Add LCM Scripts (#5727)

* add lcm scripts

* Co-authored-by: dgu8957@gmail.com

examples/consistency_distillation/README.md

+# Latent Consistency Distillation Example:
+
+[Latent Consistency Models (LCMs)](https://arxiv.org/abs/2310.04378) is method to distill latent diffusion model to enable swift inference with minimal steps. This example demonstrates how to use the latent consistency distillation to distill stable-diffusion-v1.5 for less timestep inference.
+
+## Full model distillation
+
+### Running locally with PyTorch
+
+#### Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+**Important**
+
+To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install -e .
+```
+
+Then cd in the example folder and run
+```bash
+pip install -r requirements.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+Or for a default accelerate configuration without answering questions about your environment
+
+```bash
+accelerate config default
+```
+
+Or if your environment doesn't support an interactive shell e.g. a notebook
+
+```python
+from accelerate.utils import write_basic_config
+write_basic_config()
+```
+
+When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
+
+
+#### Example with LAION-A6+ dataset
+
+```bash
+runwayml/stable-diffusion-v1-5
+PROGRAM="train_lcm_distill_sd_wds.py \
+    --pretrained_teacher_model=$MODEL_DIR \
+    --output_dir=$OUTPUT_DIR \
+    --mixed_precision=fp16 \
+    --resolution=512 \
+    --learning_rate=1e-6 --loss_type="huber" --ema_decay=0.95 --adam_weight_decay=0.0 \
+    --max_train_steps=1000 \
+    --max_train_samples=4000000 \
+    --dataloader_num_workers=8 \
+    --train_shards_path_or_url='pipe:aws s3 cp s3://muse-datasets/laion-aesthetic6plus-min512-data/{00000..01210}.tar -' \
+    --validation_steps=200 \
+    --checkpointing_steps=200 --checkpoints_total_limit=10 \
+    --train_batch_size=12 \
+    --gradient_checkpointing --enable_xformers_memory_efficient_attention \
+    --gradient_accumulation_steps=1 \
+    --use_8bit_adam \
+    --resume_from_checkpoint=latest \
+    --report_to=wandb \
+    --seed=453645634 \
+    --push_to_hub \
+```
+
+## LCM-LoRA
+
+Instead of fine-tuning the full model, we can also just train a LoRA that can be injected into any SDXL model.
+
+### Example with LAION-A6+ dataset
+    
+```bash
+runwayml/stable-diffusion-v1-5
+PROGRAM="train_lcm_distill_lora_sd_wds.py \
+    --pretrained_teacher_model=$MODEL_DIR \
+    --output_dir=$OUTPUT_DIR \
+    --mixed_precision=fp16 \
+    --resolution=512 \
+    --lora_rank=64 \
+    --learning_rate=1e-6 --loss_type="huber" --adam_weight_decay=0.0 \
+    --max_train_steps=1000 \
+    --max_train_samples=4000000 \
+    --dataloader_num_workers=8 \
+    --train_shards_path_or_url='pipe:aws s3 cp s3://muse-datasets/laion-aesthetic6plus-min512-data/{00000..01210}.tar -' \
+    --validation_steps=200 \
+    --checkpointing_steps=200 --checkpoints_total_limit=10 \
+    --train_batch_size=12 \
+    --gradient_checkpointing --enable_xformers_memory_efficient_attention \
+    --gradient_accumulation_steps=1 \
+    --use_8bit_adam \
+    --resume_from_checkpoint=latest \
+    --report_to=wandb \
+    --seed=453645634 \
+    --push_to_hub \
+```
\ No newline at end of file

examples/consistency_distillation/README_sdxl.md

+# Latent Consistency Distillation Example:
+
+[Latent Consistency Models (LCMs)](https://arxiv.org/abs/2310.04378) is method to distill latent diffusion model to enable swift inference with minimal steps. This example demonstrates how to use the latent consistency distillation to distill SDXL for less timestep inference.
+
+## Full model distillation
+
+### Running locally with PyTorch
+
+#### Installing the dependencies
+
+Before running the scripts, make sure to install the library's training dependencies:
+
+**Important**
+
+To make sure you can successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the install up to date as we update the example scripts frequently and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
+```bash
+git clone https://github.com/huggingface/diffusers
+cd diffusers
+pip install -e .
+```
+
+Then cd in the example folder and run
+```bash
+pip install -r requirements.txt
+```
+
+And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
+
+```bash
+accelerate config
+```
+
+Or for a default accelerate configuration without answering questions about your environment
+
+```bash
+accelerate config default
+```
+
+Or if your environment doesn't support an interactive shell e.g. a notebook
+
+```python
+from accelerate.utils import write_basic_config
+write_basic_config()
+```
+
+When running `accelerate config`, if we specify torch compile mode to True there can be dramatic speedups.
+
+
+#### Example with LAION-A6+ dataset
+
+```bash
+export MODEL_DIR="stabilityai/stable-diffusion-xl-base-1.0"
+PROGRAM="train_lcm_distill_sdxl_wds.py \
+    --pretrained_teacher_model=$MODEL_DIR \
+    --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
+    --output_dir=$OUTPUT_DIR \
+    --mixed_precision=fp16 \
+    --resolution=1024 \
+    --learning_rate=1e-6 --loss_type="huber" --use_fix_crop_and_size --ema_decay=0.95 --adam_weight_decay=0.0 \
+    --max_train_steps=1000 \
+    --max_train_samples=4000000 \
+    --dataloader_num_workers=8 \
+    --train_shards_path_or_url='pipe:aws s3 cp s3://muse-datasets/laion-aesthetic6plus-min512-data/{00000..01210}.tar -' \
+    --validation_steps=200 \
+    --checkpointing_steps=200 --checkpoints_total_limit=10 \
+    --train_batch_size=12 \
+    --gradient_checkpointing --enable_xformers_memory_efficient_attention \
+    --gradient_accumulation_steps=1 \
+    --use_8bit_adam \
+    --resume_from_checkpoint=latest \
+    --report_to=wandb \
+    --seed=453645634 \
+    --push_to_hub \
+```
+
+## LCM-LoRA
+
+Instead of fine-tuning the full model, we can also just train a LoRA that can be injected into any SDXL model.
+
+### Example with LAION-A6+ dataset
+    
+```bash
+export MODEL_DIR="stabilityai/stable-diffusion-xl-base-1.0"
+PROGRAM="train_lcm_distill_lora_sdxl_wds.py \
+    --pretrained_teacher_model=$MODEL_DIR \
+    --pretrained_vae_model_name_or_path=madebyollin/sdxl-vae-fp16-fix \
+    --output_dir=$OUTPUT_DIR \
+    --mixed_precision=fp16 \
+    --resolution=1024 \
+    --lora_rank=64 \
+    --learning_rate=1e-6 --loss_type="huber" --use_fix_crop_and_size --adam_weight_decay=0.0 \
+    --max_train_steps=1000 \
+    --max_train_samples=4000000 \
+    --dataloader_num_workers=8 \
+    --train_shards_path_or_url='pipe:aws s3 cp s3://muse-datasets/laion-aesthetic6plus-min512-data/{00000..01210}.tar -' \
+    --validation_steps=200 \
+    --checkpointing_steps=200 --checkpoints_total_limit=10 \
+    --train_batch_size=12 \
+    --gradient_checkpointing --enable_xformers_memory_efficient_attention \
+    --gradient_accumulation_steps=1 \
+    --use_8bit_adam \
+    --resume_from_checkpoint=latest \
+    --report_to=wandb \
+    --seed=453645634 \
+    --push_to_hub \
+```
\ No newline at end of file

examples/consistency_distillation/requirements.txt

+accelerate>=0.16.0
+torchvision
+transformers>=4.25.1
+ftfy
+tensorboard
+Jinja2
+webdataset
\ No newline at end of file

examples/consistency_distillation/train_lcm_distill_lora_sd_wds.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import argparse
+import functools
+import gc
+import itertools
+import json
+import logging
+import math
+import os
+import random
+import shutil
+from pathlib import Path
+from typing import List, Union
+
+import accelerate
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torchvision.transforms.functional as TF
+import transformers
+import webdataset as wds
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from braceexpand import braceexpand
+from huggingface_hub import create_repo
+from packaging import version
+from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
+from torch.utils.data import default_collate
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig
+from webdataset.tariterators import (
+    base_plus_ext,
+    tar_file_expander,
+    url_opener,
+    valid_sample,
+)
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    LCMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils.import_utils import is_xformers_available
+
+
+MAX_SEQ_LENGTH = 77
+
+if is_wandb_available():
+    import wandb
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.18.0.dev0")
+
+logger = get_logger(__name__)
+
+
+def get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype, adapter_name: str = "default"):
+    kohya_ss_state_dict = {}
+    for peft_key, weight in get_peft_model_state_dict(module, adapter_name=adapter_name).items():
+        kohya_key = peft_key.replace("base_model.model", prefix)
+        kohya_key = kohya_key.replace("lora_A", "lora_down")
+        kohya_key = kohya_key.replace("lora_B", "lora_up")
+        kohya_key = kohya_key.replace(".", "_", kohya_key.count(".") - 2)
+        kohya_ss_state_dict[kohya_key] = weight.to(dtype)
+
+        # Set alpha parameter
+        if "lora_down" in kohya_key:
+            alpha_key = f'{kohya_key.split(".")[0]}.alpha'
+            kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)
+
+    return kohya_ss_state_dict
+
+
+def filter_keys(key_set):
+    def _f(dictionary):
+        return {k: v for k, v in dictionary.items() if k in key_set}
+
+    return _f
+
+
+def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
+    """Return function over iterator that groups key, value pairs into samples.
+
+    :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
+    lower case (Default value = True)
+    """
+    current_sample = None
+    for filesample in data:
+        assert isinstance(filesample, dict)
+        fname, value = filesample["fname"], filesample["data"]
+        prefix, suffix = keys(fname)
+        if prefix is None:
+            continue
+        if lcase:
+            suffix = suffix.lower()
+        # FIXME webdataset version throws if suffix in current_sample, but we have a potential for
+        #  this happening in the current LAION400m dataset if a tar ends with same prefix as the next
+        #  begins, rare, but can happen since prefix aren't unique across tar files in that dataset
+        if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
+            if valid_sample(current_sample):
+                yield current_sample
+            current_sample = {"__key__": prefix, "__url__": filesample["__url__"]}
+        if suffixes is None or suffix in suffixes:
+            current_sample[suffix] = value
+    if valid_sample(current_sample):
+        yield current_sample
+
+
+def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
+    # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
+    streams = url_opener(src, handler=handler)
+    files = tar_file_expander(streams, handler=handler)
+    samples = group_by_keys_nothrow(files, handler=handler)
+    return samples
+
+
+class WebdatasetFilter:
+    def __init__(self, min_size=1024, max_pwatermark=0.5):
+        self.min_size = min_size
+        self.max_pwatermark = max_pwatermark
+
+    def __call__(self, x):
+        try:
+            if "json" in x:
+                x_json = json.loads(x["json"])
+                filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get(
+                    "original_height", 0
+                ) >= self.min_size
+                filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark
+                return filter_size and filter_watermark
+            else:
+                return False
+        except Exception:
+            return False
+
+
+class Text2ImageDataset:
+    def __init__(
+        self,
+        train_shards_path_or_url: Union[str, List[str]],
+        num_train_examples: int,
+        per_gpu_batch_size: int,
+        global_batch_size: int,
+        num_workers: int,
+        resolution: int = 512,
+        shuffle_buffer_size: int = 1000,
+        pin_memory: bool = False,
+        persistent_workers: bool = False,
+    ):
+        if not isinstance(train_shards_path_or_url, str):
+            train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
+            # flatten list using itertools
+            train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))
+
+        def transform(example):
+            # resize image
+            image = example["image"]
+            image = TF.resize(image, resolution, interpolation=transforms.InterpolationMode.BILINEAR)
+
+            # get crop coordinates and crop image
+            c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
+            image = TF.crop(image, c_top, c_left, resolution, resolution)
+            image = TF.to_tensor(image)
+            image = TF.normalize(image, [0.5], [0.5])
+
+            example["image"] = image
+            return example
+
+        processing_pipeline = [
+            wds.decode("pil", handler=wds.ignore_and_continue),
+            wds.rename(image="jpg;png;jpeg;webp", text="text;txt;caption", handler=wds.warn_and_continue),
+            wds.map(filter_keys({"image", "text"})),
+            wds.map(transform),
+            wds.to_tuple("image", "text"),
+        ]
+
+        # Create train dataset and loader
+        pipeline = [
+            wds.ResampledShards(train_shards_path_or_url),
+            tarfile_to_samples_nothrow,
+            wds.shuffle(shuffle_buffer_size),
+            *processing_pipeline,
+            wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
+        ]
+
+        num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers))  # per dataloader worker
+        num_batches = num_worker_batches * num_workers
+        num_samples = num_batches * global_batch_size
+
+        # each worker is iterating over this
+        self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)
+        self._train_dataloader = wds.WebLoader(
+            self._train_dataset,
+            batch_size=None,
+            shuffle=False,
+            num_workers=num_workers,
+            pin_memory=pin_memory,
+            persistent_workers=persistent_workers,
+        )
+        # add meta-data to dataloader instance for convenience
+        self._train_dataloader.num_batches = num_batches
+        self._train_dataloader.num_samples = num_samples
+
+    @property
+    def train_dataset(self):
+        return self._train_dataset
+
+    @property
+    def train_dataloader(self):
+        return self._train_dataloader
+
+
+def log_validation(vae, unet, args, accelerator, weight_dtype, step):
+    logger.info("Running validation... ")
+
+    unet = accelerator.unwrap_model(unet)
+    pipeline = StableDiffusionPipeline.from_pretrained(
+        args.pretrained_teacher_model,
+        vae=vae,
+        scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder="scheduler"),
+        revision=args.revision,
+        torch_dtype=weight_dtype,
+        safety_checker=None,
+    )
+    pipeline.set_progress_bar_config(disable=True)
+
+    lora_state_dict = get_module_kohya_state_dict(unet, "lora_unet", weight_dtype)
+    pipeline.load_lora_weights(lora_state_dict)
+    pipeline.fuse_lora()
+
+    pipeline = pipeline.to(accelerator.device, dtype=weight_dtype)
+    if args.enable_xformers_memory_efficient_attention:
+        pipeline.enable_xformers_memory_efficient_attention()
+
+    if args.seed is None:
+        generator = None
+    else:
+        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+
+    validation_prompts = [
+        "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
+        "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+        "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
+    ]
+
+    image_logs = []
+
+    for _, prompt in enumerate(validation_prompts):
+        images = []
+        with torch.autocast("cuda", dtype=weight_dtype):
+            images = pipeline(
+                prompt=prompt,
+                num_inference_steps=4,
+                num_images_per_prompt=4,
+                generator=generator,
+                guidance_scale=1.0,
+            ).images
+        image_logs.append({"validation_prompt": prompt, "images": images})
+
+    for tracker in accelerator.trackers:
+        if tracker.name == "tensorboard":
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                formatted_images = []
+                for image in images:
+                    formatted_images.append(np.asarray(image))
+
+                formatted_images = np.stack(formatted_images)
+
+                tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
+        elif tracker.name == "wandb":
+            formatted_images = []
+
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                for image in images:
+                    image = wandb.Image(image, caption=validation_prompt)
+                    formatted_images.append(image)
+
+            tracker.log({"validation": formatted_images})
+        else:
+            logger.warn(f"image logging not implemented for {tracker.name}")
+
+        del pipeline
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        return image_logs
+
+
+# From LatentConsistencyModel.get_guidance_scale_embedding
+def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
+    """
+    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+    Args:
+        timesteps (`torch.Tensor`):
+            generate embedding vectors at these timesteps
+        embedding_dim (`int`, *optional*, defaults to 512):
+            dimension of the embeddings to generate
+        dtype:
+            data type of the generated embeddings
+
+    Returns:
+        `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+    """
+    assert len(w.shape) == 1
+    w = w * 1000.0
+
+    half_dim = embedding_dim // 2
+    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+    emb = w.to(dtype)[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0, 1))
+    assert emb.shape == (w.shape[0], embedding_dim)
+    return emb
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
+    return x[(...,) + (None,) * dims_to_append]
+
+
+# From LCMScheduler.get_scalings_for_boundary_condition_discrete
+def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
+    c_skip = sigma_data**2 / ((timestep / 0.1) ** 2 + sigma_data**2)
+    c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5
+    return c_skip, c_out
+
+
+# Compare LCMScheduler.step, Step 4
+def predicted_origin(model_output, timesteps, sample, prediction_type, alphas, sigmas):
+    if prediction_type == "epsilon":
+        sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
+        alphas = extract_into_tensor(alphas, timesteps, sample.shape)
+        pred_x_0 = (sample - sigmas * model_output) / alphas
+    elif prediction_type == "v_prediction":
+        pred_x_0 = alphas[timesteps] * sample - sigmas[timesteps] * model_output
+    else:
+        raise ValueError(f"Prediction type {prediction_type} currently not supported.")
