Sayak Paul
985 lines
36 KiB
Python
985 lines
36 KiB
Python
import argparse
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import copy
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import itertools
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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 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 torchvision.transforms.v2 as transforms_v2
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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 set_seed
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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 peft import LoraConfig, PeftModel, get_peft_model
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from PIL import Image
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from PIL.ImageOps import exif_transpose
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from torch.utils.data import Dataset
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from tqdm.auto import tqdm
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from transformers import AutoTokenizer, CLIPTextModel
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import diffusers
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from diffusers import (
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AutoencoderKL,
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DDPMScheduler,
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DPMSolverMultistepScheduler,
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StableDiffusionInpaintPipeline,
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UNet2DConditionModel,
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)
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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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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("0.20.1")
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logger = get_logger(__name__)
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def make_mask(images, resolution, times=30):
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mask, times = torch.ones_like(images[0:1, :, :]), np.random.randint(1, times)
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min_size, max_size, margin = np.array([0.03, 0.25, 0.01]) * resolution
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max_size = min(max_size, resolution - margin * 2)
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for _ in range(times):
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width = np.random.randint(int(min_size), int(max_size))
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height = np.random.randint(int(min_size), int(max_size))
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x_start = np.random.randint(int(margin), resolution - int(margin) - width + 1)
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y_start = np.random.randint(int(margin), resolution - int(margin) - height + 1)
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mask[:, y_start : y_start + height, x_start : x_start + width] = 0
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mask = 1 - mask if random.random() < 0.5 else mask
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return mask
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def save_model_card(
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repo_id: str,
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images=None,
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base_model=str,
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repo_folder=None,
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):
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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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prompt: "a photo of sks"
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tags:
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- stable-diffusion-inpainting
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- stable-diffusion-inpainting-diffusers
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- text-to-image
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- diffusers
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- realfill
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- diffusers-training
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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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# RealFill - {repo_id}
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This is a realfill model derived from {base_model}. The weights were trained using [RealFill](https://realfill.github.io/).
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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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def log_validation(
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text_encoder,
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tokenizer,
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unet,
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args,
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accelerator,
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weight_dtype,
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epoch,
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):
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logger.info(f"Running validation... \nGenerating {args.num_validation_images} images")
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# create pipeline (note: unet and vae are loaded again in float32)
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pipeline = StableDiffusionInpaintPipeline.from_pretrained(
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args.pretrained_model_name_or_path,
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tokenizer=tokenizer,
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revision=args.revision,
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torch_dtype=weight_dtype,
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)
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# set `keep_fp32_wrapper` to True because we do not want to remove
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# mixed precision hooks while we are still training
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pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
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pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
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pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
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pipeline = pipeline.to(accelerator.device)
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pipeline.set_progress_bar_config(disable=True)
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# run inference
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generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
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target_dir = Path(args.train_data_dir) / "target"
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target_image, target_mask = target_dir / "target.png", target_dir / "mask.png"
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image, mask_image = Image.open(target_image), Image.open(target_mask)
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if image.mode != "RGB":
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image = image.convert("RGB")
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images = []
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for _ in range(args.num_validation_images):
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image = pipeline(
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prompt="a photo of sks",
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image=image,
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mask_image=mask_image,
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num_inference_steps=25,
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guidance_scale=5,
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generator=generator,
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).images[0]
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images.append(image)
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for tracker in accelerator.trackers:
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if tracker.name == "tensorboard":
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np_images = np.stack([np.asarray(img) for img in images])
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tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
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if tracker.name == "wandb":
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tracker.log({"validation": [wandb.Image(image, caption=str(i)) for i, image in enumerate(images)]})
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del pipeline
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torch.cuda.empty_cache()
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return images
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def parse_args(input_args=None):
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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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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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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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required=True,
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help="A folder containing the training data of images.",
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)
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parser.add_argument(
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"--num_validation_images",
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type=int,
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default=4,
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help="Number of images that should be generated during validation with `validation_conditioning`.",
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)
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parser.add_argument(
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"--validation_steps",
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type=int,
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default=100,
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help=(
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"Run realfill validation every X steps. RealFill validation consists of running the conditioning"
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" `args.validation_conditioning` multiple times: `args.num_validation_images`."
