latte

slurm_scripts/ffs.slurm

+#!/usr/bin/env bash
+#SBATCH --job-name=Latte-ffs # nom du job
+#SBATCH --partition group-name
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8 # nombre de GPU �|  réserver (un unique GPU ici)
+#SBATCH --cpus-per-task=16
+#SBATCH --time=500:00:00 # temps exécution maximum demande (HH:MM:SS)
+#SBATCH --output=slurm_log/%j.out # nom du fichier de sortie
+#SBATCH --error=slurm_log/%j.err # nom du fichier d'erreur (ici commun avec la sortie)
+
+source ~/.bashrc
+
+conda activate latte
+
+srun python train.py --config ./configs/ffs/ffs_train.yaml
\ No newline at end of file

slurm_scripts/sky.slurm

+#!/usr/bin/env bash
+#SBATCH --job-name=Latte-ffs # nom du job
+#SBATCH --partition group-name
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8 # nombre de GPU �|  réserver (un unique GPU ici)
+#SBATCH --cpus-per-task=16
+#SBATCH --time=500:00:00 # temps exécution maximum demande (HH:MM:SS)
+#SBATCH --output=slurm_log/%j.out # nom du fichier de sortie
+#SBATCH --error=slurm_log/%j.err # nom du fichier d'erreur (ici commun avec la sortie)
+
+source ~/.bashrc
+
+conda activate latte
+
+srun python train.py --config ./configs/sky/sky_train.yaml
\ No newline at end of file

slurm_scripts/taichi.slurm

+#!/usr/bin/env bash
+#SBATCH --job-name=Latte-ffs # nom du job
+#SBATCH --partition group-name
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8 # nombre de GPU �|  réserver (un unique GPU ici)
+#SBATCH --cpus-per-task=16
+#SBATCH --time=500:00:00 # temps exécution maximum demande (HH:MM:SS)
+#SBATCH --output=slurm_log/%j.out # nom du fichier de sortie
+#SBATCH --error=slurm_log/%j.err # nom du fichier d'erreur (ici commun avec la sortie)
+
+source ~/.bashrc
+
+conda activate latte
+
+srun python train.py --config ./configs/taichi/taichi_train.yaml
\ No newline at end of file

slurm_scripts/ucf101.slurm

+#!/usr/bin/env bash
+#SBATCH --job-name=Latte-ffs # nom du job
+#SBATCH --partition group-name
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8 # nombre de GPU �|  réserver (un unique GPU ici)
+#SBATCH --cpus-per-task=16
+#SBATCH --time=500:00:00 # temps exécution maximum demande (HH:MM:SS)
+#SBATCH --output=slurm_log/%j.out # nom du fichier de sortie
+#SBATCH --error=slurm_log/%j.err # nom du fichier d'erreur (ici commun avec la sortie)
+
+source ~/.bashrc
+
+conda activate latte
+
+srun python train.py --config ./configs/ucf101/ucf101_train.yaml
\ No newline at end of file

train.py

+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+A minimal training script for Latte using PyTorch DDP.
+"""
+
+
+import torch
+# Maybe use fp16 percision training need to set to False
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import io
+import os
+import math
+import argparse
+
+import torch.distributed as dist
+from glob import glob
+from time import time
+from copy import deepcopy
+from einops import rearrange
+from models import get_models
+from datasets import get_dataset
+from models.clip import TextEmbedder
+from diffusion import create_diffusion
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from diffusers.models import AutoencoderKL
+from diffusers.optimization import get_scheduler
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data.distributed import DistributedSampler
+from utils import (clip_grad_norm_, create_logger, update_ema, 
+                   requires_grad, cleanup, create_tensorboard, 
+                   write_tensorboard, setup_distributed,
+                   get_experiment_dir, text_preprocessing)
+import numpy as np
+from transformers import T5EncoderModel, T5Tokenizer
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    setup_distributed()
+    # dist.init_process_group("nccl")
+    # assert args.global_batch_size % dist.get_world_size() == 0, f"Batch size must be divisible by world size."
+    # rank = dist.get_rank()
+    # device = rank % torch.cuda.device_count()
+    # local_rank = rank
+
+    rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    device = torch.device("cuda", local_rank)
+
+    seed = args.global_seed + rank
+    torch.manual_seed(seed)
+    torch.cuda.set_device(device)
+    print(f"Starting rank={rank}, local rank={local_rank}, seed={seed}, world_size={dist.get_world_size()}.")
+
+    # Setup an experiment folder:
+    if rank == 0:
+        os.makedirs(args.results_dir, exist_ok=True)  # Make results folder (holds all experiment subfolders)
+        experiment_index = len(glob(f"{args.results_dir}/*"))
+        model_string_name = args.model.replace("/", "-")  # e.g., Latte-XL/2 --> Latte-XL-2 (for naming folders)
+        num_frame_string = 'F' + str(args.num_frames) + 'S' + str(args.frame_interval)
+        experiment_dir = f"{args.results_dir}/{experiment_index:03d}-{model_string_name}-{num_frame_string}-{args.dataset}"  # Create an experiment folder
+        experiment_dir = get_experiment_dir(experiment_dir, args)
+        checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        tb_writer = create_tensorboard(experiment_dir)
+        OmegaConf.save(args, os.path.join(experiment_dir, 'config.yaml'))
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+        tb_writer = None
+
+    # Create model:
+    assert args.image_size % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    sample_size = args.image_size // 8
+    args.latent_size = sample_size
+    model = get_models(args)
+    # Note that parameter initialization is done within the Latte constructor
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    # vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae").to(device)
+
+    # # use pretrained model?
+    if args.pretrained:
+        checkpoint = torch.load(args.pretrained, map_location=lambda storage, loc: storage)
+        if "ema" in checkpoint:  # supports checkpoints from train.py
+            logger.info('Using ema ckpt!')
+            checkpoint = checkpoint["ema"]
+
+        model_dict = model.state_dict()
+        # 1. filter out unnecessary keys
+        pretrained_dict = {}
+        for k, v in checkpoint.items():
+            if k in model_dict:
+                pretrained_dict[k] = v
+            else:
+                logger.info('Ignoring: {}'.format(k))
+        logger.info('Successfully Load {}% original pretrained model weights '.format(len(pretrained_dict) / len(checkpoint.items()) * 100))
+        # 2. overwrite entries in the existing state dict
+        model_dict.update(pretrained_dict)
+        model.load_state_dict(model_dict)
+        logger.info('Successfully load model at {}!'.format(args.pretrained))
+
+    if args.use_compile:
+        model = torch.compile(model)
+
+    # set distributed training
+    model = DDP(model.to(device), device_ids=[local_rank])
+    
+    if args.extras == 78:
+        # Load the tokenizers
+        tokenizer = T5Tokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
+        # Load T5
+        text_encoder = T5EncoderModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder")
+
+    logger.info(f"Model Parameters: {sum(p.numel() for p in model.parameters()):,}")
+    opt = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0)
+
+    # Freeze vae and text_encoder
+    vae.requires_grad_(False)
+    if args.extras == 78:
+        text_encoder.requires_grad_(False)
+
+    # Setup data:
+    dataset = get_dataset(args)
+
+    sampler = DistributedSampler(
+    dataset,
+    num_replicas=dist.get_world_size(),
+    rank=rank,
+    shuffle=True,
+    seed=args.global_seed
+    )
+    loader = DataLoader(
+        dataset,
+        batch_size=int(args.local_batch_size),
+        shuffle=False,
+        sampler=sampler,
+        num_workers=args.num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    logger.info(f"Dataset contains {len(dataset):,} videos ({args.data_path})")
+
+    # Scheduler
+    lr_scheduler = get_scheduler(
+        name="constant",
+        optimizer=opt,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+    )
+
+    # Prepare models for training:
+    update_ema(ema, model.module, decay=0)  # Ensure EMA is initialized with synced weights
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    train_steps = 0
+    log_steps = 0
+    running_loss = 0
+    first_epoch = 0
+    start_time = time()
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(loader))
+    # Afterwards we recalculate our number of training epochs
+    num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        # TODO, need to checkout
+        # Get the most recent checkpoint
+        dirs = os.listdir(os.path.join(experiment_dir, 'checkpoints'))
+        dirs = [d for d in dirs if d.endswith("pt")]
+        dirs = sorted(dirs, key=lambda x: int(x.split(".")[0]))
+        path = dirs[-1]
+        logger.info(f"Resuming from checkpoint {path}")
+        model.load_state(os.path.join(dirs, path))
+        train_steps = int(path.split(".")[0])
+
+        first_epoch = train_steps // num_update_steps_per_epoch
+        resume_step = train_steps % num_update_steps_per_epoch
+
+    if args.pretrained:
+        train_steps = int(args.pretrained.split("/")[-1].split('.')[0])
+
+    for epoch in range(first_epoch, num_train_epochs):
+        sampler.set_epoch(epoch)
+        for step, video_data in enumerate(loader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                continue
+
+            x = video_data['video'].to(device, non_blocking=True)
+            video_name = video_data['video_name']
+            # x = x.to(device)
+            # y = y.to(device) # y is text prompt; no need put in gpu
+            with torch.no_grad():
+                # Map input images to latent space + normalize latents:
+                b, _, _, _, _ = x.shape
+                x = rearrange(x, 'b f c h w -> (b f) c h w').contiguous()
+                x = vae.encode(x).latent_dist.sample().mul_(0.18215)
+                x = rearrange(x, '(b f) c h w -> b f c h w', b=b).contiguous()
+
+            if args.extras == 78: # text-to-video
+                raise 'T2V training are Not supported at this moment!'
+            elif args.extras == 2:
+                model_kwargs = dict(y=video_name)
+            else:
+                model_kwargs = dict(y=None)
+
+            t = torch.randint(0, diffusion.num_timesteps, (x.shape[0],), device=device)
+            loss_dict = diffusion.training_losses(model, x, t, model_kwargs)
+            loss = loss_dict["loss"].mean()
+            loss.backward()
+
+            if train_steps < args.start_clip_iter: # if train_steps >= start_clip_iter, will clip gradient
+                gradient_norm = clip_grad_norm_(model.module.parameters(), args.clip_max_norm, clip_grad=False)
+            else:
+                gradient_norm = clip_grad_norm_(model.module.parameters(), args.clip_max_norm, clip_grad=True)
+
+            opt.step()
+            lr_scheduler.step()
+            opt.zero_grad()
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                # logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}")
+                logger.info(f"(step={train_steps:07d}/epoch={epoch:04d}) Train Loss: {avg_loss:.4f}, Gradient Norm: {gradient_norm:.4f}, Train Steps/Sec: {steps_per_sec:.2f}")
+                write_tensorboard(tb_writer, 'Train Loss', avg_loss, train_steps)
+                write_tensorboard(tb_writer, 'Gradient Norm', gradient_norm, train_steps)
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save Latte checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        # "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        # "opt": opt.state_dict(),
+                        # "args": args
+                    }
+                    checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pt"
+                    torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+                dist.barrier()
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+if __name__ == "__main__":
+    # Default args here will train Latte with the hyperparameters we used in our paper (except training iters).
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="./configs/train.yaml")
+    args = parser.parse_args()
+    main(OmegaConf.load(args.config))

