diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..9166b7485ed13d74100d62758bc209a66510123d 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/kaifu_resize.png filter=lfs diff=lfs merge=lfs -text
+examples/sam_resize.png filter=lfs diff=lfs merge=lfs -text
+examples/schmidhuber_resize.png filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..68ea5ad1a232c94b6dba614b67da578c9ca811a5
--- /dev/null
+++ b/README.md
@@ -0,0 +1,14 @@
+---
+title: InstantID
+emoji: 😻
+colorFrom: gray
+colorTo: gray
+sdk: gradio
+sdk_version: 4.15.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+disable_embedding: true
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d2a786d51b4116a88b6bc61ec93a8a36522223e
--- /dev/null
+++ b/app.py
@@ -0,0 +1,675 @@
+import os
+import cv2
+import math
+#import spaces
+import torch
+import random
+import numpy as np
+import argparse
+
+import PIL
+from PIL import Image
+from typing import Tuple
+
+import diffusers
+from diffusers.utils import load_image
+from diffusers.models import ControlNetModel
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+
+from huggingface_hub import hf_hub_download
+
+import insightface
+from insightface.app import FaceAnalysis
+
+from style_template import styles
+from pipeline_stable_diffusion_xl_instantid_full import StableDiffusionXLInstantIDPipeline, draw_kps
+
+from controlnet_aux import OpenposeDetector
+
+import gradio as gr
+
+from depth_anything.dpt import DepthAnything
+from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
+
+import torch.nn.functional as F
+from torchvision.transforms import Compose
+
+# global variable
+MAX_SEED = np.iinfo(np.int32).max
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "Spring Festival"
+enable_lcm_arg = False
+
+# download checkpoints
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
+hf_hub_download(
+    repo_id="InstantX/InstantID",
+    filename="ControlNetModel/diffusion_pytorch_model.safetensors",
+    local_dir="./checkpoints",
+)
+hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")
+
+# Load face encoder
+app = FaceAnalysis(
+    name="antelopev2",
+    root="./",
+    providers=["CPUExecutionProvider"],
+)
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+
+depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(device).eval()
+
+transform = Compose([
+    Resize(
+        width=518,
+        height=518,
+        resize_target=False,
+        keep_aspect_ratio=True,
+        ensure_multiple_of=14,
+        resize_method='lower_bound',
+        image_interpolation_method=cv2.INTER_CUBIC,
+    ),
+    NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    PrepareForNet(),
+])
+
+# Path to InstantID models
+face_adapter = f"./checkpoints/ip-adapter.bin"
+controlnet_path = f"./checkpoints/ControlNetModel"
+
+# Load pipeline face ControlNetModel
+controlnet_identitynet = ControlNetModel.from_pretrained(
+    controlnet_path, torch_dtype=dtype
+)
+
+# controlnet-pose/canny/depth
+controlnet_pose_model = "thibaud/controlnet-openpose-sdxl-1.0"
+controlnet_canny_model = "diffusers/controlnet-canny-sdxl-1.0"
+controlnet_depth_model = "diffusers/controlnet-depth-sdxl-1.0-small"
+
+controlnet_pose = ControlNetModel.from_pretrained(
+    controlnet_pose_model, torch_dtype=dtype
+).to(device)
+controlnet_canny = ControlNetModel.from_pretrained(
+    controlnet_canny_model, torch_dtype=dtype
+).to(device)
+controlnet_depth = ControlNetModel.from_pretrained(
+    controlnet_depth_model, torch_dtype=dtype
+).to(device)
+
+def get_depth_map(image):
+    
+    image = np.array(image) / 255.0
+
+    h, w = image.shape[:2]
+
+    image = transform({'image': image})['image']
+    image = torch.from_numpy(image).unsqueeze(0).to("cuda")
+
+    with torch.no_grad():
+        depth = depth_anything(image)
+
+    depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
+    depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+
+    depth = depth.cpu().numpy().astype(np.uint8)
+
+    depth_image = Image.fromarray(depth)
+
+    return depth_image
+
+def get_canny_image(image, t1=100, t2=200):
+    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+    edges = cv2.Canny(image, t1, t2)
+    return Image.fromarray(edges, "L")
+
+controlnet_map = {
+    "pose": controlnet_pose,
+    "canny": controlnet_canny,
+    "depth": controlnet_depth,
+}
+controlnet_map_fn = {
+    "pose": openpose,
+    "canny": get_canny_image,
+    "depth": get_depth_map,
+}
+
+#base_model_path = "wangqixun/YamerMIX_v8"
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--inbrowser', action='store_true', help='Open in browser')
+    parser.add_argument('--server_port', type=int, default=7860, help='Server port')
+    parser.add_argument('--share', action='store_true', help='Share the Gradio UI')
+    parser.add_argument('--model_path', type=str, default='stablediffusionapi/juggernaut-xl-v8', help='Base model path')
+    parser.add_argument('--medvram', action='store_true', help='Medium VRAM settings')
+    parser.add_argument('--lowvram', action='store_true', help='Low VRAM settings')
+
+    args = parser.parse_args()
+
+base_model_path = args.model_path
+
+pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+    base_model_path,
+    controlnet=[controlnet_identitynet],
+    torch_dtype=dtype,
+    safety_checker=None,
+    feature_extractor=None,
+).to(device)
+
+pipe.scheduler = diffusers.EulerDiscreteScheduler.from_config(
+    pipe.scheduler.config
+)
+
+# load and disable LCM
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
+pipe.disable_lora()
+
+pipe.cuda()
+pipe.load_ip_adapter_instantid(face_adapter)
+pipe.image_proj_model.to("cuda")
+pipe.unet.to("cuda")
+
+def toggle_lcm_ui(value):
+    if value:
+        return (
+            gr.update(minimum=0, maximum=100, step=1, value=5),
+            gr.update(minimum=0.1, maximum=20.0, step=0.1, value=1.5),
+        )
+    else:
+        return (
+            gr.update(minimum=5, maximum=100, step=1, value=30),
+            gr.update(minimum=0.1, maximum=20.0, step=0.1, value=5),
+        )
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+def remove_tips():
+    return gr.update(visible=False)
+
+def get_example():
+    case = [
+        [
+            "./examples/yann-lecun_resize.jpg",
+            None,
+            "a man",
+            "Spring Festival",
+            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+        ],
+        [
+            "./examples/musk_resize.jpeg",
+            "./examples/poses/pose2.jpg",
+            "a man flying in the sky in Mars",
+            "Mars",
+            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+        ],
+        [
+            "./examples/sam_resize.png",
+            "./examples/poses/pose4.jpg",
+            "a man doing a silly pose wearing a suite",
+            "Jungle",
+            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, gree",
+        ],
+        [
+            "./examples/schmidhuber_resize.png",
+            "./examples/poses/pose3.jpg",
+            "a man sit on a chair",
+            "Neon",
+            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+        ],
+        [
+            "./examples/kaifu_resize.png",
+            "./examples/poses/pose.jpg",
+            "a man",
+            "Vibrant Color",
+            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+        ],
+    ]
+    return case
+
+def run_for_examples(face_file, pose_file, prompt, style, negative_prompt):
+    return generate_image(
+        face_file,
+        pose_file,
+        prompt,
+        negative_prompt,
+        style,
+        20,  # num_steps
+        0.8,  # identitynet_strength_ratio
+        0.8,  # adapter_strength_ratio
+        0.4,  # pose_strength
+        0.3,  # canny_strength
+        0.5,  # depth_strength
+        ["pose", "canny"],  # controlnet_selection
+        5.0,  # guidance_scale
+        42,  # seed
+        "EulerDiscreteScheduler",  # scheduler
+        False,  # enable_LCM
+        True,  # enable_Face_Region
+    )
+
+def convert_from_cv2_to_image(img: np.ndarray) -> Image:
+    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+def convert_from_image_to_cv2(img: Image) -> np.ndarray:
+    return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+
+def resize_img(