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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="realfill-model",
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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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"--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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"--train_batch_size", type=int, default=4, 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=1)
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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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"--checkpointing_steps",
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type=int,
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default=500,
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help=(
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"Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
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" checkpoints in case they are better than the last checkpoint, and are also 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=None,
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help=("Max number of checkpoints to store."),
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help=(
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"Whether training should be resumed from a previous checkpoint. Use a path saved by"
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' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
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),
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)
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parser.add_argument(
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"--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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"--unet_learning_rate",
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type=float,
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default=2e-4,
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help="Learning rate to use for unet.",
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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=4e-5,
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help="Learning rate to use for text encoder.",
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)
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parser.add_argument(
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"--scale_lr",
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action="store_true",
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default=False,
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help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
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)
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parser.add_argument(
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"--lr_scheduler",
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type=str,
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default="constant",
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help=(
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'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
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' "constant", "constant_with_warmup"]'
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),
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)
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parser.add_argument(
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"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument(
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"--lr_num_cycles",
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type=int,
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default=1,
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help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
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)
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parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
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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("--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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"--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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"--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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"--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(
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"--wandb_key",
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type=str,
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default=None,
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help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
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)
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parser.add_argument(
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"--wandb_project_name",
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type=str,
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default=None,
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help=("If report to option is set to wandb, project name in wandb for log tracking "),
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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("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
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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(
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"--set_grads_to_none",
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action="store_true",
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help=(
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"Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
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" behaviors, so disable this argument if it causes any problems. More info:"
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" https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
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),
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)
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parser.add_argument(
|
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"--lora_rank",
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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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)
|
|
parser.add_argument(
|
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"--lora_alpha",
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type=int,
|
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default=27,
|
|
help=("The alpha constant of the LoRA update matrices."),
|
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)
|
|
parser.add_argument(
|
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"--lora_dropout",
|
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type=float,
|
|
default=0.0,
|
|
help="The dropout rate of the LoRA update matrices.",
|
|
)
|
|
parser.add_argument(
|
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"--lora_bias",
|
|
type=str,
|
|
default="none",
|
|
help="The bias type of the Lora update matrices. Must be 'none', 'all' or 'lora_only'.",
|
|
)
|
|
|
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if input_args is not None:
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args = parser.parse_args(input_args)
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else:
|
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args = parser.parse_args()
|
|
|
|
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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|
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return args
|
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|
|
|
|
class RealFillDataset(Dataset):
|
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"""
|
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A dataset to prepare the training and conditioning images and
|
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the masks with the dummy prompt for fine-tuning the model.
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It pre-processes the images, masks and tokenizes the prompts.
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"""
|
|
|
|
def __init__(
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self,
|
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train_data_root,
|
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tokenizer,
|
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size=512,
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):
|
|
self.size = size
|
|
self.tokenizer = tokenizer
|
|
|
|
self.ref_data_root = Path(train_data_root) / "ref"
|
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self.target_image = Path(train_data_root) / "target" / "target.png"
|
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self.target_mask = Path(train_data_root) / "target" / "mask.png"
|
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if not (self.ref_data_root.exists() and self.target_image.exists() and self.target_mask.exists()):
|
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raise ValueError("Train images root doesn't exists.")
