prompts.append(item["prompt"])
+    list_negative_prompts.append(item["negative_prompt"])
+    list_seeds.append(item["seed"])
+
+
+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:
+        be.set_prompt1(list_prompts[i])
+        be.set_negative_prompt(list_negative_prompts[i])
+        be.set_prompt2(list_prompts[i + 1])
+        recycle_img1 = False
+    else:
+        be.swap_forward()
+        be.set_negative_prompt(list_negative_prompts[i+1])
+        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
+    be.run_transition(
+        recycle_img1=recycle_img1,
+        fixed_seeds=fixed_seeds)
+
+    # Save movie
+    be.write_movie_transition(fp_movie_part, duration_single_trans)
+    list_movie_parts.append(fp_movie_part)
+
+# Finally, concatente the result
+concatenate_movies(fp_movie, list_movie_parts)
+print(f"DONE! MOVIE SAVED IN {fp_movie}")

example_single_trans.py

@@ -1,8 +1,7 @@
 import torch
 import warnings
-from blending_engine import BlendingEngine
-from diffusers_holder import DiffusersHolder
 from diffusers import AutoPipelineForText2Image
+from latentblending.blending_engine import BlendingEngine
 
 warnings.filterwarnings('ignore')
 torch.set_grad_enabled(False)
@@ -12,9 +11,7 @@ torch.backends.cudnn.benchmark = False
 pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
 pipe.to("cuda")
 
-dh = DiffusersHolder(pipe)
+be = BlendingEngine(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")

latentblending/__init__.py

@@ -1,4 +1,3 @@
 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

latentblending/blending_engine.py

@@ -5,10 +5,12 @@ import warnings
 import time
 from tqdm.auto import tqdm
 from PIL import Image
-from latentblending.movie_util import MovieSaver
 from typing import List, Optional
 import lpips
-from latentblending.utils import interpolate_spherical, interpolate_linear, add_frames_linear_interp, yml_load, yml_save
+import platform
+from latentblending.diffusers_holder import DiffusersHolder
+from latentblending.utils import interpolate_spherical, interpolate_linear, add_frames_linear_interp
+from lunar_tools import MovieSaver, fill_up_frames_linear_interpolation
 warnings.filterwarnings('ignore')
 torch.backends.cudnn.benchmark = False
 torch.set_grad_enabled(False)
@@ -17,12 +19,15 @@ torch.set_grad_enabled(False)
 class BlendingEngine():
     def __init__(
             self,
-            dh: None,
+            pipe: None,
+            do_compile: bool = False,
             guidance_scale_mid_damper: float = 0.5,
             mid_compression_scaler: float = 1.2):
         r"""
         Initializes the latent blending class.
         Args:
+            pipe: diffusers pipeline (SDXL)
+            do_compile: compile pipeline for faster inference using stable fast
             guidance_scale_mid_damper: float = 0.5
                 Reduces the guidance scale towards the middle of the transition.
                 A value of 0.5 would decrease the guidance_scale towards the middle linearly by 0.5.
@@ -35,7 +40,8 @@ class BlendingEngine():
             and guidance_scale_mid_damper <= 1.0, \
             f"guidance_scale_mid_damper neees to be in interval (0,1], you provided {guidance_scale_mid_damper}"
 
-        self.dh = dh
+    
+        self.dh = DiffusersHolder(pipe)
         self.device = self.dh.device
         self.set_dimensions()
 
@@ -64,6 +70,9 @@ class BlendingEngine():
         self.multi_transition_img_first = None
         self.multi_transition_img_last = None
         self.dt_unet_step = 0
+        if platform.system() == "Darwin":
+            self.lpips = lpips.LPIPS(net='alex')
+        else:
             self.lpips = lpips.LPIPS(net='alex').cuda(self.device)
 
         self.set_prompt1("")
@@ -76,13 +85,23 @@ class BlendingEngine():
         self.benchmark_speed()
         self.set_branching()
         
+        if do_compile:
+            print("starting compilation")
+            from sfast.compilers.diffusion_pipeline_compiler import (compile, CompilationConfig)
+            self.dh.pipe.enable_xformers_memory_efficient_attention()
+            config = CompilationConfig.Default()
+            config.enable_xformers = True
+            config.enable_triton = True
+            config.enable_cuda_graph = True
+            self.dh.pipe = compile(self.dh.pipe, config)
+        
         
         
     def benchmark_speed(self):
         """
         Measures the time per diffusion step and for the vae decoding
         """
-        
+        print("starting speed benchmark...")
         text_embeddings = self.dh.get_text_embedding("test")
         latents_start = self.dh.get_noise(np.random.randint(111111))
         # warmup
@@ -96,6 +115,7 @@ class BlendingEngine():
         t0 = time.time()
         img = self.dh.latent2image(list_latents[-1])
         self.dt_vae = time.time() - t0
+        print(f"time per unet iteration: {self.dt_unet_step} time for vae: {self.dt_vae}")
 
     def set_dimensions(self, size_output=None):
         r"""
@@ -660,7 +680,6 @@ class BlendingEngine():
             img_leaf = Image.fromarray(img)
             img_leaf.save(os.path.join(dp_img, f"lowres_img_{str(i).zfill(4)}.jpg"))
         fp_yml = os.path.join(dp_img, "lowres.yaml")
-        self.save_statedict(fp_yml)
 
     def write_movie_transition(self, fp_movie, duration_transition, fps=30):
         r"""
@@ -676,7 +695,7 @@ class BlendingEngine():
         """
 
         # Let's get more cheap frames via linear interpolation (duration_transition*fps frames)
-        imgs_transition_ext = add_frames_linear_interp(self.tree_final_imgs, duration_transition, fps)
+        imgs_transition_ext = fill_up_frames_linear_interpolation(self.tree_final_imgs, duration_transition, fps)
 
         # Save as MP4
         if os.path.isfile(fp_movie):
@@ -686,12 +705,6 @@ class BlendingEngine():
             ms.write_frame(img)
         ms.finalize()
 
-    def save_statedict(self, fp_yml):
-        # Dump everything relevant into yaml
-        imgs_transition = self.tree_final_imgs
-        state_dict = self.get_state_dict()
-        state_dict['nmb_images'] = len(imgs_transition)
-        yml_save(fp_yml, state_dict)
 
     def get_state_dict(self):
         state_dict = {}
@@ -714,35 +727,6 @@ class BlendingEngine():
                         pass
         return state_dict
 
-    def randomize_seed(self):
-        r"""
-        Set a random seed for a fresh start.
-        """
-        seed = np.random.randint(999999999)
-        self.set_seed(seed)
-
-    def set_seed(self, seed: int):
-        r"""
-        Set a the seed for a fresh start.
-        """
-        self.seed = seed
-        self.dh.seed = seed
-
-    def set_width(self, width):
-        r"""
-        Set the width of the resulting image.
-        """
-        assert np.mod(width, 64) == 0, "set_width: value needs to be divisible by 64"
-        self.width = width
-        self.dh.width = width
-
-    def set_height(self, height):
-        r"""
-        Set the height of the resulting image.
-        """
-        assert np.mod(height, 64) == 0, "set_height: value needs to be divisible by 64"
-        self.height = height
-        self.dh.height = height
 
     def swap_forward(self):
         r"""
@@ -813,14 +797,18 @@ if __name__ == "__main__":
     from diffusers import AutoencoderTiny
     # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
     pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
+    pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path)
     
