Merge branch 'main' of https://github.com/huggingface/diffusers

docs/source/en/training/custom_diffusion.mdx

@@ -279,9 +279,11 @@ You can also perform inference from one of the complete checkpoint saved during
 TODO.
 
 ## 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
 
 ## Experimental results
+
 You can refer to [our webpage](https://www.cs.cmu.edu/~custom-diffusion/) that discusses our experiments in detail. 

examples/research_projects/intel_opts/textual_inversion_dfq/README.md

+# Distillation for quantization on Textual Inversion models to personalize text2image
+
+[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images._By using just 3-5 images new concepts can be taught to Stable Diffusion and the model personalized on your own images_
+The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
+We have enabled distillation for quantization in `textual_inversion.py` to do quantization aware training as well as distillation on the model generated by Textual Inversion method.
+
+## Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Prepare Datasets
+
+One picture which is from the huggingface datasets [sd-concepts-library/dicoo2](https://huggingface.co/sd-concepts-library/dicoo2) is needed, and save it to the `./dicoo` directory. The picture is shown below:
+
+<a href="https://huggingface.co/sd-concepts-library/dicoo2/blob/main/concept_images/1.jpeg">
+    <img src="https://huggingface.co/sd-concepts-library/dicoo2/resolve/main/concept_images/1.jpeg" width = "300" height="300">
+</a>
+
+## Get a FP32 Textual Inversion model
+
+Use the following command to fine-tune the Stable Diffusion model on the above dataset to obtain the FP32 Textual Inversion model.
+
+```bash
+export MODEL_NAME="CompVis/stable-diffusion-v1-4"
+export DATA_DIR="./dicoo"
+
+accelerate launch textual_inversion.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATA_DIR \
+  --learnable_property="object" \
+  --placeholder_token="<dicoo>" --initializer_token="toy" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=4 \
+  --max_train_steps=3000 \
+  --learning_rate=5.0e-04 --scale_lr \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --output_dir="dicoo_model"
+```
+
+## Do distillation for quantization
+
+Distillation for quantization is a method that combines [intermediate layer knowledge distillation](https://github.com/intel/neural-compressor/blob/master/docs/source/distillation.md#intermediate-layer-knowledge-distillation) and [quantization aware training](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#quantization-aware-training) in the same training process to improve the performance of the quantized model. Provided a FP32 model, the distillation for quantization approach will take this model itself as the teacher model and transfer the knowledges of the specified layers to the student model, i.e. quantized version of the FP32 model, during the quantization aware training process.
+
+Once you have the FP32 Textual Inversion model, the following command will take the FP32 Textual Inversion model as input to do distillation for quantization and generate the INT8 Textual Inversion model.
+
+```bash
+export FP32_MODEL_NAME="./dicoo_model"
+export DATA_DIR="./dicoo"
+
+accelerate launch textual_inversion.py \
+  --pretrained_model_name_or_path=$FP32_MODEL_NAME \
+  --train_data_dir=$DATA_DIR \
+  --use_ema --learnable_property="object" \
+  --placeholder_token="<dicoo>" --initializer_token="toy" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=4 \
+  --max_train_steps=300 \
+  --learning_rate=5.0e-04 --max_grad_norm=3 \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --output_dir="int8_model" \
+  --do_quantization --do_distillation --verify_loading
+```
+
+After the distillation for quantization process, the quantized UNet would be 4 times smaller (3279MB -> 827MB).
+
+## Inference
+
+Once you have trained a INT8 model with the above command, the inference can be done simply using the `text2images.py` script. Make sure to include the `placeholder_token` in your prompt.
