diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 1be09fdda0d6..71ee0d177e5a 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -90,6 +90,8 @@
       title: Configuration
     - local: api/outputs
       title: Outputs
+    - local: api/loaders
+      title: Loaders
     title: Main Classes
   - sections:
     - local: api/pipelines/overview
diff --git a/docs/source/en/api/loaders.mdx b/docs/source/en/api/loaders.mdx
new file mode 100644
index 000000000000..c850cdfe472b
--- /dev/null
+++ b/docs/source/en/api/loaders.mdx
@@ -0,0 +1,30 @@
+<!--Copyright 2022 The HuggingFace 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 limitations under the License.
+-->
+
+# Loaders
+
+There are many weights to train adapter neural networks for diffusion models, such as 
+- [Textual Inversion](./training/text_inversion.mdx)
+- [LoRA](https://github.com/cloneofsimo/lora)
+- [Hypernetworks](https://arxiv.org/abs/1609.09106)
+
+Such adapter neural networks often only consist of a fraction of the number of weights compared 
+to the pretrained model and as such are very portable. The Diffusers library offers an easy-to-use
+API to load such adapter neural networks via the [`loaders.py` module](https://github.com/huggingface/diffusers/blob/main/src/diffusers/loaders.py). 
+
+**Note**: This module is still highly experimental and prone to future changes.
+
+## LoaderMixins
+
+### UNet2DConditionLoadersMixin
+
+[[autodoc]] loaders.UNet2DConditionLoadersMixin
diff --git a/docs/source/en/api/logging.mdx b/docs/source/en/api/logging.mdx
index 5d75ec8e0114..b726d46d92a1 100644
--- a/docs/source/en/api/logging.mdx
+++ b/docs/source/en/api/logging.mdx
@@ -1,4 +1,4 @@
-<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+<!--Copyright 2022 The HuggingFace 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
diff --git a/examples/dreambooth/README.md b/examples/dreambooth/README.md
index 28ac0b4f6a96..e5ab64975cc7 100644
--- a/examples/dreambooth/README.md
+++ b/examples/dreambooth/README.md
@@ -5,6 +5,7 @@ The `train_dreambooth.py` script shows how to implement the training procedure a
 
 
 ## Running locally with PyTorch
+
 ### Installing the dependencies
 
 Before running the scripts, make sure to install the library's training dependencies:
@@ -235,6 +236,102 @@ image.save("dog-bucket.png")
 
 You can also perform inference from one of the checkpoints saved during the training process, if you used the `--checkpointing_steps` argument. Please, refer to [the documentation](https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint) to see how to do it.
 
