Gligen training (#7906)

* add training code of gligen

* fix code quality tests.

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

examples/research_projects/gligen/README.md

+# GLIGEN: Open-Set Grounded Text-to-Image Generation
+
+These scripts contain the code to prepare the grounding data and train the GLIGEN model on COCO dataset.
+
+### Install the requirements
+
+```bash
+conda create -n diffusers python==3.10
+conda activate diffusers
+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()
+```
+
+### Prepare the training data
+
+If you want to make your own grounding data, you need to install the requirements.
+
+I used [RAM](https://github.com/xinyu1205/recognize-anything) to tag
+images, [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO/issues?q=refer) to detect objects,
+and [BLIP2](https://huggingface.co/docs/transformers/en/model_doc/blip-2) to caption instances.
+
+Only RAM needs to be installed manually:
+
+```bash
+pip install git+https://github.com/xinyu1205/recognize-anything.git --no-deps
+```
+
+Download the pre-trained model:
+
+```bash
+huggingface-cli download --resume-download xinyu1205/recognize_anything_model ram_swin_large_14m.pth
+huggingface-cli download --resume-download IDEA-Research/grounding-dino-base
+huggingface-cli download --resume-download Salesforce/blip2-flan-t5-xxl
+huggingface-cli download --resume-download clip-vit-large-patch14
+huggingface-cli download --resume-download masterful/gligen-1-4-generation-text-box
+```
+
+Make the training data on 8 GPUs:
+
+```bash
+torchrun --master_port 17673 --nproc_per_node=8 make_datasets.py \
+    --data_root /mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017 \
+    --save_root /root/gligen_data \
+    --ram_checkpoint /root/.cache/huggingface/hub/models--xinyu1205--recognize_anything_model/snapshots/ebc52dc741e86466202a5ab8ab22eae6e7d48bf1/ram_swin_large_14m.pth
+```
+
+You can download the COCO training data from
+
+```bash
+huggingface-cli download --resume-download Hzzone/GLIGEN_COCO coco_train2017.pth
+```
+
+It's in the format of
+
+```json
+[
+  ...
+  {
+    'file_path': Path,
+    'annos': [
+      {
+        'caption': Instance
+        Caption,
+        'bbox': bbox
+        in
+        xyxy,
+        'text_embeddings_before_projection': CLIP
+        text
+        embedding
+        before
+        linear
+        projection
+      }
+    ]
+  }
+  ...
+]
+```
+
+### Training commands
+
+The training script is heavily based
+on https://github.com/huggingface/diffusers/blob/main/examples/controlnet/train_controlnet.py
+
+```bash
+accelerate launch train_gligen_text.py \
+    --data_path /root/data/zhizhonghuang/coco_train2017.pth \
+    --image_path /mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017 \
+    --train_batch_size 8 \
+    --max_train_steps 100000 \
+    --checkpointing_steps 1000 \
+    --checkpoints_total_limit 10 \
+    --learning_rate 5e-5 \
+    --dataloader_num_workers 16 \
+    --mixed_precision fp16 \
+    --report_to wandb \
+    --tracker_project_name gligen \
+    --output_dir /root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO
+```
+
+I trained the model on 8 A100 GPUs for about 11 hours (at least 24GB GPU memory). The generated images will follow the
+layout possibly at 50k iterations.
+
+Note that although the pre-trained GLIGEN model has been loaded, the parameters of `fuser` and `position_net` have been reset (see line 420 in `train_gligen_text.py`)
+
+The trained model can be downloaded from
+
+```bash
+huggingface-cli download --resume-download Hzzone/GLIGEN_COCO config.json diffusion_pytorch_model.safetensors
+```
+
+You can run `demo.ipynb` to visualize the generated images.
+
+Example prompts:
+
+```python
+prompt = 'A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky'
+boxes = [[0.041015625, 0.548828125, 0.453125, 0.859375],
+         [0.525390625, 0.552734375, 0.93359375, 0.865234375],
+         [0.12890625, 0.015625, 0.412109375, 0.279296875],
+         [0.578125, 0.08203125, 0.857421875, 0.27734375]]
+gligen_phrases = ['a green car', 'a blue truck', 'a red air balloon', 'a bird']
+```
+
+Example images:
+![alt text](generated-images-100000-00.png)
+
+### Citation
+
+```
+@article{li2023gligen,
+  title={GLIGEN: Open-Set Grounded Text-to-Image Generation},
+  author={Li, Yuheng and Liu, Haotian and Wu, Qingyang and Mu, Fangzhou and Yang, Jianwei and Gao, Jianfeng and Li, Chunyuan and Lee, Yong Jae},
+  journal={CVPR},
+  year={2023}
+}
+```
\ No newline at end of file

