wehub-resource-sync
1170 lines
50 KiB
Python
1170 lines
50 KiB
Python
# ------------------------------------------
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# TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering
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# Paper Link: https://arxiv.org/abs/2311.16465
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# Code Link: https://github.com/microsoft/unilm/tree/master/textdiffuser-2
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# Copyright (c) Microsoft Corporation.
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# ------------------------------------------
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import argparse
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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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from pathlib import Path
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import glob
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import json
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import datasets
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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 datasets import load_dataset, Dataset
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from huggingface_hub import create_repo, upload_folder
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from packaging import version
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from torchvision import transforms
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from tqdm.auto import tqdm
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from transformers import CLIPTextModel, CLIPTokenizer
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import diffusers
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from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
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from diffusers.loaders import AttnProcsLayers
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from diffusers.models.attention_processor import LoRAAttnProcessor
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from diffusers.optimization import get_scheduler
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from diffusers.utils import check_min_version, is_wandb_available
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from diffusers.utils.import_utils import is_xformers_available
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from PIL import Image
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import string
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alphabet = string.digits + string.ascii_lowercase + string.ascii_uppercase + string.punctuation + ' ' # len(aphabet) = 95
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'''alphabet
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0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~
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'''
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logger = get_logger(__name__, log_level="INFO")
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def save_model_card(repo_id: str, images=None, base_model=str, dataset_name=str, repo_folder=None):
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img_str = ""
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for i, image in enumerate(images):
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image.save(os.path.join(repo_folder, f"image_{i}.png"))
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img_str += f"\n"
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yaml = f"""
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---
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license: creativeml-openrail-m
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base_model: {base_model}
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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- lora
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inference: true
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---
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"""
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model_card = f"""
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# LoRA text2image fine-tuning - {repo_id}
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These are LoRA adaption weights for {base_model}. The weights were fine-tuned on the {dataset_name} dataset. You can find some example images in the following. \n
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{img_str}
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"""
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with open(os.path.join(repo_folder, "README.md"), "w") as f:
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f.write(yaml + model_card)
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#### check whether two boxes can be merged in the x-axis
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def check_merge(box1, box2):
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x_center1, y_center1, x_min1, y_min1, x_max1, y_max1, pred1 = box1
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x_center2, y_center2, x_min2, y_min2, x_max2, y_max2, pred2 = box2
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if y_center1 >= y_min2 and y_center1 <= y_max2:
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if y_center2 >= y_min1 and y_center2 <= y_max1:
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pass
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else:
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return False
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else:
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return False
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distance1 = x_max2 - x_min1
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distance2 = (x_max2 - x_min2) + (x_max1 - x_min1)
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if distance2 / distance1 >= 0.8:
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if x_min1 < x_min2:
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pred = pred1 + ' ' + pred2
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else:
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pred = pred2 + ' ' + pred1
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x_min = min(x_min1, x_min2)
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y_min = min(y_min1, y_min2)
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x_max = max(x_max1, x_max2)
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y_max = max(y_max1, y_max2)
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x_center = (x_min + x_max) // 2
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y_center = (y_min + y_max) // 2
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return [x_center, y_center, x_min, y_min, x_max, y_max, pred]
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else:
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return False
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#### merge boxes for training at line-level instead of word-level
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def merge_boxes(boxes):
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results = []
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while True:
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if len(boxes) == 0:
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break
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flag = False
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sample = boxes[0]
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boxes.remove(sample)
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for item in boxes:
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result = check_merge(sample, item)
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if result:
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boxes.remove(item)
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boxes.append(result)
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boxes = sorted(boxes, key=lambda x: x[0])
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flag = True
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break
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else:
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pass
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if flag is False:
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results.append(sample)
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return results
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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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"--dataset_name",
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type=str,
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default='lambdalabs/pokemon-blip-captions',
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help=(
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"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
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" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
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" or to a folder containing files that 🤗 Datasets can understand."
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),
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The config of the Dataset, leave as None if there's only one config.",
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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=(
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"A folder containing the training data. Folder contents must follow the structure described in"
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" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
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" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
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),
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)
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parser.add_argument(
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"--image_column", type=str, default="image", help="The column of the dataset containing an image."
