animatediff

requirements.txt

+diffusers==0.11.1
+transformers==4.25.1
+xformers==0.0.16
+imageio==2.27.0
+decord==0.6.0
+gdown
+einops
+omegaconf
+safetensors
+gradio
+wandb
\ No newline at end of file

scripts/animate.py

+import argparse
+import datetime
+import inspect
+import os
+from omegaconf import OmegaConf
+
+import torch
+import torchvision.transforms as transforms
+
+import diffusers
+from diffusers import AutoencoderKL, DDIMScheduler
+
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from animatediff.models.unet import UNet3DConditionModel
+from animatediff.models.sparse_controlnet import SparseControlNetModel
+from animatediff.pipelines.pipeline_animation import AnimationPipeline
+from animatediff.utils.util import save_videos_grid
+from animatediff.utils.util import load_weights
+from diffusers.utils.import_utils import is_xformers_available
+
+from einops import rearrange, repeat
+
+import csv, pdb, glob, math
+from pathlib import Path
+from PIL import Image
+import numpy as np
+
+
+@torch.no_grad()
+def main(args):
+    *_, func_args = inspect.getargvalues(inspect.currentframe())
+    func_args = dict(func_args)
+    
+    time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+    savedir = f"samples/{Path(args.config).stem}-{time_str}"
+    os.makedirs(savedir)
+
+    config  = OmegaConf.load(args.config)
+    samples = []
+
+    # create validation pipeline
+    tokenizer    = CLIPTokenizer.from_pretrained(args.pretrained_model_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_path, subfolder="text_encoder").cuda()
+    vae          = AutoencoderKL.from_pretrained(args.pretrained_model_path, subfolder="vae").cuda()
+    print("load sd completed.")
+    sample_idx = 0
+    for model_idx, model_config in enumerate(config):
+        model_config.W = model_config.get("W", args.W)
+        model_config.H = model_config.get("H", args.H)
+        model_config.L = model_config.get("L", args.L)
+
+        inference_config = OmegaConf.load(model_config.get("inference_config", args.inference_config))
+        unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)).cuda()
+
+        # load controlnet model
+        controlnet = controlnet_images = None
+        if model_config.get("controlnet_path", "") != "":
+            assert model_config.get("controlnet_images", "") != ""
+            assert model_config.get("controlnet_config", "") != ""
+            
+            unet.config.num_attention_heads = 8
+            unet.config.projection_class_embeddings_input_dim = None
+
+            controlnet_config = OmegaConf.load(model_config.controlnet_config)
+            controlnet = SparseControlNetModel.from_unet(unet, controlnet_additional_kwargs=controlnet_config.get("controlnet_additional_kwargs", {}))
+
+            print(f"loading controlnet checkpoint from {model_config.controlnet_path} ...")
+            controlnet_state_dict = torch.load(model_config.controlnet_path, map_location="cpu")
+            controlnet_state_dict = controlnet_state_dict["controlnet"] if "controlnet" in controlnet_state_dict else controlnet_state_dict
+            controlnet_state_dict.pop("animatediff_config", "")
+            controlnet.load_state_dict(controlnet_state_dict)
+            controlnet.cuda()
+
+            image_paths = model_config.controlnet_images
+            if isinstance(image_paths, str): image_paths = [image_paths]
+
+            print(f"controlnet image paths:")
+            for path in image_paths: print(path)
+            assert len(image_paths) <= model_config.L
+
+            image_transforms = transforms.Compose([
+                transforms.RandomResizedCrop(
+                    (model_config.H, model_config.W), (1.0, 1.0), 
+                    ratio=(model_config.W/model_config.H, model_config.W/model_config.H)
+                ),
+                transforms.ToTensor(),
+            ])
+
+            if model_config.get("normalize_condition_images", False):
+                def image_norm(image):
+                    image = image.mean(dim=0, keepdim=True).repeat(3,1,1)
+                    image -= image.min()
+                    image /= image.max()
+                    return image
+            else: image_norm = lambda x: x
+                
+            controlnet_images = [image_norm(image_transforms(Image.open(path).convert("RGB"))) for path in image_paths]
+
+            os.makedirs(os.path.join(savedir, "control_images"), exist_ok=True)
+            for i, image in enumerate(controlnet_images):
+                Image.fromarray((255. * (image.numpy().transpose(1,2,0))).astype(np.uint8)).save(f"{savedir}/control_images/{i}.png")
+
+            controlnet_images = torch.stack(controlnet_images).unsqueeze(0).cuda()
+            controlnet_images = rearrange(controlnet_images, "b f c h w -> b c f h w")
+
+            if controlnet.use_simplified_condition_embedding:
+                num_controlnet_images = controlnet_images.shape[2]
+                controlnet_images = rearrange(controlnet_images, "b c f h w -> (b f) c h w")
+                controlnet_images = vae.encode(controlnet_images * 2. - 1.).latent_dist.sample() * 0.18215
