1553 lines
70 KiB
Python
1553 lines
70 KiB
Python
"""LingBot-Video ti2v full-parameter training script.
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Flow-matching training of the LingBot-Video single-stream joint DiT, mirroring
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the ti2v inference path of examples/lingbot_video/predict_i2v.py:
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- The first frame is used twice, exactly like at inference time:
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1. as visual input of the frozen Qwen3-VL text encoder
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(<|vision_start|>...<|image_pad|>...<|vision_end|> tokens);
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2. as a clean latent written into the temporal prefix of the noisy latent
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(inpainting-style clamping, see LingBotVideoPipeline._apply_inpainting).
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- Loss is computed on the non-conditioned latent frames only.
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- Sigma is sampled from the same shifted FlowUniPC schedule used at inference
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(sigma' = shift * sigma / (1 + (shift - 1) * sigma)), and the transformer
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receives timestep = sigma * 1000.
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Modified from scripts/wan2.2/train.py / scripts/qwenimage/train.py.
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"""
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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import argparse
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import gc
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import logging
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import math
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import os
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import pickle
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import shutil
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import sys
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import accelerate
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import diffusers
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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import transformers
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from accelerate import Accelerator
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from accelerate.logging import get_logger
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from accelerate.utils import ProjectConfiguration, set_seed
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from diffusers.optimization import get_scheduler
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from diffusers.training_utils import (EMAModel,
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compute_density_for_timestep_sampling,
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compute_loss_weighting_for_sd3)
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from diffusers.utils import check_min_version, deprecate, is_wandb_available
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from diffusers.utils.torch_utils import is_compiled_module
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from einops import rearrange
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from packaging import version
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from PIL import Image
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from torch.utils.data import RandomSampler as TorchRandomSampler
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from torch.utils.tensorboard import SummaryWriter
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from torchvision import transforms
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from transformers import AutoProcessor
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from transformers.utils import ContextManagers
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import datasets
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current_file_path = os.path.abspath(__file__)
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project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.data import (ASPECT_RATIO_512, ASPECT_RATIO_RANDOM_CROP_512,
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ASPECT_RATIO_RANDOM_CROP_PROB,
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AspectRatioBatchImageVideoSampler,
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ImageVideoDataset, ImageVideoSampler,
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RandomSampler, get_closest_ratio)
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from videox_fun.models import (AutoencoderKLQwenImage,
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LingBotVideoTransformer3DModel,
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Qwen3VLForConditionalGeneration)
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from videox_fun.pipeline import LingBotVideoI2VPipeline
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from videox_fun.pipeline.pipeline_lingbot_video_i2v import (SPATIAL_MERGE_SIZE,
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smart_resize)
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from videox_fun.utils.discrete_sampler import DiscreteSampling
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from videox_fun.utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
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from videox_fun.utils.fsdp_ema import FSDPEMA
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from videox_fun.utils.tqdm_bar import PauseAwareTqdm
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from videox_fun.utils.utils import calculate_dimensions, save_videos_grid
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if is_wandb_available():
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import wandb
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def filter_kwargs(cls, kwargs):
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import inspect
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sig = inspect.signature(cls.__init__)
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valid_params = set(sig.parameters.keys()) - {'self', 'cls'}
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filtered_kwargs = {k: v for k, v in kwargs.items() if k in valid_params}
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return filtered_kwargs
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def get_random_downsample_ratio(sample_size, image_ratio=[],
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all_choices=False, rng=None):
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def _create_special_list(length):
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if length == 1:
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return [1.0]
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if length >= 2:
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first_element = 0.75
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remaining_sum = 1.0 - first_element
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other_elements_value = remaining_sum / (length - 1)
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special_list = [first_element] + [other_elements_value] * (length - 1)
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return special_list
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if sample_size >= 1536:
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number_list = [1, 1.25, 1.5, 2, 2.5, 3] + image_ratio
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elif sample_size >= 1024:
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number_list = [1, 1.25, 1.5, 2] + image_ratio
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elif sample_size >= 768:
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number_list = [1, 1.25, 1.5] + image_ratio
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elif sample_size >= 512:
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number_list = [1] + image_ratio
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else:
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number_list = [1]
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if all_choices:
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return number_list
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number_list_prob = np.array(_create_special_list(len(number_list)))
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if rng is None:
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return np.random.choice(number_list, p=number_list_prob)
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else:
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return rng.choice(number_list, p=number_list_prob)
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def build_sigma_table(sigma_max, sigma_min, num_steps, shift):
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"""The shifted sigma grid used by FlowUniPC at inference (see
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FlowUniPCMultistepScheduler.set_timesteps and compute_refiner_sigmas)."""
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sigmas = np.linspace(float(sigma_max), float(sigma_min), int(num_steps) + 1)[:-1]
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sigmas = shift * sigmas / (1.0 + (shift - 1.0) * sigmas)
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return torch.from_numpy(sigmas.astype(np.float32))
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def vae_latent_to_dit(vae, latents):
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"""Normalize raw VAE latents with the per-channel mean/std, matching
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LingBotVideoPipeline._vae_latent_to_dit."""
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device = latents.device
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mean = torch.tensor(vae.config.latents_mean, device=device, dtype=torch.float32).view(1, -1, 1, 1, 1)
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std_inv = (1.0 / torch.tensor(vae.config.latents_std, device=device, dtype=torch.float32)).view(1, -1, 1, 1, 1)
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return (latents.float() - mean) * std_inv
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def frame_to_vlm_image(frame):
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"""frame: (C, H, W) tensor in [-1, 1]. Returns the PIL image fed to
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Qwen3-VL, resized exactly like LingBotVideoI2VPipeline._vlm_image."""
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frame = (frame.detach().cpu().clamp(-1, 1) * 0.5 + 0.5)
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array = frame.permute(1, 2, 0).mul(255).byte().numpy()
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image = Image.fromarray(array, mode="RGB")
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return image
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def resize_vlm_image(image, patch_factor):
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resized_height, resized_width = smart_resize(image.height, image.width, factor=patch_factor)
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return image.resize((resized_width, resized_height))
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# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
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check_min_version("0.18.0.dev0")
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logger = get_logger(__name__, log_level="INFO")
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def log_validation(vae, text_encoder, processor, transformer3d, args, accelerator, weight_dtype, global_step):
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try:
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with torch.no_grad(), torch.cuda.amp.autocast(dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
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logger.info("Running validation... ")
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scheduler = FlowUniPCMultistepScheduler.from_pretrained(
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args.pretrained_model_name_or_path,
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subfolder="scheduler",
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)
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# Under FSDP the transformer3d passed here is the wrapped (sharded)
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# module; it must be used as-is because its forward is collective.
