[Training] Add `datasets` version of LCM LoRA SDXL (#5778)

* add: script to train lcm lora for sdxl with 🤗

 datasets

* suit up the args.

* remove comments.

* fix num_update_steps

* fix batch unmarshalling

* fix num_update_steps_per_epoch

* fix; dataloading.

* fix microconditions.

* unconditional predictions debug

* fix batch size.

* no need to use use_auth_token

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* make vae encoding batch size an arg

* final serialization in kohya

* style

* state dict rejigging

* feat: no separate teacher unet.

* debug

* fix state dict serialization

* debug

* debug

* debug

* remove prints.

* remove kohya utility and make style

* fix serialization

* fix

* add test

* add peft dependency.

* add: peft

* remove peft

* autocast device determination from accelerator

* autocast

* reduce lora rank.

* remove unneeded space

* Apply suggestions from code review
Co-authored-by: Suraj Patil <surajp815@gmail.com>

* style

* remove prompt dropout.

* also save in native diffusers ckpt format.

* debug

* debug

* debug

* better formation of the null embeddings.

* remove space.

* autocast fixes.

* autocast fix.

* hacky

* remove lora_sayak

* Apply suggestions from code review
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* style

* make log validation leaner.

* move back enabled in.

* fix: log_validation call.

* add: checkpointing tests

* taking my chances to see if disabling autocasting has any effect?

* start debugging

* name

* name

* name

* more debug

* more debug

* index

* remove index.

* print length

* print length

* print length

* move unet.train() after add_adapter()

* disable some prints.

* enable_adapters() manually.

* remove prints.

* some changes.

* fix params_to_optimize

* more fixes

* debug

* debug

* remove print

* disable grad for certain contexts.

* Add support for IPAdapterFull (#5911)

* Add support for IPAdapterFull
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Fix a bug in `add_noise` function  (#6085)

* fix

* copies

---------
Co-authored-by: yiyixuxu <yixu310@gmail,com>

* [Advanced Diffusion Script] Add Widget default text (#6100)

add widget

* [Advanced Training Script] Fix pipe example (#6106)

* IP-Adapter for StableDiffusionControlNetImg2ImgPipeline (#5901)

* adapter for StableDiffusionControlNetImg2ImgPipeline

* fix-copies

* fix-copies

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* IP adapter support for most pipelines (#5900)

* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py

* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py

* update tests

* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py

* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py

* support ip-adapter in src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py

* support ip-adapter in src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py

* support ip-adapter in src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

* support ip-adapter in src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py

* revert changes to sd_attend_and_excite and sd_upscale

* make style

* fix broken tests

* update ip-adapter implementation to latest

* apply suggestions from review

---------
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* fix: lora_alpha

* make vae casting conditional/

* param upcasting

* propagate comments from https://github.com/huggingface/diffusers/pull/6145

Co-authored-by: dg845 <dgu8957@gmail.com>

* [Peft] fix saving / loading when unet is not "unet" (#6046)

* [Peft] fix saving / loading when unet is not "unet"

* Update src/diffusers/loaders/lora.py
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* undo stablediffusion-xl changes

* use unet_name to get unet for lora helpers

* use unet_name

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* [Wuerstchen] fix fp16 training and correct lora args (#6245)

fix fp16 training
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* [docs] fix: animatediff docs (#6339)

fix: animatediff docs

* add: note about the new script in readme_sdxl.

* Revert "[Peft] fix saving / loading when unet is not "unet" (#6046)"

This reverts commit 4c7e983bb5929320bab08d70333eeb93f047de40.

* Revert "[Wuerstchen] fix fp16 training and correct lora args (#6245)"

This reverts commit 0bb9cf0216e501632677895de6574532092282b5.

* Revert "[docs] fix: animatediff docs (#6339)"

This reverts commit 11659a6f74b5187f601eeeeeb6f824dda73d0627.

* remove tokenize_prompt().

* assistive comments around enable_adapters() and diable_adapters().