+    input_image,
+    max_side=1280,
+    min_side=1024,
+    size=None,
+    pad_to_max_side=False,
+    mode=PIL.Image.BILINEAR,
+    base_pixel_number=64,
+):
+    w, h = input_image.size
+    if size is not None:
+        w_resize_new, h_resize_new = size
+    else:
+        ratio = min_side / min(h, w)
+        w, h = round(ratio * w), round(ratio * h)
+        ratio = max_side / max(h, w)
+        input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
+        w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
+        h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
+    input_image = input_image.resize([w_resize_new, h_resize_new], mode)
+
+    if pad_to_max_side:
+        res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
+        offset_x = (max_side - w_resize_new) // 2
+        offset_y = (max_side - h_resize_new) // 2
+        res[
+            offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new
+        ] = np.array(input_image)
+        input_image = Image.fromarray(res)
+    return input_image
+
+def apply_style(
+    style_name: str, positive: str, negative: str = ""
+) -> Tuple[str, str]:
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive), n + " " + negative
+
+#@spaces.GPU
+def generate_image(
+    face_image_path,
+    pose_image_path,
+    prompt,
+    negative_prompt,
+    style_name,
+    num_steps,
+    identitynet_strength_ratio,
+    adapter_strength_ratio,
+    pose_strength,
+    canny_strength,
+    depth_strength,
+    controlnet_selection,
+    guidance_scale,
+    seed,
+    scheduler,
+    enable_LCM,
+    enhance_face_region,
+    progress=gr.Progress(track_tqdm=True),
+):
+
+    if enable_LCM:
+        pipe.scheduler = diffusers.LCMScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_lora()
+    else:
+        pipe.disable_lora()
+        scheduler_class_name = scheduler.split("-")[0]
+
+        add_kwargs = {}
+        if len(scheduler.split("-")) > 1:
+            add_kwargs["use_karras_sigmas"] = True
+        if len(scheduler.split("-")) > 2:
+            add_kwargs["algorithm_type"] = "sde-dpmsolver++"
+        scheduler = getattr(diffusers, scheduler_class_name)
+        pipe.scheduler = scheduler.from_config(pipe.scheduler.config, **add_kwargs)
+
+    if face_image_path is None:
+        raise gr.Error(
+            f"Cannot find any input face image! Please upload the face image"
+        )
+
+    if prompt is None:
+        prompt = "a person"
+
+    # apply the style template
+    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
+
+    face_image = load_image(face_image_path)
+    face_image = resize_img(face_image, max_side=1024)
+    face_image_cv2 = convert_from_image_to_cv2(face_image)
+    height, width, _ = face_image_cv2.shape
+
+    # Extract face features
+    face_info = app.get(face_image_cv2)
+
+    if len(face_info) == 0:
+        raise gr.Error(
+            f"Unable to detect a face in the image. Please upload a different photo with a clear face."
+        )
+
+    face_info = sorted(
+        face_info,
+        key=lambda x: (x["bbox"][2] - x["bbox"][0]) * x["bbox"][3] - x["bbox"][1],
+    )[
+        -1
+    ]  # only use the maximum face
+    face_emb = face_info["embedding"]
+    face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["kps"])
+    img_controlnet = face_image
+    if pose_image_path is not None:
+        pose_image = load_image(pose_image_path)
+        pose_image = resize_img(pose_image, max_side=1024)
+        img_controlnet = pose_image
+        pose_image_cv2 = convert_from_image_to_cv2(pose_image)
+
+        face_info = app.get(pose_image_cv2)
+
+        if len(face_info) == 0:
+            raise gr.Error(
+                f"Cannot find any face in the reference image! Please upload another person image"
+            )
+
+        face_info = face_info[-1]
+        face_kps = draw_kps(pose_image, face_info["kps"])
+
+        width, height = face_kps.size
+
+    if enhance_face_region:
+        control_mask = np.zeros([height, width, 3])
+        x1, y1, x2, y2 = face_info["bbox"]
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        control_mask[y1:y2, x1:x2] = 255
+        control_mask = Image.fromarray(control_mask.astype(np.uint8))
+    else:
+        control_mask = None
+
+    if len(controlnet_selection) > 0:
+        controlnet_scales = {
+            "pose": pose_strength,
+            "canny": canny_strength,
+            "depth": depth_strength,
+        }
+        pipe.controlnet = MultiControlNetModel(
+            [controlnet_identitynet]
+            + [controlnet_map[s] for s in controlnet_selection]
+        )
+        control_scales = [float(identitynet_strength_ratio)] + [
+            controlnet_scales[s] for s in controlnet_selection
+        ]
+        control_images = [face_kps] + [
+            controlnet_map_fn[s](img_controlnet).resize((width, height))
+            for s in controlnet_selection
+        ]
+    else:
+        pipe.controlnet = controlnet_identitynet
+        control_scales = float(identitynet_strength_ratio)
+        control_images = face_kps
+
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    print("Start inference...")
+    print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}")
+
+    pipe.set_ip_adapter_scale(adapter_strength_ratio)
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image_embeds=face_emb,
+        image=control_images,
+        control_mask=control_mask,
+        controlnet_conditioning_scale=control_scales,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        height=height,
+        width=width,
+        generator=generator,
+    ).images
+
+    return images[0], gr.update(visible=True)
+    
+def clear_cuda_cache():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        
+# Description
+title = r"""
+<h1 align="center">InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1>
+"""
+
+description = r"""
+<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/InstantID/InstantID' target='_blank'><b>InstantID: Zero-shot Identity-Preserving Generation in Seconds</b></a>.<br>
+
+We are organizing a Spring Festival event with HuggingFace from 2.7 to 2.25, and you can now generate pictures of Spring Festival costumes. Happy Dragon Year 🐲 ! Share the joy with your family.<br>
+
+How to use:<br>
+1. Upload an image with a face. For images with multiple faces, we will only detect the largest face. Ensure the face is not too small and is clearly visible without significant obstructions or blurring.
+2. (Optional) You can upload another image as a reference for the face pose. If you don't, we will use the first detected face image to extract facial landmarks. If you use a cropped face at step 1, it is recommended to upload it to define a new face pose.
+3. (Optional) You can select multiple ControlNet models to control the generation process. The default is to use the IdentityNet only. The ControlNet models include pose skeleton, canny, and depth. You can adjust the strength of each ControlNet model to control the generation process.
+4. Enter a text prompt, as done in normal text-to-image models.
+5. Click the <b>Submit</b> button to begin customization.
+6. Share your customized photo with your friends and enjoy! 😊"""
+
+article = r"""
+---
+📝 **Citation**
+<br>
+If our work is helpful for your research or applications, please cite us via:
+```bibtex
+@article{wang2024instantid,
+  title={InstantID: Zero-shot Identity-Preserving Generation in Seconds},
+  author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony},
+  journal={arXiv preprint arXiv:2401.07519},
+  year={2024}
+}
+```
+📧 **Contact**
+<br>
+If you have any questions, please feel free to open an issue or directly reach us out at <b>haofanwang.ai@gmail.com</b>.
+"""
+
+tips = r"""
+### Usage tips of InstantID
+1. If you're not satisfied with the similarity, try increasing the weight of "IdentityNet Strength" and "Adapter Strength."    
+2. If you feel that the saturation is too high, first decrease the Adapter strength. If it remains too high, then decrease the IdentityNet strength.
+3. If you find that text control is not as expected, decrease Adapter strength.
+4. If you find that realistic style is not good enough, go for our Github repo and use a more realistic base model.
