1420 lines
62 KiB
Python
Executable File
1420 lines
62 KiB
Python
Executable File
"""Modified from VideoX-Fun/scripts/cogvideox_fun/train_lora.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 json
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import logging
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import math
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import os
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import random
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import shutil
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import sys
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from contextlib import contextmanager
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from typing import List, Optional, Union
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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.utils.checkpoint
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import torchvision
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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.state import AcceleratorState
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from accelerate.utils import ProjectConfiguration, set_seed
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try:
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from decord import VideoReader
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except ImportError:
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from videox_fun.data.utils import AVVideoReader as VideoReader
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from diffusers import CogVideoXDPMScheduler, DDIMScheduler
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from diffusers.optimization import get_scheduler
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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 tqdm.auto import tqdm
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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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import videox_fun.reward.reward_fn as reward_fn
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from videox_fun.models import (AutoencoderKLCogVideoX,
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CogVideoXTransformer3DModel, T5EncoderModel,
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T5Tokenizer)
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from videox_fun.pipeline.pipeline_cogvideox_fun_inpaint import (
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CogVideoXFunInpaintPipeline, get_3d_rotary_pos_embed,
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get_resize_crop_region_for_grid)
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from videox_fun.utils.lora_utils import create_network, merge_lora
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from videox_fun.utils.utils import get_image_to_video_latent, save_videos_grid
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if is_wandb_available():
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import wandb
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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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@contextmanager
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def video_reader(*args, **kwargs):
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"""A context manager to solve the memory leak of decord.
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"""
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vr = VideoReader(*args, **kwargs)
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try:
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yield vr
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finally:
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del vr
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gc.collect()
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def log_validation(vae, text_encoder, tokenizer, transformer3d, network,
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loss_fn, args, accelerator, weight_dtype, global_step, validation_prompts_idx
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):
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try:
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logger.info("Running validation... ")
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transformer3d_val = CogVideoXTransformer3DModel.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="transformer",
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).to(weight_dtype)
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transformer3d_val.load_state_dict(accelerator.unwrap_model(transformer3d).state_dict())
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scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
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if args.vae_gradient_checkpointing or args.low_vram:
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# Initialize a new vae if gradient checkpointing is enabled.
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vae = AutoencoderKLCogVideoX.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
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).to(weight_dtype)
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pipeline = CogVideoXFunInpaintPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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transformer=transformer3d_val,
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scheduler=scheduler,
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torch_dtype=weight_dtype,
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)
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if args.low_vram:
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pipeline.enable_model_cpu_offload()
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else:
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pipeline = pipeline.to(device=accelerator.device)
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lora_state_dict = accelerator.unwrap_model(network).state_dict()
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pipeline = merge_lora(pipeline, None, 1, accelerator.device, state_dict=lora_state_dict, transformer_only=True)
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to_tensor = torchvision.transforms.ToTensor()
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validation_loss, validation_reward = 0, 0
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for i in range(len(validation_prompts_idx)):
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validation_idx, validation_prompt = validation_prompts_idx[i]
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with torch.no_grad():
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with torch.autocast("cuda", dtype=weight_dtype):
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temporal_compression_ratio = vae.config.temporal_compression_ratio
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video_length = 1
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if args.video_length != 1:
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video_length += int((args.video_length - 1) // temporal_compression_ratio * temporal_compression_ratio)
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sample_size = [args.validation_sample_height, args.validation_sample_width]
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input_video, input_video_mask, _ = get_image_to_video_latent(
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None, None, video_length=video_length, sample_size=sample_size
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)
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if args.seed is None:
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generator = None
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else:
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generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
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sample = pipeline(
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validation_prompt,
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num_frames = video_length,
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negative_prompt = "The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory. Distortion. ",
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height = args.validation_sample_height,
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width = args.validation_sample_width,
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guidance_scale = 7,
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generator = generator,
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video = input_video,
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mask_video = input_video_mask,
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).videos
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sample_saved_name = f"validation_sample/sample-{global_step}-{validation_idx}.mp4"
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sample_saved_path = os.path.join(args.output_dir, sample_saved_name)
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save_videos_grid(sample, sample_saved_path, fps=8)
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num_sampled_frames = 4
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sampled_frames_list = []
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with video_reader(sample_saved_path) as vr:
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sampled_frame_idx_list = np.linspace(0, len(vr), num_sampled_frames, endpoint=False, dtype=int)
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sampled_frame_list = vr.get_batch(sampled_frame_idx_list).asnumpy()
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sampled_frames = torch.stack([to_tensor(frame) for frame in sampled_frame_list], dim=0)
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sampled_frames_list.append(sampled_frames)
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sampled_frames = torch.stack(sampled_frames_list)
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sampled_frames = rearrange(sampled_frames, "b t c h w -> b c t h w")
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loss, reward = loss_fn(sampled_frames, [validation_prompt])
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validation_loss, validation_reward = validation_loss + loss, validation_reward + reward
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validation_loss = validation_loss / len(validation_prompts_idx)
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validation_reward = validation_reward / len(validation_prompts_idx)
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del pipeline
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del transformer3d_val
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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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return validation_loss, validation_reward
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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 with info {e}")
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return None, None
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def load_prompts(prompt_path, prompt_column="prompt", start_idx=None, end_idx=None):
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prompt_list = []
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if prompt_path.endswith(".txt"):
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with open(prompt_path, "r") as f:
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for line in f:
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prompt_list.append(line.strip())
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elif prompt_path.endswith(".jsonl"):
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with open(prompt_path, "r") as f:
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for line in f.readlines():
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item = json.loads(line)
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prompt_list.append(item[prompt_column])
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else:
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raise ValueError("The prompt_path must end with .txt or .jsonl.")