---------
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Fabio Rigano <57982783+fabiorigano@users.noreply.github.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail,com>
Co-authored-by: apolinário <joaopaulo.passos@gmail.com>
Co-authored-by: Charchit Sharma <charchitsharma11@gmail.com>
Co-authored-by: Aryan V S <contact.aryanvs@gmail.com>
Co-authored-by: dg845 <dgu8957@gmail.com>
Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -161,6 +161,8 @@ tags:
 base_model: {base_model}
 instance_prompt: {instance_prompt}
 license: openrail++
+widget:
+    - text: '{validation_prompt if validation_prompt else instance_prompt}'
 ---
 """
 

examples/consistency_distillation/README_sdxl.md

@@ -112,3 +112,37 @@ accelerate launch train_lcm_distill_lora_sdxl_wds.py \
     --seed=453645634 \
     --push_to_hub \
 ```
+
+We provide another version for LCM LoRA SDXL that follows best practices of `peft` and leverages the `datasets` library for quick experimentation. The script doesn't load two UNets unlike `train_lcm_distill_lora_sdxl_wds.py` which reduces the memory requirements quite a bit. 
+
+Below is an example training command that trains an LCM LoRA on the [Pokemons dataset](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions):
+
+```bash
+export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0"
+export DATASET_NAME="lambdalabs/pokemon-blip-captions"
+export VAE_PATH="madebyollin/sdxl-vae-fp16-fix"
+
+accelerate launch train_lcm_distill_lora_sdxl.py \
+  --pretrained_teacher_model=${MODEL_NAME}  \
+  --pretrained_vae_model_name_or_path=${VAE_PATH} \
+  --output_dir="pokemons-lora-lcm-sdxl" \
+  --mixed_precision="fp16" \
+  --dataset_name=$DATASET_NAME \
+  --resolution=1024 \
+  --train_batch_size=24 \
+  --gradient_accumulation_steps=1 \
+  --gradient_checkpointing \
+  --use_8bit_adam \
+  --lora_rank=64 \
+  --learning_rate=1e-4 \
+  --report_to="wandb" \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --max_train_steps=3000 \
+  --checkpointing_steps=500 \
+  --validation_steps=50 \
+  --seed="0" \
+  --report_to="wandb" \
+  --push_to_hub
+```
+

examples/consistency_distillation/test_lcm_lora.py

+# coding=utf-8
+# Copyright 2023 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import sys
+import tempfile
+
+import safetensors
+
+
+sys.path.append("..")
+from test_examples_utils import ExamplesTestsAccelerate, run_command  # noqa: E402
+
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+stream_handler = logging.StreamHandler(sys.stdout)
+logger.addHandler(stream_handler)
+
+
+class TextToImageLCM(ExamplesTestsAccelerate):
+    def test_text_to_image_lcm_lora_sdxl(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
+                --pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe
+                --dataset_name hf-internal-testing/dummy_image_text_data
+                --resolution 64
+                --lora_rank 4
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+            # save_pretrained smoke test
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "pytorch_lora_weights.safetensors")))
+
+            # make sure the state_dict has the correct naming in the parameters.
+            lora_state_dict = safetensors.torch.load_file(os.path.join(tmpdir, "pytorch_lora_weights.safetensors"))
+            is_lora = all("lora" in k for k in lora_state_dict.keys())
+            self.assertTrue(is_lora)
+
+    def test_text_to_image_lcm_lora_sdxl_checkpointing(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+                examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
+                --pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe
+                --dataset_name hf-internal-testing/dummy_image_text_data
+                --resolution 64
+                --lora_rank 4
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 7
+                --checkpointing_steps 2
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+
+            self.assertEqual(
+                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
+                {"checkpoint-2", "checkpoint-4", "checkpoint-6"},
+            )
+
+            test_args = f"""
+                examples/consistency_distillation/train_lcm_distill_lora_sdxl.py
+                --pretrained_teacher_model hf-internal-testing/tiny-stable-diffusion-xl-pipe
+                --dataset_name hf-internal-testing/dummy_image_text_data
+                --resolution 64
+                --lora_rank 4
+                --train_batch_size 1
+                --gradient_accumulation_steps 1
+                --max_train_steps 9
+                --checkpointing_steps 2
+                --resume_from_checkpoint latest
+                --learning_rate 5.0e-04
+                --scale_lr
+                --lr_scheduler constant
+                --lr_warmup_steps 0
+                --output_dir {tmpdir}
+                """.split()
+
+            run_command(self._launch_args + test_args)
+
+            self.assertEqual(
+                {x for x in os.listdir(tmpdir) if "checkpoint" in x},
+                {"checkpoint-2", "checkpoint-4", "checkpoint-6", "checkpoint-8"},
+            )

examples/consistency_distillation/train_lcm_distill_lora_sdxl.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 The LCM team and 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 logging
+import math
+import os
+import random
+import shutil
+from pathlib import Path
+
+import accelerate
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from datasets import load_dataset
+from huggingface_hub import create_repo, upload_folder
+from packaging import version