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)
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parser.add_argument(
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"--caption_column",
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type=str,
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default="text",
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help="The column of the dataset containing a caption or a list of captions.",
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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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"--output_dir",
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type=str,
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default="sd-model-finetuned-lora",
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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(
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"--cache_dir",
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type=str,
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default=None,
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help="The directory where the downloaded models and datasets will be stored.",
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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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"--resolution",
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type=int,
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default=512,
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help=(
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"The resolution for input images, all the images in the train/validation dataset will be resized to this"
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" resolution"
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),
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)
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parser.add_argument(
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"--center_crop",
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default=False,
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action="store_true",
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help=(
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"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
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" cropped. The images will be resized to the resolution first before cropping."
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),
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)
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parser.add_argument(
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"--random_flip",
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action="store_true",
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help="whether to randomly flip images horizontally",
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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=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-4, #### lora is trained with higher lr
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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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"--text_encoder_learning_rate",
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type=float,
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default=1e-5, #### the text encoder is trained with lower lr to avoid the forgetting
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help="Initial learning rate for the text encoder (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=0, 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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"--snr_gamma",
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type=float,
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default=None,
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help="SNR weighting gamma to be used if rebalancing the loss. Recommended value is 5.0. "
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"More details here: https://arxiv.org/abs/2303.09556.",
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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(
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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("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--prediction_type",
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type=str,
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default=None,
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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.",
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)
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parser.add_argument(
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"--hub_model_id",
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type=str,
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default=None,
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help="The name of the repository to keep in sync with the local `output_dir`.",
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)
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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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"--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=2500,
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help=(
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"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
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" 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=10, # should be decreased for saving space
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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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"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
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)
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parser.add_argument("--noise_offset", type=float, default=0, help="The scale of noise offset.")
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parser.add_argument(
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"--rank",
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type=int,
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default=4,
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help=("The dimension of the LoRA update matrices."),
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)
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parser.add_argument(
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"--vis_num",
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type=int,
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default=16,
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help=("The dimension of the LoRA update matrices."),
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)
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parser.add_argument(
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"--vis_interval",
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type=int,
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default=1000,
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help="The interval for visualization."
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)
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#### newly added parameters
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parser.add_argument(
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"--granularity",
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type=int,
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default=128, #### limit the coord range to 0~128 will make the feature space compact
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help="The granularity of coordinates, ranging from 1~512."
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)
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parser.add_argument(
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"--coord_mode",
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type=str,
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default='lt',
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choices=['lt', 'center', 'ltrb'], #### l, t, r, b stand for left, top, right, bottom
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help="The way to represent coordinates"
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)
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parser.add_argument(
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"--drop_coord", #### not used in the experiment. model is hard to train without the coord guidance
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action='store_true',
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help="Whether to drop coord during training. Add more diversity."
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)
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parser.add_argument(
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"--max_length",
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default=77, #### enlarge the context length of text encoder. empirically, enlarging the context length can proceed longer sequence. However, we observe that it will be hard to render general objects
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type=int,
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help="Maximum length of the composed prompt"
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)
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parser.add_argument(
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"--index_file_path",
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type=str,
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default='/path/to/train_dataset_index.txt',
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required=True,
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help="The path of data index file, each line should follow the format 00123_0012300567 ...."
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)
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parser.add_argument(
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"--dataset_path",
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type=str,
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default='/path/to/laion-ocr-select',
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required=True,
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help="the root of the dataset, please follow the code in textdiffuser-1"
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)
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######################################################################
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args = parser.parse_args()
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env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
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if env_local_rank != -1 and env_local_rank != args.local_rank:
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args.local_rank = env_local_rank
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# Sanity checks
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if args.dataset_name is None and args.train_data_dir is None:
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raise ValueError("Need either a dataset name or a training folder.")
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return args
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|
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DATASET_NAME_MAPPING = {
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# "lambdalabs/pokemon-blip-captions": ("image", "text"),
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"MARIO-10M": ("image", "text"),
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}
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|
|
|
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def main():
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|
args = parse_args()
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logging_dir = Path(args.output_dir, args.logging_dir)
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accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
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accelerator = Accelerator(
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gradient_accumulation_steps=args.gradient_accumulation_steps,
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mixed_precision=args.mixed_precision,
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log_with=args.report_to,
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project_config=accelerator_project_config,
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)
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if args.report_to == "wandb":
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if not is_wandb_available():
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raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