train_scripts/ffs_train.sh

+export CUDA_VISIBLE_DEVICES=5
+# torchrun --nnodes=1 --nproc_per_node=2 --master_port=29509 train.py --config ./configs/ffs/ffs_train.yaml
+python train.py --config ./configs/ffs/ffs_train.yaml
\ No newline at end of file

train_scripts/sky_train.sh

+export CUDA_VISIBLE_DEVICES=4,5
+torchrun --nnodes=1 --nproc_per_node=2 --master_port=29509 train.py --config ./configs/sky/sky_train.yaml
\ No newline at end of file

train_scripts/taichi_train.sh

+export CUDA_VISIBLE_DEVICES=4,5
+torchrun --nnodes=1 --nproc_per_node=2 --master_port=29509 train.py --config ./configs/taichi/taichi_train.yaml
\ No newline at end of file

train_scripts/ucf101_train.sh

+# export CUDA_VISIBLE_DEVICES=4,5
+torchrun --nnodes=1 --nproc_per_node=2 --master_port=29509 train.py --config ./configs/ucf101/ucf101_train.yaml
\ No newline at end of file

train_with_img.py

+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+A minimal training script for Latte using PyTorch DDP.
+"""
+
+
+import torch
+# Maybe use fp16 percision training need to set to False
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+import io
+import os
+import math
+import argparse
+
+import torch.distributed as dist
+from glob import glob
+from time import time
+from copy import deepcopy
+from einops import rearrange
+from models import get_models
+from datasets import get_dataset
+from models.clip import TextEmbedder
+from diffusion import create_diffusion
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from diffusers.models import AutoencoderKL
+from diffusers.optimization import get_scheduler
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.data.distributed import DistributedSampler
+from utils import (clip_grad_norm_, create_logger, update_ema, 
+                   requires_grad, cleanup, create_tensorboard, 
+                   write_tensorboard, setup_distributed, fetch_files_by_numbers,
+                   get_experiment_dir, separation_content_motion,)
+
+
+#################################################################################
+#                                  Training Loop                                #
+#################################################################################
+
+def main(args):
+
+    assert torch.cuda.is_available(), "Training currently requires at least one GPU."
+
+    # Setup DDP:
+    setup_distributed()
+    # dist.init_process_group("nccl")
+    # assert args.global_batch_size % dist.get_world_size() == 0, f"Batch size must be divisible by world size."
+    # rank = dist.get_rank()
+    # device = rank % torch.cuda.device_count()
+    # local_rank = rank
+
+    rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    device = torch.device("cuda", local_rank)
+
+    seed = args.global_seed + rank
+    torch.manual_seed(seed)
+    torch.cuda.set_device(device)
+    print(f"Starting rank={rank}, local rank={local_rank}, seed={seed}, world_size={dist.get_world_size()}.")
+
+    # Setup an experiment folder:
+    if rank == 0:
+        os.makedirs(args.results_dir, exist_ok=True)  # Make results folder (holds all experiment subfolders)
+        experiment_index = len(glob(f"{args.results_dir}/*"))
+        model_string_name = args.model.replace("/", "-")  # e.g., Latte-XL/2 --> Latte-XL-2 (for naming folders)
+        num_frame_string = 'F' + str(args.num_frames) + 'S' + str(args.frame_interval)
+        experiment_dir = f"{args.results_dir}/{experiment_index:03d}-{model_string_name}-{num_frame_string}-{args.dataset}"  # Create an experiment folder
+        experiment_dir = get_experiment_dir(experiment_dir, args)
+        checkpoint_dir = f"{experiment_dir}/checkpoints"  # Stores saved model checkpoints
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        logger = create_logger(experiment_dir)
+        tb_writer = create_tensorboard(experiment_dir)
+        OmegaConf.save(args, os.path.join(experiment_dir, 'config.yaml'))
+        logger.info(f"Experiment directory created at {experiment_dir}")
+    else:
+        logger = create_logger(None)
+        tb_writer = None
+
+    # Create model:
+    assert args.image_size % 8 == 0, "Image size must be divisible by 8 (for the VAE encoder)."
+    sample_size = args.image_size // 8
+    args.latent_size = sample_size
+    model = get_models(args)
+    # Note that parameter initialization is done within the Latte constructor
+    ema = deepcopy(model).to(device)  # Create an EMA of the model for use after training
+    requires_grad(ema, False)
+    diffusion = create_diffusion(timestep_respacing="")  # default: 1000 steps, linear noise schedule
+    # vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-ema").to(device)
+    if args.extras == 78:
+        vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae").to(device)
+    else:
+        vae = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="sd-vae-ft-mse").to(device)
+
+    # # use pretrained model?
+    if args.pretrained:
+        checkpoint = torch.load(args.pretrained, map_location=lambda storage, loc: storage)
+        if "ema" in checkpoint:  # supports checkpoints from train.py
+            logger.info('Using ema ckpt!')
+            checkpoint = checkpoint["ema"]
+
+        model_dict = model.state_dict()
+        # 1. filter out unnecessary keys
+        pretrained_dict = {}
+        for k, v in checkpoint.items():
+            # if 'y_embedder' in k and args.dataset != 'ImageNet' and 'pretrained' in args.pretrained:
+            #     logger.info('Warning: ignoring the weights from y_embedder!')
+            #     continue
+            # if 'y_embedder' in k:
+            # # if 'y_embedder' in k and 'pretrained' in args.pretrained:
+            # if 'y_embedder' in k:
+            #     logger.info('Warning: ignoring the {} weights!'.format(k))
+            #     continue
+
+            if k in model_dict:
+                pretrained_dict[k] = v
+                # logger.info('Successfully Load weights from {}'.format(k))
+            # elif 'x_embedder' in k: # replace model parameter name
+            #     pretrained_dict['patch_embedder'] = v
+            # elif 't_embedder' in k: # replace model parameter name
+            #     pretrained_dict['timestep_embedder'] = v
+            else:
+                logger.info('Ignoring: {}'.format(k))
+        logger.info('Successfully Load {}% original pretrained model weights '.format(len(pretrained_dict) / len(checkpoint.items()) * 100))
+        # 2. overwrite entries in the existing state dict
+        model_dict.update(pretrained_dict)
+        model.load_state_dict(model_dict)
+        logger.info('Successfully load model at {}!'.format(args.pretrained))
+
+    if args.use_compile:
+        model = torch.compile(model)
+
+    if args.enable_xformers_memory_efficient_attention:
+        logger.info("Using Xformers!")
+        model.enable_xformers_memory_efficient_attention()
+
+    if args.gradient_checkpointing:
+        logger.info("Using gradient checkpointing!")
+        model.enable_gradient_checkpointing()
+
+    if args.fixed_spatial:
+        trainable_modules = (
+        "attn_temp",
+        )
+        model.requires_grad_(False)
+        for name, module in model.named_modules():
+            if name.endswith(tuple(trainable_modules)):
+                for params in module.parameters():
+                    logger.info("WARNING: Only train {} parametes!".format(name))
+                    params.requires_grad = True
+        logger.info("WARNING: Only train {} parametes!".format(trainable_modules))
+
+    # set distributed training
+    model = DDP(model.to(device), device_ids=[local_rank])
+    
+    if args.extras == 78:
+        text_encoder = TextEmbedder(args.pretrained_model_path, dropout_prob=0.1).to(device)
+
+    logger.info(f"Model Parameters: {sum(p.numel() for p in model.parameters()):,}")
+    opt = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0)
+