examples/research_projects/gligen/dataset.py

+import os
+import random
+
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+
+
+def recalculate_box_and_verify_if_valid(x, y, w, h, image_size, original_image_size, min_box_size):
+    scale = image_size / min(original_image_size)
+    crop_y = (original_image_size[1] * scale - image_size) // 2
+    crop_x = (original_image_size[0] * scale - image_size) // 2
+    x0 = max(x * scale - crop_x, 0)
+    y0 = max(y * scale - crop_y, 0)
+    x1 = min((x + w) * scale - crop_x, image_size)
+    y1 = min((y + h) * scale - crop_y, image_size)
+    if (x1 - x0) * (y1 - y0) / (image_size * image_size) < min_box_size:
+        return False, (None, None, None, None)
+    return True, (x0, y0, x1, y1)
+
+
+class COCODataset(torch.utils.data.Dataset):
+    def __init__(
+        self,
+        data_path,
+        image_path,
+        image_size=512,
+        min_box_size=0.01,
+        max_boxes_per_data=8,
+        tokenizer=None,
+    ):
+        super().__init__()
+        self.min_box_size = min_box_size
+        self.max_boxes_per_data = max_boxes_per_data
+        self.image_size = image_size
+        self.image_path = image_path
+        self.tokenizer = tokenizer
+        self.transforms = transforms.Compose(
+            [
+                transforms.Resize(image_size, interpolation=transforms.InterpolationMode.BILINEAR),
+                transforms.CenterCrop(image_size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+        self.data_list = torch.load(data_path, map_location="cpu")
+
+    def __getitem__(self, index):
+        if self.max_boxes_per_data > 99:
+            assert False, "Are you sure setting such large number of boxes per image?"
+
+        out = {}
+
+        data = self.data_list[index]
+        image = Image.open(os.path.join(self.image_path, data["file_path"])).convert("RGB")
+        original_image_size = image.size
+        out["pixel_values"] = self.transforms(image)
+
+        annos = data["annos"]
+
+        areas, valid_annos = [], []
+        for anno in annos:
+            # x, y, w, h = anno['bbox']
+            x0, y0, x1, y1 = anno["bbox"]
+            x, y, w, h = x0, y0, x1 - x0, y1 - y0
+            valid, (x0, y0, x1, y1) = recalculate_box_and_verify_if_valid(
+                x, y, w, h, self.image_size, original_image_size, self.min_box_size
+            )
+            if valid:
+                anno["bbox"] = [x0, y0, x1, y1]
+                areas.append((x1 - x0) * (y1 - y0))
+                valid_annos.append(anno)
+
+        # Sort according to area and choose the largest N objects
+        wanted_idxs = torch.tensor(areas).sort(descending=True)[1]
+        wanted_idxs = wanted_idxs[: self.max_boxes_per_data]
+        valid_annos = [valid_annos[i] for i in wanted_idxs]
+
+        out["boxes"] = torch.zeros(self.max_boxes_per_data, 4)
+        out["masks"] = torch.zeros(self.max_boxes_per_data)
+        out["text_embeddings_before_projection"] = torch.zeros(self.max_boxes_per_data, 768)
+
+        for i, anno in enumerate(valid_annos):
+            out["boxes"][i] = torch.tensor(anno["bbox"]) / self.image_size
+            out["masks"][i] = 1
+            out["text_embeddings_before_projection"][i] = anno["text_embeddings_before_projection"]
+
+        prob_drop_boxes = 0.1
+        if random.random() < prob_drop_boxes:
+            out["masks"][:] = 0
+
+        caption = random.choice(data["captions"])
+
+        prob_drop_captions = 0.5
+        if random.random() < prob_drop_captions:
+            caption = ""
+        caption = self.tokenizer(
+            caption,
+            max_length=self.tokenizer.model_max_length,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
+        )
+        out["caption"] = caption
+
+        return out
+
+    def __len__(self):
+        return len(self.data_list)

