Added multitoken training for textual inversion. Issue 369 (#661)

* Added multitoken training for textual inversion

* Updated assertion

* Removed duplicate save code

* Fixed undefined bug

* Fixed save

* Added multitoken clip model +util helper

* Removed code splitting

* Removed class

* Fixed errors

* Fixed errors

* Added loading functionality

* Loading via dict instead

* Fixed bug of invalid index being loaded

* Fixed adding placeholder token only adding 1 token

* Fixed bug when initializing tokens

* Fixed bug when initializing tokens

* Removed flawed logic

* Fixed vector shuffle

* Fixed tokenizer's inconsistent __call__ method

* Fixed tokenizer's inconsistent __call__ method

* Handling list input

* Added exception for adding invalid tokens to token map

* Removed unnecessary files and started working on progressive tokens

* Set at minimum load one token

* Changed to global step

* Added method to load automatic1111 tokens

* Fixed bug in load

* Quality+style fixes

* Update quality/style fixes

* Cast embeddings to fp16 when loading

* Fixed quality

* Started moving things over

* Clearing diffs

* Clearing diffs

* Moved everything

* Requested changes

examples/research_projects/mulit_token_textual_inversion/README.md

+## Multi Token Textual Inversion
+The author of this project is [Isamu Isozaki](https://github.com/isamu-isozaki) - please make sure to tag the author for issue and PRs as well as @patrickvonplaten.
+
+We add multi token support to textual inversion. I added
+1. num_vec_per_token for the number of used to reference that token
+2. progressive_tokens for progressively training the token from 1 token to 2 token etc
+3. progressive_tokens_max_steps for the max number of steps until we start full training
+4. vector_shuffle to shuffle vectors
+
+Feel free to add these options to your training! In practice num_vec_per_token around 10+vector shuffle works great!
+
+## Textual Inversion fine-tuning example
+
+[Textual inversion](https://arxiv.org/abs/2208.01618) is a method to personalize text2image models like stable diffusion on your own images using just 3-5 examples.
+The `textual_inversion.py` script shows how to implement the training procedure and adapt it for stable diffusion.
+
+## Running on Colab 
+
+Colab for training 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
+
+Colab for inference
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb)
+
+## Running locally with PyTorch
+### Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+**Important**
+
+To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install .
+```
+
+Then cd in the example folder  and run
+```bash
+pip install -r requirements.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+
+### Cat toy example
+
+You need to accept the model license before downloading or using the weights. In this example we'll use model version `v1-5`, so you'll need to visit [its card](https://huggingface.co/runwayml/stable-diffusion-v1-5), read the license and tick the checkbox if you agree. 
+
+You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
+
+Run the following command to authenticate your token
+
+```bash
+huggingface-cli login
+```
+
+If you have already cloned the repo, then you won't need to go through these steps. 
+
+<br>
+
+Now let's get our dataset.Download 3-4 images from [here](https://drive.google.com/drive/folders/1fmJMs25nxS_rSNqS5hTcRdLem_YQXbq5) and save them in a directory. This will be our training data.
+
+And launch the training using
+
+**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
+
+```bash
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export DATA_DIR="path-to-dir-containing-images"
+
+accelerate launch textual_inversion.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATA_DIR \
+  --learnable_property="object" \
+  --placeholder_token="<cat-toy>" --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="textual_inversion_cat"
+```
+
+A full training run takes ~1 hour on one V100 GPU.
+
+### Inference
+
+Once you have trained a model using above command, the inference can be done simply using the `StableDiffusionPipeline`. Make sure to include the `placeholder_token` in your prompt.
+
+```python
+from diffusers import StableDiffusionPipeline
+
+model_id = "path-to-your-trained-model"
+pipe = StableDiffusionPipeline.from_pretrained(model_id,torch_dtype=torch.float16).to("cuda")
+
+prompt = "A <cat-toy> backpack"
+
+image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
+
+image.save("cat-backpack.png")
+```
+
+
+## Training with Flax/JAX
+
+For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script.
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+```bash
+pip install -U -r requirements_flax.txt
+```
+
+```bash
+export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
+export DATA_DIR="path-to-dir-containing-images"
+
+python textual_inversion_flax.py \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --train_data_dir=$DATA_DIR \
+  --learnable_property="object" \
+  --placeholder_token="<cat-toy>" --initializer_token="toy" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --max_train_steps=3000 \
+  --learning_rate=5.0e-04 --scale_lr \
+  --output_dir="textual_inversion_cat"
+```
+It should be at least 70% faster than the PyTorch script with the same configuration.
+
+### 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. This is not available with the Flax/JAX implementation.