+                controlnet_images = rearrange(controlnet_images, "(b f) c h w -> b c f h w", f=num_controlnet_images)
+
+        # set xformers
+        if is_xformers_available() and (not args.without_xformers):
+            unet.enable_xformers_memory_efficient_attention()
+            if controlnet is not None: controlnet.enable_xformers_memory_efficient_attention()
+
+        pipeline = AnimationPipeline(
+            vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
+            controlnet=controlnet,
+            scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
+        ).to("cuda")
+
+        pipeline = load_weights(
+            pipeline,
+            # motion module
+            motion_module_path         = model_config.get("motion_module", ""),
+            motion_module_lora_configs = model_config.get("motion_module_lora_configs", []),
+            # domain adapter
+            adapter_lora_path          = model_config.get("adapter_lora_path", ""),
+            adapter_lora_scale         = model_config.get("adapter_lora_scale", 1.0),
+            # image layers
+            dreambooth_model_path      = model_config.get("dreambooth_path", ""),
+            lora_model_path            = model_config.get("lora_model_path", ""),
+            lora_alpha                 = model_config.get("lora_alpha", 0.8),
+        ).to("cuda")
+
+        prompts      = model_config.prompt
+        n_prompts    = list(model_config.n_prompt) * len(prompts) if len(model_config.n_prompt) == 1 else model_config.n_prompt
+        
+        random_seeds = model_config.get("seed", [-1])
+        random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)
+        random_seeds = random_seeds * len(prompts) if len(random_seeds) == 1 else random_seeds
+        
+        config[model_idx].random_seed = []
+        for prompt_idx, (prompt, n_prompt, random_seed) in enumerate(zip(prompts, n_prompts, random_seeds)):
+            
+            # manually set random seed for reproduction
+            if random_seed != -1: torch.manual_seed(random_seed)
+            else: torch.seed()
+            config[model_idx].random_seed.append(torch.initial_seed())
+            
+            print(f"current seed: {torch.initial_seed()}")
+            print(f"sampling {prompt} ...")
+            sample = pipeline(
+                prompt,
+                negative_prompt     = n_prompt,
+                num_inference_steps = model_config.steps,
+                guidance_scale      = model_config.guidance_scale,
+                width               = model_config.W,
+                height              = model_config.H,
+                video_length        = model_config.L,
+
+                controlnet_images = controlnet_images,
+                controlnet_image_index = model_config.get("controlnet_image_indexs", [0]),
+            ).videos
+            samples.append(sample)
+
+            prompt = "-".join((prompt.replace("/", "").split(" ")[:10]))
+            save_videos_grid(sample, f"{savedir}/sample/{sample_idx}-{prompt}.gif")
+            print(f"save to {savedir}/sample/{prompt}.gif")
+            
+            sample_idx += 1
+
+    samples = torch.concat(samples)
+    save_videos_grid(samples, f"{savedir}/sample.gif", n_rows=4)
+
+    OmegaConf.save(config, f"{savedir}/config.yaml")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--pretrained-model-path", type=str, default="models/StableDiffusion/stable-diffusion-v1-5",)
+    parser.add_argument("--inference-config",      type=str, default="configs/inference/inference-v1.yaml")    
+    parser.add_argument("--config",                type=str, required=True)
+    
+    parser.add_argument("--L", type=int, default=16 )
+    parser.add_argument("--W", type=int, default=256)
+    parser.add_argument("--H", type=int, default=256)
+
+    parser.add_argument("--without-xformers", action="store_true")
+
+    args = parser.parse_args()
+    main(args)

train.py

+import os
+import math
+import wandb
+import random
+import logging
+import inspect
+import argparse
+import datetime
+import subprocess
+
+from pathlib import Path
+from tqdm.auto import tqdm
+from einops import rearrange
+from omegaconf import OmegaConf
+from safetensors import safe_open
+from typing import Dict, Optional, Tuple
+
+import torch
+import torchvision
+import torch.nn.functional as F
+import torch.distributed as dist
+from torch.optim.swa_utils import AveragedModel
+from torch.utils.data.distributed import DistributedSampler
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+import diffusers
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.models import UNet2DConditionModel
+from diffusers.pipelines import StableDiffusionPipeline
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+
+import transformers
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from animatediff.data.dataset import WebVid10M
+from animatediff.models.unet import UNet3DConditionModel
+from animatediff.pipelines.pipeline_animation import AnimationPipeline
+from animatediff.utils.util import save_videos_grid, zero_rank_print
+
+
+
+def init_dist(launcher="slurm", backend='nccl', port=29500, **kwargs):
+    """Initializes distributed environment."""