examples/research_projects/gligen/demo.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda/envs/densecaption/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import torch\n",
+    "from diffusers import StableDiffusionGLIGENTextImagePipeline, StableDiffusionGLIGENPipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import diffusers\n",
+    "from diffusers import (\n",
+    "    AutoencoderKL,\n",
+    "    DDPMScheduler,\n",
+    "    UNet2DConditionModel,\n",
+    "    UniPCMultistepScheduler,\n",
+    "    EulerDiscreteScheduler,\n",
+    ")\n",
+    "from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer\n",
+    "# pretrained_model_name_or_path = 'masterful/gligen-1-4-generation-text-box'\n",
+    "\n",
+    "pretrained_model_name_or_path = '/root/data/zhizhonghuang/checkpoints/models--masterful--gligen-1-4-generation-text-box/snapshots/d2820dc1e9ba6ca082051ce79cfd3eb468ae2c83'\n",
+    "\n",
+    "tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder=\"tokenizer\")\n",
+    "noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder=\"scheduler\")\n",
+    "text_encoder = CLIPTextModel.from_pretrained(\n",
+    "    pretrained_model_name_or_path, subfolder=\"text_encoder\"\n",
+    ")\n",
+    "vae = AutoencoderKL.from_pretrained(\n",
+    "    pretrained_model_name_or_path, subfolder=\"vae\"\n",
+    ")\n",
+    "# unet = UNet2DConditionModel.from_pretrained(\n",
+    "#     pretrained_model_name_or_path, subfolder=\"unet\"\n",
+    "# )\n",
+    "\n",
+    "noise_scheduler = EulerDiscreteScheduler.from_config(noise_scheduler.config)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unet = UNet2DConditionModel.from_pretrained(\n",
+    "    '/root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion_gligen.pipeline_stable_diffusion_gligen.StableDiffusionGLIGENPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .\n"
+     ]
+    }
+   ],
+   "source": [
+    "pipe = StableDiffusionGLIGENPipeline(\n",
+    "    vae,\n",
+    "    text_encoder,\n",
+    "    tokenizer,\n",
+    "    unet,\n",
+    "    noise_scheduler,\n",
+    "    safety_checker=None,\n",
+    "    feature_extractor=None,\n",
+    ")\n",
+    "pipe = pipe.to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prompt = 'A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky'\n",
+    "# gen_boxes = [('a green car', [21, 281, 211, 159]), ('a blue truck', [269, 283, 209, 160]), ('a red air balloon', [66, 8, 145, 135]), ('a bird', [296, 42, 143, 100])]\n",
+    "\n",
+    "# prompt = 'A realistic top-down view of a wooden table with two apples on it'\n",
+    "# gen_boxes = [('a wooden table', [20, 148, 472, 216]), ('an apple', [150, 226, 100, 100]), ('an apple', [280, 226, 100, 100])]\n",
+    "\n",
+    "# prompt = 'A realistic scene of three skiers standing in a line on the snow near a palm tree'\n",
+    "# gen_boxes = [('a skier', [5, 152, 139, 168]), ('a skier', [278, 192, 121, 158]), ('a skier', [148, 173, 124, 155]), ('a palm tree', [404, 105, 103, 251])]\n",
+    "\n",
+    "prompt = 'An oil painting of a pink dolphin jumping on the left of a steam boat on the sea'\n",
+    "gen_boxes = [('a steam boat', [232, 225, 257, 149]), ('a jumping pink dolphin', [21, 249, 189, 123])]\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "boxes = np.array([x[1] for x in gen_boxes])\n",
+    "boxes = boxes / 512\n",
+    "boxes[:, 2] = boxes[:, 0] + boxes[:, 2]\n",
+    "boxes[:, 3] = boxes[:, 1] + boxes[:, 3]\n",
+    "boxes = boxes.tolist()\n",
+    "gligen_phrases = [x[0] for x in gen_boxes]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/miniconda/envs/densecaption/lib/python3.11/site-packages/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py:683: FutureWarning: Accessing config attribute `in_channels` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.in_channels'.\n",
+      "  num_channels_latents = self.unet.in_channels\n",
+      "/root/miniconda/envs/densecaption/lib/python3.11/site-packages/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py:716: FutureWarning: Accessing config attribute `cross_attention_dim` directly via 'UNet2DConditionModel' object attribute is deprecated. Please access 'cross_attention_dim' over 'UNet2DConditionModel's config object instead, e.g. 'unet.config.cross_attention_dim'.\n",
+      "  max_objs, self.unet.cross_attention_dim, device=device, dtype=self.text_encoder.dtype\n",
+      "100%|██████████| 50/50 [01:21<00:00,  1.64s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "images = pipe(\n",
+    "    prompt=prompt,\n",
+    "    gligen_phrases=gligen_phrases,\n",
+    "    gligen_boxes=boxes,\n",
+    "    gligen_scheduled_sampling_beta=1.0,\n",
+    "    output_type=\"pil\",\n",
+    "    num_inference_steps=50,\n",
+    "    negative_prompt=\"artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate\",\n",
+    "    num_images_per_prompt=16,\n",
+    ").images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "diffusers.utils.make_image_grid(images, 4, len(images)//4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "densecaption",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/research_projects/gligen/make_datasets.py