     
-    pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
+    # pipe.to("mps")
     pipe.to("cuda")
-    pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
+    
-    pipe.vae = pipe.vae.cuda()
+    # pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
+    # pipe.vae = pipe.vae.cuda()
 
     dh = DiffusersHolder(pipe)
+    
+    xxx
     # %% Next let's set up all parameters
     prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
     prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
@@ -829,19 +817,20 @@ if __name__ == "__main__":
     duration_transition = 12  # In seconds
 
     # Spawn latent blending
-    lb = LatentBlending(dh)
+    be = BlendingEngine(dh)
-    lb.set_prompt1(prompt1)
+    be.set_prompt1(prompt1)
-    lb.set_prompt2(prompt2)
+    be.set_prompt2(prompt2)
-    lb.set_negative_prompt(negative_prompt)
+    be.set_negative_prompt(negative_prompt)
 
     # Run latent blending
     t0 = time.time()
-    lb.run_transition(fixed_seeds=[420, 421])
+    be.run_transition(fixed_seeds=[420, 421])
     dt = time.time() - t0
+    print(f"dt = {dt}")
 
     # Save movie
     fp_movie = f'test.mp4'
-    lb.write_movie_transition(fp_movie, duration_transition)
+    be.write_movie_transition(fp_movie, duration_transition)
     
 
 

latentblending/gradio_ui.py

@@ -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
 torch.backends.cudnn.benchmark = False
@@ -20,481 +5,340 @@ torch.set_grad_enabled(False)
 import numpy as np
 import warnings
 warnings.filterwarnings('ignore')
-import warnings
 from tqdm.auto import tqdm
 from PIL import Image
-from movie_util import MovieSaver, concatenate_movies
-from latent_blending import LatentBlending
-from stable_diffusion_holder import StableDiffusionHolder
 import gradio as gr
-from dotenv import find_dotenv, load_dotenv
 import shutil
 import uuid
-from utils import get_time, add_frames_linear_interp
+from diffusers import AutoPipelineForText2Image
-from huggingface_hub import hf_hub_download
+from latentblending.blending_engine import BlendingEngine
+import datetime
+import tempfile
+import json
+from lunar_tools import concatenate_movies
+import argparse
 
+"""
+TODO
+- time per segment
+- init phase (model, res, nmb iter)
+- recycle existing movies
+- hf spaces integration
+"""
 
-class BlendingFrontend():
+class MultiUserRouter():
     def __init__(
             self,
-            sdh,
+            do_compile=False
-            share=False):
+        ):
+        self.user_blendingvariableholder = {}
+        self.do_compile = do_compile
+        self.list_models = ["stabilityai/sdxl-turbo", "stabilityai/stable-diffusion-xl-base-1.0"]
+
+        self.init_models()
+
+    def init_models(self):
+        self.dict_blendingengines = {}
+        for m in self.list_models:
+            pipe = AutoPipelineForText2Image.from_pretrained(m, torch_dtype=torch.float16, variant="fp16")
+            pipe.to("cuda")
+            be = BlendingEngine(pipe, do_compile=self.do_compile)
+            
+            self.dict_blendingengines[m] = be
+
+    def register_new_user(self, model, width, height):
+        user_id = str(uuid.uuid4().hex.upper()[0:8])
+        be = self.dict_blendingengines[model]
+        be.set_dimensions((width, height))
+        self.user_blendingvariableholder[user_id] = BlendingVariableHolder(be)
+        return user_id
+
+    def user_overflow_protection(self):
+        pass
+
+    def preview_img_selected(self, user_id, data: gr.SelectData, button):
+        return self.user_blendingvariableholder[user_id].preview_img_selected(data, button)
+
+    def movie_img_selected(self, user_id, data: gr.SelectData, button):
+        return self.user_blendingvariableholder[user_id].movie_img_selected(data, button)
+
+    def compute_imgs(self, user_id, prompt, negative_prompt):
+        return self.user_blendingvariableholder[user_id].compute_imgs(prompt, negative_prompt)
+    
+    def get_list_images_movie(self, user_id):
+        return self.user_blendingvariableholder[user_id].get_list_images_movie()
+    
+    def init_new_movie(self, user_id):
+        return self.user_blendingvariableholder[user_id].init_new_movie()
+    
+    def write_json(self, user_id):
+        return self.user_blendingvariableholder[user_id].write_json()
+    
+    def add_image_to_video(self, user_id):
+        return self.user_blendingvariableholder[user_id].add_image_to_video()
+    
+    def img_movie_delete(self, user_id):
+        return self.user_blendingvariableholder[user_id].img_movie_delete()
+    
+    def img_movie_later(self, user_id):
+        return self.user_blendingvariableholder[user_id].img_movie_later()
+    
+    def img_movie_earlier(self, user_id):
+        return self.user_blendingvariableholder[user_id].img_movie_earlier()
+    
+    def generate_movie(self, user_id, t_per_segment):
+        return self.user_blendingvariableholder[user_id].generate_movie(t_per_segment)
+
+#%% BlendingVariableHolder Class
+class BlendingVariableHolder():
+    def __init__(
+            self,
+            be):
         r"""
         Gradio Helper Class to collect UI data and start latent blending.
         Args:
-            sdh:
+            be:
-                StableDiffusionHolder
+                Blendingengine
             share: bool
                 Set true to get a shareable gradio link (e.g. for running a remote server)
         """
-        self.share = share
+        self.be = be
 
         # UI Defaults
-        self.num_inference_steps = 30
-        self.depth_strength = 0.25
         self.seed1 = 420
         self.seed2 = 420
         self.prompt1 = ""
         self.prompt2 = ""
         self.negative_prompt = ""
-        self.fps = 30
+        self.nmb_preview_images = 4
-        self.duration_video = 8
-        self.t_compute_max_allowed = 10
-
-        self.lb = LatentBlending(sdh)
-        self.lb.sdh.num_inference_steps = self.num_inference_steps
-        self.init_parameters_from_lb()
-        self.init_save_dir()
 
         # Vars
-        self.list_fp_imgs_current = []
+        self.prompt = None
-        self.recycle_img1 = False
+        self.negative_prompt = None
-        self.recycle_img2 = False
+        self.list_seeds = []
-        self.list_all_segments = []
+        self.idx_movie = 0
-        self.dp_session = ""
+        self.list_seeds = []
-        self.user_id = None
+        self.list_images_preview = []
+        self.data = []
+        self.idx_img_preview_selected = None
+        self.idx_img_movie_selected = None
+        self.jpg_quality = 80 
+        self.fp_movie = ''
 