+    # Freeze vae and text_encoder
+    vae.requires_grad_(False)
+    if args.extras == 78:
+        text_encoder.requires_grad_(False)
+
+    if args.dataset == 'webvideo2mlaion':
+        # Setup video dataset:
+        file_list = os.listdir(args.image_data_path) # all file format must be the same!
+        file_count = int(len(file_list) / dist.get_world_size())
+        args.laion_meta_files = fetch_files_by_numbers(rank * file_count, file_count, file_list)
+
+        file_list = os.listdir(args.webvideo_data_path) # all file format must be the same!
+        file_count = int(len(file_list) / dist.get_world_size())
+        args.webvideo_meta_files = fetch_files_by_numbers(rank * file_count, file_count, file_list)
+
+    if args.test_run:
+        args.laion_meta_files = ['file_000.csv']
+        args.webvideo_meta_files = ['file_000.csv']
+
+    # Setup data:
+    dataset = get_dataset(args)
+
+    if args.dataset == 'webvideo2mlaion':
+        sampler = DistributedSampler(
+            dataset,
+            num_replicas=1, # important
+            rank=0, # important
+            shuffle=True,
+            seed=args.global_seed
+    )
+    else:
+        sampler = DistributedSampler(
+        dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    sampler = DistributedSampler(
+        dataset,
+        num_replicas=dist.get_world_size(),
+        rank=rank,
+        shuffle=True,
+        seed=args.global_seed
+    )
+    loader = DataLoader(
+        dataset,
+        batch_size=int(args.local_batch_size),
+        shuffle=False,
+        sampler=sampler,
+        num_workers=args.num_workers,
+        pin_memory=True,
+        drop_last=True
+    )
+    logger.info(f"Dataset contains {len(dataset):,} videos ({args.webvideo_data_path})")
+
+    # Scheduler
+    lr_scheduler = get_scheduler(
+        name="constant",
+        optimizer=opt,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+    )
+
+    # Prepare models for training:
+    update_ema(ema, model.module, decay=0)  # Ensure EMA is initialized with synced weights
+    model.train()  # important! This enables embedding dropout for classifier-free guidance
+    ema.eval()  # EMA model should always be in eval mode
+
+    # Variables for monitoring/logging purposes:
+    train_steps = 0
+    log_steps = 0
+    running_loss = 0
+    first_epoch = 0
+    start_time = time()
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(loader))
+    # Afterwards we recalculate our number of training epochs
+    num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        # TODO, need to checkout
+        # Get the most recent checkpoint
+        dirs = os.listdir(os.path.join(experiment_dir, 'checkpoints'))
+        dirs = [d for d in dirs if d.endswith("pt")]
+        dirs = sorted(dirs, key=lambda x: int(x.split(".")[0]))
+        path = dirs[-1]
+        logger.info(f"Resuming from checkpoint {path}")
+        model.load_state(os.path.join(dirs, path))
+        train_steps = int(path.split(".")[0])
+
+        first_epoch = train_steps // num_update_steps_per_epoch
+        resume_step = train_steps % num_update_steps_per_epoch
+
+    for epoch in range(first_epoch, num_train_epochs):
+        sampler.set_epoch(epoch)
+        for step, video_data in enumerate(loader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                continue
+
+            x = video_data['video'].to(device, non_blocking=True)
+            video_name = video_data['video_name']
+            if args.dataset == "ucf101_img":
+                image_name = video_data['image_name']
+                image_names = []
+                for caption in image_name:
+                    single_caption = [int(item) for item in caption.split('=====')]
+                    image_names.append(torch.as_tensor(single_caption))
+            # x = x.to(device)
+            # y = y.to(device) # y is text prompt; no need put in gpu
+            with torch.no_grad():
+                # Map input images to latent space + normalize latents:
+                b, _, _, _, _ = x.shape
+                x = rearrange(x, 'b f c h w -> (b f) c h w').contiguous()
+                x = vae.encode(x).latent_dist.sample().mul_(0.18215)
+                x = rearrange(x, '(b f) c h w -> b f c h w', b=b).contiguous()
+
+            if args.extras == 78: # text-to-video
+                raise 'T2V training are Not supported at this moment!'
+            elif args.extras == 2:
+                if args.dataset == "ucf101_img":
+                    model_kwargs = dict(y=video_name, y_image=image_names, use_image_num=args.use_image_num) # tav unet
+                else:
+                    model_kwargs = dict(y=video_name) # tav unet
+            else:
+                model_kwargs = dict(y=None, use_image_num=args.use_image_num)
+
+            t = torch.randint(0, diffusion.num_timesteps, (x.shape[0],), device=device)
+            loss_dict = diffusion.training_losses(model, x, t, model_kwargs)
+            loss = loss_dict["loss"].mean()
+            loss.backward()
+
+            if train_steps < args.start_clip_iter: # if train_steps >= start_clip_iter, will clip gradient
+                gradient_norm = clip_grad_norm_(model.module.parameters(), args.clip_max_norm, clip_grad=False)
+            else:
+                gradient_norm = clip_grad_norm_(model.module.parameters(), args.clip_max_norm, clip_grad=True)
+
+            opt.step()
+            lr_scheduler.step()
+            opt.zero_grad()
+            update_ema(ema, model.module)
+
+            # Log loss values:
+            running_loss += loss.item()
+            log_steps += 1
+            train_steps += 1
+            if train_steps % args.log_every == 0:
+                # Measure training speed:
+                torch.cuda.synchronize()
+                end_time = time()
+                steps_per_sec = log_steps / (end_time - start_time)
+                # Reduce loss history over all processes:
+                avg_loss = torch.tensor(running_loss / log_steps, device=device)
+                dist.all_reduce(avg_loss, op=dist.ReduceOp.SUM)
+                avg_loss = avg_loss.item() / dist.get_world_size()
+                # logger.info(f"(step={train_steps:07d}) Train Loss: {avg_loss:.4f}, Train Steps/Sec: {steps_per_sec:.2f}")
+                logger.info(f"(step={train_steps:07d}/epoch={epoch:04d}) Train Loss: {avg_loss:.4f}, Gradient Norm: {gradient_norm:.4f}, Train Steps/Sec: {steps_per_sec:.2f}")
+                write_tensorboard(tb_writer, 'Train Loss', avg_loss, train_steps)
+                write_tensorboard(tb_writer, 'Gradient Norm', gradient_norm, train_steps)
+                # Reset monitoring variables:
+                running_loss = 0
+                log_steps = 0
+                start_time = time()
+
+            # Save Latte checkpoint:
+            if train_steps % args.ckpt_every == 0 and train_steps > 0:
+                if rank == 0:
+                    checkpoint = {
+                        # "model": model.module.state_dict(),
+                        "ema": ema.state_dict(),
+                        # "opt": opt.state_dict(),
+                        # "args": args
+                    }
+
+                    checkpoint_path = f"{checkpoint_dir}/{train_steps:07d}.pt"
+                    torch.save(checkpoint, checkpoint_path)
+                    logger.info(f"Saved checkpoint to {checkpoint_path}")
+                dist.barrier()
+
+    model.eval()  # important! This disables randomized embedding dropout
+    # do any sampling/FID calculation/etc. with ema (or model) in eval mode ...
+
+    logger.info("Done!")
+    cleanup()
+
+
+if __name__ == "__main__":
+    # Default args here will train Latte-XL/2 with the hyperparameters we used in our paper (except training iters).
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml")
+    args = parser.parse_args()
+    main(OmegaConf.load(args.config))