+import argparse
+import os
+import random
+
+import torch
+import torchvision
+import torchvision.transforms as TS
+from PIL import Image
+from ram import inference_ram
+from ram.models import ram
+from tqdm import tqdm
+from transformers import (
+    AutoModelForZeroShotObjectDetection,
+    AutoProcessor,
+    Blip2ForConditionalGeneration,
+    Blip2Processor,
+    CLIPTextModel,
+    CLIPTokenizer,
+)
+
+
+torch.autograd.set_grad_enabled(False)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Caption Generation script", add_help=False)
+    parser.add_argument("--data_root", type=str, required=True, help="path to COCO")
+    parser.add_argument("--save_root", type=str, required=True, help="path to save")
+    parser.add_argument("--ram_checkpoint", type=str, required=True, help="path to save")
+    args = parser.parse_args()
+
+    # ram_checkpoint = '/root/.cache/huggingface/hub/models--xinyu1205--recognize_anything_model/snapshots/ebc52dc741e86466202a5ab8ab22eae6e7d48bf1/ram_swin_large_14m.pth'
+    # data_root = '/mnt/workspace/workgroup/zhizhonghuang/dataset/COCO/train2017'
+    # save_root = '/root/gligen_data'
+    box_threshold = 0.25
+    text_threshold = 0.2
+
+    import torch.distributed as dist
+
+    dist.init_process_group(backend="nccl", init_method="env://")
+    local_rank = torch.distributed.get_rank() % torch.cuda.device_count()
+    device = f"cuda:{local_rank}"
+    torch.cuda.set_device(local_rank)
+
+    ram_model = ram(pretrained=args.ram_checkpoint, image_size=384, vit="swin_l").cuda().eval()
+    ram_processor = TS.Compose(
+        [TS.Resize((384, 384)), TS.ToTensor(), TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]
+    )
+
+    grounding_dino_processor = AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-base")
+    grounding_dino_model = AutoModelForZeroShotObjectDetection.from_pretrained(
+        "IDEA-Research/grounding-dino-base"
+    ).cuda()
+
+    blip2_processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xxl")
+    blip2_model = Blip2ForConditionalGeneration.from_pretrained(
+        "Salesforce/blip2-flan-t5-xxl", torch_dtype=torch.float16
+    ).cuda()
+
+    clip_text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14").cuda()
+    clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+
+    image_paths = [os.path.join(args.data_root, x) for x in os.listdir(args.data_root)]
+    random.shuffle(image_paths)
+
+    for image_path in tqdm.tqdm(image_paths):
+        pth_path = os.path.join(args.save_root, os.path.basename(image_path))
+        if os.path.exists(pth_path):
+            continue
+
+        sample = {"file_path": os.path.basename(image_path), "annos": []}
+
+        raw_image = Image.open(image_path).convert("RGB")
+
+        res = inference_ram(ram_processor(raw_image).unsqueeze(0).cuda(), ram_model)
+
+        text = res[0].replace(" |", ".")
+
+        inputs = grounding_dino_processor(images=raw_image, text=text, return_tensors="pt")
+        inputs = {k: v.cuda() for k, v in inputs.items()}
+        outputs = grounding_dino_model(**inputs)
+
+        results = grounding_dino_processor.post_process_grounded_object_detection(
+            outputs,
+            inputs["input_ids"],
+            box_threshold=box_threshold,
+            text_threshold=text_threshold,
+            target_sizes=[raw_image.size[::-1]],
+        )
+        boxes = results[0]["boxes"]
+        labels = results[0]["labels"]
+        scores = results[0]["scores"]
+        indices = torchvision.ops.nms(boxes, scores, 0.5)
+        boxes = boxes[indices]
+        category_names = [labels[i] for i in indices]
+
+        for i, bbox in enumerate(boxes):
+            bbox = bbox.tolist()
+            inputs = blip2_processor(images=raw_image.crop(bbox), return_tensors="pt")
+            inputs = {k: v.cuda().to(torch.float16) for k, v in inputs.items()}
+            outputs = blip2_model.generate(**inputs)
+            caption = blip2_processor.decode(outputs[0], skip_special_tokens=True)
+            inputs = clip_tokenizer(
+                caption,
+                padding="max_length",
+                max_length=clip_tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            inputs = {k: v.cuda() for k, v in inputs.items()}
+            text_embeddings_before_projection = clip_text_encoder(**inputs).pooler_output.squeeze(0)
+
+            sample["annos"].append(
+                {
+                    "caption": caption,
+                    "bbox": bbox,
+                    "text_embeddings_before_projection": text_embeddings_before_projection,
+                }
+            )
+        torch.save(sample, pth_path)