-    def init_parameters_from_lb(self):
+    def preview_img_selected(self, data: gr.SelectData, button):
-        r"""
+        self.idx_img_preview_selected = data.index
-        Automatically init parameters from latentblending instance
+        print(f"preview image {self.idx_img_preview_selected} selected, seed {self.list_seeds[self.idx_img_preview_selected]}")
-        """
-        self.height = self.lb.sdh.height
-        self.width = self.lb.sdh.width
-        self.guidance_scale = self.lb.guidance_scale
-        self.guidance_scale_mid_damper = self.lb.guidance_scale_mid_damper
-        self.mid_compression_scaler = self.lb.mid_compression_scaler
-        self.branch1_crossfeed_power = self.lb.branch1_crossfeed_power
-        self.branch1_crossfeed_range = self.lb.branch1_crossfeed_range
-        self.branch1_crossfeed_decay = self.lb.branch1_crossfeed_decay
-        self.parental_crossfeed_power = self.lb.parental_crossfeed_power
-        self.parental_crossfeed_range = self.lb.parental_crossfeed_range
-        self.parental_crossfeed_power_decay = self.lb.parental_crossfeed_power_decay
 
-    def init_save_dir(self):
+    def movie_img_selected(self, data: gr.SelectData, button):
-        r"""
+        self.idx_img_movie_selected = data.index
-        Initializes the directory where stuff is being saved.
+        print(f"movie image {self.idx_img_movie_selected} selected")
-        You can specify this directory in a ".env" file in your latentblending root, setting
-        DIR_OUT='/path/to/saving'
-        """
-        load_dotenv(find_dotenv(), verbose=False)
-        self.dp_out = os.getenv("DIR_OUT")
-        if self.dp_out is None:
-            self.dp_out = ""
-        self.dp_imgs = os.path.join(self.dp_out, "imgs")
-        os.makedirs(self.dp_imgs, exist_ok=True)
-        self.dp_movies = os.path.join(self.dp_out, "movies")
-        os.makedirs(self.dp_movies, exist_ok=True)
-        self.save_empty_image()
 
-    def save_empty_image(self):
+    def compute_imgs(self, prompt, negative_prompt):
-        r"""
+        self.prompt = prompt
-        Saves an empty/black dummy image.
+        self.negative_prompt = negative_prompt
-        """
+        self.be.set_prompt1(prompt)
-        self.fp_img_empty = os.path.join(self.dp_imgs, 'empty.jpg')
+        self.be.set_prompt2(prompt)
-        Image.fromarray(np.zeros((self.height, self.width, 3), dtype=np.uint8)).save(self.fp_img_empty, quality=5)
+        self.be.set_negative_prompt(negative_prompt)
+        self.list_seeds = []
+        self.list_images_preview = []
+        self.idx_img_preview_selected = None
+        for i in range(self.nmb_preview_images):
+            seed = np.random.randint(0, np.iinfo(np.int32).max)
+            self.be.seed1 = seed
+            self.list_seeds.append(seed)
+            img = self.be.compute_latents1(return_image=True)
+            fn_img_tmp = f"image_{uuid.uuid4()}.jpg"
+            temp_img_path = os.path.join(tempfile.gettempdir(), fn_img_tmp)
+            img.save(temp_img_path)
+            img.save(temp_img_path, quality=self.jpg_quality, optimize=True)
+            self.list_images_preview.append(temp_img_path)
+        return self.list_images_preview 
     
-    def randomize_seed1(self):
-        r"""
-        Randomizes the first seed
-        """
-        seed = np.random.randint(0, 10000000)
-        self.seed1 = int(seed)
-        print(f"randomize_seed1: new seed = {self.seed1}")
-        return seed
 
-    def randomize_seed2(self):
+    def get_list_images_movie(self):
-        r"""
+        return [entry["preview_image"] for entry in self.data]
-        Randomizes the second seed
-        """
-        seed = np.random.randint(0, 10000000)
-        self.seed2 = int(seed)
-        print(f"randomize_seed2: new seed = {self.seed2}")
-        return seed
 
-    def setup_lb(self, list_ui_vals):
-        r"""
-        Sets all parameters from the UI. Since gradio does not support to pass dictionaries,
-        we have to instead pass keys (list_ui_keys, global) and values (list_ui_vals)
-        """
-        # Collect latent blending variables
-        self.lb.set_width(list_ui_vals[list_ui_keys.index('width')])
-        self.lb.set_height(list_ui_vals[list_ui_keys.index('height')])
-        self.lb.set_prompt1(list_ui_vals[list_ui_keys.index('prompt1')])
-        self.lb.set_prompt2(list_ui_vals[list_ui_keys.index('prompt2')])
-        self.lb.set_negative_prompt(list_ui_vals[list_ui_keys.index('negative_prompt')])
-        self.lb.guidance_scale = list_ui_vals[list_ui_keys.index('guidance_scale')]
-        self.lb.guidance_scale_mid_damper = list_ui_vals[list_ui_keys.index('guidance_scale_mid_damper')]
-        self.t_compute_max_allowed = list_ui_vals[list_ui_keys.index('duration_compute')]
-        self.lb.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')]
-        self.lb.sdh.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')]
-        self.duration_video = list_ui_vals[list_ui_keys.index('duration_video')]
-        self.lb.seed1 = list_ui_vals[list_ui_keys.index('seed1')]
-        self.lb.seed2 = list_ui_vals[list_ui_keys.index('seed2')]
-        self.lb.branch1_crossfeed_power = list_ui_vals[list_ui_keys.index('branch1_crossfeed_power')]
-        self.lb.branch1_crossfeed_range = list_ui_vals[list_ui_keys.index('branch1_crossfeed_range')]
-        self.lb.branch1_crossfeed_decay = list_ui_vals[list_ui_keys.index('branch1_crossfeed_decay')]
-        self.lb.parental_crossfeed_power = list_ui_vals[list_ui_keys.index('parental_crossfeed_power')]
-        self.lb.parental_crossfeed_range = list_ui_vals[list_ui_keys.index('parental_crossfeed_range')]
-        self.lb.parental_crossfeed_power_decay = list_ui_vals[list_ui_keys.index('parental_crossfeed_power_decay')]
-        self.num_inference_steps = list_ui_vals[list_ui_keys.index('num_inference_steps')]
-        self.depth_strength = list_ui_vals[list_ui_keys.index('depth_strength')]
 
-        if len(list_ui_vals[list_ui_keys.index('user_id')]) > 1:
+    def init_new_movie(self):
-            self.user_id = list_ui_vals[list_ui_keys.index('user_id')]
+        current_time = datetime.datetime.now()
+        self.fp_movie = "movie_" + current_time.strftime("%y%m%d_%H%M") + ".mp4"
+        self.fp_json = "movie_" + current_time.strftime("%y%m%d_%H%M") + ".json"
+        
+
+    def write_json(self):
+        # Write the data list to a JSON file
+        data_copy = self.data.copy()
+        data_copy.insert(0, {"settings": "sdxl", "width": self.be.dh.width_img, "height": self.be.dh.height_img, "num_inference_steps": self.be.dh.num_inference_steps})
+        with open(self.fp_json, 'w') as f:
+            json.dump(data_copy, f, indent=4)
+
+    def add_image_to_video(self):
+        if self.prompt is None:
+            print("Cannot take because no prompt was set!")
+            return self.get_list_images_movie()
+        if self.idx_movie == 0:
+            self.init_new_movie()
+
+        self.data.append({"iteration": self.idx_movie, 
+        "seed": self.list_seeds[self.idx_img_preview_selected], 
+        "prompt": self.prompt, 
+        "negative_prompt": self.negative_prompt,
+        "preview_image": self.list_images_preview[self.idx_img_preview_selected]
+        })
+
+        self.write_json()
+        self.idx_movie += 1
+        return self.get_list_images_movie()
+
+    def img_movie_delete(self):
+        if self.idx_img_movie_selected is not None and 0 <= self.idx_img_movie_selected < len(self.data)+1:
+            del self.data[self.idx_img_movie_selected]
+            self.idx_img_movie_selected = None
         else:
-            # generate new user id
+            print(f"Invalid movie image index for deletion: {self.idx_img_movie_selected}")
-            self.user_id = uuid.uuid4().hex
+        return self.get_list_images_movie()
-            print(f"made new user_id: {self.user_id} at {get_time('second')}")
 