+"""
+
+css = """
+.gradio-container {width: 85% !important}
+"""
+with gr.Blocks(css=css) as demo:
+    # description
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Row(equal_height=True):
+                # upload face image
+                face_file = gr.Image(
+                    label="Upload a photo of your face", type="filepath"
+                )
+                # optional: upload a reference pose image
+                pose_file = gr.Image(
+                    label="Upload a reference pose image (Optional)",
+                    type="filepath",
+                )
+
+            # prompt
+            prompt = gr.Textbox(
+                label="Prompt",
+                info="Give simple prompt is enough to achieve good face fidelity",
+                placeholder="A photo of a person",
+                value="",
+            )
+
+            submit = gr.Button("Submit", variant="primary")
+            enable_LCM = gr.Checkbox(
+                label="Enable Fast Inference with LCM", value=enable_lcm_arg,
+                info="LCM speeds up the inference step, the trade-off is the quality of the generated image. It performs better with portrait face images rather than distant faces",
+            )
+            style = gr.Dropdown(
+                label="Style template",
+                choices=STYLE_NAMES,
+                value=DEFAULT_STYLE_NAME,
+            )
+
+            # strength
+            identitynet_strength_ratio = gr.Slider(
+                label="IdentityNet strength (for fidelity)",
+                minimum=0,
+                maximum=1.5,
+                step=0.05,
+                value=0.80,
+            )
+            adapter_strength_ratio = gr.Slider(
+                label="Image adapter strength (for detail)",
+                minimum=0,
+                maximum=1.5,
+                step=0.05,
+                value=0.80,
+            )
+            with gr.Accordion("Controlnet"):
+                controlnet_selection = gr.CheckboxGroup(
+                    ["pose", "canny", "depth"], label="Controlnet", value=["pose"],
+                    info="Use pose for skeleton inference, canny for edge detection, and depth for depth map estimation. You can try all three to control the generation process"
+                )
+                pose_strength = gr.Slider(
+                    label="Pose strength",
+                    minimum=0,
+                    maximum=1.5,
+                    step=0.05,
+                    value=0.40,
+                )
+                canny_strength = gr.Slider(
+                    label="Canny strength",
+                    minimum=0,
+                    maximum=1.5,
+                    step=0.05,
+                    value=0.40,
+                )
+                depth_strength = gr.Slider(
+                    label="Depth strength",
+                    minimum=0,
+                    maximum=1.5,
+                    step=0.05,
+                    value=0.40,
+                )
+            with gr.Accordion(open=False, label="Advanced Options"):
+                negative_prompt = gr.Textbox(
+                    label="Negative Prompt",
+                    placeholder="low quality",
+                    value="(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+                )
+                num_steps = gr.Slider(
+                    label="Number of sample steps",
+                    minimum=1,
+                    maximum=100,
+                    step=1,
+                    value=5 if enable_lcm_arg else 30,
+                )
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.1,
+                    maximum=20.0,
+                    step=0.1,
+                    value=0.0 if enable_lcm_arg else 5.0,
+                )
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=42,
+                )
+                schedulers = [
+                    "DEISMultistepScheduler",
+                    "HeunDiscreteScheduler",
+                    "EulerDiscreteScheduler",
+                    "DPMSolverMultistepScheduler",
+                    "DPMSolverMultistepScheduler-Karras",
+                    "DPMSolverMultistepScheduler-Karras-SDE",
+                ]
+                scheduler = gr.Dropdown(
+                    label="Schedulers",
+                    choices=schedulers,
+                    value="EulerDiscreteScheduler",
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                enhance_face_region = gr.Checkbox(label="Enhance non-face region", value=True)
+
+        with gr.Column(scale=1):
+            gallery = gr.Image(label="Generated Images")
+            usage_tips = gr.Markdown(
+                label="InstantID Usage Tips", value=tips, visible=False
+            )
+
+        submit.click(
+            fn=remove_tips,
+            outputs=usage_tips,
+        ).then(
+            fn=randomize_seed_fn,
+            inputs=[seed, randomize_seed],
+            outputs=seed,
+            queue=False,
+            api_name=False,
+        ).then(
+            fn=generate_image,
+            inputs=[
+                face_file,
+                pose_file,
+                prompt,
+                negative_prompt,
+                style,
+                num_steps,
+                identitynet_strength_ratio,
+                adapter_strength_ratio,
+                pose_strength,
+                canny_strength,
+                depth_strength,
+                controlnet_selection,
+                guidance_scale,
+                seed,
+                scheduler,
+                enable_LCM,
+                enhance_face_region,
+            ],
+            outputs=[gallery, usage_tips],
+        ).then(
+            fn=clear_cuda_cache            
+        )
+
+        enable_LCM.input(
+            fn=toggle_lcm_ui,
+            inputs=[enable_LCM],
+            outputs=[num_steps, guidance_scale],
+            queue=False,
+        )
+
+    gr.Examples(
+        examples=get_example(),
+        inputs=[face_file, pose_file, prompt, style, negative_prompt],
+        fn=run_for_examples,
+        outputs=[gallery, usage_tips],
+        cache_examples=False,
+    )
+
+    gr.Markdown(article)
+
+
+demo.launch(inbrowser=args.inbrowser, server_port=args.server_port, share=args.share)
diff --git a/checkpoints/ControlNetModel/config.json b/checkpoints/ControlNetModel/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..7360f57d816de85660f607272eeae301a4eb0dcd
--- /dev/null
+++ b/checkpoints/ControlNetModel/config.json
@@ -0,0 +1,57 @@
+{
+  "_class_name": "ControlNetModel",
+  "_diffusers_version": "0.21.2",
+  "_name_or_path": "/mnt/nj-aigc/usr/guiwan/workspace/diffusion_output/face_xl_ipc_v4_2_XiezhenAnimeForeigner/checkpoint-150000/ControlNetModel",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "class_embed_type": null,
+  "conditioning_channels": 3,
+  "conditioning_embedding_out_channels": [
+    16,
+    32,
+    96,
+    256
+  ],
+  "controlnet_conditioning_channel_order": "rgb",
+  "cross_attention_dim": 2048,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "global_pool_conditions": false,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_time_scale_shift": "default",
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "upcast_attention": null,
+  "use_linear_projection": true
+}
diff --git a/checkpoints/ControlNetModel/diffusion_pytorch_model.safetensors b/checkpoints/ControlNetModel/diffusion_pytorch_model.safetensors
new file mode 100644
index 0000000000000000000000000000000000000000..610fcb2cc99d3414c88fc2de4d75cc594dd48b07
--- /dev/null
+++ b/checkpoints/ControlNetModel/diffusion_pytorch_model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8127be9f174101ebdafee9964d856b49b634435cf6daa396d3f593cf0bbbb05
+size 2502139136
diff --git a/checkpoints/ip-adapter.bin b/checkpoints/ip-adapter.bin
new file mode 100644
index 0000000000000000000000000000000000000000..55c98e90c7047768538ad83e8f06f44c017fc329
--- /dev/null
+++ b/checkpoints/ip-adapter.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02b3618e36d803784166660520098089a81388e61a93ef8002aa79a5b1c546e1
+size 1691134141
diff --git a/depth_anything/__pycache__/blocks.cpython-310.pyc b/depth_anything/__pycache__/blocks.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..053b9637d4309af02f028ca698abe9a0951f5ae1
Binary files /dev/null and b/depth_anything/__pycache__/blocks.cpython-310.pyc differ
diff --git a/depth_anything/__pycache__/dpt.cpython-310.pyc b/depth_anything/__pycache__/dpt.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8e28527451f3290d7dc1e936fe49fd1702027b17
Binary files /dev/null and b/depth_anything/__pycache__/dpt.cpython-310.pyc differ
diff --git a/depth_anything/blocks.py b/depth_anything/blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..38dbcfeffc0c38ef51bcb20dfd347e50b2a60616
--- /dev/null
+++ b/depth_anything/blocks.py
@@ -0,0 +1,153 @@
+import torch.nn as nn
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    if len(in_shape) >= 4:
+        out_shape4 = out_shape
+
+    if expand:
+        out_shape1 = out_shape
+        out_shape2 = out_shape*2
+        out_shape3 = out_shape*4
+        if len(in_shape) >= 4:
+            out_shape4 = out_shape*8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    if len(in_shape) >= 4:
+        scratch.layer4_rn = nn.Conv2d(
+            in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+        )
+
+    return scratch
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+        
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+
+        if self.bn==True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn==True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn==True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+        self.size=size
+
+    def forward(self, *xs, size=None):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+
+        output = self.resConfUnit2(output)
+
+        if (size is None) and (self.size is None):
+            modifier = {"scale_factor": 2}
+        elif size is None:
+            modifier = {"size": self.size}
+        else:
+            modifier = {"size": size}
+
+        output = nn.functional.interpolate(