+
+```bash
+export INT8_MODEL_NAME="./int8_model"
+
+python text2images.py \
+  --pretrained_model_name_or_path=$INT8_MODEL_NAME \
+  --caption "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings." \
+  --images_num 4
+```
+
+Here is the comparison of images generated by the FP32 model (left) and INT8 model (right) respectively:
+
+<p float="left">
+  <img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/FP32.png" width = "300" height = "300" alt="FP32" align=center />
+  <img src="https://huggingface.co/datasets/Intel/textual_inversion_dicoo_dfq/resolve/main/INT8.png" width = "300" height = "300" alt="INT8" align=center />
+</p>
+

examples/research_projects/intel_opts/textual_inversion_dfq/requirements.txt

+accelerate
+torchvision
+transformers>=4.25.0
+ftfy
+tensorboard
+modelcards
+neural-compressor
\ No newline at end of file

examples/research_projects/intel_opts/textual_inversion_dfq/text2images.py

+import argparse
+import math
+import os
+
+import torch
+from neural_compressor.utils.pytorch import load
+from PIL import Image
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, StableDiffusionPipeline, UNet2DConditionModel
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-m",
+        "--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(
+        "-c",
+        "--caption",
+        type=str,
+        default="robotic cat with wings",
+        help="Text used to generate images.",
+    )
+    parser.add_argument(
+        "-n",
+        "--images_num",
+        type=int,
+        default=4,
+        help="How much images to generate.",
+    )
+    parser.add_argument(
+        "-s",
+        "--seed",
+        type=int,
+        default=42,
+        help="Seed for random process.",
+    )
+    parser.add_argument(
+        "-ci",
+        "--cuda_id",
+        type=int,
+        default=0,
+        help="cuda_id.",
+    )
+    args = parser.parse_args()
+    return args
+
+
+def image_grid(imgs, rows, cols):
+    if not len(imgs) == rows * cols:
+        raise ValueError("The specified number of rows and columns are not correct.")
+
+    w, h = imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+    grid_w, grid_h = grid.size
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+def generate_images(
+    pipeline,
+    prompt="robotic cat with wings",
+    guidance_scale=7.5,
+    num_inference_steps=50,
+    num_images_per_prompt=1,
+    seed=42,
+):
+    generator = torch.Generator(pipeline.device).manual_seed(seed)
+    images = pipeline(
+        prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        num_images_per_prompt=num_images_per_prompt,
+    ).images
+    _rows = int(math.sqrt(num_images_per_prompt))
+    grid = image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)
+    return grid, images
+
+
+args = parse_args()
+# Load models and create wrapper for stable diffusion
+tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
+vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+
+pipeline = StableDiffusionPipeline.from_pretrained(
+    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
+)
+pipeline.safety_checker = lambda images, clip_input: (images, False)
+if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
+    unet = load(args.pretrained_model_name_or_path, model=unet)
+    unet.eval()
+    setattr(pipeline, "unet", unet)
+else:
+    unet = unet.to(torch.device("cuda", args.cuda_id))
+pipeline = pipeline.to(unet.device)
+grid, images = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
+grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
+dirname = os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
+os.makedirs(dirname, exist_ok=True)
+for idx, image in enumerate(images):
+    image.save(os.path.join(dirname, "{}.png".format(idx + 1)))

examples/research_projects/intel_opts/textual_inversion_dfq/textual_inversion.py

+import argparse
+import itertools
+import math
+import os
+import random
+from pathlib import Path
+from typing import Iterable, Optional
+
+import numpy as np
+import PIL
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from huggingface_hub import HfFolder, Repository, whoami
+from neural_compressor.utils import logger
+from packaging import version
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+
+
+if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.Resampling.BILINEAR,
+        "bilinear": PIL.Image.Resampling.BILINEAR,
+        "bicubic": PIL.Image.Resampling.BICUBIC,
+        "lanczos": PIL.Image.Resampling.LANCZOS,
+        "nearest": PIL.Image.Resampling.NEAREST,
+    }
+else:
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.LINEAR,
+        "bilinear": PIL.Image.BILINEAR,
+        "bicubic": PIL.Image.BICUBIC,
+        "lanczos": PIL.Image.LANCZOS,
+        "nearest": PIL.Image.NEAREST,
+    }
+# ------------------------------------------------------------------------------
+
+
+def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
+    logger.info("Saving embeddings")
+    learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
+    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
+    torch.save(learned_embeds_dict, save_path)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Example of distillation for quantization on Textual Inversion.")
