Add realfill (#5456)

* Add realfill

* Move realfill folder

* Fix some format issues

examples/research_projects/realfill/README.md

+# RealFill
+
+[RealFill](https://arxiv.org/abs/2309.16668) is a method to personalize text2image inpainting models like stable diffusion inpainting given just a few(1~5) images of a scene.
+The `train_realfill.py` script shows how to implement the training procedure for stable diffusion inpainting.
+
+
+## Running locally with PyTorch
+
+### Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+cd to the realfill folder and run
+```bash
+cd realfill
+pip install -r requirements.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+Or for a default accelerate configuration without answering questions about your environment
+
+```bash
+accelerate config default
+```
+
+Or if your environment doesn't support an interactive shell e.g. a notebook
+
+```python
+from accelerate.utils import write_basic_config
+write_basic_config()
+```
+
+When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups. 
+
+### Toy example
+
+Now let's fill the real. For this example, we will use some images of the flower girl example from the paper.
+
+We already provide some images for testing in [this link](https://github.com/thuanz123/realfill/tree/main/data/flowerwoman)
+
+You only have to launch the training using:
+
+```bash
+export MODEL_NAME="stabilityai/stable-diffusion-2-inpainting"
+export TRAIN_DIR="data/flowerwoman"
+export OUTPUT_DIR="flowerwoman-model"
+
+accelerate launch train_realfill.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$TRAIN_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --resolution=512 \
+  --train_batch_size=16 \
+  --gradient_accumulation_steps=1 \
+  --unet_learning_rate=2e-4 \
+  --text_encoder_learning_rate=4e-5 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=100 \
+  --max_train_steps=2000 \
+  --lora_rank=8 \
+  --lora_dropout=0.1 \
+  --lora_alpha=16 \
+```
+
+### Training on a low-memory GPU:
+
+It is possible to run realfill on a low-memory GPU by using the following optimizations:
+- [gradient checkpointing and the 8-bit optimizer](#training-with-gradient-checkpointing-and-8-bit-optimizers)
+- [xformers](#training-with-xformers)
+- [setting grads to none](#set-grads-to-none)
+
+```bash
+export MODEL_NAME="stabilityai/stable-diffusion-2-inpainting"
+export TRAIN_DIR="data/flowerwoman"
+export OUTPUT_DIR="flowerwoman-model"
+
+accelerate launch train_realfill.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$TRAIN_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --resolution=512 \
+  --train_batch_size=16 \
+  --gradient_accumulation_steps=1 --gradient_checkpointing \
+  --use_8bit_adam \
+  --enable_xformers_memory_efficient_attention \
+  --set_grads_to_none \
+  --unet_learning_rate=2e-4 \
+  --text_encoder_learning_rate=4e-5 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=100 \
+  --max_train_steps=2000 \
+  --lora_rank=8 \
+  --lora_dropout=0.1 \
+  --lora_alpha=16 \
+```
+
+### Training with gradient checkpointing and 8-bit optimizers:
+
+With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train realfill on a 16GB GPU.
+
+To install `bitsandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
+
+### Training with xformers:
+You can enable memory efficient attention by [installing xFormers](https://github.com/facebookresearch/xformers#installing-xformers) and padding the `--enable_xformers_memory_efficient_attention` argument to the script.
+
+### Set grads to none
+
+To save even more memory, pass the `--set_grads_to_none` argument to the script. This will set grads to None instead of zero. However, be aware that it changes certain behaviors, so if you start experiencing any problems, remove this argument.
+
+More info: https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html
+
+## Acknowledge
+This repo is built upon the code of DreamBooth from diffusers and we thank the developers for their great works and efforts to release source code. Furthermore, a special "thank you" to RealFill's authors for publishing such an amazing work.

