# 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, sys
import torch
torch.backends.cudnn.benchmark = False
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import warnings
import torch
from tqdm.auto import tqdm
from PIL import Image
import torch
from movie_util import MovieSaver, concatenate_movies
from typing import Callable, List, Optional, Union
from latent_blending import get_time, yml_save, LatentBlending, add_frames_linear_interp, compare_dicts
from stable_diffusion_holder import StableDiffusionHolder
torch.set_grad_enabled(False)
import gradio as gr
import copy
from dotenv import find_dotenv, load_dotenv
import shutil
"""
never hit compute trans -> multi movie add fail
"""
#%%
class BlendingFrontend():
def __init__(self, sdh=None):
self.num_inference_steps = 30
if sdh is None:
self.use_debug = True
self.height = 768
self.width = 768
else:
self.use_debug = False
self.lb = LatentBlending(sdh)
self.lb.sdh.num_inference_steps = self.num_inference_steps
self.height = self.lb.sdh.height
self.width = self.lb.sdh.width
self.init_save_dir()
self.save_empty_image()
self.share = False
self.depth_strength = 0.25
self.seed1 = 420
self.seed2 = 420
self.guidance_scale = 4.0
self.guidance_scale_mid_damper = 0.5
self.mid_compression_scaler = 1.2
self.prompt1 = ""
self.prompt2 = ""
self.negative_prompt = ""
self.state_current = {}
self.branch1_influence = 0.3
self.branch1_max_depth_influence = 0.6
self.branch1_influence_decay = 0.3
self.parental_influence = 0.1
self.parental_max_depth_influence = 1.0
self.parental_influence_decay = 1.0
self.nmb_branches_final = 9
self.nmb_imgs_show = 5 # don't change
self.fps = 30
self.duration_video = 10
self.t_compute_max_allowed = 10
self.list_fp_imgs_current = []
self.current_timestamp = None
self.recycle_img1 = False
self.recycle_img2 = False
self.fp_img1 = None
self.fp_img2 = None
self.multi_idx_current = -1
self.multi_list_concat = []
self.list_imgs_shown_last = 5*[self.fp_img_empty]
self.nmb_trans_stack = 6
def init_save_dir(self):
load_dotenv(find_dotenv(), verbose=False)
self.dp_out = os.getenv("dp_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)
# make dummy image
def save_empty_image(self):
self.fp_img_empty = os.path.join(self.dp_imgs, 'empty.jpg')
Image.fromarray(np.zeros((self.height, self.width, 3), dtype=np.uint8)).save(self.fp_img_empty, quality=5)
def randomize_seed1(self):
seed = np.random.randint(0, 10000000)
self.seed1 = int(seed)
print(f"randomize_seed1: new seed = {self.seed1}")
return seed
def randomize_seed2(self):
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_elem):
# Collect latent blending variables
self.state_current = self.get_state_dict()
self.lb.set_width(list_ui_elem[list_ui_keys.index('width')])
self.lb.set_height(list_ui_elem[list_ui_keys.index('height')])
self.lb.set_prompt1(list_ui_elem[list_ui_keys.index('prompt1')])
self.lb.set_prompt2(list_ui_elem[list_ui_keys.index('prompt2')])
self.lb.set_negative_prompt(list_ui_elem[list_ui_keys.index('negative_prompt')])
self.lb.guidance_scale = list_ui_elem[list_ui_keys.index('guidance_scale')]
self.lb.guidance_scale_mid_damper = list_ui_elem[list_ui_keys.index('guidance_scale_mid_damper')]
self.t_compute_max_allowed = list_ui_elem[list_ui_keys.index('duration_compute')]
self.lb.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
self.lb.sdh.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
self.duration_video = list_ui_elem[list_ui_keys.index('duration_video')]
self.lb.seed1 = list_ui_elem[list_ui_keys.index('seed1')] #seed
self.lb.seed2 = list_ui_elem[list_ui_keys.index('seed2')]
self.lb.branch1_influence = list_ui_elem[list_ui_keys.index('branch1_influence')]
self.lb.branch1_max_depth_influence = list_ui_elem[list_ui_keys.index('branch1_max_depth_influence')]
self.lb.branch1_influence_decay = list_ui_elem[list_ui_keys.index('branch1_influence_decay')]
self.lb.parental_influence = list_ui_elem[list_ui_keys.index('parental_influence')]
self.lb.parental_max_depth_influence = list_ui_elem[list_ui_keys.index('parental_max_depth_influence')]
self.lb.parental_influence_decay = list_ui_elem[list_ui_keys.index('parental_influence_decay')]
self.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
self.depth_strength = list_ui_elem[list_ui_keys.index('depth_strength')]
def compute_img1(self, *args):
list_ui_elem = args
self.setup_lb(list_ui_elem)
self.fp_img1 = os.path.join(self.dp_imgs, f"img1_{get_time('second')}.jpg")
img1 = Image.fromarray(self.lb.compute_latents1(return_image=True))
img1.save(self.fp_img1)
self.recycle_img1 = True
self.recycle_img2 = False
return [self.fp_img1, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
def compute_img2(self, *args):
list_ui_elem = args
self.setup_lb(list_ui_elem)
self.fp_img2 = os.path.join(self.dp_imgs, f"img2_{get_time('second')}.jpg")
img2 = Image.fromarray(self.lb.compute_latents2(return_image=True))
img2.save(self.fp_img2)
self.recycle_img2 = True
return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img2]
def compute_transition(self, *args):
if not self.recycle_img1:
print("compute first image before transition")
return
if not self.recycle_img2:
print("compute last image before transition")
return
list_ui_elem = args
self.setup_lb(list_ui_elem)
print("STARTING DIFFUSION!")