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prompt_list = prompt_list[start_idx:end_idx]
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return prompt_list
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# Modified from cogvideox.pipeline.pipeline_cogvideox_inpaint.CogVideoXFunInpaintPipeline._get_t5_prompt_embeds
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def get_t5_prompt_embeds(
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tokenizer: T5Tokenizer,
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text_encoder: T5EncoderModel,
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prompt: Union[str, List[str]] = None,
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num_videos_per_prompt: int = 1,
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max_sequence_length: int = 226,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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text_inputs = tokenizer(
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prompt,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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add_special_tokens=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because `max_sequence_length` is set to "
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f" {max_sequence_length} tokens: {removed_text}"
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)
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prompt_embeds = text_encoder(text_input_ids.to(device))[0]
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prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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_, seq_len, _ = prompt_embeds.shape
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prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
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return prompt_embeds
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# Modified from cogvideox.pipeline.pipeline_cogvideox_inpaint.CogVideoXFunInpaintPipeline.encode_prompt
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def encode_prompt(
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tokenizer: T5Tokenizer,
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text_encoder: T5EncoderModel,
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prompt: Union[str, List[str]],
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negative_prompt: Optional[Union[str, List[str]]] = None,
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do_classifier_free_guidance: bool = True,
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num_videos_per_prompt: int = 1,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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max_sequence_length: int = 226,
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device: Optional[torch.device] = None,
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dtype: Optional[torch.dtype] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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"""
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prompt = [prompt] if isinstance(prompt, str) else prompt
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if prompt is not None:
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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prompt_embeds = get_t5_prompt_embeds(
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tokenizer,
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text_encoder,
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prompt=prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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max_sequence_length=max_sequence_length,
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device=device,
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dtype=dtype,
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)
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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negative_prompt = negative_prompt or ""
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negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
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if prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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negative_prompt_embeds = get_t5_prompt_embeds(
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tokenizer,
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text_encoder,
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prompt=negative_prompt,
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num_videos_per_prompt=num_videos_per_prompt,
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max_sequence_length=max_sequence_length,
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device=device,
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dtype=dtype,
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)
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return prompt_embeds, negative_prompt_embeds
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# Modified from cogvideox.pipeline.pipeline_cogvideox_inpaint.CogVideoXFunInpaintPipeline.prepare_extra_step_kwargs
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def prepare_extra_step_kwargs(scheduler, generator, eta):
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# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
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# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
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# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
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# and should be between [0, 1]
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import inspect
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accepts_eta = "eta" in set(inspect.signature(scheduler.step).parameters.keys())
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extra_step_kwargs = {}
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if accepts_eta:
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extra_step_kwargs["eta"] = eta
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# check if the scheduler accepts generator
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accepts_generator = "generator" in set(inspect.signature(scheduler.step).parameters.keys())
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if accepts_generator:
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extra_step_kwargs["generator"] = generator
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return extra_step_kwargs
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# Modified from cogvideox.pipeline.pipeline_cogvideox_inpaint.CogVideoXFunInpaintPipeline._prepare_rotary_positional_embeddings
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def prepare_rotary_positional_embeddings(
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height: int,
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width: int,
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num_frames: int,
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vae_scale_factor_spatial: int = 8,
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patch_size: int = 2,
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patch_size_t: int = 2,
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attention_head_dim: int = 64,
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sample_height: int = 720,
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sample_width: int = 480,
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device: torch.device = "cpu"
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):
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grid_height = height // (vae_scale_factor_spatial * patch_size)
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grid_width = width // (vae_scale_factor_spatial * patch_size)
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base_size_height = sample_height // patch_size
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base_size_width = sample_width // patch_size
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if patch_size_t is None:
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# CogVideoX 1.0
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grid_crops_coords = get_resize_crop_region_for_grid(
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(grid_height, grid_width), base_size_width, base_size_height
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)
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freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
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embed_dim=attention_head_dim,
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crops_coords=grid_crops_coords,
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grid_size=(grid_height, grid_width),
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temporal_size=num_frames,
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use_real=True,
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)
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else:
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# CogVideoX 1.5