+## Training with Low-Rank Adaptation of Large Language Models (LoRA)
+
+Low-Rank Adaption of Large Language Models was first introduced by Microsoft in [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) by *Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen*
+
+In a nutshell, LoRA allows to adapt pretrained models by adding pairs of rank-decomposition matrices to existing weights and **only** training those newly added weights. This has a couple of advantages:
+- Previous pretrained weights are kept frozen so that the model is not prone to [catastrophic forgetting](https://www.pnas.org/doi/10.1073/pnas.1611835114)
+- Rank-decomposition matrices have significantly fewer parameters than the original model, which means that trained LoRA weights are easily portable.
+- LoRA attention layers allow to control to which extent the model is adapted torwards new training images via a `scale` parameter.
+
+[cloneofsimo](https://github.com/cloneofsimo) was the first to try out LoRA training for Stable Diffusion in 
+the popular [lora](https://github.com/cloneofsimo/lora) GitHub repository.
+
+### Training
+
+Let's get started with a simple example. We will re-use the dog example of the [previous section](#dog-toy-example).
+
+First, you need to set-up your dreambooth training example as is explained in the [installation section](#Installing-the-dependencies).
+Next, let's download the dog dataset. Download images from [here](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ) and save them in a directory. Make sure to set `INSTANCE_DIR` to the name of your directory further below. This will be our training data.
+
+Now, you can launch the training. Here we will use [Stable Diffusion 1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
+
+**___Note: Change the `resolution` to 768 if you are using the [stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) 768x768 model.___**
+
+**___Note: It is quite useful to monitor the training progress by regularly generating sample images during training. [wandb](https://docs.wandb.ai/quickstart) is a nice solution to easily see generating images during training. All you need to do is to run `pip install wandb` before training and pass `--report_to="wandb"` to automatically log images.___**
+
+
+```bash
+export MODEL_NAME="runwayml/stable-diffusion-v1-5"
+export INSTANCE_DIR="path-to-instance-images"
+export OUTPUT_DIR="path-to-save-model"
+```
+
+For this example we want to directly store the trained LoRA embeddings on the Hub, so 
+we need to be logged in and add the `--push_to_hub` flag.
+
+```bash
+huggingface-cli login
+```
+
+Now we can start training!
+
+```bash
+accelerate launch train_dreambooth_lora.py \
+  --pretrained_model_name_or_path=$MODEL_NAME  \
+  --instance_data_dir=$INSTANCE_DIR \
+  --output_dir=$OUTPUT_DIR \
+  --instance_prompt="a photo of sks dog" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=1 \
+  --checkpointing_steps=100 \
+  --learning_rate=1e-4 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=500 \
+  --validation_prompt="A photo of sks dog in a bucket" \
+  --validation_epochs=50 \
+  --seed="0" \
+  --push_to_hub
+```
+
+**___Note: When using LoRA we can use a much higher learning rate compared to vanilla dreambooth. Here we 
+use *1e-4* instead of the usual *2e-6*.___**
+
+The final LoRA embedding weights have been uploaded to [patrickvonplaten/lora_dreambooth_dog_example](https://huggingface.co/patrickvonplaten/lora_dreambooth_dog_example). **___Note: [The final weights](https://huggingface.co/patrickvonplaten/lora/blob/main/pytorch_attn_procs.bin) are only 3 MB in size which is orders of magnitudes smaller than the original model.**
+
+The training results are summarized [here](https://api.wandb.ai/report/patrickvonplaten/xm6cd5q5).
+You can use the `Step` slider to see how the model learned the features of our subject while the model trained.
+
+### Inference
+
+After training, LoRA weights can be loaded very easily into the original pipeline. First, you need to 
+load the original pipeline:
+
+```python
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+import torch
+
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.to("cuda")
+```
+
+Next, we can load the adapter layers into the UNet with the [`load_attn_procs` function](https://huggingface.co/docs/diffusers/api/loaders#diffusers.loaders.UNet2DConditionLoadersMixin.load_attn_procs).
+
+```python
+pipe.load_attn_procs("patrickvonplaten/lora")
+```
+
+Finally, we can run the model in inference.
+
+```python
+image = pipe("A picture of a sks dog in a bucket", num_inference_steps=25).images[0]
+```
+
 ## 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.
diff --git a/examples/dreambooth/train_dreambooth.py b/examples/dreambooth/train_dreambooth.py
index aac720ffa474..654616eb9849 100644
--- a/examples/dreambooth/train_dreambooth.py
+++ b/examples/dreambooth/train_dreambooth.py
@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 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 hashlib
 import itertools
diff --git a/examples/dreambooth/train_dreambooth_lora.py b/examples/dreambooth/train_dreambooth_lora.py
new file mode 100644
index 000000000000..66986e6c8f00
--- /dev/null
+++ b/examples/dreambooth/train_dreambooth_lora.py
@@ -0,0 +1,950 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 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 hashlib
+import logging
+import math
+import os
+import warnings
+from pathlib import Path
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.utils.data import Dataset
+
+import datasets
+import diffusers
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    DPMSolverMultistepScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.loaders import AttnProcsLayers
+from diffusers.models.cross_attention import LoRACrossAttnProcessor
+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
+from huggingface_hub import HfFolder, Repository, create_repo, whoami
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, PretrainedConfig
+
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.12.0.dev0")
+
+logger = get_logger(__name__)
+
+
+def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path,
+        subfolder="text_encoder",
+        revision=revision,
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "RobertaSeriesModelWithTransformation":
+        from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
+
+        return RobertaSeriesModelWithTransformation
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--pretrained_model_name_or_path",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--instance_data_dir",
+        type=str,
+        default=None,
+        required=True,
+        help="A folder containing the training data of instance images.",
+    )
+    parser.add_argument(
+        "--class_data_dir",
+        type=str,
+        default=None,
+        required=False,
+        help="A folder containing the training data of class images.",
+    )
+    parser.add_argument(
+        "--instance_prompt",
+        type=str,
+        default=None,
+        required=True,
+        help="The prompt with identifier specifying the instance",
+    )
+    parser.add_argument(
+        "--class_prompt",
+        type=str,
+        default=None,
+        help="The prompt to specify images in the same class as provided instance images.",
+    )
+    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 dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
+            " `args.validation_prompt` multiple times: `args.num_validation_images`."
+        ),
+    )
+    parser.add_argument(
+        "--with_prior_preservation",
+        default=False,
+        action="store_true",
+        help="Flag to add prior preservation loss.",
+    )
+    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
+    parser.add_argument(
+        "--num_class_images",
+        type=int,
+        default=100,
+        help=(
+            "Minimal class images for prior preservation loss. If there are not enough images already present in"
+            " class_data_dir, additional images will be sampled with class_prompt."
+        ),
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lora-dreambooth-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=4, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument(
+        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=1)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
+            " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-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(
+        "--lr_num_cycles",
+        type=int,
+        default=1,
+        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
+    )
+    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
+    parser.add_argument(
+        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--hub_model_id",
+        type=str,
+        default=None,
+        help="The name of the repository to keep in sync with the local `output_dir`.",
+    )
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--prior_generation_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp32", "fp16", "bf16"],
+        help=(
+            "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to  fp16 if a GPU is available else fp32."
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+
+    if input_args is not None:
+        args = parser.parse_args(input_args)
+    else:
+        args = parser.parse_args()
+
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    if args.with_prior_preservation:
+        if args.class_data_dir is None:
+            raise ValueError("You must specify a data directory for class images.")
+        if args.class_prompt is None:
+            raise ValueError("You must specify prompt for class images.")
+    else:
+        # logger is not available yet
+        if args.class_data_dir is not None:
+            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
+        if args.class_prompt is not None:
+            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
+
+    return args
+
+
+class DreamBoothDataset(Dataset):
+    """
+    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
+    It pre-processes the images and the tokenizes prompts.
+    """
+
+    def __init__(
+        self,
+        instance_data_root,
+        instance_prompt,
+        tokenizer,
+        class_data_root=None,
+        class_prompt=None,
+        size=512,
+        center_crop=False,
+    ):
+        self.size = size
+        self.center_crop = center_crop
+        self.tokenizer = tokenizer
+
+        self.instance_data_root = Path(instance_data_root)
+        if not self.instance_data_root.exists():
+            raise ValueError("Instance images root doesn't exists.")
+
+        self.instance_images_path = list(Path(instance_data_root).iterdir())
+        self.num_instance_images = len(self.instance_images_path)
+        self.instance_prompt = instance_prompt
+        self._length = self.num_instance_images
+
+        if class_data_root is not None:
+            self.class_data_root = Path(class_data_root)
+            self.class_data_root.mkdir(parents=True, exist_ok=True)
+            self.class_images_path = list(self.class_data_root.iterdir())
+            self.num_class_images = len(self.class_images_path)
+            self._length = max(self.num_class_images, self.num_instance_images)
+            self.class_prompt = class_prompt
+        else:
+            self.class_data_root = None
+
+        self.image_transforms = transforms.Compose(
+            [
+                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, index):
+        example = {}
+        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
+        if not instance_image.mode == "RGB":
+            instance_image = instance_image.convert("RGB")
+        example["instance_images"] = self.image_transforms(instance_image)
+        example["instance_prompt_ids"] = self.tokenizer(
+            self.instance_prompt,
+            truncation=True,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids
+
+        if self.class_data_root:
+            class_image = Image.open(self.class_images_path[index % self.num_class_images])
+            if not class_image.mode == "RGB":
+                class_image = class_image.convert("RGB")
+            example["class_images"] = self.image_transforms(class_image)
+            example["class_prompt_ids"] = self.tokenizer(
+                self.class_prompt,
+                truncation=True,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                return_tensors="pt",
+            ).input_ids
+
+        return example
+
+
+def collate_fn(examples, with_prior_preservation=False):
+    input_ids = [example["instance_prompt_ids"] for example in examples]
+    pixel_values = [example["instance_images"] for example in examples]
+
+    # Concat class and instance examples for prior preservation.
+    # We do this to avoid doing two forward passes.
+    if with_prior_preservation:
+        input_ids += [example["class_prompt_ids"] for example in examples]
+        pixel_values += [example["class_images"] for example in examples]
+
+    pixel_values = torch.stack(pixel_values)
+    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+
+    input_ids = torch.cat(input_ids, dim=0)
+
+    batch = {
+        "input_ids": input_ids,
+        "pixel_values": pixel_values,
+    }
+    return batch
+
+
+class PromptDataset(Dataset):
+    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
+
+    def __init__(self, prompt, num_samples):
+        self.prompt = prompt
+        self.num_samples = num_samples
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, index):
+        example = {}
+        example["prompt"] = self.prompt
+        example["index"] = index
+        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):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        logging_dir=logging_dir,
+    )
+
+    if args.report_to == "wandb":
+        if not is_wandb_available():
+            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
+        import wandb
+
+    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
+    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
+    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
+    # 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:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Generate class images if prior preservation is enabled.
+    if args.with_prior_preservation:
+        class_images_dir = Path(args.class_data_dir)
+        if not class_images_dir.exists():
+            class_images_dir.mkdir(parents=True)
+        cur_class_images = len(list(class_images_dir.iterdir()))
+
+        if cur_class_images < args.num_class_images:
+            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
+            if args.prior_generation_precision == "fp32":
+                torch_dtype = torch.float32
+            elif args.prior_generation_precision == "fp16":
+                torch_dtype = torch.float16
+            elif args.prior_generation_precision == "bf16":
+                torch_dtype = torch.bfloat16
+            pipeline = DiffusionPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                torch_dtype=torch_dtype,
+                safety_checker=None,
+                revision=args.revision,
+            )
+            pipeline.set_progress_bar_config(disable=True)
+
+            num_new_images = args.num_class_images - cur_class_images
+            logger.info(f"Number of class images to sample: {num_new_images}.")
+
+            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
+            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
+
+            sample_dataloader = accelerator.prepare(sample_dataloader)
+            pipeline.to(accelerator.device)
+
+            for example in tqdm(
+                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
+            ):
+                images = pipeline(example["prompt"]).images
+
+                for i, image in enumerate(images):
+                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
+                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
+                    image.save(image_filename)
+
+            del pipeline
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+
+    # 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_name = create_repo(repo_name, exist_ok=True)
+            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
+    if args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
+    elif args.pretrained_model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            args.pretrained_model_name_or_path,
+            subfolder="tokenizer",
+            revision=args.revision,
+            use_fast=False,
+        )
+
+    # import correct text encoder class
+    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
+
+    # Load scheduler and models
+    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
+    text_encoder = text_encoder_cls.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
+    )
+
+    # We only train the additional adapter LoRA layers
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    unet.requires_grad_(False)
+
+    # For mixed precision training we cast the text_encoder 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 unet, vae and text_encoder to device and cast to weight_dtype
+    unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # now we will add new LoRA weights to the attention layers
+    # It's important to realize here how many attention weights will be added and of which sizes
+    # The sizes of the attention layers consist only of two different variables:
+    # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
+    # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
+
+    # Let's first see how many attention processors we will have to set.
+    # For Stable Diffusion, it should be equal to:
+    # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
+    # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
+    # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