examples/research_projects/mulit_token_textual_inversion/multi_token_clip.py

+"""
+The main idea for this code is to provide a way for users to not need to bother with the hassle of multiple tokens for a concept by typing
+a photo of <concept>_0 <concept>_1 ... and so on
+and instead just do
+a photo of <concept>
+which gets translated to the above. This needs to work for both inference and training.
+For inference,
+the tokenizer encodes the text. So, we would want logic for our tokenizer to replace the placeholder token with
+it's underlying vectors
+For training,
+we would want to abstract away some logic like
+1. Adding tokens
+2. Updating gradient mask
+3. Saving embeddings
+to our Util class here.
+so
+TODO:
+1. have tokenizer keep track of concept, multiconcept pairs and replace during encode call x
+2. have mechanism for adding tokens x
+3. have mech for saving emebeddings x
+4. get mask to update x
+5. Loading tokens from embedding x
+6. Integrate to training x
+7. Test
+"""
+import copy
+import random
+
+from transformers import CLIPTokenizer
+
+
+class MultiTokenCLIPTokenizer(CLIPTokenizer):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.token_map = {}
+
+    def try_adding_tokens(self, placeholder_token, *args, **kwargs):
+        num_added_tokens = super().add_tokens(placeholder_token, *args, **kwargs)
+        if num_added_tokens == 0:
+            raise ValueError(
+                f"The tokenizer already contains the token {placeholder_token}. Please pass a different"
+                " `placeholder_token` that is not already in the tokenizer."
+            )
+
+    def add_placeholder_tokens(self, placeholder_token, *args, num_vec_per_token=1, **kwargs):
+        output = []
+        if num_vec_per_token == 1:
+            self.try_adding_tokens(placeholder_token, *args, **kwargs)
+            output.append(placeholder_token)
+        else:
+            output = []
+            for i in range(num_vec_per_token):
+                ith_token = placeholder_token + f"_{i}"
+                self.try_adding_tokens(ith_token, *args, **kwargs)
+                output.append(ith_token)
+        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
+        for token in self.token_map:
+            if token in placeholder_token:
+                raise ValueError(
+                    f"The tokenizer already has placeholder token {token} that can get confused with"
+                    f" {placeholder_token}keep placeholder tokens independent"
+                )
+        self.token_map[placeholder_token] = output
+
+    def replace_placeholder_tokens_in_text(self, text, vector_shuffle=False, prop_tokens_to_load=1.0):
+        """
+        Here, we replace the placeholder tokens in text recorded in token_map so that the text_encoder
+        can encode them
+        vector_shuffle was inspired by https://github.com/rinongal/textual_inversion/pull/119
+        where shuffling tokens were found to force the model to learn the concepts more descriptively.
+        """
+        if isinstance(text, list):
+            output = []
+            for i in range(len(text)):
+                output.append(self.replace_placeholder_tokens_in_text(text[i], vector_shuffle=vector_shuffle))
+            return output
+        for placeholder_token in self.token_map:
+            if placeholder_token in text:
+                tokens = self.token_map[placeholder_token]
+                tokens = tokens[: 1 + int(len(tokens) * prop_tokens_to_load)]
+                if vector_shuffle:
+                    tokens = copy.copy(tokens)
+                    random.shuffle(tokens)
+                text = text.replace(placeholder_token, " ".join(tokens))
+        return text
+
+    def __call__(self, text, *args, vector_shuffle=False, prop_tokens_to_load=1.0, **kwargs):
+        return super().__call__(
+            self.replace_placeholder_tokens_in_text(
+                text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load
+            ),
+            *args,
+            **kwargs,
+        )
+
+    def encode(self, text, *args, vector_shuffle=False, prop_tokens_to_load=1.0, **kwargs):
+        return super().encode(
+            self.replace_placeholder_tokens_in_text(
+                text, vector_shuffle=vector_shuffle, prop_tokens_to_load=prop_tokens_to_load
+            ),
+            *args,
+            **kwargs,
+        )

examples/research_projects/mulit_token_textual_inversion/requirements.txt

+accelerate
+torchvision
+transformers>=4.25.1
+ftfy
+tensorboard
+Jinja2

examples/research_projects/mulit_token_textual_inversion/requirements_flax.txt

+transformers>=4.25.1
+flax
+optax
+torch
+torchvision
+ftfy
+tensorboard
+Jinja2