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base_num_frames = (num_frames + patch_size_t - 1) // patch_size_t
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freqs_cos, freqs_sin = get_3d_rotary_pos_embed(
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embed_dim=attention_head_dim,
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crops_coords=None,
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grid_size=(grid_height, grid_width),
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temporal_size=base_num_frames,
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grid_type="slice",
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max_size=(base_size_height, base_size_width),
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)
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freqs_cos = freqs_cos.to(device=device)
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freqs_sin = freqs_sin.to(device=device)
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return freqs_cos, freqs_sin
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def parse_args():
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parser = argparse.ArgumentParser(description="Simple example of a 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 pretrained model or model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default=None,
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required=False,
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help="Revision of pretrained model identifier from huggingface.co/models.",
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)
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parser.add_argument(
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"--variant",
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type=str,
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default=None,
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help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
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)
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parser.add_argument(
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"--validation_prompt_path",
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type=str,
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default=None,
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help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
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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_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--validation_batch_size",
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type=int,
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default=1,
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help=("A set of prompts evaluated every `--validation_epochs` and logged to `--report_to`."),
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)
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parser.add_argument(
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"--validation_sample_height",
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type=int,
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default=512,
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help="The height of sampling videos in validation.",
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)
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parser.add_argument(
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"--validation_sample_width",
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type=int,
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default=512,
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help="The width of sampling videos in validation.",
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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="sd-model-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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"--use_came",
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action="store_true",
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help="whether to use came",
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)
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parser.add_argument(
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"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
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)
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parser.add_argument("--num_train_epochs", type=int, default=200)
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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,
|
|
help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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|
parser.add_argument(
|
|
"--gradient_checkpointing",
|
|
action="store_true",
|
|
help="Whether or not to use gradient checkpointing (for DiT) to save memory at the expense of slower backward pass.",
|
|
)
|
|
parser.add_argument(
|
|
"--vae_gradient_checkpointing",
|
|
action="store_true",
|
|
help="Whether or not to use gradient checkpointing (for VAE) to save memory at the expense of slower backward pass.",
|
|
)
|
|
parser.add_argument(
|
|
"--learning_rate",
|
|
type=float,
|
|
default=1e-4,
|
|
help="Initial learning rate (after the potential warmup period) to use.",
|
|
)
|
|
parser.add_argument(
|
|
"--scale_lr",
|
|
action="store_true",
|
|
default=False,
|
|
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
|
|
)
|
|
parser.add_argument(
|
|
"--lr_scheduler",
|
|
type=str,
|
|
default="constant",
|
|
help=(
|
|
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
|
|
' "constant", "constant_with_warmup"]'
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
|
|
)
|
|
parser.add_argument(
|
|
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
|
|
)
|
|
parser.add_argument(
|
|
"--allow_tf32",
|
|
action="store_true",
|
|
help=(
|
|
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
|
|
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
|
|
),
|
|
)
|
|
parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
|
|
parser.add_argument(
|
|
"--non_ema_revision",
|
|
type=str,
|
|
default=None,
|
|
required=False,
|
|
help=(
|
|
"Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or"
|
|
" remote repository specified with --pretrained_model_name_or_path."
|
|
),
|
|
)
|
|
parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
|
|
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
|
|
parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
|
|
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
|
|
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
|
|
parser.add_argument(
|
|
"--prediction_type",
|
|
type=str,
|
|
default=None,
|
|
help="The prediction_type that shall be used for training. Choose between 'epsilon' or 'v_prediction' or leave `None`. If left to `None` the default prediction type of the scheduler: `noise_scheduler.config.prediciton_type` is chosen.",
|
|
)
|
|
parser.add_argument(
|
|
"--logging_dir",
|
|
type=str,
|
|
default="logs",
|
|
help=(
|
|
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
|
|
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--mixed_precision",
|
|
type=str,
|
|
default=None,
|
|
choices=["no", "fp16", "bf16"],
|
|
help=(
|
|
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
|
|
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
|
|
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--report_to",
|
|
type=str,
|
|
default="tensorboard",
|
|
help=(
|
|
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
|
|
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
|
|
),
|
|
)
|
|
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
|
parser.add_argument(
|
|
"--checkpointing_steps",
|
|
type=int,
|
|
default=500,
|
|
help=(
|
|
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
|
|
" training using `--resume_from_checkpoint`."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--checkpoints_total_limit",
|
|
type=int,
|
|
default=None,
|
|
help=("Max number of checkpoints to store."),
|
|
)
|
|
parser.add_argument(
|
|
"--resume_from_checkpoint",
|
|
type=str,
|
|
default=None,
|
|
help=(
|
|
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
|
|
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--validation_epochs",
|
|
type=int,
|
|
default=5,
|
|
help="Run validation every X epochs.",
|
|
)
|
|
parser.add_argument(
|
|
"--validation_steps",
|
|
type=int,
|
|
default=2000,
|
|
help="Run validation every X steps.",
|
|
)
|
|
parser.add_argument(
|
|
"--tracker_project_name",
|
|
type=str,
|
|
default="text2image-fine-tune",
|
|
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"
|
|
),
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--rank",
|
|
type=int,
|
|
default=128,
|
|
help=("The dimension of the LoRA update matrices."),
|
|
)
|
|
parser.add_argument(
|
|
"--network_alpha",
|
|
type=int,
|
|
default=64,
|
|
help=("The dimension of the LoRA update matrices."),
|
|
)
|
|
parser.add_argument(
|
|
"--train_text_encoder",
|
|
action="store_true",
|
|
help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
|
|
)
|
|
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("--save_state", action="store_true", help="Whether or not to save state.")