+    # => 32 layers
+
+    # Set correct lora layers
+    lora_attn_procs = {}
+    for name in unet.attn_processors.keys():
+        cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+        if name.startswith("mid_block"):
+            hidden_size = unet.config.block_out_channels[-1]
+        elif name.startswith("up_blocks"):
+            block_id = int(name[len("up_blocks.")])
+            hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+        elif name.startswith("down_blocks"):
+            block_id = int(name[len("down_blocks.")])
+            hidden_size = unet.config.block_out_channels[block_id]
+
+        lora_attn_procs[name] = LoRACrossAttnProcessor(
+            hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
+        )
+
+    unet.set_attn_processor(lora_attn_procs)
+    lora_layers = AttnProcsLayers(unet.attn_processors)
+
+    accelerator.register_for_checkpointing(lora_layers)
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    # 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
+        )
+
+    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
+    if args.use_8bit_adam:
+        try:
+            import bitsandbytes as bnb
+        except ImportError:
+            raise ImportError(
+                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
+            )
+
+        optimizer_class = bnb.optim.AdamW8bit
+    else:
+        optimizer_class = torch.optim.AdamW
+
+    # Optimizer creation
+    optimizer = optimizer_class(
+        lora_layers.parameters(),
+        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 = DreamBoothDataset(
+        instance_data_root=args.instance_data_dir,
+        instance_prompt=args.instance_prompt,
+        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
+        class_prompt=args.class_prompt,
+        tokenizer=tokenizer,
+        size=args.resolution,
+        center_crop=args.center_crop,
+    )
+
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        batch_size=args.train_batch_size,
+        shuffle=True,
+        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
+        num_workers=1,
+    )
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+        num_cycles=args.lr_num_cycles,
+        power=args.lr_power,
+    )
+
+    # Prepare everything with our `accelerator`.
+    lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        lora_layers, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        accelerator.init_trackers("dreambooth-lora", 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 batches each epoch = {len(train_dataloader)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the mos recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1]
+        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 = resume_global_step // num_update_steps_per_epoch
+        resume_step = resume_global_step % num_update_steps_per_epoch
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar.set_description("Steps")
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        unet.train()
+        for step, batch in enumerate(train_dataloader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % args.gradient_accumulation_steps == 0:
+                    progress_bar.update(1)
+                continue
+
+            with accelerator.accumulate(unet):
+                # Convert images to latent space
+                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
+                latents = latents * 0.18215
+
+                # 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]
+
+                # 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}")
+
+                if args.with_prior_preservation:
+                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
+                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
+                    target, target_prior = torch.chunk(target, 2, dim=0)
+
+                    # Compute instance loss
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                    # Compute prior loss
+                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
+
+                    # Add the prior loss to the instance loss.
+                    loss = loss + args.prior_loss_weight * prior_loss
+                else:
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    params_to_clip = lora_layers.parameters()
+                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            # 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.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
+            pipeline = DiffusionPipeline.from_pretrained(
+                args.pretrained_model_name_or_path,
+                unet=accelerator.unwrap_model(unet),
+                text_encoder=accelerator.unwrap_model(text_encoder),
+                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 = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+            prompt = args.num_validation_images * [args.validation_prompt]
+            images = pipeline(prompt, num_inference_steps=25, generator=generator).images
+
+            for tracker in accelerator.trackers:
+                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()
+
+    # Save the lora layers
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unet = unet.to(torch.float32)
+        unet.save_attn_procs(args.output_dir)
+
+        if args.push_to_hub:
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+
+    # Final inference
+    # Load previous pipeline
+    pipeline = DiffusionPipeline.from_pretrained(
+        args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
+    )
+    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+    pipeline = pipeline.to(accelerator.device)
+
+    # load attention processors
+    pipeline.unet.load_attn_procs(args.output_dir)
+
+    # run inference
+    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+    prompt = args.num_validation_images * [args.validation_prompt]
+    images = pipeline(prompt, num_inference_steps=25, generator=generator).images
+
+    for tracker in accelerator.trackers:
+        if tracker.name == "wandb":
+            tracker.log(
+                {
+                    "test": [
+                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
+                    ]
+                }
+            )
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)
diff --git a/examples/text_to_image/train_text_to_image.py b/examples/text_to_image/train_text_to_image.py
index 841849dcf3bf..b77f90566799 100644
--- a/examples/text_to_image/train_text_to_image.py
+++ b/examples/text_to_image/train_text_to_image.py
@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 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 copy
 import logging
diff --git a/examples/textual_inversion/textual_inversion.py b/examples/textual_inversion/textual_inversion.py
index 11e145e63a3f..b58abc0dcd29 100644
--- a/examples/textual_inversion/textual_inversion.py
+++ b/examples/textual_inversion/textual_inversion.py
@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2022 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
diff --git a/src/diffusers/dependency_versions_check.py b/src/diffusers/dependency_versions_check.py
index bbf863222a52..c6330299700c 100644
--- a/src/diffusers/dependency_versions_check.py
+++ b/src/diffusers/dependency_versions_check.py
@@ -1,4 +1,4 @@
-# Copyright 2020 The HuggingFace Team. All rights reserved.
+# Copyright 2022 The HuggingFace 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.
diff --git a/src/diffusers/dependency_versions_table.py b/src/diffusers/dependency_versions_table.py
index 1ef1edc14629..7fc779fc543e 100644
--- a/src/diffusers/dependency_versions_table.py
+++ b/src/diffusers/dependency_versions_table.py
@@ -4,7 +4,7 @@
 deps = {
     "Pillow": "Pillow",
     "accelerate": "accelerate>=0.11.0",
-    "black": "black==22.8",
+    "black": "black==22.12",
     "datasets": "datasets",
     "filelock": "filelock",
     "flake8": "flake8>=3.8.3",
diff --git a/src/diffusers/loaders.py b/src/diffusers/loaders.py
new file mode 100644
index 000000000000..61754ed6d8ed
--- /dev/null
+++ b/src/diffusers/loaders.py
@@ -0,0 +1,243 @@
+# Copyright 2022 The HuggingFace 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
+# limitations under the License.
+import os
+from collections import defaultdict
+from typing import Callable, Dict, Union
+
+import torch
+
+from .models.cross_attention import LoRACrossAttnProcessor
+from .models.modeling_utils import _get_model_file
+from .utils import DIFFUSERS_CACHE, HF_HUB_OFFLINE, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+LORA_WEIGHT_NAME = "pytorch_lora_weights.bin"
+
+
+class AttnProcsLayers(torch.nn.Module):
+    def __init__(self, state_dict: Dict[str, torch.Tensor]):
+        super().__init__()
+        self.layers = torch.nn.ModuleList(state_dict.values())
+        self.mapping = {k: v for k, v in enumerate(state_dict.keys())}
+        self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}
+
+        # we add a hook to state_dict() and load_state_dict() so that the
+        # naming fits with `unet.attn_processors`
+        def map_to(module, state_dict, *args, **kwargs):
+            new_state_dict = {}
+            for key, value in state_dict.items():
+                num = int(key.split(".")[1])  # 0 is always "layers"
+                new_key = key.replace(f"layers.{num}", module.mapping[num])
+                new_state_dict[new_key] = value
+
+            return new_state_dict
+
+        def map_from(module, state_dict, *args, **kwargs):
+            all_keys = list(state_dict.keys())
+            for key in all_keys:
+                replace_key = key.split(".processor")[0] + ".processor"
+                new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
+                state_dict[new_key] = state_dict[key]
+                del state_dict[key]
+
+        self._register_state_dict_hook(map_to)
+        self._register_load_state_dict_pre_hook(map_from, with_module=True)
+
+
+class UNet2DConditionLoadersMixin:
+    def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
+        r"""
+        Load pretrained attention processor layers into `UNet2DConditionModel`. Attention processor layers have to be
+        defined in
+        [cross_attention.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py)
+        and be a `torch.nn.Module` class.
+
+        <Tip warning={true}>
+
+            This function is experimental and might change in the future
+
+        </Tip>
+
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                Can be either:
+
+                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
+                    - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
+                      `./my_model_directory/`.
+                    - A [torch state
+                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
+
+            cache_dir (`Union[str, os.PathLike]`, *optional*):
+                Path to a directory in which a downloaded pretrained model configuration should be cached if the
+                standard cache should not be used.
+            force_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+            resume_download (`bool`, *optional*, defaults to `False`):
+                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
+                file exists.
+            proxies (`Dict[str, str]`, *optional*):
+                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            local_files_only(`bool`, *optional*, defaults to `False`):
+                Whether or not to only look at local files (i.e., do not try to download the model).
+            use_auth_token (`str` or *bool*, *optional*):
+                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
+                when running `diffusers-cli login` (stored in `~/.huggingface`).
+            revision (`str`, *optional*, defaults to `"main"`):
+                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
+                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
+                identifier allowed by git.
+            subfolder (`str`, *optional*, defaults to `""`):
+                In case the relevant files are located inside a subfolder of the model repo (either remote in
+                huggingface.co or downloaded locally), you can specify the folder name here.
+
+            mirror (`str`, *optional*):
+                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
+                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
+                Please refer to the mirror site for more information.
+
+        <Tip>
+
+         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
+         models](https://huggingface.co/docs/hub/models-gated#gated-models).
+
+        </Tip>
+
+        <Tip>
+
+        Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
+        this method in a firewalled environment.
+
+        </Tip>
+        """
+
+        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
+        force_download = kwargs.pop("force_download", False)
+        resume_download = kwargs.pop("resume_download", False)
+        proxies = kwargs.pop("proxies", None)
+        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+        revision = kwargs.pop("revision", None)
+        subfolder = kwargs.pop("subfolder", None)
+        weight_name = kwargs.pop("weight_name", LORA_WEIGHT_NAME)
+
+        user_agent = {
+            "file_type": "attn_procs_weights",
+            "framework": "pytorch",
+        }
+
+        if not isinstance(pretrained_model_name_or_path_or_dict, dict):
+            model_file = _get_model_file(
+                pretrained_model_name_or_path_or_dict,
+                weights_name=weight_name,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+                revision=revision,
+                subfolder=subfolder,
+                user_agent=user_agent,
+            )
+            state_dict = torch.load(model_file, map_location="cpu")
+        else:
+            state_dict = pretrained_model_name_or_path_or_dict
+
+        # fill attn processors
+        attn_processors = {}
+
+        is_lora = all("lora" in k for k in state_dict.keys())
+
+        if is_lora:
+            lora_grouped_dict = defaultdict(dict)
+            for key, value in state_dict.items():
+                attn_processor_key, sub_key = ".".join(key.split(".")[:-3]), ".".join(key.split(".")[-3:])
+                lora_grouped_dict[attn_processor_key][sub_key] = value
+
+            for key, value_dict in lora_grouped_dict.items():
+                rank = value_dict["to_k_lora.down.weight"].shape[0]
+                cross_attention_dim = value_dict["to_k_lora.down.weight"].shape[1]
+                hidden_size = value_dict["to_k_lora.up.weight"].shape[0]
+
+                attn_processors[key] = LoRACrossAttnProcessor(
+                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=rank
+                )
+                attn_processors[key].load_state_dict(value_dict)
+
+        else:
+            raise ValueError(f"{model_file} does not seem to be in the correct format expected by LoRA training.")
+
+        # set correct dtype & device
+        attn_processors = {k: v.to(device=self.device, dtype=self.dtype) for k, v in attn_processors.items()}
+
+        # set layers
+        self.set_attn_processor(attn_processors)
+
+    def save_attn_procs(
+        self,
+        save_directory: Union[str, os.PathLike],
+        is_main_process: bool = True,
+        weights_name: str = LORA_WEIGHT_NAME,
+        save_function: Callable = None,
+    ):
+        r"""
+        Save an attention procesor to a directory, so that it can be re-loaded using the
+        `[`~loaders.UNet2DConditionLoadersMixin.load_attn_procs`]` method.
+
+        Arguments:
+            save_directory (`str` or `os.PathLike`):
+                Directory to which to save. Will be created if it doesn't exist.
+            is_main_process (`bool`, *optional*, defaults to `True`):
+                Whether the process calling this is the main process or not. Useful when in distributed training like
+                TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
+                the main process to avoid race conditions.
+            save_function (`Callable`):
+                The function to use to save the state dictionary. Useful on distributed training like TPUs when one
+                need to replace `torch.save` by another method. Can be configured with the environment variable
+                `DIFFUSERS_SAVE_MODE`.
+        """
+        if os.path.isfile(save_directory):
+            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
+            return
+
+        if save_function is None:
+            save_function = torch.save
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        model_to_save = AttnProcsLayers(self.attn_processors)
+
+        # Save the model
+        state_dict = model_to_save.state_dict()
+
+        # Clean the folder from a previous save
+        for filename in os.listdir(save_directory):
+            full_filename = os.path.join(save_directory, filename)
+            # If we have a shard file that is not going to be replaced, we delete it, but only from the main process
+            # in distributed settings to avoid race conditions.
+            weights_no_suffix = weights_name.replace(".bin", "")
+            if filename.startswith(weights_no_suffix) and os.path.isfile(full_filename) and is_main_process:
+                os.remove(full_filename)
+
+        # Save the model
+        save_function(state_dict, os.path.join(save_directory, weights_name))
+
+        logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}")
diff --git a/src/diffusers/models/cross_attention.py b/src/diffusers/models/cross_attention.py
index b2dc52db1558..d4da50c23f66 100644
--- a/src/diffusers/models/cross_attention.py
+++ b/src/diffusers/models/cross_attention.py
@@ -246,6 +246,68 @@ def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=No
         return hidden_states
 