+from peft import LoraConfig, get_peft_model_state_dict
+from torchvision import transforms
+from torchvision.transforms.functional import crop
+from tqdm.auto import tqdm
+from transformers import AutoTokenizer, PretrainedConfig
+
+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
+
+
+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("0.24.0.dev0")
+
+logger = get_logger(__name__)
+
+DATASET_NAME_MAPPING = {
+    "lambdalabs/pokemon-blip-captions": ("image", "text"),
+}
+
+
+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 log_validation(vae, args, accelerator, weight_dtype, step, unet=None, is_final_validation=False):
+    logger.info("Running validation... ")
+
+    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,
+    ).to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=True)
+
+    to_load = None
+    if not is_final_validation:
+        if unet is None:
+            raise ValueError("Must provide a `unet` when doing intermediate validation.")
+        unet = accelerator.unwrap_model(unet)
+        state_dict = get_peft_model_state_dict(unet)
+        to_load = state_dict
+    else:
+        to_load = args.output_dir
+
+    pipeline.load_lora_weights(to_load)
+    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 = [
+        "cute sundar pichai character",
+        "robotic cat with wings",
+        "a photo of yoda",
+        "a cute creature with blue eyes",
+    ]
+
+    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)
+            logger_name = "test" if is_final_validation else "validation"
+            tracker.log({logger_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 get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas):
+    alphas = extract_into_tensor(alphas, timesteps, sample.shape)
+    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
+    if prediction_type == "epsilon":
+        pred_x_0 = (sample - sigmas * model_output) / alphas
+    elif prediction_type == "sample":
+        pred_x_0 = model_output
+    elif prediction_type == "v_prediction":
+        pred_x_0 = alphas * sample - sigmas * model_output
+    else:
+        raise ValueError(
+            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
+            f" are supported."
+        )
+
+    return pred_x_0
+
+
+# Based on step 4 in DDIMScheduler.step
+def get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas):
+    alphas = extract_into_tensor(alphas, timesteps, sample.shape)
+    sigmas = extract_into_tensor(sigmas, timesteps, sample.shape)
+    if prediction_type == "epsilon":
+        pred_epsilon = model_output
+    elif prediction_type == "sample":
+        pred_epsilon = (sample - alphas * model_output) / sigmas
+    elif prediction_type == "v_prediction":
+        pred_epsilon = alphas * model_output + sigmas * sample
+    else:
+        raise ValueError(
+            f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`"
+            f" are supported."
+        )
+
+    return pred_epsilon
+
+
+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)))
+
+
+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
+    )
+    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(
+        "--dataset_name",
+        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(
+        "--dataset_config_name",
+        type=str,
+        default=None,
+        help="The config of the Dataset, leave as None if there's only one config.",
+    )
+    parser.add_argument(
+        "--train_data_dir",
+        type=str,
+        default=None,
+        help=(
+            "A folder containing the training data. Folder contents must follow the structure described in"
+            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
+            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
+        ),
+    )
+    parser.add_argument(
+        "--image_column", type=str, default="image", help="The column of the dataset containing an image."
+    )
+    parser.add_argument(
+        "--caption_column",
+        type=str,
+        default="text",
+        help="The column of the dataset containing a caption or a list of captions.",
+    )
+    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(
+        "--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",
+    )
+    parser.add_argument(
+        "--encode_batch_size",
+        type=int,
+        default=8,
+        help="Batch size to use for VAE encoding of the images for efficient processing.",
+    )
+    # ----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-6,
+        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----
+    # ----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"
+        ),
+    )
+    # ----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
+
+    return args
+
+
+# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
+def encode_prompt(prompt_batch, text_encoders, tokenizers, is_train=True):
+    prompt_embeds_list = []
+
+    captions = []
+    for caption in prompt_batch:
+        if 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:
+            repo_id = 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
+    )
+
+    # DDPMScheduler calculates the alpha and sigma noise schedules (based on the alpha bars) for us
+    alpha_schedule = torch.sqrt(noise_scheduler.alphas_cumprod)
+    sigma_schedule = torch.sqrt(1 - noise_scheduler.alphas_cumprod)
+    # Initialize the DDIM ODE solver for distillation.
+    solver = DDIMSolver(
+        noise_scheduler.alphas_cumprod.numpy(),
+        timesteps=noise_scheduler.config.num_train_timesteps,
+        ddim_timesteps=args.num_ddim_timesteps,