examples/research_projects/mulit_token_textual_inversion/textual_inversion.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import argparse
+import logging
+import math
+import os
+import random
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import PIL
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+from multi_token_clip import MultiTokenCLIPTokenizer
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+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
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    StableDiffusionPipeline,
+    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 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,
+    }
+# ------------------------------------------------------------------------------
+
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.14.0.dev0")
+
+logger = get_logger(__name__)
+
+
+def add_tokens(tokenizer, text_encoder, placeholder_token, num_vec_per_token=1, initializer_token=None):
+    """
+    Add tokens to the tokenizer and set the initial value of token embeddings
+    """
+    tokenizer.add_placeholder_tokens(placeholder_token, num_vec_per_token=num_vec_per_token)
+    text_encoder.resize_token_embeddings(len(tokenizer))
+    token_embeds = text_encoder.get_input_embeddings().weight.data
+    placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
+    if initializer_token:
+        token_ids = tokenizer.encode(initializer_token, add_special_tokens=False)
+        for i, placeholder_token_id in enumerate(placeholder_token_ids):
+            token_embeds[placeholder_token_id] = token_embeds[token_ids[i * len(token_ids) // num_vec_per_token]]
+    else:
+        for i, placeholder_token_id in enumerate(placeholder_token_ids):
+            token_embeds[placeholder_token_id] = torch.randn_like(token_embeds[placeholder_token_id])
+    return placeholder_token
+
+
+def save_progress(tokenizer, text_encoder, accelerator, save_path):
+    for placeholder_token in tokenizer.token_map:
+        placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
+        learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_ids]
+        if len(placeholder_token_ids) == 1:
+            learned_embeds = learned_embeds[None]
+        learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}
+        torch.save(learned_embeds_dict, save_path)
+
+
+def load_multitoken_tokenizer(tokenizer, text_encoder, learned_embeds_dict):
+    for placeholder_token in learned_embeds_dict:
+        placeholder_embeds = learned_embeds_dict[placeholder_token]
+        num_vec_per_token = placeholder_embeds.shape[0]
+        placeholder_embeds = placeholder_embeds.to(dtype=text_encoder.dtype)
+        add_tokens(tokenizer, text_encoder, placeholder_token, num_vec_per_token=num_vec_per_token)
+        placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
+        token_embeds = text_encoder.get_input_embeddings().weight.data
+        for i, placeholder_token_id in enumerate(placeholder_token_ids):
+            token_embeds[placeholder_token_id] = placeholder_embeds[i]
+
+
+def load_multitoken_tokenizer_from_automatic(tokenizer, text_encoder, automatic_dict, placeholder_token):
+    """
+    Automatic1111's tokens have format
+    {'string_to_token': {'*': 265}, 'string_to_param': {'*': tensor([[ 0.0833,  0.0030,  0.0057,  ..., -0.0264, -0.0616, -0.0529],
+        [ 0.0058, -0.0190, -0.0584,  ..., -0.0025, -0.0945, -0.0490],
+        [ 0.0916,  0.0025,  0.0365,  ..., -0.0685, -0.0124,  0.0728],
+        [ 0.0812, -0.0199, -0.0100,  ..., -0.0581, -0.0780,  0.0254]],
+       requires_grad=True)}, 'name': 'FloralMarble-400', 'step': 399, 'sd_checkpoint': '4bdfc29c', 'sd_checkpoint_name': 'SD2.1-768'}
+    """
+    learned_embeds_dict = {}
+    learned_embeds_dict[placeholder_token] = automatic_dict["string_to_param"]["*"]
+    load_multitoken_tokenizer(tokenizer, text_encoder, learned_embeds_dict)
+
+
+def get_mask(tokenizer, accelerator):
+    # Get the mask of the weights that won't change
+    mask = torch.ones(len(tokenizer)).to(accelerator.device, dtype=torch.bool)
+    for placeholder_token in tokenizer.token_map:
+        placeholder_token_ids = tokenizer.encode(placeholder_token, add_special_tokens=False)
+        for i in range(len(placeholder_token_ids)):
+            mask = mask & (torch.arange(len(tokenizer)) != placeholder_token_ids[i]).to(accelerator.device)
+    return mask
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--progressive_tokens_max_steps",
+        type=int,
+        default=2000,
+        help="The number of steps until all tokens will be used.",
+    )
+    parser.add_argument(
+        "--progressive_tokens",
+        action="store_true",
+        help="Progressively train the tokens. For example, first train for 1 token, then 2 tokens and so on.",
+    )
+    parser.add_argument("--vector_shuffle", action="store_true", help="Shuffling tokens durint training")
+    parser.add_argument(
+        "--num_vec_per_token",
+        type=int,
+        default=1,
+        help=(
+            "The number of vectors used to represent the placeholder token. The higher the number, the better the"
+            " result at the cost of editability. This can be fixed by prompt editing."
+        ),
+    )
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=500,