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pipeline = LingBotVideoI2VPipeline(
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transformer=transformer3d,
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vae=vae,
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text_encoder=text_encoder,
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processor=processor,
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scheduler=scheduler,
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)
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if not args.use_fsdp:
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pipeline = pipeline.to(accelerator.device)
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if args.seed is None:
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generator = None
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else:
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rank_seed = args.seed + accelerator.process_index
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generator = torch.Generator(device=accelerator.device).manual_seed(rank_seed)
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logger.info(f"Rank {accelerator.process_index} using seed: {rank_seed}")
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video_length = int((args.video_sample_n_frames - 1) // pipeline.vae_scale_factor_temporal * pipeline.vae_scale_factor_temporal) + 1 if args.video_sample_n_frames != 1 else 1
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for i in range(len(args.validation_prompts)):
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image = Image.open(args.validation_paths[i]).convert("RGB")
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width, height = calculate_dimensions(
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args.image_sample_size * args.image_sample_size, image.width / image.height)
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width, height = int(width), int(height)
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sample = pipeline(
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args.validation_prompts[i],
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image = image,
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num_frames = video_length,
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height = height,
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width = width,
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generator = generator,
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guidance_scale = 3.0,
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shift = args.train_shift,
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num_inference_steps = 25,
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).videos
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os.makedirs(os.path.join(args.output_dir, "sample"), exist_ok=True)
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save_videos_grid(
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sample,
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os.path.join(
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args.output_dir,
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f"sample/sample-{global_step}-rank{accelerator.process_index}-image-{i}.mp4"
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),
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fps=24,
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)
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del pipeline
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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vae.to(accelerator.device if not args.low_vram else "cpu")
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text_encoder.to(accelerator.device if not args.low_vram else "cpu")
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except Exception as e:
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gc.collect()
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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print(f"Eval error on rank {accelerator.process_index} with info {e}")
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vae.to(accelerator.device if not args.low_vram else "cpu")
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text_encoder.to(accelerator.device if not args.low_vram else "cpu")
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def linear_decay(initial_value, final_value, total_steps, current_step):
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if current_step >= total_steps:
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return final_value
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current_step = max(0, current_step)
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step_size = (final_value - initial_value) / total_steps
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current_value = initial_value + step_size * current_step
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return current_value
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def parse_args():
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parser = argparse.ArgumentParser(description="LingBot-Video ti2v training script.")
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parser.add_argument(
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"--pretrained_model_name_or_path",
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type=str,
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default=None,
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required=True,
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help="Path to the LingBot-Video model root (contains transformer/ vae/ text_encoder/ processor/ scheduler/).",
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)
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parser.add_argument(
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"--train_data_dir",
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type=str,
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default=None,
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help="A folder containing the training data.",
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)
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parser.add_argument(
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"--train_data_meta",
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type=str,
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default=None,
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help="A json containing the training data.",
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)
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parser.add_argument(
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"--max_train_samples",
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type=int,
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default=None,
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help=(
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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),
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)
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parser.add_argument(
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"--validation_prompts",
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type=str,
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default=None,
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nargs="+",
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help="A set of prompts evaluated every `--validation_steps` and logged to `--report_to`. "
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"Must be structured JSON captions (rewriter schema), same as the training data.",
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)
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parser.add_argument(
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"--validation_paths",
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type=str,
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default=None,
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nargs="+",
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help="A set of condition (first-frame) images evaluated every `--validation_steps`, paired with `--validation_prompts`.",
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)
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parser.add_argument(
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"--output_dir",
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type=str,
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default="lingbot-video-finetuned",
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help="The output directory where the model predictions and checkpoints will be written.",
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)
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument(
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"--vae_mini_batch", type=int, default=1, help="mini batch size for vae."
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)
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parser.add_argument("--num_train_epochs", type=int, default=100)
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--gradient_checkpointing",
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action="store_true",
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help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=1e-5,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument(
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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument(
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"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
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)
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parser.add_argument(
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"--allow_tf32",
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action="store_true",
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help=(
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"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
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" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
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),
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)
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parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
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parser.add_argument(
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"--dataloader_num_workers",
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type=int,
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default=0,
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help=(
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"Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
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),
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)
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parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
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parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
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parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
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parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
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parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
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parser.add_argument(
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"--logging_dir",
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type=str,
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default="logs",
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help=(
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"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
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" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
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),
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)
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parser.add_argument(
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"--report_model_info", action="store_true", help="Whether or not to report more info about model (such as norm, grad)."
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)
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parser.add_argument(
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"--mixed_precision",
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type=str,
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default=None,
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choices=["no", "fp16", "bf16"],
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help=(
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"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
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" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
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" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
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),
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="tensorboard",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
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' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
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),
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)
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parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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parser.add_argument(
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"--checkpointing_steps",
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type=int,
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default=500,
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help=(
|
|
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
|
|
" training using `--resume_from_checkpoint`."
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),
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)
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parser.add_argument(
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"--checkpoints_total_limit",
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type=int,
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default=None,
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help=("Max number of checkpoints to store."),
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
|
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help=(
|
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"Whether training should be resumed from a previous checkpoint. Use a path saved by"
|
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' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
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),
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)
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parser.add_argument(
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"--validation_epochs",
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type=int,
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default=5,
|
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help="Run validation every X epochs.",
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)
|
|
parser.add_argument(
|
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"--validation_steps",
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type=int,
|
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default=2000,
|
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help="Run validation every X steps.",
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)
|
|
parser.add_argument(
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"--tracker_project_name",
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type=str,
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default="lingbot-video-fine-tune",
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help=(
|
|
"The `project_name` argument passed to Accelerator.init_trackers for"
|
|
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
|
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),
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)
|
|
parser.add_argument(
|
|
"--uniform_sampling", action="store_true", help="Whether or not to use uniform_sampling."
|
|
)
|
|
parser.add_argument(
|
|
"--enable_bucket", action="store_true", help="Whether enable bucket sample in datasets."
|
|
)
|
|
parser.add_argument(
|
|
"--random_ratio_crop", action="store_true", help="Whether enable random ratio crop sample in datasets."
|
|
)
|
|
parser.add_argument(
|
|
"--random_hw_adapt", action="store_true", help="Whether enable random adapt height and width in datasets."
|
|
)
|
|
parser.add_argument(
|
|
"--training_with_video_token_length", action="store_true", help="Keep the video token length constant while adapting height and width.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_sampling_steps",
|
|
type=int,
|
|
default=1000,
|
|
help="Number of discrete sigma levels used for training-time sigma sampling.",
|
|
)
|
|
parser.add_argument(
|
|
"--train_shift",
|
|
type=float,
|
|
default=3.0,
|
|
help="Shift of the flow-matching sigma schedule, same as the inference `shift` (3.0 for LingBot-Video).",
|
|
)
|
|
parser.add_argument(
|
|
"--token_sample_size",
|
|
type=int,
|
|
default=512,
|
|
help="Sample size of the token.",
|
|
)
|
|
parser.add_argument(
|
|
"--video_sample_size",
|
|
type=int,
|
|
default=512,
|
|
help="Sample size of the video.",
|
|
)
|
|
parser.add_argument(
|
|
"--image_sample_size",
|
|
type=int,
|
|
default=512,
|
|
help="Sample size of the image.",
|
|
)
|
|
parser.add_argument(
|
|
"--fix_sample_size",
|
|
nargs=2, type=int, default=None,
|
|
help="Fix Sample size [height, width] when using bucket and collate_fn."