examples/research_projects/realfill/infer.py

+import argparse
+import os
+
+import torch
+from PIL import Image, ImageFilter
+from transformers import CLIPTextModel
+
+from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, UNet2DConditionModel
+
+
+parser = argparse.ArgumentParser(description="Inference")
+parser.add_argument(
+    "--model_path",
+    type=str,
+    default=None,
+    required=True,
+    help="Path to pretrained model or model identifier from huggingface.co/models.",
+)
+parser.add_argument(
+    "--validation_image",
+    type=str,
+    default=None,
+    required=True,
+    help="The directory of the validation image",
+)
+parser.add_argument(
+    "--validation_mask",
+    type=str,
+    default=None,
+    required=True,
+    help="The directory of the validation mask",
+)
+parser.add_argument(
+    "--output_dir",
+    type=str,
+    default="./test-infer/",
+    help="The output directory where predictions are saved",
+)
+parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible inference.")
+
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    os.makedirs(args.output_dir, exist_ok=True)
+    generator = None
+
+    # create & load model
+    pipe = StableDiffusionInpaintPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-2-inpainting", torch_dtype=torch.float32, revision=None
+    )
+
+    pipe.unet = UNet2DConditionModel.from_pretrained(
+        args.model_path,
+        subfolder="unet",
+        revision=None,
+    )
+    pipe.text_encoder = CLIPTextModel.from_pretrained(
+        args.model_path,
+        subfolder="text_encoder",
+        revision=None,
+    )
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+    pipe = pipe.to("cuda")
+
+    if args.seed is not None:
+        generator = torch.Generator(device="cuda").manual_seed(args.seed)
+
+    image = Image.open(args.validation_image)
+    mask_image = Image.open(args.validation_mask)
+
+    results = pipe(
+        ["a photo of sks"] * 16,
+        image=image,
+        mask_image=mask_image,
+        num_inference_steps=25,
+        guidance_scale=5,
+        generator=generator,
+    ).images
+
+    erode_kernel = ImageFilter.MaxFilter(3)
+    mask_image = mask_image.filter(erode_kernel)
+
+    blur_kernel = ImageFilter.BoxBlur(1)
+    mask_image = mask_image.filter(blur_kernel)
+
+    for idx, result in enumerate(results):
+        result = Image.composite(result, image, mask_image)
+        result.save(f"{args.output_dir}/{idx}.png")
+
+    del pipe
+    torch.cuda.empty_cache()

examples/research_projects/realfill/requirements.txt

+diffusers==0.20.1
+accelerate==0.23.0
+transformers==4.34.0
+peft==0.5.0
+torch==2.0.1
+torchvision==0.15.2
+ftfy==6.1.1
+tensorboard==2.14.0
+Jinja2==3.1.2