examples/research_projects/gligen/requirements.txt

+accelerate>=0.16.0
+torchvision
+transformers>=4.25.1
+ftfy
+tensorboard
+Jinja2
+diffusers
+scipy
+timm
+fairscale
+wandb
\ No newline at end of file

examples/research_projects/gligen/train_gligen_text.py

+# from accelerate.utils import write_basic_config
+#
+# write_basic_config()
+import argparse
+import logging
+import math
+import os
+import shutil
+from pathlib import Path
+
+import accelerate
+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 packaging import version
+from tqdm.auto import tqdm
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    EulerDiscreteScheduler,
+    StableDiffusionGLIGENPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.optimization import get_scheduler
+from diffusers.utils import is_wandb_available, make_image_grid
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.utils.torch_utils import is_compiled_module
+
+
+if is_wandb_available():
+    pass
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+# check_min_version("0.28.0.dev0")
+
+logger = get_logger(__name__)
+
+
+@torch.no_grad()
+def log_validation(vae, text_encoder, tokenizer, unet, noise_scheduler, args, accelerator, step, weight_dtype):
+    if accelerator.is_main_process:
+        print("generate test images...")
+    unet = accelerator.unwrap_model(unet)
+    vae.to(accelerator.device, dtype=torch.float32)
+
+    pipeline = StableDiffusionGLIGENPipeline(
+        vae,
+        text_encoder,
+        tokenizer,
+        unet,
+        EulerDiscreteScheduler.from_config(noise_scheduler.config),
+        safety_checker=None,
+        feature_extractor=None,
+    )
+    pipeline = pipeline.to(accelerator.device)
+    pipeline.set_progress_bar_config(disable=not accelerator.is_main_process)
+    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)
+
+    prompt = "A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky"
+    boxes = [
+        [0.041015625, 0.548828125, 0.453125, 0.859375],
+        [0.525390625, 0.552734375, 0.93359375, 0.865234375],
+        [0.12890625, 0.015625, 0.412109375, 0.279296875],
+        [0.578125, 0.08203125, 0.857421875, 0.27734375],
+    ]
+    gligen_phrases = ["a green car", "a blue truck", "a red air balloon", "a bird"]
+    images = pipeline(
+        prompt=prompt,
+        gligen_phrases=gligen_phrases,
+        gligen_boxes=boxes,
+        gligen_scheduled_sampling_beta=1.0,
+        output_type="pil",
+        num_inference_steps=50,
+        negative_prompt="artifacts, blurry, smooth texture, bad quality, distortions, unrealistic, distorted image, bad proportions, duplicate",
+        num_images_per_prompt=4,
+        generator=generator,
+    ).images
+    os.makedirs(os.path.join(args.output_dir, "images"), exist_ok=True)
+    make_image_grid(images, 1, 4).save(
+        os.path.join(args.output_dir, "images", f"generated-images-{step:06d}-{accelerator.process_index:02d}.png")
+    )
+
+    vae.to(accelerator.device, dtype=weight_dtype)
+
+
+def parse_args(input_args=None):
+    parser = argparse.ArgumentParser(description="Simple example of a ControlNet training script.")
+    parser.add_argument(
+        "--data_path",
+        type=str,
+        default="coco_train2017.pth",
+        help="Path to training dataset.",
+    )
+    parser.add_argument(
+        "--image_path",
+        type=str,
+        default="coco_train2017.pth",
+        help="Path to training images.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="controlnet-model",
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=1)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=None,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
+            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
+            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
+            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
+            "instructions."
+        ),
+    )
+    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.'
+        ),
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=5e-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(
+        "--lr_num_cycles",
+        type=int,
+        default=1,
+        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
+    )
+    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
+    parser.add_argument(
+        "--dataloader_num_workers",
+        type=int,
+        default=0,
+        help=(
+            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+        ),
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default=None,
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
+            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+        ),
+    )
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+    parser.add_argument(
+        "--set_grads_to_none",
+        action="store_true",