|
|
)
|
|
parser.add_argument(
|
|
"--video_sample_stride",
|
|
type=int,
|
|
default=1,
|
|
help="Sample stride of the video.",
|
|
)
|
|
parser.add_argument(
|
|
"--video_sample_n_frames",
|
|
type=int,
|
|
default=81,
|
|
help="Num frame of video.",
|
|
)
|
|
parser.add_argument(
|
|
"--video_repeat",
|
|
type=int,
|
|
default=0,
|
|
help="Num of repeat video.",
|
|
)
|
|
parser.add_argument(
|
|
"--transformer_path",
|
|
type=str,
|
|
default=None,
|
|
help=("If you want to load the weight from other transformers, input its path."),
|
|
)
|
|
parser.add_argument(
|
|
"--vae_path",
|
|
type=str,
|
|
default=None,
|
|
help=("If you want to load the weight from other vaes, input its path."),
|
|
)
|
|
parser.add_argument(
|
|
'--trainable_modules',
|
|
nargs='+',
|
|
default=["."],
|
|
help='Enter a list of trainable modules (substring match).'
|
|
)
|
|
parser.add_argument(
|
|
'--trainable_modules_low_learning_rate',
|
|
nargs='+',
|
|
default=[],
|
|
help='Enter a list of trainable modules with lower learning rate'
|
|
)
|
|
parser.add_argument(
|
|
"--use_fsdp", action="store_true", help="Whether or not to use fsdp."
|
|
)
|
|
parser.add_argument(
|
|
"--low_vram", action="store_true", help="Whether enable low_vram mode."
|
|
)
|
|
parser.add_argument(
|
|
"--abnormal_norm_clip_start",
|
|
type=int,
|
|
default=1000,
|
|
help=('When do we start doing additional processing on abnormal gradients.'),
|
|
)
|
|
parser.add_argument(
|
|
"--initial_grad_norm_ratio",
|
|
type=int,
|
|
default=5,
|
|
help=('The initial gradient is relative to the multiple of the max_grad_norm.'),
|
|
)
|
|
parser.add_argument(
|
|
"--weighting_scheme",
|
|
type=str,
|
|
default="none",
|
|
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
|
|
help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
|
|
)
|
|
parser.add_argument(
|
|
"--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
|
|
)
|
|
parser.add_argument(
|
|
"--mode_scale",
|
|
type=float,
|
|
default=1.29,
|
|
help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
|
|
if env_local_rank != -1 and env_local_rank != args.local_rank:
|
|
args.local_rank = env_local_rank
|
|
|
|
if args.validation_prompts is not None and args.validation_paths is not None:
|
|
assert len(args.validation_prompts) == len(args.validation_paths), \
|
|
"validation_prompts and validation_paths must be paired one-to-one."
|
|
|
|
return args
|
|
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
logging_dir = os.path.join(args.output_dir, args.logging_dir)
|
|
|
|
accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
|
|
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with=args.report_to,
|
|
project_config=accelerator_project_config,
|
|
)
|
|
|
|
fsdp_plugin = accelerator.state.fsdp_plugin if hasattr(accelerator.state, "fsdp_plugin") else None
|
|
if fsdp_plugin is not None:
|
|
from torch.distributed.fsdp import ShardingStrategy
|
|
zero_stage = 0
|
|
if fsdp_plugin.sharding_strategy in (ShardingStrategy.FULL_SHARD, None): # The fsdp_plugin.sharding_strategy is None in FSDP 2.
|
|
fsdp_stage = 3
|
|
elif fsdp_plugin.sharding_strategy is ShardingStrategy.SHARD_GRAD_OP:
|
|
fsdp_stage = 2
|
|
else:
|
|
fsdp_stage = 0
|
|
print(f"Using FSDP stage: {fsdp_stage}")
|
|
|
|
args.use_fsdp = True
|
|
if fsdp_stage == 3:
|
|
print(f"Auto set save_state to True because fsdp_stage == 3")
|
|
args.save_state = True
|
|
else:
|
|
zero_stage = 0
|
|
fsdp_stage = 0
|
|
print("FSDP is not enabled.")
|
|
|
|
if accelerator.is_main_process:
|
|
writer = SummaryWriter(log_dir=logging_dir)
|
|
|
|
# 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:
|
|
datasets.utils.logging.set_verbosity_warning()
|
|
transformers.utils.logging.set_verbosity_warning()
|
|
diffusers.utils.logging.set_verbosity_info()
|
|
else:
|
|
datasets.utils.logging.set_verbosity_error()
|
|
transformers.utils.logging.set_verbosity_error()
|
|
diffusers.utils.logging.set_verbosity_error()
|
|
|
|
# If passed along, set the training seed now.
|
|
if args.seed is not None:
|
|
set_seed(args.seed)
|
|
rng = np.random.default_rng(np.random.PCG64(args.seed + accelerator.process_index))
|
|
torch_rng = torch.Generator(accelerator.device).manual_seed(args.seed + accelerator.process_index)
|
|
else:
|
|
rng = None
|
|
torch_rng = None
|
|
print(f"Init rng with seed {args.seed + accelerator.process_index if args.seed is not None else None}. Process_index is {accelerator.process_index}")
|
|
|
|
# Handle the repository creation
|
|
if accelerator.is_main_process:
|
|
if args.output_dir is not None:
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
# For mixed precision training we cast all non-trainable weights (vae, text_encoder)
|
|
# to half-precision as these weights 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
|
|
args.mixed_precision = accelerator.mixed_precision
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
args.mixed_precision = accelerator.mixed_precision
|
|
|
|
# Load the sigma scheduler (only used to build the training sigma table).
|
|
noise_scheduler = FlowUniPCMultistepScheduler.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="scheduler",
|
|
)
|
|
|
|
# Get Processor (Qwen3-VL tokenizer + image processor)
|
|
processor = AutoProcessor.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, "processor"),
|
|
)
|
|
vision_patch_size = getattr(getattr(processor, "image_processor", None), "config", None)
|
|
vision_patch_size = getattr(vision_patch_size, "patch_size", 16) if vision_patch_size is not None else 16
|
|
vlm_patch_factor = int(vision_patch_size) * SPATIAL_MERGE_SIZE
|
|
|
|
def deepspeed_zero_init_disabled_context_manager():
|
|
"""
|
|
returns a list of context managers to disable deepspeed zero init
|
|
"""
|
|
deepspeed_plugin = accelerate.state.AcceleratorState().deepspeed_plugin if accelerate.state.is_initialized() else None
|
|
if deepspeed_plugin is None:
|
|
return []
|
|
return [deepspeed_plugin.zero3_init_context_manager(enable=False)]
|
|
|
|
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
|
|
# Get Text encoder (frozen Qwen3-VL)
|
|
text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, "text_encoder"),
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
text_encoder = text_encoder.eval()
|
|
# Get Vae
|
|
vae = AutoencoderKLQwenImage.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, "vae"),
|
|
)
|
|
vae.eval()