+    if launcher == 'pytorch':
+        rank = int(os.environ['RANK'])
+        num_gpus = torch.cuda.device_count()
+        local_rank = rank % num_gpus
+        torch.cuda.set_device(local_rank)
+        dist.init_process_group(backend=backend, **kwargs)
+        
+    elif launcher == 'slurm':
+        proc_id = int(os.environ['SLURM_PROCID'])
+        ntasks = int(os.environ['SLURM_NTASKS'])
+        node_list = os.environ['SLURM_NODELIST']
+        num_gpus = torch.cuda.device_count()
+        local_rank = proc_id % num_gpus
+        torch.cuda.set_device(local_rank)
+        addr = subprocess.getoutput(
+            f'scontrol show hostname {node_list} | head -n1')
+        os.environ['MASTER_ADDR'] = addr
+        os.environ['WORLD_SIZE'] = str(ntasks)
+        os.environ['RANK'] = str(proc_id)
+        port = os.environ.get('PORT', port)
+        os.environ['MASTER_PORT'] = str(port)
+        dist.init_process_group(backend=backend)
+        zero_rank_print(f"proc_id: {proc_id}; local_rank: {local_rank}; ntasks: {ntasks}; node_list: {node_list}; num_gpus: {num_gpus}; addr: {addr}; port: {port}")
+        
+    else:
+        raise NotImplementedError(f'Not implemented launcher type: `{launcher}`!')
+    
+    return local_rank
+
+
+
+def main(
+    image_finetune: bool,
+    
+    name: str,
+    use_wandb: bool,
+    launcher: str,
+    
+    output_dir: str,
+    pretrained_model_path: str,
+
+    train_data: Dict,
+    validation_data: Dict,
+    cfg_random_null_text: bool = True,
+    cfg_random_null_text_ratio: float = 0.1,
+    
+    unet_checkpoint_path: str = "",
+    unet_additional_kwargs: Dict = {},
+    ema_decay: float = 0.9999,
+    noise_scheduler_kwargs = None,
+    
+    max_train_epoch: int = -1,
+    max_train_steps: int = 100,
+    validation_steps: int = 100,
+    validation_steps_tuple: Tuple = (-1,),
+
+    learning_rate: float = 3e-5,
+    scale_lr: bool = False,
+    lr_warmup_steps: int = 0,
+    lr_scheduler: str = "constant",
+
+    trainable_modules: Tuple[str] = (None, ),
+    num_workers: int = 32,
+    train_batch_size: int = 1,
+    adam_beta1: float = 0.9,
+    adam_beta2: float = 0.999,
+    adam_weight_decay: float = 1e-2,
+    adam_epsilon: float = 1e-08,
+    max_grad_norm: float = 1.0,
+    gradient_accumulation_steps: int = 1,
+    gradient_checkpointing: bool = False,
+    checkpointing_epochs: int = 5,
+    checkpointing_steps: int = -1,
+
+    mixed_precision_training: bool = True,
+    enable_xformers_memory_efficient_attention: bool = True,
+
+    global_seed: int = 42,
+    is_debug: bool = False,
+):
+    check_min_version("0.10.0.dev0")
+
+    # Initialize distributed training
+    local_rank      = init_dist(launcher=launcher)
+    global_rank     = dist.get_rank()
+    num_processes   = dist.get_world_size()
+    is_main_process = global_rank == 0
+
+    seed = global_seed + global_rank
+    torch.manual_seed(seed)
+    
+    # Logging folder
+    folder_name = "debug" if is_debug else name + datetime.datetime.now().strftime("-%Y-%m-%dT%H-%M-%S")
+    output_dir = os.path.join(output_dir, folder_name)
+    if is_debug and os.path.exists(output_dir):
+        os.system(f"rm -rf {output_dir}")
+
+    *_, config = inspect.getargvalues(inspect.currentframe())
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+
+    if is_main_process and (not is_debug) and use_wandb:
+        run = wandb.init(project="animatediff", name=folder_name, config=config)
+
+    # Handle the output folder creation
+    if is_main_process:
+        os.makedirs(output_dir, exist_ok=True)
+        os.makedirs(f"{output_dir}/samples", exist_ok=True)
+        os.makedirs(f"{output_dir}/sanity_check", exist_ok=True)
+        os.makedirs(f"{output_dir}/checkpoints", exist_ok=True)
+        OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
+
+    # Load scheduler, tokenizer and models.