-    def save_latents(self, fp_latents, list_latents):
+    def img_movie_later(self):
-        r"""
+        if self.idx_img_movie_selected is not None and self.idx_img_movie_selected < len(self.data):
-        Saves a latent trajectory on disk, in npy format.
+            # Swap the selected image with the next one
-        """
+            self.data[self.idx_img_movie_selected], self.data[self.idx_img_movie_selected + 1] = \
-        list_latents_cpu = [l.cpu().numpy() for l in list_latents]
+                self.data[self.idx_img_movie_selected+1], self.data[self.idx_img_movie_selected]
-        np.save(fp_latents, list_latents_cpu)
+            self.idx_img_movie_selected = None
+        else:
+            print("Cannot move the image later in the sequence.")
+        return self.get_list_images_movie()
 
-    def load_latents(self, fp_latents):
+    def img_movie_earlier(self):
-        r"""
+        if self.idx_img_movie_selected is not None and self.idx_img_movie_selected > 0:
-        Loads a latent trajectory from disk, converts to torch tensor.
+            # Swap the selected image with the previous one
-        """
+            self.data[self.idx_img_movie_selected-1], self.data[self.idx_img_movie_selected] = \
-        list_latents_cpu = np.load(fp_latents)
+                self.data[self.idx_img_movie_selected], self.data[self.idx_img_movie_selected-1]
-        list_latents = [torch.from_numpy(l).to(self.lb.device) for l in list_latents_cpu]
+            self.idx_img_movie_selected = None
-        return list_latents
+        else:
+            print("Cannot move the image earlier in the sequence.")
+        return self.get_list_images_movie()
     
-    def compute_img1(self, *args):
-        r"""
-        Computes the first transition image and returns it for display.
-        Sets all other transition images and last image to empty (as they are obsolete with this operation)
-        """
-        list_ui_vals = args
-        self.setup_lb(list_ui_vals)
-        fp_img1 = os.path.join(self.dp_imgs, f"img1_{self.user_id}")
-        img1 = Image.fromarray(self.lb.compute_latents1(return_image=True))
-        img1.save(fp_img1 + ".jpg")
-        self.save_latents(fp_img1 + ".npy", self.lb.tree_latents[0])
-        self.recycle_img1 = True
-        self.recycle_img2 = False
-        return [fp_img1 + ".jpg", self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.user_id]
 
-    def compute_img2(self, *args):
+    def generate_movie(self, t_per_segment=10):
-        r"""
+        print("starting movie gen")
-        Computes the last transition image and returns it for display.
+        list_prompts = []
-        Sets all other transition images to empty (as they are obsolete with this operation)
+        list_negative_prompts = []
-        """
+        list_seeds = []
-        if not os.path.isfile(os.path.join(self.dp_imgs, f"img1_{self.user_id}.jpg")):  # don't do anything
-            return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.user_id]
-        list_ui_vals = args
-        self.setup_lb(list_ui_vals)
 
-        self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
+        # Extract prompts, negative prompts, and seeds from the data
-        fp_img2 = os.path.join(self.dp_imgs, f"img2_{self.user_id}")
+        for item in self.data: 
-        img2 = Image.fromarray(self.lb.compute_latents2(return_image=True))
+            list_prompts.append(item["prompt"])
-        img2.save(fp_img2 + '.jpg')
+            list_negative_prompts.append(item["negative_prompt"])
-        self.save_latents(fp_img2 + ".npy", self.lb.tree_latents[-1])
+            list_seeds.append(item["seed"])
-        self.recycle_img2 = True
-        # fixme save seeds. change filenames?
-        return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, fp_img2 + ".jpg", self.user_id]
 
-    def compute_transition(self, *args):
+        list_movie_parts = []
-        r"""
+        for i in range(len(list_prompts) - 1):
-        Computes transition images and movie.
+            # For a multi transition we can save some computation time and recycle the latents
-        """
+            if i == 0:
-        list_ui_vals = args
+                self.be.set_prompt1(list_prompts[i])
-        self.setup_lb(list_ui_vals)
+                self.be.set_negative_prompt(list_negative_prompts[i])
-        print("STARTING TRANSITION...")
+                self.be.set_prompt2(list_prompts[i + 1])
-        fixed_seeds = [self.seed1, self.seed2]
+                recycle_img1 = False
-        # Inject loaded latents (other user interference)
+            else:
-        self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
+                self.be.swap_forward()
-        self.lb.tree_latents[-1] = self.load_latents(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy"))
+                self.be.set_negative_prompt(list_negative_prompts[i+1])
-        imgs_transition = self.lb.run_transition(
+                self.be.set_prompt2(list_prompts[i + 1])
-            recycle_img1=self.recycle_img1,
+                recycle_img1 = True
-            recycle_img2=self.recycle_img2,
+
-            num_inference_steps=self.num_inference_steps,
+            fp_movie_part = f"tmp_part_{str(i).zfill(3)}.mp4"
-            depth_strength=self.depth_strength,
+            fixed_seeds = list_seeds[i:i + 2]
-            t_compute_max_allowed=self.t_compute_max_allowed,
+            # Run latent blending
+            self.be.run_transition(
+                recycle_img1=recycle_img1,
                 fixed_seeds=fixed_seeds)
-        print(f"Latent Blending pass finished ({get_time('second')}). Resulted in {len(imgs_transition)} images")
 
-        # Subselect three preview images
+            # Save movie
-        idx_img_prev = np.round(np.linspace(0, len(imgs_transition) - 1, 5)[1:-1]).astype(np.int32)
+            self.be.write_movie_transition(fp_movie_part, t_per_segment)
+            list_movie_parts.append(fp_movie_part)
 