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import wandb
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|
|
# Make one log on every process with the configuration for debugging.
|
|
logging.basicConfig(
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|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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|
datefmt="%m/%d/%Y %H:%M:%S",
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|
level=logging.INFO,
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)
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|
logger.info(accelerator.state, main_process_only=False)
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|
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)
|
|
|
|
# Handle the repository creation
|
|
if accelerator.is_main_process:
|
|
if args.output_dir is not None:
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
if args.push_to_hub:
|
|
repo_id = create_repo(
|
|
repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
|
|
).repo_id
|
|
# Load scheduler, tokenizer and models.
|
|
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
|
|
tokenizer = CLIPTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision
|
|
)
|
|
|
|
#### additional tokens are introduced, including coordinate tokens and character tokens
|
|
print('[Size of the original tokenizer] ', len(tokenizer))
|
|
for i in range(520):
|
|
tokenizer.add_tokens(['l' + str(i) ]) # left
|
|
tokenizer.add_tokens(['t' + str(i) ]) # top
|
|
tokenizer.add_tokens(['r' + str(i) ]) # width
|
|
tokenizer.add_tokens(['b' + str(i) ]) # height
|
|
for c in alphabet:
|
|
tokenizer.add_tokens([f'[{c}]']) # character-level embedding
|
|
print('[Size of the modified tokenizer] ', len(tokenizer))
|
|
|
|
if args.max_length == 77:
|
|
text_encoder = CLIPTextModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
|
|
)
|
|
else:
|
|
#### enlarge the context length of text encoder. empirically, enlarging the context length can proceed longer sequence. However, we observe that it will be hard to render general objects
|
|
text_encoder = CLIPTextModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision, max_position_embeddings=args.max_length, ignore_mismatched_sizes=True
|
|
)
|
|
text_encoder.resize_token_embeddings(len(tokenizer))
|
|
|
|
vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
|
|
|
|
unet = UNet2DConditionModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
|
|
)
|
|
|
|
# freeze parameters of models to save more memory
|
|
unet.requires_grad_(False) # unet is not trained since lora is used
|
|
vae.requires_grad_(False)
|
|
|
|
#### the text_encoder should be trainable to learn the newly-added tokens
|
|
text_encoder.requires_grad_(True)
|
|
|
|
# For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) 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
|
|
elif accelerator.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
|
|
# Move unet, vae and text_encoder to device and cast to weight_dtype
|
|
#### only operate parameters
|
|
unet.to(accelerator.device, dtype=weight_dtype)
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
# text_encoder.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
# now we will add new LoRA weights to the attention layers
|
|
# It's important to realize here how many attention weights will be added and of which sizes
|
|
# The sizes of the attention layers consist only of two different variables:
|
|
# 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
|
|
# 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
|
|
|
|
# Let's first see how many attention processors we will have to set.
|
|
# For Stable Diffusion, it should be equal to:
|
|
# - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
|
|
# - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
|
|
# - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
|
|
# => 32 layers
|
|
|
|
# Set correct lora layers
|
|
lora_attn_procs = {}
|
|
for name in unet.attn_processors.keys():
|
|
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
|
|
if name.startswith("mid_block"):
|
|
hidden_size = unet.config.block_out_channels[-1]
|
|
elif name.startswith("up_blocks"):
|
|
block_id = int(name[len("up_blocks.")])
|
|
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
|
|
elif name.startswith("down_blocks"):
|
|
block_id = int(name[len("down_blocks.")])
|
|
hidden_size = unet.config.block_out_channels[block_id]
|
|
|
|
lora_attn_procs[name] = LoRAAttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
rank=args.rank,
|
|
)
|
|
|
|
unet.set_attn_processor(lora_attn_procs)
|
|
|
|
if args.enable_xformers_memory_efficient_attention:
|
|
if is_xformers_available():
|
|
import xformers
|
|
|
|
xformers_version = version.parse(xformers.__version__)
|
|
if xformers_version == version.parse("0.0.16"):
|
|
logger.warn(
|
|
"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
|
|
)
|
|
unet.enable_xformers_memory_efficient_attention()
|
|
else:
|
|
raise ValueError("xformers is not available. Make sure it is installed correctly")
|
|
|
|
def compute_snr(timesteps):
|
|
"""
|
|
Computes SNR as per https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
|
|
"""