|
|
|
|
# Get Transformer. `.to(weight_dtype)` keeps the fp32-sensitive modules
|
|
# (norms / router / modulation / scale_shift_table) in fp32 automatically.
|
|
transformer3d = LingBotVideoTransformer3DModel.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, "transformer"),
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
).to(weight_dtype)
|
|
|
|
# FSDP flattens parameters of one wrap unit into a single flat buffer and
|
|
# requires a uniform dtype. The custom `.to()` above keeps fp32-sensitive
|
|
# modules (norms / router / modulation / scale_shift_table) in fp32, so under
|
|
# FSDP we force-cast everything to weight_dtype (matches bf16 inference).
|
|
if fsdp_stage != 0:
|
|
for _name, _param in transformer3d.named_parameters():
|
|
_param.data = _param.data.to(weight_dtype)
|
|
for _name, _buffer in transformer3d.named_buffers():
|
|
_buffer.data = _buffer.data.to(weight_dtype)
|
|
|
|
# Freeze vae and text_encoder and set transformer3d to trainable
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
transformer3d.requires_grad_(False)
|
|
|
|
if args.transformer_path is not None:
|
|
print(f"From checkpoint: {args.transformer_path}")
|
|
if args.transformer_path.endswith("safetensors"):
|
|
from safetensors.torch import load_file
|
|
state_dict = load_file(args.transformer_path)
|
|
else:
|
|
state_dict = torch.load(args.transformer_path, map_location="cpu")
|
|
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
|
|
|
|
m, u = transformer3d.load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
assert len(u) == 0
|
|
|
|
if args.vae_path is not None:
|
|
print(f"From checkpoint: {args.vae_path}")
|
|
if args.vae_path.endswith("safetensors"):
|
|
from safetensors.torch import load_file
|
|
state_dict = load_file(args.vae_path)
|
|
else:
|
|
state_dict = torch.load(args.vae_path, map_location="cpu")
|
|
state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
|
|
|
|
m, u = vae.load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
assert len(u) == 0
|
|
|
|
transformer3d.train()
|
|
if accelerator.is_main_process:
|
|
accelerator.print(
|
|
f"Trainable modules '{args.trainable_modules}'."
|
|
)
|
|
for name, param in transformer3d.named_parameters():
|
|
for trainable_module_name in args.trainable_modules + args.trainable_modules_low_learning_rate:
|
|
if trainable_module_name in name:
|
|
param.requires_grad = True
|
|
break
|
|
|
|
# Create EMA for the transformer3d.
|
|
if args.use_ema:
|
|
if fsdp_stage == 3:
|
|
raise NotImplementedError("DeepSpeed Zero-3 does not support EMA.")
|
|
|
|
ema_module = LingBotVideoTransformer3DModel.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, "transformer"),
|
|
low_cpu_mem_usage=True,
|
|
torch_dtype=weight_dtype,
|
|
)
|
|
if args.use_fsdp:
|
|
# The EMA copy gets the same FSDP wrap as the live model so that
|
|
# every local shard of the copy pairs 1:1 with the live shard.
|
|
ema_transformer3d = FSDPEMA(ema_module, source=transformer3d, accelerator=accelerator, fsdp_plugin=fsdp_plugin)
|
|
else:
|
|
ema_module = ema_module.to(weight_dtype)
|
|
ema_transformer3d = EMAModel(ema_module.parameters(), model_cls=LingBotVideoTransformer3DModel, model_config=ema_module.config)
|
|
|
|
# `accelerate` 0.16.0 will have better support for customized saving
|
|
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
|
|
if fsdp_stage != 0:
|
|
def save_model_hook(models, weights, output_dir):
|
|
accelerate_state_dict = accelerator.get_state_dict(models[-1], unwrap=True)
|
|
if accelerator.is_main_process:
|
|
from safetensors.torch import save_file
|
|
|
|
safetensor_save_path = os.path.join(output_dir, f"diffusion_pytorch_model.safetensors")
|
|
accelerate_state_dict = {k: v.to(dtype=weight_dtype) for k, v in accelerate_state_dict.items()}
|
|
save_file(accelerate_state_dict, safetensor_save_path, metadata={"format": "pt"})
|
|
|
|
with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file:
|
|
pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file)
|
|
if args.use_ema:
|
|
# Every rank joins the FULL_STATE_DICT all-gather inside.
|
|
ema_transformer3d.save_pretrained(os.path.join(output_dir, "transformer_ema"))
|
|
|
|
def load_model_hook(models, input_dir):
|
|
if args.use_ema:
|
|
ema_transformer3d.load_pretrained(os.path.join(input_dir, "transformer_ema"))
|
|
pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl")
|
|
if os.path.exists(pkl_path):
|
|
with open(pkl_path, 'rb') as file:
|
|
loaded_number, _ = pickle.load(file)
|
|
batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0)
|
|
print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.")
|
|
else:
|
|
# create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
|
|
def save_model_hook(models, weights, output_dir):
|
|
if accelerator.is_main_process:
|
|
if args.use_ema:
|
|
ema_transformer3d.save_pretrained(os.path.join(output_dir, "transformer_ema"))
|
|
|
|
models[0].save_pretrained(os.path.join(output_dir, "transformer"))
|
|
weights.pop()
|
|
|
|
with open(os.path.join(output_dir, "sampler_pos_start.pkl"), 'wb') as file:
|
|
pickle.dump([batch_sampler.sampler._pos_start, first_epoch], file)
|
|
|
|
def load_model_hook(models, input_dir):
|
|
if args.use_ema:
|
|
ema_path = os.path.join(input_dir, "transformer_ema")
|
|
_, ema_kwargs = LingBotVideoTransformer3DModel.load_config(ema_path, return_unused_kwargs=True)
|
|
load_model = LingBotVideoTransformer3DModel.from_pretrained(
|
|
input_dir, subfolder="transformer_ema",
|
|
low_cpu_mem_usage=True,
|
|
)
|
|
load_model = EMAModel(load_model.parameters(), model_cls=LingBotVideoTransformer3DModel, model_config=load_model.config)
|
|
load_model.load_state_dict(ema_kwargs)
|
|
|
|
ema_transformer3d.load_state_dict(load_model.state_dict())
|
|
ema_transformer3d.to(accelerator.device)
|
|
del load_model
|
|
|
|
for i in range(len(models)):
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
# load diffusers style into model
|
|
load_model = LingBotVideoTransformer3DModel.from_pretrained(
|
|
input_dir, subfolder="transformer",
|
|
low_cpu_mem_usage=True,
|
|
)
|
|
model.register_to_config(**load_model.config)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
del load_model
|
|
|
|
pkl_path = os.path.join(input_dir, "sampler_pos_start.pkl")
|
|
if os.path.exists(pkl_path):
|
|
with open(pkl_path, 'rb') as file:
|
|
loaded_number, _ = pickle.load(file)
|
|
batch_sampler.sampler._pos_start = max(loaded_number - args.dataloader_num_workers * accelerator.num_processes * 2, 0)
|
|
print(f"Load pkl from {pkl_path}. Get loaded_number = {loaded_number}.")