-        list_imgs_preview = []
+        # Finally, concatenate the result
-        for j in idx_img_prev:
+        concatenate_movies(self.fp_movie, list_movie_parts)
-            list_imgs_preview.append(Image.fromarray(imgs_transition[j]))
+        print(f"DONE! MOVIE SAVED IN {self.fp_movie}")
-
+        return self.fp_movie
-        # Save the preview imgs as jpgs on disk so we are not sending umcompressed data around
-        current_timestamp = get_time('second')
-        self.list_fp_imgs_current = []
-        for i in range(len(list_imgs_preview)):
-            fp_img = os.path.join(self.dp_imgs, f"img_preview_{i}_{current_timestamp}.jpg")
-            list_imgs_preview[i].save(fp_img)
-            self.list_fp_imgs_current.append(fp_img)
-        # Insert cheap frames for the movie
-        imgs_transition_ext = add_frames_linear_interp(imgs_transition, self.duration_video, self.fps)
-
-        # Save as movie
-        self.fp_movie = self.get_fp_video_last()
-        if os.path.isfile(self.fp_movie):
-            os.remove(self.fp_movie)
-        ms = MovieSaver(self.fp_movie, fps=self.fps)
-        for img in tqdm(imgs_transition_ext):
-            ms.write_frame(img)
-        ms.finalize()
-        print("DONE SAVING MOVIE! SENDING BACK...")
-
-        # Assemble Output, updating the preview images and le movie
-        list_return = self.list_fp_imgs_current + [self.fp_movie]
-        return list_return
-
-    def stack_forward(self, prompt2, seed2):
-        r"""
-        Allows to generate multi-segment movies. Sets last image -> first image with all
-        relevant parameters.
-        """
-        # Save preview images, prompts and seeds into dictionary for stacking
-        if len(self.list_all_segments) == 0:
-            timestamp_session = get_time('second')
-            self.dp_session = os.path.join(self.dp_out, f"session_{timestamp_session}")
-            os.makedirs(self.dp_session)
-
-        idx_segment = len(self.list_all_segments)
-        dp_segment = os.path.join(self.dp_session, f"segment_{str(idx_segment).zfill(3)}")
-
-        self.list_all_segments.append(dp_segment)
-        self.lb.write_imgs_transition(dp_segment)
-
-        fp_movie_last = self.get_fp_video_last()
-        fp_movie_next = self.get_fp_video_next()
-
-        shutil.copyfile(fp_movie_last, fp_movie_next)
-
-        self.lb.tree_latents[0] = self.load_latents(os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
-        self.lb.tree_latents[-1] = self.load_latents(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy"))
-        self.lb.swap_forward()
-
-        shutil.copyfile(os.path.join(self.dp_imgs, f"img2_{self.user_id}.npy"), os.path.join(self.dp_imgs, f"img1_{self.user_id}.npy"))
-        fp_multi = self.multi_concat()
-        list_out = [fp_multi]
-
-        list_out.extend([os.path.join(self.dp_imgs, f"img2_{self.user_id}.jpg")])
-        list_out.extend([self.fp_img_empty] * 4)
-        list_out.append(gr.update(interactive=False, value=prompt2))
-        list_out.append(gr.update(interactive=False, value=seed2))
-        list_out.append("")
-        list_out.append(np.random.randint(0, 10000000))
-        print(f"stack_forward: fp_multi {fp_multi}")
-        return list_out
-
-    def multi_concat(self):
-        r"""
-        Concatentates all stacked segments into one long movie.
-        """
-        list_fp_movies = self.get_fp_video_all()
-        # Concatenate movies and save
-        fp_final = os.path.join(self.dp_session, f"concat_{self.user_id}.mp4")
-        concatenate_movies(fp_final, list_fp_movies)
-        return fp_final
-
-    def get_fp_video_all(self):
-        r"""
-        Collects all stacked movie segments.
-        """
-        list_all = os.listdir(self.dp_movies)
-        str_beg = f"movie_{self.user_id}_"
-        list_user = [l for l in list_all if str_beg in l]
-        list_user.sort()
-        list_user = [os.path.join(self.dp_movies, l) for l in list_user]
-        return list_user
-
-    def get_fp_video_next(self):
-        r"""
-        Gets the filepath of the next movie segment.
-        """
-        list_videos = self.get_fp_video_all()
-        if len(list_videos) == 0:
-            idx_next = 0
-        else:
-            idx_next = len(list_videos)
-        fp_video_next = os.path.join(self.dp_movies, f"movie_{self.user_id}_{str(idx_next).zfill(3)}.mp4")
-        return fp_video_next
-
-    def get_fp_video_last(self):
-        r"""
-        Gets the current video that was saved.
-        """
-        fp_video_last = os.path.join(self.dp_movies, f"last_{self.user_id}.mp4")
-        return fp_video_last
 
+#%% Runtime engine
 
 if __name__ == "__main__":
-#    fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt")
-    fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.ckpt")
-    bf = BlendingFrontend(StableDiffusionHolder(fp_ckpt))
-    # self = BlendingFrontend(None)
 
+    # Change Parameters below
+    parser = argparse.ArgumentParser(description="Latent Blending GUI")
+    parser.add_argument("--do_compile", type=bool, default=False)
+    parser.add_argument("--nmb_preview_images", type=int, default=4)
+    parser.add_argument("--server_name", type=str, default=None)
+    try:
+        args = parser.parse_args()
+        nmb_preview_images = args.nmb_preview_images
+        do_compile = args.do_compile
+        server_name = args.server_name
+
+    except SystemExit:
+        # If the script is run in an interactive environment (like Jupyter), parse_args might fail.
+        nmb_preview_images = 4
+        do_compile = False # compile SD pipes with sdfast
+        server_name = None
+
+    mur = MultiUserRouter(do_compile=do_compile)
     with gr.Blocks() as demo:
-        gr.HTML("""<h1>Latent Blending</h1>
+        with gr.Accordion("Setup", open=True) as accordion_setup:
-<p>Create butter-smooth transitions between prompts, powered by stable diffusion</p>
+            # New user registration, model selection, ...
-<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-<br/>
-<a href="https://huggingface.co/spaces/lunarring/latentblending?duplicate=true">
-<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-</p>""")
-
             with gr.Row():
-            prompt1 = gr.Textbox(label="prompt 1")
+                model = gr.Dropdown(mur.list_models, value=mur.list_models[0], label="model")
-            prompt2 = gr.Textbox(label="prompt 2")
+                width = gr.Slider(256, 2048, 512, step=128, label='width', interactive=True)
+                height = gr.Slider(256, 2048, 512, step=128, label='height', interactive=True)
+                user_id = gr.Textbox(label="user id (filled automatically)", interactive=False)
+                b_start_session = gr.Button('start session', variant='primary')
 