|
|
|
|
parser.add_argument(
|
|
"--use_deepspeed", action="store_true", help="Whether or not to use deepspeed."
|
|
)
|
|
parser.add_argument(
|
|
"--low_vram", action="store_true", help="Whether enable low_vram mode."
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--prompt_path",
|
|
type=str,
|
|
default="normal",
|
|
help="The path to the training prompt file.",
|
|
)
|
|
parser.add_argument(
|
|
'--train_sample_height',
|
|
type=int,
|
|
default=384,
|
|
help='The height of sampling videos in training'
|
|
)
|
|
parser.add_argument(
|
|
'--train_sample_width',
|
|
type=int,
|
|
default=672,
|
|
help='The width of sampling videos in training'
|
|
)
|
|
parser.add_argument(
|
|
"--video_length",
|
|
type=int,
|
|
default=49,
|
|
help="The number of frames to generate in training and validation."
|
|
)
|
|
parser.add_argument(
|
|
'--eta',
|
|
type=float,
|
|
default=0.0,
|
|
help='eta parameter for the DDIM sampler. this controls the amount of noise injected into the sampling process, '
|
|
'with 0.0 being fully deterministic and 1.0 being equivalent to the DDPM sampler.'
|
|
)
|
|
parser.add_argument(
|
|
"--guidance_scale",
|
|
type=float,
|
|
default=6.0,
|
|
help="The classifier-free diffusion guidance."
|
|
)
|
|
parser.add_argument(
|
|
"--num_inference_steps",
|
|
type=int,
|
|
default=50,
|
|
help="The number of denoising steps in training and validation."
|
|
)
|
|
parser.add_argument(
|
|
"--num_decoded_latents",
|
|
type=int,
|
|
default=3,
|
|
help="The number of latents to be decoded."
|
|
)
|
|
parser.add_argument(
|
|
"--num_sampled_frames",
|
|
type=int,
|
|
default=None,
|
|
help="The number of sampled frames for the reward function."
|
|
)
|
|
parser.add_argument(
|
|
"--reward_fn",
|
|
type=str,
|
|
default="HPSReward",
|
|
help='The reward function.'
|
|
)
|
|
parser.add_argument(
|
|
"--reward_fn_kwargs",
|
|
type=str,
|
|
default=None,
|
|
help='The keyword arguments of the reward function.'
|
|
)
|
|
parser.add_argument(
|
|
"--backprop",
|
|
action="store_true",
|
|
default=False,
|
|
help="Whether to use the reward backprop training mode.",
|
|
)
|
|
parser.add_argument(
|
|
"--backprop_step_list",
|
|
nargs="+",
|
|
type=int,
|
|
default=None,
|
|
help="The preset step list for reward backprop. If provided, overrides `backprop_strategy`."
|
|
)
|
|
parser.add_argument(
|
|
"--backprop_strategy",
|
|
choices=["last", "tail", "uniform", "random"],
|
|
default="last",
|
|
help="The strategy for reward backprop."
|
|
)
|
|
parser.add_argument(
|
|
"--stop_latent_model_input_gradient",
|
|
action="store_true",
|
|
default=False,
|
|
help="Whether to stop the gradient of the latents during reward backprop.",
|
|
)
|
|
parser.add_argument(
|
|
"--backprop_random_start_step",
|
|
type=int,
|
|
default=0,
|
|
help="The random start step for reward backprop. Only used when `backprop_strategy` is random."
|
|
)
|
|
parser.add_argument(
|
|
"--backprop_random_end_step",
|
|
type=int,
|
|
default=50,
|
|
help="The random end step for reward backprop. Only used when `backprop_strategy` is random."
|
|
)
|
|
parser.add_argument(
|
|
"--backprop_num_steps",
|
|
type=int,
|
|
default=5,
|
|
help="The number of steps for backprop. Only used when `backprop_strategy` is tail/uniform/random."
|
|
)
|
|
|
|
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
|
|
|
|
# default to using the same revision for the non-ema model if not specified
|
|
if args.non_ema_revision is None:
|
|
args.non_ema_revision = args.revision
|
|
|
|
return args
|
|
|
|
|
|
def main():
|
|
args = parse_args()
|
|
|
|
if args.report_to == "wandb" and args.hub_token is not None:
|
|
raise ValueError(
|
|
"You cannot use both --report_to=wandb and --hub_token due to a security risk of exposing your token."