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=500,
+        help="Save learned_embeds.bin every X updates steps.",
+    )
+    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."
+    )
+    parser.add_argument(
+        "--placeholder_token",
+        type=str,
+        default=None,
+        required=True,
+        help="A token to use as a placeholder for the concept.",
+    )
+    parser.add_argument(
+        "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
+    )
+    parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
+    parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="text-inversion-model",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument("--seed", type=int, default=42, 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(
+        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=5000,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    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(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument("--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("--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(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        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."
+        ),
+    )
+    parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--do_quantization", action="store_true", help="Whether or not to do quantization.")
+    parser.add_argument("--do_distillation", action="store_true", help="Whether or not to do distillation.")
+    parser.add_argument(
+        "--verify_loading", action="store_true", help="Whether or not to verify the loading of the quantized model."
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    if args.train_data_dir is None:
+        raise ValueError("You must specify a train data directory.")
+
+    return args
+
+
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_style_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+
+# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14
+class EMAModel:
+    """
+    Exponential Moving Average of models weights
+    """
+
+    def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999):
+        parameters = list(parameters)
+        self.shadow_params = [p.clone().detach() for p in parameters]
+
+        self.decay = decay
+        self.optimization_step = 0
+
+    def get_decay(self, optimization_step):
+        """
+        Compute the decay factor for the exponential moving average.
+        """
+        value = (1 + optimization_step) / (10 + optimization_step)
+        return 1 - min(self.decay, value)
+
+    @torch.no_grad()
+    def step(self, parameters):
+        parameters = list(parameters)
+
+        self.optimization_step += 1
+        self.decay = self.get_decay(self.optimization_step)
+
+        for s_param, param in zip(self.shadow_params, parameters):
+            if param.requires_grad:
+                tmp = self.decay * (s_param - param)
+                s_param.sub_(tmp)
+            else:
+                s_param.copy_(param)
+
+        torch.cuda.empty_cache()
+
+    def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None:
+        """
+        Copy current averaged parameters into given collection of parameters.
+        Args:
+            parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+                updated with the stored moving averages. If `None`, the
+                parameters with which this `ExponentialMovingAverage` was
+                initialized will be used.
+        """
+        parameters = list(parameters)
+        for s_param, param in zip(self.shadow_params, parameters):
+            param.data.copy_(s_param.data)
+
+    def to(self, device=None, dtype=None) -> None:
+        r"""Move internal buffers of the ExponentialMovingAverage to `device`.
+        Args:
+            device: like `device` argument to `torch.Tensor.to`
+        """
+        # .to() on the tensors handles None correctly
+        self.shadow_params = [
+            p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device)
+            for p in self.shadow_params
+        ]
+
+
+class TextualInversionDataset(Dataset):
+    def __init__(
+        self,
+        data_root,
+        tokenizer,
+        learnable_property="object",  # [object, style]
+        size=512,
+        repeats=100,
+        interpolation="bicubic",
+        flip_p=0.5,
+        set="train",
+        placeholder_token="*",
+        center_crop=False,
+    ):
+        self.data_root = data_root
+        self.tokenizer = tokenizer
+        self.learnable_property = learnable_property
+        self.size = size
+        self.placeholder_token = placeholder_token
+        self.center_crop = center_crop
+        self.flip_p = flip_p
+
+        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
+
+        self.num_images = len(self.image_paths)
+        self._length = self.num_images
+
+        if set == "train":
+            self._length = self.num_images * repeats
+
+        self.interpolation = {
+            "linear": PIL_INTERPOLATION["linear"],
+            "bilinear": PIL_INTERPOLATION["bilinear"],
+            "bicubic": PIL_INTERPOLATION["bicubic"],
+            "lanczos": PIL_INTERPOLATION["lanczos"],
+        }[interpolation]
+
+        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
+        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = {}
+        image = Image.open(self.image_paths[i % self.num_images])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        placeholder_string = self.placeholder_token
+        text = random.choice(self.templates).format(placeholder_string)
+
+        example["input_ids"] = self.tokenizer(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids[0]
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+
+        if self.center_crop:
+            crop = min(img.shape[0], img.shape[1])
+            (
+                h,
+                w,
+            ) = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
+
+        image = Image.fromarray(img)
+        image = image.resize((self.size, self.size), resample=self.interpolation)
+
+        image = self.flip_transform(image)
+        image = np.array(image).astype(np.uint8)
+        image = (image / 127.5 - 1.0).astype(np.float32)
+
+        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
+        return example
+
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
+
+
+def freeze_params(params):
+    for param in params:
+        param.requires_grad = False
+
+
+def image_grid(imgs, rows, cols):
+    if not len(imgs) == rows * cols:
+        raise ValueError("The specified number of rows and columns are not correct.")