+        help="Save learned_embeds.bin every X updates steps.",
+    )
+    parser.add_argument(
+        "--only_save_embeds",
+        action="store_true",
+        default=False,
+        help="Save only the embeddings for the new concept.",
+    )
+    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("--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(
+        "--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(
+        "--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(
+        "--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(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    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(
+        "--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(
+        "--validation_prompt",
+        type=str,
+        default=None,
+        help="A prompt that is used during validation to verify that the model is learning.",
+    )
+    parser.add_argument(
+        "--num_validation_images",
+        type=int,
+        default=4,
+        help="Number of images that should be generated during validation with `validation_prompt`.",
+    )
+    parser.add_argument(
+        "--validation_epochs",
+        type=int,
+        default=50,
+        help=(
+            "Run validation every X epochs. Validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`"
+            " and logging the images."
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=None,
+        help=(
+            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
+            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
+            " for more docs"
+        ),
+    )
+    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(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+
+    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 {}",
+]
+
+
+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,
+        vector_shuffle=False,
+        progressive_tokens=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.vector_shuffle = vector_shuffle
+        self.progressive_tokens = progressive_tokens
+        self.prop_tokens_to_load = 0
+
+        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.encode(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+            vector_shuffle=self.vector_shuffle,
+            prop_tokens_to_load=self.prop_tokens_to_load if self.progressive_tokens else 1.0,
+        )[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 main():
+    args = parse_args()
+    logging_dir = os.path.join(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        logging_dir=logging_dir,
+        project_config=accelerator_project_config,
+    )
+
+    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.")
+        import wandb
+
+    # 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.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
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+
+            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 tokenizer
+    if args.tokenizer_name:
+        tokenizer = MultiTokenCLIPTokenizer.from_pretrained(args.tokenizer_name)
+    elif args.pretrained_model_name_or_path:
+        tokenizer = MultiTokenCLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
+
+    # 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
+    )
+    if is_xformers_available():
+        try:
+            unet.enable_xformers_memory_efficient_attention()
+        except Exception as e:
+            logger.warning(
+                "Could not enable memory efficient attention. Make sure xformers is installed"
+                f" correctly and a GPU is available: {e}"
+            )
+    add_tokens(tokenizer, text_encoder, args.placeholder_token, args.num_vec_per_token, args.initializer_token)
+
+    # Freeze vae and unet
+    vae.requires_grad_(False)
+    unet.requires_grad_(False)
+    # Freeze all parameters except for the token embeddings in text encoder
+    text_encoder.text_model.encoder.requires_grad_(False)
+    text_encoder.text_model.final_layer_norm.requires_grad_(False)
+    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
+
+    if args.gradient_checkpointing:
+        # Keep unet in train mode if we are using gradient checkpointing to save memory.
+        # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
+        unet.train()
+        text_encoder.gradient_checkpointing_enable()
+        unet.enable_gradient_checkpointing()
+
+    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")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    # Initialize the optimizer
+    optimizer = torch.optim.AdamW(
+        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
+        lr=args.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 = 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, num_workers=args.dataloader_num_workers
+    )
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+    )
+
+    # Prepare everything with our `accelerator`.
+    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # For mixed precision training we cast the unet and vae weights to half-precision
+    # as these models 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 and unet to device and cast to weight_dtype
+    unet.to(accelerator.device, dtype=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:
+        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}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1] if len(dirs) > 0 else None
+
+        if path is None:
+            accelerator.print(
+                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+            )
+            args.resume_from_checkpoint = None
+        else:
+            accelerator.print(f"Resuming from checkpoint {path}")
+            accelerator.load_state(os.path.join(args.output_dir, path))
+            global_step = int(path.split("-")[1])
+
+            resume_global_step = global_step * args.gradient_accumulation_steps
+            first_epoch = global_step // num_update_steps_per_epoch
+            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar.set_description("Steps")
+
+    # keep original embeddings as reference
+    orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        text_encoder.train()
+        for step, batch in enumerate(train_dataloader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % args.gradient_accumulation_steps == 0:
+                    progress_bar.update(1)
+                continue
+            if args.progressive_tokens:
+                train_dataset.prop_tokens_to_load = float(global_step) / args.progressive_tokens_max_steps
+
+            with accelerator.accumulate(text_encoder):
+                # Convert images to latent space
+                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
+                latents = latents * vae.config.scaling_factor
+
+                # 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)
+
+                # Get the text embedding for conditioning
+                encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
+
+                # Predict the noise residual
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                # Get the target for loss depending on the prediction type
+                if noise_scheduler.config.prediction_type == "epsilon":
+                    target = noise
+                elif noise_scheduler.config.prediction_type == "v_prediction":
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                accelerator.backward(loss)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+                # Let's make sure we don't update any embedding weights besides the newly added token
+                index_no_updates = get_mask(tokenizer, accelerator)
+                with torch.no_grad():
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
+                        index_no_updates
+                    ] = orig_embeds_params[index_no_updates]
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+                if global_step % args.save_steps == 0:
+                    save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
+                    save_progress(tokenizer, text_encoder, accelerator, save_path)
+
+                if global_step % args.checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+            logs = {"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
+
+        if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
+            logger.info(
+                f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+                f" {args.validation_prompt}."
+            )
+            # create pipeline (note: unet and vae are loaded again in float32)
+            pipeline = DiffusionPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                text_encoder=accelerator.unwrap_model(text_encoder),
+                tokenizer=tokenizer,
+                unet=unet,
+                vae=vae,
+                revision=args.revision,
+                torch_dtype=weight_dtype,
+            )
+            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)
+            )
+            images = []
+            for _ in range(args.num_validation_images):
+                with torch.autocast("cuda"):
+                    image = pipeline(args.validation_prompt, num_inference_steps=25, 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=f"{i}: {args.validation_prompt}")
+                                for i, image in enumerate(images)
+                            ]
+                        }
+                    )
+
+            del pipeline
+            torch.cuda.empty_cache()
+
+    # Create the pipeline using using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        if args.push_to_hub and args.only_save_embeds:
+            logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
+            save_full_model = True
+        else:
+            save_full_model = not args.only_save_embeds
+        if save_full_model:
+            pipeline = StableDiffusionPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                text_encoder=accelerator.unwrap_model(text_encoder),
+                vae=vae,
+                unet=unet,
+                tokenizer=tokenizer,
+            )
+            pipeline.save_pretrained(args.output_dir)
+        # Save the newly trained embeddings
+        save_path = os.path.join(args.output_dir, "learned_embeds.bin")
+        save_progress(tokenizer, text_encoder, accelerator, save_path)
+
+        if args.push_to_hub:
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    main()