|
|
" Please use `huggingface-cli login` to authenticate with the Hub."
|
|
)
|
|
|
|
if args.non_ema_revision is not None:
|
|
deprecate(
|
|
"non_ema_revision!=None",
|
|
"0.15.0",
|
|
message=(
|
|
"Downloading 'non_ema' weights from revision branches of the Hub is deprecated. Please make sure to"
|
|
" use `--variant=non_ema` instead."
|
|
),
|
|
)
|
|
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,
|
|
)
|
|
|
|
# Sanity check for validation
|
|
do_validation = (args.validation_prompt_path is not None or args.validation_prompts is not None)
|
|
if do_validation:
|
|
if not (os.path.exists(args.validation_prompt_path) or args.validation_prompt_path.endswith(".txt")):
|
|
raise ValueError("The `--validation_prompt_path` must be a txt file containing prompts.")
|
|
if args.validation_batch_size < accelerator.num_processes or args.validation_batch_size % accelerator.num_processes != 0:
|
|
raise ValueError("The `--validation_batch_size` must be divisible by the number of processes.")
|
|
|
|
# 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, device_specific=True)
|
|
|
|
# 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 weigths (vae, non-lora text_encoder and non-lora transformer3d) 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 scheduler, tokenizer and models.
|
|
# Use DDIM instead of DDPM to sample training videos.
|
|
noise_scheduler = DDIMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
noise_scheduler.set_timesteps(args.num_inference_steps, device=accelerator.device)
|
|
|
|
tokenizer = T5Tokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
|
|
)
|
|
|
|
def deepspeed_zero_init_disabled_context_manager():
|
|
"""
|
|
returns either a context list that includes one that will disable zero.Init or an empty context list
|
|
"""
|
|
deepspeed_plugin = 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)]
|
|
|
|
# Currently Accelerate doesn't know how to handle multiple models under Deepspeed ZeRO stage 3.
|
|
# For this to work properly all models must be run through `accelerate.prepare`. But accelerate
|
|
# will try to assign the same optimizer with the same weights to all models during
|
|
# `deepspeed.initialize`, which of course doesn't work.
|
|
#
|
|
# For now the following workaround will partially support Deepspeed ZeRO-3, by excluding the 2
|
|
# frozen models from being partitioned during `zero.Init` which gets called during
|
|
# `from_pretrained` So CLIPTextModel and AutoencoderKL will not enjoy the parameter sharding
|
|
# across multiple gpus and only UNet2DConditionModel will get ZeRO sharded.
|
|
with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
|
|
text_encoder = T5EncoderModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, variant=args.variant,
|
|
torch_dtype=weight_dtype
|
|
)
|
|
|
|
vae = AutoencoderKLCogVideoX.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision, variant=args.variant
|
|
)
|
|
|
|
transformer3d = CogVideoXTransformer3DModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="transformer"
|
|
)
|
|
|
|
# Freeze vae and text_encoder and set transformer3d to trainable
|
|
vae.requires_grad_(False)
|
|
text_encoder.requires_grad_(False)
|
|
transformer3d.requires_grad_(False)
|
|
|
|
# Lora will work with this...
|
|
network = create_network(
|
|
1.0,
|
|
args.rank,
|
|
args.network_alpha,
|
|
text_encoder,
|
|
transformer3d,
|
|
neuron_dropout=None,
|
|
add_lora_in_attn_temporal=True,
|
|
)
|
|
network.apply_to(text_encoder, transformer3d, args.train_text_encoder, True)
|
|
|
|
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, safe_open
|
|
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, safe_open
|
|
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
|
|
|
|
vae_scale_factor_spatial = 2 ** (len(vae.config.block_out_channels) - 1)
|
|
vae_scale_factor_temporal = vae.config.temporal_compression_ratio
|
|
num_channels_latent = vae.config.latent_channels
|
|
|
|
# `accelerate` 0.16.0 will have better support for customized saving
|
|
if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
|
|
# 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:
|
|
safetensor_save_path = os.path.join(output_dir, f"lora_diffusion_pytorch_model.safetensors")
|
|
save_model(safetensor_save_path, accelerator.unwrap_model(models[-1]))
|
|
if not args.use_deepspeed:
|
|
for _ in range(len(weights)):
|
|
weights.pop()
|
|
|
|
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()
|
|
|
|
if args.vae_gradient_checkpointing:
|
|
# Since 3D casual VAE need a cache to decode all latents autoregressively, .Thus, gradient checkpointing can only be
|
|
# enabled when decoding the first batch (i.e. the first three) of latents, in which case the cache is not being used.
|
|
if args.num_decoded_latents > 3:
|
|
raise ValueError("The vae_gradient_checkpointing is not supported for num_decoded_latents > 3.")