+    )
+
+    # 2. Load tokenizers from SDXL 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 SDXL 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 SDXL 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,
+    )
+
+    # 6. Freeze teacher vae, text_encoders.
+    vae.requires_grad_(False)
+    text_encoder_one.requires_grad_(False)
+    text_encoder_two.requires_grad_(False)
+
+    # 7. Create online student U-Net.
+    unet = UNet2DConditionModel.from_pretrained(
+        args.pretrained_teacher_model, subfolder="unet", revision=args.teacher_revision
+    )
+    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}"
+        )
+
+    # 8. 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.
+    unet.to(accelerator.device, dtype=weight_dtype)
+    if args.pretrained_vae_model_name_or_path is None:
+        vae.to(accelerator.device, dtype=torch.float32)
+    else:
+        vae.to(accelerator.device, dtype=weight_dtype)
+    text_encoder_one.to(accelerator.device, dtype=weight_dtype)
+    text_encoder_two.to(accelerator.device, dtype=weight_dtype)
+
+    # 9. 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,
+        lora_alpha=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.add_adapter(lora_config)
+
+    # Make sure the trainable params are in float32.
+    if args.mixed_precision == "fp16":
+        for param in unet.parameters():
+            # only upcast trainable parameters (LoRA) into fp32
+            if param.requires_grad:
+                param.data = param.to(torch.float32)
+
+    # 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)
+                # also save the checkpoints in native `diffusers` format so that it can be easily
+                # be independently loaded via `load_lora_weights()`.
+                state_dict = get_peft_model_state_dict(unet_)
+                StableDiffusionXLPipeline.save_lora_weights(output_dir, unet_lora_layers=state_dict)
+
+                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)
+            lora_state_dict, network_alphas = StableDiffusionXLPipeline.lora_state_dict(input_dir)
+            StableDiffusionXLPipeline.load_lora_into_unet(lora_state_dict, network_alphas=network_alphas, unet=unet_)
+
+            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()
+        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
+    params_to_optimize = filter(lambda p: p.requires_grad, unet.parameters())
+    optimizer = optimizer_class(
+        params_to_optimize,
+        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
+    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
+    # download the dataset.
+    if args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        dataset = load_dataset(
+            args.dataset_name,
+            args.dataset_config_name,
+            cache_dir=args.cache_dir,
+        )
+    else:
+        data_files = {}
+        if args.train_data_dir is not None:
+            data_files["train"] = os.path.join(args.train_data_dir, "**")
+        dataset = load_dataset(
+            "imagefolder",
+            data_files=data_files,
+            cache_dir=args.cache_dir,
+        )
+        # See more about loading custom images at
+        # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder
+
+    # Preprocessing the datasets.
+    column_names = dataset["train"].column_names
+
+    # Get the column names for input/target.
+    dataset_columns = DATASET_NAME_MAPPING.get(args.dataset_name, None)
+    if args.image_column is None:
+        image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
+    else:
+        image_column = args.image_column
+        if image_column not in column_names:
+            raise ValueError(
+                f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}"
+            )
+    if args.caption_column is None:
+        caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
+    else:
+        caption_column = args.caption_column
+        if caption_column not in column_names:
+            raise ValueError(
+                f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}"
+            )
+
+    # Preprocessing the datasets.
+    train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)
+    train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)
+    train_flip = transforms.RandomHorizontalFlip(p=1.0)
+    train_transforms = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])
+
+    def preprocess_train(examples):
+        images = [image.convert("RGB") for image in examples[image_column]]
+        # image aug
+        original_sizes = []
+        all_images = []
+        crop_top_lefts = []
+        for image in images:
+            original_sizes.append((image.height, image.width))
+            image = train_resize(image)
+            if args.center_crop:
+                y1 = max(0, int(round((image.height - args.resolution) / 2.0)))
+                x1 = max(0, int(round((image.width - args.resolution) / 2.0)))
+                image = train_crop(image)
+            else:
+                y1, x1, h, w = train_crop.get_params(image, (args.resolution, args.resolution))
+                image = crop(image, y1, x1, h, w)
+            if args.random_flip and random.random() < 0.5:
+                # flip
+                x1 = image.width - x1
+                image = train_flip(image)
+            crop_top_left = (y1, x1)
+            crop_top_lefts.append(crop_top_left)
+            image = train_transforms(image)
+            all_images.append(image)
+
+        examples["original_sizes"] = original_sizes
+        examples["crop_top_lefts"] = crop_top_lefts