|
|
alphas_cumprod = noise_scheduler.alphas_cumprod
|
|
sqrt_alphas_cumprod = alphas_cumprod**0.5
|
|
sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
|
|
|
|
# Expand the tensors.
|
|
# Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
|
|
sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
|
|
while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
|
|
sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
|
|
alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
|
|
|
|
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(device=timesteps.device)[timesteps].float()
|
|
while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
|
|
sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
|
|
sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
|
|
|
|
# Compute SNR.
|
|
snr = (alpha / sigma) ** 2
|
|
return snr
|
|
|
|
lora_layers = AttnProcsLayers(unet.attn_processors)
|
|
|
|
# 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
|
|
|
|
# optimizer = optimizer_cls(
|
|
# lora_layers.parameters(),
|
|
# lr=args.learning_rate,
|
|
# betas=(args.adam_beta1, args.adam_beta2),
|
|
# weight_decay=args.adam_weight_decay,
|
|
# eps=args.adam_epsilon,
|
|
# )
|
|
|
|
#### the optimizer is modified to train both the text_encoder and the lora
|
|
optimizer = optimizer_cls(
|
|
[
|
|
{'params': text_encoder.parameters(), 'lr': args.text_encoder_learning_rate}, # 1e-5
|
|
{'params': lora_layers.parameters(), 'lr': args.learning_rate}, # 1e-4
|
|
],
|
|
lr=args.learning_rate,
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon
|
|
)
|
|
|
|
#### load the data through the index file path
|
|
lines = open(args.index_file_path).readlines()
|
|
random.shuffle(lines)
|
|
train_dataset = Dataset.from_dict({"image": lines, "text": lines}) # 一些line列表，这个还是好处理的
|
|
dataset = {
|
|
'train': train_dataset,
|
|
}
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to tokenize inputs and targets.
|
|
column_names = dataset["train"].column_names
|
|
|
|
# 6. Get the column names for input/target.
|
|
dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
|
|
if args.image_column is None:
|
|
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
|
|
else:
|
|
image_column = args.image_column
|
|
if image_column not in column_names:
|
|
raise ValueError(
|
|
f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
if args.caption_column is None:
|
|
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
|
|
else:
|
|
caption_column = args.caption_column
|
|
if caption_column not in column_names:
|
|
raise ValueError(
|
|
f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
|
|
)
|
|
|
|
#### many augmentations can not be used in the text rendering task
|
|
train_transforms = transforms.Compose(
|
|
[
|
|
# transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
# transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
|
|
# transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
|
|
transforms.ToTensor(),
|
|
]
|
|
)
|
|
|
|
#### process the training data
|
|
def preprocess_train(examples):
|
|
images = []
|
|
prompts_train = []
|
|
prompts_cond = []
|
|
prompts_nocond = []
|
|
for image in examples[image_column]:
|
|
image = image.strip()
|
|
first, second = image.split('_')
|
|
|
|
#### get image
|
|
image_path = f'{args.dataset_path}/{first}/{second}/image.jpg'
|
|
image = Image.open(image_path).convert("RGB")
|
|
images.append(image)
|
|
|
|
#### get caption
|
|
try: #### note that few cases do not contain valid captions
|
|
caption = open(f'{args.dataset_path}/{first}/{second}/caption.txt').readlines()[0]
|
|
except:
|
|
caption = 'null'
|
|
print('erorr of caption')
|
|
|
|
#### get ocr
|
|
#### since the original ocr annotations are word-level, we need to merge some boxes to construct line-level ocr
|
|
ocrs = open(f'{args.dataset_path}/{first}/{second}/ocr.txt').readlines()
|
|
|
|
ocrs_temp = []
|