utils.py

+import os
+import math
+import torch
+import logging
+import random
+import subprocess
+import numpy as np
+import torch.distributed as dist
+
+# from torch._six import inf
+from torch import inf
+from PIL import Image
+from typing import Union, Iterable
+from collections import OrderedDict
+from torch.utils.tensorboard import SummaryWriter   
+
+from diffusers.utils import is_bs4_available, is_ftfy_available
+
+import html
+import re
+import urllib.parse as ul
+
+if is_bs4_available():
+    from bs4 import BeautifulSoup
+
+if is_ftfy_available():
+    import ftfy
+
+_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
+
+
+#################################################################################
+#                             Training Clip Gradients                           #
+#################################################################################
+
+def get_grad_norm(
+        parameters: _tensor_or_tensors, norm_type: float = 2.0) -> torch.Tensor:
+    r"""
+    Copy from torch.nn.utils.clip_grad_norm_
+
+    Clips gradient norm of an iterable of parameters.
+
+    The norm is computed over all gradients together, as if they were
+    concatenated into a single vector. Gradients are modified in-place.
+
+    Args:
+        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
+            single Tensor that will have gradients normalized
+        max_norm (float or int): max norm of the gradients
+        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
+            infinity norm.
+        error_if_nonfinite (bool): if True, an error is thrown if the total
+            norm of the gradients from :attr:`parameters` is ``nan``,
+            ``inf``, or ``-inf``. Default: False (will switch to True in the future)
+
+    Returns:
+        Total norm of the parameter gradients (viewed as a single vector).
+    """
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    grads = [p.grad for p in parameters if p.grad is not None]
+    norm_type = float(norm_type)
+    if len(grads) == 0:
+        return torch.tensor(0.)
+    device = grads[0].device
+    if norm_type == inf:
+        norms = [g.detach().abs().max().to(device) for g in grads]
+        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
+    else:
+        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
+    return total_norm
+
+def clip_grad_norm_(
+        parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0,
+        error_if_nonfinite: bool = False, clip_grad = True) -> torch.Tensor:
+    r"""
+    Copy from torch.nn.utils.clip_grad_norm_
+
+    Clips gradient norm of an iterable of parameters.
+
+    The norm is computed over all gradients together, as if they were
+    concatenated into a single vector. Gradients are modified in-place.
+
+    Args:
+        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
+            single Tensor that will have gradients normalized
+        max_norm (float or int): max norm of the gradients
+        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
+            infinity norm.
+        error_if_nonfinite (bool): if True, an error is thrown if the total
+            norm of the gradients from :attr:`parameters` is ``nan``,
+            ``inf``, or ``-inf``. Default: False (will switch to True in the future)
+
+    Returns:
+        Total norm of the parameter gradients (viewed as a single vector).
+    """
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    grads = [p.grad for p in parameters if p.grad is not None]
+    max_norm = float(max_norm)
+    norm_type = float(norm_type)
+    if len(grads) == 0:
+        return torch.tensor(0.)
+    device = grads[0].device
+    if norm_type == inf:
+        norms = [g.detach().abs().max().to(device) for g in grads]
+        total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
+    else:
+        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
+    # print(total_norm)
+
+    if clip_grad:
+        if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
+            raise RuntimeError(
+                f'The total norm of order {norm_type} for gradients from '
+                '`parameters` is non-finite, so it cannot be clipped. To disable '
+                'this error and scale the gradients by the non-finite norm anyway, '
+                'set `error_if_nonfinite=False`')
+        clip_coef = max_norm / (total_norm + 1e-6)
+        # Note: multiplying by the clamped coef is redundant when the coef is clamped to 1, but doing so
+        # avoids a `if clip_coef < 1:` conditional which can require a CPU <=> device synchronization
+        # when the gradients do not reside in CPU memory.
+        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+        for g in grads:
+            g.detach().mul_(clip_coef_clamped.to(g.device))
+        # gradient_cliped = torch.norm(torch.stack([torch.norm(g.detach(), norm_type).to(device) for g in grads]), norm_type)
+        # print(gradient_cliped)
+    return total_norm
+
+def get_experiment_dir(root_dir, args):
+    # if args.pretrained is not None and 'Latte-XL-2-256x256.pt' not in args.pretrained:
+    #     root_dir += '-WOPRE'
+    if args.use_compile:
+        root_dir += '-Compile' # speedup by torch compile
+    if args.fixed_spatial:
+        root_dir += '-FixedSpa'
+    if args.enable_xformers_memory_efficient_attention:
+        root_dir += '-Xfor'
+    if args.gradient_checkpointing:
+        root_dir += '-Gc'
+    if args.mixed_precision:
+        root_dir += '-Amp'
+    if args.image_size == 512:
+        root_dir += '-512'
+    return root_dir
+
+#################################################################################
+#                             Training Logger                                   #
+#################################################################################
+
+def create_logger(logging_dir):
+    """
+    Create a logger that writes to a log file and stdout.
+    """
+    if dist.get_rank() == 0:  # real logger
+        logging.basicConfig(
+            level=logging.INFO,
+            # format='[\033[34m%(asctime)s\033[0m] %(message)s',
+            format='[%(asctime)s] %(message)s',
+            datefmt='%Y-%m-%d %H:%M:%S',
+            handlers=[logging.StreamHandler(), logging.FileHandler(f"{logging_dir}/log.txt")]
+        )
+        logger = logging.getLogger(__name__)
+        
+    else:  # dummy logger (does nothing)
+        logger = logging.getLogger(__name__)
+        logger.addHandler(logging.NullHandler())
+    return logger
+
+
+def create_tensorboard(tensorboard_dir):
+    """
+    Create a tensorboard that saves losses.
+    """
+    if dist.get_rank() == 0:  # real tensorboard 
+        # tensorboard 
+        writer = SummaryWriter(tensorboard_dir)
+
+    return writer
+
+def write_tensorboard(writer, *args):
+    '''
+    write the loss information to a tensorboard file.
+    Only for pytorch DDP mode.
+    '''
+    if dist.get_rank() == 0:  # real tensorboard
+        writer.add_scalar(args[0], args[1], args[2])
+
+#################################################################################
+#                      EMA Update/ DDP Training Utils                           #
+#################################################################################
+
+@torch.no_grad()
+def update_ema(ema_model, model, decay=0.9999):
+    """
+    Step the EMA model towards the current model.
+    """
+    ema_params = OrderedDict(ema_model.named_parameters())
+    model_params = OrderedDict(model.named_parameters())
+
+    for name, param in model_params.items():
+        # TODO: Consider applying only to params that require_grad to avoid small numerical changes of pos_embed
+        ema_params[name].mul_(decay).add_(param.data, alpha=1 - decay)
+
+def requires_grad(model, flag=True):
+    """
+    Set requires_grad flag for all parameters in a model.
+    """
+    for p in model.parameters():
+        p.requires_grad = flag
+
+def cleanup():
+    """
+    End DDP training.
+    """
+    dist.destroy_process_group()
+    
+
+def setup_distributed(backend="nccl", port=None):
+    """Initialize distributed training environment.
+    support both slurm and torch.distributed.launch
+    see torch.distributed.init_process_group() for more details
+    """
+    num_gpus = torch.cuda.device_count()
+
+    if "SLURM_JOB_ID" in os.environ:
+        rank = int(os.environ["SLURM_PROCID"])
+        world_size = int(os.environ["SLURM_NTASKS"])
+        node_list = os.environ["SLURM_NODELIST"]
+        addr = subprocess.getoutput(f"scontrol show hostname {node_list} | head -n1")
+        # specify master port
+        if port is not None:
+            os.environ["MASTER_PORT"] = str(port)
+        elif "MASTER_PORT" not in os.environ:
+            # os.environ["MASTER_PORT"] = "29566"
+            os.environ["MASTER_PORT"] = str(29567 + num_gpus)
+        if "MASTER_ADDR" not in os.environ:
+            os.environ["MASTER_ADDR"] = addr
+        os.environ["WORLD_SIZE"] = str(world_size)
+        os.environ["LOCAL_RANK"] = str(rank % num_gpus)
+        os.environ["RANK"] = str(rank)
+    else:
+        rank = int(os.environ["RANK"])
+        world_size = int(os.environ["WORLD_SIZE"])
+
+    # torch.cuda.set_device(rank % num_gpus)
+
+    dist.init_process_group(
+        backend=backend,
+        world_size=world_size,
+        rank=rank,
+    )
+
+#################################################################################
+#                             Testing  Utils                                    #
+#################################################################################
+
+def save_video_grid(video, nrow=None):
+    b, t, h, w, c = video.shape
+    
+    if nrow is None:
+        nrow = math.ceil(math.sqrt(b))
+    ncol = math.ceil(b / nrow)
+    padding = 1
+    video_grid = torch.zeros((t, (padding + h) * nrow + padding,
+                           (padding + w) * ncol + padding, c), dtype=torch.uint8)
+    
+    print(video_grid.shape)
+    for i in range(b):
+        r = i // ncol
+        c = i % ncol
+        start_r = (padding + h) * r
+        start_c = (padding + w) * c
+        video_grid[:, start_r:start_r + h, start_c:start_c + w] = video[i]
+    
+    return video_grid
+
+
+#################################################################################
+#                             MMCV  Utils                                    #
+#################################################################################
+
+
+def collect_env():
+    # Copyright (c) OpenMMLab. All rights reserved.
+    from mmcv.utils import collect_env as collect_base_env
+    from mmcv.utils import get_git_hash
+    """Collect the information of the running environments."""
+    
+    env_info = collect_base_env()
+    env_info['MMClassification'] = get_git_hash()[:7]
+
+    for name, val in env_info.items():
+        print(f'{name}: {val}')
+    
+    print(torch.cuda.get_arch_list())
+    print(torch.version.cuda)
+
+
+#################################################################################
+#                          Pixart-alpha  Utils                                  #
+#################################################################################
+
+bad_punct_regex = re.compile(
+        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
+)  
+
+def text_preprocessing(text, clean_caption=False):
+    if clean_caption and not is_bs4_available():
+        clean_caption = False
+
+    if clean_caption and not is_ftfy_available():
+        clean_caption = False
+
+    if not isinstance(text, (tuple, list)):
+        text = [text]
+
+    def process(text: str):
+        if clean_caption:
+            text = clean_caption(text)
+            text = clean_caption(text)
+        else:
+            text = text.lower().strip()
+        return text
+
+    return [process(t) for t in text]
+
+# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
+def clean_caption(caption):
+    caption = str(caption)
+    caption = ul.unquote_plus(caption)
+    caption = caption.strip().lower()
+    caption = re.sub("<person>", "person", caption)
+    # urls:
+    caption = re.sub(
+        r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+        "",
+        caption,
+    )  # regex for urls
+    caption = re.sub(
+        r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))",  # noqa
+        "",
+        caption,
+    )  # regex for urls
+    # html:
+    caption = BeautifulSoup(caption, features="html.parser").text
+
+    # @<nickname>
+    caption = re.sub(r"@[\w\d]+\b", "", caption)
+
+    # 31C0—31EF CJK Strokes
+    # 31F0—31FF Katakana Phonetic Extensions
+    # 3200—32FF Enclosed CJK Letters and Months
+    # 3300—33FF CJK Compatibility
+    # 3400—4DBF CJK Unified Ideographs Extension A
+    # 4DC0—4DFF Yijing Hexagram Symbols
+    # 4E00—9FFF CJK Unified Ideographs
+    caption = re.sub(r"[\u31c0-\u31ef]+", "", caption)
+    caption = re.sub(r"[\u31f0-\u31ff]+", "", caption)
+    caption = re.sub(r"[\u3200-\u32ff]+", "", caption)
+    caption = re.sub(r"[\u3300-\u33ff]+", "", caption)
+    caption = re.sub(r"[\u3400-\u4dbf]+", "", caption)
+    caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption)
+    caption = re.sub(r"[\u4e00-\u9fff]+", "", caption)
+    #######################################################
+
+    # все виды тире / all types of dash --> "-"
+    caption = re.sub(
+        r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+",  # noqa
+        "-",
+        caption,
+    )
+
+    # кавычки к одному стандарту
+    caption = re.sub(r"[`´«»“”¨]", '"', caption)
+    caption = re.sub(r"[‘’]", "'", caption)
+
+    # &quot;
+    caption = re.sub(r"&quot;?", "", caption)
+    # &amp
+    caption = re.sub(r"&amp", "", caption)
+
+    # ip adresses:
+    caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption)
+
+    # article ids:
+    caption = re.sub(r"\d:\d\d\s+$", "", caption)
+
+    # \n
+    caption = re.sub(r"\\n", " ", caption)
+
+    # "#123"
+    caption = re.sub(r"#\d{1,3}\b", "", caption)
+    # "#12345.."
+    caption = re.sub(r"#\d{5,}\b", "", caption)
+    # "123456.."
+    caption = re.sub(r"\b\d{6,}\b", "", caption)
+    # filenames:
+    caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)
+
+    #
+    caption = re.sub(r"[\"\']{2,}", r'"', caption)  # """AUSVERKAUFT"""
+    caption = re.sub(r"[\.]{2,}", r" ", caption)  # """AUSVERKAUFT"""
+
+    caption = re.sub(bad_punct_regex, r" ", caption)  # ***AUSVERKAUFT***, #AUSVERKAUFT
+    caption = re.sub(r"\s+\.\s+", r" ", caption)  # " . "
+
+    # this-is-my-cute-cat / this_is_my_cute_cat
+    regex2 = re.compile(r"(?:\-|\_)")
+    if len(re.findall(regex2, caption)) > 3:
+        caption = re.sub(regex2, " ", caption)
+
+    caption = ftfy.fix_text(caption)
+    caption = html.unescape(html.unescape(caption))
+
+    caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption)  # jc6640
+    caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption)  # jc6640vc
+    caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption)  # 6640vc231
+
+    caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
+    caption = re.sub(r"(free\s)?download(\sfree)?", "", caption)
+    caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption)
+    caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption)
+    caption = re.sub(r"\bpage\s+\d+\b", "", caption)
+
+    caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption)  # j2d1a2a...
+
+    caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption)
+
+    caption = re.sub(r"\b\s+\:\s+", r": ", caption)
+    caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption)
+    caption = re.sub(r"\s+", " ", caption)
+
+    caption.strip()
+
+    caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
+    caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
+    caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
+    caption = re.sub(r"^\.\S+$", "", caption)
+
+    return caption.strip()
+
+
+
+    
+
+