+
+    w, h = imgs[0].size
+    grid = Image.new("RGB", size=(cols * w, rows * h))
+    grid_w, grid_h = grid.size
+
+    for i, img in enumerate(imgs):
+        grid.paste(img, box=(i % cols * w, i // cols * h))
+    return grid
+
+
+def generate_images(pipeline, prompt="", guidance_scale=7.5, num_inference_steps=50, num_images_per_prompt=1, seed=42):
+    generator = torch.Generator(pipeline.device).manual_seed(seed)
+    images = pipeline(
+        prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        num_images_per_prompt=num_images_per_prompt,
+    ).images
+    _rows = int(math.sqrt(num_images_per_prompt))
+    grid = image_grid(images, rows=_rows, cols=num_images_per_prompt // _rows)
+    return grid
+
+
+def main():
+    args = parse_args()
+    logging_dir = os.path.join(args.output_dir, args.logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with="tensorboard",
+        logging_dir=logging_dir,
+    )
+
+    # 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.push_to_hub:
+            if args.hub_model_id is None:
+                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+            repo = Repository(args.output_dir, clone_from=repo_name)
+
+            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "step_*" not in gitignore:
+                    gitignore.write("step_*\n")
+                if "epoch_*" not in gitignore:
+                    gitignore.write("epoch_*\n")
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    # Load the tokenizer and add the placeholder token as a additional special token
+    if args.tokenizer_name:
+        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
+    elif args.pretrained_model_name_or_path:
+        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+
+    # Load models and create wrapper for stable diffusion
+    noise_scheduler = DDPMScheduler.from_config(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,
+    )
+
+    train_unet = False
+    # Freeze vae and unet
+    freeze_params(vae.parameters())
+    if not args.do_quantization and not args.do_distillation:
+        # Add the placeholder token in tokenizer
+        num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
+        if num_added_tokens == 0:
+            raise ValueError(
+                f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
+                " `placeholder_token` that is not already in the tokenizer."
+            )
+
+        # Convert the initializer_token, placeholder_token to ids
+        token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
+        # Check if initializer_token is a single token or a sequence of tokens
+        if len(token_ids) > 1:
+            raise ValueError("The initializer token must be a single token.")