+        with gr.Accordion("Latent Blending (expand with arrow on right side after you clicked 'start session')", open=False) as accordion_latentblending:
             with gr.Row():
-            duration_compute = gr.Slider(10, 25, bf.t_compute_max_allowed, step=1, label='waiting time', interactive=True)
+                prompt = gr.Textbox(label="prompt")
-            duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='video duration', interactive=True)
-            height = gr.Slider(256, 1024, bf.height, step=128, label='height', interactive=True)
-            width = gr.Slider(256, 1024, bf.width, step=128, label='width', interactive=True)
-
-        with gr.Accordion("Advanced Settings (click to expand)", open=False):
-
-            with gr.Accordion("Diffusion settings", open=True):
-                with gr.Row():
-                    num_inference_steps = gr.Slider(5, 100, bf.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
-                    guidance_scale = gr.Slider(1, 25, bf.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
                 negative_prompt = gr.Textbox(label="negative prompt")
-
+                b_compute = gr.Button('generate preview images', variant='primary')
-            with gr.Accordion("Seed control: adjust seeds for first and last images", open=True):
+                b_select = gr.Button('add selected image to video', variant='primary')        
-                with gr.Row():
-                    b_newseed1 = gr.Button("randomize seed 1", variant='secondary')
-                    seed1 = gr.Number(bf.seed1, label="seed 1", interactive=True)
-                    seed2 = gr.Number(bf.seed2, label="seed 2", interactive=True)
-                    b_newseed2 = gr.Button("randomize seed 2", variant='secondary')
-
-            with gr.Accordion("Last image crossfeeding.", open=True):
-                with gr.Row():
-                    branch1_crossfeed_power = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_power, step=0.01, label='branch1 crossfeed power', interactive=True)
-                    branch1_crossfeed_range = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_range, step=0.01, label='branch1 crossfeed range', interactive=True)
-                    branch1_crossfeed_decay = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_decay, step=0.01, label='branch1 crossfeed decay', interactive=True)
-
-            with gr.Accordion("Transition settings", open=True):
-                with gr.Row():
-                    parental_crossfeed_power = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power, step=0.01, label='parental crossfeed power', interactive=True)
-                    parental_crossfeed_range = gr.Slider(0.0, 1.0, bf.parental_crossfeed_range, step=0.01, label='parental crossfeed range', interactive=True)
-                    parental_crossfeed_power_decay = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power_decay, step=0.01, label='parental crossfeed decay', interactive=True)
-                with gr.Row():
-                    depth_strength = gr.Slider(0.01, 0.99, bf.depth_strength, step=0.01, label='depth_strength', interactive=True)
-                    guidance_scale_mid_damper = gr.Slider(0.01, 2.0, bf.guidance_scale_mid_damper, step=0.01, label='guidance_scale_mid_damper', interactive=True)
 
             with gr.Row():
-            b_compute1 = gr.Button('step1: compute first image', variant='primary')
+                gallery_preview = gr.Gallery(
-            b_compute2 = gr.Button('step2: compute last image', variant='primary')
+            label="Generated images", show_label=False, elem_id="gallery"
-            b_compute_transition = gr.Button('step3: compute transition', variant='primary')
+        , columns=[nmb_preview_images], rows=[1], object_fit="contain", height="auto", allow_preview=False, interactive=False)
+
 
             with gr.Row():
-            img1 = gr.Image(label="1/5")
+                gr.Markdown("Your movie contains the following images (see below)")
-            img2 = gr.Image(label="2/5", show_progress=False)
+            with gr.Row():
-            img3 = gr.Image(label="3/5", show_progress=False)
+                gallery_movie = gr.Gallery(
-            img4 = gr.Image(label="4/5", show_progress=False)
+            label="Generated images", show_label=False, elem_id="gallery"
-            img5 = gr.Image(label="5/5")
+        , columns=[20], rows=[1], object_fit="contain", height="auto", allow_preview=False, interactive=False)        
+                
             
             with gr.Row():
-            vid_single = gr.Video(label="current single trans")
+                b_delete = gr.Button('delete selected image')
-            vid_multi = gr.Video(label="concatented multi trans")
+                b_move_earlier = gr.Button('move image to earlier time')
+                b_move_later = gr.Button('move image to later time')
 
             with gr.Row():
-            b_stackforward = gr.Button('append last movie segment (left) to multi movie (right)', variant='primary')
+                b_generate_movie = gr.Button('generate movie', variant='primary')
+                t_per_segment = gr.Slider(1, 30, 10, step=0.1, label='time per segment', interactive=True)
 
             with gr.Row():
-            gr.Markdown(
+                movie = gr.Video()
-                """
-                # Parameters
-                ## Main
-                - waiting time: set your waiting time for the transition. high values = better quality
-                - video duration: seconds per segment
-                - height/width: in pixels
 
-                ## Diffusion settings
+            # bindings
-                - num_inference_steps: number of diffusion steps
+            b_start_session.click(mur.register_new_user, inputs=[model, width, height], outputs=user_id)
-                - guidance_scale: latent blending seems to prefer lower values here
+            b_compute.click(mur.compute_imgs, inputs=[user_id, prompt, negative_prompt], outputs=gallery_preview)
-                - negative prompt: enter negative prompt here, applied for all images
+            b_select.click(mur.add_image_to_video, user_id, gallery_movie)
+            gallery_preview.select(mur.preview_img_selected, user_id, None)
+            gallery_movie.select(mur.movie_img_selected, user_id, None)
+            b_delete.click(mur.img_movie_delete, user_id, gallery_movie)
+            b_move_earlier.click(mur.img_movie_earlier, user_id, gallery_movie)
+            b_move_later.click(mur.img_movie_later, user_id, gallery_movie)
+            b_generate_movie.click(mur.generate_movie, [user_id, t_per_segment], movie)
 
-                ## Last image crossfeeding
-                - branch1_crossfeed_power: Controls the level of cross-feeding between the first and last image branch. For preserving structures.
-                - branch1_crossfeed_range: Sets the duration of active crossfeed during development. High values enforce strong structural similarity.
-                - branch1_crossfeed_decay: Sets decay for branch1_crossfeed_power. Lower values make the decay stronger across the range.
 
-                ## Transition settings
+    if server_name is None:
-                - parental_crossfeed_power: Similar to branch1_crossfeed_power, however applied for the images withinin the transition.
+        demo.launch(share=False, inbrowser=True, inline=False)
-                - parental_crossfeed_range: Similar to branch1_crossfeed_range, however applied for the images withinin the transition.
+    else:
-                - parental_crossfeed_power_decay: Similar to branch1_crossfeed_decay, however applied for the images withinin the transition.
+        demo.launch(share=False, inbrowser=True, inline=False, server_name=server_name)
-                - depth_strength: Determines when the blending process will begin in terms of diffusion steps. Low values more inventive but can cause motion.
-                - guidance_scale_mid_damper: Decreases the guidance scale in the middle of a transition.
-                """)
-
-        with gr.Row():
-            user_id = gr.Textbox(label="user id", interactive=False)
-
-        # Collect all UI elemts in list to easily pass as inputs in gradio
-        dict_ui_elem = {}
-        dict_ui_elem["prompt1"] = prompt1
-        dict_ui_elem["negative_prompt"] = negative_prompt
-        dict_ui_elem["prompt2"] = prompt2
-
-        dict_ui_elem["duration_compute"] = duration_compute
-        dict_ui_elem["duration_video"] = duration_video
-        dict_ui_elem["height"] = height
-        dict_ui_elem["width"] = width
-
-        dict_ui_elem["depth_strength"] = depth_strength
-        dict_ui_elem["branch1_crossfeed_power"] = branch1_crossfeed_power
-        dict_ui_elem["branch1_crossfeed_range"] = branch1_crossfeed_range
-        dict_ui_elem["branch1_crossfeed_decay"] = branch1_crossfeed_decay
-
-        dict_ui_elem["num_inference_steps"] = num_inference_steps
-        dict_ui_elem["guidance_scale"] = guidance_scale
-        dict_ui_elem["guidance_scale_mid_damper"] = guidance_scale_mid_damper
-        dict_ui_elem["seed1"] = seed1
-        dict_ui_elem["seed2"] = seed2
-
-        dict_ui_elem["parental_crossfeed_range"] = parental_crossfeed_range
-        dict_ui_elem["parental_crossfeed_power"] = parental_crossfeed_power
-        dict_ui_elem["parental_crossfeed_power_decay"] = parental_crossfeed_power_decay
-        dict_ui_elem["user_id"] = user_id
-
-        # Convert to list, as gradio doesn't seem to accept dicts
-        list_ui_vals = []
-        list_ui_keys = []
-        for k in dict_ui_elem.keys():
-            list_ui_vals.append(dict_ui_elem[k])
-            list_ui_keys.append(k)
-        bf.list_ui_keys = list_ui_keys
-
-        b_newseed1.click(bf.randomize_seed1, outputs=seed1)
-        b_newseed2.click(bf.randomize_seed2, outputs=seed2)
-        b_compute1.click(bf.compute_img1, inputs=list_ui_vals, outputs=[img1, img2, img3, img4, img5, user_id])
-        b_compute2.click(bf.compute_img2, inputs=list_ui_vals, outputs=[img2, img3, img4, img5, user_id])
-        b_compute_transition.click(bf.compute_transition,
-                                   inputs=list_ui_vals,
-                                   outputs=[img2, img3, img4, vid_single])
-
-        b_stackforward.click(bf.stack_forward,
-                             inputs=[prompt2, seed2],
-                             outputs=[vid_multi, img1, img2, img3, img4, img5, prompt1, seed1, prompt2])
-
-    demo.launch(share=bf.share, inbrowser=True, inline=False)