+            output, **modifier, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output
diff --git a/depth_anything/dpt.py b/depth_anything/dpt.py
new file mode 100644
index 0000000000000000000000000000000000000000..56b9545cb2cd787bb9e1c5a85cb6001f038f50b4
--- /dev/null
+++ b/depth_anything/dpt.py
@@ -0,0 +1,187 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
+
+from depth_anything.blocks import FeatureFusionBlock, _make_scratch
+
+
+def _make_fusion_block(features, use_bn, size = None):
+    return FeatureFusionBlock(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+        size=size,
+    )
+
+
+class DPTHead(nn.Module):
+    def __init__(self, nclass, in_channels, features=256, use_bn=False, out_channels=[256, 512, 1024, 1024], use_clstoken=False):
+        super(DPTHead, self).__init__()
+        
+        self.nclass = nclass
+        self.use_clstoken = use_clstoken
+        
+        self.projects = nn.ModuleList([
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channel,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ) for out_channel in out_channels
+        ])
+        
+        self.resize_layers = nn.ModuleList([
+            nn.ConvTranspose2d(
+                in_channels=out_channels[0],
+                out_channels=out_channels[0],
+                kernel_size=4,
+                stride=4,
+                padding=0),
+            nn.ConvTranspose2d(
+                in_channels=out_channels[1],
+                out_channels=out_channels[1],
+                kernel_size=2,
+                stride=2,
+                padding=0),
+            nn.Identity(),
+            nn.Conv2d(
+                in_channels=out_channels[3],
+                out_channels=out_channels[3],
+                kernel_size=3,
+                stride=2,
+                padding=1)
+        ])
+        
+        if use_clstoken:
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(
+                    nn.Sequential(
+                        nn.Linear(2 * in_channels, in_channels),
+                        nn.GELU()))
+        
+        self.scratch = _make_scratch(
+            out_channels,
+            features,
+            groups=1,
+            expand=False,
+        )
+
+        self.scratch.stem_transpose = None
+        
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        head_features_1 = features
+        head_features_2 = 32
+        
+        if nclass > 1:
+            self.scratch.output_conv = nn.Sequential(
+                nn.Conv2d(head_features_1, head_features_1, kernel_size=3, stride=1, padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(head_features_1, nclass, kernel_size=1, stride=1, padding=0),
+            )
+        else:
+            self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1)
+            
+            self.scratch.output_conv2 = nn.Sequential(
+                nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
+                nn.ReLU(True),
+                nn.Identity(),
+            )
+            
+    def forward(self, out_features, patch_h, patch_w):
+        out = []
+        for i, x in enumerate(out_features):
+            if self.use_clstoken:
+                x, cls_token = x[0], x[1]
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+            else:
+                x = x[0]
+            
+            x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
+            
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            
+            out.append(x)
+        
+        layer_1, layer_2, layer_3, layer_4 = out
+        
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+        
+        path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+        
+        out = self.scratch.output_conv1(path_1)
+        out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
+        out = self.scratch.output_conv2(out)
+        
+        return out
+        
+        
+class DPT_DINOv2(nn.Module):
+    def __init__(self, encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024], use_bn=False, use_clstoken=False, localhub=True):
+        super(DPT_DINOv2, self).__init__()
+        
+        assert encoder in ['vits', 'vitb', 'vitl']
+        
+        # in case the Internet connection is not stable, please load the DINOv2 locally
+        if localhub:
+            self.pretrained = torch.hub.load('torchhub/facebookresearch_dinov2_main', 'dinov2_{:}14'.format(encoder), source='local', pretrained=False)
+        else:
+            self.pretrained = torch.hub.load('facebookresearch/dinov2', 'dinov2_{:}14'.format(encoder))
+        
+        dim = self.pretrained.blocks[0].attn.qkv.in_features
+        
+        self.depth_head = DPTHead(1, dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken)
+        
+    def forward(self, x):
+        h, w = x.shape[-2:]
+        
+        features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True)
+        
+        patch_h, patch_w = h // 14, w // 14
+
+        depth = self.depth_head(features, patch_h, patch_w)
+        depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
+        depth = F.relu(depth)
+
+        return depth.squeeze(1)
+
+
+class DepthAnything(DPT_DINOv2, PyTorchModelHubMixin):
+    def __init__(self, config):
+        super().__init__(**config)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--encoder",
+        default="vits",
+        type=str,
+        choices=["vits", "vitb", "vitl"],
+    )
+    args = parser.parse_args()
+    
+    model = DepthAnything.from_pretrained("LiheYoung/depth_anything_{:}14".format(args.encoder))
+    
+    print(model)
+    
\ No newline at end of file
diff --git a/depth_anything/util/__pycache__/transform.cpython-310.pyc b/depth_anything/util/__pycache__/transform.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..89dd0fcf298ca0e6515928a0ddfcb4e8bec00fa5
Binary files /dev/null and b/depth_anything/util/__pycache__/transform.cpython-310.pyc differ
diff --git a/depth_anything/util/transform.py b/depth_anything/util/transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..7beab14a9a1e18a8e46e7666fe5bdec223074155
--- /dev/null
+++ b/depth_anything/util/transform.py
@@ -0,0 +1,248 @@
+import random
+from PIL import Image, ImageOps, ImageFilter
+import torch
+from torchvision import transforms
+import torch.nn.functional as F
+
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            if "semseg_mask" in sample:
+                # sample["semseg_mask"] = cv2.resize(
+                #     sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
+                # )
+                sample["semseg_mask"] = F.interpolate(torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode='nearest').numpy()[0, 0]
+                
+            if "mask" in sample:
+                sample["mask"] = cv2.resize(
+                    sample["mask"].astype(np.float32),
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+                # sample["mask"] = sample["mask"].astype(bool)
+
+        # print(sample['image'].shape, sample['depth'].shape)
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+        
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+            
+        if "semseg_mask" in sample:
+            sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
+            sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
+
+        return sample
diff --git a/examples/.DS_Store b/examples/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..b19f745c17abfbd2e9601143678743fa5230df1e
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diff --git a/examples/kaifu_resize.png b/examples/kaifu_resize.png
new file mode 100644
index 0000000000000000000000000000000000000000..1d15a0479a2bb438ff82591fd5a81422c096f4da
--- /dev/null
+++ b/examples/kaifu_resize.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b7302f0f7d0ff61be67bf13d172ad2393b6cb2bc985f048089f4e901145324d7
+size 1059403
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diff --git a/examples/poses/pose2.jpg b/examples/poses/pose2.jpg
new file mode 100644
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diff --git a/examples/poses/pose3.jpg b/examples/poses/pose3.jpg
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diff --git a/examples/sam_resize.png b/examples/sam_resize.png
new file mode 100644
index 0000000000000000000000000000000000000000..5ef6abe23f9576dd862930eaa2ef3fd0e153ef21
--- /dev/null
+++ b/examples/sam_resize.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1390d8a9a1be7b8f5388c0bc8483b2d5cca6c0f0adeb6eecd970a4413b1f1deb
+size 2362631
diff --git a/examples/schmidhuber_resize.png b/examples/schmidhuber_resize.png
new file mode 100644
index 0000000000000000000000000000000000000000..07085ef18b0d942823f7e036fb8509999c39e664
--- /dev/null
+++ b/examples/schmidhuber_resize.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51beaa72d1eb9f56118118fae8775bda818bcb56b03220f3cd39daa425f57a9a
+size 3225696
diff --git a/examples/yann-lecun_resize.jpg b/examples/yann-lecun_resize.jpg
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index 0000000000000000000000000000000000000000..4a15ae380e5e9224637c70f79cb72f37a91166b6
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diff --git a/ip_adapter/__pycache__/attention_processor.cpython-310.pyc b/ip_adapter/__pycache__/attention_processor.cpython-310.pyc
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diff --git a/ip_adapter/__pycache__/utils.cpython-310.pyc b/ip_adapter/__pycache__/utils.cpython-310.pyc
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diff --git a/ip_adapter/attention_processor.py b/ip_adapter/attention_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..745143d85d1ee312d8968664d4f3b6ff3d41aecd
--- /dev/null
+++ b/ip_adapter/attention_processor.py
@@ -0,0 +1,446 @@
+# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+try:
+    import xformers
+    import xformers.ops
+    xformers_available = True
+except Exception as e:
+    xformers_available = False
+
+class RegionControler(object):
+    def __init__(self) -> None:
+        self.prompt_image_conditioning = []
+region_control = RegionControler()
+
+class AttnProcessor(nn.Module):
+    r"""
+    Default processor for performing attention-related computations.
