代码拉取完成,页面将自动刷新
import argparse
import datetime
import logging
import inspect
import math
import os
from typing import Dict, Optional, Tuple
from omegaconf import OmegaConf
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import diffusers
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
from tuneavideo.models.unet import UNet3DConditionModel
from tuneavideo.data.dataset import TuneAVideoDataset
from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline
from tuneavideo.util import save_videos_grid, ddim_inversion
from einops import rearrange
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")
logger = get_logger(__name__, log_level="INFO")
def main(
pretrained_model_path: str,
output_dir: str,
train_data: Dict,
validation_data: Dict,
validation_steps: int = 100,
trainable_modules: Tuple[str] = (
"attn1.to_q",
"attn2.to_q",
"attn_temp",
),
train_batch_size: int = 1,
max_train_steps: int = 500,
learning_rate: float = 3e-5,
scale_lr: bool = False,
lr_scheduler: str = "constant",
lr_warmup_steps: int = 0,
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 = True,
checkpointing_steps: int = 500,
resume_from_checkpoint: Optional[str] = None,
mixed_precision: Optional[str] = "fp16",
use_8bit_adam: bool = False,
enable_xformers_memory_efficient_attention: bool = True,
seed: Optional[int] = None,
):
*_, config = inspect.getargvalues(inspect.currentframe())
accelerator = Accelerator(
gradient_accumulation_steps=gradient_accumulation_steps,
mixed_precision=mixed_precision,
)
# 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,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if seed is not None:
set_seed(seed)
# Handle the output folder creation
if accelerator.is_main_process:
# now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
# output_dir = os.path.join(output_dir, now)
os.makedirs(output_dir, exist_ok=True)
os.makedirs(f"{output_dir}/samples", exist_ok=True)
os.makedirs(f"{output_dir}/inv_latents", exist_ok=True)
OmegaConf.save(config, os.path.join(output_dir, 'config.yaml'))
# Load scheduler, tokenizer and models.
noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
unet = UNet3DConditionModel.from_pretrained_2d(pretrained_model_path, subfolder="unet")
# Freeze vae and text_encoder
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
unet.requires_grad_(False)
for name, module in unet.named_modules():
if name.endswith(tuple(trainable_modules)):
for params in module.parameters():
params.requires_grad = True
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")
if gradient_checkpointing:
unet.enable_gradient_checkpointing()
if scale_lr:
learning_rate = (
learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
)
# Initialize the optimizer
if use_8bit_adam:
try:
import bitsandbytes as bnb
except ImportError:
raise ImportError(
"Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
)
optimizer_cls = bnb.optim.AdamW8bit
else:
optimizer_cls = torch.optim.AdamW
optimizer = optimizer_cls(
unet.parameters(),
lr=learning_rate,
betas=(adam_beta1, adam_beta2),
weight_decay=adam_weight_decay,
eps=adam_epsilon,
)
# Get the training dataset
train_dataset = TuneAVideoDataset(**train_data)
# Preprocessing the dataset
train_dataset.prompt_ids = tokenizer(
train_dataset.prompt, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt"
).input_ids[0]
# DataLoaders creation:
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_batch_size
)
# Get the validation pipeline
validation_pipeline = TuneAVideoPipeline(
vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet,
scheduler=DDIMScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
)
validation_pipeline.enable_vae_slicing()
ddim_inv_scheduler = DDIMScheduler.from_pretrained(pretrained_model_path, subfolder='scheduler')
ddim_inv_scheduler.set_timesteps(validation_data.num_inv_steps)
# 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,
)
# Prepare everything with our `accelerator`.
unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
unet, optimizer, train_dataloader, lr_scheduler
)
# For mixed precision training we cast the text_encoder and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move text_encode and vae to gpu and cast to weight_dtype
text_encoder.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
# 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)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
accelerator.init_trackers("text2video-fine-tune")
# Train!
total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {max_train_steps}")
global_step = 0
first_epoch = 0
# Potentially load in the weights and states from a previous save
if resume_from_checkpoint:
if resume_from_checkpoint != "latest":
path = os.path.basename(resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1]
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(output_dir, path))
global_step = int(path.split("-")[1])
first_epoch = global_step // num_update_steps_per_epoch
resume_step = global_step % num_update_steps_per_epoch
# Only show the progress bar once on each machine.
progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process)
progress_bar.set_description("Steps")
for epoch in range(first_epoch, num_train_epochs):
unet.train()
train_loss = 0.0
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(unet):
# Convert videos to latent space
pixel_values = batch["pixel_values"].to(weight_dtype)
video_length = pixel_values.shape[1]
pixel_values = rearrange(pixel_values, "b f c h w -> (b f) c h w")
latents = vae.encode(pixel_values).latent_dist.sample()
latents = rearrange(latents, "(b f) c h w -> b c f h w", f=video_length)
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.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
encoder_hidden_states = text_encoder(batch["prompt_ids"])[0]
# Get the target for loss depending on the prediction type
if noise_scheduler.prediction_type == "epsilon":
target = noise
elif noise_scheduler.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.prediction_type}")
# Predict the noise residual and compute loss
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
# Gather the losses across all processes for logging (if we use distributed training).
avg_loss = accelerator.gather(loss.repeat(train_batch_size)).mean()
train_loss += avg_loss.item() / gradient_accumulation_steps
# Backpropagate
accelerator.backward(loss)
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(unet.parameters(), max_grad_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
accelerator.log({"train_loss": train_loss}, step=global_step)
train_loss = 0.0
if global_step % checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
if global_step % validation_steps == 0:
if accelerator.is_main_process:
samples = []
generator = torch.Generator(device=latents.device)
generator.manual_seed(seed)
ddim_inv_latent = None
if validation_data.use_inv_latent:
inv_latents_path = os.path.join(output_dir, f"inv_latents/ddim_latent-{global_step}.pt")
ddim_inv_latent = ddim_inversion(
validation_pipeline, ddim_inv_scheduler, video_latent=latents,
num_inv_steps=validation_data.num_inv_steps, prompt="")[-1].to(weight_dtype)
torch.save(ddim_inv_latent, inv_latents_path)
for idx, prompt in enumerate(validation_data.prompts):
sample = validation_pipeline(prompt, generator=generator, latents=ddim_inv_latent,
**validation_data).videos
save_videos_grid(sample, f"{output_dir}/samples/sample-{global_step}/{prompt}.gif")
samples.append(sample)
samples = torch.concat(samples)
save_path = f"{output_dir}/samples/sample-{global_step}.gif"
save_videos_grid(samples, save_path)
logger.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
# Create the pipeline using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
unet = accelerator.unwrap_model(unet)
pipeline = TuneAVideoPipeline.from_pretrained(
pretrained_model_path,
text_encoder=text_encoder,
vae=vae,
unet=unet,
)
pipeline.save_pretrained(output_dir)
accelerator.end_training()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default="./configs/tuneavideo.yaml")
args = parser.parse_args()
main(**OmegaConf.load(args.config))
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。