if self.use_debug:
list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
list_imgs = [Image.fromarray(l) for l in list_imgs]
print("DONE! SENDING BACK RESULTS")
return list_imgs
fixed_seeds = [self.seed1, self.seed2]
# Run Latent Blending
imgs_transition = self.lb.run_transition(
recycle_img1=self.recycle_img1,
recycle_img2=self.recycle_img2,
num_inference_steps=self.num_inference_steps,
depth_strength=self.depth_strength,
t_compute_max_allowed=self.t_compute_max_allowed,
fixed_seeds=fixed_seeds
)
print(f"Latent Blending pass finished. Resulted in {len(imgs_transition)} images")
# Subselect three preview images
idx_img_prev = np.round(np.linspace(0, len(imgs_transition)-1, 5)[1:-1]).astype(np.int32)
list_imgs_preview = []
for j in idx_img_prev:
list_imgs_preview.append(Image.fromarray(imgs_transition[j]))
# Save the preview imgs as jpgs on disk so we are not sending umcompressed data around
self.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}_{self.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 = os.path.join(self.dp_movies, f"movie_{self.current_timestamp}.mp4")
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):
# Save preview images, prompts and seeds into dictionary for stacking
# self.list_imgs_shown_last = self.get_multi_trans_imgs_preview(f"lowres_{self.current_timestamp}")[0:5]
timestamp_section = get_time('second')
self.lb.write_imgs_transition(os.path.join(self.dp_out, f"lowres_{timestamp_section}"))
self.lb.write_imgs_transition(os.path.join(self.dp_out, "lowres_current"))
shutil.copyfile(self.fp_movie, os.path.join(self.dp_out, f"lowres_{timestamp_section}", "movie.mp4"))
self.lb.swap_forward()
self.multi_append()
fp_multi = self.multi_concat()
list_out = [fp_multi]
list_out.extend([self.fp_img2])
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 get_list_all_stacked(self):
list_all = os.listdir(os.path.join(self.dp_out))
list_all = [l for l in list_all if l[:8]=="lowres_2"]
list_all.sort()
return list_all
def multi_append(self):
list_all = self.get_list_all_stacked()
dn = list_all[self.multi_idx_current]
self.multi_list_concat.append(dn)
list_short = [dn[7:] for dn in self.multi_list_concat]
str_out = "\n".join(list_short)
return str_out
def multi_reset(self):
self.multi_list_concat = []
str_out = ""
return str_out
def multi_concat(self):
# Make new output directory
dp_multi = os.path.join(self.dp_out, f"multi_{get_time('second')}")
os.makedirs(dp_multi, exist_ok=False)
# Copy all low-res folders (prepending multi001_xxxx), however leave out the movie.mp4
# also collect all movie.mp4
list_fp_movies = []
for i, dn in enumerate(self.multi_list_concat):
dp_source = os.path.join(self.dp_out, dn)
dp_sequence = os.path.join(dp_multi, f"{str(i).zfill(3)}_{dn}")
os.makedirs(dp_sequence, exist_ok=False)
list_source = os.listdir(dp_source)
list_source = [l for l in list_source if not l.endswith(".mp4")]
for fn in list_source:
shutil.copyfile(os.path.join(dp_source, fn), os.path.join(dp_sequence, fn))
list_fp_movies.append(os.path.join(dp_source, "movie.mp4"))
# Concatenate movies and save
fp_final = os.path.join(dp_multi, "movie.mp4")
concatenate_movies(fp_final, list_fp_movies)
return fp_final
def get_state_dict(self):
state_dict = {}
grab_vars = ['prompt1', 'prompt2', 'seed1', 'seed2', 'height', 'width',
'num_inference_steps', 'depth_strength', 'guidance_scale',
'guidance_scale_mid_damper', 'mid_compression_scaler']
for v in grab_vars:
state_dict[v] = getattr(self, v)
return state_dict
def get_img_rand():
return (255*np.random.rand(self.height,self.width,3)).astype(np.uint8)
def generate_list_output(
prompt1,
prompt2,
seed1,
seed2,
list_fp_imgs,
):
list_output = []
list_output.append(prompt1)
list_output.append(prompt2)
list_output.append(seed1)
list_output.append(seed2)
for fp_img in list_fp_imgs:
list_output.append(fp_img)
return list_output
if __name__ == "__main__":
# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
self = BlendingFrontend(StableDiffusionHolder(fp_ckpt)) # Yes this is possible in python and yes it is an awesome trick
# self = BlendingFrontend(None) # Yes this is possible in python and yes it is an awesome trick
dict_ui_elem = {}
with gr.Blocks() as demo:
with gr.Tab("Single Transition"):
with gr.Row():