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
if args.gradient_checkpointing:
|
|
transformer3d.enable_gradient_checkpointing()
|
|
|
|
# Enable TF32 for faster training on Ampere GPUs,
|
|
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
|
|
if args.allow_tf32:
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
|
|
if args.scale_lr:
|
|
args.learning_rate = (
|
|
args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
|
|
)
|
|
|
|
# Initialize the optimizer
|
|
if args.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
|
|
|
|
trainable_params = list(filter(lambda p: p.requires_grad, transformer3d.parameters()))
|
|
trainable_params_optim = [
|
|
{'params': [], 'lr': args.learning_rate},
|
|
{'params': [], 'lr': args.learning_rate / 2},
|
|
]
|
|
in_already = []
|
|
for name, param in transformer3d.named_parameters():
|
|
high_lr_flag = False
|
|
if name in in_already:
|
|
continue
|
|
for trainable_module_name in args.trainable_modules:
|
|
if trainable_module_name in name:
|
|
in_already.append(name)
|
|
high_lr_flag = True
|
|
trainable_params_optim[0]['params'].append(param)
|
|
if accelerator.is_main_process:
|
|
print(f"Set {name} to lr : {args.learning_rate}")
|
|
break
|
|
if high_lr_flag:
|
|
continue
|
|
for trainable_module_name in args.trainable_modules_low_learning_rate:
|
|
if trainable_module_name in name:
|
|
in_already.append(name)
|
|
trainable_params_optim[1]['params'].append(param)
|
|
if accelerator.is_main_process:
|
|
print(f"Set {name} to lr : {args.learning_rate / 2}")
|
|
break
|
|
|
|
optimizer = optimizer_cls(
|
|
trainable_params_optim,
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Get the training dataset
|
|
# AutoencoderKLQwenImage's config has no compression-ratio fields; the
|
|
# pipeline constants are temporal=4 / spatial=8.
|
|
sample_n_frames_bucket_interval = getattr(vae.config, "temporal_compression_ratio", 4)
|
|
spatial_compression_ratio = getattr(vae.config, "spatial_compression_ratio", 8)
|
|
|
|
if args.fix_sample_size is not None and args.enable_bucket:
|
|
args.video_sample_size = max(max(args.fix_sample_size), args.video_sample_size)
|
|
args.image_sample_size = max(max(args.fix_sample_size), args.image_sample_size)
|
|
args.training_with_video_token_length = False
|
|
args.random_hw_adapt = False
|
|
|
|
# Get the dataset
|
|
train_dataset = ImageVideoDataset(
|
|
args.train_data_meta, args.train_data_dir,
|
|
video_sample_size=args.video_sample_size, video_sample_stride=args.video_sample_stride, video_sample_n_frames=args.video_sample_n_frames,
|
|
video_repeat=args.video_repeat,
|
|
image_sample_size=args.image_sample_size,
|
|
enable_bucket=args.enable_bucket, enable_inpaint=False,
|
|
)
|
|
|
|
# The DiT was trained on structured JSON captions only; plain natural
|
|
# language captions are out-of-distribution and degrade fine-tuning.
|
|
from videox_fun.models.lingbot_video_rewriter import is_valid_caption
|
|
n_plain = sum(1 for d in train_dataset.dataset if not is_valid_caption(d.get("text", "")))
|
|
if n_plain:
|
|
logger.warning(
|
|
f"{n_plain}/{len(train_dataset.dataset)} dataset captions are NOT structured JSON captions. "
|
|
"LingBot-Video expects rewriter-style JSON captions; training on natural-language text "
|
|
"feeds out-of-distribution prompts to the DiT. All captions must go through the rewriter "
|
|
"(no hand-written captions) — convert the dataset with "
|
|
"scripts/lingbot_video/prepare_captions.py (batch) or "
|
|
"videox_fun.models.lingbot_video_rewriter.ensure_json_caption (single)."
|
|
)
|
|
|
|
if args.enable_bucket:
|
|
aspect_ratio_sample_size = {key: [x / 512 * args.video_sample_size for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
|
batch_sampler_generator = torch.Generator().manual_seed(args.seed)
|
|
batch_sampler = AspectRatioBatchImageVideoSampler(
|
|
sampler=RandomSampler(train_dataset, generator=batch_sampler_generator), dataset=train_dataset.dataset,
|
|
batch_size=args.train_batch_size, train_folder=args.train_data_dir, drop_last=True,
|
|
aspect_ratios=aspect_ratio_sample_size,
|
|
)
|
|
|
|
def get_length_to_frame_num(token_length):
|
|
if args.image_sample_size > args.video_sample_size:
|
|
sample_sizes = list(range(args.video_sample_size, args.image_sample_size + 1, 128))
|
|
|
|
if sample_sizes[-1] != args.image_sample_size:
|
|
sample_sizes.append(args.image_sample_size)
|
|
else:
|
|
sample_sizes = [args.image_sample_size]
|
|
|
|
length_to_frame_num = {
|
|
sample_size: min(token_length / sample_size / sample_size, args.video_sample_n_frames) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1 for sample_size in sample_sizes
|
|
}
|
|
|
|
return length_to_frame_num
|
|
|
|
def collate_fn(examples):
|
|
# Get token length
|
|
target_token_length = args.video_sample_n_frames * args.token_sample_size * args.token_sample_size
|
|
length_to_frame_num = get_length_to_frame_num(target_token_length)
|
|
|
|
# Create new output
|
|
new_examples = {}
|
|
new_examples["target_token_length"] = target_token_length
|
|
new_examples["pixel_values"] = []
|
|
new_examples["text"] = []
|
|
|
|
# Get downsample ratio in image and videos
|
|
pixel_value = examples[0]["pixel_values"]
|
|
data_type = examples[0]["data_type"]
|
|
f, h, w, c = np.shape(pixel_value)
|
|
if data_type == 'image':
|
|
random_downsample_ratio = 1 if not args.random_hw_adapt else get_random_downsample_ratio(args.image_sample_size, image_ratio=[args.image_sample_size / args.video_sample_size], rng=rng)
|
|
|
|
aspect_ratio_sample_size = {key: [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
|
aspect_ratio_random_crop_sample_size = {key: [x / 512 * args.image_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
|
|
|
|
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
|
|
else:
|
|
if args.random_hw_adapt:
|
|
if args.training_with_video_token_length:
|
|
local_min_size = np.min(np.array([np.mean(np.array([np.shape(example["pixel_values"])[1], np.shape(example["pixel_values"])[2]])) for example in examples]))