examples/research_projects/mulit_token_textual_inversion/textual_inversion_flax.py

+import argparse
+import logging
+import math
+import os
+import random
+from pathlib import Path
+from typing import Optional
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+import PIL
+import torch
+import torch.utils.checkpoint
+import transformers
+from flax import jax_utils
+from flax.training import train_state
+from flax.training.common_utils import shard
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+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 CLIPFeatureExtractor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
+
+from diffusers import (
+    FlaxAutoencoderKL,
+    FlaxDDPMScheduler,
+    FlaxPNDMScheduler,
+    FlaxStableDiffusionPipeline,
+    FlaxUNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
+from diffusers.utils import check_min_version
+
+
+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,
+    }
+# ------------------------------------------------------------------------------
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.14.0.dev0")
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    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(
+        "--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("--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(
+        "--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=True,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    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_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("--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(
+        "--use_auth_token",
+        action="store_true",
+        help=(
+            "Will use the token generated when running `huggingface-cli login` (necessary to use this script with"
+            " private models)."
+        ),
+    )
+    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("--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 {}",
+]
+
+
+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 resize_token_embeddings(model, new_num_tokens, initializer_token_id, placeholder_token_id, rng):
+    if model.config.vocab_size == new_num_tokens or new_num_tokens is None:
+        return
+    model.config.vocab_size = new_num_tokens
+
+    params = model.params
+    old_embeddings = params["text_model"]["embeddings"]["token_embedding"]["embedding"]
+    old_num_tokens, emb_dim = old_embeddings.shape
+
+    initializer = jax.nn.initializers.normal()
+
+    new_embeddings = initializer(rng, (new_num_tokens, emb_dim))
+    new_embeddings = new_embeddings.at[:old_num_tokens].set(old_embeddings)
+    new_embeddings = new_embeddings.at[placeholder_token_id].set(new_embeddings[initializer_token_id])
+    params["text_model"]["embeddings"]["token_embedding"]["embedding"] = new_embeddings
+
+    model.params = params
+    return model
+
+
+def get_params_to_save(params):
+    return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params))
+
+
+def main():
+    args = parse_args()
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    if jax.process_index() == 0:
+        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
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+
+            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)
+
+    # 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,
+    )
+    # Setup logging, we only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+
+    # 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")
+
+    # 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)
+
+    # Load models and create wrapper for stable diffusion
+    text_encoder = FlaxCLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
+    vae, vae_params = FlaxAutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
+    unet, unet_params = FlaxUNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
+
+    # Create sampling rng
+    rng = jax.random.PRNGKey(args.seed)
+    rng, _ = jax.random.split(rng)
+    # Resize the token embeddings as we are adding new special tokens to the tokenizer
+    text_encoder = resize_token_embeddings(
+        text_encoder, len(tokenizer), initializer_token_id, placeholder_token_id, rng
+    )
+    original_token_embeds = text_encoder.params["text_model"]["embeddings"]["token_embedding"]["embedding"]
+
+    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",
+    )
+
+    def collate_fn(examples):
+        pixel_values = torch.stack([example["pixel_values"] for example in examples])
+        input_ids = torch.stack([example["input_ids"] for example in examples])
+
+        batch = {"pixel_values": pixel_values, "input_ids": input_ids}
+        batch = {k: v.numpy() for k, v in batch.items()}
+
+        return batch
+
+    total_train_batch_size = args.train_batch_size * jax.local_device_count()
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset, batch_size=total_train_batch_size, shuffle=True, drop_last=True, collate_fn=collate_fn
+    )
+
+    # Optimization
+    if args.scale_lr:
+        args.learning_rate = args.learning_rate * total_train_batch_size
+
+    constant_scheduler = optax.constant_schedule(args.learning_rate)
+
+    optimizer = optax.adamw(
+        learning_rate=constant_scheduler,
+        b1=args.adam_beta1,
+        b2=args.adam_beta2,
+        eps=args.adam_epsilon,
+        weight_decay=args.adam_weight_decay,
+    )
+
+    def create_mask(params, label_fn):
+        def _map(params, mask, label_fn):
+            for k in params:
+                if label_fn(k):
+                    mask[k] = "token_embedding"
+                else:
+                    if isinstance(params[k], dict):
+                        mask[k] = {}
+                        _map(params[k], mask[k], label_fn)
+                    else:
+                        mask[k] = "zero"
+
+        mask = {}
+        _map(params, mask, label_fn)
+        return mask
+
+    def zero_grads():
+        # from https://github.com/deepmind/optax/issues/159#issuecomment-896459491
+        def init_fn(_):
+            return ()
+
+        def update_fn(updates, state, params=None):
+            return jax.tree_util.tree_map(jnp.zeros_like, updates), ()
+
+        return optax.GradientTransformation(init_fn, update_fn)
+
+    # Zero out gradients of layers other than the token embedding layer
+    tx = optax.multi_transform(