+        examples["pixel_values"] = all_images
+        examples["captions"] = list(examples[caption_column])
+        return examples
+
+    with accelerator.main_process_first():
+        if args.max_train_samples is not None:
+            dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
+        # Set the training transforms
+        train_dataset = dataset["train"].with_transform(preprocess_train)
+
+    def collate_fn(examples):
+        pixel_values = torch.stack([example["pixel_values"] for example in examples])
+        pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
+        original_sizes = [example["original_sizes"] for example in examples]
+        crop_top_lefts = [example["crop_top_lefts"] for example in examples]
+        captions = [example["captions"] for example in examples]
+
+        return {
+            "pixel_values": pixel_values,
+            "captions": captions,
+            "original_sizes": original_sizes,
+            "crop_top_lefts": crop_top_lefts,
+        }
+
+    # DataLoaders creation:
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset,
+        shuffle=True,
+        collate_fn=collate_fn,
+        batch_size=args.train_batch_size,
+        num_workers=args.dataloader_num_workers,
+    )
+
+    # 14. Embeddings for the UNet.
+    # Adapted from pipeline.StableDiffusionXLPipeline._get_add_time_ids
+    def compute_embeddings(prompt_batch, original_sizes, crop_coords, text_encoders, tokenizers, is_train=True):
+        def compute_time_ids(original_size, crops_coords_top_left):
+            target_size = (args.resolution, args.resolution)
+            add_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_time_ids = torch.tensor([add_time_ids])
+            add_time_ids = add_time_ids.to(accelerator.device, dtype=weight_dtype)
+            return add_time_ids
+
+        prompt_embeds, pooled_prompt_embeds = encode_prompt(prompt_batch, text_encoders, tokenizers, is_train)
+        add_text_embeds = pooled_prompt_embeds
+
+        add_time_ids = torch.cat([compute_time_ids(s, c) for s, c in zip(original_sizes, crop_coords)])
+
+        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}
+
+    text_encoders = [text_encoder_one, text_encoder_two]
+    tokenizers = [tokenizer_one, tokenizer_two]
+
+    compute_embeddings_fn = functools.partial(compute_embeddings, text_encoders=text_encoders, tokenizers=tokenizers)
+
+    # 15. LR Scheduler creation
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    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 * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
+    )
+
+    # 16. Prepare for training
+    # Prepare everything with our `accelerator`.
+    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        unet, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    # 17. Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1] if len(dirs) > 0 else None
+
+        if path is None:
+            accelerator.print(
+                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+            )
+            args.resume_from_checkpoint = None
+            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,
+    )
+
+    unet.train()
+    for epoch in range(first_epoch, args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(unet):
+                # 1. Load and process the image and text conditioning
+                pixel_values, text, orig_size, crop_coords = (
+                    batch["pixel_values"],
+                    batch["captions"],
+                    batch["original_sizes"],
+                    batch["crop_top_lefts"],
+                )
+
+                encoded_text = compute_embeddings_fn(text, orig_size, crop_coords)
+
+                # encode pixel values with batch size of at most 8
+                pixel_values = pixel_values.to(dtype=vae.dtype)
+                latents = []
+                for i in range(0, pixel_values.shape[0], args.encode_batch_size):
+                    latents.append(vae.encode(pixel_values[i : i + args.encode_batch_size]).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)
+
+                # 2. Sample a random timestep for each image t_n from the ODE solver timesteps without bias.
+                # For the DDIM solver, the timestep schedule is [T - 1, T - k - 1, T - 2 * k - 1, ...]
+                bsz = latents.shape[0]
+                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)
+
+                # 3. 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]]
+
+                # 4. Sample noise from the prior and add it to the latents according to the noise magnitude at each
+                # timestep (this is the forward diffusion process) [z_{t_{n + k}} in Algorithm 1]
+                noise = torch.randn_like(latents)
+                noisy_model_input = noise_scheduler.add_noise(latents, noise, start_timesteps)
+
+                # 5. Sample a random guidance scale w from U[w_min, w_max]
+                # Note that for LCM-LoRA distillation it is not necessary to use a guidance scale embedding
+                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)
+
+                # 6. Prepare prompt embeds and unet_added_conditions
+                prompt_embeds = encoded_text.pop("prompt_embeds")
+
+                # 7. Get online LCM prediction on z_{t_{n + k}} (noisy_model_input), w, c, t_{n + k} (start_timesteps)
+                noise_pred = unet(
+                    noisy_model_input,
+                    start_timesteps,
+                    encoder_hidden_states=prompt_embeds,
+                    added_cond_kwargs=encoded_text,