|
for line in ocrs:
|
|
line = line.strip()
|
|
pred, box, prob = line.split()
|
|
items = box.split(',')
|
|
x1, y1, x2, y2, x3, y3, x4, y4 = int(items[0]), int(items[1]), int(items[2]), int(items[3]), int(items[4]), int(items[5]), int(items[6]), int(items[7])
|
|
x_min = min(x1, x2, x3, x4)
|
|
y_min = min(y1, y2, y3, y4)
|
|
x_max = max(x1, x2, x3, x4)
|
|
y_max = max(y1, y2, y3, y4)
|
|
x_center = (x_min + x_max) // 2
|
|
y_center = (y_min + y_max) // 2
|
|
ocrs_temp.append([x_center, y_center, x_min, y_min, x_max, y_max, pred])
|
|
ocrs_temp = sorted(ocrs_temp, key=lambda x: x[0])
|
|
ocrs_temp = merge_boxes(ocrs_temp)
|
|
ocrs_temp = sorted(ocrs_temp, key=lambda x: x[1])
|
|
|
|
random.shuffle(ocrs_temp) #### augment the ocr sequence for robust training
|
|
|
|
ocr_ids = [] #### concat with the prompt tokens
|
|
for line in ocrs_temp:
|
|
|
|
x_center, y_center, x_min, y_min, x_max, y_max, pred = line
|
|
|
|
# choose coord mode
|
|
if args.coord_mode == 'lt':
|
|
x_left = x_min
|
|
y_top = y_min
|
|
x_left = x_left // (512 // args.granularity)
|
|
y_top = y_top // (512 // args.granularity)
|
|
x_left = np.clip(x_left, 0, args.granularity)
|
|
y_top = np.clip(y_top, 0, args.granularity)
|
|
ocr_ids.extend(['l'+str(x_left), 't'+str(y_top)])
|
|
|
|
elif args.coord_mode == 'center':
|
|
x_center = x_center // (512 // args.granularity)
|
|
y_center = y_center // (512 // args.granularity)
|
|
x_center = np.clip(x_center, 0, args.granularity)
|
|
y_center = np.clip(y_center, 0, args.granularity)
|
|
ocr_ids.extend(['l'+str(x_center), 't'+str(y_center)])
|
|
|
|
elif args.coord_mode == 'ltrb':
|
|
x_left = x_min
|
|
y_top = y_min
|
|
x_right = x_max
|
|
y_bottom = y_max
|
|
x_left = x_left // (512 // args.granularity)
|
|
y_top = y_top // (512 // args.granularity)
|
|
x_right = x_right // (512 // args.granularity)
|
|
y_bottom = y_bottom // (512 // args.granularity)
|
|
x_left = np.clip(x_left, 0, args.granularity)
|
|
y_top = np.clip(y_top, 0, args.granularity)
|
|
x_right = np.clip(x_right, 0, args.granularity)
|
|
y_bottom = np.clip(y_bottom, 0, args.granularity)
|
|
ocr_ids.extend(['l'+str(x_left), 't'+str(y_top), 'r'+str(x_right), 'b'+str(y_bottom)])
|
|
|
|
char_list = list(pred)
|
|
char_list = [f'[{i}]' for i in char_list]
|
|
ocr_ids.extend(char_list)
|
|
ocr_ids.append(tokenizer.eos_token_id)
|
|
ocr_ids.append(tokenizer.eos_token_id)
|
|
|
|
ocr_ids = tokenizer.encode(ocr_ids)
|
|
|
|
caption_ids = tokenizer(
|
|
caption, truncation=True, return_tensors="pt"
|
|
).input_ids[0].tolist()
|
|
|
|
prompt = caption_ids + ocr_ids
|
|
prompt = prompt[:args.max_length]
|
|
while len(prompt) < args.max_length:
|
|
prompt.append(tokenizer.pad_token_id)
|
|
|
|
prompts_cond.append(prompt)
|
|
prompts_nocond.append([tokenizer.pad_token_id]*args.max_length)
|
|
|
|
#### classifier-free guidance
|
|
if random.random() < 0.1:
|
|
prompts_train.append([tokenizer.pad_token_id]*args.max_length)
|
|
else:
|
|
prompts_train.append(prompt)
|
|
|
|
examples["images"] = [train_transforms(image).sub_(0.5).div_(0.5) for image in images]
|
|
examples["prompts_train"] = prompts_train
|
|
examples["prompts_cond"] = prompts_cond
|
|
examples["prompts_nocond"] = prompts_nocond
|
|
|
|
return examples
|
|
|
|
with accelerator.main_process_first():
|
|
if args.max_train_samples is not None:
|
|
dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
|
|
# Set the training transforms
|
|
train_dataset = dataset["train"].with_transform(preprocess_train)
|
|
|
|
|
|
def collate_fn(examples):
|
|
images = torch.stack([example["images"] for example in examples])
|
|
images = images.to(memory_format=torch.contiguous_format).float()
|
|
|
|
prompts_train = torch.Tensor([example["prompts_train"] for example in examples]).long()
|
|
prompts_cond = torch.Tensor([example["prompts_cond"] for example in examples]).long()
|
|