 
+class LoRALinearLayer(nn.Module):
+    def __init__(self, in_features, out_features, rank=4):
+        super().__init__()
+
+        if rank > min(in_features, out_features):
+            raise ValueError(f"LoRA rank {rank} must be less or equal than {min(in_features, out_features)}")
+
+        self.down = nn.Linear(in_features, rank, bias=False)
+        self.up = nn.Linear(rank, out_features, bias=False)
+        self.scale = 1.0
+
+        nn.init.normal_(self.down.weight, std=1 / rank)
+        nn.init.zeros_(self.up.weight)
+
+    def forward(self, hidden_states):
+        orig_dtype = hidden_states.dtype
+        dtype = self.down.weight.dtype
+
+        down_hidden_states = self.down(hidden_states.to(dtype))
+        up_hidden_states = self.up(down_hidden_states)
+
+        return up_hidden_states.to(orig_dtype)
+
+
+class LoRACrossAttnProcessor(nn.Module):
+    def __init__(self, hidden_size, cross_attention_dim=None, rank=4):
+        super().__init__()
+
+        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size)
+        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size)
+
+    def __call__(
+        self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0
+    ):
+        batch_size, sequence_length, _ = hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
+
+        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
+        query = attn.head_to_batch_dim(query)
+
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+
+        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
+
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        return hidden_states
+
+
 class CrossAttnAddedKVProcessor:
     def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None):
         residual = hidden_states
@@ -312,6 +374,41 @@ def __call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=No
         hidden_states = attn.to_out[0](hidden_states)
         # dropout
         hidden_states = attn.to_out[1](hidden_states)
+        return hidden_states
+
+
+class LoRAXFormersCrossAttnProcessor(nn.Module):
+    def __init__(self, hidden_size, cross_attention_dim, rank=4):
+        super().__init__()
+
+        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size)
+        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size)
+        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size)
+
+    def __call__(
+        self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None, scale=1.0
+    ):
+        batch_size, sequence_length, _ = hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
+
+        query = attn.to_q(hidden_states) + scale * self.to_q_lora(hidden_states)
+        query = attn.head_to_batch_dim(query).contiguous()
+
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+
+        key = attn.to_k(encoder_hidden_states) + scale * self.to_k_lora(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states) + scale * self.to_v_lora(encoder_hidden_states)
+
+        key = attn.head_to_batch_dim(key).contiguous()
+        value = attn.head_to_batch_dim(value).contiguous()
+
+        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states) + scale * self.to_out_lora(hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
 