|
|
vae.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
|
|
elif args.use_came:
|
|
try:
|
|
from came_pytorch import CAME
|
|
except Exception:
|
|
raise ImportError(
|
|
"Please install came_pytorch to use CAME. You can do so by running `pip install came_pytorch`"
|
|
)
|
|
|
|
optimizer_cls = CAME
|
|
else:
|
|
optimizer_cls = torch.optim.AdamW
|
|
|
|
logging.info("Add network parameters")
|
|
trainable_params = list(filter(lambda p: p.requires_grad, network.parameters()))
|
|
trainable_params_optim = network.prepare_optimizer_params(args.learning_rate / 2, args.learning_rate, args.learning_rate)
|
|
|
|
if args.use_came:
|
|
optimizer = optimizer_cls(
|
|
trainable_params_optim,
|
|
lr=args.learning_rate,
|
|
betas=(0.9, 0.999, 0.9999),
|
|
eps=(1e-30, 1e-16)
|
|
)
|
|
else:
|
|
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,
|
|
)
|
|
|
|
# loss function
|
|
reward_fn_kwargs = {}
|
|
if args.reward_fn_kwargs is not None:
|
|
reward_fn_kwargs = json.loads(args.reward_fn_kwargs)
|
|
if accelerator.is_main_process:
|
|
# Check if the model is downloaded in the main process.
|
|
loss_fn = getattr(reward_fn, args.reward_fn)(device="cpu", dtype=weight_dtype, **reward_fn_kwargs)
|
|
accelerator.wait_for_everyone()
|
|
loss_fn = getattr(reward_fn, args.reward_fn)(device=accelerator.device, dtype=weight_dtype, **reward_fn_kwargs)
|
|
|
|
# Get RL training prompts
|
|
prompt_list = load_prompts(args.prompt_path)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(prompt_list) / 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`.
|
|
network, optimizer, lr_scheduler = accelerator.prepare(
|
|
network, optimizer, lr_scheduler
|
|
)
|
|
|
|
# Move text_encode and vae to gpu and cast to weight_dtype
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
transformer3d.to(accelerator.device, dtype=weight_dtype)
|
|
text_encoder.to(accelerator.device)
|
|
|
|
# Enable auto split process for vae
|
|
vae.enable_auto_split_process()
|
|
|
|
# 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(prompt_list) / args.train_batch_size / 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(prompt_list)}")
|
|
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:
|
|
from safetensors.torch import load_file, safe_open
|
|
state_dict = load_file(os.path.join(os.path.join(args.output_dir, path), "lora_diffusion_pytorch_model.safetensors"))
|
|
m, u = accelerator.unwrap_model(network).load_state_dict(state_dict, strict=False)
|
|
print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
|
|
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
accelerator.load_state(os.path.join(args.output_dir, path))
|
|
global_step = int(path.split("-")[1])
|
|
|
|
initial_global_step = global_step
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
else:
|
|
initial_global_step = 0
|
|
|
|
# function for saving/removing
|
|
def save_model(ckpt_file, unwrapped_nw):
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
accelerator.print(f"\nsaving checkpoint: {ckpt_file}")
|
|
unwrapped_nw.save_weights(ckpt_file, weight_dtype, None)
|
|
|
|
progress_bar = tqdm(
|
|
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,
|
|
)
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
train_loss = 0.0
|
|
train_reward = 0.0
|
|
# In the following training loop, randomly select training prompts and use the
|
|
# `CogVideoXFunInpaintPipeline` to sample videos, calculate rewards, and update the network.
|
|
for _ in range(num_update_steps_per_epoch):
|
|
# train_prompt = random.sample(prompt_list, args.train_batch_size)
|
|
train_prompt = random.choices(prompt_list, k=args.train_batch_size)
|
|
logger.info(f"train_prompt: {train_prompt}")
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = args.guidance_scale > 1.0
|
|
|
|
# Reduce the vram by offload text encoders
|
|
if args.low_vram:
|
|
torch.cuda.empty_cache()
|
|
text_encoder.to(accelerator.device)
|
|
|
|
# Encode input prompt
|
|
prompt_embeds, negative_prompt_embeds = encode_prompt(
|
|
tokenizer,
|
|
text_encoder,
|
|
train_prompt,
|
|
do_classifier_free_guidance=do_classifier_free_guidance,
|
|
negative_prompt="",
|
|
dtype=weight_dtype,
|
|
device=accelerator.device,
|
|
)
|
|
if do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
|
|
|
# Reduce the vram by offload text encoders
|
|
if args.low_vram:
|
|
text_encoder.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
|
|
# Prepare timesteps
|
|
timesteps = noise_scheduler.timesteps
|
|
|
|
# Prepare latents
|
|
latent_shape = [
|
|
len(train_prompt),
|
|
(args.video_length - 1) // vae_scale_factor_temporal + 1,
|
|
num_channels_latent,
|
|
args.train_sample_height // vae_scale_factor_spatial,
|
|
args.train_sample_width // vae_scale_factor_spatial,
|
|
]
|
|
|
|
with accelerator.accumulate(transformer3d):
|
|
with accelerator.autocast():
|
|
latents = torch.randn(*latent_shape, device=accelerator.device, dtype=weight_dtype)
|
|
latents = latents * noise_scheduler.init_noise_sigma
|
|
|
|
mask_latents = torch.zeros_like(latents)[:, :, :1].to(latents.device, latents.dtype)
|
|
masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype)
|
|
mask_input = torch.cat([mask_latents] * 2) if do_classifier_free_guidance else mask_latents
|
|
masked_video_latents_input = (
|
|
torch.cat([masked_video_latents] * 2) if do_classifier_free_guidance else masked_video_latents
|
|
)
|
|
inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=2).to(latents.dtype)