+    """
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+
+    def forward(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+    
+    
+class IPAttnProcessor(nn.Module):
+    r"""
+    Attention processor for IP-Adapater.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def forward(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = encoder_hidden_states[:, :end_pos, :], encoder_hidden_states[:, end_pos:, :]
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+
+        if xformers_available:
+            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+        else:
+            attention_probs = attn.get_attention_scores(query, key, attention_mask)
+            hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+        
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+        
+        ip_key = attn.head_to_batch_dim(ip_key)
+        ip_value = attn.head_to_batch_dim(ip_value)
+        
+        if xformers_available:
+            ip_hidden_states = self._memory_efficient_attention_xformers(query, ip_key, ip_value, None)
+        else:
+            ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
+            ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
+        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
+
+        # region control
+        if len(region_control.prompt_image_conditioning) == 1:
+            region_mask = region_control.prompt_image_conditioning[0].get('region_mask', None)
+            if region_mask is not None:
+                h, w = region_mask.shape[:2]
+                ratio = (h * w / query.shape[1]) ** 0.5
+                mask = F.interpolate(region_mask[None, None], scale_factor=1/ratio, mode='nearest').reshape([1, -1, 1])
+            else:
+                mask = torch.ones_like(ip_hidden_states)
+            ip_hidden_states = ip_hidden_states * mask     
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+
+    def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
+        # TODO attention_mask
+        query = query.contiguous()
+        key = key.contiguous()
+        value = value.contiguous()
+        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+        # hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
+        return hidden_states
+
+
+class AttnProcessor2_0(torch.nn.Module):
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+    def __init__(
+        self,
+        hidden_size=None,
+        cross_attention_dim=None,
+    ):
+        super().__init__()
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+    def forward(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
+
+class IPAttnProcessor2_0(torch.nn.Module):
+    r"""
+    Attention processor for IP-Adapater for PyTorch 2.0.
+    Args:
+        hidden_size (`int`):
+            The hidden size of the attention layer.
+        cross_attention_dim (`int`):
+            The number of channels in the `encoder_hidden_states`.
+        scale (`float`, defaults to 1.0):
+            the weight scale of image prompt.
+        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
+            The context length of the image features.
+    """
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.hidden_size = hidden_size
+        self.cross_attention_dim = cross_attention_dim
+        self.scale = scale
+        self.num_tokens = num_tokens
+
+        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
+
+    def forward(
+        self,
+        attn,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        temb=None,
+    ):
+        residual = hidden_states
+
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+
+        input_ndim = hidden_states.ndim
+
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        batch_size, sequence_length, _ = (
+            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        )
+
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
+            # scaled_dot_product_attention expects attention_mask shape to be
+            # (batch, heads, source_length, target_length)
+            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
+
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = attn.to_q(hidden_states)
+
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        else:
+            # get encoder_hidden_states, ip_hidden_states
+            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
+            encoder_hidden_states, ip_hidden_states = (
+                encoder_hidden_states[:, :end_pos, :],
+                encoder_hidden_states[:, end_pos:, :],
+            )
+            if attn.norm_cross:
+                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
+
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        # for ip-adapter
+        ip_key = self.to_k_ip(ip_hidden_states)
+        ip_value = self.to_v_ip(ip_hidden_states)
+
+        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # the output of sdp = (batch, num_heads, seq_len, head_dim)
+        # TODO: add support for attn.scale when we move to Torch 2.1
+        ip_hidden_states = F.scaled_dot_product_attention(
+            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
+        )
+        with torch.no_grad():
+            self.attn_map = query @ ip_key.transpose(-2, -1).softmax(dim=-1)
+            #print(self.attn_map.shape)
+
+        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        ip_hidden_states = ip_hidden_states.to(query.dtype)
+
+        # region control
+        if len(region_control.prompt_image_conditioning) == 1:
+            region_mask = region_control.prompt_image_conditioning[0].get('region_mask', None)
+            if region_mask is not None:
+                h, w = region_mask.shape[:2]
+                ratio = (h * w / query.shape[1]) ** 0.5
+                mask = F.interpolate(region_mask[None, None], scale_factor=1/ratio, mode='nearest').reshape([1, -1, 1])
+            else:
+                mask = torch.ones_like(ip_hidden_states)
+            ip_hidden_states = ip_hidden_states * mask
+
+        hidden_states = hidden_states + self.scale * ip_hidden_states
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / attn.rescale_output_factor
+
+        return hidden_states
\ No newline at end of file
diff --git a/ip_adapter/resampler.py b/ip_adapter/resampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..4521c8c3e6f17caf4547c3dd84118da760e5179f
--- /dev/null
+++ b/ip_adapter/resampler.py
@@ -0,0 +1,121 @@
+# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
+import math
+
+import torch
+import torch.nn as nn
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+    
+    
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+        
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+        
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+        
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class Resampler(nn.Module):
+    def __init__(
+        self,
+        dim=1024,
+        depth=8,
+        dim_head=64,
+        heads=16,
+        num_queries=8,
+        embedding_dim=768,
+        output_dim=1024,
+        ff_mult=4,
+    ):
+        super().__init__()
+        
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+        
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+        
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                        FeedForward(dim=dim, mult=ff_mult),
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        
+        latents = self.latents.repeat(x.size(0), 1, 1)
+        
+        x = self.proj_in(x)
+        
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+            
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
\ No newline at end of file
diff --git a/ip_adapter/utils.py b/ip_adapter/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a105f3701c15e8d3bbf838d79bacc51e91d0696
--- /dev/null
+++ b/ip_adapter/utils.py
@@ -0,0 +1,5 @@
+import torch.nn.functional as F
+
+
+def is_torch2_available():
+    return hasattr(F, "scaled_dot_product_attention")
diff --git a/models/antelopev2/1k3d68.onnx b/models/antelopev2/1k3d68.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..221aa2f02a6faccddb2723529e1f93c7db2edbdc
--- /dev/null
+++ b/models/antelopev2/1k3d68.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df5c06b8a0c12e422b2ed8947b8869faa4105387f199c477af038aa01f9a45cc
+size 143607619
diff --git a/models/antelopev2/2d106det.onnx b/models/antelopev2/2d106det.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..cdb163d88b5f51396855ebc795e0114322c98b6b
--- /dev/null
+++ b/models/antelopev2/2d106det.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f001b856447c413801ef5c42091ed0cd516fcd21f2d6b79635b1e733a7109dbf
+size 5030888
diff --git a/models/antelopev2/genderage.onnx b/models/antelopev2/genderage.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..fcf638481cea978e99ddabd914ccd3b70c8401cb
--- /dev/null
+++ b/models/antelopev2/genderage.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fde69b1c810857b88c64a335084f1c3fe8f01246c9a191b48c7bb756d6652fb
+size 1322532
diff --git a/models/antelopev2/glintr100.onnx b/models/antelopev2/glintr100.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..9d221846df998a9c85239fd74a9fe5685193775f