+
+    return pred_x_0
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+class DDIMSolver:
+    def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
+        # DDIM sampling parameters
+        step_ratio = timesteps // ddim_timesteps
+        self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
+        self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
+        self.ddim_alpha_cumprods_prev = np.asarray(
+            [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
+        )
+        # convert to torch tensors
+        self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
+        self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
+        self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
+
+    def to(self, device):
+        self.ddim_timesteps = self.ddim_timesteps.to(device)
+        self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
+        self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
+        return self
+
+    def ddim_step(self, pred_x0, pred_noise, timestep_index):
+        alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
+        dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
+        x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
+        return x_prev
+
+
+@torch.no_grad()
+def update_ema(target_params, source_params, rate=0.99):
+    """
+    Update target parameters to be closer to those of source parameters using
+    an exponential moving average.
+
+    :param target_params: the target parameter sequence.
+    :param source_params: the source parameter sequence.
+    :param rate: the EMA rate (closer to 1 means slower).
+    """
+    for targ, src in zip(target_params, source_params):
+        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
+
+
+def import_model_class_from_model_name_or_path(
+    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
+):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path, subfolder=subfolder, revision=revision, use_auth_token=True
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "CLIPTextModelWithProjection":
+        from transformers import CLIPTextModelWithProjection
+
+        return CLIPTextModelWithProjection
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    # ----------Model Checkpoint Loading Arguments----------
+    parser.add_argument(
+        "--pretrained_teacher_model",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_vae_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
+    )
+    parser.add_argument(
+        "--teacher_revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM model identifier from huggingface.co/models.",
+    )
+    # ----------Training Arguments----------
+    # ----General Training Arguments----
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lcm-xl-distilled",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    # ----Logging----
+    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(
+        "--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.'
+        ),
+    )
+    # ----Checkpointing----
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=None,
+        help=("Max number of checkpoints to store."),
+    )
+    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.'
+        ),
+    )
+    # ----Image Processing----
+    parser.add_argument(
+        "--train_shards_path_or_url",
+        type=str,
+        default=None,
+        help=(
+            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
+            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
+            " or to a folder containing files that 🤗 Datasets can understand."
+        ),
+    )
+    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",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+    parser.add_argument(
+        "--random_flip",
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
+    # ----Dataloader----
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    # ----Batch Size and Training Steps----
+    parser.add_argument(
+        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--max_train_samples",
+        type=int,
+        default=None,
+        help=(
+            "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        ),
+    )
+    # ----Learning Rate----
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    # ----Optimizer (Adam)----
+    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.")
+    # ----Diffusion Training Arguments----
+    parser.add_argument(
+        "--proportion_empty_prompts",
+        type=float,
+        default=0,
+        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
+    )
+    # ----Latent Consistency Distillation (LCD) Specific Arguments----
+    parser.add_argument(
+        "--w_min",
+        type=float,
+        default=5.0,
+        required=False,
+        help=(
+            "The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--w_max",
+        type=float,
+        default=15.0,
+        required=False,
+        help=(
+            "The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--num_ddim_timesteps",
+        type=int,
+        default=50,
+        help="The number of timesteps to use for DDIM sampling.",
+    )
+    parser.add_argument(
+        "--loss_type",
+        type=str,
+        default="l2",
+        choices=["l2", "huber"],
+        help="The type of loss to use for the LCD loss.",
+    )
+    parser.add_argument(
+        "--huber_c",
+        type=float,
+        default=0.001,
+        help="The huber loss parameter. Only used if `--loss_type=huber`.",
+    )
+    parser.add_argument(
+        "--lora_rank",
+        type=int,
+        default=64,
+        help="The rank of the LoRA projection matrix.",
+    )
+    # ----Mixed Precision----
+    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(
+        "--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(
+        "--cast_teacher_unet",
+        action="store_true",
+        help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
+    )
+    # ----Training Optimizations----
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+    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.",
+    )
+    # ----Distributed Training----
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    # ----------Validation Arguments----------
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=200,
+        help="Run validation every X steps.",
+    )
+    # ----------Huggingface Hub Arguments-----------
+    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`.",
+    )
+    # ----------Accelerate Arguments----------
+    parser.add_argument(
+        "--tracker_project_name",
+        type=str,
+        default="text2image-fine-tune",
+        help=(
+            "The `project_name` argument passed to Accelerator.init_trackers for"
+            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
+        ),
+    )
+
+    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.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
+        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
+
+    return args
+
+
+# Adapted from pipelines.StableDiffusionPipeline.encode_prompt
+def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):
+    captions = []
+    for caption in prompt_batch:
+        if random.random() < proportion_empty_prompts:
+            captions.append("")
+        elif isinstance(caption, str):
+            captions.append(caption)
+        elif isinstance(caption, (list, np.ndarray)):
+            # take a random caption if there are multiple
+            captions.append(random.choice(caption) if is_train else caption[0])
+
+    with torch.no_grad():
+        text_inputs = tokenizer(
+            captions,
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device))[0]
+
+    return prompt_embeds
+
+
+def main(args):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+        if args.push_to_hub:
+            create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name,
+                exist_ok=True,
+                token=args.hub_token,
+                private=True,
+            ).repo_id
+
+    # 1. Create the noise scheduler and the desired noise schedule.
+    noise_scheduler = DDPMScheduler.from_pretrained(
+        args.pretrained_teacher_model, subfolder="scheduler", revision=args.teacher_revision
+    )
+
+    # The scheduler calculates the alpha and sigma schedule for us
+    alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
+    sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
+    solver = DDIMSolver(
+        noise_scheduler.alphas_cumprod.numpy(),
+        timesteps=noise_scheduler.config.num_train_timesteps,
+        ddim_timesteps=args.num_ddim_timesteps,
+    )
+
+    # 2. Load tokenizers from SD-XL checkpoint.
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.pretrained_teacher_model, subfolder="tokenizer", revision=args.teacher_revision, use_fast=False
+    )
+
+    # 3. Load text encoders from SD-1.5 checkpoint.
+    # import correct text encoder classes
+    text_encoder = CLIPTextModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="text_encoder", revision=args.teacher_revision
+    )
+
+    # 4. Load VAE from SD-XL checkpoint (or more stable VAE)
+    vae = AutoencoderKL.from_pretrained(
+        args.pretrained_teacher_model,
+        subfolder="vae",
+        revision=args.teacher_revision,
+    )
+
+    # 5. Load teacher U-Net from SD-XL checkpoint
+    teacher_unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
+    )
+
+    # 6. Freeze teacher vae, text_encoder, and teacher_unet
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    teacher_unet.requires_grad_(False)
+
+    # 7. Create online (`unet`) student U-Nets.
+    unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
+    )
+    unet.train()
+
+    # Check that all trainable models are in full precision
+    low_precision_error_string = (
+        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
+        " doing mixed precision training, copy of the weights should still be float32."
+    )
+
+    if accelerator.unwrap_model(unet).dtype != torch.float32:
+        raise ValueError(
+            f"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
+        )
+
+    # 8. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.
+    lora_config = LoraConfig(
+        r=args.lora_rank,
+        target_modules=[
+            "to_q",
+            "to_k",
+            "to_v",
+            "to_out.0",
+            "proj_in",
+            "proj_out",
+            "ff.net.0.proj",
+            "ff.net.2",
+            "conv1",
+            "conv2",
+            "conv_shortcut",
+            "downsamplers.0.conv",
+            "upsamplers.0.conv",
+            "time_emb_proj",
+        ],
+    )
+    unet = get_peft_model(unet, lora_config)
+
+    # 9. Handle mixed precision and device placement
+    # For mixed precision training we cast all non-trainable weigths to half-precision
+    # as these weights are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move unet, vae and text_encoder to device and cast to weight_dtype
+    # The VAE is in float32 to avoid NaN losses.
+    vae.to(accelerator.device)
+    if args.pretrained_vae_model_name_or_path is not None:
+        vae.to(dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    # Move teacher_unet to device, optionally cast to weight_dtype
+    teacher_unet.to(accelerator.device)
+    if args.cast_teacher_unet:
+        teacher_unet.to(dtype=weight_dtype)
+
+    # Also move the alpha and sigma noise schedules to accelerator.device.
+    alpha_schedule = alpha_schedule.to(accelerator.device)
+    sigma_schedule = sigma_schedule.to(accelerator.device)
+    solver = solver.to(accelerator.device)
+
+    # 10. Handle saving and loading of checkpoints
+    # `accelerate` 0.16.0 will have better support for customized saving
+    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
+        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
+        def save_model_hook(models, weights, output_dir):
+            if accelerator.is_main_process:
+                unet_ = accelerator.unwrap_model(unet)
+                lora_state_dict = get_peft_model_state_dict(unet_, adapter_name="default")
+                StableDiffusionPipeline.save_lora_weights(os.path.join(output_dir, "unet_lora"), lora_state_dict)
+                # save weights in peft format to be able to load them back
+                unet_.save_pretrained(output_dir)
+
+                for _, model in enumerate(models):
+                    # make sure to pop weight so that corresponding model is not saved again
+                    weights.pop()
+
+        def load_model_hook(models, input_dir):
+            # load the LoRA into the model
+            unet_ = accelerator.unwrap_model(unet)
+            unet_.load_adapter(input_dir, "default", is_trainable=True)
+
+            for _ in range(len(models)):
+                # pop models so that they are not loaded again
+                models.pop()
+
+        accelerator.register_save_state_pre_hook(save_model_hook)
+        accelerator.register_load_state_pre_hook(load_model_hook)
+
+    # 11. Enable optimizations
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = version.parse(xformers.__version__)
+            if xformers_version == version.parse("0.0.16"):
+                logger.warn(
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                )
+            unet.enable_xformers_memory_efficient_attention()
+            teacher_unet.enable_xformers_memory_efficient_attention()
+            # target_unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
+    # 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
+
+    # 12. Optimizer creation
+    optimizer = optimizer_class(
+        unet.parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    # Here, we compute not just the text embeddings but also the additional embeddings
+    # needed for the SD XL UNet to operate.
+    def compute_embeddings(prompt_batch, proportion_empty_prompts, text_encoder, tokenizer, is_train=True):
+        prompt_embeds = encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train)
+        return {"prompt_embeds": prompt_embeds}
+
+    dataset = Text2ImageDataset(
+        train_shards_path_or_url=args.train_shards_path_or_url,
+        num_train_examples=args.max_train_samples,
+        per_gpu_batch_size=args.train_batch_size,
+        global_batch_size=args.train_batch_size * accelerator.num_processes,
+        num_workers=args.dataloader_num_workers,
+        resolution=args.resolution,
+        shuffle_buffer_size=1000,
+        pin_memory=True,
+        persistent_workers=True,
+    )
+    train_dataloader = dataset.train_dataloader
+
+    compute_embeddings_fn = functools.partial(
+        compute_embeddings,
+        proportion_empty_prompts=0,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+    )
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / 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,
+        num_training_steps=args.max_train_steps,
+    )
+
+    # Prepare everything with our `accelerator`.
+    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, 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(train_dataloader.num_batches / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    uncond_input_ids = tokenizer(
+        [""] * args.train_batch_size, return_tensors="pt", padding="max_length", max_length=77
+    ).input_ids.to(accelerator.device)
+    uncond_prompt_embeds = text_encoder(uncond_input_ids)[0]
+
+    # Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num batches each epoch = {train_dataloader.num_batches}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1] if len(dirs) > 0 else None
+
+        if path is None:
+            accelerator.print(
+                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+            )
+            args.resume_from_checkpoint = None
+            initial_global_step = 0
+        else:
+            accelerator.print(f"Resuming from checkpoint {path}")
+            accelerator.load_state(os.path.join(args.output_dir, path))
+            global_step = int(path.split("-")[1])
+
+            initial_global_step = global_step
+            first_epoch = global_step // num_update_steps_per_epoch
+    else:
+        initial_global_step = 0
+
+    progress_bar = tqdm(
+        range(0, args.max_train_steps),
+        initial=initial_global_step,
+        desc="Steps",
+        # Only show the progress bar once on each machine.
+        disable=not accelerator.is_local_main_process,
+    )
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(unet):
+                image, text, _, _ = batch
+
+                image = image.to(accelerator.device, non_blocking=True)
+                encoded_text = compute_embeddings_fn(text)
+
+                pixel_values = image.to(dtype=weight_dtype)
+                if vae.dtype != weight_dtype:
+                    vae.to(dtype=weight_dtype)
+
+                # encode pixel values with batch size of at most 32
+                latents = []
+                for i in range(0, pixel_values.shape[0], 32):
+                    latents.append(vae.encode(pixel_values[i : i + 32]).latent_dist.sample())
+                latents = torch.cat(latents, dim=0)
+
+                latents = latents * vae.config.scaling_factor
+                latents = latents.to(weight_dtype)
+
+                # 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 t_n ~ U[0, N - k - 1] without bias.
+                topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps
+                index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()
+                start_timesteps = solver.ddim_timesteps[index]
+                timesteps = start_timesteps - topk
+                timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
+
+                # 20.4.4. Get boundary scalings for start_timesteps and (end) timesteps.