|
|
|
|
self.train_images_path = list(self.ref_data_root.iterdir()) + [self.target_image]
|
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self.num_train_images = len(self.train_images_path)
|
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self.train_prompt = "a photo of sks"
|
|
|
|
self.transform = transforms_v2.Compose(
|
|
[
|
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transforms_v2.ToImage(),
|
|
transforms_v2.RandomResize(size, int(1.125 * size)),
|
|
transforms_v2.RandomCrop(size),
|
|
transforms_v2.ToDtype(torch.float32, scale=True),
|
|
transforms_v2.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
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|
|
def __len__(self):
|
|
return self.num_train_images
|
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|
|
def __getitem__(self, index):
|
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example = {}
|
|
|
|
image = Image.open(self.train_images_path[index])
|
|
image = exif_transpose(image)
|
|
|
|
if not image.mode == "RGB":
|
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image = image.convert("RGB")
|
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|
|
if index < len(self) - 1:
|
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weighting = Image.new("L", image.size)
|
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else:
|
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weighting = Image.open(self.target_mask)
|
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weighting = exif_transpose(weighting)
|
|
|
|
image, weighting = self.transform(image, weighting)
|
|
example["images"], example["weightings"] = image, weighting < 0
|
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|
|
if random.random() < 0.1:
|
|
example["masks"] = torch.ones_like(example["images"][0:1, :, :])
|
|
else:
|
|
example["masks"] = make_mask(example["images"], self.size)
|
|
|
|
example["conditioning_images"] = example["images"] * (example["masks"] < 0.5)
|
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|
|
train_prompt = "" if random.random() < 0.1 else self.train_prompt
|
|
example["prompt_ids"] = self.tokenizer(
|
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train_prompt,
|
|
truncation=True,
|
|
padding="max_length",
|
|
max_length=self.tokenizer.model_max_length,
|
|
return_tensors="pt",
|
|
).input_ids
|
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|
|
return example
|
|
|
|
|
|
def collate_fn(examples):
|
|
input_ids = [example["prompt_ids"] for example in examples]
|
|
images = [example["images"] for example in examples]
|
|
|
|
masks = [example["masks"] for example in examples]
|
|
weightings = [example["weightings"] for example in examples]
|
|
conditioning_images = [example["conditioning_images"] for example in examples]
|
|
|
|
images = torch.stack(images)
|
|
images = images.to(memory_format=torch.contiguous_format).float()
|
|
|
|
masks = torch.stack(masks)
|
|
masks = masks.to(memory_format=torch.contiguous_format).float()
|
|
|
|
weightings = torch.stack(weightings)
|
|
weightings = weightings.to(memory_format=torch.contiguous_format).float()
|
|
|
|
conditioning_images = torch.stack(conditioning_images)
|
|
conditioning_images = conditioning_images.to(memory_format=torch.contiguous_format).float()
|
|
|
|
input_ids = torch.cat(input_ids, dim=0)
|
|
|
|
batch = {
|
|
"input_ids": input_ids,
|
|
"images": images,
|
|
"masks": masks,
|
|
"weightings": weightings,
|
|
"conditioning_images": conditioning_images,
|
|
}
|
|
return batch
|
|
|
|
|
|
def main(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."
|
|
)
|
|
|
|
logging_dir = Path(args.output_dir, args.logging_dir)
|
|
|
|
accelerator = Accelerator(
|
|
gradient_accumulation_steps=args.gradient_accumulation_steps,
|
|
mixed_precision=args.mixed_precision,
|
|
log_with=args.report_to,
|
|
project_dir=logging_dir,
|
|
)
|
|
|
|
if args.report_to == "wandb":
|
|
if not is_wandb_available():
|
|
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
|
|
|
|
wandb.login(key=args.wandb_key)
|
|
wandb.init(project=args.wandb_project_name)
|
|
|
|
# Make one log on every process with the configuration for debugging.
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
level=logging.INFO,
|
|
)
|
|
logger.info(accelerator.state, main_process_only=False)
|
|
if accelerator.is_local_main_process:
|
|
transformers.utils.logging.set_verbosity_warning()
|
|
diffusers.utils.logging.set_verbosity_info()
|
|
else:
|
|
transformers.utils.logging.set_verbosity_error()
|
|
diffusers.utils.logging.set_verbosity_error()