+
+        initializer_token_id = token_ids[0]
+        placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
+        # Resize the token embeddings as we are adding new special tokens to the tokenizer
+        text_encoder.resize_token_embeddings(len(tokenizer))
+
+        # Initialise the newly added placeholder token with the embeddings of the initializer token
+        token_embeds = text_encoder.get_input_embeddings().weight.data
+        token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
+
+        freeze_params(unet.parameters())
+        # Freeze all parameters except for the token embeddings in text encoder
+        params_to_freeze = itertools.chain(
+            text_encoder.text_model.encoder.parameters(),
+            text_encoder.text_model.final_layer_norm.parameters(),
+            text_encoder.text_model.embeddings.position_embedding.parameters(),
+        )
+        freeze_params(params_to_freeze)
+    else:
+        train_unet = True
+        freeze_params(text_encoder.parameters())
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    # Initialize the optimizer
+    optimizer = torch.optim.AdamW(
+        # only optimize the unet or embeddings of text_encoder
+        unet.parameters() if train_unet else text_encoder.get_input_embeddings().parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    train_dataset = TextualInversionDataset(
+        data_root=args.train_data_dir,
+        tokenizer=tokenizer,
+        size=args.resolution,
+        placeholder_token=args.placeholder_token,
+        repeats=args.repeats,
+        learnable_property=args.learnable_property,
+        center_crop=args.center_crop,
+        set="train",
+    )
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
+
+    # 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,
+    )
+
+    if not train_unet:
+        text_encoder = accelerator.prepare(text_encoder)
+        unet.to(accelerator.device)
+        unet.eval()
+    else:
+        unet = accelerator.prepare(unet)
+        text_encoder.to(accelerator.device)
+        text_encoder.eval()
+    optimizer, train_dataloader, lr_scheduler = accelerator.prepare(optimizer, train_dataloader, lr_scheduler)
+
+    # Move vae to device
+    vae.to(accelerator.device)
+
+    # Keep vae in eval model as we don't train these
+    vae.eval()
+
+    compression_manager = None
+
+    def train_func(model):
+        if train_unet:
+            unet_ = model
+            text_encoder_ = text_encoder
+        else:
+            unet_ = unet
+            text_encoder_ = model
+        # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+        num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+        if overrode_max_train_steps:
+            args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        # Afterwards we recalculate our number of training epochs
+        args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+        # We need to initialize the trackers we use, and also store our configuration.
+        # The trackers initializes automatically on the main process.
+        if accelerator.is_main_process:
+            accelerator.init_trackers("textual_inversion", config=vars(args))
+
+        # Train!
+        total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+        logger.info("***** Running training *****")
+        logger.info(f"  Num examples = {len(train_dataset)}")
+        logger.info(f"  Num Epochs = {args.num_train_epochs}")
+        logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+        logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+        logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+        logger.info(f"  Total optimization steps = {args.max_train_steps}")
+        # Only show the progress bar once on each machine.
+        progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
+        progress_bar.set_description("Steps")
+        global_step = 0
+
+        if train_unet and args.use_ema:
+            ema_unet = EMAModel(unet_.parameters())
+
+        for epoch in range(args.num_train_epochs):
+            model.train()
+            train_loss = 0.0
+            for step, batch in enumerate(train_dataloader):
+                with accelerator.accumulate(model):
+                    # Convert images to latent space
+                    latents = vae.encode(batch["pixel_values"]).latent_dist.sample().detach()
+                    latents = latents * 0.18215
+
+                    # Sample noise that we'll add to the latents
+                    noise = torch.randn(latents.shape).to(latents.device)
+                    bsz = latents.shape[0]
+                    # Sample a random timestep for each image
+                    timesteps = torch.randint(
+                        0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device
+                    ).long()
+
+                    # Add noise to the latents according to the noise magnitude at each timestep
+                    # (this is the forward diffusion process)
+                    noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                    # Get the text embedding for conditioning
+                    encoder_hidden_states = text_encoder_(batch["input_ids"])[0]
+
+                    # Predict the noise residual
+                    model_pred = unet_(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                    loss = F.mse_loss(model_pred, noise, reduction="none").mean([1, 2, 3]).mean()
+                    if train_unet and compression_manager:
+                        unet_inputs = {
+                            "sample": noisy_latents,
+                            "timestep": timesteps,
+                            "encoder_hidden_states": encoder_hidden_states,
+                        }
+                        loss = compression_manager.callbacks.on_after_compute_loss(unet_inputs, model_pred, loss)
+
+                    # Gather the losses across all processes for logging (if we use distributed training).