|
|
# The video will be resized to a lower resolution than its own.
|
|
choice_list = [length for length in list(length_to_frame_num.keys()) if length < local_min_size * 1.25]
|
|
if len(choice_list) == 0:
|
|
choice_list = list(length_to_frame_num.keys())
|
|
if rng is None:
|
|
local_video_sample_size = np.random.choice(choice_list)
|
|
else:
|
|
local_video_sample_size = rng.choice(choice_list)
|
|
batch_video_length = length_to_frame_num[local_video_sample_size]
|
|
random_downsample_ratio = args.video_sample_size / local_video_sample_size
|
|
else:
|
|
random_downsample_ratio = get_random_downsample_ratio(
|
|
args.video_sample_size, rng=rng)
|
|
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
|
|
else:
|
|
random_downsample_ratio = 1
|
|
batch_video_length = args.video_sample_n_frames + sample_n_frames_bucket_interval
|
|
|
|
aspect_ratio_sample_size = {key: [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
|
|
aspect_ratio_random_crop_sample_size = {key: [x / 512 * args.video_sample_size / random_downsample_ratio for x in ASPECT_RATIO_RANDOM_CROP_512[key]] for key in ASPECT_RATIO_RANDOM_CROP_512.keys()}
|
|
|
|
if args.fix_sample_size is not None:
|
|
fix_sample_size = [int(x / spatial_compression_ratio / 2) * spatial_compression_ratio * 2 for x in args.fix_sample_size]
|
|
elif args.random_ratio_crop:
|
|
if rng is None:
|
|
random_sample_size = aspect_ratio_random_crop_sample_size[
|
|
np.random.choice(list(aspect_ratio_random_crop_sample_size.keys()), p=ASPECT_RATIO_RANDOM_CROP_PROB)
|
|
]
|
|
else:
|
|
random_sample_size = aspect_ratio_random_crop_sample_size[
|
|
rng.choice(list(aspect_ratio_random_crop_sample_size.keys()), p=ASPECT_RATIO_RANDOM_CROP_PROB)
|
|
]
|
|
random_sample_size = [int(x / spatial_compression_ratio / 2) * spatial_compression_ratio * 2 for x in random_sample_size]
|
|
else:
|
|
closest_size, closest_ratio = get_closest_ratio(h, w, ratios=aspect_ratio_sample_size)
|
|
closest_size = [int(x / spatial_compression_ratio / 2) * spatial_compression_ratio * 2 for x in closest_size]
|
|
|
|
min_example_length = min(
|
|
[example["pixel_values"].shape[0] for example in examples]
|
|
)
|
|
batch_video_length = int(min(batch_video_length, min_example_length))
|
|
|
|
# VAE needs the number of frames to be 4n + 1.
|
|
batch_video_length = (batch_video_length - 1) // sample_n_frames_bucket_interval * sample_n_frames_bucket_interval + 1
|
|
|
|
if batch_video_length <= 0:
|
|
batch_video_length = 1
|
|
|
|
for example in examples:
|
|
if args.fix_sample_size is not None:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
fix_sample_size = list(map(lambda x: int(x), fix_sample_size))
|
|
transform = transforms.Compose([
|
|
transforms.Resize(fix_sample_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(fix_sample_size),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
elif args.random_ratio_crop:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
b, c, h, w = pixel_values.size()
|
|
th, tw = random_sample_size
|
|
if th / tw > h / w:
|
|
nh = int(th)
|
|
nw = int(w / h * nh)
|
|
else:
|
|
nw = int(tw)
|
|
nh = int(h / w * nw)
|
|
|
|
transform = transforms.Compose([
|
|
transforms.Resize([nh, nw]),
|
|
transforms.CenterCrop([int(x) for x in random_sample_size]),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
else:
|
|
# To 0~1
|
|
pixel_values = torch.from_numpy(example["pixel_values"]).permute(0, 3, 1, 2).contiguous()
|
|
pixel_values = pixel_values / 255.
|
|
|
|
# Get adapt hw for resize
|
|
closest_size = list(map(lambda x: int(x), closest_size))
|
|
if closest_size[0] / h > closest_size[1] / w:
|
|
resize_size = closest_size[0], int(w * closest_size[0] / h)
|
|
else:
|
|
resize_size = int(h * closest_size[1] / w), closest_size[1]
|
|
|
|
transform = transforms.Compose([
|
|
transforms.Resize(resize_size, interpolation=transforms.InterpolationMode.BILINEAR), # Image.BICUBIC
|
|
transforms.CenterCrop(closest_size),
|
|
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
|
|
])
|
|
|
|
new_examples["pixel_values"].append(transform(pixel_values)[:batch_video_length])
|
|
new_examples["text"].append(example["text"])
|
|
|
|
# Limit the number of frames to the same
|
|
new_examples["pixel_values"] = torch.stack([example for example in new_examples["pixel_values"]])
|
|
|
|
return new_examples
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
batch_sampler=batch_sampler,
|
|
collate_fn=collate_fn,
|
|
persistent_workers=True if args.dataloader_num_workers != 0 else False,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
else:
|
|
# DataLoaders creation:
|
|
batch_sampler_generator = torch.Generator().manual_seed(args.seed)
|
|
batch_sampler = ImageVideoSampler(RandomSampler(train_dataset, generator=batch_sampler_generator), train_dataset, args.train_batch_size)
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
batch_sampler=batch_sampler,
|
|
persistent_workers=True if args.dataloader_num_workers != 0 else False,
|
|
num_workers=args.dataloader_num_workers,
|
|
)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
args.lr_scheduler,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
|
|
num_training_steps=args.max_train_steps * accelerator.num_processes,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
transformer3d, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
if fsdp_stage != 0:
|
|
from functools import partial
|
|
|
|
from videox_fun.dist import shard_model
|
|
# Qwen3-VL has no `.blocks`; wrap the text decoder layers and vision
|
|
# blocks explicitly via the transformer-class policy.
|
|
shard_fn = partial(
|
|
shard_model, device_id=accelerator.device, param_dtype=weight_dtype,
|
|
transformer_layer_cls_to_wrap=["Qwen3VLTextDecoderLayer", "Qwen3VLVisionBlock"],
|
|
)
|
|
text_encoder = shard_fn(text_encoder)
|
|
|
|
if args.use_ema:
|
|
ema_transformer3d.to(accelerator.device)
|
|
|
|
# Move text_encoder and vae to gpu and cast to weight_dtype
|
|
vae.to(accelerator.device if not args.low_vram else "cpu", dtype=weight_dtype)
|
|
text_encoder.to(accelerator.device if not args.low_vram else "cpu", 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) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.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:
|
|
tracker_config = dict(vars(args))
|
|
keys_to_pop = [k for k, v in tracker_config.items() if isinstance(v, list)]
|
|
for k in keys_to_pop:
|
|
tracker_config.pop(k)
|
|
print(f"Removed tracker_config['{k}']")
|
|
accelerator.init_trackers(args.tracker_project_name, tracker_config)
|
|
|
|
# Function for unwrapping if model was compiled with `torch.compile`.
|
|
def unwrap_model(model):
|
|
model = accelerator.unwrap_model(model)