+                ).sample
+                pred_x_0 = get_predicted_original_sample(
+                    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
+
+                # 8. Compute the conditional and unconditional teacher model predictions to get CFG estimates of the
+                # predicted noise eps_0 and predicted original sample x_0, then run the ODE solver using these
+                # estimates to predict the data point in the augmented PF-ODE trajectory corresponding to the next ODE
+                # solver timestep.
+
+                # With the adapters disabled, the `unet` is the regular teacher model.
+                unet.disable_adapters()
+                with torch.no_grad():
+                    # 1. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and conditional embedding c
+                    cond_teacher_output = unet(
+                        noisy_model_input,
+                        start_timesteps,
+                        encoder_hidden_states=prompt_embeds,
+                        added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},
+                    ).sample
+                    cond_pred_x0 = get_predicted_original_sample(
+                        cond_teacher_output,
+                        start_timesteps,
+                        noisy_model_input,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+                    cond_pred_noise = get_predicted_noise(
+                        cond_teacher_output,
+                        start_timesteps,
+                        noisy_model_input,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+
+                    # 2. Get teacher model prediction on noisy_model_input z_{t_{n + k}} and unconditional embedding 0
+                    uncond_prompt_embeds = torch.zeros_like(prompt_embeds)
+                    uncond_pooled_prompt_embeds = torch.zeros_like(encoded_text["text_embeds"])
+                    uncond_added_conditions = copy.deepcopy(encoded_text)
+                    uncond_added_conditions["text_embeds"] = uncond_pooled_prompt_embeds
+                    uncond_teacher_output = unet(
+                        noisy_model_input,
+                        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 = get_predicted_original_sample(
+                        uncond_teacher_output,
+                        start_timesteps,
+                        noisy_model_input,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+                    uncond_pred_noise = get_predicted_noise(
+                        uncond_teacher_output,
+                        start_timesteps,
+                        noisy_model_input,
+                        noise_scheduler.config.prediction_type,
+                        alpha_schedule,
+                        sigma_schedule,
+                    )
+
+                    # 3. Calculate the CFG estimate of x_0 (pred_x0) and eps_0 (pred_noise)
+                    # Note that this uses the LCM paper's CFG formulation rather than the Imagen CFG formulation
+                    pred_x0 = cond_pred_x0 + w * (cond_pred_x0 - uncond_pred_x0)
+                    pred_noise = cond_pred_noise + w * (cond_pred_noise - uncond_pred_noise)
+                    # 4. Run one step of the ODE solver to estimate the next point x_prev on the
+                    # augmented PF-ODE trajectory (solving backward in time)
+                    # Note that the DDIM step depends on both the predicted x_0 and source noise eps_0.
+                    x_prev = solver.ddim_step(pred_x0, pred_noise, index).to(unet.dtype)
+
+                # re-enable unet adapters to turn the `unet` into a student unet.
+                unet.enable_adapters()
+
+                # 9. Get target LCM prediction on x_prev, w, c, t_n (timesteps)
+                # Note that we do not use a separate target network for LCM-LoRA distillation.
+                with torch.no_grad():
+                    target_noise_pred = unet(
+                        x_prev,
+                        timesteps,
+                        encoder_hidden_states=prompt_embeds,
+                        added_cond_kwargs={k: v.to(weight_dtype) for k, v in encoded_text.items()},
+                    ).sample
+                    pred_x_0 = get_predicted_original_sample(
+                        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
+
+                # 10. 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
+                    )
+
+                # 11. Backpropagate on the online student model (`unet`) (only LoRA)
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(params_to_optimize, 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, args, accelerator, weight_dtype, global_step, unet=unet, is_final_validation=False
+                        )
+
+            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_lora_state_dict = get_peft_model_state_dict(unet)
+        StableDiffusionXLPipeline.save_lora_weights(args.output_dir, unet_lora_layers=unet_lora_state_dict)
+
+        if args.push_to_hub:
+            upload_folder(
+                repo_id=repo_id,
+                folder_path=args.output_dir,
+                commit_message="End of training",
+                ignore_patterns=["step_*", "epoch_*"],
+            )
+
+        del unet
+        torch.cuda.empty_cache()
+
+        # Final inference.
+        if args.validation_steps is not None:
+            log_validation(vae, args, accelerator, weight_dtype, step=global_step, unet=None, is_final_validation=True)
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)