+                    avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
+                    train_loss += avg_loss.item() / args.gradient_accumulation_steps
+
+                    # Backpropagate
+                    accelerator.backward(loss)
+
+                    if train_unet:
+                        if accelerator.sync_gradients:
+                            accelerator.clip_grad_norm_(unet_.parameters(), args.max_grad_norm)
+                    else:
+                        # Zero out the gradients for all token embeddings except the newly added
+                        # embeddings for the concept, as we only want to optimize the concept embeddings
+                        if accelerator.num_processes > 1:
+                            grads = text_encoder_.module.get_input_embeddings().weight.grad
+                        else:
+                            grads = text_encoder_.get_input_embeddings().weight.grad
+                        # Get the index for tokens that we want to zero the grads for
+                        index_grads_to_zero = torch.arange(len(tokenizer)) != placeholder_token_id
+                        grads.data[index_grads_to_zero, :] = grads.data[index_grads_to_zero, :].fill_(0)
+
+                    optimizer.step()
+                    lr_scheduler.step()
+                    optimizer.zero_grad()
+
+                # Checks if the accelerator has performed an optimization step behind the scenes
+                if accelerator.sync_gradients:
+                    if train_unet and args.use_ema:
+                        ema_unet.step(unet_.parameters())
+                    progress_bar.update(1)
+                    global_step += 1
+                    accelerator.log({"train_loss": train_loss}, step=global_step)
+                    train_loss = 0.0
+                    if not train_unet and global_step % args.save_steps == 0:
+                        save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
+                        save_progress(text_encoder_, placeholder_token_id, accelerator, args, save_path)
+
+                logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
+                progress_bar.set_postfix(**logs)
+                accelerator.log(logs, step=global_step)
+
+                if global_step >= args.max_train_steps:
+                    break
+            accelerator.wait_for_everyone()
+
+        if train_unet and args.use_ema:
+            ema_unet.copy_to(unet_.parameters())
+
+        if not train_unet:
+            return text_encoder_
+
+    if not train_unet:
+        text_encoder = train_func(text_encoder)
+    else:
+        import copy
+
+        model = copy.deepcopy(unet)
+        confs = []
+        if args.do_quantization:
+            from neural_compressor import QuantizationAwareTrainingConfig
+
+            q_conf = QuantizationAwareTrainingConfig()
+            confs.append(q_conf)
+
+        if args.do_distillation:
+            teacher_model = copy.deepcopy(model)
+
+            def attention_fetcher(x):
+                return x.sample
+
+            layer_mappings = [
+                [
+                    [
+                        "conv_in",
+                    ]
+                ],
+                [
+                    [
+                        "time_embedding",
+                    ]
+                ],
+                [["down_blocks.0.attentions.0", attention_fetcher]],
+                [["down_blocks.0.attentions.1", attention_fetcher]],
+                [
+                    [
+                        "down_blocks.0.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.0.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.0.downsamplers.0",
+                    ]
+                ],
+                [["down_blocks.1.attentions.0", attention_fetcher]],
+                [["down_blocks.1.attentions.1", attention_fetcher]],
+                [
+                    [
+                        "down_blocks.1.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.1.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.1.downsamplers.0",
+                    ]
+                ],
+                [["down_blocks.2.attentions.0", attention_fetcher]],
+                [["down_blocks.2.attentions.1", attention_fetcher]],
+                [
+                    [
+                        "down_blocks.2.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.2.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.2.downsamplers.0",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.3.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "down_blocks.3.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.0.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.0.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.0.resnets.2",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.0.upsamplers.0",
+                    ]
+                ],
+                [["up_blocks.1.attentions.0", attention_fetcher]],
+                [["up_blocks.1.attentions.1", attention_fetcher]],
+                [["up_blocks.1.attentions.2", attention_fetcher]],
+                [
+                    [
+                        "up_blocks.1.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.1.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.1.resnets.2",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.1.upsamplers.0",
+                    ]
+                ],
+                [["up_blocks.2.attentions.0", attention_fetcher]],
+                [["up_blocks.2.attentions.1", attention_fetcher]],
+                [["up_blocks.2.attentions.2", attention_fetcher]],
+                [
+                    [
+                        "up_blocks.2.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.2.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.2.resnets.2",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.2.upsamplers.0",
+                    ]
+                ],
+                [["up_blocks.3.attentions.0", attention_fetcher]],
+                [["up_blocks.3.attentions.1", attention_fetcher]],
+                [["up_blocks.3.attentions.2", attention_fetcher]],
+                [
+                    [
+                        "up_blocks.3.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.3.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "up_blocks.3.resnets.2",
+                    ]
+                ],
+                [["mid_block.attentions.0", attention_fetcher]],
+                [
+                    [
+                        "mid_block.resnets.0",
+                    ]
+                ],
+                [
+                    [
+                        "mid_block.resnets.1",
+                    ]
+                ],
+                [
+                    [
+                        "conv_out",
+                    ]
+                ],
+            ]
+            layer_names = [layer_mapping[0][0] for layer_mapping in layer_mappings]
+            if not set(layer_names).issubset([n[0] for n in model.named_modules()]):
+                raise ValueError(
+                    "Provided model is not compatible with the default layer_mappings, "
+                    'please use the model fine-tuned from "CompVis/stable-diffusion-v1-4", '
+                    "or modify the layer_mappings variable to fit your model."