|
|
|
|
# If passed along, set the training seed now.
|
|
if 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 the tokenizer
|
|
if args.tokenizer_name:
|
|
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
|
|
elif args.pretrained_model_name_or_path:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
subfolder="tokenizer",
|
|
revision=args.revision,
|
|
use_fast=False,
|
|
)
|
|
|
|
# Load scheduler and models
|
|
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
|
|
text_encoder = CLIPTextModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
|
|
)
|
|
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
|
|
)
|
|
|
|
config = LoraConfig(
|
|
r=args.lora_rank,
|
|
lora_alpha=args.lora_alpha,
|
|
target_modules=["to_k", "to_q", "to_v", "key", "query", "value"],
|
|
lora_dropout=args.lora_dropout,
|
|
bias=args.lora_bias,
|
|
)
|
|
unet = get_peft_model(unet, config)
|
|
|
|
config = LoraConfig(
|
|
r=args.lora_rank,
|
|
lora_alpha=args.lora_alpha,
|
|
target_modules=["k_proj", "q_proj", "v_proj"],
|
|
lora_dropout=args.lora_dropout,
|
|
bias=args.lora_bias,
|
|
)
|
|
text_encoder = get_peft_model(text_encoder, config)
|
|
|
|
vae.requires_grad_(False)
|
|
|
|
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.warning(
|
|
"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")
|
|
|
|
if args.gradient_checkpointing:
|
|
unet.enable_gradient_checkpointing()
|
|
text_encoder.gradient_checkpointing_enable()
|
|
|
|
# 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:
|
|
for model in models:
|
|
sub_dir = (
|
|
"unet"
|
|
if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))
|
|
else "text_encoder"
|
|
)
|
|
model.save_pretrained(os.path.join(output_dir, sub_dir))
|
|
|
|
# make sure to pop weight so that corresponding model is not saved again
|
|
weights.pop()
|
|
|
|
def load_model_hook(models, input_dir):
|
|
while len(models) > 0:
|
|
# pop models so that they are not loaded again
|
|
model = models.pop()
|
|
|
|
sub_dir = (
|
|
"unet"
|
|
if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))
|
|
else "text_encoder"
|
|
)
|
|
model_cls = (
|
|
UNet2DConditionModel
|
|
if isinstance(model.base_model.model, type(accelerator.unwrap_model(unet).base_model.model))
|
|
else CLIPTextModel
|
|
)
|
|
|
|
load_model = model_cls.from_pretrained(args.pretrained_model_name_or_path, subfolder=sub_dir)
|
|
load_model = PeftModel.from_pretrained(load_model, input_dir, subfolder=sub_dir)
|
|
|
|
model.load_state_dict(load_model.state_dict())
|
|
del load_model
|
|
|
|
accelerator.register_save_state_pre_hook(save_model_hook)
|
|
accelerator.register_load_state_pre_hook(load_model_hook)
|
|
|
|
# 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.unet_learning_rate = (
|
|
args.unet_learning_rate
|
|
* args.gradient_accumulation_steps
|
|
* args.train_batch_size
|
|
* accelerator.num_processes
|
|
)
|
|
|
|
args.text_encoder_learning_rate = (
|
|
args.text_encoder_learning_rate
|
|
* args.gradient_accumulation_steps
|
|
* args.train_batch_size
|
|
* accelerator.num_processes
|
|
)
|
|
|
|
# Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
|
|
if args.use_8bit_adam:
|
|
try:
|
|
import bitsandbytes as bnb
|
|
except ImportError:
|
|
raise ImportError(
|
|
"To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
|
|
)
|
|
|
|
optimizer_class = bnb.optim.AdamW8bit
|
|
else:
|
|
optimizer_class = torch.optim.AdamW
|
|
|
|
# Optimizer creation
|
|
optimizer = optimizer_class(
|
|
[
|
|
{"params": unet.parameters(), "lr": args.unet_learning_rate},
|
|
{"params": text_encoder.parameters(), "lr": args.text_encoder_learning_rate},
|
|
],
|
|
betas=(args.adam_beta1, args.adam_beta2),
|
|
weight_decay=args.adam_weight_decay,
|
|
eps=args.adam_epsilon,
|
|
)
|
|
|
|
# Dataset and DataLoaders creation:
|
|
train_dataset = RealFillDataset(
|
|
train_data_root=args.train_data_dir,
|
|
tokenizer=tokenizer,
|
|
size=args.resolution,
|
|
)
|
|
|
|
train_dataloader = torch.utils.data.DataLoader(
|
|
train_dataset,
|
|
batch_size=args.train_batch_size,
|
|
shuffle=True,
|
|
collate_fn=collate_fn,
|
|
num_workers=1,
|
|
)
|
|
|
|
# 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,
|
|
num_cycles=args.lr_num_cycles,
|
|
power=args.lr_power,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