prompt1 = gr.Textbox(label="prompt 1")
prompt2 = gr.Textbox(label="prompt 2")
with gr.Row():
duration_compute = gr.Slider(5, 45, self.t_compute_max_allowed, step=1, label='compute budget for transition (seconds)', interactive=True)
duration_video = gr.Slider(0.1, 30, self.duration_video, step=0.1, label='result video duration (seconds)', interactive=True)
height = gr.Slider(256, 2048, self.height, step=128, label='height', interactive=True)
width = gr.Slider(256, 2048, self.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, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
negative_prompt = gr.Textbox(label="negative prompt")
with gr.Accordion("Seeds control", open=True):
with gr.Row():
b_newseed1 = gr.Button("randomize seed 1", variant='secondary')
seed1 = gr.Number(self.seed1, label="seed 1", interactive=True)
seed2 = gr.Number(self.seed2, label="seed 2", interactive=True)
b_newseed2 = gr.Button("randomize seed 2", variant='secondary')
with gr.Accordion("Crossfeeding for last image", open=True):
with gr.Row():
branch1_influence = gr.Slider(0.0, 1.0, self.branch1_influence, step=0.01, label='crossfeed power', interactive=True)
branch1_max_depth_influence = gr.Slider(0.0, 1.0, self.branch1_max_depth_influence, step=0.01, label='crossfeed range', interactive=True)
branch1_influence_decay = gr.Slider(0.0, 1.0, self.branch1_influence_decay, step=0.01, label='crossfeed decay', interactive=True)
with gr.Accordion("Transition settings", open=True):
with gr.Row():
parental_influence = gr.Slider(0.0, 1.0, self.parental_influence, step=0.01, label='parental power', interactive=True)
parental_max_depth_influence = gr.Slider(0.0, 1.0, self.parental_max_depth_influence, step=0.01, label='parental range', interactive=True)
parental_influence_decay = gr.Slider(0.0, 1.0, self.parental_influence_decay, step=0.01, label='parental decay', interactive=True)
with gr.Row():
depth_strength = gr.Slider(0.01, 0.99, self.depth_strength, step=0.01, label='depth_strength', interactive=True)
guidance_scale_mid_damper = gr.Slider(0.01, 2.0, self.guidance_scale_mid_damper, step=0.01, label='guidance_scale_mid_damper', interactive=True)
with gr.Row():
b_compute1 = gr.Button('compute first image', variant='primary')
b_compute_transition = gr.Button('compute transition', variant='primary')
b_compute2 = gr.Button('compute last image', variant='primary')
with gr.Row():
img1 = gr.Image(label="1/5")
img2 = gr.Image(label="2/5", show_progress=False)
img3 = gr.Image(label="3/5", show_progress=False)
img4 = gr.Image(label="4/5", show_progress=False)
img5 = gr.Image(label="5/5")
with gr.Row():
vid_single = gr.Video(label="single trans")
vid_multi = gr.Video(label="multi trans")
with gr.Row():
# b_restart = gr.Button("RESTART EVERYTHING")
b_stackforward = gr.Button('multi-movie start next segment (move last image -> first image)', variant='primary')
# Collect all UI elemts in list to easily pass as inputs
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_influence"] = branch1_influence
dict_ui_elem["branch1_max_depth_influence"] = branch1_max_depth_influence
dict_ui_elem["branch1_influence_decay"] = branch1_influence_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_max_depth_influence"] = parental_max_depth_influence
dict_ui_elem["parental_influence"] = parental_influence
dict_ui_elem["parental_influence_decay"] = parental_influence_decay
# Convert to list, as gradio doesn't seem to accept dicts
list_ui_elem = []
list_ui_keys = []
for k in dict_ui_elem.keys():
list_ui_elem.append(dict_ui_elem[k])
list_ui_keys.append(k)
self.list_ui_keys = list_ui_keys
b_newseed1.click(self.randomize_seed1, outputs=seed1)
b_newseed2.click(self.randomize_seed2, outputs=seed2)
b_compute1.click(self.compute_img1, inputs=list_ui_elem, outputs=[img1, img2, img3, img4, img5])
b_compute2.click(self.compute_img2, inputs=list_ui_elem, outputs=[img2, img3, img4, img5])
b_compute_transition.click(self.compute_transition,
inputs=list_ui_elem,
outputs=[img2, img3, img4, vid_single])
b_stackforward.click(self.stack_forward,
inputs=[prompt2, seed2],
outputs=[vid_multi, img1, img2, img3, img4, img5, prompt1, seed1, prompt2])
# b_restart.click(self.multi_reset)
demo.launch(share=self.share, inbrowser=True, inline=False)