+                c_skip_start, c_out_start = scalings_for_boundary_conditions(start_timesteps)
+                c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]
+                c_skip, c_out = scalings_for_boundary_conditions(timesteps)
+                c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]
+
+                # 20.4.5. Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]
+                noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)
+
+                # 20.4.6. Sample a random guidance scale w from U[w_min, w_max] and embed it
+                w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
+                w = w.reshape(bsz, 1, 1, 1)
+                w = w.to(device=latents.device, dtype=latents.dtype)
+
+                # 20.4.8. Prepare prompt embeds and unet_added_conditions
+                prompt_embeds = encoded_text.pop("prompt_embeds")
+
+                # 20.4.9. Get online LCM prediction on z_{t_{n + k}}, w, c, t_{n + k}
+                noise_pred = unet(
+                    noisy_model_input,
+                    start_timesteps,
+                    timestep_cond=None,
+                    encoder_hidden_states=prompt_embeds.float(),
+                    added_cond_kwargs=encoded_text,
+                ).sample
+
+                pred_x_0 = predicted_origin(
+                    noise_pred,
+                    start_timesteps,
+                    noisy_model_input,
+                    noise_scheduler.config.prediction_type,
+                    alpha_schedule,
+                    sigma_schedule,
+                )
+
+                model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
+
+                # 20.4.10. Use the ODE solver to predict the kth step in the augmented PF-ODE trajectory after
+                # noisy_latents with both the conditioning embedding c and unconditional embedding 0
+                # Get teacher model prediction on noisy_latents and conditional embedding
+                with torch.no_grad():
+                    with torch.autocast("cuda"):
+                        cond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=prompt_embeds.to(weight_dtype),
+                        ).sample
+                        cond_pred_x0 = predicted_origin(
+                            cond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # Get teacher model prediction on noisy_latents and unconditional embedding
+                        uncond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),
+                        ).sample
+                        uncond_pred_x0 = predicted_origin(
+                            uncond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # 20.4.11. Perform "CFG" to get x_prev estimate (using the LCM paper's CFG formulation)
+                        pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
+                        pred_noise = cond_teacher_output + w * (cond_teacher_output - uncond_teacher_output)
+                        x_prev = solver.ddim_step(pred_x0, pred_noise, index)
+
+                # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n
+                with torch.no_grad():
+                    with torch.autocast("cuda", dtype=weight_dtype):
+                        target_noise_pred = unet(
+                            x_prev.float(),
+                            timesteps,
+                            timestep_cond=None,
+                            encoder_hidden_states=prompt_embeds.float(),
+                        ).sample
+                    pred_x_0 = predicted_origin(
+                        target_noise_pred,
+                        timesteps,
+                        x_prev,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+                    target = c_skip * x_prev + c_out * pred_x_0
+
+                # 20.4.13. Calculate loss
+                if args.loss_type == "l2":
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+                elif args.loss_type == "huber":
+                    loss = torch.mean(
+                        torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c
+                    )
+
+                # 20.4.14. Backpropagate on the online student model (`unet`)
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                if accelerator.is_main_process:
+                    if global_step % args.checkpointing_steps == 0:
+                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
+                        if args.checkpoints_total_limit is not None:
+                            checkpoints = os.listdir(args.output_dir)
+                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
+                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
+
+                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
+                            if len(checkpoints) >= args.checkpoints_total_limit:
+                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
+                                removing_checkpoints = checkpoints[0:num_to_remove]
+
+                                logger.info(
+                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+                                )
+                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
+
+                                for removing_checkpoint in removing_checkpoints:
+                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
+                                    shutil.rmtree(removing_checkpoint)
+
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+                    if global_step % args.validation_steps == 0:
+                        log_validation(vae, unet, args, accelerator, weight_dtype, global_step)
+
+            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
+
+    # Create the pipeline using using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unet = accelerator.unwrap_model(unet)
+        unet.save_pretrained(args.output_dir)
+        lora_state_dict = get_peft_model_state_dict(unet, adapter_name="default")
+        StableDiffusionPipeline.save_lora_weights(os.path.join(args.output_dir, "unet_lora"), lora_state_dict)
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import argparse
+import copy
+import functools
+import gc
+import itertools
+import json
+import logging
+import math
+import os
+import random
+import shutil
+from pathlib import Path
+from typing import List, Union
+
+import accelerate
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torchvision.transforms.functional as TF
+import transformers
+import webdataset as wds
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from braceexpand import braceexpand
+from huggingface_hub import create_repo
+from packaging import version
+from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
+from torch.utils.data import default_collate
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, PretrainedConfig
+from webdataset.tariterators import (
+    base_plus_ext,
+    tar_file_expander,
+    url_opener,
+    valid_sample,
+)
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    LCMScheduler,
+    StableDiffusionXLPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils.import_utils import is_xformers_available
+
+
+MAX_SEQ_LENGTH = 77
+
+if is_wandb_available():
+    import wandb
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.18.0.dev0")
+
+logger = get_logger(__name__)
+
+
+def get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype, adapter_name: str = "default"):
+    kohya_ss_state_dict = {}
+    for peft_key, weight in get_peft_model_state_dict(module, adapter_name=adapter_name).items():
+        kohya_key = peft_key.replace("base_model.model", prefix)
+        kohya_key = kohya_key.replace("lora_A", "lora_down")
+        kohya_key = kohya_key.replace("lora_B", "lora_up")
+        kohya_key = kohya_key.replace(".", "_", kohya_key.count(".") - 2)
+        kohya_ss_state_dict[kohya_key] = weight.to(dtype)
+
+        # Set alpha parameter
+        if "lora_down" in kohya_key:
+            alpha_key = f'{kohya_key.split(".")[0]}.alpha'
+            kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)
+
+    return kohya_ss_state_dict
+
+
+def filter_keys(key_set):
+    def _f(dictionary):
+        return {k: v for k, v in dictionary.items() if k in key_set}
+
+    return _f
+
+
+def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
+    """Return function over iterator that groups key, value pairs into samples.
+
+    :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
+    lower case (Default value = True)
+    """
+    current_sample = None
+    for filesample in data:
+        assert isinstance(filesample, dict)
+        fname, value = filesample["fname"], filesample["data"]
+        prefix, suffix = keys(fname)
+        if prefix is None:
+            continue
+        if lcase:
+            suffix = suffix.lower()
+        # FIXME webdataset version throws if suffix in current_sample, but we have a potential for
+        #  this happening in the current LAION400m dataset if a tar ends with same prefix as the next
+        #  begins, rare, but can happen since prefix aren't unique across tar files in that dataset
+        if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
+            if valid_sample(current_sample):
+                yield current_sample
+            current_sample = {"__key__": prefix, "__url__": filesample["__url__"]}
+        if suffixes is None or suffix in suffixes:
+            current_sample[suffix] = value
+    if valid_sample(current_sample):
+        yield current_sample
+
+
+def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
+    # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
+    streams = url_opener(src, handler=handler)
+    files = tar_file_expander(streams, handler=handler)
+    samples = group_by_keys_nothrow(files, handler=handler)
+    return samples
+
+
+class WebdatasetFilter:
+    def __init__(self, min_size=1024, max_pwatermark=0.5):
+        self.min_size = min_size
+        self.max_pwatermark = max_pwatermark
+
+    def __call__(self, x):
+        try:
+            if "json" in x:
+                x_json = json.loads(x["json"])
+                filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get(
+                    "original_height", 0
+                ) >= self.min_size
+                filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark
+                return filter_size and filter_watermark
+            else:
+                return False
+        except Exception:
+            return False
+
+
+class Text2ImageDataset:
+    def __init__(
+        self,
+        train_shards_path_or_url: Union[str, List[str]],
+        num_train_examples: int,
+        per_gpu_batch_size: int,
+        global_batch_size: int,
+        num_workers: int,
+        resolution: int = 1024,
+        shuffle_buffer_size: int = 1000,
+        pin_memory: bool = False,
+        persistent_workers: bool = False,
+        use_fix_crop_and_size: bool = False,
+    ):
+        if not isinstance(train_shards_path_or_url, str):
+            train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
+            # flatten list using itertools
+            train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))
+
+        def get_orig_size(json):
+            if use_fix_crop_and_size:
+                return (resolution, resolution)
+            else:
+                return (int(json.get("original_width", 0.0)), int(json.get("original_height", 0.0)))
+
+        def transform(example):
+            # resize image
+            image = example["image"]
+            image = TF.resize(image, resolution, interpolation=transforms.InterpolationMode.BILINEAR)
+
+            # get crop coordinates and crop image
+            c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
+            image = TF.crop(image, c_top, c_left, resolution, resolution)
+            image = TF.to_tensor(image)
+            image = TF.normalize(image, [0.5], [0.5])
+
+            example["image"] = image
+            example["crop_coords"] = (c_top, c_left) if not use_fix_crop_and_size else (0, 0)
+            return example
+
+        processing_pipeline = [
+            wds.decode("pil", handler=wds.ignore_and_continue),
+            wds.rename(
+                image="jpg;png;jpeg;webp", text="text;txt;caption", orig_size="json", handler=wds.warn_and_continue
+            ),
+            wds.map(filter_keys({"image", "text", "orig_size"})),
+            wds.map_dict(orig_size=get_orig_size),
+            wds.map(transform),
+            wds.to_tuple("image", "text", "orig_size", "crop_coords"),
+        ]
+
+        # Create train dataset and loader
+        pipeline = [
+            wds.ResampledShards(train_shards_path_or_url),
+            tarfile_to_samples_nothrow,
+            wds.select(WebdatasetFilter(min_size=960)),
+            wds.shuffle(shuffle_buffer_size),
+            *processing_pipeline,
+            wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
+        ]
+
+        num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers))  # per dataloader worker
+        num_batches = num_worker_batches * num_workers
+        num_samples = num_batches * global_batch_size
+
+        # each worker is iterating over this
+        self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)
+        self._train_dataloader = wds.WebLoader(
+            self._train_dataset,
+            batch_size=None,
+            shuffle=False,
+            num_workers=num_workers,
+            pin_memory=pin_memory,
+            persistent_workers=persistent_workers,
+        )
+        # add meta-data to dataloader instance for convenience
+        self._train_dataloader.num_batches = num_batches
+        self._train_dataloader.num_samples = num_samples
+
+    @property
+    def train_dataset(self):
+        return self._train_dataset
+
+    @property
+    def train_dataloader(self):
+        return self._train_dataloader
+
+
+def log_validation(vae, unet, args, accelerator, weight_dtype, step):
+    logger.info("Running validation... ")
+
+    unet = accelerator.unwrap_model(unet)
+    pipeline = StableDiffusionXLPipeline.from_pretrained(
+        args.pretrained_teacher_model,
+        vae=vae,
+        scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder="scheduler"),
+        revision=args.revision,
+        torch_dtype=weight_dtype,
+    )
+    pipeline = pipeline.to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=True)
+
+    lora_state_dict = get_module_kohya_state_dict(unet, "lora_unet", weight_dtype)
+    pipeline.load_lora_weights(lora_state_dict)
+    pipeline.fuse_lora()
+
+    if args.enable_xformers_memory_efficient_attention:
+        pipeline.enable_xformers_memory_efficient_attention()
+
+    if args.seed is None:
+        generator = None
+    else:
+        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+
+    validation_prompts = [
+        "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
+        "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+        "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
+    ]
+
+    image_logs = []
+
+    for _, prompt in enumerate(validation_prompts):
+        images = []
+        with torch.autocast("cuda", dtype=weight_dtype):
+            images = pipeline(
+                prompt=prompt,
+                num_inference_steps=4,
+                num_images_per_prompt=4,
+                generator=generator,
+                guidance_scale=0.0,
+            ).images
+        image_logs.append({"validation_prompt": prompt, "images": images})
+
+    for tracker in accelerator.trackers:
+        if tracker.name == "tensorboard":
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                formatted_images = []
+                for image in images:
+                    formatted_images.append(np.asarray(image))
+
+                formatted_images = np.stack(formatted_images)
+
+                tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
+        elif tracker.name == "wandb":
+            formatted_images = []
+
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                for image in images:
+                    image = wandb.Image(image, caption=validation_prompt)
+                    formatted_images.append(image)
+
+            tracker.log({"validation": formatted_images})
+        else:
+            logger.warn(f"image logging not implemented for {tracker.name}")
+
+        del pipeline
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        return image_logs
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
+    return x[(...,) + (None,) * dims_to_append]
+
+
+# From LCMScheduler.get_scalings_for_boundary_condition_discrete
+def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
+    c_skip = sigma_data**2 / ((timestep / 0.1) ** 2 + sigma_data**2)
+    c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5
+    return c_skip, c_out
+
+
+# Compare LCMScheduler.step, Step 4
+def predicted_origin(model_output, timesteps, sample, prediction_type, alphas, sigmas):
+    if prediction_type == "epsilon":
+        sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
+        alphas = extract_into_tensor(alphas, timesteps, sample.shape)
+        pred_x_0 = (sample - sigmas * model_output) / alphas
+    elif prediction_type == "v_prediction":
+        pred_x_0 = alphas[timesteps] * sample - sigmas[timesteps] * model_output
+    else:
+        raise ValueError(f"Prediction type {prediction_type} currently not supported.")
+
+    return pred_x_0
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+class DDIMSolver:
+    def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
+        # DDIM sampling parameters
+        step_ratio = timesteps // ddim_timesteps
+
+        self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
+        self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
+        self.ddim_alpha_cumprods_prev = np.asarray(
+            [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
+        )
+        # convert to torch tensors
+        self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
+        self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
+        self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
+
+    def to(self, device):
+        self.ddim_timesteps = self.ddim_timesteps.to(device)
+        self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
+        self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
+        return self
+
+    def ddim_step(self, pred_x0, pred_noise, timestep_index):
+        alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
+        dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
+        x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
+        return x_prev
+
+
+def import_model_class_from_model_name_or_path(
+    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
+):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path, subfolder=subfolder, revision=revision, use_auth_token=True
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "CLIPTextModelWithProjection":
+        from transformers import CLIPTextModelWithProjection
+
+        return CLIPTextModelWithProjection
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    # ----------Model Checkpoint Loading Arguments----------
+    parser.add_argument(
+        "--pretrained_teacher_model",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_vae_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
+    )
+    parser.add_argument(
+        "--teacher_revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM model identifier from huggingface.co/models.",
+    )
+    # ----------Training Arguments----------
+    # ----General Training Arguments----
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lcm-xl-distilled",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    # ----Logging----
+    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(
+        "--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.'
+        ),
+    )
+    # ----Checkpointing----
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=None,
+        help=("Max number of checkpoints to store."),
+    )
+    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.'
+        ),
+    )
+    # ----Image Processing----
+    parser.add_argument(
+        "--train_shards_path_or_url",
+        type=str,
+        default=None,
+        help=(
+            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
+            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
+            " or to a folder containing files that 🤗 Datasets can understand."
+        ),
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=1024,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--use_fix_crop_and_size",
+        action="store_true",
+        help="Whether or not to use the fixed crop and size for the teacher model.",
+        default=False,
+    )
+    parser.add_argument(
+        "--center_crop",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+    parser.add_argument(
+        "--random_flip",
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
+    # ----Dataloader----
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    # ----Batch Size and Training Steps----
+    parser.add_argument(
+        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--max_train_samples",
+        type=int,
+        default=None,
+        help=(
+            "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        ),
+    )
+    # ----Learning Rate----
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    # ----Optimizer (Adam)----
+    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.")
+    # ----Diffusion Training Arguments----
+    parser.add_argument(
+        "--proportion_empty_prompts",
+        type=float,
+        default=0,
+        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
+    )
+    # ----Latent Consistency Distillation (LCD) Specific Arguments----
+    parser.add_argument(
+        "--w_min",
+        type=float,
+        default=3.0,
+        required=False,
+        help=(
+            "The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--w_max",
+        type=float,
+        default=15.0,
+        required=False,
+        help=(
+            "The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--num_ddim_timesteps",
+        type=int,
+        default=50,
+        help="The number of timesteps to use for DDIM sampling.",
+    )
+    parser.add_argument(
+        "--loss_type",
+        type=str,
+        default="l2",
+        choices=["l2", "huber"],
+        help="The type of loss to use for the LCD loss.",
+    )
+    parser.add_argument(
+        "--huber_c",
+        type=float,
+        default=0.001,
+        help="The huber loss parameter. Only used if `--loss_type=huber`.",
+    )
+    parser.add_argument(
+        "--lora_rank",
+        type=int,
+        default=64,
+        help="The rank of the LoRA projection matrix.",
+    )
+    # ----Mixed Precision----
+    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(
+        "--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(
+        "--cast_teacher_unet",
+        action="store_true",
+        help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
+    )
+    # ----Training Optimizations----
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+    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.",
+    )
+    # ----Distributed Training----
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    # ----------Validation Arguments----------
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=200,
+        help="Run validation every X steps.",
+    )
+    # ----------Huggingface Hub Arguments-----------
+    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`.",
+    )
+    # ----------Accelerate Arguments----------
+    parser.add_argument(
+        "--tracker_project_name",
+        type=str,
+        default="text2image-fine-tune",
+        help=(
+            "The `project_name` argument passed to Accelerator.init_trackers for"
+            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
+        ),
+    )
+
+    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.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
+        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
+
+    return args
+
+
+# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
+def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):
+    prompt_embeds_list = []
+
+    captions = []
+    for caption in prompt_batch:
+        if random.random() < proportion_empty_prompts:
+            captions.append("")
+        elif isinstance(caption, str):
+            captions.append(caption)
+        elif isinstance(caption, (list, np.ndarray)):
+            # take a random caption if there are multiple
+            captions.append(random.choice(caption) if is_train else caption[0])
+
+    with torch.no_grad():
+        for tokenizer, text_encoder in zip(tokenizers, text_encoders):
+            text_inputs = tokenizer(
+                captions,
+                padding="max_length",
+                max_length=tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            prompt_embeds = text_encoder(
+                text_input_ids.to(text_encoder.device),
+                output_hidden_states=True,
+            )
+
+            # We are only ALWAYS interested in the pooled output of the final text encoder
+            pooled_prompt_embeds = prompt_embeds[0]
+            prompt_embeds = prompt_embeds.hidden_states[-2]
+            bs_embed, seq_len, _ = prompt_embeds.shape
+            prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
+            prompt_embeds_list.append(prompt_embeds)
+
+    prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+    pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
+    return prompt_embeds, pooled_prompt_embeds
+
+
+def main(args):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+        if args.push_to_hub:
+            create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name,
+                exist_ok=True,
+                token=args.hub_token,
+                private=True,
+            ).repo_id
+
+    # 1. Create the noise scheduler and the desired noise schedule.