+        {"token_embedding": optimizer, "zero": zero_grads()},
+        create_mask(text_encoder.params, lambda s: s == "token_embedding"),
+    )
+
+    state = train_state.TrainState.create(apply_fn=text_encoder.__call__, params=text_encoder.params, tx=tx)
+
+    noise_scheduler = FlaxDDPMScheduler(
+        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000
+    )
+    noise_scheduler_state = noise_scheduler.create_state()
+
+    # Initialize our training
+    train_rngs = jax.random.split(rng, jax.local_device_count())
+
+    # Define gradient train step fn
+    def train_step(state, vae_params, unet_params, batch, train_rng):
+        dropout_rng, sample_rng, new_train_rng = jax.random.split(train_rng, 3)
+
+        def compute_loss(params):
+            vae_outputs = vae.apply(
+                {"params": vae_params}, batch["pixel_values"], deterministic=True, method=vae.encode
+            )
+            latents = vae_outputs.latent_dist.sample(sample_rng)
+            # (NHWC) -> (NCHW)
+            latents = jnp.transpose(latents, (0, 3, 1, 2))
+            latents = latents * vae.config.scaling_factor
+
+            noise_rng, timestep_rng = jax.random.split(sample_rng)
+            noise = jax.random.normal(noise_rng, latents.shape)
+            bsz = latents.shape[0]
+            timesteps = jax.random.randint(
+                timestep_rng,
+                (bsz,),
+                0,
+                noise_scheduler.config.num_train_timesteps,
+            )
+            noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps)
+            encoder_hidden_states = state.apply_fn(
+                batch["input_ids"], params=params, dropout_rng=dropout_rng, train=True
+            )[0]
+            # Predict the noise residual and compute loss
+            model_pred = unet.apply(
+                {"params": unet_params}, noisy_latents, timesteps, encoder_hidden_states, train=False
+            ).sample
+
+            # Get the target for loss depending on the prediction type
+            if noise_scheduler.config.prediction_type == "epsilon":
+                target = noise
+            elif noise_scheduler.config.prediction_type == "v_prediction":
+                target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps)
+            else:
+                raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+            loss = (target - model_pred) ** 2
+            loss = loss.mean()
+
+            return loss
+
+        grad_fn = jax.value_and_grad(compute_loss)
+        loss, grad = grad_fn(state.params)
+        grad = jax.lax.pmean(grad, "batch")
+        new_state = state.apply_gradients(grads=grad)
+
+        # Keep the token embeddings fixed except the newly added embeddings for the concept,
+        # as we only want to optimize the concept embeddings
+        token_embeds = original_token_embeds.at[placeholder_token_id].set(
+            new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"][placeholder_token_id]
+        )
+        new_state.params["text_model"]["embeddings"]["token_embedding"]["embedding"] = token_embeds
+
+        metrics = {"loss": loss}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
+        return new_state, metrics, new_train_rng
+
+    # Create parallel version of the train and eval step
+    p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
+
+    # Replicate the train state on each device
+    state = jax_utils.replicate(state)
+    vae_params = jax_utils.replicate(vae_params)
+    unet_params = jax_utils.replicate(unet_params)
+
+    # Train!
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader))
+
+    # Scheduler and math around the number of training steps.
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    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) = {total_train_batch_size}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+
+    global_step = 0
+
+    epochs = tqdm(range(args.num_train_epochs), desc=f"Epoch ... (1/{args.num_train_epochs})", position=0)
+    for epoch in epochs:
+        # ======================== Training ================================
+
+        train_metrics = []
+
+        steps_per_epoch = len(train_dataset) // total_train_batch_size
+        train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
+        # train
+        for batch in train_dataloader:
+            batch = shard(batch)
+            state, train_metric, train_rngs = p_train_step(state, vae_params, unet_params, batch, train_rngs)
+            train_metrics.append(train_metric)
+
+            train_step_progress_bar.update(1)
+            global_step += 1
+
+            if global_step >= args.max_train_steps:
+                break
+
+        train_metric = jax_utils.unreplicate(train_metric)
+
+        train_step_progress_bar.close()
+        epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})")
+
+    # Create the pipeline using using the trained modules and save it.
+    if jax.process_index() == 0:
+        scheduler = FlaxPNDMScheduler(
+            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
+        )
+        safety_checker = FlaxStableDiffusionSafetyChecker.from_pretrained(
+            "CompVis/stable-diffusion-safety-checker", from_pt=True
+        )
+        pipeline = FlaxStableDiffusionPipeline(
+            text_encoder=text_encoder,
+            vae=vae,
+            unet=unet,
+            tokenizer=tokenizer,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
+        )
+
+        pipeline.save_pretrained(
+            args.output_dir,
+            params={
+                "text_encoder": get_params_to_save(state.params),
+                "vae": get_params_to_save(vae_params),
+                "unet": get_params_to_save(unet_params),
+                "safety_checker": safety_checker.params,
+            },
+        )
+
+        # Also save the newly trained embeddings
+        learned_embeds = get_params_to_save(state.params)["text_model"]["embeddings"]["token_embedding"]["embedding"][
+            placeholder_token_id
+        ]
+        learned_embeds_dict = {args.placeholder_token: learned_embeds}
+        jnp.save(os.path.join(args.output_dir, "learned_embeds.npy"), learned_embeds_dict)
+
+        if args.push_to_hub:
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+
+
+if __name__ == "__main__":
+    main()