# 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
from typing import Callable, List, Optional, Union
from latent_blending import get_time, yml_save, LatentBlending, add_frames_linear_interp
from stable_diffusion_holder import StableDiffusionHolder
torch.set_grad_enabled(False)
import gradio as gr
import copy
#%%
def compare_dicts(a, b):
"""
Compares two dictionaries a and b and returns a dictionary c, with all
keys,values that have shared keys in a and b but same values in a and b.
The values of a and b are stacked together in the output.
Example:
a = {}; a['bobo'] = 4
b = {}; b['bobo'] = 5
c = dict_compare(a,b)
c = {"bobo",[4,5]}
"""
c = {}
for key in a.keys():
if key in b.keys():
val_a = a[key]
val_b = b[key]
if val_a != val_b:
c[key] = [val_a, val_b]
return c
class BlendingFrontend():
def __init__(self):
self.use_debug = False
self.share = True
self.num_inference_steps = 30
self.depth_strength = 0.25
self.seed1 = 42
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.dp_base = "/home/lugo/latentblending"
self.list_settings = []
self.state_prev = {}
self.state_current = {}
self.showing_current = True
self.branch1_influence = 0.3
self.imgs_show_last = []
self.imgs_show_current = []
self.nmb_branches_final = 13
self.nmb_imgs_show = 5
self.fps = 30
self.duration = 10
if not self.use_debug:
self.init_diffusion()
self.height = self.lb.sdh.height
self.width = self.lb.sdh.width
else:
self.height = 420
self.width = 420
def init_diffusion(self):
fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
fp_config = 'configs/v2-inference.yaml'
# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
# fp_config = 'configs/v2-inference-v.yaml'
sdh = StableDiffusionHolder(fp_ckpt, fp_config, num_inference_steps=self.num_inference_steps)
self.lb = LatentBlending(sdh)
self.use_debug = False
def change_depth_strength(self, value):
self.depth_strength = value
print(f"changed depth_strength to {value}")
def change_num_inference_steps(self, value):
self.num_inference_steps = value
print(f"changed num_inference_steps to {value}")
def change_guidance_scale(self, value):
self.guidance_scale = value
self.lb.set_guidance_scale(value)
print(f"changed guidance_scale to {value}")
def change_guidance_scale_mid_damper(self, value):
self.guidance_scale_mid_damper = value
print(f"changed guidance_scale_mid_damper to {value}")
def change_mid_compression_scaler(self, value):
self.mid_compression_scaler = value
print(f"changed mid_compression_scaler to {value}")
def change_branch1_influence(self, value):
self.branch1_influence = value
print(f"changed branch1_influence to {value}")
def change_height(self, value):
self.height = value
print(f"changed height to {value}")
def change_width(self, value):
self.width = value
print(f"changed width to {value}")
def change_nmb_branches_final(self, value):
self.nmb_branches_final = value
print(f"changed nmb_branches_final to {value}")
def change_duration(self, value):
self.duration = value
print(f"changed duration to {value}")
def change_fps(self, value):
self.fps = value
print(f"changed fps to {value}")
def change_prompt1(self, value):
self.prompt1 = value
# print(f"changed prompt1 to {value}")
def change_prompt2(self, value):
self.prompt2 = value
# print(f"changed prompt2 to {value}")
def change_negative_prompt(self, value):
self.negative_prompt = value
def change_seed1(self, value):
self.seed1 = int(value)
def change_seed2(self, value):
self.seed2 = int(value)
def randomize_seed1(self):
seed = np.random.randint(0, 10000000)
self.change_seed1(seed)
print(f"randomize_seed1: new seed = {self.seed1}")
return seed
def randomize_seed2(self):
seed = np.random.randint(0, 10000000)
self.change_seed2(seed)
print(f"randomize_seed2: new seed = {self.seed2}")
return seed
def run(self, x):
print("STARTING DIFFUSION!")
self.state_prev = self.state_current.copy()
self.state_current = self.get_state_dict()
# Copy last iteration
self.imgs_show_last = copy.deepcopy(self.imgs_show_current)
if self.use_debug:
list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
self.imgs_show_current = copy.deepcopy(list_imgs)
return list_imgs
# FIXME TODO ASSERTS
self.lb.sdh.height = self.height
self.lb.sdh.width = self.width
self.lb.autosetup_branching(
depth_strength = self.depth_strength,
num_inference_steps = self.num_inference_steps,
nmb_branches_final = self.nmb_branches_final,
nmb_mindist = 3)
self.lb.set_prompt1(self.prompt1)
self.lb.set_prompt2(self.prompt2)
self.lb.set_negative_prompt(self.negative_prompt)
self.lb.guidance_scale = self.guidance_scale
self.lb.guidance_scale_mid_damper = self.guidance_scale_mid_damper
self.lb.mid_compression_scaler = self.mid_compression_scaler
self.lb.branch1_influence = self.branch1_influence
fixed_seeds = [self.seed1, self.seed2]
imgs_transition = self.lb.run_transition(fixed_seeds=fixed_seeds)
imgs_transition = [Image.fromarray(l) for l in imgs_transition]
print(f"DONE DIFFUSION! Resulted in {len(imgs_transition)} images")
assert np.mod((self.nmb_branches_final-self.nmb_imgs_show)/4, 1)==0, 'self.nmb_branches_final illegal value!'