+    noise_scheduler = DDPMScheduler.from_pretrained(
+        args.pretrained_teacher_model, subfolder="scheduler", revision=args.teacher_revision
+    )
+
+    # The scheduler calculates the alpha and sigma schedule for us
+    alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
+    sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
+    solver = DDIMSolver(
+        noise_scheduler.alphas_cumprod.numpy(),
+        timesteps=noise_scheduler.config.num_train_timesteps,
+        ddim_timesteps=args.num_ddim_timesteps,
+    )
+
+    # 2. Load tokenizers from SD-XL checkpoint.
+    tokenizer_one = AutoTokenizer.from_pretrained(
+        args.pretrained_teacher_model, subfolder="tokenizer", revision=args.teacher_revision, use_fast=False
+    )
+    tokenizer_two = AutoTokenizer.from_pretrained(
+        args.pretrained_teacher_model, subfolder="tokenizer_2", revision=args.teacher_revision, use_fast=False
+    )
+
+    # 3. Load text encoders from SD-XL checkpoint.
+    # import correct text encoder classes
+    text_encoder_cls_one = import_model_class_from_model_name_or_path(
+        args.pretrained_teacher_model, args.teacher_revision
+    )
+    text_encoder_cls_two = import_model_class_from_model_name_or_path(
+        args.pretrained_teacher_model, args.teacher_revision, subfolder="text_encoder_2"
+    )
+
+    text_encoder_one = text_encoder_cls_one.from_pretrained(
+        args.pretrained_teacher_model, subfolder="text_encoder", revision=args.teacher_revision
+    )
+    text_encoder_two = text_encoder_cls_two.from_pretrained(
+        args.pretrained_teacher_model, subfolder="text_encoder_2", revision=args.teacher_revision
+    )
+
+    # 4. Load VAE from SD-XL checkpoint (or more stable VAE)
+    vae_path = (
+        args.pretrained_teacher_model
+        if args.pretrained_vae_model_name_or_path is None
+        else args.pretrained_vae_model_name_or_path
+    )
+    vae = AutoencoderKL.from_pretrained(
+        vae_path,
+        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
+        revision=args.teacher_revision,
+    )
+
+    # 5. Load teacher U-Net from SD-XL checkpoint
+    teacher_unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
+    )
+
+    # 6. Freeze teacher vae, text_encoders, and teacher_unet
+    vae.requires_grad_(False)
+    text_encoder_one.requires_grad_(False)
+    text_encoder_two.requires_grad_(False)
+    teacher_unet.requires_grad_(False)
+
+    # 7. Create online (`unet`) student U-Nets.
+    unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
+    )
+    unet.train()
+
+    # Check that all trainable models are in full precision
+    low_precision_error_string = (
+        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
+        " doing mixed precision training, copy of the weights should still be float32."
+    )
+
+    if accelerator.unwrap_model(unet).dtype != torch.float32:
+        raise ValueError(
+            f"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
+        )
+
+    # 8. Add LoRA to the student U-Net, only the LoRA projection matrix will be updated by the optimizer.
+    lora_config = LoraConfig(
+        r=args.lora_rank,
+        target_modules=[
+            "to_q",
+            "to_k",
+            "to_v",
+            "to_out.0",
+            "proj_in",
+            "proj_out",
+            "ff.net.0.proj",
+            "ff.net.2",
+            "conv1",
+            "conv2",
+            "conv_shortcut",
+            "downsamplers.0.conv",
+            "upsamplers.0.conv",
+            "time_emb_proj",
+        ],
+    )
+    unet = get_peft_model(unet, lora_config)
+
+    # 9. Handle mixed precision and device placement
+    # For mixed precision training we cast all non-trainable weigths to half-precision
+    # as these weights are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move unet, vae and text_encoder to device and cast to weight_dtype
+    # The VAE is in float32 to avoid NaN losses.
+    vae.to(accelerator.device)
+    if args.pretrained_vae_model_name_or_path is not None:
+        vae.to(dtype=weight_dtype)
+    text_encoder_one.to(accelerator.device, dtype=weight_dtype)
+    text_encoder_two.to(accelerator.device, dtype=weight_dtype)
+
+    # Move teacher_unet to device, optionally cast to weight_dtype
+    teacher_unet.to(accelerator.device)
+    if args.cast_teacher_unet:
+        teacher_unet.to(dtype=weight_dtype)
+
+    # Also move the alpha and sigma noise schedules to accelerator.device.
+    alpha_schedule = alpha_schedule.to(accelerator.device)
+    sigma_schedule = sigma_schedule.to(accelerator.device)
+    solver = solver.to(accelerator.device)
+
+    # 10. Handle saving and loading of checkpoints
+    # `accelerate` 0.16.0 will have better support for customized saving
+    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
+        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
+        def save_model_hook(models, weights, output_dir):
+            if accelerator.is_main_process:
+                unet_ = accelerator.unwrap_model(unet)
+                lora_state_dict = get_peft_model_state_dict(unet_, adapter_name="default")
+                StableDiffusionXLPipeline.save_lora_weights(os.path.join(output_dir, "unet_lora"), lora_state_dict)
+                # save weights in peft format to be able to load them back
+                unet_.save_pretrained(output_dir)
+
+                for _, model in enumerate(models):
+                    # make sure to pop weight so that corresponding model is not saved again
+                    weights.pop()
+
+        def load_model_hook(models, input_dir):
+            # load the LoRA into the model
+            unet_ = accelerator.unwrap_model(unet)
+            unet_.load_adapter(input_dir, "default", is_trainable=True)
+
+            for _ in range(len(models)):
+                # pop models so that they are not loaded again
+                models.pop()
+
+        accelerator.register_save_state_pre_hook(save_model_hook)
+        accelerator.register_load_state_pre_hook(load_model_hook)
+
+    # 11. Enable optimizations
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = version.parse(xformers.__version__)
+            if xformers_version == version.parse("0.0.16"):
+                logger.warn(
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                )
+            unet.enable_xformers_memory_efficient_attention()
+            teacher_unet.enable_xformers_memory_efficient_attention()
+            # target_unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
+    # 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
+
+    # 12. Optimizer creation
+    optimizer = optimizer_class(
+        unet.parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    # 13. Dataset creation and data processing
+    # Here, we compute not just the text embeddings but also the additional embeddings
+    # needed for the SD XL UNet to operate.
+    def compute_embeddings(
+        prompt_batch, original_sizes, crop_coords, proportion_empty_prompts, text_encoders, tokenizers, is_train=True
+    ):
+        target_size = (args.resolution, args.resolution)
+        original_sizes = list(map(list, zip(*original_sizes)))
+        crops_coords_top_left = list(map(list, zip(*crop_coords)))
+
+        original_sizes = torch.tensor(original_sizes, dtype=torch.long)
+        crops_coords_top_left = torch.tensor(crops_coords_top_left, dtype=torch.long)
+
+        prompt_embeds, pooled_prompt_embeds = encode_prompt(
+            prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train
+        )
+        add_text_embeds = pooled_prompt_embeds
+
+        # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
+        add_time_ids = list(target_size)
+        add_time_ids = torch.tensor([add_time_ids])
+        add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)
+        add_time_ids = torch.cat([original_sizes, crops_coords_top_left, add_time_ids], dim=-1)
+        add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)
+
+        prompt_embeds = prompt_embeds.to(accelerator.device)
+        add_text_embeds = add_text_embeds.to(accelerator.device)
+        unet_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+        return {"prompt_embeds": prompt_embeds, **unet_added_cond_kwargs}
+
+    dataset = Text2ImageDataset(
+        train_shards_path_or_url=args.train_shards_path_or_url,
+        num_train_examples=args.max_train_samples,
+        per_gpu_batch_size=args.train_batch_size,
+        global_batch_size=args.train_batch_size * accelerator.num_processes,
+        num_workers=args.dataloader_num_workers,
+        resolution=args.resolution,
+        shuffle_buffer_size=1000,
+        pin_memory=True,
+        persistent_workers=True,
+        use_fix_crop_and_size=args.use_fix_crop_and_size,
+    )
+    train_dataloader = dataset.train_dataloader
+
+    # Let's first compute all the embeddings so that we can free up the text encoders
+    # from memory.
+    text_encoders = [text_encoder_one, text_encoder_two]
+    tokenizers = [tokenizer_one, tokenizer_two]
+
+    compute_embeddings_fn = functools.partial(
+        compute_embeddings,
+        proportion_empty_prompts=0,
+        text_encoders=text_encoders,
+        tokenizers=tokenizers,
+    )
+
+    # 14. LR Scheduler creation
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / 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
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps,
+        num_training_steps=args.max_train_steps,
+    )
+
+    # 15. Prepare for training
+    # Prepare everything with our `accelerator`.
+    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, 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(train_dataloader.num_batches / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    # Create uncond embeds for classifier free guidance
+    uncond_prompt_embeds = torch.zeros(args.train_batch_size, 77, 2048).to(accelerator.device)
+    uncond_pooled_prompt_embeds = torch.zeros(args.train_batch_size, 1280).to(accelerator.device)
+
+    # 16. Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num batches each epoch = {train_dataloader.num_batches}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1] if len(dirs) > 0 else None
+
+        if path is None:
+            accelerator.print(
+                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+            )
+            args.resume_from_checkpoint = None
+            initial_global_step = 0
+        else:
+            accelerator.print(f"Resuming from checkpoint {path}")
+            accelerator.load_state(os.path.join(args.output_dir, path))
+            global_step = int(path.split("-")[1])
+
+            initial_global_step = global_step
+            first_epoch = global_step // num_update_steps_per_epoch
+    else:
+        initial_global_step = 0
+
+    progress_bar = tqdm(
+        range(0, args.max_train_steps),
+        initial=initial_global_step,
+        desc="Steps",
+        # Only show the progress bar once on each machine.
+        disable=not accelerator.is_local_main_process,
+    )
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(unet):
+                image, text, orig_size, crop_coords = batch
+
+                image = image.to(accelerator.device, non_blocking=True)
+                encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)
+
+                if args.pretrained_vae_model_name_or_path is not None:
+                    pixel_values = image.to(dtype=weight_dtype)
+                    if vae.dtype != weight_dtype:
+                        vae.to(dtype=weight_dtype)
+                else:
+                    pixel_values = image
+
+                # encode pixel values with batch size of at most 8
+                latents = []
+                for i in range(0, pixel_values.shape[0], 8):
+                    latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample())
+                latents = torch.cat(latents, dim=0)
+
+                latents = latents * vae.config.scaling_factor
+                if args.pretrained_vae_model_name_or_path is None:
+                    latents = latents.to(weight_dtype)
+
+                # 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 t_n ~ U[0, N - k - 1] without bias.
+                topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps
+                index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()
+                start_timesteps = solver.ddim_timesteps[index]
+                timesteps = start_timesteps - topk
+                timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
+
+                # 20.4.4. Get boundary scalings for start_timesteps and (end) timesteps.
+                c_skip_start, c_out_start = scalings_for_boundary_conditions(start_timesteps)
+                c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]
+                c_skip, c_out = scalings_for_boundary_conditions(timesteps)
+                c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]
+
+                # 20.4.5. Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]
+                noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)
+
+                # 20.4.6. Sample a random guidance scale w from U[w_min, w_max] and embed it
+                w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
+                w = w.reshape(bsz, 1, 1, 1)
+                w = w.to(device=latents.device, dtype=latents.dtype)
+
+                # 20.4.8. Prepare prompt embeds and unet_added_conditions
+                prompt_embeds = encoded_text.pop("prompt_embeds")
+
+                # 20.4.9. Get online LCM prediction on z_{t_{n + k}}, w, c, t_{n + k}
+                noise_pred = unet(
+                    noisy_model_input,
+                    start_timesteps,
+                    timestep_cond=None,
+                    encoder_hidden_states=prompt_embeds.float(),
+                    added_cond_kwargs=encoded_text,
+                ).sample
+
+                pred_x_0 = predicted_origin(
+                    noise_pred,
+                    start_timesteps,
+                    noisy_model_input,
+                    noise_scheduler.config.prediction_type,
+                    alpha_schedule,
+                    sigma_schedule,
+                )
+
+                model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
+
+                # 20.4.10. Use the ODE solver to predict the kth step in the augmented PF-ODE trajectory after
+                # noisy_latents with both the conditioning embedding c and unconditional embedding 0
+                # Get teacher model prediction on noisy_latents and conditional embedding
+                with torch.no_grad():
+                    with torch.autocast("cuda"):
+                        cond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=prompt_embeds.to(weight_dtype),
+                            added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},
+                        ).sample
+                        cond_pred_x0 = predicted_origin(
+                            cond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # Get teacher model prediction on noisy_latents and unconditional embedding
+                        uncond_added_conditions = copy.deepcopy(encoded_text)
+                        uncond_added_conditions["text_embeds"] = uncond_pooled_prompt_embeds
+                        uncond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),
+                            added_cond_kwargs={k: v.to(weight_dtype) for k, v in uncond_added_conditions.items()},
+                        ).sample
+                        uncond_pred_x0 = predicted_origin(
+                            uncond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # 20.4.11. Perform "CFG" to get x_prev estimate (using the LCM paper's CFG formulation)
+                        pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
+                        pred_noise = cond_teacher_output + w * (cond_teacher_output - uncond_teacher_output)
+                        x_prev = solver.ddim_step(pred_x0, pred_noise, index)
+
+                # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n
+                with torch.no_grad():
+                    with torch.autocast("cuda", enabled=True, dtype=weight_dtype):
+                        target_noise_pred = unet(
+                            x_prev.float(),
+                            timesteps,
+                            timestep_cond=None,
+                            encoder_hidden_states=prompt_embeds.float(),
+                            added_cond_kwargs=encoded_text,
+                        ).sample
+                    pred_x_0 = predicted_origin(
+                        target_noise_pred,
+                        timesteps,
+                        x_prev,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+                    target = c_skip * x_prev + c_out * pred_x_0
+
+                # 20.4.13. Calculate loss
+                if args.loss_type == "l2":
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+                elif args.loss_type == "huber":
+                    loss = torch.mean(
+                        torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c
+                    )
+
+                # 20.4.14. Backpropagate on the online student model (`unet`)
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                if accelerator.is_main_process:
+                    if global_step % args.checkpointing_steps == 0:
+                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
+                        if args.checkpoints_total_limit is not None:
+                            checkpoints = os.listdir(args.output_dir)
+                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
+                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
+
+                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
+                            if len(checkpoints) >= args.checkpoints_total_limit:
+                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
+                                removing_checkpoints = checkpoints[0:num_to_remove]
+
+                                logger.info(
+                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+                                )
+                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
+
+                                for removing_checkpoint in removing_checkpoints:
+                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
+                                    shutil.rmtree(removing_checkpoint)
+
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+                    if global_step % args.validation_steps == 0:
+                        log_validation(vae, unet, args, accelerator, weight_dtype, global_step)
+
+            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
+
+    # Create the pipeline using using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unet = accelerator.unwrap_model(unet)
+        unet.save_pretrained(args.output_dir)
+        lora_state_dict = get_peft_model_state_dict(unet, adapter_name="default")
+        StableDiffusionXLPipeline.save_lora_weights(os.path.join(args.output_dir, "unet_lora"), lora_state_dict)
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

examples/consistency_distillation/train_lcm_distill_sd_wds.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import argparse
+import functools
+import gc
+import itertools
+import json
+import logging
+import math
+import os
+import random
+import shutil
+from pathlib import Path
+from typing import List, Union
+
+import accelerate
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torchvision.transforms.functional as TF
+import transformers
+import webdataset as wds
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from braceexpand import braceexpand
+from huggingface_hub import create_repo
+from packaging import version
+from torch.utils.data import default_collate
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig
+from webdataset.tariterators import (
+    base_plus_ext,
+    tar_file_expander,
+    url_opener,
+    valid_sample,
+)
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    LCMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils.import_utils import is_xformers_available
+
+
+MAX_SEQ_LENGTH = 77
+
+if is_wandb_available():
+    import wandb
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.18.0.dev0")
+
+logger = get_logger(__name__)
+
+
+def filter_keys(key_set):
+    def _f(dictionary):
+        return {k: v for k, v in dictionary.items() if k in key_set}
+
+    return _f
+
+
+def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
+    """Return function over iterator that groups key, value pairs into samples.