--- /dev/null
+++ b/models/antelopev2/glintr100.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ab1d6435d639628a6f3e5008dd4f929edf4c4124b1a7169e1048f9fef534cdf
+size 260665334
diff --git a/models/antelopev2/scrfd_10g_bnkps.onnx b/models/antelopev2/scrfd_10g_bnkps.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..aa586e034379fa5ea5babc8aa73d47afcd0fa6c2
--- /dev/null
+++ b/models/antelopev2/scrfd_10g_bnkps.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5838f7fe053675b1c7a08b633df49e7af5495cee0493c7dcf6697200b85b5b91
+size 16923827
diff --git a/pipeline_stable_diffusion_xl_instantid_full.py b/pipeline_stable_diffusion_xl_instantid_full.py
new file mode 100644
index 0000000000000000000000000000000000000000..4145ef3862c0112cce4accb1b6e8c4dbf73d0fc2
--- /dev/null
+++ b/pipeline_stable_diffusion_xl_instantid_full.py
@@ -0,0 +1,1204 @@
+# Copyright 2024 The InstantX Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import cv2
+import math
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+
+from diffusers.image_processor import PipelineImageInput
+
+from diffusers.models import ControlNetModel
+
+from diffusers.utils import (
+    deprecate,
+    logging,
+    replace_example_docstring,
+)
+from diffusers.utils.torch_utils import is_compiled_module, is_torch_version
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
+
+from diffusers import StableDiffusionXLControlNetPipeline
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.utils.import_utils import is_xformers_available
+
+from ip_adapter.resampler import Resampler
+from ip_adapter.utils import is_torch2_available
+
+from ip_adapter.attention_processor import IPAttnProcessor, AttnProcessor
+from ip_adapter.attention_processor import region_control
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> # !pip install opencv-python transformers accelerate insightface
+        >>> import diffusers
+        >>> from diffusers.utils import load_image
+        >>> from diffusers.models import ControlNetModel
+
+        >>> import cv2
+        >>> import torch
+        >>> import numpy as np
+        >>> from PIL import Image
+        
+        >>> from insightface.app import FaceAnalysis
+        >>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
+
+        >>> # download 'antelopev2' under ./models
+        >>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
+        >>> app.prepare(ctx_id=0, det_size=(640, 640))
+        
+        >>> # download models under ./checkpoints
+        >>> face_adapter = f'./checkpoints/ip-adapter.bin'
+        >>> controlnet_path = f'./checkpoints/ControlNetModel'
+        
+        >>> # load IdentityNet
+        >>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
+        
+        >>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
+        ... )
+        >>> pipe.cuda()
+        
+        >>> # load adapter
+        >>> pipe.load_ip_adapter_instantid(face_adapter)
+
+        >>> prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality"
+        >>> negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured"
+
+        >>> # load an image
+        >>> image = load_image("your-example.jpg")
+        
+        >>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]
+        >>> face_emb = face_info['embedding']
+        >>> face_kps = draw_kps(face_image, face_info['kps'])
+        
+        >>> pipe.set_ip_adapter_scale(0.8)
+
+        >>> # generate image
+        >>> image = pipe(
+        ...     prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8
+        ... ).images[0]
+        ```
+"""
+
+from transformers import CLIPTokenizer
+from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipeline
+class LongPromptWeight(object):
+    
+    """
+    Copied from https://github.com/huggingface/diffusers/blob/main/examples/community/lpw_stable_diffusion_xl.py
+    """
+    
+    def __init__(self) -> None:
+        pass
+
+    def parse_prompt_attention(self, text):
+        """
+        Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
+        Accepted tokens are:
+        (abc) - increases attention to abc by a multiplier of 1.1
+        (abc:3.12) - increases attention to abc by a multiplier of 3.12
+        [abc] - decreases attention to abc by a multiplier of 1.1
+        \( - literal character '('
+        \[ - literal character '['
+        \) - literal character ')'
+        \] - literal character ']'
+        \\ - literal character '\'
+        anything else - just text
+
+        >>> parse_prompt_attention('normal text')
+        [['normal text', 1.0]]
+        >>> parse_prompt_attention('an (important) word')
+        [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
+        >>> parse_prompt_attention('(unbalanced')
+        [['unbalanced', 1.1]]
+        >>> parse_prompt_attention('\(literal\]')
+        [['(literal]', 1.0]]
+        >>> parse_prompt_attention('(unnecessary)(parens)')
+        [['unnecessaryparens', 1.1]]
+        >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
+        [['a ', 1.0],
+        ['house', 1.5730000000000004],
+        [' ', 1.1],
+        ['on', 1.0],
+        [' a ', 1.1],
+        ['hill', 0.55],
+        [', sun, ', 1.1],
+        ['sky', 1.4641000000000006],
+        ['.', 1.1]]
+        """
+        import re
+
+        re_attention = re.compile(
+            r"""
+                \\\(|\\\)|\\\[|\\]|\\\\|\\|\(|\[|:([+-]?[.\d]+)\)|
+                \)|]|[^\\()\[\]:]+|:
+            """,
+            re.X,
+        )
+
+        re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
+
+        res = []
+        round_brackets = []
+        square_brackets = []
+
+        round_bracket_multiplier = 1.1
+        square_bracket_multiplier = 1 / 1.1
+
+        def multiply_range(start_position, multiplier):
+            for p in range(start_position, len(res)):
+                res[p][1] *= multiplier
+
+        for m in re_attention.finditer(text):
+            text = m.group(0)
+            weight = m.group(1)
+
+            if text.startswith("\\"):
+                res.append([text[1:], 1.0])
+            elif text == "(":
+                round_brackets.append(len(res))
+            elif text == "[":
+                square_brackets.append(len(res))
+            elif weight is not None and len(round_brackets) > 0:
+                multiply_range(round_brackets.pop(), float(weight))
+            elif text == ")" and len(round_brackets) > 0:
+                multiply_range(round_brackets.pop(), round_bracket_multiplier)
+            elif text == "]" and len(square_brackets) > 0:
+                multiply_range(square_brackets.pop(), square_bracket_multiplier)
+            else:
+                parts = re.split(re_break, text)
+                for i, part in enumerate(parts):
+                    if i > 0:
+                        res.append(["BREAK", -1])
+                    res.append([part, 1.0])
+
+        for pos in round_brackets:
+            multiply_range(pos, round_bracket_multiplier)
+
+        for pos in square_brackets:
+            multiply_range(pos, square_bracket_multiplier)
+
+        if len(res) == 0:
+            res = [["", 1.0]]
+
+        # merge runs of identical weights
+        i = 0
+        while i + 1 < len(res):
+            if res[i][1] == res[i + 1][1]:
+                res[i][0] += res[i + 1][0]
+                res.pop(i + 1)
+            else:
+                i += 1
+
+        return res
+
+    def get_prompts_tokens_with_weights(self, clip_tokenizer: CLIPTokenizer, prompt: str):
+        """
+        Get prompt token ids and weights, this function works for both prompt and negative prompt
+
+        Args:
+            pipe (CLIPTokenizer)
+                A CLIPTokenizer
+            prompt (str)
+                A prompt string with weights
+
+        Returns:
+            text_tokens (list)
+                A list contains token ids
+            text_weight (list)
+                A list contains the correspodent weight of token ids
+
+        Example:
+            import torch
+            from transformers import CLIPTokenizer
+
+            clip_tokenizer = CLIPTokenizer.from_pretrained(
+                "stablediffusionapi/deliberate-v2"
+                , subfolder = "tokenizer"
+                , dtype = torch.float16
+            )
+
+            token_id_list, token_weight_list = get_prompts_tokens_with_weights(
+                clip_tokenizer = clip_tokenizer
+                ,prompt = "a (red:1.5) cat"*70
+            )
+        """
+        texts_and_weights = self.parse_prompt_attention(prompt)
+        text_tokens, text_weights = [], []
+        for word, weight in texts_and_weights:
+            # tokenize and discard the starting and the ending token
+            token = clip_tokenizer(word, truncation=False).input_ids[1:-1]  # so that tokenize whatever length prompt
+            # the returned token is a 1d list: [320, 1125, 539, 320]
+
+            # merge the new tokens to the all tokens holder: text_tokens
+            text_tokens = [*text_tokens, *token]
+
+            # each token chunk will come with one weight, like ['red cat', 2.0]
+            # need to expand weight for each token.