         return hidden_states
 
diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py
index 26e913c4b9be..1822bb49d561 100644
--- a/src/diffusers/models/modeling_utils.py
+++ b/src/diffusers/models/modeling_utils.py
@@ -438,7 +438,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
         model_file = None
         if from_flax:
-            model_file = cls._get_model_file(
+            model_file = _get_model_file(
                 pretrained_model_name_or_path,
                 weights_name=FLAX_WEIGHTS_NAME,
                 cache_dir=cache_dir,
@@ -474,7 +474,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         else:
             if is_safetensors_available():
                 try:
-                    model_file = cls._get_model_file(
+                    model_file = _get_model_file(
                         pretrained_model_name_or_path,
                         weights_name=SAFETENSORS_WEIGHTS_NAME,
                         cache_dir=cache_dir,
@@ -490,7 +490,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 except:
                     pass
             if model_file is None:
-                model_file = cls._get_model_file(
+                model_file = _get_model_file(
                     pretrained_model_name_or_path,
                     weights_name=WEIGHTS_NAME,
                     cache_dir=cache_dir,
@@ -599,92 +599,6 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
         return model
 
-    @classmethod
-    def _get_model_file(
-        cls,
-        pretrained_model_name_or_path,
-        *,
-        weights_name,
-        subfolder,
-        cache_dir,
-        force_download,
-        proxies,
-        resume_download,
-        local_files_only,
-        use_auth_token,
-        user_agent,
-        revision,
-    ):
-        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-        if os.path.isdir(pretrained_model_name_or_path):
-            if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
-                # Load from a PyTorch checkpoint
-                model_file = os.path.join(pretrained_model_name_or_path, weights_name)
-            elif subfolder is not None and os.path.isfile(
-                os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
-            ):
-                model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name)
-            else:
-                raise EnvironmentError(
-                    f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
-                )
-            return model_file
-        else:
-            try:
-                # Load from URL or cache if already cached
-                model_file = hf_hub_download(
-                    pretrained_model_name_or_path,
-                    filename=weights_name,
-                    cache_dir=cache_dir,
-                    force_download=force_download,
-                    proxies=proxies,
-                    resume_download=resume_download,
-                    local_files_only=local_files_only,
-                    use_auth_token=use_auth_token,
-                    user_agent=user_agent,
-                    subfolder=subfolder,
-                    revision=revision,
-                )
-                return model_file
-
-            except RepositoryNotFoundError:
-                raise EnvironmentError(
-                    f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
-                    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
-                    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
-                    "login`."
-                )
-            except RevisionNotFoundError:
-                raise EnvironmentError(
-                    f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
-                    "this model name. Check the model page at "
-                    f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
-                )
-            except EntryNotFoundError:
-                raise EnvironmentError(
-                    f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."
-                )
-            except HTTPError as err:
-                raise EnvironmentError(
-                    "There was a specific connection error when trying to load"
-                    f" {pretrained_model_name_or_path}:\n{err}"
-                )
-            except ValueError:
-                raise EnvironmentError(
-                    f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
-                    f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
-                    f" directory containing a file named {weights_name} or"
-                    " \nCheckout your internet connection or see how to run the library in"
-                    " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
-                )
-            except EnvironmentError:
-                raise EnvironmentError(
-                    f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
-                    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
-                    f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
-                    f"containing a file named {weights_name}"
-                )
-
     @classmethod
     def _load_pretrained_model(
         cls,
@@ -848,7 +762,9 @@ def _get_model_file(
     revision,
 ):
     pretrained_model_name_or_path = str(pretrained_model_name_or_path)
-    if os.path.isdir(pretrained_model_name_or_path):
+    if os.path.isfile(pretrained_model_name_or_path):
+        return pretrained_model_name_or_path
+    elif os.path.isdir(pretrained_model_name_or_path):
         if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)):
             # Load from a PyTorch checkpoint
             model_file = os.path.join(pretrained_model_name_or_path, weights_name)
diff --git a/src/diffusers/models/unet_2d_condition.py b/src/diffusers/models/unet_2d_condition.py
index 8099cd8421fb..ee7bb812e88b 100644
--- a/src/diffusers/models/unet_2d_condition.py
+++ b/src/diffusers/models/unet_2d_condition.py
@@ -19,6 +19,7 @@
 import torch.utils.checkpoint
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..loaders import UNet2DConditionLoadersMixin
 from ..utils import BaseOutput, logging
 from .cross_attention import AttnProcessor
 from .embeddings import TimestepEmbedding, Timesteps
@@ -49,7 +50,7 @@ class UNet2DConditionOutput(BaseOutput):
     sample: torch.FloatTensor
 