+
+    :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
+    lower case (Default value = True)
+    """
+    current_sample = None
+    for filesample in data:
+        assert isinstance(filesample, dict)
+        fname, value = filesample["fname"], filesample["data"]
+        prefix, suffix = keys(fname)
+        if prefix is None:
+            continue
+        if lcase:
+            suffix = suffix.lower()
+        # FIXME webdataset version throws if suffix in current_sample, but we have a potential for
+        #  this happening in the current LAION400m dataset if a tar ends with same prefix as the next
+        #  begins, rare, but can happen since prefix aren't unique across tar files in that dataset
+        if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
+            if valid_sample(current_sample):
+                yield current_sample
+            current_sample = {"__key__": prefix, "__url__": filesample["__url__"]}
+        if suffixes is None or suffix in suffixes:
+            current_sample[suffix] = value
+    if valid_sample(current_sample):
+        yield current_sample
+
+
+def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
+    # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
+    streams = url_opener(src, handler=handler)
+    files = tar_file_expander(streams, handler=handler)
+    samples = group_by_keys_nothrow(files, handler=handler)
+    return samples
+
+
+class WebdatasetFilter:
+    def __init__(self, min_size=1024, max_pwatermark=0.5):
+        self.min_size = min_size
+        self.max_pwatermark = max_pwatermark
+
+    def __call__(self, x):
+        try:
+            if "json" in x:
+                x_json = json.loads(x["json"])
+                filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get(
+                    "original_height", 0
+                ) >= self.min_size
+                filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark
+                return filter_size and filter_watermark
+            else:
+                return False
+        except Exception:
+            return False
+
+
+class Text2ImageDataset:
+    def __init__(
+        self,
+        train_shards_path_or_url: Union[str, List[str]],
+        num_train_examples: int,
+        per_gpu_batch_size: int,
+        global_batch_size: int,
+        num_workers: int,
+        resolution: int = 512,
+        shuffle_buffer_size: int = 1000,
+        pin_memory: bool = False,
+        persistent_workers: bool = False,
+    ):
+        if not isinstance(train_shards_path_or_url, str):
+            train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
+            # flatten list using itertools
+            train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))
+
+        def transform(example):
+            # resize image
+            image = example["image"]
+            image = TF.resize(image, resolution, interpolation=transforms.InterpolationMode.BILINEAR)
+
+            # get crop coordinates and crop image
+            c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
+            image = TF.crop(image, c_top, c_left, resolution, resolution)
+            image = TF.to_tensor(image)
+            image = TF.normalize(image, [0.5], [0.5])
+
+            example["image"] = image
+            return example
+
+        processing_pipeline = [
+            wds.decode("pil", handler=wds.ignore_and_continue),
+            wds.rename(image="jpg;png;jpeg;webp", text="text;txt;caption", handler=wds.warn_and_continue),
+            wds.map(filter_keys({"image", "text"})),
+            wds.map(transform),
+            wds.to_tuple("image", "text"),
+        ]
+
+        # Create train dataset and loader
+        pipeline = [
+            wds.ResampledShards(train_shards_path_or_url),
+            tarfile_to_samples_nothrow,
+            wds.shuffle(shuffle_buffer_size),
+            *processing_pipeline,
+            wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
+        ]
+
+        num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers))  # per dataloader worker
+        num_batches = num_worker_batches * num_workers
+        num_samples = num_batches * global_batch_size
+
+        # each worker is iterating over this
+        self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)
+        self._train_dataloader = wds.WebLoader(
+            self._train_dataset,
+            batch_size=None,
+            shuffle=False,
+            num_workers=num_workers,
+            pin_memory=pin_memory,
+            persistent_workers=persistent_workers,
+        )
+        # add meta-data to dataloader instance for convenience
+        self._train_dataloader.num_batches = num_batches
+        self._train_dataloader.num_samples = num_samples
+
+    @property
+    def train_dataset(self):
+        return self._train_dataset
+
+    @property
+    def train_dataloader(self):
+        return self._train_dataloader
+
+
+def log_validation(vae, unet, args, accelerator, weight_dtype, step, name="target"):
+    logger.info("Running validation... ")
+
+    unet = accelerator.unwrap_model(unet)
+    pipeline = StableDiffusionPipeline.from_pretrained(
+        args.pretrained_teacher_model,
+        vae=vae,
+        unet=unet,
+        scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder="scheduler"),
+        revision=args.revision,
+        torch_dtype=weight_dtype,
+    )
+    pipeline = pipeline.to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=True)
+
+    if args.enable_xformers_memory_efficient_attention:
+        pipeline.enable_xformers_memory_efficient_attention()
+
+    if args.seed is None:
+        generator = None
+    else:
+        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+
+    validation_prompts = [
+        "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
+        "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+        "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
+    ]
+
+    image_logs = []
+
+    for _, prompt in enumerate(validation_prompts):
+        images = []
+        with torch.autocast("cuda"):
+            images = pipeline(
+                prompt=prompt,
+                num_inference_steps=4,
+                num_images_per_prompt=4,
+                generator=generator,
+            ).images
+        image_logs.append({"validation_prompt": prompt, "images": images})
+
+    for tracker in accelerator.trackers:
+        if tracker.name == "tensorboard":
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                formatted_images = []
+                for image in images:
+                    formatted_images.append(np.asarray(image))
+
+                formatted_images = np.stack(formatted_images)
+
+                tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
+        elif tracker.name == "wandb":
+            formatted_images = []
+
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                for image in images:
+                    image = wandb.Image(image, caption=validation_prompt)
+                    formatted_images.append(image)
+
+            tracker.log({f"validation/{name}": formatted_images})
+        else:
+            logger.warn(f"image logging not implemented for {tracker.name}")
+
+        del pipeline
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        return image_logs
+
+
+# From LatentConsistencyModel.get_guidance_scale_embedding
+def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
+    """
+    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+    Args:
+        timesteps (`torch.Tensor`):
+            generate embedding vectors at these timesteps
+        embedding_dim (`int`, *optional*, defaults to 512):
+            dimension of the embeddings to generate
+        dtype:
+            data type of the generated embeddings
+
+    Returns:
+        `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+    """
+    assert len(w.shape) == 1
+    w = w * 1000.0
+
+    half_dim = embedding_dim // 2
+    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+    emb = w.to(dtype)[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0, 1))
+    assert emb.shape == (w.shape[0], embedding_dim)
+    return emb
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
+    return x[(...,) + (None,) * dims_to_append]
+
+
+# From LCMScheduler.get_scalings_for_boundary_condition_discrete
+def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
+    c_skip = sigma_data**2 / ((timestep / 0.1) ** 2 + sigma_data**2)
+    c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5
+    return c_skip, c_out
+
+
+# Compare LCMScheduler.step, Step 4
+def predicted_origin(model_output, timesteps, sample, prediction_type, alphas, sigmas):
+    if prediction_type == "epsilon":
+        sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
+        alphas = extract_into_tensor(alphas, timesteps, sample.shape)
+        pred_x_0 = (sample - sigmas * model_output) / alphas
+    elif prediction_type == "v_prediction":
+        pred_x_0 = alphas[timesteps] * sample - sigmas[timesteps] * model_output
+    else:
+        raise ValueError(f"Prediction type {prediction_type} currently not supported.")
+
+    return pred_x_0
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+class DDIMSolver:
+    def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
+        # DDIM sampling parameters
+        step_ratio = timesteps // ddim_timesteps
+        self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
+        self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
+        self.ddim_alpha_cumprods_prev = np.asarray(
+            [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
+        )
+        # convert to torch tensors
+        self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
+        self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
+        self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
+
+    def to(self, device):
+        self.ddim_timesteps = self.ddim_timesteps.to(device)
+        self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
+        self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
+        return self
+
+    def ddim_step(self, pred_x0, pred_noise, timestep_index):
+        alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
+        dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
+        x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
+        return x_prev
+
+
+@torch.no_grad()
+def update_ema(target_params, source_params, rate=0.99):
+    """
+    Update target parameters to be closer to those of source parameters using
+    an exponential moving average.
+
+    :param target_params: the target parameter sequence.
+    :param source_params: the source parameter sequence.
+    :param rate: the EMA rate (closer to 1 means slower).
+    """
+    for targ, src in zip(target_params, source_params):
+        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
+
+
+def import_model_class_from_model_name_or_path(
+    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
+):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path, subfolder=subfolder, revision=revision, use_auth_token=True
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "CLIPTextModelWithProjection":
+        from transformers import CLIPTextModelWithProjection
+
+        return CLIPTextModelWithProjection
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    # ----------Model Checkpoint Loading Arguments----------
+    parser.add_argument(
+        "--pretrained_teacher_model",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_vae_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
+    )
+    parser.add_argument(
+        "--teacher_revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM model identifier from huggingface.co/models.",
+    )
+    # ----------Training Arguments----------
+    # ----General Training Arguments----
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lcm-xl-distilled",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    # ----Logging----
+    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(
+        "--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.'
+        ),
+    )
+    # ----Checkpointing----
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=None,
+        help=("Max number of checkpoints to store."),
+    )
+    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.'
+        ),
+    )
+    # ----Image Processing----
+    parser.add_argument(
+        "--train_shards_path_or_url",
+        type=str,
+        default=None,
+        help=(
+            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
+            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
+            " or to a folder containing files that 🤗 Datasets can understand."
+        ),
+    )
+    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",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+    parser.add_argument(
+        "--random_flip",
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
+    # ----Dataloader----
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    # ----Batch Size and Training Steps----
+    parser.add_argument(
+        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--max_train_samples",
+        type=int,
+        default=None,
+        help=(
+            "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        ),
+    )
+    # ----Learning Rate----
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    # ----Optimizer (Adam)----
+    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.")
+    # ----Diffusion Training Arguments----
+    parser.add_argument(
+        "--proportion_empty_prompts",
+        type=float,
+        default=0,
+        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
+    )
+    # ----Latent Consistency Distillation (LCD) Specific Arguments----
+    parser.add_argument(
+        "--w_min",
+        type=float,
+        default=5.0,
+        required=False,
+        help=(
+            "The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--w_max",
+        type=float,
+        default=15.0,
+        required=False,
+        help=(
+            "The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--num_ddim_timesteps",
+        type=int,
+        default=50,
+        help="The number of timesteps to use for DDIM sampling.",
+    )
+    parser.add_argument(
+        "--loss_type",
+        type=str,
+        default="l2",
+        choices=["l2", "huber"],
+        help="The type of loss to use for the LCD loss.",
+    )
+    parser.add_argument(
+        "--huber_c",
+        type=float,
+        default=0.001,
+        help="The huber loss parameter. Only used if `--loss_type=huber`.",
+    )
+    # ----Exponential Moving Average (EMA)----
+    parser.add_argument(
+        "--ema_decay",
+        type=float,
+        default=0.95,
+        required=False,
+        help="The exponential moving average (EMA) rate or decay factor.",
+    )
+    # ----Mixed Precision----
+    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(
+        "--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(
+        "--cast_teacher_unet",
+        action="store_true",
+        help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
+    )
+    # ----Training Optimizations----
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+    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.",
+    )
+    # ----Distributed Training----
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    # ----------Validation Arguments----------
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=200,
+        help="Run validation every X steps.",
+    )
+    # ----------Huggingface Hub Arguments-----------
+    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`.",
+    )
+    # ----------Accelerate Arguments----------
+    parser.add_argument(
+        "--tracker_project_name",
+        type=str,
+        default="text2image-fine-tune",
+        help=(
+            "The `project_name` argument passed to Accelerator.init_trackers for"
+            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
+        ),
+    )
+
+    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.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
+        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
+
+    return args
+
+
+# Adapted from pipelines.StableDiffusionPipeline.encode_prompt
+def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):
+    captions = []
+    for caption in prompt_batch:
+        if random.random() < proportion_empty_prompts:
+            captions.append("")
+        elif isinstance(caption, str):
+            captions.append(caption)
+        elif isinstance(caption, (list, np.ndarray)):
+            # take a random caption if there are multiple
+            captions.append(random.choice(caption) if is_train else caption[0])
+
+    with torch.no_grad():
+        text_inputs = tokenizer(
+            captions,
+            padding="max_length",
+            max_length=tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        prompt_embeds = text_encoder(text_input_ids.to(text_encoder.device))[0]
+
+    return prompt_embeds
+
+
+def main(args):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+        if args.push_to_hub:
+            create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name,
+                exist_ok=True,
+                token=args.hub_token,
+                private=True,
+            ).repo_id
+
+    # 1. Create the noise scheduler and the desired noise schedule.
+    noise_scheduler = DDPMScheduler.from_pretrained(
+        args.pretrained_teacher_model, subfolder="scheduler", revision=args.teacher_revision
+    )
+
+    # The scheduler calculates the alpha and sigma schedule for us
+    alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
+    sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
+    solver = DDIMSolver(
+        noise_scheduler.alphas_cumprod.numpy(),
+        timesteps=noise_scheduler.config.num_train_timesteps,
+        ddim_timesteps=args.num_ddim_timesteps,
+    )
+
+    # 2. Load tokenizers from SD-XL checkpoint.
+    tokenizer = AutoTokenizer.from_pretrained(
+        args.pretrained_teacher_model, subfolder="tokenizer", revision=args.teacher_revision, use_fast=False
+    )
+
+    # 3. Load text encoders from SD-1.5 checkpoint.
+    # import correct text encoder classes
+    text_encoder = CLIPTextModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="text_encoder", revision=args.teacher_revision
+    )
+
+    # 4. Load VAE from SD-XL checkpoint (or more stable VAE)
+    vae = AutoencoderKL.from_pretrained(
+        args.pretrained_teacher_model,
+        subfolder="vae",
+        revision=args.teacher_revision,
+    )
+
+    # 5. Load teacher U-Net from SD-XL checkpoint
+    teacher_unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
+    )
+
+    # 6. Freeze teacher vae, text_encoder, and teacher_unet
+    vae.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+    teacher_unet.requires_grad_(False)
+
+    # 8. Create online (`unet`) student U-Nets. This will be updated by the optimizer (e.g. via backpropagation.)