+            chunk_weights = [weight] * len(token)
+
+            # append the weight back to the weight holder: text_weights
+            text_weights = [*text_weights, *chunk_weights]
+        return text_tokens, text_weights
+
+    def group_tokens_and_weights(self, token_ids: list, weights: list, pad_last_block=False):
+        """
+        Produce tokens and weights in groups and pad the missing tokens
+
+        Args:
+            token_ids (list)
+                The token ids from tokenizer
+            weights (list)
+                The weights list from function get_prompts_tokens_with_weights
+            pad_last_block (bool)
+                Control if fill the last token list to 75 tokens with eos
+        Returns:
+            new_token_ids (2d list)
+            new_weights (2d list)
+
+        Example:
+            token_groups,weight_groups = group_tokens_and_weights(
+                token_ids = token_id_list
+                , weights = token_weight_list
+            )
+        """
+        bos, eos = 49406, 49407
+
+        # this will be a 2d list
+        new_token_ids = []
+        new_weights = []
+        while len(token_ids) >= 75:
+            # get the first 75 tokens
+            head_75_tokens = [token_ids.pop(0) for _ in range(75)]
+            head_75_weights = [weights.pop(0) for _ in range(75)]
+
+            # extract token ids and weights
+            temp_77_token_ids = [bos] + head_75_tokens + [eos]
+            temp_77_weights = [1.0] + head_75_weights + [1.0]
+
+            # add 77 token and weights chunk to the holder list
+            new_token_ids.append(temp_77_token_ids)
+            new_weights.append(temp_77_weights)
+
+        # padding the left
+        if len(token_ids) >= 0:
+            padding_len = 75 - len(token_ids) if pad_last_block else 0
+
+            temp_77_token_ids = [bos] + token_ids + [eos] * padding_len + [eos]
+            new_token_ids.append(temp_77_token_ids)
+
+            temp_77_weights = [1.0] + weights + [1.0] * padding_len + [1.0]
+            new_weights.append(temp_77_weights)
+
+        return new_token_ids, new_weights
+
+    def get_weighted_text_embeddings_sdxl(
+        self,
+        pipe: StableDiffusionXLPipeline,
+        prompt: str = "",
+        prompt_2: str = None,
+        neg_prompt: str = "",
+        neg_prompt_2: str = None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        negative_pooled_prompt_embeds=None,
+        extra_emb=None,
+        extra_emb_alpha=0.6,
+    ):
+        """
+        This function can process long prompt with weights, no length limitation
+        for Stable Diffusion XL
+
+        Args:
+            pipe (StableDiffusionPipeline)
+            prompt (str)
+            prompt_2 (str)
+            neg_prompt (str)
+            neg_prompt_2 (str)
+        Returns:
+            prompt_embeds (torch.Tensor)
+            neg_prompt_embeds (torch.Tensor)
+        """
+        # 
+        if prompt_embeds is not None and \
+            negative_prompt_embeds is not None and \
+            pooled_prompt_embeds is not None and \
+            negative_pooled_prompt_embeds is not None:
+            return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+        if prompt_2:
+            prompt = f"{prompt} {prompt_2}"
+
+        if neg_prompt_2:
+            neg_prompt = f"{neg_prompt} {neg_prompt_2}"
+
+        eos = pipe.tokenizer.eos_token_id
+
+        # tokenizer 1
+        prompt_tokens, prompt_weights = self.get_prompts_tokens_with_weights(pipe.tokenizer, prompt)
+        neg_prompt_tokens, neg_prompt_weights = self.get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt)
+
+        # tokenizer 2
+        # prompt_tokens_2, prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer_2, prompt)
+        # neg_prompt_tokens_2, neg_prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer_2, neg_prompt)
+        # tokenizer 2 遇到 !! !!!! 等多感叹号和tokenizer 1的效果不一致
+        prompt_tokens_2, prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer, prompt)
+        neg_prompt_tokens_2, neg_prompt_weights_2 = self.get_prompts_tokens_with_weights(pipe.tokenizer, neg_prompt)
+
+        # padding the shorter one for prompt set 1
+        prompt_token_len = len(prompt_tokens)
+        neg_prompt_token_len = len(neg_prompt_tokens)
+
+        if prompt_token_len > neg_prompt_token_len:
+            # padding the neg_prompt with eos token
+            neg_prompt_tokens = neg_prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
+            neg_prompt_weights = neg_prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
+        else:
+            # padding the prompt
+            prompt_tokens = prompt_tokens + [eos] * abs(prompt_token_len - neg_prompt_token_len)
+            prompt_weights = prompt_weights + [1.0] * abs(prompt_token_len - neg_prompt_token_len)
+
+        # padding the shorter one for token set 2
+        prompt_token_len_2 = len(prompt_tokens_2)
+        neg_prompt_token_len_2 = len(neg_prompt_tokens_2)
+
+        if prompt_token_len_2 > neg_prompt_token_len_2:
+            # padding the neg_prompt with eos token
+            neg_prompt_tokens_2 = neg_prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+            neg_prompt_weights_2 = neg_prompt_weights_2 + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+        else:
+            # padding the prompt
+            prompt_tokens_2 = prompt_tokens_2 + [eos] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+            prompt_weights_2 = prompt_weights + [1.0] * abs(prompt_token_len_2 - neg_prompt_token_len_2)
+
+        embeds = []
+        neg_embeds = []
+
+        prompt_token_groups, prompt_weight_groups = self.group_tokens_and_weights(prompt_tokens.copy(), prompt_weights.copy())
+
+        neg_prompt_token_groups, neg_prompt_weight_groups = self.group_tokens_and_weights(
+            neg_prompt_tokens.copy(), neg_prompt_weights.copy()
+        )
+
+        prompt_token_groups_2, prompt_weight_groups_2 = self.group_tokens_and_weights(
+            prompt_tokens_2.copy(), prompt_weights_2.copy()
+        )
+
+        neg_prompt_token_groups_2, neg_prompt_weight_groups_2 = self.group_tokens_and_weights(
+            neg_prompt_tokens_2.copy(), neg_prompt_weights_2.copy()
+        )
+
+        # get prompt embeddings one by one is not working.