 
-class UNet2DConditionModel(ModelMixin, ConfigMixin):
+class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
     r"""
     UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep
     and returns sample shaped output.
@@ -266,17 +267,59 @@ def __init__(
         self.conv_act = nn.SiLU()
         self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
 
-    def set_attn_processor(self, processor: AttnProcessor):
+    @property
+    def attn_processors(self) -> Dict[str, AttnProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
         # set recursively
-        def fn_recursive_attn_processor(module: torch.nn.Module):
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttnProcessor]):
             if hasattr(module, "set_processor"):
-                module.set_processor(processor)
+                processors[f"{name}.processor"] = module.processor
 
-            for child in module.children():
-                fn_recursive_attn_processor(child)
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
 
-        for module in self.children():
-            fn_recursive_attn_processor(module)
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]):
+        r"""
+        Parameters:
+            `processor (`dict` of `AttnProcessor` or `AttnProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                of **all** `CrossAttention` layers.
+            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.:
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
 
     def set_attention_slice(self, slice_size):
         r"""
diff --git a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
index 9b647811285e..e52ebcbafc9c 100644
--- a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
+++ b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
@@ -353,17 +353,59 @@ def __init__(
         self.conv_act = nn.SiLU()
         self.conv_out = LinearMultiDim(block_out_channels[0], out_channels, kernel_size=3, padding=1)
 
-    def set_attn_processor(self, processor: AttnProcessor):
+    @property
+    def attn_processors(self) -> Dict[str, AttnProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
         # set recursively
-        def fn_recursive_attn_processor(module: torch.nn.Module):
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttnProcessor]):
             if hasattr(module, "set_processor"):
-                module.set_processor(processor)
+                processors[f"{name}.processor"] = module.processor
 
-            for child in module.children():
-                fn_recursive_attn_processor(child)
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
 
-        for module in self.children():
-            fn_recursive_attn_processor(module)
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]):
+        r"""
+        Parameters:
+            `processor (`dict` of `AttnProcessor` or `AttnProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                of **all** `CrossAttention` layers.
+            In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.:
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
 
     def set_attention_slice(self, slice_size):
         r"""
diff --git a/src/diffusers/utils/__init__.py b/src/diffusers/utils/__init__.py
index 3d059f3f944e..e1d4d88e71cd 100644
--- a/src/diffusers/utils/__init__.py
+++ b/src/diffusers/utils/__init__.py
@@ -58,6 +58,7 @@
     is_transformers_available,
     is_transformers_version,
     is_unidecode_available,
+    is_wandb_available,
     is_xformers_available,
     requires_backends,
 )
diff --git a/src/diffusers/utils/import_utils.py b/src/diffusers/utils/import_utils.py
index 91d1d091889a..7aae03033c51 100644
--- a/src/diffusers/utils/import_utils.py
+++ b/src/diffusers/utils/import_utils.py
@@ -217,6 +217,13 @@
 except importlib_metadata.PackageNotFoundError:
     _k_diffusion_available = False
 
+_wandb_available = importlib.util.find_spec("wandb") is not None
+try:
+    _wandb_version = importlib_metadata.version("wandb")
+    logger.debug(f"Successfully imported k-diffusion version {_wandb_version }")
+except importlib_metadata.PackageNotFoundError:
+    _wandb_available = False
+
 
 def is_torch_available():
     return _torch_available
@@ -274,6 +281,10 @@ def is_k_diffusion_available():
     return _k_diffusion_available
 
 
+def is_wandb_available():
+    return _wandb_available
+
+
 # docstyle-ignore
 FLAX_IMPORT_ERROR = """
 {0} requires the FLAX library but it was not found in your environment. Checkout the instructions on the
@@ -328,6 +339,12 @@ def is_k_diffusion_available():
 install k-diffusion`
 """
 
+# docstyle-ignore
+WANDB_IMPORT_ERROR = """
+{0} requires the wandb library but it was not found in your environment. You can install it with pip: `pip
+install wandb`
+"""
+
 
 BACKENDS_MAPPING = OrderedDict(
     [
@@ -340,6 +357,7 @@ def is_k_diffusion_available():
         ("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
         ("librosa", (is_librosa_available, LIBROSA_IMPORT_ERROR)),
         ("k_diffusion", (is_k_diffusion_available, K_DIFFUSION_IMPORT_ERROR)),
+        ("wandb", (is_wandb_available, WANDB_IMPORT_ERROR)),
     ]
 )
 
diff --git a/src/diffusers/utils/logging.py b/src/diffusers/utils/logging.py
index 8c1c77d10b2a..39a021562dae 100644
--- a/src/diffusers/utils/logging.py
+++ b/src/diffusers/utils/logging.py
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 Optuna, Hugging Face
+# Copyright 2022 Optuna, Hugging Face
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/tests/models/test_models_unet_2d.py b/tests/models/test_models_unet_2d.py
index 91192f17fb00..39cd98a14726 100644
--- a/tests/models/test_models_unet_2d.py
+++ b/tests/models/test_models_unet_2d.py
@@ -20,18 +20,8 @@
 
 import torch
 
-from diffusers import UNet2DConditionModel, UNet2DModel
-from diffusers.utils import (
-    floats_tensor,
-    load_hf_numpy,
-    logging,
-    require_torch_gpu,
-    slow,
-    torch_all_close,
-    torch_device,
-)
-from diffusers.utils.import_utils import is_xformers_available
-from parameterized import parameterized
+from diffusers import UNet2DModel
+from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
 
 from ..test_modeling_common import ModelTesterMixin
 
@@ -218,237 +208,6 @@ def test_output_pretrained(self):
         self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3))
 
 
-class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
-    model_class = UNet2DConditionModel
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_channels = 4
-        sizes = (32, 32)
-
-        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-        time_step = torch.tensor([10]).to(torch_device)
-        encoder_hidden_states = floats_tensor((batch_size, 4, 32)).to(torch_device)
-
-        return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
-
-    @property
-    def input_shape(self):
-        return (4, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (4, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "block_out_channels": (32, 64),
-            "down_block_types": ("CrossAttnDownBlock2D", "DownBlock2D"),
-            "up_block_types": ("UpBlock2D", "CrossAttnUpBlock2D"),
-            "cross_attention_dim": 32,
-            "attention_head_dim": 8,
-            "out_channels": 4,
-            "in_channels": 4,
-            "layers_per_block": 2,
-            "sample_size": 32,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_enable_works(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-
-        model.enable_xformers_memory_efficient_attention()
-
-        assert (
-            model.mid_block.attentions[0].transformer_blocks[0].attn1._use_memory_efficient_attention_xformers
-        ), "xformers is not enabled"
-
-    @unittest.skipIf(torch_device == "mps", "Gradient checkpointing skipped on MPS")
-    def test_gradient_checkpointing(self):
-        # enable deterministic behavior for gradient checkpointing
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        assert not model.is_gradient_checkpointing and model.training
-
-        out = model(**inputs_dict).sample
-        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
-        # we won't calculate the loss and rather backprop on out.sum()
-        model.zero_grad()
-
-        labels = torch.randn_like(out)
-        loss = (out - labels).mean()
-        loss.backward()
-
-        # re-instantiate the model now enabling gradient checkpointing
-        model_2 = self.model_class(**init_dict)
-        # clone model
-        model_2.load_state_dict(model.state_dict())
-        model_2.to(torch_device)
-        model_2.enable_gradient_checkpointing()
-
-        assert model_2.is_gradient_checkpointing and model_2.training
-
-        out_2 = model_2(**inputs_dict).sample
-        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
-        # we won't calculate the loss and rather backprop on out.sum()
-        model_2.zero_grad()
-        loss_2 = (out_2 - labels).mean()
-        loss_2.backward()
-
-        # compare the output and parameters gradients
-        self.assertTrue((loss - loss_2).abs() < 1e-5)
-        named_params = dict(model.named_parameters())
-        named_params_2 = dict(model_2.named_parameters())
-        for name, param in named_params.items():
-            self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
-
-    def test_model_with_attention_head_dim_tuple(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = (8, 16)
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            output = model(**inputs_dict)
-
-            if isinstance(output, dict):
-                output = output.sample
-
-        self.assertIsNotNone(output)
-        expected_shape = inputs_dict["sample"].shape
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-    def test_model_with_use_linear_projection(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["use_linear_projection"] = True
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        with torch.no_grad():
-            output = model(**inputs_dict)
-
-            if isinstance(output, dict):
-                output = output.sample
-
-        self.assertIsNotNone(output)
-        expected_shape = inputs_dict["sample"].shape
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-    def test_model_attention_slicing(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = (8, 16)
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-        model.eval()
-
-        model.set_attention_slice("auto")
-        with torch.no_grad():
-            output = model(**inputs_dict)
-        assert output is not None
-
-        model.set_attention_slice("max")
-        with torch.no_grad():
-            output = model(**inputs_dict)
-        assert output is not None
-
-        model.set_attention_slice(2)
-        with torch.no_grad():
-            output = model(**inputs_dict)
-        assert output is not None
-
-    def test_model_slicable_head_dim(self):
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = (8, 16)
-
-        model = self.model_class(**init_dict)
-
-        def check_slicable_dim_attr(module: torch.nn.Module):
-            if hasattr(module, "set_attention_slice"):
-                assert isinstance(module.sliceable_head_dim, int)
-
-            for child in module.children():
-                check_slicable_dim_attr(child)
-
-        # retrieve number of attention layers
-        for module in model.children():
-            check_slicable_dim_attr(module)
-
-    def test_special_attn_proc(self):
-        class AttnEasyProc(torch.nn.Module):
-            def __init__(self, num):
-                super().__init__()
-                self.weight = torch.nn.Parameter(torch.tensor(num))
-                self.is_run = False
-                self.number = 0
-                self.counter = 0
-
-            def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, number=None):
-                batch_size, sequence_length, _ = hidden_states.shape
-                attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
-
-                query = attn.to_q(hidden_states)
-
-                encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
-                key = attn.to_k(encoder_hidden_states)
-                value = attn.to_v(encoder_hidden_states)
-
-                query = attn.head_to_batch_dim(query)
-                key = attn.head_to_batch_dim(key)
-                value = attn.head_to_batch_dim(value)
-
-                attention_probs = attn.get_attention_scores(query, key, attention_mask)
-                hidden_states = torch.bmm(attention_probs, value)
-                hidden_states = attn.batch_to_head_dim(hidden_states)
-
-                # linear proj
-                hidden_states = attn.to_out[0](hidden_states)
-                # dropout
-                hidden_states = attn.to_out[1](hidden_states)
-
-                hidden_states += self.weight
-
-                self.is_run = True
-                self.counter += 1
-                self.number = number
-
-                return hidden_states
-
-        # enable deterministic behavior for gradient checkpointing
-        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
-
-        init_dict["attention_head_dim"] = (8, 16)
-
-        model = self.model_class(**init_dict)
-        model.to(torch_device)
-
-        processor = AttnEasyProc(5.0)
-
-        model.set_attn_processor(processor)
-        model(**inputs_dict, cross_attention_kwargs={"number": 123}).sample
-
-        assert processor.counter == 12
-        assert processor.is_run
-        assert processor.number == 123
-
-
 class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
     model_class = UNet2DModel
 