+    # Add `time_cond_proj_dim` to the student U-Net if `teacher_unet.config.time_cond_proj_dim` is None
+    if teacher_unet.config.time_cond_proj_dim is None:
+        teacher_unet.config["time_cond_proj_dim"] = args.unet_time_cond_proj_dim
+    unet = UNet2DConditionModel(**teacher_unet.config)
+    # load teacher_unet weights into unet
+    unet.load_state_dict(teacher_unet.state_dict(), strict=False)
+    unet.train()
+
+    # 9. Create target (`ema_unet`) student U-Net parameters. This will be updated via EMA updates (polyak averaging).
+    # Initialize from unet
+    target_unet = UNet2DConditionModel(**teacher_unet.config)
+    target_unet.load_state_dict(unet.state_dict())
+    target_unet.train()
+    target_unet.requires_grad_(False)
+
+    # Check that all trainable models are in full precision
+    low_precision_error_string = (
+        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
+        " doing mixed precision training, copy of the weights should still be float32."
+    )
+
+    if accelerator.unwrap_model(unet).dtype != torch.float32:
+        raise ValueError(
+            f"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
+        )
+
+    # 10. Handle mixed precision and device placement
+    # For mixed precision training we cast all non-trainable weigths to half-precision
+    # as these weights are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move unet, vae and text_encoder to device and cast to weight_dtype
+    # The VAE is in float32 to avoid NaN losses.
+    vae.to(accelerator.device)
+    if args.pretrained_vae_model_name_or_path is not None:
+        vae.to(dtype=weight_dtype)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    # Move teacher_unet to device, optionally cast to weight_dtype
+    target_unet.to(accelerator.device)
+    teacher_unet.to(accelerator.device)
+    if args.cast_teacher_unet:
+        teacher_unet.to(dtype=weight_dtype)
+
+    # Also move the alpha and sigma noise schedules to accelerator.device.
+    alpha_schedule = alpha_schedule.to(accelerator.device)
+    sigma_schedule = sigma_schedule.to(accelerator.device)
+    solver = solver.to(accelerator.device)
+
+    # 11. Handle saving and loading of checkpoints
+    # `accelerate` 0.16.0 will have better support for customized saving
+    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
+        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
+        def save_model_hook(models, weights, output_dir):
+            if accelerator.is_main_process:
+                target_unet.save_pretrained(os.path.join(output_dir, "unet_target"))
+
+                for i, model in enumerate(models):
+                    model.save_pretrained(os.path.join(output_dir, "unet"))
+
+                    # make sure to pop weight so that corresponding model is not saved again
+                    weights.pop()
+
+        def load_model_hook(models, input_dir):
+            load_model = UNet2DConditionModel.from_pretrained(os.path.join(input_dir, "unet_target"))
+            target_unet.load_state_dict(load_model.state_dict())
+            target_unet.to(accelerator.device)
+            del load_model
+
+            for i in range(len(models)):
+                # pop models so that they are not loaded again
+                model = models.pop()
+
+                # load diffusers style into model
+                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
+                model.register_to_config(**load_model.config)
+
+                model.load_state_dict(load_model.state_dict())
+                del load_model
+
+        accelerator.register_save_state_pre_hook(save_model_hook)
+        accelerator.register_load_state_pre_hook(load_model_hook)
+
+    # 12. Enable optimizations
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = version.parse(xformers.__version__)
+            if xformers_version == version.parse("0.0.16"):
+                logger.warn(
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                )
+            unet.enable_xformers_memory_efficient_attention()
+            teacher_unet.enable_xformers_memory_efficient_attention()
+            target_unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
+    # 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
+
+    # 12. Optimizer creation
+    optimizer = optimizer_class(
+        unet.parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    # Here, we compute not just the text embeddings but also the additional embeddings
+    # needed for the SD XL UNet to operate.
+    def compute_embeddings(prompt_batch, proportion_empty_prompts, text_encoder, tokenizer, is_train=True):
+        prompt_embeds = encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train)
+        return {"prompt_embeds": prompt_embeds}
+
+    dataset = Text2ImageDataset(
+        train_shards_path_or_url=args.train_shards_path_or_url,
+        num_train_examples=args.max_train_samples,
+        per_gpu_batch_size=args.train_batch_size,
+        global_batch_size=args.train_batch_size * accelerator.num_processes,
+        num_workers=args.dataloader_num_workers,
+        resolution=args.resolution,
+        shuffle_buffer_size=1000,
+        pin_memory=True,
+        persistent_workers=True,
+    )
+    train_dataloader = dataset.train_dataloader
+
+    compute_embeddings_fn = functools.partial(
+        compute_embeddings,
+        proportion_empty_prompts=0,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+    )
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / 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,
+        num_training_steps=args.max_train_steps,
+    )
+
+    # Prepare everything with our `accelerator`.
+    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, 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(train_dataloader.num_batches / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    uncond_input_ids = tokenizer(
+        [""] * args.train_batch_size, return_tensors="pt", padding="max_length", max_length=77
+    ).input_ids.to(accelerator.device)
+    uncond_prompt_embeds = text_encoder(uncond_input_ids)[0]
+
+    # Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num batches each epoch = {train_dataloader.num_batches}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1] if len(dirs) > 0 else None
+
+        if path is None:
+            accelerator.print(
+                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+            )
+            args.resume_from_checkpoint = None
+            initial_global_step = 0
+        else:
+            accelerator.print(f"Resuming from checkpoint {path}")
+            accelerator.load_state(os.path.join(args.output_dir, path))
+            global_step = int(path.split("-")[1])
+
+            initial_global_step = global_step
+            first_epoch = global_step // num_update_steps_per_epoch
+    else:
+        initial_global_step = 0
+
+    progress_bar = tqdm(
+        range(0, args.max_train_steps),
+        initial=initial_global_step,
+        desc="Steps",
+        # Only show the progress bar once on each machine.
+        disable=not accelerator.is_local_main_process,
+    )
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(unet):
+                image, text, _, _ = batch
+
+                image = image.to(accelerator.device, non_blocking=True)
+                encoded_text = compute_embeddings_fn(text)
+
+                pixel_values = image.to(dtype=weight_dtype)
+                if vae.dtype != weight_dtype:
+                    vae.to(dtype=weight_dtype)
+
+                # encode pixel values with batch size of at most 32
+                latents = []
+                for i in range(0, pixel_values.shape[0], 32):
+                    latents.append(vae.encode(pixel_values[i : i + 32]).latent_dist.sample())
+                latents = torch.cat(latents, dim=0)
+
+                latents = latents * vae.config.scaling_factor
+                latents = latents.to(weight_dtype)
+
+                # 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 t_n ~ U[0, N - k - 1] without bias.
+                topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps
+                index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()
+                start_timesteps = solver.ddim_timesteps[index]
+                timesteps = start_timesteps - topk
+                timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
+
+                # 20.4.4. Get boundary scalings for start_timesteps and (end) timesteps.
+                c_skip_start, c_out_start = scalings_for_boundary_conditions(start_timesteps)
+                c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]
+                c_skip, c_out = scalings_for_boundary_conditions(timesteps)
+                c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]
+
+                # 20.4.5. Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]
+                noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)
+
+                # 20.4.6. Sample a random guidance scale w from U[w_min, w_max] and embed it
+                w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
+                w_embedding = guidance_scale_embedding(w, embedding_dim=args.unet_time_cond_proj_dim)
+                w = w.reshape(bsz, 1, 1, 1)
+                # Move to U-Net device and dtype
+                w = w.to(device=latents.device, dtype=latents.dtype)
+                w_embedding = w_embedding.to(device=latents.device, dtype=latents.dtype)
+
+                # 20.4.8. Prepare prompt embeds and unet_added_conditions
+                prompt_embeds = encoded_text.pop("prompt_embeds")
+
+                # 20.4.9. Get online LCM prediction on z_{t_{n + k}}, w, c, t_{n + k}
+                noise_pred = unet(
+                    noisy_model_input,
+                    start_timesteps,
+                    timestep_cond=w_embedding,
+                    encoder_hidden_states=prompt_embeds.float(),
+                    added_cond_kwargs=encoded_text,
+                ).sample
+
+                pred_x_0 = predicted_origin(
+                    noise_pred,
+                    start_timesteps,
+                    noisy_model_input,
+                    noise_scheduler.config.prediction_type,
+                    alpha_schedule,
+                    sigma_schedule,
+                )
+
+                model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
+
+                # 20.4.10. Use the ODE solver to predict the kth step in the augmented PF-ODE trajectory after
+                # noisy_latents with both the conditioning embedding c and unconditional embedding 0
+                # Get teacher model prediction on noisy_latents and conditional embedding
+                with torch.no_grad():
+                    with torch.autocast("cuda"):
+                        cond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=prompt_embeds.to(weight_dtype),
+                        ).sample
+                        cond_pred_x0 = predicted_origin(
+                            cond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # Get teacher model prediction on noisy_latents and unconditional embedding
+                        uncond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),
+                        ).sample
+                        uncond_pred_x0 = predicted_origin(
+                            uncond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # 20.4.11. Perform "CFG" to get x_prev estimate (using the LCM paper's CFG formulation)
+                        pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
+                        pred_noise = cond_teacher_output + w * (cond_teacher_output - uncond_teacher_output)
+                        x_prev = solver.ddim_step(pred_x0, pred_noise, index)
+
+                # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n
+                with torch.no_grad():
+                    with torch.autocast("cuda", dtype=weight_dtype):
+                        target_noise_pred = target_unet(
+                            x_prev.float(),
+                            timesteps,
+                            timestep_cond=w_embedding,
+                            encoder_hidden_states=prompt_embeds.float(),
+                        ).sample
+                    pred_x_0 = predicted_origin(
+                        target_noise_pred,
+                        timesteps,
+                        x_prev,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+                    target = c_skip * x_prev + c_out * pred_x_0
+
+                # 20.4.13. Calculate loss
+                if args.loss_type == "l2":
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+                elif args.loss_type == "huber":
+                    loss = torch.mean(
+                        torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c
+                    )
+
+                # 20.4.14. Backpropagate on the online student model (`unet`)
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                # 20.4.15. Make EMA update to target student model parameters
+                update_ema(target_unet.parameters(), unet.parameters(), args.ema_decay)
+                progress_bar.update(1)
+                global_step += 1
+
+                if accelerator.is_main_process:
+                    if global_step % args.checkpointing_steps == 0:
+                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
+                        if args.checkpoints_total_limit is not None:
+                            checkpoints = os.listdir(args.output_dir)
+                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
+                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
+
+                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
+                            if len(checkpoints) >= args.checkpoints_total_limit:
+                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
+                                removing_checkpoints = checkpoints[0:num_to_remove]
+
+                                logger.info(
+                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+                                )
+                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
+
+                                for removing_checkpoint in removing_checkpoints:
+                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
+                                    shutil.rmtree(removing_checkpoint)
+
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+                    if global_step % args.validation_steps == 0:
+                        log_validation(vae, target_unet, args, accelerator, weight_dtype, global_step, "target")
+                        log_validation(vae, unet, args, accelerator, weight_dtype, global_step, "online")
+
+            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
+
+    # Create the pipeline using using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unet = accelerator.unwrap_model(unet)
+        unet.save_pretrained(os.path.join(args.output_dir, "unet"))
+
+        target_unet = accelerator.unwrap_model(target_unet)
+        target_unet.save_pretrained(os.path.join(args.output_dir, "unet_target"))
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

examples/consistency_distillation/train_lcm_distill_sdxl_wds.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+
+import argparse
+import copy
+import functools
+import gc
+import itertools
+import json
+import logging
+import math
+import os
+import random
+import shutil
+from pathlib import Path
+from typing import List, Union
+
+import accelerate
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torchvision.transforms.functional as TF
+import transformers
+import webdataset as wds
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from braceexpand import braceexpand
+from huggingface_hub import create_repo
+from packaging import version
+from torch.utils.data import default_collate
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, PretrainedConfig
+from webdataset.tariterators import (
+    base_plus_ext,
+    tar_file_expander,
+    url_opener,
+    valid_sample,
+)
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    LCMScheduler,
+    StableDiffusionXLPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version, is_wandb_available
+from diffusers.utils.import_utils import is_xformers_available
+
+
+MAX_SEQ_LENGTH = 77
+
+if is_wandb_available():
+    import wandb
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.18.0.dev0")
+
+logger = get_logger(__name__)
+
+
+def filter_keys(key_set):
+    def _f(dictionary):
+        return {k: v for k, v in dictionary.items() if k in key_set}
+
+    return _f
+
+
+def group_by_keys_nothrow(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
+    """Return function over iterator that groups key, value pairs into samples.