examples/research_projects/realfill/train_realfill.py

+import argparse
+import copy
+import itertools
+import logging
+import math
+import os
+import random
+import shutil
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torchvision.transforms.v2 as transforms_v2
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from huggingface_hub import create_repo, upload_folder
+from packaging import version
+from peft import LoraConfig, PeftModel, get_peft_model
+from PIL import Image
+from PIL.ImageOps import exif_transpose
+from torch.utils.data import Dataset
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, CLIPTextModel
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DPMSolverMultistepScheduler,
+    StableDiffusionInpaintPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils.import_utils import is_xformers_available
+
+
+if is_wandb_available():
+    import wandb
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.20.1")
+
+logger = get_logger(__name__)
+
+
+def make_mask(images, resolution, times=30):
+    mask, times = torch.ones_like(images[0:1, :, :]), np.random.randint(1, times)
+    min_size, max_size, margin = np.array([0.03, 0.25, 0.01]) * resolution
+    max_size = min(max_size, resolution - margin * 2)
+
+    for _ in range(times):
+        width = np.random.randint(int(min_size), int(max_size))
+        height = np.random.randint(int(min_size), int(max_size))
+
+        x_start = np.random.randint(int(margin), resolution - int(margin) - width + 1)
+        y_start = np.random.randint(int(margin), resolution - int(margin) - height + 1)
+        mask[:, y_start : y_start + height, x_start : x_start + width] = 0
+
+    mask = 1 - mask if random.random() < 0.5 else mask
+    return mask
+
+
+def save_model_card(
+    repo_id: str,
+    images=None,
+    base_model=str,
+    repo_folder=None,
+):
+    img_str = ""
+    for i, image in enumerate(images):
+        image.save(os.path.join(repo_folder, f"image_{i}.png"))
+        img_str += f"![img_{i}](./image_{i}.png)\n"
+
+    yaml = f"""
+---
+license: creativeml-openrail-m
+base_model: {base_model}
+prompt: "a photo of sks"
+tags:
+- stable-diffusion-inpainting
+- stable-diffusion-inpainting-diffusers
+- text-to-image
+- diffusers
+- realfill
+inference: true
+---
+    """
+    model_card = f"""
+# RealFill - {repo_id}
+
+This is a realfill model derived from {base_model}. The weights were trained using [RealFill](https://realfill.github.io/).
+You can find some example images in the following. \n
+{img_str}
+"""
+    with open(os.path.join(repo_folder, "README.md"), "w") as f:
+        f.write(yaml + model_card)
+
+
+def log_validation(
+    text_encoder,
+    tokenizer,
+    unet,
+    args,
+    accelerator,
+    weight_dtype,
+    epoch,
+):
+    logger.info(f"Running validation... \nGenerating {args.num_validation_images} images")
+
+    # create pipeline (note: unet and vae are loaded again in float32)
+    pipeline = StableDiffusionInpaintPipeline.from_pretrained(
+        args.pretrained_model_name_or_path,
+        tokenizer=tokenizer,
+        revision=args.revision,
+        torch_dtype=weight_dtype,
+    )
+
+    # set `keep_fp32_wrapper` to True because we do not want to remove
+    # mixed precision hooks while we are still training
+    pipeline.unet = accelerator.unwrap_model(unet, keep_fp32_wrapper=True)
+    pipeline.text_encoder = accelerator.unwrap_model(text_encoder, keep_fp32_wrapper=True)
+    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+
+    pipeline = pipeline.to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=True)
+
+    # run inference
+    generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
+
+    target_dir = Path(args.train_data_dir) / "target"
+    target_image, target_mask = target_dir / "target.png", target_dir / "mask.png"
+    image, mask_image = Image.open(target_image), Image.open(target_mask)
+
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    images = []
+    for _ in range(args.num_validation_images):
+        image = pipeline(
+            prompt="a photo of sks",
+            image=image,
+            mask_image=mask_image,
+            num_inference_steps=25,
+            guidance_scale=5,
+            generator=generator,
+        ).images[0]
+        images.append(image)
+
+    for tracker in accelerator.trackers:
+        if tracker.name == "tensorboard":
+            np_images = np.stack([np.asarray(img) for img in images])
+            tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
+        if tracker.name == "wandb":
+            tracker.log({"validation": [wandb.Image(image, caption=str(i)) for i, image in enumerate(images)]})
+
+    del pipeline
+    torch.cuda.empty_cache()
+
+    return images
+
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--train_data_dir",
+        type=str,
+        default=None,
+        required=True,
+        help="A folder containing the training data of images.",
+    )
+    parser.add_argument(
+        "--num_validation_images",
+        type=int,
+        default=4,
+        help="Number of images that should be generated during validation with `validation_conditioning`.",
+    )
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=100,
+        help=(
+            "Run realfill validation every X steps. RealFill validation consists of running the conditioning"
+            " `args.validation_conditioning` multiple times: `args.num_validation_images`."