+    noise_scheduler = DDIMScheduler(**OmegaConf.to_container(noise_scheduler_kwargs))
+
+    vae          = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
+    tokenizer    = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
+    if not image_finetune:
+        unet = UNet3DConditionModel.from_pretrained_2d(
+            pretrained_model_path, subfolder="unet", 
+            unet_additional_kwargs=OmegaConf.to_container(unet_additional_kwargs)
+        )
+    else:
+        unet = UNet2DConditionModel.from_pretrained(pretrained_model_path, subfolder="unet")
+        
+    # Load pretrained unet weights
+    if unet_checkpoint_path != "":
+        zero_rank_print(f"from checkpoint: {unet_checkpoint_path}")
+        unet_checkpoint_path = torch.load(unet_checkpoint_path, map_location="cpu")
+        if "global_step" in unet_checkpoint_path: zero_rank_print(f"global_step: {unet_checkpoint_path['global_step']}")
+        state_dict = unet_checkpoint_path["state_dict"] if "state_dict" in unet_checkpoint_path else unet_checkpoint_path
+
+        print(state_dict)
+        
+        m, u = unet.load_state_dict(state_dict, strict=False)
+        zero_rank_print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
+        # assert len(u) == 0
+        
+    # Freeze vae and text_encoder
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    
+    # Set unet trainable parameters
+    unet.requires_grad_(False)
+    for name, param in unet.named_parameters():
+        for trainable_module_name in trainable_modules:
+            if trainable_module_name in name:
+                param.requires_grad = True
+                break
+            
+    trainable_params = list(filter(lambda p: p.requires_grad, unet.parameters()))
+    optimizer = torch.optim.AdamW(
+        trainable_params,
+        lr=learning_rate,
+        betas=(adam_beta1, adam_beta2),
+        weight_decay=adam_weight_decay,
+        eps=adam_epsilon,
+    )
+
+    if is_main_process:
+        zero_rank_print(f"trainable params number: {len(trainable_params)}")
+        zero_rank_print(f"trainable params scale: {sum(p.numel() for p in trainable_params) / 1e6:.3f} M")
+
+    # Enable xformers
+    if enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # Enable gradient checkpointing
+    if gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
+    # Move models to GPU
+    vae.to(local_rank)
+    text_encoder.to(local_rank)
+
+    # Get the training dataset
+    train_dataset = WebVid10M(**train_data, is_image=image_finetune)
+    distributed_sampler = DistributedSampler(
+        train_dataset,
+        num_replicas=num_processes,
+        rank=global_rank,
+        shuffle=True,
+        seed=global_seed,
+    )
+
+    # DataLoaders creation:
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=train_batch_size,
+        shuffle=False,
+        sampler=distributed_sampler,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=True,
+    )
+
+    # Get the training iteration
+    if max_train_steps == -1:
+        assert max_train_epoch != -1
+        max_train_steps = max_train_epoch * len(train_dataloader)
+        
+    if checkpointing_steps == -1:
+        assert checkpointing_epochs != -1
+        checkpointing_steps = checkpointing_epochs * len(train_dataloader)
+
+    if scale_lr:
+        learning_rate = (learning_rate * gradient_accumulation_steps * train_batch_size * num_processes)
+
+    # Scheduler
+    lr_scheduler = get_scheduler(
+        lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
+        num_training_steps=max_train_steps * gradient_accumulation_steps,
+    )
+
+    # Validation pipeline
+    if not image_finetune:
+        validation_pipeline = AnimationPipeline(
+            unet=unet, vae=vae, tokenizer=tokenizer, text_encoder=text_encoder, scheduler=noise_scheduler,
+        ).to("cuda")
+    else:
+        validation_pipeline = StableDiffusionPipeline.from_pretrained(
+            pretrained_model_path,
+            unet=unet, vae=vae, tokenizer=tokenizer, text_encoder=text_encoder, scheduler=noise_scheduler, safety_checker=None,
+        )
+    validation_pipeline.enable_vae_slicing()
+
+    # DDP warpper
+    unet.to(local_rank)
+    unet = DDP(unet, device_ids=[local_rank], output_device=local_rank)
+
+    # 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(train_dataloader) / gradient_accumulation_steps)
+    # Afterwards we recalculate our number of training epochs
+    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
+
+    # Train!