+
+    :param keys: function that splits the key into key and extension (base_plus_ext) :param lcase: convert suffixes to
+    lower case (Default value = True)
+    """
+    current_sample = None
+    for filesample in data:
+        assert isinstance(filesample, dict)
+        fname, value = filesample["fname"], filesample["data"]
+        prefix, suffix = keys(fname)
+        if prefix is None:
+            continue
+        if lcase:
+            suffix = suffix.lower()
+        # FIXME webdataset version throws if suffix in current_sample, but we have a potential for
+        #  this happening in the current LAION400m dataset if a tar ends with same prefix as the next
+        #  begins, rare, but can happen since prefix aren't unique across tar files in that dataset
+        if current_sample is None or prefix != current_sample["__key__"] or suffix in current_sample:
+            if valid_sample(current_sample):
+                yield current_sample
+            current_sample = {"__key__": prefix, "__url__": filesample["__url__"]}
+        if suffixes is None or suffix in suffixes:
+            current_sample[suffix] = value
+    if valid_sample(current_sample):
+        yield current_sample
+
+
+def tarfile_to_samples_nothrow(src, handler=wds.warn_and_continue):
+    # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw
+    streams = url_opener(src, handler=handler)
+    files = tar_file_expander(streams, handler=handler)
+    samples = group_by_keys_nothrow(files, handler=handler)
+    return samples
+
+
+class WebdatasetFilter:
+    def __init__(self, min_size=1024, max_pwatermark=0.5):
+        self.min_size = min_size
+        self.max_pwatermark = max_pwatermark
+
+    def __call__(self, x):
+        try:
+            if "json" in x:
+                x_json = json.loads(x["json"])
+                filter_size = (x_json.get("original_width", 0.0) or 0.0) >= self.min_size and x_json.get(
+                    "original_height", 0
+                ) >= self.min_size
+                filter_watermark = (x_json.get("pwatermark", 1.0) or 1.0) <= self.max_pwatermark
+                return filter_size and filter_watermark
+            else:
+                return False
+        except Exception:
+            return False
+
+
+class Text2ImageDataset:
+    def __init__(
+        self,
+        train_shards_path_or_url: Union[str, List[str]],
+        num_train_examples: int,
+        per_gpu_batch_size: int,
+        global_batch_size: int,
+        num_workers: int,
+        resolution: int = 1024,
+        shuffle_buffer_size: int = 1000,
+        pin_memory: bool = False,
+        persistent_workers: bool = False,
+        use_fix_crop_and_size: bool = False,
+    ):
+        if not isinstance(train_shards_path_or_url, str):
+            train_shards_path_or_url = [list(braceexpand(urls)) for urls in train_shards_path_or_url]
+            # flatten list using itertools
+            train_shards_path_or_url = list(itertools.chain.from_iterable(train_shards_path_or_url))
+
+        def get_orig_size(json):
+            if use_fix_crop_and_size:
+                return (resolution, resolution)
+            else:
+                return (int(json.get("original_width", 0.0)), int(json.get("original_height", 0.0)))
+
+        def transform(example):
+            # resize image
+            image = example["image"]
+            image = TF.resize(image, resolution, interpolation=transforms.InterpolationMode.BILINEAR)
+
+            # get crop coordinates and crop image
+            c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(resolution, resolution))
+            image = TF.crop(image, c_top, c_left, resolution, resolution)
+            image = TF.to_tensor(image)
+            image = TF.normalize(image, [0.5], [0.5])
+
+            example["image"] = image
+            example["crop_coords"] = (c_top, c_left) if not use_fix_crop_and_size else (0, 0)
+            return example
+
+        processing_pipeline = [
+            wds.decode("pil", handler=wds.ignore_and_continue),
+            wds.rename(
+                image="jpg;png;jpeg;webp", text="text;txt;caption", orig_size="json", handler=wds.warn_and_continue
+            ),
+            wds.map(filter_keys({"image", "text", "orig_size"})),
+            wds.map_dict(orig_size=get_orig_size),
+            wds.map(transform),
+            wds.to_tuple("image", "text", "orig_size", "crop_coords"),
+        ]
+
+        # Create train dataset and loader
+        pipeline = [
+            wds.ResampledShards(train_shards_path_or_url),
+            tarfile_to_samples_nothrow,
+            wds.select(WebdatasetFilter(min_size=960)),
+            wds.shuffle(shuffle_buffer_size),
+            *processing_pipeline,
+            wds.batched(per_gpu_batch_size, partial=False, collation_fn=default_collate),
+        ]
+
+        num_worker_batches = math.ceil(num_train_examples / (global_batch_size * num_workers))  # per dataloader worker
+        num_batches = num_worker_batches * num_workers
+        num_samples = num_batches * global_batch_size
+
+        # each worker is iterating over this
+        self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(num_worker_batches)
+        self._train_dataloader = wds.WebLoader(
+            self._train_dataset,
+            batch_size=None,
+            shuffle=False,
+            num_workers=num_workers,
+            pin_memory=pin_memory,
+            persistent_workers=persistent_workers,
+        )
+        # add meta-data to dataloader instance for convenience
+        self._train_dataloader.num_batches = num_batches
+        self._train_dataloader.num_samples = num_samples
+
+    @property
+    def train_dataset(self):
+        return self._train_dataset
+
+    @property
+    def train_dataloader(self):
+        return self._train_dataloader
+
+
+def log_validation(vae, unet, args, accelerator, weight_dtype, step, name="target"):
+    logger.info("Running validation... ")
+
+    unet = accelerator.unwrap_model(unet)
+    pipeline = StableDiffusionXLPipeline.from_pretrained(
+        args.pretrained_teacher_model,
+        vae=vae,
+        unet=unet,
+        scheduler=LCMScheduler.from_pretrained(args.pretrained_teacher_model, subfolder="scheduler"),
+        revision=args.revision,
+        torch_dtype=weight_dtype,
+    )
+    pipeline = pipeline.to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=True)
+
+    if args.enable_xformers_memory_efficient_attention:
+        pipeline.enable_xformers_memory_efficient_attention()
+
+    if args.seed is None:
+        generator = None
+    else:
+        generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+
+    validation_prompts = [
+        "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour, style by Dan Winters, Russell James, Steve McCurry, centered, extremely detailed, Nikon D850, award winning photography",
+        "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+        "A photo of beautiful mountain with realistic sunset and blue lake, highly detailed, masterpiece",
+    ]
+
+    image_logs = []
+
+    for _, prompt in enumerate(validation_prompts):
+        images = []
+        with torch.autocast("cuda"):
+            images = pipeline(
+                prompt=prompt,
+                num_inference_steps=4,
+                num_images_per_prompt=4,
+                generator=generator,
+            ).images
+        image_logs.append({"validation_prompt": prompt, "images": images})
+
+    for tracker in accelerator.trackers:
+        if tracker.name == "tensorboard":
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                formatted_images = []
+                for image in images:
+                    formatted_images.append(np.asarray(image))
+
+                formatted_images = np.stack(formatted_images)
+
+                tracker.writer.add_images(validation_prompt, formatted_images, step, dataformats="NHWC")
+        elif tracker.name == "wandb":
+            formatted_images = []
+
+            for log in image_logs:
+                images = log["images"]
+                validation_prompt = log["validation_prompt"]
+                for image in images:
+                    image = wandb.Image(image, caption=validation_prompt)
+                    formatted_images.append(image)
+
+            tracker.log({f"validation/{name}": formatted_images})
+        else:
+            logger.warn(f"image logging not implemented for {tracker.name}")
+
+        del pipeline
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        return image_logs
+
+
+def append_dims(x, target_dims):
+    """Appends dimensions to the end of a tensor until it has target_dims dimensions."""
+    dims_to_append = target_dims - x.ndim
+    if dims_to_append < 0:
+        raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less")
+    return x[(...,) + (None,) * dims_to_append]
+
+
+# From LCMScheduler.get_scalings_for_boundary_condition_discrete
+def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0):
+    c_skip = sigma_data**2 / ((timestep / 0.1) ** 2 + sigma_data**2)
+    c_out = (timestep / 0.1) / ((timestep / 0.1) ** 2 + sigma_data**2) ** 0.5
+    return c_skip, c_out
+
+
+# Compare LCMScheduler.step, Step 4
+def predicted_origin(model_output, timesteps, sample, prediction_type, alphas, sigmas):
+    if prediction_type == "epsilon":
+        sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
+        alphas = extract_into_tensor(alphas, timesteps, sample.shape)
+        pred_x_0 = (sample - sigmas * model_output) / alphas
+    elif prediction_type == "v_prediction":
+        pred_x_0 = alphas[timesteps] * sample - sigmas[timesteps] * model_output
+    else:
+        raise ValueError(f"Prediction type {prediction_type} currently not supported.")
+
+    return pred_x_0
+
+
+def extract_into_tensor(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+@torch.no_grad()
+def update_ema(target_params, source_params, rate=0.99):
+    """
+    Update target parameters to be closer to those of source parameters using
+    an exponential moving average.
+
+    :param target_params: the target parameter sequence.
+    :param source_params: the source parameter sequence.
+    :param rate: the EMA rate (closer to 1 means slower).
+    """
+    for targ, src in zip(target_params, source_params):
+        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
+
+
+# From LatentConsistencyModel.get_guidance_scale_embedding
+def guidance_scale_embedding(w, embedding_dim=512, dtype=torch.float32):
+    """
+    See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+    Args:
+        timesteps (`torch.Tensor`):
+            generate embedding vectors at these timesteps
+        embedding_dim (`int`, *optional*, defaults to 512):
+            dimension of the embeddings to generate
+        dtype:
+            data type of the generated embeddings
+
+    Returns:
+        `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+    """
+    assert len(w.shape) == 1
+    w = w * 1000.0
+
+    half_dim = embedding_dim // 2
+    emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+    emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+    emb = w.to(dtype)[:, None] * emb[None, :]
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+    if embedding_dim % 2 == 1:  # zero pad
+        emb = torch.nn.functional.pad(emb, (0, 1))
+    assert emb.shape == (w.shape[0], embedding_dim)
+    return emb
+
+
+class DDIMSolver:
+    def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50):
+        # DDIM sampling parameters
+        step_ratio = timesteps // ddim_timesteps
+
+        self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1
+        self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps]
+        self.ddim_alpha_cumprods_prev = np.asarray(
+            [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist()
+        )
+        # convert to torch tensors
+        self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long()
+        self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods)
+        self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev)
+
+    def to(self, device):
+        self.ddim_timesteps = self.ddim_timesteps.to(device)
+        self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device)
+        self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device)
+        return self
+
+    def ddim_step(self, pred_x0, pred_noise, timestep_index):
+        alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape)
+        dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise
+        x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt
+        return x_prev
+
+
+def import_model_class_from_model_name_or_path(
+    pretrained_model_name_or_path: str, revision: str, subfolder: str = "text_encoder"
+):
+    text_encoder_config = PretrainedConfig.from_pretrained(
+        pretrained_model_name_or_path, subfolder=subfolder, revision=revision, use_auth_token=True
+    )
+    model_class = text_encoder_config.architectures[0]
+
+    if model_class == "CLIPTextModel":
+        from transformers import CLIPTextModel
+
+        return CLIPTextModel
+    elif model_class == "CLIPTextModelWithProjection":
+        from transformers import CLIPTextModelWithProjection
+
+        return CLIPTextModelWithProjection
+    else:
+        raise ValueError(f"{model_class} is not supported.")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    # ----------Model Checkpoint Loading Arguments----------
+    parser.add_argument(
+        "--pretrained_teacher_model",
+        type=str,
+        default=None,
+        required=True,
+        help="Path to pretrained LDM teacher model or model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--pretrained_vae_model_name_or_path",
+        type=str,
+        default=None,
+        help="Path to pretrained VAE model with better numerical stability. More details: https://github.com/huggingface/diffusers/pull/4038.",
+    )
+    parser.add_argument(
+        "--teacher_revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM teacher model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained LDM model identifier from huggingface.co/models.",
+    )
+    # ----------Training Arguments----------
+    # ----General Training Arguments----
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="lcm-xl-distilled",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument(
+        "--cache_dir",
+        type=str,
+        default=None,
+        help="The directory where the downloaded models and datasets will be stored.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    # ----Logging----
+    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(
+        "--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.'
+        ),
+    )
+    # ----Checkpointing----
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--checkpoints_total_limit",
+        type=int,
+        default=None,
+        help=("Max number of checkpoints to store."),
+    )
+    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.'
+        ),
+    )
+    # ----Image Processing----
+    parser.add_argument(
+        "--train_shards_path_or_url",
+        type=str,
+        default=None,
+        help=(
+            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
+            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
+            " or to a folder containing files that 🤗 Datasets can understand."
+        ),
+    )
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=1024,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--use_fix_crop_and_size",
+        action="store_true",
+        help="Whether or not to use the fixed crop and size for the teacher model.",
+        default=False,
+    )
+    parser.add_argument(
+        "--center_crop",
+        default=False,
+        action="store_true",
+        help=(
+            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
+            " cropped. The images will be resized to the resolution first before cropping."
+        ),
+    )
+    parser.add_argument(
+        "--random_flip",
+        action="store_true",
+        help="whether to randomly flip images horizontally",
+    )
+    # ----Dataloader----
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    # ----Batch Size and Training Steps----
+    parser.add_argument(
+        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--max_train_samples",
+        type=int,
+        default=None,
+        help=(
+            "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        ),
+    )
+    # ----Learning Rate----
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=False,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    # ----Optimizer (Adam)----
+    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.")
+    # ----Diffusion Training Arguments----
+    parser.add_argument(
+        "--proportion_empty_prompts",
+        type=float,
+        default=0,
+        help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).",
+    )
+    # ----Latent Consistency Distillation (LCD) Specific Arguments----
+    parser.add_argument(
+        "--w_min",
+        type=float,
+        default=3.0,
+        required=False,
+        help=(
+            "The minimum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--w_max",
+        type=float,
+        default=15.0,
+        required=False,
+        help=(
+            "The maximum guidance scale value for guidance scale sampling. Note that we are using the Imagen CFG"
+            " formulation rather than the LCM formulation, which means all guidance scales have 1 added to them as"
+            " compared to the original paper."
+        ),
+    )
+    parser.add_argument(
+        "--num_ddim_timesteps",
+        type=int,
+        default=50,
+        help="The number of timesteps to use for DDIM sampling.",
+    )
+    parser.add_argument(
+        "--loss_type",
+        type=str,
+        default="l2",
+        choices=["l2", "huber"],
+        help="The type of loss to use for the LCD loss.",
+    )
+    parser.add_argument(
+        "--huber_c",
+        type=float,
+        default=0.001,
+        help="The huber loss parameter. Only used if `--loss_type=huber`.",
+    )
+    # ----Exponential Moving Average (EMA)----
+    parser.add_argument(
+        "--ema_decay",
+        type=float,
+        default=0.95,
+        required=False,
+        help="The exponential moving average (EMA) rate or decay factor.",
+    )
+    # ----Mixed Precision----
+    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(
+        "--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(
+        "--cast_teacher_unet",
+        action="store_true",
+        help="Whether to cast the teacher U-Net to the precision specified by `--mixed_precision`.",
+    )
+    # ----Training Optimizations----
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+    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.",
+    )
+    # ----Distributed Training----
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    # ----------Validation Arguments----------
+    parser.add_argument(
+        "--validation_steps",
+        type=int,
+        default=200,
+        help="Run validation every X steps.",
+    )
+    # ----------Huggingface Hub Arguments-----------
+    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`.",
+    )
+    # ----------Accelerate Arguments----------
+    parser.add_argument(
+        "--tracker_project_name",
+        type=str,
+        default="text2image-fine-tune",
+        help=(
+            "The `project_name` argument passed to Accelerator.init_trackers for"
+            " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
+        ),
+    )
+
+    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.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1:
+        raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].")
+
+    return args
+
+
+# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
+def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train=True):
+    prompt_embeds_list = []
+
+    captions = []
+    for caption in prompt_batch:
+        if random.random() < proportion_empty_prompts:
+            captions.append("")
+        elif isinstance(caption, str):
+            captions.append(caption)
+        elif isinstance(caption, (list, np.ndarray)):
+            # take a random caption if there are multiple
+            captions.append(random.choice(caption) if is_train else caption[0])
+
+    with torch.no_grad():
+        for tokenizer, text_encoder in zip(tokenizers, text_encoders):
+            text_inputs = tokenizer(
+                captions,
+                padding="max_length",
+                max_length=tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            prompt_embeds = text_encoder(
+                text_input_ids.to(text_encoder.device),
+                output_hidden_states=True,
+            )
+
+            # We are only ALWAYS interested in the pooled output of the final text encoder
+            pooled_prompt_embeds = prompt_embeds[0]
+            prompt_embeds = prompt_embeds.hidden_states[-2]
+            bs_embed, seq_len, _ = prompt_embeds.shape
+            prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
+            prompt_embeds_list.append(prompt_embeds)
+
+    prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+    pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
+    return prompt_embeds, pooled_prompt_embeds
+
+
+def main(args):
+    logging_dir = Path(args.output_dir, args.logging_dir)
+
+    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        project_config=accelerator_project_config,
+        split_batches=True,  # It's important to set this to True when using webdataset to get the right number of steps for lr scheduling. If set to False, the number of steps will be devide by the number of processes assuming batches are multiplied by the number of processes
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+        if args.push_to_hub:
+            create_repo(
+                repo_id=args.hub_model_id or Path(args.output_dir).name,
+                exist_ok=True,
+                token=args.hub_token,
+                private=True,
+            ).repo_id
+
+    # 1. Create the noise scheduler and the desired noise schedule.
+    noise_scheduler = DDPMScheduler.from_pretrained(
+        args.pretrained_teacher_model, subfolder="scheduler", revision=args.teacher_revision
+    )
+
+    # The scheduler calculates the alpha and sigma schedule for us
+    alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
+    sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
+    solver = DDIMSolver(
+        noise_scheduler.alphas_cumprod.numpy(),
+        timesteps=noise_scheduler.config.num_train_timesteps,
+        ddim_timesteps=args.num_ddim_timesteps,
+    )
+
+    # 2. Load tokenizers from SD-XL checkpoint.
+    tokenizer_one = AutoTokenizer.from_pretrained(
+        args.pretrained_teacher_model, subfolder="tokenizer", revision=args.teacher_revision, use_fast=False
+    )
+    tokenizer_two = AutoTokenizer.from_pretrained(
+        args.pretrained_teacher_model, subfolder="tokenizer_2", revision=args.teacher_revision, use_fast=False
+    )
+
+    # 3. Load text encoders from SD-XL checkpoint.