prompts_nocond = torch.Tensor([example["prompts_nocond"] for example in examples]).long()
|
|
return {"images": images, "prompts_train": prompts_train, "prompts_cond": prompts_cond, "prompts_nocond": prompts_nocond}
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
shuffle=True,
|
|
collate_fn=collate_fn,
|
|
batch_size=args.train_batch_size,
|
|
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 * args.gradient_accumulation_steps,
|
|
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
#### please note that "text_encoder" should be added for training
|
|
lora_layers, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
|
|
lora_layers, text_encoder, optimizer, train_dataloader, lr_scheduler
|
|
)
|
|
|
|
# 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:
|
|
accelerator.init_trackers("text2image-fine-tune", config=vars(args))
|
|
|
|
# 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
|
|
else:
|
|
accelerator.print(f"Resuming from checkpoint {path}")
|
|
#### modified. directly load ckpt from args.resume_from_checkpoint
|
|
accelerator.load_state(args.resume_from_checkpoint)
|
|
|
|
global_step = int(path.split("-")[1])
|
|
resume_global_step = global_step * args.gradient_accumulation_steps
|
|
first_epoch = global_step // num_update_steps_per_epoch
|
|
resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
|
|
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
|
|
progress_bar.set_description("Steps")
|
|
|
|
for epoch in range(first_epoch, args.num_train_epochs):
|
|
unet.train()
|
|
text_encoder.train()
|
|
train_loss = 0.0
|
|
for step, batch in enumerate(train_dataloader):
|
|
|
|
# # Skip steps until we reach the resumed step
|
|
# if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
|
|
# if step % args.gradient_accumulation_steps == 0:
|
|
# progress_bar.update(1)
|
|
# continue
|
|
|
|
with accelerator.accumulate(unet):
|
|
# # Convert images to latent space
|
|
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
latents = latents * vae.config.scaling_factor
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
if args.noise_offset:
|
|
# https://www.crosslabs.org//blog/diffusion-with-offset-noise
|
|
noise += args.noise_offset * torch.randn(
|
|
(latents.shape[0], latents.shape[1], 1, 1), device=latents.device
|
|
)
|
|
|
|
bsz = latents.shape[0]
|
|
# Sample a random timestep for each image
|
|
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
|
|
timesteps = timesteps.long()
|
|
|
|
# Add noise to the latents according to the noise magnitude at each timestep
|
|
# (this is the forward diffusion process)
|
|
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
|
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(batch["prompts_train"])[0]
|
|
|
|
# Get the target for loss depending on the prediction type
|
|
if args.prediction_type is not None:
|
|
# set prediction_type of scheduler if defined
|
|
noise_scheduler.register_to_config(prediction_type=args.prediction_type)
|
|
|
|
if noise_scheduler.config.prediction_type == "epsilon":
|
|
target = noise
|
|
elif noise_scheduler.config.prediction_type == "v_prediction":
|
|
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
|
else:
|
|
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
|
|
|
# Predict the noise residual and compute loss
|
|
# model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
|
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
|
|
|
if args.snr_gamma is None:
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
|
else:
|
|
# Compute loss-weights as per Section 3.4 of https://arxiv.org/abs/2303.09556.
|
|
# Since we predict the noise instead of x_0, the original formulation is slightly changed.
|
|
# This is discussed in Section 4.2 of the same paper.
|
|
snr = compute_snr(timesteps)
|
|
mse_loss_weights = (
|
|
torch.stack([snr, args.snr_gamma * torch.ones_like(timesteps)], dim=1).min(dim=1)[0] / snr
|
|
)