+                    f"\nDefault layer_mappings are as such:\n{layer_mappings}"
+                )
+            from neural_compressor.config import DistillationConfig, IntermediateLayersKnowledgeDistillationLossConfig
+
+            distillation_criterion = IntermediateLayersKnowledgeDistillationLossConfig(
+                layer_mappings=layer_mappings,
+                loss_types=["MSE"] * len(layer_mappings),
+                loss_weights=[1.0 / len(layer_mappings)] * len(layer_mappings),
+                add_origin_loss=True,
+            )
+            d_conf = DistillationConfig(teacher_model=teacher_model, criterion=distillation_criterion)
+            confs.append(d_conf)
+
+        from neural_compressor.training import prepare_compression
+
+        compression_manager = prepare_compression(model, confs)
+        compression_manager.callbacks.on_train_begin()
+        model = compression_manager.model
+        train_func(model)
+        compression_manager.callbacks.on_train_end()
+
+        # Save the resulting model and its corresponding configuration in the given directory
+        model.save(args.output_dir)
+
+        logger.info(f"Optimized model saved to: {args.output_dir}.")
+
+        # change to framework model for further use
+        model = model.model
+
+    # Create the pipeline using using the trained modules and save it.
+    templates = imagenet_style_templates_small if args.learnable_property == "style" else imagenet_templates_small
+    prompt = templates[0].format(args.placeholder_token)
+    if accelerator.is_main_process:
+        pipeline = StableDiffusionPipeline.from_pretrained(
+            args.pretrained_model_name_or_path,
+            text_encoder=accelerator.unwrap_model(text_encoder),
+            vae=vae,
+            unet=accelerator.unwrap_model(unet),
+            tokenizer=tokenizer,
+        )
+        pipeline.save_pretrained(args.output_dir)
+        pipeline = pipeline.to(unet.device)
+        baseline_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
+        baseline_model_images.save(
+            os.path.join(args.output_dir, "{}_baseline_model.png".format("_".join(prompt.split())))
+        )
+
+        if not train_unet:
+            # Also save the newly trained embeddings
+            save_path = os.path.join(args.output_dir, "learned_embeds.bin")
+            save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
+        else:
+            setattr(pipeline, "unet", accelerator.unwrap_model(model))
+            if args.do_quantization:
+                pipeline = pipeline.to(torch.device("cpu"))
+
+            optimized_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
+            optimized_model_images.save(
+                os.path.join(args.output_dir, "{}_optimized_model.png".format("_".join(prompt.split())))
+            )
+
+        if args.push_to_hub:
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+
+    accelerator.end_training()
+
+    if args.do_quantization and args.verify_loading:
+        # Load the model obtained after Intel Neural Compressor quantization
+        from neural_compressor.utils.pytorch import load
+
+        loaded_model = load(args.output_dir, model=unet)
+        loaded_model.eval()
+
+        setattr(pipeline, "unet", loaded_model)
+        if args.do_quantization:
+            pipeline = pipeline.to(torch.device("cpu"))
+
+        loaded_model_images = generate_images(pipeline, prompt=prompt, seed=args.seed)
+        if loaded_model_images != optimized_model_images:
+            logger.info("The quantized model was not successfully loaded.")
+        else:
+            logger.info("The quantized model was successfully loaded.")
+
+
+if __name__ == "__main__":
+    main()