visuals/architecture.svg

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" 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" 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/w8tHP8de/+Xt88Yv/il2OFB/HTYjKxajmWvQhYR8jtvqM5fuYip7A4YpjmnnXxP3VMOpk6VpZthPq+ZFWa+TrcRqUAOsJq0spKyVMWJqUlK64cWenzFY6cNsFa1opQWWUVNZ2Ksi6pl72JdUkqMzMViagTsa8Xo4T7WiYkKuY4CixsmR9kSdYjn+E7e2PcbD3MU73X+PhyWv8yas/w6+/+O94dP9X7MP7WGAoiITuYXX5HrbjT5DYfI745kdU9Rn8C8dwMjFNTUeYdX0M9YyTAqqdJOAE7wIqx8Cv9V+D7aOy0rNSwslSlvlK9+24wHUl/VRU86hxwrnJ3mxo4SxixmxNqSlrmGbYx1Gywr5kmbl2WbIPEI5+oNTcSbzEKdV8cvopnt3/DF98/hf47OPfYHf7Nfy+QwSZgFZXHmB/8yl2eBLbrwj6ms77lAnqUClqnVlOmtGoGzrdrHrSoNNZOG4mMTI8wTNO2DGWtJ5nlMCjhE32bV/vYErdfppTDzq7LuhRNTMrqSidVvZNFQ5kJeoYoQFRzT4J7W5uJsmSNc9y7nGUBJafcJTIG36JEyp5evACh1sPcbb3EveOP1GQkcUzZtw9LFK1+OojnB1+qj6UzcQniK09T+Zchv9ZxyrMlkX16MWgHn1yXdNN8UzTnMx8PamObngsVcbjBB1Tp18zRIVFVc7Vbo6Zrq5vAS1PJqBaBoNalm0SUkcDGkcH1ezVOtTMHCOkcln3HjzB+1iKPcf2zqcsww9xuP0h4tFjRBcTWI+yRFceYYljJEA1/fP7CDP3bsYe4mj/M2ypD+cVIsuPaEQncEnZWrn1WMKcoz4akgfj3Ev1I1bCzcCgtyTP6CS/ZySslHO6Z5MKDw6IE7N0e/rQ1d3D2XoBqBhQFROQgNbLb67auNV3jqGDavZzzxwY4cxkpjWYmUdlZgZoQKLm+gvEN55ji/MxEX+KSDBBBfe4lh0iFDiAnxk2xIgnr5cXdvgzH2Bv5xPEWerRtQ+xyN6d9x1QzQ0FKo9dTCY/zJPcS8edGNPbCCxnmpBWKm3BqN5EUM5XgmoHRVGdUlPTJ+4roBqqysDf1fFNUDGhKp66RgZiQrbL89QeI3o0k+iXJwej8xg1MRjQZW1zh/CFOTOZgDZpJvLGtwiwtcGUwzkZ4lq24NtRj0n8XMsEciW0x8X7BNtMTGJaGxxFYc7RBaopQWFmdg2TLFszVzTzpI9LuJeqyuNPGw8VHbNifGyGmw1BWb56nZkzVlSlooM6wg6hf4B9qmFwIGgXy7ej84KsW1nL3qzvRCOjnlKTZdsjT8v7zRgcZkoxBLiZrGKKoF6WbGjpMVZXP8TR0efcRl5ia/0pF+szLIfvcfc8ocPuYoErWIjrWZS5NhY5QmL9Gfa2P8XW1iv+3Q9UOvIz9skeOmNjTp4OEzLI46XzOnkcHDWzPFRUIHlE0fEx9u2wiaCiqoGjh2akIMWIpEeZfTleGuou+JVENXfNennW0sZU0cGh2z2G7r4JaAZt0LJsRyfCyc2EvekjaISgosr+/idqE1kOy+hgzuWuueA/gNcdV6Ari/scJw9Ysi+wu/MZzUnUfK7iX4Dm5GUacjEozNhWMGPl/8McwKTJzePC5IQTlkk7S3gGpolZvp6lolNUWsxpguVLWDElurCAaqhmT4+Ml35Ullbhds6tb4LWNXajmQbU0iaQRn46U+gbmMGQnu5nWoRlhgbk3MFc4JSr1UPGt0fYWHuC9dh9Dv9jludesmwDewjM72CJ+2ZkYRfrK6dY48/uUsntxMdqbkZjz5TT+viBeFjaTnn6x7EyOxNRz3YnTR4Cy3HBbLLztZ0qOwhqV5BSvqpXR0xUNjlbpXxlxMjzpJLCUtzKuY1b2Te/CdpEp20haGc3N/peEzTyAJolOyrPZS3LsNr5hjwHnHeEokIrEebVCHsvcoBoZB8rYRkf+wjKk4TALr/eSarJcl6L8QNZf676eY0GJGqGFu+rkeL2xOHiWBHQGYYFiyXA41WgUxY3pghrmXQqWNOEjXfpWTEmjhu9EXo956qWinJ+NlUx3eWXIe9aPm5l3cbtzNvfBG1tlcV1SJVsTy8NiGoO65wwTizAMh2Fg2p6ZegzwkU59MPBpILLYXHUbYQXttiPW3TZdRqR3DewHCIof3aVgSBGFdc5MyXMy1q2sHAKrycBD9WUZ7pybFZ5OhiC1RqgsnOwTnl4T8IqRQkpx2QUU5qEwWBisGB79WhRX9GAguuFuJ0tSibPzawLSreNvdnBcNDdw0V3gGoO2aEf89IJw3TEDX7yO2pnXODIiPHNL4eOeH9IRY8UYDCQwJx7hW98GXOeVc7OdUSXThRgbPW5Mq6VFY4ftamcYJ5zdc5DY6OiHtc6nI4YnPYV9Sh0xrpAyHlYp+XXFO4UrJOwNCaCThjpwONTNCI9umg6RTcKcSfnDu5k3aGSNwko5xauZ974Jqg8HpRf6PRrzAzSNta9A2OGeUxNsXfs8kY2MD+X4Jp1TMOh8YRZukv3+HqXwOxbfwK+uXXMU1Hf/CqCC3HEaFJray95nivYNGiQmXd+bldBzrk2FKg88nQ7o7DNBAkbJOw8Zma8PLxbvZimupYpqmqmAxvMHC8G1JXVouhmMfKz8ghJFa/ewo2rNxSgnNyMa98E7WDZ9vRIyuAOOGSjq0nZ+tUvhmZp/U77Kt/YJkv2SD2+XIqcEviYM5PGEz5gGTP5SCmzZ8OhLZY0lV+mUXGMKDWZh5cjjxFmHFxkVfjm2AoE9c3FMe8W0Bgc9jAcjkV17HY/TwC2WR9sNh9LWv5hhzzrHUd9TSOKbxcjL/tO8lBJ6ccbV28qwGtXryP36jXkZOR+E7S7W4KxDGBat85OV3PTEELq1wTyO5I5jougfxuhhQOODyYfKhph+crTPHleux57QBfmbF0Rk9qjE59hg7l2bfVp0oC4lC/RgCLBY/53dgkoFbAJn0d+XSG/m1mByxnhqInA7SawcwF21wLbRmD9BPXQcIZRcquYY+M28nhExdtKyZsKUo5AXsu4jixCZl+5AFT+8ZL89ko3PMMQTWcbd9PaF6joElzsHR/fjJSn/PbL417FItULEzYWvY84x8w2s+4WI2Bs+ZA594zh/QE2CS4zc5V9uiLPiDhjl/jhiCvL89yAd5OjKI6AnyXsidIHluHxLKVAqawrjFlnCBarB5VlNbiZfYvK3eRJ3Ql2k6V6g2Dq8GspV1Ey80oOrvKk8N5cQ1oLIa2EZPoY5ZA20t7NQdg52zzOFfWrBC9NxkmzcXs26JoHDAr3qeIjbG485jbCQL/2EGvLp4R8SMgHfP2My/VzqvoEUf7sSuQex9CJAg34tlWFiIkF6dhe7yrm/ezTuWWqusjdNMwTgd0RVKH9Vo4odkPBpc9NqneTgNevUMUr15DLk0MVswTy/WxcuZz5TVDZ5g1jTvamjaF5VoFap0JKTY8rShWjNKQV9k2MI+aIxvKAalI15tvtLQn0H2B99X6qhB+oILG5/gFhuatGH6l+jdK8BFR+FxPikfsig0ZwIUFHj3P5XudsZZvMUVVvhHM7zCBgQP7NIkKlAEVN3m+wB69l5BAyF9ffz0UuT9b7OcjmybychYzLV/H+pSsXKDo0rUp2lDughGn5lyKzdECHfZEDfYmqxpQ7ejj7IpH7hEwmo8Sm5NePCEpF1x/zPFLAkn23Nhj6JT2xV1ejTFMEXWbQiEQYOsJyjmhOOzz7rBDO3uAGZ3UMcz6WMEHFdYvzSnFDlSr7ka56K+NGUkWWZ+6VbHWuXc5G9uUcZPGepSAzqGbGxaCDLN1RvZ2B2aHWIwtjmHXKCzut3mVn+bpWaUgcHT4mIM7BGNORmI88DtnbfqUU3dx4qsp3i6UsrxNc3QRefk7SUYywKyuEZXkvK1jGRIIuRbjKhdmzoqifFTS/pB6CFdBZb6RmYlrJNOg1lqeCfD8LOTyZhJRz9VImrhBQIC8EFSMaU6ASomdhnnBgmjHMTlVdjjBB+SkTVkxEZqn0nADsJV