+    total_batch_size = train_batch_size * num_processes * gradient_accumulation_steps
+
+    if is_main_process:
+        logging.info("***** Running training *****")
+        logging.info(f"  Num examples = {len(train_dataset)}")
+        logging.info(f"  Num Epochs = {num_train_epochs}")
+        logging.info(f"  Instantaneous batch size per device = {train_batch_size}")
+        logging.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+        logging.info(f"  Gradient Accumulation steps = {gradient_accumulation_steps}")
+        logging.info(f"  Total optimization steps = {max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(global_step, max_train_steps), disable=not is_main_process)
+    progress_bar.set_description("Steps")
+
+    # Support mixed-precision training
+    scaler = torch.cuda.amp.GradScaler() if mixed_precision_training else None
+
+    for epoch in range(first_epoch, num_train_epochs):
+        train_dataloader.sampler.set_epoch(epoch)
+        unet.train()
+        
+        for step, batch in enumerate(train_dataloader):
+            if cfg_random_null_text:
+                batch['text'] = [name if random.random() > cfg_random_null_text_ratio else "" for name in batch['text']]
+                
+            # Data batch sanity check
+            if epoch == first_epoch and step == 0:
+                pixel_values, texts = batch['pixel_values'].cpu(), batch['text']
+                if not image_finetune:
+                    pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
+                    for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)):
+                        pixel_value = pixel_value[None, ...]
+                        save_videos_grid(pixel_value, f"{output_dir}/sanity_check/{'-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_rank}-{idx}'}.gif", rescale=True)
+                else:
+                    for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)):
+                        pixel_value = pixel_value / 2. + 0.5
+                        torchvision.utils.save_image(pixel_value, f"{output_dir}/sanity_check/{'-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_rank}-{idx}'}.png")
+                    
+            ### >>>> Training >>>> ###
+            
+            # Convert videos to latent space            
+            pixel_values = batch["pixel_values"].to(local_rank)
+            video_length = pixel_values.shape[1]
+            with torch.no_grad():
+                if not image_finetune:
+                    pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
+                    latents = vae.encode(pixel_values).latent_dist
+                    latents = latents.sample()
+                    latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
+                else:
+                    latents = vae.encode(pixel_values).latent_dist
+                    latents = latents.sample()
+
+                latents = latents * 0.18215
+
+            # Sample noise that we'll add to the latents
+            noise = torch.randn_like(latents)
+            bsz = latents.shape[0]
+            
+            # Sample a random timestep for each video
+            timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
+            timesteps = timesteps.long()
+            
+            # Add noise to the latents according to the noise magnitude at each timestep
+            # (this is the forward diffusion process)
+            noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+            
+            # Get the text embedding for conditioning
+            with torch.no_grad():
+                prompt_ids = tokenizer(
+                    batch['text'], max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
+                ).input_ids.to(latents.device)
+                encoder_hidden_states = text_encoder(prompt_ids)[0]
+                
+            # Get the target for loss depending on the prediction type
+            if noise_scheduler.config.prediction_type == "epsilon":
+                target = noise
+            elif noise_scheduler.config.prediction_type == "v_prediction":
+                raise NotImplementedError
+            else:
+                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+            # Predict the noise residual and compute loss
+            # Mixed-precision training
+            with torch.cuda.amp.autocast(enabled=mixed_precision_training):
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+            optimizer.zero_grad()
+
+            # Backpropagate
+            if mixed_precision_training:
+                scaler.scale(loss).backward()
+                """ >>> gradient clipping >>> """
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(unet.parameters(), max_grad_norm)
