407 lines
19 KiB
Python
407 lines
19 KiB
Python
# Copyright 2022 Lunar Ring. All rights reserved.
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# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os, sys
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import torch
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torch.backends.cudnn.benchmark = False
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import numpy as np
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import warnings
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warnings.filterwarnings('ignore')
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import warnings
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import torch
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from tqdm.auto import tqdm
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from PIL import Image
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import torch
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from movie_util import MovieSaver, concatenate_movies
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from typing import Callable, List, Optional, Union
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from latent_blending import get_time, yml_save, LatentBlending, add_frames_linear_interp, compare_dicts
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from stable_diffusion_holder import StableDiffusionHolder
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torch.set_grad_enabled(False)
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import gradio as gr
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import copy
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from dotenv import find_dotenv, load_dotenv
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import shutil
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"""
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never hit compute trans -> multi movie add fail
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"""
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#%%
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class BlendingFrontend():
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def __init__(self, sdh=None):
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self.num_inference_steps = 30
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if sdh is None:
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self.use_debug = True
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self.height = 768
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self.width = 768
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else:
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self.use_debug = False
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self.lb = LatentBlending(sdh)
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self.lb.sdh.num_inference_steps = self.num_inference_steps
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self.height = self.lb.sdh.height
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self.width = self.lb.sdh.width
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self.init_save_dir()
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self.save_empty_image()
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self.share = True
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self.transition_can_be_computed = False
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self.depth_strength = 0.25
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self.seed1 = 420
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self.seed2 = 420
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self.guidance_scale = 4.0
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self.guidance_scale_mid_damper = 0.5
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self.mid_compression_scaler = 1.2
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self.prompt1 = ""
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self.prompt2 = ""
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self.negative_prompt = ""
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self.state_current = {}
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self.branch1_crossfeed_power = self.lb.branch1_crossfeed_power
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self.branch1_crossfeed_range = self.lb.branch1_crossfeed_range
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self.branch1_crossfeed_decay = self.lb.branch1_crossfeed_decay
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self.parental_crossfeed_power = self.lb.parental_crossfeed_power
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self.parental_crossfeed_range = self.lb.parental_crossfeed_range
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self.parental_crossfeed_power_decay = self.lb.parental_crossfeed_power_decay
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self.fps = 30
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self.duration_video = 10
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self.t_compute_max_allowed = 10
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self.list_fp_imgs_current = []
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self.current_timestamp = None
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self.recycle_img1 = False
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self.recycle_img2 = False
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self.fp_img1 = None
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self.fp_img2 = None
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self.multi_idx_current = -1
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self.list_imgs_shown_last = 5*[self.fp_img_empty]
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self.list_all_segments = []
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self.dp_session = ""
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def init_save_dir(self):
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load_dotenv(find_dotenv(), verbose=False)
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self.dp_out = os.getenv("DIR_OUT")
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if self.dp_out is None:
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self.dp_out = ""
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self.dp_imgs = os.path.join(self.dp_out, "imgs")
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os.makedirs(self.dp_imgs, exist_ok=True)
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self.dp_movies = os.path.join(self.dp_out, "movies")
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os.makedirs(self.dp_movies, exist_ok=True)
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# make dummy image
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def save_empty_image(self):
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self.fp_img_empty = os.path.join(self.dp_imgs, 'empty.jpg')
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Image.fromarray(np.zeros((self.height, self.width, 3), dtype=np.uint8)).save(self.fp_img_empty, quality=5)
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def randomize_seed1(self):
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# Dont randomize seed if we are in a multi concat mode. we don't want to change this one otherwise the movie breaks
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if len(self.list_all_segments) > 0:
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seed = self.seed1
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else:
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seed = np.random.randint(0, 10000000)
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self.seed1 = int(seed)
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print(f"randomize_seed1: new seed = {self.seed1}")
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return seed
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def randomize_seed2(self):
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seed = np.random.randint(0, 10000000)
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self.seed2 = int(seed)
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print(f"randomize_seed2: new seed = {self.seed2}")
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return seed
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def setup_lb(self, list_ui_elem):
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# Collect latent blending variables
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self.state_current = self.get_state_dict()
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self.lb.set_width(list_ui_elem[list_ui_keys.index('width')])
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self.lb.set_height(list_ui_elem[list_ui_keys.index('height')])
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self.lb.set_prompt1(list_ui_elem[list_ui_keys.index('prompt1')])
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self.lb.set_prompt2(list_ui_elem[list_ui_keys.index('prompt2')])
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self.lb.set_negative_prompt(list_ui_elem[list_ui_keys.index('negative_prompt')])
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self.lb.guidance_scale = list_ui_elem[list_ui_keys.index('guidance_scale')]
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self.lb.guidance_scale_mid_damper = list_ui_elem[list_ui_keys.index('guidance_scale_mid_damper')]
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self.t_compute_max_allowed = list_ui_elem[list_ui_keys.index('duration_compute')]
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self.lb.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
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self.lb.sdh.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
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self.duration_video = list_ui_elem[list_ui_keys.index('duration_video')]
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self.lb.seed1 = list_ui_elem[list_ui_keys.index('seed1')] #seed
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self.lb.seed2 = list_ui_elem[list_ui_keys.index('seed2')]
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self.lb.branch1_crossfeed_power = list_ui_elem[list_ui_keys.index('branch1_crossfeed_power')]
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self.lb.branch1_crossfeed_range = list_ui_elem[list_ui_keys.index('branch1_crossfeed_range')]
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self.lb.branch1_crossfeed_decay = list_ui_elem[list_ui_keys.index('branch1_crossfeed_decay')]
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self.lb.parental_crossfeed_power = list_ui_elem[list_ui_keys.index('parental_crossfeed_power')]
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self.lb.parental_crossfeed_range = list_ui_elem[list_ui_keys.index('parental_crossfeed_range')]
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self.lb.parental_crossfeed_power_decay = list_ui_elem[list_ui_keys.index('parental_crossfeed_power_decay')]
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self.num_inference_steps = list_ui_elem[list_ui_keys.index('num_inference_steps')]
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self.depth_strength = list_ui_elem[list_ui_keys.index('depth_strength')]
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def compute_img1(self, *args):
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list_ui_elem = args
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self.setup_lb(list_ui_elem)
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self.fp_img1 = os.path.join(self.dp_imgs, f"img1_{get_time('second')}.jpg")
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img1 = Image.fromarray(self.lb.compute_latents1(return_image=True))
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img1.save(self.fp_img1)
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self.recycle_img1 = True
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self.recycle_img2 = False
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return [self.fp_img1, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
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def compute_img2(self, *args):
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if self.fp_img1 is None: # don't do anything
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return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
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list_ui_elem = args
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self.setup_lb(list_ui_elem)
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self.fp_img2 = os.path.join(self.dp_imgs, f"img2_{get_time('second')}.jpg")
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img2 = Image.fromarray(self.lb.compute_latents2(return_image=True))
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img2.save(self.fp_img2)
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self.recycle_img2 = True
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self.transition_can_be_computed = True
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return [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img2]
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def compute_transition(self, *args):
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if not self.transition_can_be_computed:
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list_return = [self.fp_img_empty, self.fp_img_empty, self.fp_img_empty, self.fp_img_empty]
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return list_return
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list_ui_elem = args
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self.setup_lb(list_ui_elem)
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print("STARTING TRANSITION...")
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if self.use_debug:
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list_imgs = [(255*np.random.rand(self.height,self.width,3)).astype(np.uint8) for l in range(5)]
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list_imgs = [Image.fromarray(l) for l in list_imgs]
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print("DONE! SENDING BACK RESULTS")
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return list_imgs
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fixed_seeds = [self.seed1, self.seed2]
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# Run Latent Blending
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imgs_transition = self.lb.run_transition(
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recycle_img1=self.recycle_img1,
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recycle_img2=self.recycle_img2,
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num_inference_steps=self.num_inference_steps,
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depth_strength=self.depth_strength,
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t_compute_max_allowed=self.t_compute_max_allowed,
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fixed_seeds=fixed_seeds
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)
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print(f"Latent Blending pass finished. Resulted in {len(imgs_transition)} images")
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# Subselect three preview images
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idx_img_prev = np.round(np.linspace(0, len(imgs_transition)-1, 5)[1:-1]).astype(np.int32)
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list_imgs_preview = []
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for j in idx_img_prev:
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list_imgs_preview.append(Image.fromarray(imgs_transition[j]))
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# Save the preview imgs as jpgs on disk so we are not sending umcompressed data around
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self.current_timestamp = get_time('second')
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self.list_fp_imgs_current = []
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for i in range(len(list_imgs_preview)):
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fp_img = os.path.join(self.dp_imgs, f"img_preview_{i}_{self.current_timestamp}.jpg")
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list_imgs_preview[i].save(fp_img)
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self.list_fp_imgs_current.append(fp_img)
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# Insert cheap frames for the movie
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imgs_transition_ext = add_frames_linear_interp(imgs_transition, self.duration_video, self.fps)
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# Save as movie
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self.fp_movie = os.path.join(self.dp_movies, f"movie_{self.current_timestamp}.mp4")
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if os.path.isfile(self.fp_movie):
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os.remove(self.fp_movie)
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ms = MovieSaver(self.fp_movie, fps=self.fps)
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for img in tqdm(imgs_transition_ext):
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ms.write_frame(img)
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ms.finalize()
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print("DONE SAVING MOVIE! SENDING BACK...")