unet, text_encoder, optimizer, train_dataloader = accelerator.prepare(
|
|
unet, text_encoder, optimizer, train_dataloader
|
|
)
|
|
|
|
# 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 vae to device and cast to weight_dtype
|
|
vae.to(accelerator.device, dtype=weight_dtype)
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / 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 = vars(copy.deepcopy(args))
|
|
accelerator.init_trackers("realfill", config=tracker_config)
|
|
|
|
# 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 batches each epoch = {len(train_dataloader)}")
|
|
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 mos 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:
|
|
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
|
|
|
|
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):
|
|
unet.train()
|
|
text_encoder.train()
|
|
|
|
for step, batch in enumerate(train_dataloader):
|
|
with accelerator.accumulate(unet, text_encoder):
|
|
# Convert images to latent space
|
|
latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
latents = latents * 0.18215
|
|
|
|
# Convert masked images to latent space
|
|
conditionings = vae.encode(batch["conditioning_images"].to(dtype=weight_dtype)).latent_dist.sample()
|
|
conditionings = conditionings * 0.18215
|
|
|
|
# Downsample mask and weighting so that they match with the latents
|
|
masks, size = batch["masks"].to(dtype=weight_dtype), latents.shape[2:]
|
|
masks = F.interpolate(masks, size=size)
|
|
|
|
weightings = batch["weightings"].to(dtype=weight_dtype)
|
|
weightings = F.interpolate(weightings, size=size)
|
|
|
|
# Sample noise that we'll add to the latents
|
|
noise = torch.randn_like(latents)
|
|
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)
|
|
|
|
# Concatenate noisy latents, masks and conditionings to get inputs to unet
|
|
inputs = torch.cat([noisy_latents, masks, conditionings], dim=1)
|
|
|
|
# Get the text embedding for conditioning
|
|
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
|
|
|
|
# Predict the noise residual
|
|
model_pred = unet(inputs, timesteps, encoder_hidden_states).sample
|
|
|
|
# Compute the diffusion loss
|
|
assert noise_scheduler.config.prediction_type == "epsilon"
|
|
loss = (weightings * F.mse_loss(model_pred.float(), noise.float(), reduction="none")).mean()
|
|
|
|
# Backpropagate
|
|
accelerator.backward(loss)
|
|
if accelerator.sync_gradients:
|
|
params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())
|
|
accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
|
|
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad(set_to_none=args.set_grads_to_none)
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
if args.report_to == "wandb":
|
|
accelerator.print(progress_bar)
|
|
global_step += 1
|
|
|
|
if accelerator.is_main_process:
|
|
if global_step % args.checkpointing_steps == 0:
|
|
# _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}")
|
|
|
|
if global_step % args.validation_steps == 0:
|
|
log_validation(
|
|
text_encoder,
|
|
tokenizer,
|
|
unet,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
logs = {"loss": loss.detach().item()}
|
|
progress_bar.set_postfix(**logs)
|
|
accelerator.log(logs, step=global_step)
|
|
|
|
if global_step >= args.max_train_steps:
|
|
break
|
|
|
|
# Save the lora layers
|
|
accelerator.wait_for_everyone()
|
|
if accelerator.is_main_process:
|
|
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
|
|
args.pretrained_model_name_or_path,
|
|
unet=accelerator.unwrap_model(unet, keep_fp32_wrapper=True).merge_and_unload(),
|
|
text_encoder=accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True).merge_and_unload(),
|
|
revision=args.revision,
|
|
)
|
|
|
|
pipeline.save_pretrained(args.output_dir)
|
|
|
|
# Final inference
|
|
images = log_validation(
|
|
text_encoder,
|
|
tokenizer,
|
|
unet,
|
|
args,
|
|
accelerator,
|
|
weight_dtype,
|
|
global_step,
|
|
)
|
|
|
|
if args.push_to_hub:
|
|
save_model_card(
|
|
repo_id,
|
|
images=images,
|
|
base_model=args.pretrained_model_name_or_path,
|
|
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_*"],
|
|
)
|
|
|
|
accelerator.end_training()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_args()
|
|
main(args)
|