|
|
# We first calculate the original loss. Then we mean over the non-batch dimensions and
|
|
# rebalance the sample-wise losses with their respective loss weights.
|
|
# Finally, we take the mean of the rebalanced loss.
|
|
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none")
|
|
loss = loss.mean(dim=list(range(1, len(loss.shape)))) * mse_loss_weights
|
|
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:
|
|
# accelerator.clip_grad_norm_(text_encoder.parameters(), args.max_grad_norm)
|
|
params_to_clip = lora_layers.parameters()
|
|
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
#### visualization during training
|
|
if True:
|
|
# if accelerator.is_main_process:
|
|
|
|
cfg = 7
|
|
if (step + 0) % args.vis_interval == 0:
|
|
scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
scheduler.set_timesteps(50)
|
|
noise = torch.randn((args.vis_num, 4, 64, 64)).to("cuda")
|
|
input = noise
|
|
|
|
encoder_hidden_states_cond = text_encoder(batch["prompts_cond"])[0]
|
|
encoder_hidden_states_nocond = text_encoder(batch["prompts_nocond"])[0]
|
|
|
|
texts = batch["prompts_cond"]
|
|
|
|
f = open(f'{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_prompt_{args.local_rank}.txt', 'w+')
|
|
for text in texts:
|
|
# also need to call a function to map back
|
|
sentence = tokenizer.decode(text)
|
|
f.write(sentence + '\n')
|
|
f.close()
|
|
|
|
for t in tqdm(scheduler.timesteps):
|
|
with torch.no_grad(): # classifier free guidance
|
|
|
|
noise_pred_cond = unet(sample=input.half(), timestep=t, encoder_hidden_states=encoder_hidden_states_cond[:args.vis_num]).sample # b, 4, 64, 64
|
|
noise_pred_uncond = unet(sample=input.half(), timestep=t, encoder_hidden_states=encoder_hidden_states_nocond[:args.vis_num]).sample # b, 4, 64, 64
|
|
noisy_residual = noise_pred_uncond + cfg * (noise_pred_cond - noise_pred_uncond) # b, 4, 64, 64
|
|
prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
|
|
input = prev_noisy_sample
|
|
|
|
# decode
|
|
input = 1 / vae.config.scaling_factor * input
|
|
images = vae.decode(input.half(), return_dict=False)[0]
|
|
## save predicted images
|
|
width, height = 512, 512
|
|
new_image = Image.new('RGB', (4*width, 4*height))
|
|
for index, image in enumerate(images.float()):
|
|
image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0)
|
|
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
|
|
image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB')
|
|
row = index // 4
|
|
col = index % 4
|
|
new_image.paste(image, (col*width, row*height))
|
|
new_image.save(f'{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_pred_img_cfg{cfg}_{args.local_rank}.jpg')
|
|
## save original images
|
|
width, height = 512, 512
|
|
new_image = Image.new('RGB', (4*width, 4*height))
|
|
for index, image in enumerate(batch["images"][:args.vis_num]):
|
|
image = (image / 2 + 0.5).clamp(0, 1).unsqueeze(0)
|
|
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
|
|
image = Image.fromarray((image * 255).round().astype("uint8")).convert('RGB')
|
|
# pred_images.append(image)
|
|
row = index // 4
|
|
col = index % 4
|
|
new_image.paste(image, (col*width, row*height))
|
|
new_image.save(f'{args.output_dir}/[{epoch}]_{(step + 1) // args.vis_interval}_orig_img_{args.local_rank}.jpg')
|
|
|
|
scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
scheduler.set_timesteps(50)
|
|
noise = torch.randn((args.vis_num, 4, 64, 64)).to("cuda")
|
|
input = noise
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
global_step += 1
|
|
accelerator.log({"train_loss": train_loss}, step=global_step)
|
|
train_loss = 0.0
|
|
|
|
if global_step % args.checkpointing_steps == 0:
|
|
if 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)
|
|
|
|
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}")
|
|
|
|
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
|
|
|
|
# Save the lora layers
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
unet = unet.to(torch.float32)
|
|
unet.save_attn_procs(args.output_dir)
|
|
|
|
if args.push_to_hub:
|
|
save_model_card(
|
|
repo_id,
|
|
images=images,
|
|
base_model=args.pretrained_model_name_or_path,
|
|
dataset_name=args.dataset_name,
|
|
repo_folder=args.output_dir,
|
|
)
|
|
upload_folder(
|
|
repo_id=repo_id,
|
|
folder_path=args.output_dir,
|
|
commit_message="End of training",
|
|
ignore_patterns=["step_*", "epoch_*"],
|
|
)
|
|
|
|
# Final inference
|
|
# Load previous pipeline
|
|
pipeline = DiffusionPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
|
|
)
|
|
pipeline = pipeline.to(accelerator.device)
|
|
|
|
# load attention processors
|
|
pipeline.unet.load_attn_procs(args.output_dir)
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|