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# Assemble Output, updating the preview images and le movie
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list_return = self.list_fp_imgs_current + [self.fp_movie]
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return list_return
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def stack_forward(self, prompt2, seed2):
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# Save preview images, prompts and seeds into dictionary for stacking
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if len(self.list_all_segments) == 0:
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timestamp_session = get_time('second')
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self.dp_session = os.path.join(self.dp_out, f"session_{timestamp_session}")
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os.makedirs(self.dp_session)
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self.transition_can_be_computed = False
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idx_segment = len(self.list_all_segments)
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dp_segment = os.path.join(self.dp_session, f"segment_{str(idx_segment).zfill(3)}")
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self.list_all_segments.append(dp_segment)
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self.lb.write_imgs_transition(dp_segment)
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shutil.copyfile(self.fp_movie, os.path.join(dp_segment, "movie.mp4"))
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self.lb.swap_forward()
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fp_multi = self.multi_concat()
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list_out = [fp_multi]
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list_out.extend([self.fp_img2])
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list_out.extend([self.fp_img_empty]*4)
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list_out.append(gr.update(interactive=False, value=prompt2))
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list_out.append(gr.update(interactive=False, value=seed2))
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list_out.append("")
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list_out.append(np.random.randint(0, 10000000))
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print(f"stack_forward: fp_multi {fp_multi}")
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return list_out
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def multi_concat(self):
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list_fp_movies = []
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for dp_segment in self.list_all_segments:
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list_fp_movies.append(os.path.join(dp_segment, "movie.mp4"))
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# Concatenate movies and save
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fp_final = os.path.join(self.dp_session, "movie.mp4")
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concatenate_movies(fp_final, list_fp_movies)
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return fp_final
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def get_state_dict(self):
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state_dict = {}
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grab_vars = ['prompt1', 'prompt2', 'seed1', 'seed2', 'height', 'width',
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'num_inference_steps', 'depth_strength', 'guidance_scale',
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'guidance_scale_mid_damper', 'mid_compression_scaler']
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for v in grab_vars:
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state_dict[v] = getattr(self, v)
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return state_dict
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if __name__ == "__main__":
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# fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_768-ema-pruned.ckpt"
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fp_ckpt = "../stable_diffusion_models/ckpt/v2-1_512-ema-pruned.ckpt"
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bf = BlendingFrontend(StableDiffusionHolder(fp_ckpt))
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# self = BlendingFrontend(None)
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with gr.Blocks() as demo:
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with gr.Tab("Single Transition"):
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with gr.Row():
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prompt1 = gr.Textbox(label="prompt 1")
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prompt2 = gr.Textbox(label="prompt 2")
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with gr.Row():
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duration_compute = gr.Slider(5, 200, bf.t_compute_max_allowed, step=1, label='compute budget for transition (seconds)', interactive=True)
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duration_video = gr.Slider(1, 100, bf.duration_video, step=0.1, label='result video duration (seconds)', interactive=True)
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height = gr.Slider(256, 2048, bf.height, step=128, label='height', interactive=True)
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width = gr.Slider(256, 2048, bf.width, step=128, label='width', interactive=True)
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with gr.Accordion("Advanced Settings (click to expand)", open=False):
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with gr.Accordion("Diffusion settings", open=True):
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with gr.Row():
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num_inference_steps = gr.Slider(5, 100, bf.num_inference_steps, step=1, label='num_inference_steps', interactive=True)
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guidance_scale = gr.Slider(1, 25, bf.guidance_scale, step=0.1, label='guidance_scale', interactive=True)
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negative_prompt = gr.Textbox(label="negative prompt")
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with gr.Accordion("Seed control: adjust seeds for first and last images", open=True):
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with gr.Row():
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b_newseed1 = gr.Button("randomize seed 1", variant='secondary')
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seed1 = gr.Number(bf.seed1, label="seed 1", interactive=True)
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seed2 = gr.Number(bf.seed2, label="seed 2", interactive=True)
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b_newseed2 = gr.Button("randomize seed 2", variant='secondary')
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with gr.Accordion("Last image crossfeeding.", open=True):
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with gr.Row():