|
|
model = model._orig_mod if is_compiled_module(model) else model
|
|
return model
|
|
|
|
# Train!
|
|
total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
|
global_step = 0
|
|
first_epoch = 0
|
|
|
|
# Potentially load in the weights and states from a previous save
|
|
if args.resume_from_checkpoint:
|
|
if args.resume_from_checkpoint != "latest":
|
|
path = os.path.basename(args.resume_from_checkpoint)
|
|
else:
|
|
# Get the most recent checkpoint
|
|
dirs = os.listdir(args.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] if len(dirs) > 0 else None
|
|
|
|
if path is None:
|
|
accelerator.print(
|
|
f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
|
|
)
|
|
args.resume_from_checkpoint = None
|
|
initial_global_step = 0
|
|
else:
|
|
global_step = int(path.split("-")[1])
|
|
|
|
initial_global_step = global_step
|
|
|
|
pkl_path = os.path.join(os.path.join(args.output_dir, path), "sampler_pos_start.pkl")
|
|
if os.path.exists(pkl_path):
|
|
with open(pkl_path, 'rb') as file:
|
|
_, first_epoch = pickle.load(file)
|
|
else:
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
print(f"Load pkl from {pkl_path}. Get first_epoch = {first_epoch}.")
|
|
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
else:
|
|
initial_global_step = 0
|
|
|
|
progress_bar = PauseAwareTqdm(
|
|
range(0, args.max_train_steps),
|
|
initial=initial_global_step,
|
|
desc="Steps",
|
|
# Only show the progress bar once on each machine.
|
|
disable=not accelerator.is_local_main_process,
|
|
)
|
|
|
|
# Training-time sigma table: the same shifted FlowUniPC grid used at inference.
|
|
sigma_table = build_sigma_table(
|
|
noise_scheduler.sigma_max, noise_scheduler.sigma_min,
|
|
args.train_sampling_steps, args.train_shift,
|
|
)
|
|
idx_sampling = DiscreteSampling(args.train_sampling_steps, uniform_sampling=args.uniform_sampling)
|
|
|
|
# A throw-away pipeline used only to reuse the inference-time prompt encoding
|
|
# (chat template + image tokens + system-prefix cropping).
|
|
encode_pipeline = LingBotVideoI2VPipeline(
|
|
transformer=None,
|
|
vae=vae,
|
|
text_encoder=text_encoder,
|
|
processor=processor,
|
|
scheduler=None,
|
|
)
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
train_loss = 0.0
|
|
batch_sampler.sampler.generator = torch.Generator().manual_seed(args.seed + epoch)
|
|
for step, batch in enumerate(train_dataloader):
|
|
# Data batch sanity check
|
|
if epoch == first_epoch and step == 0:
|
|
pixel_values, texts = batch['pixel_values'].cpu(), batch['text']
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
os.makedirs(os.path.join(args.output_dir, "sanity_check"), exist_ok=True)
|
|
for idx, (pixel_value, text) in enumerate(zip(pixel_values, texts)):
|
|
pixel_value = pixel_value[None, ...]
|
|
gif_name = '-'.join(text.replace('/', '').split()[:10]) if not text == '' else f'{global_step}-{idx}'
|
|
save_videos_grid(pixel_value, f"{args.output_dir}/sanity_check/{gif_name[:10]}.mp4", rescale=True)
|
|
|
|
with accelerator.accumulate(transformer3d):
|
|
# Convert videos to latent space
|
|
pixel_values = batch["pixel_values"].to(weight_dtype)
|
|
pixel_values = rearrange(pixel_values, "b f c h w -> b c f h w")
|
|
|
|
if args.low_vram:
|
|
torch.cuda.empty_cache()
|
|
vae.to(accelerator.device)
|
|
|
|
with torch.no_grad():
|
|
# This way is quicker when batch grows up
|
|
def _batch_encode_vae(pixel_values):
|
|
bs = args.vae_mini_batch
|
|
new_pixel_values = []
|
|
for i in range(0, pixel_values.shape[0], bs):
|
|
pixel_values_bs = pixel_values[i : i + bs]
|
|
pixel_values_bs = vae.encode(pixel_values_bs.to(vae.dtype)).latent_dist.sample()
|
|
new_pixel_values.append(pixel_values_bs)
|
|
return torch.cat(new_pixel_values, dim=0)
|
|
|
|
latents = vae_latent_to_dit(vae, _batch_encode_vae(pixel_values))
|
|
|
|
# ti2v condition: the first frame encoded on its own, exactly
|
|
# like LingBotVideoI2VPipeline.encode_image_latent at inference.
|
|
cond_latent = vae_latent_to_dit(vae, _batch_encode_vae(pixel_values[:, :, :1]))
|
|
|
|
if args.low_vram:
|
|
vae.to('cpu')
|
|
torch.cuda.empty_cache()
|
|
|
|
# Text (+ first-frame image) encoding with the frozen Qwen3-VL.
|
|
with torch.no_grad():
|
|
if args.low_vram:
|
|
text_encoder.to(accelerator.device)
|
|
vlm_images = [
|
|
resize_vlm_image(frame_to_vlm_image(frame), vlm_patch_factor)
|
|
for frame in pixel_values[:, :, 0]
|
|
]
|
|
prompt_embeds, prompt_mask = encode_pipeline.encode_prompt(
|
|
batch['text'], images=vlm_images, device=accelerator.device)
|
|
prompt_embeds = prompt_embeds.to(device=latents.device)
|
|
prompt_mask = prompt_mask.to(device=latents.device)
|
|
if args.low_vram:
|
|
text_encoder.to('cpu')
|
|
torch.cuda.empty_cache()
|
|
|
|
bsz, channel, num_frames, height, width = latents.size()
|
|
noise = torch.randn(latents.size(), device=latents.device, generator=torch_rng, dtype=weight_dtype)
|
|
|
|
# Sample sigmas from the shifted table.
|
|
if not args.uniform_sampling:
|
|
u = compute_density_for_timestep_sampling(
|
|
weighting_scheme=args.weighting_scheme,
|
|
batch_size=bsz,
|
|
logit_mean=args.logit_mean,
|
|
logit_std=args.logit_std,
|
|
mode_scale=args.mode_scale,
|
|
)
|
|
indices = (u * args.train_sampling_steps).long()
|
|
else:
|
|
indices = idx_sampling(bsz, generator=torch_rng, device=latents.device)
|
|
indices = indices.long().cpu()
|
|
sigmas = sigma_table[indices].to(device=latents.device)
|
|
|
|
# Add noise according to flow matching.
|
|
# zt = (1 - sigma) * x0 + sigma * noise
|
|
sigmas = sigmas.view(-1, 1, 1, 1, 1).to(latents.dtype)