5ih9tLgnACKIYk/bqb4EK+xdWNrzep9obq12d0YJZx7AzR6CkN6gTLS/tUeAeLi1zpQuxRKjmsM+JuYTlTzh0FeZOQ4qzp3ryhejFbAWaxDzN5z2BPykkreeU9notA5Zeu4rQGLrmyNdiYN+3TAcwypTg539w0pDn3Bg1khzPyhKVIs1l/QsjnhHyqQLcJJYBH+x/hYJfbyjZhEy+wRdU3WcZx/twaS3t1nf0du8cjrs0ZvLyNhUVCehY54kZQcKeYDvsm4aRBRUlx11yqmaVUvIqrAkolM/hazCfjUgZVZel+m6Kyycu/8TEaZlm2LswS1DWzANfsoop1Xo4UP0fL4sIe907ZSO5RoSfqycImy3Ob9z1CnR5+gpODV9jfFtAX2NnmrrrF0SPq0rQ24uzZOOft+gmhjxFbZwBZ2sQYt5GiPJmRLFE56cyaCue3OE6ucXRcJ6Somc1ezPoSNIt3qkrADCqZVjTjQtARrj+jM2qbn56kEU37FeicgLJHvXReGe6hwDaX6GP1r0ukJ+WpQoJvfocw+7sfUc1XONz7CPs7/PrgNXapbGKboNsETbBvtxnuN1n2W2cEPuHXJxwfozSdEqacWwwCybRzh+eWzE3JrATMpcPm0lGTxpOpQLMvXU2pKqC8p8pVAAX4yr+6spnCe3ON6izQD1swSce1mudgY6h2cotw20Lw0nVVYODMW1zYRjS4y2X6lEo+UpCJzac43H2JvdQ52H3N8wonR7zvU9n959jdE+BnhOXc3aaRJe5hnIt0+d1atTveIdAdKnmHXys1Wa43MpOA2VdoOOKwBM2lu2YTSk6WlCmhFSihBTQNePny5dwU2tevMZ0ZE+zPaW730yxdK9cku9UPD41onmoGGNaDvs3UnrnHqHePfflEzVF5KHZEEFFxl4CH+x9TzVc4Pf4Yx8cvcXTK7x8+V5BSrnZHAD3dwyi6VaLinPTi7Vz2IEFvskRlZl5PGU7mlUzVjzmEzBHIS1T0kqjJ0qXDCqT0qJRxxnuX//JbAdNXEnQaZoMNUyYHFXXDMRMgaAR+N0MCY5qoGeaAX1lizOMYkYinXHbrKQ52XmJ/7zUOCXhy9LFS8/T4FY7PWManLxDffshw7kchF+Pb2XkEzGP/ESyL4yLrOlXlxqFyKoOA6sNUDyrQdKkKpCjLD4HgMjNlnHB+/r9/FjB9GUYIqrcQdAY2LrqOaTfc9iCDNyF9G4TcUhtKhLNPxkLSiJ6qVLS38wEO9l7i+PA1TqiiAJ6cvOR5TtCXOOR9eNiI4julSi05EucEUspTzvWruSrpCKCaiwTMeJ+gPAKs+pKQOZcZFPhhKBX5AWS995Yyvegyjk3CSNhpzlAbl1wHg7SbOdPvjbE3Rc0Es+oxS5aOy9Gwxd6UObm380L1oSh4dvIxnfYJTEYHg3qUGXefCcvJtapNBfL0LJQgfp0RLglIg0nNxGx543IIKYAyE9OgknpyGPOyVY5lKHhbH77tGh+dhHlsClbjLEcLFbV5OVYWseCjCREywpJd5ZDf4FayxfEgSkow2Nvl3DwS9V5zUzmFbtCkzORufinu5OYjj9u/GvypbUMN/FSEu5aRzbsASK8l1ROINFwm75l8nS3jRABF5ctX//k+fNs1MWaGxThFI7IxKLg5P/2cnRGW7RohaUCRfcY+liwD+w5V29lm2W4+wf7BB5yvMRpWGP1dOuQRTnpMjhiKAMqeKCqqoS8ly6PGBd94LktVYKRM00eglYr8fqa4LkuVG8lv34dvu8ZHTQrUap5NlW0A89wNQ4GNFCiDQuyYvXlGZQ+To4eJSdujR2NFMyrzKphcONS5Tcjie0M91uAMlDLN5GsB5vafLFdJN2/KVSknQfyroEpJAc2lkte+W5ledJkNFsyYbXDOuOCenefsDFGpFSz617ldbCEc3GQJr0M/ZIKmbQDF14tQfKMERVRQPZQSKO6F1xm6BeqmpBmW7HUajpwbjHQ35GsBVZtHMqcKkJSuyq38nirfVBlnXf6Offi2yzo5w5J1MA25MWcPwMvN3ueJIMRleCkUx3DfKMrkX5VdK0D5zWIUMsHkq3gmPUeAFKR6+qbMRqAIyBkpRz3Lka1DelOND/adKEooUVWOwIkBXb505ffrw7dddoudaroZ97yEZPRzBjE/F0GA5dvT0ou71wtRRmO5ey0fxTn5KKDh3GRJXpc3n9ruBUQA1XOb1LMbdeiwuUrFpJJfK1tRjwlHevLKpYx/mT582yVqOmycnY55uu0C3C4Gets8DP1jKGOCKczNQwGHfAHLUT1xk8GuDEXefBJSPX2T76fu6tGGJBquT8klOblDJktVBn0SUm0b33Vc/K6XbcqO2RnOT5sn+SSco6aeObQmv/zLsK2efksGpXsmF1+qQ4gc+VrAUkee26gj2TT1czJK0mHgy6wqKv5QgOlrcmJKPfVuaWhDVXkt8m8WJCMae+urT76VWgpC3nTKSFJqyV2gRcGkkkkFlYo8YjxiQPJ3si9lfH99+Lar4HYBgzXNJfcOHZLjQJxS+o0KyZqULEcmEwEUUAbsr8F+7Uj/JdUTSPlazUSeq1d+h1z6fVzJWZeec3RGnuRQf1OeaTCBVwqmXn/bSY4QGRfiqNm4ciXnhy3Tiy5l+wRLBmuWm8AwWyb7L3WkBFMA/xQy+b03r9N9qIzmdw3e3+cl5aiUS5WbHEk5SXMRRZMgaQA5XwVTke0rf5Zy1D9MH77tyk4ZiHpcIaqKYtKLSsVvh/wq2Jfn0tU/bB++7VIlqp54J8HSb/qfAn31pH8m/fU7V6YXXcpseGTBTQNkvp9MLAqEEF+FUs9reDJT3//B5+F3veSxhEDKm1cgzJxy0pAS1dRJwcm5yj/PeBf78G2XwKVB0pBfBVXqpb5WkO/9ALn0+7jUm09BpEs0fWguSTie7Pf45+96H77tkmei6bJNAoqaPJdoRKlHiz+aPnzbJQ9+lZpUT6kopavAGeEu5/y4+vBtVxpSHYKK0WS+y/Pwu16ZKUWlfCWbvvPz8Lte8nBKfjnzk+jDt11XLv0Wv7f44/XH6x25fvaz/w/39Iyr/pCq6QAAAABJRU5ErkJggg==" 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2pWqeYcmZqe6+OmSXpH1uWYdLXPXMkwfATiax+IFvbH8vdO5/Ikwc/lad3P5a/+8Vv5f/8wz/JP/z6f8u9vdd04D3MxENUfSqrKw8lSajKkQiNiu6sDcrtXhK7ndlNGnd3O+jKI8DagR5FaQUetGA7erj2SSdqd3b0EgPmZmhaa902NjKPqeM04puTAVWnVKyPKSuYrWarYSlvwDzQfRvaxs1cbWd16x6KyPD4mriCtyW49EQWEx9Qpx/J/v7H8vjeJ/KU+Ojpv5ff/PJ38vf/6Xfy/N6nEgkdYCbuycryE+bsA9laeygbyUdG3XDkvszMbhnYcdeC2W/7+1zUrROFHYDauY6Ya3cXNdw5mFZ7SLraFRKVTYpTzyaVdf5iKRveksZG0RLqtLTRdN8KZqvOVRtOydbAPtlqqdrWFyR9mamuVXEGMAGLj2QmSqNZeyWbWx/JAc3nyeFreXrntfyHV7+U3/zn38tff/ArWV95LEvUajR8T5Iom0o+lK31R2burqw8B/YIJ7UlAX9CPONLZqEf6B+XQaK/z4FPdhJjACssKgPcw1Vr2UpvTete07haAW1ubpcmohHgNOKbU1iMySd1S+jAZTimKkB1PbI16FYzKo1aq2w2nYyavlHsnGdDvLNHMhVhdCReyvr2p7K9+4k8vENj2n1fXtz/jMb09/KLz34rj45+LnFqUmFXIndlP/VUjvZeyt7WK0ltfkwaP5VQ6LaB9QOri7yueLr5DPa7gR2XgT63DHLt6x0xwKpybzfA3WmFUbqDejXKAtzSYin7VlhV1nhgzH45BqISA1FTx0qFLWxsHZXWbr8B7WfMaPd1TeJjQ49kLk6j2fpENoHd2/1Q7h8Ajg8+2H4hT+//TO4ffibray8ljJsKzaZkNX5XjvY/kNv7GAvMxjqpH166zyKwJ5M0J31k43KEZGxkklT206i8BrRfYY3SKNw7Kv1Eb4/CDgCrQdMilbs6+0xnbmlh5qKsRhrxzVFVS5mp5WoLSWF9AlHPqGnUXbXTQ/edpk5pHKTvmGdTvDSl2ehzWd74WDZ3PqUxvZadjWeyzr4aCwGFHdxae25qM7x4iCfelfDspmyz+dw9eCW7Ox8C+lqSdO95vPLUVArYJCkcE5dT17wpC5iuPDTgk+EBtwwBOzToksEBBzeA1O5VhYep6yEMSJ8B76RptbUxhlrbqVtS+W3KFpXQlBQWVasZNTaW8sYWuzSRvm3sqvq0sNcexhLicCZ2ZYYxEyF9kyiTSn2II3oM3BNZAWphjq0mtC/RRTadhT2u+7I8vyUrcxuyvfYAs/GhbG9/KInk+xKjWenWE2Rx9/vi4nEvAjsrTse0OMamUddHeElrP+AeGR5ykt4Ao3B/3wihCmunRtUuOnInadwObBt1i7qNTW8ZPUX6jXZFo6nVuoY+qW8CthkT3umVjp4p6SaF9avF8YltCcwcygJ1trL2QjY28b67H8nuNkZi8ymGgdESY4zMsvHM7cji/I6shHcluQgsN2J787nskAV6g5ZXqHc2oilck59F3+tZQtl5FvsZgOfTSzygdg+wPoKr3S0jQ+MAAz00ispjlsK9/YypHoCZuR00KZRtArSmuv7LsMXamBg3NahaR1NqbMZ0tzukHQPR0z8v/ag66mbXnNiRaYx7GKu3BugD7ODBzmvZXEXV6D1s4JEkVp5Rn3vsrvoUcVtWI5iI+B3ZQHntvlssB+uYihj77Dw7rC4APhZ4rydsnjSOO2fEMToJrJ9U9nMNyMhIGnR43ITdxBhpPUbXtktf3xCwvcxebCSqas0WF5bJ5ZwrX4atIH0tVem+jYPS1DLCHXJLj36zZmfUOJbF4d2QqRAeNvzQLNubbDI6VzeBSACzHLZGy1KInZWGE0Pltei+rMUOgXwhW3x+e+tTGtYrVNVxo7N1VwKkcMC/In5PVLyMHaOsIwjwhIwBPTYaIPz8jLoAjwI+ynV4yIH5cMrg4DDprF1ZYXtQs07yr9+US+evyMVzb/mHmtqUamhKDRh9BdV/z9CpTyCoVbsjJg7PqgSmDyQ4fyiLrG8Ku8LsXInfB/SuJGO3DWyEJT0S2iO2JBHeoWHtSyKyj6pPsZEfmZUvufrSeOLQwh2z2inohC8mAW9MfNSswrqd0wBPUrsBwv95jAE8NuIlPLxGabsD2CHp6x+iVrulvLRG8q7ckKsoeunMRbmYffHLsNW1bPbUalPriDTrqGnDhNOYBu2MAZZy38SWTM0dyiyqLUZIY9I1snibTrtL+tKBF0nZ0LYszG5wTREbshrdxUzcZVl/iPrvy+bmh7KSfGFUjWL+Q6G7MhVkgQjEDayGH6uodavhck4QPgPsIsb52THmIzwEoCOqLGMIVdsYN6WFFXIl56pcyr4kl84CevaCXAQ4jfjm1DJTG0xT0lp1oqqXP4QGMbogLpbyickdUu42du+uhMN3ZYnGE6MWV0jRSGhT4qgYodvOTi9LdGFdwnOr3IAtWQV0Nclui29OYvyXUTSG8V+Kqhfel5kpSmNyGeikBAMrqBsWv1e/JZgV9/ikCZeLK+EZD1rwTi+w4zI6qktCv9RW1snVC9cMpKatqnnBxAXJycp5C2w9KQxsS5vWqoc/xE9qBM3TfI9vzaRbiGayxBiJAhkO7ZhHKwobXUzJcmRHFqaTMj0Zk7mpZZpTUuKkbzLBkk7aJoFdXn7OEoAPDj9gth7ILA1sZnqd0bMq0/y3wcAyKoeZt2HMBbDuILCEJ0jzmjIxPo7KwNqHxrCJvVJ0o0SuXbgul85dBtKCVdBcVD0PaPbJc1+GtTWSwqjaTvr2dHtQlWE+xB/u1if6q8agzwQ3zNyMhQ+MYsnlB6Twoawn7qLsrizOrsoiis5PJxg5G1hEalmfN7G3rhDLy8xVNpww2RGaP5A57OHMFLBTqzI7lZDpYEwmJyLU8CLdGVjfHDd6RrzeafHpFdjRESwjRqKyrFauX843kJfPXpYrhMKqmrlncyXndK6cO3X+X4Ft6KVWVVWFVZfipyZoFJ4Yg567PRGXkH4ZtUQDItZ0W4kdGWUTNKdk/NCAJ5dpUigdR/3Esq5wT7gxOCkMRDz+lKxQR6Ww++aRqgLPTq2R/iuoGyPipHSUsiGVAyHxBWbZcee5AbN06gmpKbPJNYXMtuAuKehpgE+RtqcBPX0e0Byj6jlAz53M/jJsQ9MAKTwqHZ1qw7BmgxMG1sW65WPYTwUTskDjCS9gFGhEqlo8csdAra3cR10cFHZxLXGPlN6T9dXHxBNZW31OveoTCRZ7VI3SycOLR/xZ+8zYHTzxDsDU+mySdI4b2OCUwoYZSQvMYAJord2C6yVGxctngNTQtD19US6duiAXTuVKLqE1eh5FFTL7vTNy9r3TX4bVUdPe6ZTuHt0svMwwnW/cTUbBZCCGEhsyP7sO9DJ1tsEGgw0ENqGgq4yVjeeyvc5CzrxdXb4r6yi/yg1Yxx+rgUgwqpZpSjqHNY0XsZIhRlRoHqc1n+I16s6tmJgCdnIqIkGAgxNLpPCs1NU0y4Xzmqa5psNqXCBysyzQnFPngFQ1FfQsoKflzIksySLSiG9Oe4dLuns9+EzdMDzUqw70aTMOtJ5maSBTpPLkhKZaynxtoY9C11BvK/VSdjANG6uPSOlDFvL7KHyf6xNGDrDcjCTg8eh9Ojgja4lZDezS4h4q73HjyJaFDQM8N6ff+JHK01FubATQeUZKpWk6F6nFi2c0AMxCSSLnZI7kvndezquSpG32SdQE0AI9JSeJNOKb02UgfVgvRg5r1Qgbh9s1z52NMRriAOsXT6RYgHReoE6T1mMVhUlt4I42XwD1yCi6jtobyQcs58/4Pamtj0357AobUBxLqcAx9lrNjGjkEKXZiJa2+HPXASadZ4GdAXZqSTpaeuQK9XkJSJ2dlzRtUfOipq1Cvoei72Wb65kTZw2kwp5+1wJ9722w3d2kL7DamEYBdbJx6Lzze+bMKFDoaRqJqrpEza0kFAxVNz/ACmq8b1ySxiqqav1ub7zP1XrfNLSVB3RkIk5dx+4Bfkh3viMRTEl4aZsZTqmEVmVmjlKZjUqTrVXyLuRZcKiZAdW4gKI5AJraNGmbDegZA3sG0Kw07Ml3T75F2W5dij3A6nah/0wnKOOsWb5xuqFvwQz+6elN6nZHoqShjp1NILZJ4T22mK3N9yWFkqrs5tpjtptnpDbvAb25TkdG9aSmeRKFFTp+RHe2YKNYyqXwhiwuraPuGrW6KC1NHXIVRS8rKF32IqPki8Bao1ZtAkmczcCmQU+lU/i9t8H29DpZjklf9kddp5xjE+LBnyrshI8mNcGcBXZxfptGc0gHfmC2GE1fDQVNkbJGUV3jtl4YtRU2Bbh+dn0DhQFPckMSiQc0LdSloUWiWxKNbvLn0xP8C9JQ2yJ5lxgvCkbXvUwjumSAibSq595TUFXyjJw2cZbUPf0l0LfC9vezRQyyKFOvDrYMt3MKU85881K3gQiGYtUs5ep/l8N7dNx7RtnUOjVLF94CYpuOvLv9gv1Wn0S8NK8P9l7JDq83uQEbwK5vPkZlQoGp7ZXEbcbSDg0pJo11HZJ/5ZaxexfPMlY00mPG+FxqNpexkpMeK5qyWpunTeoCDGwG8IuRRnxzBvt1QaZe7X6zbRjYcerVH6YLR4FdAXYD37uNkbhNc7pHyj4G+InpwgqraXuw/1oODj4C+APZA3Jv5wPZ5/X21nPZ2ubmbDOS1lB1VeNIkmuHOKQ5ybtakDbwNCM1DICaOaqGHvjzmIXsrPTsPGGNFYVUJbPe5fU7Z+QUV63RDKSqe+LfndxKI745+uTOPuiWMbuXWp2UgDskk94FCfoxFFi42akV7OC68cFRDH5y+YjUfAyoduSHpCyqGrBXqAkwiu4Te/v6JAOVd98nFPYhax43apNFP74jxbcq5SJ75wWF03TVGaqAZo5Sm7ihc6eoRx0pxDkDaymqoKfToKff0RQ+CTCQhEKeOHHiXBrvX57hPoeMDDqp1XQKEwHXtAR9IQOrfjccYpuZX5cYnTNBg9FGlNJnv5tP0goCSNze/xB1WexNoPA+N2KfhrXHXF5nDm8eYfLVKLTKFQNqwWWajzEJaveyGCenqEUgz2ASzprUPW1AtS5V0bPvkMJ/BSjXrHeAfefk//pXITPH3u+Q0UGPOKnXcYC9NKiAMyjTKDsbjGPw18yOGl1knYvukMZHpLF2ZH3QrbX6/ucpq49JNZ339l8CSw3fpmHtPaIZpdhUBqQgr1Tyr96yIKlFVfBzUDUJOlaoy9OnSM2TwBlQTd0zGAXrquPmrF4NbJac+DqQmWPvGwXWJU67RzyjfvEBO+WZlxltTqxtC7NrJn3jEWo2vmc8sCq7tUlzSqEaNZlpSLcPUBjIA4XcfyJ3Dp+Zxyk3rhUYwAtmO9E1TLcT4HBC2mVzsXo5at6Zn1qbWYAqbBbQ+vo076lLOm/GzjkDe+YdVH7nlDeN8fWOwo6Rxu4RnwGdcE3JtDck86gaIoWXSN9omL01tksKH5haTW3o6GHWArqzg4J7H5C+L5nHzOTJJRNz+Nzq8gbS9RoKWt1VgXPT9ajbSQ4dVu3eOVOT6ZGCkhaoXklRrdW0JTyjqvPzya+qy686owNj4hweR1ULNuielhnfEgs5jYkUji6lSF2dr0TyiFp9CCgpvPWUxqOg2EUaz+CABy9bJfnXC9hQ0t1Vd83MKCEs2FxAMe00H4VT466hzedzb6uw3IDTah5Q8yw3JQvQkyfO/GmQmeMYdoprxC0+lJ1wTZLCszLLyFli9QrjWWOkb1JBV+9iEh4RdOGN+7JDh43Fd6nzoLTWd0v+xRs0Ft08zqFYLjumtZ2YMCmcVpXPnAc0s6F8EdJqRtQmsGfSoEbRE9lfvy6/6owNOkhhj3gdfplwT0rQOyNzkxFZMqpukr47wO5jBm4DecQYSlLHSfMPOmxVTZJ3MR+HcwkIfUJgrVtm/QIyV5dqAPVRiYkzpC6dVtPWmpsWqM7PzGujtKY13Tj7ZPY/fyOQmeO0uwD1idcVwDVNyzRL83wwSgdeQ9WUxMKb1O6y9HXapbm6VerK6qUkr0yu6bOfNJAqmQOkXrXpGCX5nT4iySFtrTrlqrWaTt/PwUwqW65I56elMHHyT6zLrzqeMa94nX4JeIJsOHOk8IKE2CfD8/pPZ1fFPTohxVeKJO/8dcnPyZPr2VfkiqYm6agpmUujUUBV08QX1dQrsApsbgaqZmAV1DSmtMoZ2JPvfAuQmeNx+sQ/HpAJP104OMe4UWXDMjsZlhZbG4DX5ca5a8BelRvn8yTv3FVjzq0nBNlcgTbKWqFpbCnKZ0wzUjU1rM9n4CxVrTCgf5X1ybcGmTk+D7PVE2B3xTVNzLDSsdYF5mSkd0QKLuSzQF+Rq3TVq6qoPgei8ZxnLlp/eUJnZAZUl2rg9emeecKnnjb9OW1IlpLWNQPJavbNNJ+vc/weHJNv0ig7TLNyjvmkurhSKm8UWw+5TOictFYtVTQHhVQlhchRcAUGVHfMs9pF05H5jKWodt83itKU/jnrxIm69F/jz3NczNjW+japrbBJ6a1yuaLflaiF05oDzjymTBsADTXkGYXUEJzXn40hOGuZgTSM+QyQBprQ/y4Dmn3iW6zLrzrXqcnLdFZN11xqTR9oXdSGY8YEDUifx6bHxZuwVDLghJr0U3TQLO2iCmM+Y4Hq3DRKEme+K8jMyQXogj76UEBVz9QgcOlUfdNBM90zU3NfqDtCd0wFMmEg9bEJv+cGMGb+fHX5VccAZepKw8Bk6s0KC05hVdG0Uhk4ruY9/fldTDtX9bPmscm7Wd+sKfi3nvPYMQOjIJp6mppfUDAD/zlgOjLQxuLx8zkW6TPvnjU1q+mc9V2n7NtO5usCBVVl9amAAryBtVTNhAGlRs8Blq0Puvg5S1U8QYPi/ZPfR8jMsZRU5ayGYh6DfAEuA6iRqc/sd/lvjJJWU9L4N28kf46TSduzjA59MpBJyz8MrclMI1JIVVV/znr3j3hS8F0fAwOwaSrEH0JmAPX7E01ZfW1+fvfU96v5fJ2Tbb4n0e75LyEzoPqAK/PwOQP7Jz8p+K6PPpL8Q1D9KkGvmS+JrCd6xLe5kfw5jvUMNv10PQObDv2dUfaHVJdfdb4I+bmy5rElFvDHApk5f6imZfmo43fO/HGPKX8IJ/NVXyadf/B1+VUnA/qD7bB/zPnR1eVfzv835yc/+X/6Ld9TJyCD9wAAAABJRU5ErkJggg==" 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" 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" 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fill="#DAE3F3"/><text font-family="Times New Roman,Times New Roman_MSFontService,sans-serif" font-weight="400" font-size="19" transform="matrix(1 0 0 1.00017 277.956 439)">Embedding</text><rect x="260" y="350.061" width="122" height="32.0056" fill="#FBE5D6"/><text font-family="Times New Roman,Times New Roman_MSFontService,sans-serif" font-weight="400" font-size="21" transform="matrix(1 0 0 1.00017 291.646 374)">Spatial</text><rect x="260" y="293.051" width="122" height="32.0056" fill="#FBE5D6"/><text font-family="Times New Roman,Times New Roman_MSFontService,sans-serif" font-weight="400" font-size="21" transform="matrix(1 0 0 1.00017 280.558 317)">Temporal</text><rect x="260" y="123.021" width="122" height="33.0058" fill="#FBE5D6"/><text font-family="Times New Roman,Times New Roman_MSFontService,sans-serif" font-weight="400" font-size="21" transform="matrix(1 0 0 1.00017 291.646 148)">Spatial</text><rect x="260" y="180.031" width="122" height="32.0056" fill="#FBE5D6"/><text 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0.333347 14.4962-0.333203 14.4962-0.333275 1.65315e-06ZM3.99937 13.163 0.000104969 21.1617-3.99924 13.1631Z" transform="matrix(1 0 0 -1.00017 319.5 235.703)"/><path d="M0.333275-1.65298e-06 0.333347 14.4984-0.333203 14.4984-0.333275 1.65298e-06ZM3.99937 13.1652 0.000104969 21.1639-3.99924 13.1653Z" transform="matrix(1 0 0 -1.00017 319.5 178.695)"/><path d="M0.333275-1.65298e-06 0.333347 14.4984-0.333203 14.4984-0.333275 1.65298e-06ZM3.99937 13.1652 0.000104969 21.1639-3.99924 13.1653Z" transform="matrix(1 0 0 -1.00017 319.5 121.685)"/><text font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-weight="400" font-size="19" transform="matrix(1 0 0 1.00017 292.666 409)">𝑧</text><text font-family="Cambria Math,Cambria Math_MSFontService,sans-serif" font-weight="400" font-size="14" transform="matrix(1 0 0 1.00017 301.211 413)">𝑠</text><path d="M319.167 411.803 319.167 388.233 319.833 388.233 319.833 411.803ZM315.501 389.567 319.5 381.567 323.499 389.566Z"/><path 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