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branch1_crossfeed_power = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_power, step=0.01, label='branch1 crossfeed power', interactive=True)
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branch1_crossfeed_range = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_range, step=0.01, label='branch1 crossfeed range', interactive=True)
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branch1_crossfeed_decay = gr.Slider(0.0, 1.0, bf.branch1_crossfeed_decay, step=0.01, label='branch1 crossfeed decay', interactive=True)
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with gr.Accordion("Transition settings", open=True):
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with gr.Row():
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parental_crossfeed_power = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power, step=0.01, label='parental crossfeed power', interactive=True)
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parental_crossfeed_range = gr.Slider(0.0, 1.0, bf.parental_crossfeed_range, step=0.01, label='parental crossfeed range', interactive=True)
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parental_crossfeed_power_decay = gr.Slider(0.0, 1.0, bf.parental_crossfeed_power_decay, step=0.01, label='parental crossfeed decay', interactive=True)
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with gr.Row():
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depth_strength = gr.Slider(0.01, 0.99, bf.depth_strength, step=0.01, label='depth_strength', interactive=True)
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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)
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with gr.Row():
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b_compute1 = gr.Button('compute first image', variant='primary')
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b_compute_transition = gr.Button('compute transition', variant='primary')
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b_compute2 = gr.Button('compute last image', variant='primary')
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with gr.Row():
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img1 = gr.Image(label="1/5")
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img2 = gr.Image(label="2/5", show_progress=False)
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img3 = gr.Image(label="3/5", show_progress=False)
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img4 = gr.Image(label="4/5", show_progress=False)
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img5 = gr.Image(label="5/5")
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with gr.Row():
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vid_single = gr.Video(label="single trans")
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vid_multi = gr.Video(label="multi trans")
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with gr.Row():
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# b_restart = gr.Button("RESTART EVERYTHING")
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b_stackforward = gr.Button('append last movie segment (left) to multi movie (right)', variant='primary')
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# Collect all UI elemts in list to easily pass as inputs in gradio
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dict_ui_elem = {}
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dict_ui_elem["prompt1"] = prompt1
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dict_ui_elem["negative_prompt"] = negative_prompt
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dict_ui_elem["prompt2"] = prompt2
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dict_ui_elem["duration_compute"] = duration_compute
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dict_ui_elem["duration_video"] = duration_video
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dict_ui_elem["height"] = height
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dict_ui_elem["width"] = width
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dict_ui_elem["depth_strength"] = depth_strength
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dict_ui_elem["branch1_crossfeed_power"] = branch1_crossfeed_power
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dict_ui_elem["branch1_crossfeed_range"] = branch1_crossfeed_range
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dict_ui_elem["branch1_crossfeed_decay"] = branch1_crossfeed_decay
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dict_ui_elem["num_inference_steps"] = num_inference_steps
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dict_ui_elem["guidance_scale"] = guidance_scale
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dict_ui_elem["guidance_scale_mid_damper"] = guidance_scale_mid_damper
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dict_ui_elem["seed1"] = seed1
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dict_ui_elem["seed2"] = seed2
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dict_ui_elem["parental_crossfeed_range"] = parental_crossfeed_range
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dict_ui_elem["parental_crossfeed_power"] = parental_crossfeed_power
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dict_ui_elem["parental_crossfeed_power_decay"] = parental_crossfeed_power_decay
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# Convert to list, as gradio doesn't seem to accept dicts
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list_ui_elem = []
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list_ui_keys = []
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for k in dict_ui_elem.keys():
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list_ui_elem.append(dict_ui_elem[k])
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list_ui_keys.append(k)
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bf.list_ui_keys = list_ui_keys
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b_newseed1.click(bf.randomize_seed1, outputs=seed1)
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b_newseed2.click(bf.randomize_seed2, outputs=seed2)
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b_compute1.click(bf.compute_img1, inputs=list_ui_elem, outputs=[img1, img2, img3, img4, img5])
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b_compute2.click(bf.compute_img2, inputs=list_ui_elem, outputs=[img2, img3, img4, img5])
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b_compute_transition.click(bf.compute_transition,
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inputs=list_ui_elem,
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outputs=[img2, img3, img4, vid_single])
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b_stackforward.click(bf.stack_forward,
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inputs=[prompt2, seed2],
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outputs=[vid_multi, img1, img2, img3, img4, img5, prompt1, seed1, prompt2])
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demo.launch(share=bf.share, inbrowser=True, inline=False)
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