idx_list = np.linspace(0, self.nmb_branches_final-1, self.nmb_imgs_show).astype(np.int32)
list_imgs = []
for j in idx_list:
list_imgs.append(imgs_transition[j])
self.imgs_show_current = copy.deepcopy(list_imgs)
return list_imgs
def save(self):
if self.lb.tree_final_imgs[0] is None:
return
print("save is called!")
dp_img = os.path.join(self.dp_base, get_time("second"))
imgs_transition = self.lb.tree_final_imgs
self.lb.write_imgs_transition(dp_img, imgs_transition)
fps = self.fps
# Let's get more cheap frames via linear interpolation (duration_transition*fps frames)
imgs_transition_ext = add_frames_linear_interp(imgs_transition, self.duration, fps)
# Save as MP4
fp_movie = os.path.join(dp_img, "movie_lowres.mp4")
if os.path.isfile(fp_movie):
os.remove(fp_movie)
ms = MovieSaver(fp_movie, fps=fps)
for img in tqdm(imgs_transition_ext):
ms.write_frame(img)
ms.finalize()
return fp_movie
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 compare_last(self):
if len(self.state_prev) == 0 or len(self.state_current) == 0:
return ""
if self.showing_current:
# inject the last images that were shown and return str of changes
str_fill = "showing last version: "
list_return = self.imgs_show_last
idx = 0
verb = 'was'
self.showing_current = False
elif not self.showing_current:
# inject the current images and show no string
str_fill = "showing current version: "
verb = 'is'
idx = 1
list_return = self.imgs_show_current
self.showing_current = True
dict_diff = compare_dicts(self.state_prev, self.state_current)
for key in dict_diff:
str_fill += f"{key} {verb} {dict_diff[key][idx]}, "
str_fill = str_fill[:-2]
list_return.extend([str_fill])
return list_return
self = BlendingFrontend()
with gr.Blocks() as demo:
with gr.Row():
prompt1 = gr.Textbox(label="prompt 1")
prompt2 = gr.Textbox(label="prompt 2")
negative_prompt = gr.Textbox(label="negative prompt")
with gr.Row():
nmb_branches_final = gr.Slider(5, 125, self.nmb_branches_final, step=4, label='nmb trans images', 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.Row():
num_inference_steps = gr.Slider(5, 100, self.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
branch1_influence = gr.Slider(0.0, 1.0, self.branch1_influence, step=0.01, label='branch1_influence', interactive=True)
guidance_scale = gr.Slider(1, 25, self.guidance_scale, step=0.1, label='guidance_scale', 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)
mid_compression_scaler = gr.Slider(1.0, 2.0, self.mid_compression_scaler, step=0.01, label='mid_compression_scaler', interactive=True)
with gr.Row():
b_newseed1 = gr.Button("rand seed 1")
seed1 = gr.Number(42, label="seed 1", interactive=True)
b_newseed2 = gr.Button("rand seed 2")
seed2 = gr.Number(420, label="seed 2", interactive=True)
b_compare = gr.Button("compare")
with gr.Row():
b_run = gr.Button('run preview!')
with gr.Row():
img1 = gr.Image(label="1/5")
img2 = gr.Image(label="2/5")
img3 = gr.Image(label="3/5")
img4 = gr.Image(label="4/5")
img5 = gr.Image(label="5/5")
with gr.Row():
compare_text = gr.Textbox(label="")
with gr.Row():
fps = gr.Slider(1, 120, self.fps, step=1, label='fps', interactive=True)
duration = gr.Slider(0.1, 30, self.duration, step=0.1, label='duration', interactive=True)
b_save = gr.Button('save video')
with gr.Row():
vid = gr.Video()
# Bind the on-change methods
depth_strength.change(fn=self.change_depth_strength, inputs=depth_strength)
num_inference_steps.change(fn=self.change_num_inference_steps, inputs=num_inference_steps)
nmb_branches_final.change(fn=self.change_nmb_branches_final, inputs=nmb_branches_final)
guidance_scale.change(fn=self.change_guidance_scale, inputs=guidance_scale)
guidance_scale_mid_damper.change(fn=self.change_guidance_scale_mid_damper, inputs=guidance_scale_mid_damper)
mid_compression_scaler.change(fn=self.change_mid_compression_scaler, inputs=mid_compression_scaler)
height.change(fn=self.change_height, inputs=height)
width.change(fn=self.change_width, inputs=width)
prompt1.change(fn=self.change_prompt1, inputs=prompt1)
prompt2.change(fn=self.change_prompt2, inputs=prompt2)
negative_prompt.change(fn=self.change_negative_prompt, inputs=negative_prompt)
seed1.change(fn=self.change_seed1, inputs=seed1)
seed2.change(fn=self.change_seed2, inputs=seed2)
fps.change(fn=self.change_fps, inputs=fps)
duration.change(fn=self.change_duration, inputs=duration)
branch1_influence.change(fn=self.change_branch1_influence, inputs=branch1_influence)
b_newseed1.click(self.randomize_seed1, outputs=seed1)
b_newseed2.click(self.randomize_seed2, outputs=seed2)
b_compare.click(self.compare_last, outputs=[img1, img2, img3, img4, img5, compare_text])
b_run.click(self.run, outputs=[img1, img2, img3, img4, img5])
b_save.click(self.save, outputs=vid)
demo.launch(share=self.share)