+        help=(
+            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
+            " behaviors, so disable this argument if it causes any problems. More info:"
+            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
+        ),
+    )
+    parser.add_argument(
+        "--tracker_project_name",
+        type=str,
+        default="train_controlnet",
+        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()
+    return args
+
+
+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,
+    )
+
+    # Disable AMP for MPS.
+    if torch.backends.mps.is_available():
+        accelerator.native_amp = False
+
+    # 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)
+
+    # import correct text encoder class
+    # text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
+    # Load scheduler and models
+    from transformers import CLIPTextModel, CLIPTokenizer
+
+    pretrained_model_name_or_path = "masterful/gligen-1-4-generation-text-box"
+    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer")
+    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler")
+    text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder="text_encoder")
+
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae")
+    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name_or_path, subfolder="unet")
+
+    # Taken from [Sayak Paul's Diffusers PR #6511](https://github.com/huggingface/diffusers/pull/6511/files)
+    def unwrap_model(model):
+        model = accelerator.unwrap_model(model)
+        model = model._orig_mod if is_compiled_module(model) else model
+        return model
+
+    # `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:
+                i = len(weights) - 1
+
+                while len(weights) > 0:
+                    weights.pop()
+                    model = models[i]
+
+                    sub_dir = "unet"
+                    model.save_pretrained(os.path.join(output_dir, sub_dir))
+
+                    i -= 1
+
+        def load_model_hook(models, input_dir):
+            while len(models) > 0:
+                # pop models so that they are not loaded again
+                model = models.pop()
+
+                # load diffusers style into model
+                load_model = unet.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)
+
+    vae.requires_grad_(False)
+    unet.requires_grad_(False)
+    text_encoder.requires_grad_(False)
+
+    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.warning(
+                    "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()
+            # controlnet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # if args.gradient_checkpointing:
+    #     controlnet.enable_gradient_checkpointing()
+
+    # 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 unwrap_model(unet).dtype != torch.float32:
+        raise ValueError(f"Controlnet loaded as datatype {unwrap_model(unet).dtype}. {low_precision_error_string}")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    optimizer_class = torch.optim.AdamW
+    # Optimizer creation
+    for n, m in unet.named_modules():
+        if ("fuser" in n) or ("position_net" in n):
+            import torch.nn as nn
+
+            if isinstance(m, (nn.Linear, nn.LayerNorm)):
+                m.reset_parameters()
+    params_to_optimize = []
+    for n, p in unet.named_parameters():
+        if ("fuser" in n) or ("position_net" in n):
+            p.requires_grad = True
+            params_to_optimize.append(p)
+    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,
+    )
+
+    from dataset import COCODataset
+
+    train_dataset = COCODataset(
+        data_path=args.data_path,
+        image_path=args.image_path,
+        tokenizer=tokenizer,
+        image_size=args.resolution,
+        max_boxes_per_data=30,
+    )
+
+    print("num samples: ", len(train_dataset))
+
+    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,
+    )
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+        num_training_steps=args.max_train_steps * accelerator.num_processes,
+        num_cycles=args.lr_num_cycles,
+        power=args.lr_power,
+    )
+
+    # Prepare everything with our `accelerator`.
+    unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        unet, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # For mixed precision training we cast the text_encoder and vae weights to half-precision
+    # as these models are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move vae, unet and text_encoder to device and cast to weight_dtype
+    vae.to(accelerator.device, dtype=weight_dtype)
+    # unet.to(accelerator.device, dtype=weight_dtype)
+    unet.to(accelerator.device, dtype=torch.float32)
+    text_encoder.to(accelerator.device, dtype=weight_dtype)
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        tracker_config = dict(vars(args))