|
|
|
|
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
|
|
# Prepare extra step kwargs.
|
|
extra_step_kwargs = prepare_extra_step_kwargs(noise_scheduler, generator, args.eta)
|
|
|
|
# Create rotary embeds if required
|
|
image_rotary_emb = (
|
|
prepare_rotary_positional_embeddings(
|
|
height = args.train_sample_height,
|
|
width = args.train_sample_width,
|
|
num_frames = latents.size(1),
|
|
vae_scale_factor_spatial = vae_scale_factor_spatial,
|
|
patch_size = unwrap_model(transformer3d).config.patch_size,
|
|
patch_size_t = unwrap_model(transformer3d).config.patch_size_t,
|
|
attention_head_dim = unwrap_model(transformer3d).config.attention_head_dim,
|
|
sample_height = unwrap_model(transformer3d).config.sample_height,
|
|
sample_width = unwrap_model(transformer3d).config.sample_width,
|
|
device = accelerator.device
|
|
)
|
|
if unwrap_model(transformer3d).config.use_rotary_positional_embeddings
|
|
else None
|
|
)
|
|
|
|
# Denoising loop
|
|
if args.backprop:
|
|
if args.backprop_step_list is None:
|
|
if args.backprop_strategy == "last":
|
|
backprop_step_list = [args.num_inference_steps - 1]
|
|
elif args.backprop_strategy == "tail":
|
|
backprop_step_list = list(range(args.num_inference_steps))[-args.backprop_num_steps:]
|
|
elif args.backprop_strategy == "uniform":
|
|
interval = args.num_inference_steps // args.backprop_num_steps
|
|
random_start = random.randint(0, interval)
|
|
backprop_step_list = [random_start + i * interval for i in range(args.backprop_num_steps)]
|
|
elif args.backprop_strategy == "random":
|
|
backprop_step_list = random.sample(
|
|
range(args.backprop_random_start_step, args.backprop_random_end_step + 1), args.backprop_num_steps
|
|
)
|
|
else:
|
|
raise ValueError(f"Invalid backprop strategy: {args.backprop_strategy}.")
|
|
else:
|
|
backprop_step_list = args.backprop_step_list
|
|
|
|
for i, t in enumerate(tqdm(timesteps)):
|
|
# for DPM-solver++
|
|
old_pred_original_sample = None
|
|
|
|
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
|
latent_model_input = noise_scheduler.scale_model_input(latent_model_input, t)
|
|
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latent_model_input.shape[0])
|
|
|
|
# Whether to enable DRTune: https://arxiv.org/abs/2405.00760
|
|
if args.stop_latent_model_input_gradient:
|
|
latent_model_input = latent_model_input.detach()
|
|
|
|
# predict noise model_output
|
|
noise_pred = transformer3d(
|
|
hidden_states=latent_model_input,
|
|
encoder_hidden_states=prompt_embeds,
|
|
timestep=timestep,
|
|
image_rotary_emb=image_rotary_emb,
|
|
return_dict=False,
|
|
inpaint_latents=inpaint_latents
|
|
)[0]
|
|
noise_pred = noise_pred.float()
|
|
|
|
# Optimize the denoising results only for the specified steps.
|
|
if i in backprop_step_list:
|
|
noise_pred = noise_pred
|
|
else:
|
|
noise_pred = noise_pred.detach()
|
|
|
|
# perform guidance
|
|
guidance_scale = args.guidance_scale
|
|
# if args.use_dynamic_cfg:
|
|
# guidance_scale = 1 + guidance_scale * (
|
|
# (1 - math.cos(math.pi * ((args.num_inference_steps - t.item()) / args.num_inference_steps) ** 5.0)) / 2
|
|
# )
|
|
if do_classifier_free_guidance:
|
|
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
|
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
if not isinstance(noise_scheduler, CogVideoXDPMScheduler):
|
|
latents = noise_scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
|
else:
|
|
latents, old_pred_original_sample = noise_scheduler.step(
|
|
noise_pred,
|
|
old_pred_original_sample,
|
|
t,
|
|
timesteps[i - 1] if i > 0 else None,
|
|
latents,
|
|
**extra_step_kwargs,
|
|
return_dict=False,
|
|
)
|
|
latents = latents.to(prompt_embeds.dtype)
|
|
|
|
# decode latents (tensor)
|
|
latents = latents.permute(0, 2, 1, 3, 4) # [B, C, T, H, W]