@@ -564,310 +323,3 @@ def test_output_pretrained_ve_large(self):
     def test_forward_with_norm_groups(self):
         # not required for this model
         pass
-
-
-@slow
-class UNet2DConditionModelIntegrationTests(unittest.TestCase):
-    def get_file_format(self, seed, shape):
-        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
-
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
-        dtype = torch.float16 if fp16 else torch.float32
-        image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
-        return image
-
-    def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
-        revision = "fp16" if fp16 else None
-        torch_dtype = torch.float16 if fp16 else torch.float32
-
-        model = UNet2DConditionModel.from_pretrained(
-            model_id, subfolder="unet", torch_dtype=torch_dtype, revision=revision
-        )
-        model.to(torch_device).eval()
-
-        return model
-
-    def test_set_attention_slice_auto(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        unet = self.get_unet_model()
-        unet.set_attention_slice("auto")
-
-        latents = self.get_latents(33)
-        encoder_hidden_states = self.get_encoder_hidden_states(33)
-        timestep = 1
-
-        with torch.no_grad():
-            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-
-        assert mem_bytes < 5 * 10**9
-
-    def test_set_attention_slice_max(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        unet = self.get_unet_model()
-        unet.set_attention_slice("max")
-
-        latents = self.get_latents(33)
-        encoder_hidden_states = self.get_encoder_hidden_states(33)
-        timestep = 1
-
-        with torch.no_grad():
-            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-
-        assert mem_bytes < 5 * 10**9
-
-    def test_set_attention_slice_int(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        unet = self.get_unet_model()
-        unet.set_attention_slice(2)
-
-        latents = self.get_latents(33)
-        encoder_hidden_states = self.get_encoder_hidden_states(33)
-        timestep = 1
-
-        with torch.no_grad():
-            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-
-        assert mem_bytes < 5 * 10**9
-
-    def test_set_attention_slice_list(self):
-        torch.cuda.empty_cache()
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.reset_peak_memory_stats()
-
-        # there are 32 slicable layers
-        slice_list = 16 * [2, 3]
-        unet = self.get_unet_model()
-        unet.set_attention_slice(slice_list)
-
-        latents = self.get_latents(33)
-        encoder_hidden_states = self.get_encoder_hidden_states(33)
-        timestep = 1
-
-        with torch.no_grad():
-            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-
-        assert mem_bytes < 5 * 10**9
-
-    def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):
-        dtype = torch.float16 if fp16 else torch.float32
-        hidden_states = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
-        return hidden_states
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, 4, [-0.4424, 0.1510, -0.1937, 0.2118, 0.3746, -0.3957, 0.0160, -0.0435]],
-            [47, 0.55, [-0.1508, 0.0379, -0.3075, 0.2540, 0.3633, -0.0821, 0.1719, -0.0207]],
-            [21, 0.89, [-0.6479, 0.6364, -0.3464, 0.8697, 0.4443, -0.6289, -0.0091, 0.1778]],
-            [9, 1000, [0.8888, -0.5659, 0.5834, -0.7469, 1.1912, -0.3923, 1.1241, -0.4424]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_compvis_sd_v1_4(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4")
-        latents = self.get_latents(seed)
-        encoder_hidden_states = self.get_encoder_hidden_states(seed)
-
-        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
-
-        with torch.no_grad():
-            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        assert sample.shape == latents.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
-            [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
-            [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
-            [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_compvis_sd_v1_4_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4", fp16=True)
-        latents = self.get_latents(seed, fp16=True)
-        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
-
-        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
-
-        with torch.no_grad():
-            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        assert sample.shape == latents.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, 4, [-0.4430, 0.1570, -0.1867, 0.2376, 0.3205, -0.3681, 0.0525, -0.0722]],
-            [47, 0.55, [-0.1415, 0.0129, -0.3136, 0.2257, 0.3430, -0.0536, 0.2114, -0.0436]],
-            [21, 0.89, [-0.7091, 0.6664, -0.3643, 0.9032, 0.4499, -0.6541, 0.0139, 0.1750]],
-            [9, 1000, [0.8878, -0.5659, 0.5844, -0.7442, 1.1883, -0.3927, 1.1192, -0.4423]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5")
-        latents = self.get_latents(seed)
-        encoder_hidden_states = self.get_encoder_hidden_states(seed)
-
-        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
-
-        with torch.no_grad():
-            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        assert sample.shape == latents.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [83, 4, [-0.2695, -0.1669, 0.0073, -0.3181, -0.1187, -0.1676, -0.1395, -0.5972]],
-            [17, 0.55, [-0.1290, -0.2588, 0.0551, -0.0916, 0.3286, 0.0238, -0.3669, 0.0322]],
-            [8, 0.89, [-0.5283, 0.1198, 0.0870, -0.1141, 0.9189, -0.0150, 0.5474, 0.4319]],
-            [3, 1000, [-0.5601, 0.2411, -0.5435, 0.1268, 1.1338, -0.2427, -0.0280, -1.0020]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5", fp16=True)
-        latents = self.get_latents(seed, fp16=True)
-        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
-
-        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
-
-        with torch.no_grad():
-            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        assert sample.shape == latents.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [33, 4, [-0.7639, 0.0106, -0.1615, -0.3487, -0.0423, -0.7972, 0.0085, -0.4858]],
-            [47, 0.55, [-0.6564, 0.0795, -1.9026, -0.6258, 1.8235, 1.2056, 1.2169, 0.9073]],
-            [21, 0.89, [0.0327, 0.4399, -0.6358, 0.3417, 0.4120, -0.5621, -0.0397, -1.0430]],
-            [9, 1000, [0.1600, 0.7303, -1.0556, -0.3515, -0.7440, -1.2037, -1.8149, -1.8931]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting")
-        latents = self.get_latents(seed, shape=(4, 9, 64, 64))
-        encoder_hidden_states = self.get_encoder_hidden_states(seed)
-
-        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
-
-        with torch.no_grad():
-            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        assert sample.shape == (4, 4, 64, 64)
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [83, 4, [-0.1047, -1.7227, 0.1067, 0.0164, -0.5698, -0.4172, -0.1388, 1.1387]],
-            [17, 0.55, [0.0975, -0.2856, -0.3508, -0.4600, 0.3376, 0.2930, -0.2747, -0.7026]],
-            [8, 0.89, [-0.0952, 0.0183, -0.5825, -0.1981, 0.1131, 0.4668, -0.0395, -0.3486]],
-            [3, 1000, [0.4790, 0.4949, -1.0732, -0.7158, 0.7959, -0.9478, 0.1105, -0.9741]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting", fp16=True)
-        latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
-        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
-
-        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
-
-        with torch.no_grad():
-            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        assert sample.shape == (4, 4, 64, 64)
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
-
-    @parameterized.expand(
-        [
-            # fmt: off
-            [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
-            [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
-            [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
-            [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
-            # fmt: on
-        ]
-    )
-    @require_torch_gpu
-    def test_stabilityai_sd_v2_fp16(self, seed, timestep, expected_slice):
-        model = self.get_unet_model(model_id="stabilityai/stable-diffusion-2", fp16=True)
-        latents = self.get_latents(seed, shape=(4, 4, 96, 96), fp16=True)
-        encoder_hidden_states = self.get_encoder_hidden_states(seed, shape=(4, 77, 1024), fp16=True)
-
-        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
-
-        with torch.no_grad():
-            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
-
-        assert sample.shape == latents.shape
-
-        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
-        expected_output_slice = torch.tensor(expected_slice)
-
-        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
diff --git a/tests/models/test_models_unet_2d_condition.py b/tests/models/test_models_unet_2d_condition.py
new file mode 100644
index 000000000000..f8f29e9f232d
--- /dev/null
+++ b/tests/models/test_models_unet_2d_condition.py
@@ -0,0 +1,688 @@
+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# 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
+# limitations under the License.
+
+import gc
+import tempfile
+import unittest
+
+import torch
+
+from diffusers import UNet2DConditionModel
+from diffusers.models.cross_attention import LoRACrossAttnProcessor
+from diffusers.utils import (
+    floats_tensor,
+    load_hf_numpy,
+    logging,
+    require_torch_gpu,
+    slow,
+    torch_all_close,
+    torch_device,
+)
+from diffusers.utils.import_utils import is_xformers_available
+from parameterized import parameterized
+
+from ..test_modeling_common import ModelTesterMixin
+
+
+logger = logging.get_logger(__name__)
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
+    model_class = UNet2DConditionModel
+
+    @property
+    def dummy_input(self):
+        batch_size = 4
+        num_channels = 4
+        sizes = (32, 32)
+
+        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
+        time_step = torch.tensor([10]).to(torch_device)
+        encoder_hidden_states = floats_tensor((batch_size, 4, 32)).to(torch_device)
+
+        return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
+
+    @property
+    def input_shape(self):
+        return (4, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (4, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "block_out_channels": (32, 64),
+            "down_block_types": ("CrossAttnDownBlock2D", "DownBlock2D"),
+            "up_block_types": ("UpBlock2D", "CrossAttnUpBlock2D"),
+            "cross_attention_dim": 32,
+            "attention_head_dim": 8,
+            "out_channels": 4,
+            "in_channels": 4,
+            "layers_per_block": 2,
+            "sample_size": 32,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_enable_works(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+
+        model.enable_xformers_memory_efficient_attention()
+
+        assert (
+            model.mid_block.attentions[0].transformer_blocks[0].attn1._use_memory_efficient_attention_xformers
+        ), "xformers is not enabled"
+
+    @unittest.skipIf(torch_device == "mps", "Gradient checkpointing skipped on MPS")
+    def test_gradient_checkpointing(self):
+        # enable deterministic behavior for gradient checkpointing
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+
+        assert not model.is_gradient_checkpointing and model.training
+
+        out = model(**inputs_dict).sample
+        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
+        # we won't calculate the loss and rather backprop on out.sum()
+        model.zero_grad()
+
+        labels = torch.randn_like(out)
+        loss = (out - labels).mean()
+        loss.backward()
+
+        # re-instantiate the model now enabling gradient checkpointing
+        model_2 = self.model_class(**init_dict)
+        # clone model
+        model_2.load_state_dict(model.state_dict())
+        model_2.to(torch_device)
+        model_2.enable_gradient_checkpointing()
+
+        assert model_2.is_gradient_checkpointing and model_2.training
+
+        out_2 = model_2(**inputs_dict).sample
+        # run the backwards pass on the model. For backwards pass, for simplicity purpose,
+        # we won't calculate the loss and rather backprop on out.sum()
+        model_2.zero_grad()
+        loss_2 = (out_2 - labels).mean()
+        loss_2.backward()
+
+        # compare the output and parameters gradients
+        self.assertTrue((loss - loss_2).abs() < 1e-5)
+        named_params = dict(model.named_parameters())
+        named_params_2 = dict(model_2.named_parameters())
+        for name, param in named_params.items():
+            self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
+
+    def test_model_with_attention_head_dim_tuple(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            output = model(**inputs_dict)
+
+            if isinstance(output, dict):
+                output = output.sample
+
+        self.assertIsNotNone(output)
+        expected_shape = inputs_dict["sample"].shape
+        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
+
+    def test_model_with_use_linear_projection(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["use_linear_projection"] = True
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            output = model(**inputs_dict)
+
+            if isinstance(output, dict):
+                output = output.sample
+
+        self.assertIsNotNone(output)
+        expected_shape = inputs_dict["sample"].shape
+        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
+
+    def test_model_attention_slicing(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        model.set_attention_slice("auto")
+        with torch.no_grad():
+            output = model(**inputs_dict)
+        assert output is not None
+
+        model.set_attention_slice("max")
+        with torch.no_grad():
+            output = model(**inputs_dict)
+        assert output is not None
+
+        model.set_attention_slice(2)
+        with torch.no_grad():
+            output = model(**inputs_dict)
+        assert output is not None
+
+    def test_model_slicable_head_dim(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        model = self.model_class(**init_dict)
+
+        def check_slicable_dim_attr(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                assert isinstance(module.sliceable_head_dim, int)