+
+        # tensorboard cannot handle list types for config
+        # tracker_config.pop("validation_prompt")
+        # tracker_config.pop("validation_image")
+
+        accelerator.init_trackers(args.tracker_project_name, config=tracker_config)
+
+    # Train!
+    # total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    # logger.info("***** Running training *****")
+    # logger.info(f"  Num examples = {len(train_dataset)}")
+    # logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
+    # logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    # logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    # logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    # logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    # logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the 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,
+    )
+
+    log_validation(
+        vae,
+        text_encoder,
+        tokenizer,
+        unet,
+        noise_scheduler,
+        args,
+        accelerator,
+        global_step,
+        weight_dtype,
+    )
+
+    # image_logs = None
+    for epoch in range(first_epoch, args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(unet):
+                # Convert images to latent space
+                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
+                latents = latents * vae.config.scaling_factor
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                with torch.no_grad():
+                    # Get the text embedding for conditioning
+                    encoder_hidden_states = text_encoder(
+                        batch["caption"]["input_ids"].squeeze(1),
+                        # batch['caption']['attention_mask'].squeeze(1),
+                        return_dict=False,
+                    )[0]
+
+                cross_attention_kwargs = {}
+                cross_attention_kwargs["gligen"] = {
+                    "boxes": batch["boxes"],
+                    "positive_embeddings": batch["text_embeddings_before_projection"],
+                    "masks": batch["masks"],
+                }
+                # Predict the noise residual
+                model_pred = unet(
+                    noisy_latents,
+                    timesteps,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # Get the target for loss depending on the prediction type
+                if noise_scheduler.config.prediction_type == "epsilon":
+                    target = noise
+                elif noise_scheduler.config.prediction_type == "v_prediction":
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                accelerator.backward(loss)
+                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=args.set_grads_to_none)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                if global_step % args.checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        # _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:06d}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+                    # if args.validation_prompt is not None and global_step % args.validation_steps == 0:
+                    log_validation(
+                        vae,
+                        text_encoder,
+                        tokenizer,
+                        unet,
+                        noise_scheduler,
+                        args,
+                        accelerator,
+                        global_step,
+                        weight_dtype,
+                    )
+            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 = unwrap_model(unet)
+        unet.save_pretrained(args.output_dir)
+    #
+    #     # Run a final round of validation.
+    #     image_logs = None
+    #     if args.validation_prompt is not None:
+    #         image_logs = log_validation(
+    #             vae=vae,
+    #             text_encoder=text_encoder,
+    #             tokenizer=tokenizer,
+    #             unet=unet,
+    #             controlnet=None,
+    #             args=args,
+    #             accelerator=accelerator,
+    #             weight_dtype=weight_dtype,
+    #             step=global_step,
+    #             is_final_validation=True,
+    #         )
+    #
+    #     if args.push_to_hub:
+    #         save_model_card(
+    #             repo_id,
+    #             image_logs=image_logs,
+    #             base_model=args.pretrained_model_name_or_path,
+    #             repo_folder=args.output_dir,
+    #         )
+    #         upload_folder(
+    #             repo_id=repo_id,
+    #             folder_path=args.output_dir,
+    #             commit_message="End of training",
+    #             ignore_patterns=["step_*", "epoch_*"],
+    #         )
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)