latentblending/movie_util.py

@@ -1,301 +0,0 @@
-# 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 subprocess
-import os
-import numpy as np
-from tqdm import tqdm
-import cv2
-from typing import List
-import ffmpeg  # pip install ffmpeg-python. if error with broken pipe: conda update ffmpeg
-
-
-class MovieSaver():
-    def __init__(
-            self,
-            fp_out: str,
-            fps: int = 24,
-            shape_hw: List[int] = None,
-            crf: int = 21,
-            codec: str = 'libx264',
-            preset: str = 'fast',
-            pix_fmt: str = 'yuv420p',
-            silent_ffmpeg: bool = True):
-        r"""
-        Initializes movie saver class - a human friendly ffmpeg wrapper.
-        After you init the class, you can dump numpy arrays x into moviesaver.write_frame(x).
-        Don't forget toi finalize movie file with moviesaver.finalize().
-        Args:
-            fp_out: str
-                Output file name. If it already exists, it will be deleted.
-            fps: int
-                Frames per second.
-            shape_hw: List[int, int]
-                Output shape, optional argument. Can be initialized automatically when first frame is written.
-            crf: int
-                ffmpeg doc: the range of the CRF scale is 0–51, where 0 is lossless
-                (for 8 bit only, for 10 bit use -qp 0), 23 is the default, and 51 is worst quality possible.
-                A lower value generally leads to higher quality, and a subjectively sane range is 17–28.
-                Consider 17 or 18 to be visually lossless or nearly so;
-                it should look the same or nearly the same as the input but it isn't technically lossless.
-                The range is exponential, so increasing the CRF value +6 results in
-                roughly half the bitrate / file size, while -6 leads to roughly twice the bitrate.
-            codec: int
-                Number of diffusion steps. Larger values will take more compute time.
-            preset: str
-                Choose between ultrafast, superfast, veryfast, faster, fast, medium, slow, slower, veryslow.
-                ffmpeg doc: A preset is a collection of options that will provide a certain encoding speed
-                to compression ratio. A slower preset will provide better compression
-                (compression is quality per filesize).
-                This means that, for example, if you target a certain file size or constant bit rate,
-                you will achieve better quality with a slower preset. Similarly, for constant quality encoding,
-                you will simply save bitrate by choosing a slower preset.
-            pix_fmt: str
-                Pixel format. Run 'ffmpeg -pix_fmts' in your shell to see all options.
-            silent_ffmpeg: bool
-                Surpress the output from ffmpeg.
-        """
-        if len(os.path.split(fp_out)[0]) > 0:
-            assert os.path.isdir(os.path.split(fp_out)[0]), "Directory does not exist!"
-
-        self.fp_out = fp_out
-        self.fps = fps
-        self.crf = crf
-        self.pix_fmt = pix_fmt
-        self.codec = codec
-        self.preset = preset
-        self.silent_ffmpeg = silent_ffmpeg
-
-        if os.path.isfile(fp_out):
-            os.remove(fp_out)
-
-        self.init_done = False
-        self.nmb_frames = 0
-        if shape_hw is None:
-            self.shape_hw = [-1, 1]
-        else:
-            if len(shape_hw) == 2:
-                shape_hw.append(3)
-            self.shape_hw = shape_hw
-            self.initialize()
-
-        print(f"MovieSaver initialized. fps={fps} crf={crf} pix_fmt={pix_fmt} codec={codec} preset={preset}")
-
-    def initialize(self):
-        args = (
-            ffmpeg
-            .input('pipe:', format='rawvideo', pix_fmt='rgb24', s='{}x{}'.format(self.shape_hw[1], self.shape_hw[0]), framerate=self.fps)
-            .output(self.fp_out, crf=self.crf, pix_fmt=self.pix_fmt, c=self.codec, preset=self.preset)
-            .overwrite_output()
-            .compile()
-        )
-        if self.silent_ffmpeg:
-            self.ffmpg_process = subprocess.Popen(args, stdin=subprocess.PIPE, stderr=subprocess.DEVNULL, stdout=subprocess.DEVNULL)
-        else:
-            self.ffmpg_process = subprocess.Popen(args, stdin=subprocess.PIPE)
-        self.init_done = True
-        self.shape_hw = tuple(self.shape_hw)
-        print(f"Initialization done. Movie shape: {self.shape_hw}")
-
-    def write_frame(self, out_frame: np.ndarray):
-        r"""
-        Function to dump a numpy array as frame of a movie.
-        Args:
-            out_frame: np.ndarray
-                Numpy array, in np.uint8 format. Convert with np.astype(x, np.uint8).
-                Dim 0: y
-                Dim 1: x
-                Dim 2: RGB
-        """
-        assert out_frame.dtype == np.uint8, "Convert to np.uint8 before"
-        assert len(out_frame.shape) == 3, "out_frame needs to be three dimensional, Y X C"
-        assert out_frame.shape[2] == 3, f"need three color channels, but you provided {out_frame.shape[2]}."
-
-        if not self.init_done:
-            self.shape_hw = out_frame.shape
-            self.initialize()
-
-        assert self.shape_hw == out_frame.shape, f"You cannot change the image size after init. Initialized with {self.shape_hw}, out_frame {out_frame.shape}"
-
-        # write frame
-        self.ffmpg_process.stdin.write(
-            out_frame
-            .astype(np.uint8)
-            .tobytes()
-        )
-
-        self.nmb_frames += 1
-
-    def finalize(self):
-        r"""
-        Call this function to finalize the movie. If you forget to call it your movie will be garbage.
-        """
-        if self.nmb_frames == 0:
-            print("You did not write any frames yet! nmb_frames = 0. Cannot save.")
-            return
-        self.ffmpg_process.stdin.close()
-        self.ffmpg_process.wait()
-        duration = int(self.nmb_frames / self.fps)
-        print(f"Movie saved, {duration}s playtime, watch here: \n{self.fp_out}")
-
-
-def concatenate_movies(fp_final: str, list_fp_movies: List[str]):
-    r"""