|
|
noisy_latents = (1.0 - sigmas) * latents + sigmas * noise
|
|
|
|
# Clamp the condition frame(s) back to the clean latent, matching
|
|
# LingBotVideoPipeline._apply_inpainting at inference. The loss is
|
|
# masked on these frames accordingly.
|
|
cond_t = cond_latent.shape[2]
|
|
frame_mask = torch.ones_like(latents[:, :, :1].expand(-1, -1, num_frames, -1, -1))
|
|
if num_frames > cond_t:
|
|
noisy_latents[:, :, :cond_t] = cond_latent.to(noisy_latents.dtype)
|
|
frame_mask[:, :, :cond_t] = 0.0
|
|
else:
|
|
# Degenerate single-latent-frame sample: nothing to generate.
|
|
frame_mask[:, :, :] = 0.0
|
|
|
|
# Predict the noise residual
|
|
timesteps = (sigmas.reshape(-1) * 1000.0).float()
|
|
with torch.cuda.amp.autocast(dtype=weight_dtype), torch.cuda.device(device=accelerator.device):
|
|
noise_pred = transformer3d(
|
|
noisy_latents,
|
|
timesteps,
|
|
prompt_embeds.to(weight_dtype),
|
|
encoder_attention_mask=prompt_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
target = noise - latents
|
|
|
|
weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas.squeeze())
|
|
weighting = weighting.view(-1, 1, 1, 1, 1).float()
|
|
|
|
mse_loss = F.mse_loss(noise_pred.float(), target.float(), reduction='none')
|
|
masked_loss = mse_loss * frame_mask * weighting
|
|
denom = frame_mask.sum() * channel * height * width
|
|
loss = masked_loss.sum() / denom.clamp(min=1.0)
|
|
loss = loss.mean()
|
|
|
|
# Gather the losses across all processes for logging (if we use distributed training).
|
|
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
|
|
train_loss += avg_loss.item() / args.gradient_accumulation_steps
|
|
|
|
# Backpropagate
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
if not args.use_fsdp:
|
|
trainable_params_grads = [p.grad for p in trainable_params if p.grad is not None]
|
|
trainable_params_total_norm = torch.norm(torch.stack([torch.norm(g.detach(), 2) for g in trainable_params_grads]), 2)
|
|
max_grad_norm = linear_decay(args.max_grad_norm * args.initial_grad_norm_ratio, args.max_grad_norm, args.abnormal_norm_clip_start, global_step)
|
|
if trainable_params_total_norm / max_grad_norm > 5 and global_step > args.abnormal_norm_clip_start:
|
|
actual_max_grad_norm = max_grad_norm / min((trainable_params_total_norm / max_grad_norm), 10)
|
|
else:
|
|
actual_max_grad_norm = max_grad_norm
|
|
else:
|
|
actual_max_grad_norm = args.max_grad_norm
|
|
|
|
if not args.use_fsdp and args.report_model_info and accelerator.is_main_process:
|
|
if trainable_params_total_norm > 1 and global_step > args.abnormal_norm_clip_start:
|
|
for name, param in transformer3d.named_parameters():
|
|
if param.requires_grad:
|
|
writer.add_scalar(f'gradients/before_clip_norm/{name}', param.grad.norm(), global_step=global_step)
|
|
|
|
norm_sum = accelerator.clip_grad_norm_(trainable_params, actual_max_grad_norm)
|
|
if not args.use_fsdp and args.report_model_info and accelerator.is_main_process:
|
|
writer.add_scalar(f'gradients/norm_sum', norm_sum, global_step=global_step)
|
|
writer.add_scalar(f'gradients/actual_max_grad_norm', actual_max_grad_norm, global_step=global_step)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
|
|
if args.use_ema:
|
|
ema_transformer3d.step(transformer3d.parameters())
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
accelerator.log({"train_loss": train_loss}, step=global_step)
|
|
train_loss = 0.0
|
|
|
|
if global_step % args.checkpointing_steps == 0:
|
|
if args.use_fsdp or accelerator.is_main_process:
|
|
# _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
|
|
if args.checkpoints_total_limit is not None:
|
|
checkpoints = os.listdir(args.output_dir)
|
|
checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
|
|
checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
|
|
|
|
# before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
|
|
if len(checkpoints) >= args.checkpoints_total_limit:
|
|
num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
|
|
removing_checkpoints = checkpoints[0:num_to_remove]
|
|
|
|
logger.info(
|
|
f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
|
|
)
|
|
logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
|
|
|
|
for removing_checkpoint in removing_checkpoints:
|
|
removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
|
|
shutil.rmtree(removing_checkpoint)
|
|
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
# Keep the checkpoint out of the progress bar rate: a minute-long save would
|
|
# otherwise land in the next step's interval and be shown as a slow step. The
|
|
# save also stages the whole state in host RAM (safetensors materializes every
|
|
# tensor as bytes) and leaves the freed blocks in the allocator caches, so the
|
|
# cache flushes run inside the same window.
|
|
with progress_bar.paused():
|
|
accelerator.save_state(save_path)
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
if args.validation_prompts is not None and global_step % args.validation_steps == 0:
|
|
with progress_bar.paused():
|
|
if args.use_ema:
|
|
# Store the transformer parameters temporarily and load the EMA parameters to perform inference.
|
|
ema_transformer3d.store(transformer3d.parameters())
|
|
ema_transformer3d.copy_to(transformer3d.parameters())
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
processor,
|
|
transformer3d,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
if args.use_ema:
|
|
# Switch back to the original transformer3d parameters.
|
|
ema_transformer3d.restore(transformer3d.parameters())
|
|
|
|
logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
if args.validation_prompts is not None and epoch % args.validation_epochs == 0:
|
|
with progress_bar.paused():
|
|
if args.use_ema:
|
|
# Store the transformer parameters temporarily and load the EMA parameters to perform inference.
|
|
ema_transformer3d.store(transformer3d.parameters())
|
|
ema_transformer3d.copy_to(transformer3d.parameters())
|
|
log_validation(
|
|
vae,
|
|
text_encoder,
|
|
processor,
|
|
transformer3d,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
if args.use_ema:
|
|
# Switch back to the original transformer3d parameters.
|
|
ema_transformer3d.restore(transformer3d.parameters())
|
|
|
|
# Close the bar before the end-of-run checkpoint: tqdm keeps redrawing a live bar whenever
|
|
# something else writes to the console. PauseAwareTqdm.close() rebases the closing line onto
|
|
# the smoothed rate, so the worker warm-up and the first dataloader fetch do not dilute it.
|
|
progress_bar.close()
|
|
|
|
# Create the pipeline using the trained modules and save it.
|
|
accelerator.wait_for_everyone()
|
|
if args.use_ema and args.use_fsdp:
|
|
# Under FSDP every rank must write its own shards, and the shards only
|
|
# exist while the model is still wrapped, so this runs before the
|
|
# `unwrap_model` below.
|
|
ema_transformer3d.copy_to(transformer3d.parameters())
|
|
if accelerator.is_main_process:
|
|
transformer3d = unwrap_model(transformer3d)
|
|
if args.use_ema and not args.use_fsdp:
|
|
ema_transformer3d.copy_to(transformer3d.parameters())
|
|
|
|
if args.use_fsdp or accelerator.is_main_process:
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(save_path)
|
|
logger.info(f"Saved state to {save_path}")
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|