+        ),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="realfill-model",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=1)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
+            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=None,
+        help=("Max number of checkpoints to store."),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--unet_learning_rate",
+        type=float,
+        default=2e-4,
+        help="Learning rate to use for unet.",
+    )
+    parser.add_argument(
+        "--text_encoder_learning_rate",
+        type=float,
+        default=4e-5,
+        help="Learning rate to use for text encoder.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--lr_num_cycles",
+        type=int,
+        default=1,
+        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
+    )
+    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
+    parser.add_argument(
+        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--hub_model_id",
+        type=str,
+        default=None,
+        help="The name of the repository to keep in sync with the local `output_dir`.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument(
+        "--wandb_key",
+        type=str,
+        default=None,
+        help=("If report to option is set to wandb, api-key for wandb used for login to wandb "),
+    )
+    parser.add_argument(
+        "--wandb_project_name",
+        type=str,
+        default=None,
+        help=("If report to option is set to wandb, project name in wandb for log tracking  "),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+    parser.add_argument(
+        "--set_grads_to_none",
+        action="store_true",
+        help=(
+            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
+            " behaviors, so disable this argument if it causes any problems. More info:"
+            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
+        ),
+    )
+    parser.add_argument(
+        "--lora_rank",
+        type=int,
+        default=16,
+        help=("The dimension of the LoRA update matrices."),
+    )
+    parser.add_argument(
+        "--lora_alpha",
+        type=int,
+        default=27,
+        help=("The alpha constant of the LoRA update matrices."),
+    )
+    parser.add_argument(
+        "--lora_dropout",
+        type=float,
+        default=0.0,
+        help="The dropout rate of the LoRA update matrices.",
+    )
+    parser.add_argument(
+        "--lora_bias",
+        type=str,
+        default="none",
+        help="The bias type of the Lora update matrices. Must be 'none', 'all' or 'lora_only'.",
+    )
+
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    return args
+
+
+class RealFillDataset(Dataset):
+    """
+    A dataset to prepare the training and conditioning images and
+    the masks with the dummy prompt for fine-tuning the model.
+    It pre-processes the images, masks and tokenizes the prompts.
+    """
+
+    def __init__(
+        self,
+        train_data_root,
+        tokenizer,
+        size=512,
+    ):
+        self.size = size
+        self.tokenizer = tokenizer
+
+        self.ref_data_root = Path(train_data_root) / "ref"
+        self.target_image = Path(train_data_root) / "target" / "target.png"
+        self.target_mask = Path(train_data_root) / "target" / "mask.png"
+        if not (self.ref_data_root.exists() and self.target_image.exists() and self.target_mask.exists()):
+            raise ValueError("Train images root doesn't exists.")
+
+        self.train_images_path = list(self.ref_data_root.iterdir()) + [self.target_image]
+        self.num_train_images = len(self.train_images_path)
+        self.train_prompt = "a photo of sks"
+
+        self.transform = transforms_v2.Compose(
+            [
+                transforms_v2.RandomResize(size, int(1.125 * size)),
+                transforms_v2.RandomCrop(size),
+                transforms_v2.ToImageTensor(),
+                transforms_v2.ConvertImageDtype(),
+                transforms_v2.Normalize([0.5], [0.5]),
+            ]
+        )
+
+    def __len__(self):
+        return self.num_train_images
+
+    def __getitem__(self, index):
+        example = {}
+
+        image = Image.open(self.train_images_path[index])
+        image = exif_transpose(image)
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        if index < len(self) - 1:
+            weighting = Image.new("L", image.size)
+        else:
+            weighting = Image.open(self.target_mask)
+            weighting = exif_transpose(weighting)
+
+        image, weighting = self.transform(image, weighting)
+        example["images"], example["weightings"] = image, weighting < 0
+
+        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)
+
+        train_prompt = "" if random.random() < 0.1 else self.train_prompt
+        example["prompt_ids"] = self.tokenizer(
+            train_prompt,
+            truncation=True,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids
+
+        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):
+    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.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")
+
+    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.merge_and_unload(), keep_fp32_wrapper=True),
+            text_encoder=accelerator.unwrap_model(text_encoder.merge_and_unload(), keep_fp32_wrapper=True),
+            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)