-    Concatenate multiple movie segments into one long movie, using ffmpeg.
-
-    Parameters
-    ----------
-    fp_final : str
-        Full path of the final movie file. Should end with .mp4
-    list_fp_movies : list[str]
-        List of full paths of movie segments.
-    """
-    assert fp_final[-4] == ".", "fp_final seems to miss file extension: {fp_final}"
-    for fp in list_fp_movies:
-        assert os.path.isfile(fp), f"Input movie does not exist: {fp}"
-        assert os.path.getsize(fp) > 100, f"Input movie seems empty: {fp}"
-
-    if os.path.isfile(fp_final):
-        os.remove(fp_final)
-
-    # make a list for ffmpeg
-    list_concat = []
-    for fp_part in list_fp_movies:
-        list_concat.append(f"""file '{fp_part}'""")
-
-    # save this list
-    fp_list = "tmp_move.txt"
-    with open(fp_list, "w") as fa:
-        for item in list_concat:
-            fa.write("%s\n" % item)
-
-    cmd = f'ffmpeg -f concat -safe 0 -i {fp_list} -c copy {fp_final}'
-    subprocess.call(cmd, shell=True)
-    os.remove(fp_list)
-    if os.path.isfile(fp_final):
-        print(f"concatenate_movies: success! Watch here: {fp_final}")
-        
-    
-def add_sound(fp_final, fp_silentmovie, fp_sound):
-    cmd = f'ffmpeg -i {fp_silentmovie} -i {fp_sound} -c copy -map 0:v:0 -map 1:a:0 {fp_final}'
-    subprocess.call(cmd, shell=True)
-    if os.path.isfile(fp_final):
-        print(f"add_sound: success! Watch here: {fp_final}")
-    
-    
-def add_subtitles_to_video(
-        fp_input: str,
-        fp_output: str,
-        subtitles: list,
-        fontsize: int = 50,
-        font_name: str = "Arial",
-        color: str = 'yellow'
-    ):
-    from moviepy.editor import VideoFileClip, TextClip, CompositeVideoClip
-    r"""
-    Function to add subtitles to a video.
-    
-    Args:
-        fp_input (str): File path of the input video.
-        fp_output (str): File path of the output video with subtitles.
-        subtitles (list): List of dictionaries containing subtitle information 
-            (start, duration, text). Example:
-            subtitles = [
-                {"start": 1, "duration": 3, "text": "hello test"},
-                {"start": 4, "duration": 2, "text": "this works"},
-            ]
-        fontsize (int): Font size of the subtitles.
-        font_name (str): Font name of the subtitles.
-        color (str): Color of the subtitles.
-    """
-    
-    # Check if the input file exists
-    if not os.path.isfile(fp_input):
-        raise FileNotFoundError(f"Input file not found: {fp_input}")
-    
-    # Check the subtitles format and sort them by the start time
-    time_points = []
-    for subtitle in subtitles:
-        if not isinstance(subtitle, dict):
-            raise ValueError("Each subtitle must be a dictionary containing 'start', 'duration' and 'text'.")
-        if not all(key in subtitle for key in ["start", "duration", "text"]):
-            raise ValueError("Each subtitle dictionary must contain 'start', 'duration' and 'text'.")
-        if subtitle['start'] < 0 or subtitle['duration'] <= 0:
-            raise ValueError("'start' should be non-negative and 'duration' should be positive.")
-        time_points.append((subtitle['start'], subtitle['start'] + subtitle['duration']))
-
-    # Check for overlaps
-    time_points.sort()
-    for i in range(1, len(time_points)):
-        if time_points[i][0] < time_points[i - 1][1]:
-            raise ValueError("Subtitle time intervals should not overlap.")
-    
-    # Load the video clip
-    video = VideoFileClip(fp_input)
-    
-    # Create a list to store subtitle clips
-    subtitle_clips = []
-    
-    # Loop through the subtitle information and create TextClip for each
-    for subtitle in subtitles:
-        text_clip = TextClip(subtitle["text"], fontsize=fontsize, color=color, font=font_name)
-        text_clip = text_clip.set_position(('center', 'bottom')).set_start(subtitle["start"]).set_duration(subtitle["duration"])
-        subtitle_clips.append(text_clip)
-    
-    # Overlay the subtitles on the video
-    video = CompositeVideoClip([video] + subtitle_clips)
-    
-    # Write the final clip to a new file
-    video.write_videofile(fp_output)
-
-
-
-class MovieReader():
-    r"""
-    Class to read in a movie.
-    """
-
-    def __init__(self, fp_movie):
-        self.video_player_object = cv2.VideoCapture(fp_movie)
-        self.nmb_frames = int(self.video_player_object.get(cv2.CAP_PROP_FRAME_COUNT))
-        self.fps_movie = int(self.video_player_object.get(cv2.CAP_PROP_FPS))
-        self.shape = [100, 100, 3]
-        self.shape_is_set = False
-
-    def get_next_frame(self):
-        success, image = self.video_player_object.read()
-        if success:
-            if not self.shape_is_set:
-                self.shape_is_set = True
-                self.shape = image.shape
-            return image
-        else:
-            return np.zeros(self.shape)
-
-
-if __name__ == "__main__":
-    fps = 2
-    list_fp_movies = []
-    for k in range(4):
-        fp_movie = f"/tmp/my_random_movie_{k}.mp4"
-        list_fp_movies.append(fp_movie)
-        ms = MovieSaver(fp_movie, fps=fps)
-        for fn in tqdm(range(30)):
-            img = (np.random.rand(512, 1024, 3) * 255).astype(np.uint8)
-            ms.write_frame(img)
-        ms.finalize()
-
-    fp_final = "/tmp/my_concatenated_movie.mp4"
-    concatenate_movies(fp_final, list_fp_movies)

requirements.txt

@@ -1,6 +1,6 @@
 lpips==0.1.4
 opencv-python
-ffmpeg-python
 diffusers==0.25.0
 transformers
 pytest
+accelerate

setup.py

@@ -6,14 +6,14 @@ with open('requirements.txt') as f:
 
 setup(
     name='latentblending',
-    version='0.2',
+    version='0.3',
     url='https://github.com/lunarring/latentblending',
     description='Butter-smooth video transitions',
     long_description=open('README.md').read(),
-    install_requires=required,
+    install_requires=[
-    dependency_links=[
+        'lunar_tools @ git+https://github.com/lunarring/lunar_tools.git#egg=lunar_tools'
-        'git+https://github.com/lunarring/lunar_tools#egg=lunar_tools'
+    ] + required,
-    ],
     include_package_data=False,
 )
 
+