+    # import correct text encoder classes
+    text_encoder_cls_one = import_model_class_from_model_name_or_path(
+        args.pretrained_teacher_model, args.teacher_revision
+    )
+    text_encoder_cls_two = import_model_class_from_model_name_or_path(
+        args.pretrained_teacher_model, args.teacher_revision, subfolder="text_encoder_2"
+    )
+
+    text_encoder_one = text_encoder_cls_one.from_pretrained(
+        args.pretrained_teacher_model, subfolder="text_encoder", revision=args.teacher_revision
+    )
+    text_encoder_two = text_encoder_cls_two.from_pretrained(
+        args.pretrained_teacher_model, subfolder="text_encoder_2", revision=args.teacher_revision
+    )
+
+    # 4. Load VAE from SD-XL checkpoint (or more stable VAE)
+    vae_path = (
+        args.pretrained_teacher_model
+        if args.pretrained_vae_model_name_or_path is None
+        else args.pretrained_vae_model_name_or_path
+    )
+    vae = AutoencoderKL.from_pretrained(
+        vae_path,
+        subfolder="vae" if args.pretrained_vae_model_name_or_path is None else None,
+        revision=args.teacher_revision,
+    )
+
+    # 5. Load teacher U-Net from SD-XL checkpoint
+    teacher_unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
+    )
+
+    # 6. Freeze teacher vae, text_encoders, and teacher_unet
+    vae.requires_grad_(False)
+    text_encoder_one.requires_grad_(False)
+    text_encoder_two.requires_grad_(False)
+    teacher_unet.requires_grad_(False)
+
+    # 8. Create online (`unet`) student U-Nets. This will be updated by the optimizer (e.g. via backpropagation.)
+    # Add `time_cond_proj_dim` to the student U-Net if `teacher_unet.config.time_cond_proj_dim` is None
+    if teacher_unet.config.time_cond_proj_dim is None:
+        teacher_unet.config["time_cond_proj_dim"] = args.unet_time_cond_proj_dim
+    unet = UNet2DConditionModel(**teacher_unet.config)
+    # load teacher_unet weights into unet
+    unet.load_state_dict(teacher_unet.state_dict(), strict=False)
+    unet.train()
+
+    # 9. Create target (`ema_unet`) student U-Net parameters. This will be updated via EMA updates (polyak averaging).
+    # Initialize from unet
+    target_unet = UNet2DConditionModel(**teacher_unet.config)
+    target_unet.load_state_dict(unet.state_dict())
+    target_unet.train()
+    target_unet.requires_grad_(False)
+
+    # Check that all trainable models are in full precision
+    low_precision_error_string = (
+        " Please make sure to always have all model weights in full float32 precision when starting training - even if"
+        " doing mixed precision training, copy of the weights should still be float32."
+    )
+
+    if accelerator.unwrap_model(unet).dtype != torch.float32:
+        raise ValueError(
+            f"Controlnet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
+        )
+
+    # 9. Handle mixed precision and device placement
+    # For mixed precision training we cast all non-trainable weigths to half-precision
+    # as these weights are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move unet, vae and text_encoder to device and cast to weight_dtype
+    # The VAE is in float32 to avoid NaN losses.
+    vae.to(accelerator.device)
+    if args.pretrained_vae_model_name_or_path is not None:
+        vae.to(dtype=weight_dtype)
+    text_encoder_one.to(accelerator.device, dtype=weight_dtype)
+    text_encoder_two.to(accelerator.device, dtype=weight_dtype)
+    target_unet.to(accelerator.device)
+    # Move teacher_unet to device, optionally cast to weight_dtype
+    teacher_unet.to(accelerator.device)
+    if args.cast_teacher_unet:
+        teacher_unet.to(dtype=weight_dtype)
+
+    # Also move the alpha and sigma noise schedules to accelerator.device.
+    alpha_schedule = alpha_schedule.to(accelerator.device)
+    sigma_schedule = sigma_schedule.to(accelerator.device)
+    solver = solver.to(accelerator.device)
+
+    # 10. Handle saving and loading of checkpoints
+    # `accelerate` 0.16.0 will have better support for customized saving
+    if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
+        # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
+        def save_model_hook(models, weights, output_dir):
+            if accelerator.is_main_process:
+                target_unet.save_pretrained(os.path.join(output_dir, "unet_target"))
+
+                for i, model in enumerate(models):
+                    model.save_pretrained(os.path.join(output_dir, "unet"))
+
+                    # make sure to pop weight so that corresponding model is not saved again
+                    weights.pop()
+
+        def load_model_hook(models, input_dir):
+            load_model = UNet2DConditionModel.from_pretrained(os.path.join(input_dir, "unet_target"))
+            target_unet.load_state_dict(load_model.state_dict())
+            target_unet.to(accelerator.device)
+            del load_model
+
+            for i in range(len(models)):
+                # pop models so that they are not loaded again
+                model = models.pop()
+
+                # load diffusers style into model
+                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
+                model.register_to_config(**load_model.config)
+
+                model.load_state_dict(load_model.state_dict())
+                del load_model
+
+        accelerator.register_save_state_pre_hook(save_model_hook)
+        accelerator.register_load_state_pre_hook(load_model_hook)
+
+    # 11. Enable optimizations
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = version.parse(xformers.__version__)
+            if xformers_version == version.parse("0.0.16"):
+                logger.warn(
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                )
+            unet.enable_xformers_memory_efficient_attention()
+            teacher_unet.enable_xformers_memory_efficient_attention()
+            target_unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+
+    # 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
+
+    # 12. Optimizer creation
+    optimizer = optimizer_class(
+        unet.parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    # 13. Dataset creation and data processing
+    # Here, we compute not just the text embeddings but also the additional embeddings
+    # needed for the SD XL UNet to operate.
+    def compute_embeddings(
+        prompt_batch, original_sizes, crop_coords, proportion_empty_prompts, text_encoders, tokenizers, is_train=True
+    ):
+        target_size = (args.resolution, args.resolution)
+        original_sizes = list(map(list, zip(*original_sizes)))
+        crops_coords_top_left = list(map(list, zip(*crop_coords)))
+
+        original_sizes = torch.tensor(original_sizes, dtype=torch.long)
+        crops_coords_top_left = torch.tensor(crops_coords_top_left, dtype=torch.long)
+
+        prompt_embeds, pooled_prompt_embeds = encode_prompt(
+            prompt_batch, text_encoders, tokenizers, proportion_empty_prompts, is_train
+        )
+        add_text_embeds = pooled_prompt_embeds
+
+        # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
+        add_time_ids = list(target_size)
+        add_time_ids = torch.tensor([add_time_ids])
+        add_time_ids = add_time_ids.repeat(len(prompt_batch), 1)
+        add_time_ids = torch.cat([original_sizes, crops_coords_top_left, add_time_ids], dim=-1)
+        add_time_ids = add_time_ids.to(accelerator.device, dtype=prompt_embeds.dtype)
+
+        prompt_embeds = prompt_embeds.to(accelerator.device)
+        add_text_embeds = add_text_embeds.to(accelerator.device)
+        unet_added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+        return {"prompt_embeds": prompt_embeds, **unet_added_cond_kwargs}
+
+    dataset = Text2ImageDataset(
+        train_shards_path_or_url=args.train_shards_path_or_url,
+        num_train_examples=args.max_train_samples,
+        per_gpu_batch_size=args.train_batch_size,
+        global_batch_size=args.train_batch_size * accelerator.num_processes,
+        num_workers=args.dataloader_num_workers,
+        resolution=args.resolution,
+        shuffle_buffer_size=1000,
+        pin_memory=True,
+        persistent_workers=True,
+        use_fix_crop_and_size=args.use_fix_crop_and_size,
+    )
+    train_dataloader = dataset.train_dataloader
+
+    # Let's first compute all the embeddings so that we can free up the text encoders
+    # from memory.
+    text_encoders = [text_encoder_one, text_encoder_two]
+    tokenizers = [tokenizer_one, tokenizer_two]
+
+    compute_embeddings_fn = functools.partial(
+        compute_embeddings,
+        proportion_empty_prompts=0,
+        text_encoders=text_encoders,
+        tokenizers=tokenizers,
+    )
+
+    # 14. LR Scheduler creation
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / 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
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps,
+        num_training_steps=args.max_train_steps,
+    )
+
+    # 15. Prepare for training
+    # Prepare everything with our `accelerator`.
+    unet, optimizer, lr_scheduler = accelerator.prepare(unet, optimizer, 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(train_dataloader.num_batches / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    # Create uncond embeds for classifier free guidance
+    uncond_prompt_embeds = torch.zeros(args.train_batch_size, 77, 2048).to(accelerator.device)
+    uncond_pooled_prompt_embeds = torch.zeros(args.train_batch_size, 1280).to(accelerator.device)
+
+    # 16. Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num batches each epoch = {train_dataloader.num_batches}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1] if len(dirs) > 0 else None
+
+        if path is None:
+            accelerator.print(
+                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+            )
+            args.resume_from_checkpoint = None
+            initial_global_step = 0
+        else:
+            accelerator.print(f"Resuming from checkpoint {path}")
+            accelerator.load_state(os.path.join(args.output_dir, path))
+            global_step = int(path.split("-")[1])
+
+            initial_global_step = global_step
+            first_epoch = global_step // num_update_steps_per_epoch
+    else:
+        initial_global_step = 0
+
+    progress_bar = tqdm(
+        range(0, args.max_train_steps),
+        initial=initial_global_step,
+        desc="Steps",
+        # Only show the progress bar once on each machine.
+        disable=not accelerator.is_local_main_process,
+    )
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(unet):
+                image, text, orig_size, crop_coords = batch
+
+                image = image.to(accelerator.device, non_blocking=True)
+                encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)
+
+                if args.pretrained_vae_model_name_or_path is not None:
+                    pixel_values = image.to(dtype=weight_dtype)
+                    if vae.dtype != weight_dtype:
+                        vae.to(dtype=weight_dtype)
+                else:
+                    pixel_values = image
+
+                # encode pixel values with batch size of at most 8
+                latents = []
+                for i in range(0, pixel_values.shape[0], 8):
+                    latents.append(vae.encode(pixel_values[i : i + 8]).latent_dist.sample())
+                latents = torch.cat(latents, dim=0)
+
+                latents = latents * vae.config.scaling_factor
+                if args.pretrained_vae_model_name_or_path is None:
+                    latents = latents.to(weight_dtype)
+
+                # 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 t_n ~ U[0, N - k - 1] without bias.
+                topk = noise_scheduler.config.num_train_timesteps // args.num_ddim_timesteps
+                index = torch.randint(0, args.num_ddim_timesteps, (bsz,), device=latents.device).long()
+                start_timesteps = solver.ddim_timesteps[index]
+                timesteps = start_timesteps - topk
+                timesteps = torch.where(timesteps < 0, torch.zeros_like(timesteps), timesteps)
+
+                # 20.4.4. Get boundary scalings for start_timesteps and (end) timesteps.
+                c_skip_start, c_out_start = scalings_for_boundary_conditions(start_timesteps)
+                c_skip_start, c_out_start = [append_dims(x, latents.ndim) for x in [c_skip_start, c_out_start]]
+                c_skip, c_out = scalings_for_boundary_conditions(timesteps)
+                c_skip, c_out = [append_dims(x, latents.ndim) for x in [c_skip, c_out]]
+
+                # 20.4.5. Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]
+                noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)
+
+                # 20.4.6. Sample a random guidance scale w from U[w_min, w_max] and embed it
+                w = (args.w_max - args.w_min) * torch.rand((bsz,)) + args.w_min
+                w = w.reshape(bsz, 1, 1, 1)
+                w = w.to(device=latents.device, dtype=latents.dtype)
+
+                # 20.4.8. Prepare prompt embeds and unet_added_conditions
+                prompt_embeds = encoded_text.pop("prompt_embeds")
+
+                # 20.4.9. Get online LCM prediction on z_{t_{n + k}}, w, c, t_{n + k}
+                noise_pred = unet(
+                    noisy_model_input,
+                    start_timesteps,
+                    timestep_cond=None,
+                    encoder_hidden_states=prompt_embeds.float(),
+                    added_cond_kwargs=encoded_text,
+                ).sample
+
+                pred_x_0 = predicted_origin(
+                    noise_pred,
+                    start_timesteps,
+                    noisy_model_input,
+                    noise_scheduler.config.prediction_type,
+                    alpha_schedule,
+                    sigma_schedule,
+                )
+
+                model_pred = c_skip_start * noisy_model_input + c_out_start * pred_x_0
+
+                # 20.4.10. Use the ODE solver to predict the kth step in the augmented PF-ODE trajectory after
+                # noisy_latents with both the conditioning embedding c and unconditional embedding 0
+                # Get teacher model prediction on noisy_latents and conditional embedding
+                with torch.no_grad():
+                    with torch.autocast("cuda"):
+                        cond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=prompt_embeds.to(weight_dtype),
+                            added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},
+                        ).sample
+                        cond_pred_x0 = predicted_origin(
+                            cond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # Get teacher model prediction on noisy_latents and unconditional embedding
+                        uncond_added_conditions = copy.deepcopy(encoded_text)
+                        uncond_added_conditions["text_embeds"] = uncond_pooled_prompt_embeds
+                        uncond_teacher_output = teacher_unet(
+                            noisy_model_input.to(weight_dtype),
+                            start_timesteps,
+                            encoder_hidden_states=uncond_prompt_embeds.to(weight_dtype),
+                            added_cond_kwargs={k: v.to(weight_dtype) for k, v in uncond_added_conditions.items()},
+                        ).sample
+                        uncond_pred_x0 = predicted_origin(
+                            uncond_teacher_output,
+                            start_timesteps,
+                            noisy_model_input,
+                            noise_scheduler.config.prediction_type,
+                            alpha_schedule,
+                            sigma_schedule,
+                        )
+
+                        # 20.4.11. Perform "CFG" to get x_prev estimate (using the LCM paper's CFG formulation)
+                        pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
+                        pred_noise = cond_teacher_output + w * (cond_teacher_output - uncond_teacher_output)
+                        x_prev = solver.ddim_step(pred_x0, pred_noise, index)
+
+                # 20.4.12. Get target LCM prediction on x_prev, w, c, t_n
+                with torch.no_grad():
+                    with torch.autocast("cuda", dtype=weight_dtype):
+                        target_noise_pred = target_unet(
+                            x_prev.float(),
+                            timesteps,
+                            timestep_cond=None,
+                            encoder_hidden_states=prompt_embeds.float(),
+                            added_cond_kwargs=encoded_text,
+                        ).sample
+                    pred_x_0 = predicted_origin(
+                        target_noise_pred,
+                        timesteps,
+                        x_prev,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+                    target = c_skip * x_prev + c_out * pred_x_0
+
+                # 20.4.13. Calculate loss
+                if args.loss_type == "l2":
+                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+                elif args.loss_type == "huber":
+                    loss = torch.mean(
+                        torch.sqrt((model_pred.float() - target.float()) ** 2 + args.huber_c**2) - args.huber_c
+                    )
+
+                # 20.4.14. Backpropagate on the online student model (`unet`)
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm)
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                # 20.4.15. Make EMA update to target student model parameters
+                update_ema(target_unet.parameters(), unet.parameters(), args.ema_decay)
+                progress_bar.update(1)
+                global_step += 1
+
+                if accelerator.is_main_process:
+                    if global_step % args.checkpointing_steps == 0:
+                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
+                        if args.checkpoints_total_limit is not None:
+                            checkpoints = os.listdir(args.output_dir)
+                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
+                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
+
+                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
+                            if len(checkpoints) >= args.checkpoints_total_limit:
+                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
+                                removing_checkpoints = checkpoints[0:num_to_remove]
+
+                                logger.info(
+                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+                                )
+                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
+
+                                for removing_checkpoint in removing_checkpoints:
+                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
+                                    shutil.rmtree(removing_checkpoint)
+
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+                    if global_step % args.validation_steps == 0:
+                        log_validation(vae, target_unet, args, accelerator, weight_dtype, global_step, "target")
+                        log_validation(vae, unet, args, accelerator, weight_dtype, global_step, "online")
+
+            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
+
+    # Create the pipeline using using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        unet = accelerator.unwrap_model(unet)
+        unet.save_pretrained(os.path.join(args.output_dir, "unet"))
+
+        target_unet = accelerator.unwrap_model(target_unet)
+        target_unet.save_pretrained(os.path.join(args.output_dir, "unet_target"))
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)