|
|
# Since the casual VAE decoding consumes a large amount of VRAM, and we need to keep the decoding
|
|
# operation within the computational graph. Thus, we only decode the first args.num_decoded_latents
|
|
# to calculate the reward.
|
|
sampled_frame_indices = list(range(args.num_decoded_latents))
|
|
sampled_latents = latents[:, :, sampled_frame_indices, :, :]
|
|
sampled_latents = 1 / vae.config.scaling_factor * sampled_latents
|
|
sampled_frames = vae.decode(sampled_latents).sample
|
|
sampled_frames = (sampled_frames / 2 + 0.5).clamp(0, 1) # [-1, 1] -> [0, 1]
|
|
|
|
if global_step % args.checkpointing_steps == 0:
|
|
saved_file = f"sample-{global_step}-{accelerator.process_index}.mp4"
|
|
save_videos_grid(
|
|
sampled_frames.to(torch.float32).detach().cpu(),
|
|
os.path.join(args.output_dir, "train_sample", saved_file),
|
|
fps=8
|
|
)
|
|
|
|
if args.num_sampled_frames is not None:
|
|
num_frames = sampled_frames.size(2) - 1
|
|
sampled_frames_indices = torch.linspace(0, num_frames, steps=args.num_sampled_frames).long()
|
|
sampled_frames = sampled_frames[:, :, sampled_frames_indices, :, :]
|
|
# compute loss and reward
|
|
loss, reward = loss_fn(sampled_frames, train_prompt)
|
|
|
|
# Gather the losses and rewards across all processes for logging (if we use distributed training).
|
|
avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
|
|
avg_reward = accelerator.gather(reward.repeat(args.train_batch_size)).mean()
|
|
train_loss += avg_loss.item() / args.gradient_accumulation_steps
|
|
train_reward += avg_reward.item() / args.gradient_accumulation_steps
|
|
|
|
# Backpropagate
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
total_norm = accelerator.clip_grad_norm_(trainable_params, args.max_grad_norm)
|
|
# If use_deepspeed, `total_norm` cannot be logged by accelerator.
|
|
if not args.use_deepspeed:
|
|
accelerator.log({"total_norm": total_norm}, step=global_step)
|
|
else:
|
|
if hasattr(optimizer, "optimizer") and hasattr(optimizer.optimizer, "_global_grad_norm"):
|
|
accelerator.log({"total_norm": optimizer.optimizer._global_grad_norm}, 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:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
accelerator.log({"train_loss": train_loss, "train_reward": train_reward}, step=global_step)
|
|
train_loss = 0.0
|
|
train_reward = 0.0
|
|
|
|
if global_step % args.checkpointing_steps == 0:
|
|
# DeepSpeed requires saving weights on every device; saving weights only on the main process would cause issues.
|
|
if args.use_deepspeed 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()
|
|
if not args.save_state:
|
|
safetensor_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}.safetensors")
|
|
save_model(safetensor_save_path, accelerator.unwrap_model(network))
|
|
logger.info(f"Saved safetensor to {safetensor_save_path}")
|
|
else:
|
|
accelerator_save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
|
accelerator.save_state(accelerator_save_path)
|
|
logger.info(f"Saved state to {accelerator_save_path}")
|
|
|
|
# Validation (distributed)
|
|
if do_validation and (global_step % args.validation_steps) == 0:
|
|
if args.validation_prompts is None and args.validation_prompt_path.endswith(".txt"):
|
|
validation_prompts = []
|
|
with open(args.validation_prompt_path, "r") as f:
|
|
for line in f:
|
|
validation_prompts.append(line.strip())
|
|
# Do not select randomly to ensure that `args.validation_prompts` is the same for each process.
|
|
args.validation_prompts = validation_prompts[:args.validation_batch_size]
|
|
|
|
validation_prompts_idx = [(i, p) for i, p in enumerate(args.validation_prompts)]
|
|
|
|
if hasattr(vae, "enable_cache_in_vae"):
|
|
vae.enable_cache_in_vae()
|
|
accelerator.wait_for_everyone()
|
|
with accelerator.split_between_processes(validation_prompts_idx) as splitted_prompts_idx:
|
|
validation_loss, validation_reward = log_validation(
|
|
vae,
|
|
text_encoder,
|
|
tokenizer,
|
|
transformer3d,
|
|
network,
|
|
loss_fn,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
splitted_prompts_idx
|
|
)
|
|
avg_validation_loss = accelerator.gather(validation_loss).mean()
|
|
avg_validation_reward = accelerator.gather(validation_reward).mean()
|
|
if accelerator.is_main_process:
|
|
accelerator.log({"validation_loss": avg_validation_loss, "validation_reward": avg_validation_reward}, step=global_step)
|
|
accelerator.wait_for_everyone()
|
|
|
|
logs = {"step_loss": loss.detach().item(), "step_reward": reward.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
|
|
progress_bar.set_postfix(**logs)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|