+
+            for child in module.children():
+                check_slicable_dim_attr(child)
+
+        # retrieve number of attention layers
+        for module in model.children():
+            check_slicable_dim_attr(module)
+
+    def test_special_attn_proc(self):
+        class AttnEasyProc(torch.nn.Module):
+            def __init__(self, num):
+                super().__init__()
+                self.weight = torch.nn.Parameter(torch.tensor(num))
+                self.is_run = False
+                self.number = 0
+                self.counter = 0
+
+            def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, number=None):
+                batch_size, sequence_length, _ = hidden_states.shape
+                attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
+
+                query = attn.to_q(hidden_states)
+
+                encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+                key = attn.to_k(encoder_hidden_states)
+                value = attn.to_v(encoder_hidden_states)
+
+                query = attn.head_to_batch_dim(query)
+                key = attn.head_to_batch_dim(key)
+                value = attn.head_to_batch_dim(value)
+
+                attention_probs = attn.get_attention_scores(query, key, attention_mask)
+                hidden_states = torch.bmm(attention_probs, value)
+                hidden_states = attn.batch_to_head_dim(hidden_states)
+
+                # linear proj
+                hidden_states = attn.to_out[0](hidden_states)
+                # dropout
+                hidden_states = attn.to_out[1](hidden_states)
+
+                hidden_states += self.weight
+
+                self.is_run = True
+                self.counter += 1
+                self.number = number
+
+                return hidden_states
+
+        # enable deterministic behavior for gradient checkpointing
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+
+        processor = AttnEasyProc(5.0)
+
+        model.set_attn_processor(processor)
+        model(**inputs_dict, cross_attention_kwargs={"number": 123}).sample
+
+        assert processor.counter == 12
+        assert processor.is_run
+        assert processor.number == 123
+
+    def test_lora_processors(self):
+        # enable deterministic behavior for gradient checkpointing
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+
+        with torch.no_grad():
+            sample1 = model(**inputs_dict).sample
+
+        lora_attn_procs = {}
+        for name in model.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else model.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = model.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(model.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = model.config.block_out_channels[block_id]
+
+            lora_attn_procs[name] = LoRACrossAttnProcessor(
+                hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
+            )
+
+            # add 1 to weights to mock trained weights
+            with torch.no_grad():
+                lora_attn_procs[name].to_q_lora.up.weight += 1
+                lora_attn_procs[name].to_k_lora.up.weight += 1
+                lora_attn_procs[name].to_v_lora.up.weight += 1
+                lora_attn_procs[name].to_out_lora.up.weight += 1
+
+        # make sure we can set a list of attention processors
+        model.set_attn_processor(lora_attn_procs)
+        model.to(torch_device)
+
+        # test that attn processors can be set to itself
+        model.set_attn_processor(model.attn_processors)
+
+        with torch.no_grad():
+            sample2 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.0}).sample
+            sample3 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
+            sample4 = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
+
+        assert (sample1 - sample2).abs().max() < 1e-4
+        assert (sample3 - sample4).abs().max() < 1e-4
+
+        # sample 2 and sample 3 should be different
+        assert (sample2 - sample3).abs().max() > 1e-4
+
+    def test_lora_save_load(self):
+        # enable deterministic behavior for gradient checkpointing
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        torch.manual_seed(0)
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+
+        with torch.no_grad():
+            old_sample = model(**inputs_dict).sample
+
+        lora_attn_procs = {}
+        for name in model.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else model.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = model.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(model.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = model.config.block_out_channels[block_id]
+
+            lora_attn_procs[name] = LoRACrossAttnProcessor(
+                hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
+            )
+            lora_attn_procs[name] = lora_attn_procs[name].to(model.device)
+
+            # add 1 to weights to mock trained weights
+            with torch.no_grad():
+                lora_attn_procs[name].to_q_lora.up.weight += 1
+                lora_attn_procs[name].to_k_lora.up.weight += 1
+                lora_attn_procs[name].to_v_lora.up.weight += 1
+                lora_attn_procs[name].to_out_lora.up.weight += 1
+
+        model.set_attn_processor(lora_attn_procs)
+
+        with torch.no_grad():
+            sample = model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_attn_procs(tmpdirname)
+            torch.manual_seed(0)
+            new_model = self.model_class(**init_dict)
+            new_model.to(torch_device)
+            new_model.load_attn_procs(tmpdirname)
+
+        with torch.no_grad():
+            new_sample = new_model(**inputs_dict, cross_attention_kwargs={"scale": 0.5}).sample
+
+        assert (sample - new_sample).abs().max() < 1e-4
+
+        # LoRA and no LoRA should NOT be the same
+        assert (sample - old_sample).abs().max() > 1e-4
+
+
+@slow
+class UNet2DConditionModelIntegrationTests(unittest.TestCase):
+    def get_file_format(self, seed, shape):
+        return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
+
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
+        dtype = torch.float16 if fp16 else torch.float32
+        image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
+        return image
+
+    def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
+        revision = "fp16" if fp16 else None
+        torch_dtype = torch.float16 if fp16 else torch.float32
+
+        model = UNet2DConditionModel.from_pretrained(
+            model_id, subfolder="unet", torch_dtype=torch_dtype, revision=revision
+        )
+        model.to(torch_device).eval()
+
+        return model
+
+    def test_set_attention_slice_auto(self):
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+
+        unet = self.get_unet_model()
+        unet.set_attention_slice("auto")
+
+        latents = self.get_latents(33)
+        encoder_hidden_states = self.get_encoder_hidden_states(33)
+        timestep = 1
+
+        with torch.no_grad():
+            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+
+        assert mem_bytes < 5 * 10**9
+
+    def test_set_attention_slice_max(self):
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+
+        unet = self.get_unet_model()
+        unet.set_attention_slice("max")
+
+        latents = self.get_latents(33)
+        encoder_hidden_states = self.get_encoder_hidden_states(33)
+        timestep = 1
+
+        with torch.no_grad():
+            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+
+        assert mem_bytes < 5 * 10**9
+
+    def test_set_attention_slice_int(self):
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+
+        unet = self.get_unet_model()
+        unet.set_attention_slice(2)
+
+        latents = self.get_latents(33)
+        encoder_hidden_states = self.get_encoder_hidden_states(33)
+        timestep = 1
+
+        with torch.no_grad():
+            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+
+        assert mem_bytes < 5 * 10**9
+
+    def test_set_attention_slice_list(self):
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+
+        # there are 32 slicable layers
+        slice_list = 16 * [2, 3]
+        unet = self.get_unet_model()
+        unet.set_attention_slice(slice_list)
+
+        latents = self.get_latents(33)
+        encoder_hidden_states = self.get_encoder_hidden_states(33)
+        timestep = 1
+
+        with torch.no_grad():
+            _ = unet(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+
+        assert mem_bytes < 5 * 10**9
+
+    def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):
+        dtype = torch.float16 if fp16 else torch.float32
+        hidden_states = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
+        return hidden_states
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, 4, [-0.4424, 0.1510, -0.1937, 0.2118, 0.3746, -0.3957, 0.0160, -0.0435]],
+            [47, 0.55, [-0.1508, 0.0379, -0.3075, 0.2540, 0.3633, -0.0821, 0.1719, -0.0207]],
+            [21, 0.89, [-0.6479, 0.6364, -0.3464, 0.8697, 0.4443, -0.6289, -0.0091, 0.1778]],
+            [9, 1000, [0.8888, -0.5659, 0.5834, -0.7469, 1.1912, -0.3923, 1.1241, -0.4424]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_v1_4(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4")
+        latents = self.get_latents(seed)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed)
+
+        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
+            [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
+            [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
+            [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_v1_4_fp16(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4", fp16=True)
+        latents = self.get_latents(seed, fp16=True)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
+
+        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, 4, [-0.4430, 0.1570, -0.1867, 0.2376, 0.3205, -0.3681, 0.0525, -0.0722]],
+            [47, 0.55, [-0.1415, 0.0129, -0.3136, 0.2257, 0.3430, -0.0536, 0.2114, -0.0436]],
+            [21, 0.89, [-0.7091, 0.6664, -0.3643, 0.9032, 0.4499, -0.6541, 0.0139, 0.1750]],
+            [9, 1000, [0.8878, -0.5659, 0.5844, -0.7442, 1.1883, -0.3927, 1.1192, -0.4423]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5")
+        latents = self.get_latents(seed)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed)
+
+        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [83, 4, [-0.2695, -0.1669, 0.0073, -0.3181, -0.1187, -0.1676, -0.1395, -0.5972]],
+            [17, 0.55, [-0.1290, -0.2588, 0.0551, -0.0916, 0.3286, 0.0238, -0.3669, 0.0322]],
+            [8, 0.89, [-0.5283, 0.1198, 0.0870, -0.1141, 0.9189, -0.0150, 0.5474, 0.4319]],
+            [3, 1000, [-0.5601, 0.2411, -0.5435, 0.1268, 1.1338, -0.2427, -0.0280, -1.0020]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5", fp16=True)
+        latents = self.get_latents(seed, fp16=True)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
+
+        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, 4, [-0.7639, 0.0106, -0.1615, -0.3487, -0.0423, -0.7972, 0.0085, -0.4858]],
+            [47, 0.55, [-0.6564, 0.0795, -1.9026, -0.6258, 1.8235, 1.2056, 1.2169, 0.9073]],
+            [21, 0.89, [0.0327, 0.4399, -0.6358, 0.3417, 0.4120, -0.5621, -0.0397, -1.0430]],
+            [9, 1000, [0.1600, 0.7303, -1.0556, -0.3515, -0.7440, -1.2037, -1.8149, -1.8931]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting")
+        latents = self.get_latents(seed, shape=(4, 9, 64, 64))
+        encoder_hidden_states = self.get_encoder_hidden_states(seed)
+
+        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == (4, 4, 64, 64)
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [83, 4, [-0.1047, -1.7227, 0.1067, 0.0164, -0.5698, -0.4172, -0.1388, 1.1387]],
+            [17, 0.55, [0.0975, -0.2856, -0.3508, -0.4600, 0.3376, 0.2930, -0.2747, -0.7026]],
+            [8, 0.89, [-0.0952, 0.0183, -0.5825, -0.1981, 0.1131, 0.4668, -0.0395, -0.3486]],
+            [3, 1000, [0.4790, 0.4949, -1.0732, -0.7158, 0.7959, -0.9478, 0.1105, -0.9741]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting", fp16=True)
+        latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
+
+        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == (4, 4, 64, 64)
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
+            [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
+            [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
+            [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_stabilityai_sd_v2_fp16(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="stabilityai/stable-diffusion-2", fp16=True)
+        latents = self.get_latents(seed, shape=(4, 4, 96, 96), fp16=True)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed, shape=(4, 77, 1024), fp16=True)
+
+        timestep = torch.tensor([timestep], dtype=torch.long, device=torch_device)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
diff --git a/utils/check_inits.py b/utils/check_inits.py
index c5e25182a460..0bc0a54d4e27 100644
--- a/utils/check_inits.py
+++ b/utils/check_inits.py
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
+# Copyright 2022 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/utils/check_repo.py b/utils/check_repo.py
index 80c63a79a335..c495c9ded94b 100644
--- a/utils/check_repo.py
+++ b/utils/check_repo.py
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
+# Copyright 2022 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/utils/check_table.py b/utils/check_table.py
index 28c6ea88913f..7636c6bde3de 100644
--- a/utils/check_table.py
+++ b/utils/check_table.py
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
+# Copyright 2022 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/utils/custom_init_isort.py b/utils/custom_init_isort.py
index a204a155feb1..2e18bc4c9212 100644
--- a/utils/custom_init_isort.py
+++ b/utils/custom_init_isort.py
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2021 The HuggingFace Inc. team.
+# Copyright 2022 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
diff --git a/utils/get_modified_files.py b/utils/get_modified_files.py
index 44c60e96abba..d2c6bbf874b2 100644
--- a/utils/get_modified_files.py
+++ b/utils/get_modified_files.py
@@ -1,5 +1,5 @@
 # coding=utf-8
-# Copyright 2020 The HuggingFace Inc. team.
+# Copyright 2022 The HuggingFace Inc. team.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.