+                """ <<< gradient clipping <<< """
+                scaler.step(optimizer)
+                scaler.update()
+            else:
+                loss.backward()
+                """ >>> gradient clipping >>> """
+                torch.nn.utils.clip_grad_norm_(unet.parameters(), max_grad_norm)
+                """ <<< gradient clipping <<< """
+                optimizer.step()
+
+            lr_scheduler.step()
+            progress_bar.update(1)
+            global_step += 1
+            
+            ### <<<< Training <<<< ###
+            
+            # Wandb logging
+            if is_main_process and (not is_debug) and use_wandb:
+                wandb.log({"train_loss": loss.item()}, step=global_step)
+                
+            # Save checkpoint
+            if is_main_process and (global_step % checkpointing_steps == 0 or step == len(train_dataloader) - 1):
+                save_path = os.path.join(output_dir, f"checkpoints")
+                state_dict = {
+                    "epoch": epoch,
+                    "global_step": global_step,
+                    "state_dict": unet.state_dict(),
+                }
+                if step == len(train_dataloader) - 1:
+                    torch.save(state_dict, os.path.join(save_path, f"checkpoint-epoch-{epoch+1}.ckpt"))
+                else:
+                    torch.save(state_dict, os.path.join(save_path, f"checkpoint.ckpt"))
+                logging.info(f"Saved state to {save_path} (global_step: {global_step})")
+                
+            # Periodically validation
+            if is_main_process and (global_step % validation_steps == 0 or global_step in validation_steps_tuple):
+                samples = []
+                
+                generator = torch.Generator(device=latents.device)
+                generator.manual_seed(global_seed)
+                
+                height = train_data.sample_size[0] if not isinstance(train_data.sample_size, int) else train_data.sample_size
+                width  = train_data.sample_size[1] if not isinstance(train_data.sample_size, int) else train_data.sample_size
+
+                prompts = validation_data.prompts[:2] if global_step < 1000 and (not image_finetune) else validation_data.prompts
+
+                for idx, prompt in enumerate(prompts):
+                    if not image_finetune:
+                        sample = validation_pipeline(
+                            prompt,
+                            generator    = generator,
+                            video_length = train_data.sample_n_frames,
+                            height       = height,
+                            width        = width,
+                            **validation_data,
+                        ).videos
+                        save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}/{idx}.gif")
+                        samples.append(sample)
+                        
+                    else:
+                        sample = validation_pipeline(
+                            prompt,
+                            generator           = generator,
+                            height              = height,
+                            width               = width,
+                            num_inference_steps = validation_data.get("num_inference_steps", 25),
+                            guidance_scale      = validation_data.get("guidance_scale", 8.),
+                        ).images[0]
+                        sample = torchvision.transforms.functional.to_tensor(sample)
+                        samples.append(sample)
+                
+                if not image_finetune:
+                    samples = torch.concat(samples)
+                    save_path = f"{output_dir}/samples/sample-{global_step}.gif"
+                    save_videos_grid(samples, save_path)
+                    
+                else:
+                    samples = torch.stack(samples)
+                    save_path = f"{output_dir}/samples/sample-{global_step}.png"
+                    torchvision.utils.save_image(samples, save_path, nrow=4)
+
+                logging.info(f"Saved samples to {save_path}")
+                
+            logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            
+            if global_step >= max_train_steps:
+                break
+            
+    dist.destroy_process_group()
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config",   type=str, required=True)
+    parser.add_argument("--launcher", type=str, choices=["pytorch", "slurm"], default="pytorch")
+    parser.add_argument("--wandb",    action="store_true")
+    args = parser.parse_args()
+
+    name   = Path(args.config).stem
+    config = OmegaConf.load(args.config)
+
+    main(name=name, launcher=args.launcher, use_wandb=args.wandb, **config)