+        for i in range(len(prompt_token_groups)):
+            # get positive prompt embeddings with weights
+            token_tensor = torch.tensor([prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
+            weight_tensor = torch.tensor(prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
+
+            token_tensor_2 = torch.tensor([prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
+
+            # use first text encoder
+            prompt_embeds_1 = pipe.text_encoder(token_tensor.to(pipe.device), output_hidden_states=True)
+            prompt_embeds_1_hidden_states = prompt_embeds_1.hidden_states[-2]
+
+            # use second text encoder
+            prompt_embeds_2 = pipe.text_encoder_2(token_tensor_2.to(pipe.device), output_hidden_states=True)
+            prompt_embeds_2_hidden_states = prompt_embeds_2.hidden_states[-2]
+            pooled_prompt_embeds = prompt_embeds_2[0]
+
+            prompt_embeds_list = [prompt_embeds_1_hidden_states, prompt_embeds_2_hidden_states]
+            token_embedding = torch.concat(prompt_embeds_list, dim=-1).squeeze(0)
+
+            for j in range(len(weight_tensor)):
+                if weight_tensor[j] != 1.0:
+                    token_embedding[j] = (
+                        token_embedding[-1] + (token_embedding[j] - token_embedding[-1]) * weight_tensor[j]
+                    )
+
+            token_embedding = token_embedding.unsqueeze(0)
+            embeds.append(token_embedding)
+
+            # get negative prompt embeddings with weights
+            neg_token_tensor = torch.tensor([neg_prompt_token_groups[i]], dtype=torch.long, device=pipe.device)
+            neg_token_tensor_2 = torch.tensor([neg_prompt_token_groups_2[i]], dtype=torch.long, device=pipe.device)
+            neg_weight_tensor = torch.tensor(neg_prompt_weight_groups[i], dtype=torch.float16, device=pipe.device)
+
+            # use first text encoder
+            neg_prompt_embeds_1 = pipe.text_encoder(neg_token_tensor.to(pipe.device), output_hidden_states=True)
+            neg_prompt_embeds_1_hidden_states = neg_prompt_embeds_1.hidden_states[-2]
+
+            # use second text encoder
+            neg_prompt_embeds_2 = pipe.text_encoder_2(neg_token_tensor_2.to(pipe.device), output_hidden_states=True)
+            neg_prompt_embeds_2_hidden_states = neg_prompt_embeds_2.hidden_states[-2]
+            negative_pooled_prompt_embeds = neg_prompt_embeds_2[0]
+
+            neg_prompt_embeds_list = [neg_prompt_embeds_1_hidden_states, neg_prompt_embeds_2_hidden_states]
+            neg_token_embedding = torch.concat(neg_prompt_embeds_list, dim=-1).squeeze(0)
+
+            for z in range(len(neg_weight_tensor)):
+                if neg_weight_tensor[z] != 1.0:
+                    neg_token_embedding[z] = (
+                        neg_token_embedding[-1] + (neg_token_embedding[z] - neg_token_embedding[-1]) * neg_weight_tensor[z]
+                    )
+
+            neg_token_embedding = neg_token_embedding.unsqueeze(0)
+            neg_embeds.append(neg_token_embedding)
+
+        prompt_embeds = torch.cat(embeds, dim=1)
+        negative_prompt_embeds = torch.cat(neg_embeds, dim=1)
+
+        if extra_emb is not None:
+            extra_emb = extra_emb.to(prompt_embeds.device, dtype=prompt_embeds.dtype) * extra_emb_alpha
+            prompt_embeds = torch.cat([prompt_embeds, extra_emb], 1)
+            negative_prompt_embeds = torch.cat([negative_prompt_embeds, torch.zeros_like(extra_emb)], 1)
+            print(f'fix prompt_embeds, extra_emb_alpha={extra_emb_alpha}')
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+    def get_prompt_embeds(self, *args, **kwargs):
+        prompt_embeds, negative_prompt_embeds, _, _ = self.get_weighted_text_embeddings_sdxl(*args, **kwargs)
+        prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+        return prompt_embeds
+
+def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
+    
+    stickwidth = 4
+    limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
+    kps = np.array(kps)
+
+    w, h = image_pil.size
+    out_img = np.zeros([h, w, 3])
+
+    for i in range(len(limbSeq)):
+        index = limbSeq[i]
+        color = color_list[index[0]]
+
+        x = kps[index][:, 0]
+        y = kps[index][:, 1]
+        length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
+        polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
+    out_img = (out_img * 0.6).astype(np.uint8)
+
+    for idx_kp, kp in enumerate(kps):
+        color = color_list[idx_kp]
+        x, y = kp
+        out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
+
+    out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
+    return out_img_pil
+    
+class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
+    
+    def cuda(self, dtype=torch.float16, use_xformers=False):
+        self.to('cuda', dtype)
+        
+        if hasattr(self, 'image_proj_model'):
+            self.image_proj_model.to(self.unet.device).to(self.unet.dtype)
+        
+        if use_xformers:
+            if is_xformers_available():
+                import xformers
+                from packaging import version
+
+                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."
+                    )
+                self.enable_xformers_memory_efficient_attention()
+            else:
+                raise ValueError("xformers is not available. Make sure it is installed correctly")
+    
+    def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):     
+        self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)
+        self.set_ip_adapter(model_ckpt, num_tokens, scale)
+        
+    def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):
+        
+        image_proj_model = Resampler(
+            dim=1280,
+            depth=4,
+            dim_head=64,
+            heads=20,
+            num_queries=num_tokens,
+            embedding_dim=image_emb_dim,
+            output_dim=self.unet.config.cross_attention_dim,
+            ff_mult=4,
+        )
+
+        image_proj_model.eval()
+        
+        self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype)
+        state_dict = torch.load(model_ckpt, map_location="cpu")
+        if 'image_proj' in state_dict:
+            state_dict = state_dict["image_proj"]
+        self.image_proj_model.load_state_dict(state_dict)
+        
+        self.image_proj_model_in_features = image_emb_dim
+    
+    def set_ip_adapter(self, model_ckpt, num_tokens, scale):
+        
+        unet = self.unet
+        attn_procs = {}
+        for name in unet.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = unet.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = unet.config.block_out_channels[block_id]
+            if cross_attention_dim is None:
+                attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype)
+            else:
+                attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size, 
+                                                   cross_attention_dim=cross_attention_dim, 
+                                                   scale=scale,
+                                                   num_tokens=num_tokens).to(unet.device, dtype=unet.dtype)
+        unet.set_attn_processor(attn_procs)
+        
+        state_dict = torch.load(model_ckpt, map_location="cpu")
+        ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
+        if 'ip_adapter' in state_dict:
+            state_dict = state_dict['ip_adapter']
+        ip_layers.load_state_dict(state_dict)
+    
+    def set_ip_adapter_scale(self, scale):
+        unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
+        for attn_processor in unet.attn_processors.values():
+            if isinstance(attn_processor, IPAttnProcessor):
+                attn_processor.scale = scale
+
+    def _encode_prompt_image_emb(self, prompt_image_emb, device, num_images_per_prompt, dtype, do_classifier_free_guidance):
+        
+        if isinstance(prompt_image_emb, torch.Tensor):
+            prompt_image_emb = prompt_image_emb.clone().detach()
+        else:
+            prompt_image_emb = torch.tensor(prompt_image_emb)
+            
+        prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)
+        prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])
+        
+        if do_classifier_free_guidance:
+            prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)
+        else:
+            prompt_image_emb = torch.cat([prompt_image_emb], dim=0)
+        
+        prompt_image_emb = self.image_proj_model(prompt_image_emb)
+
+        bs_embed, seq_len, _ = prompt_image_emb.shape
+        prompt_image_emb = prompt_image_emb.repeat(1, num_images_per_prompt, 1)
+        prompt_image_emb = prompt_image_emb.view(bs_embed * num_images_per_prompt, seq_len, -1)
+        
+        return prompt_image_emb
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        image_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
+        control_guidance_start: Union[float, List[float]] = 0.0,
+        control_guidance_end: Union[float, List[float]] = 1.0,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+
+        # IP adapter
+        ip_adapter_scale=None,
+
+        # Enhance Face Region
+        control_mask = None,
+
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders.
+            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
+                The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
+                specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
+                accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
+                and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
+                `init`, images must be passed as a list such that each element of the list can be correctly batched for
+                input to a single ControlNet.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated image. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated image. Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 5.0):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
+                and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, pooled text embeddings are generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
+                weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
+                argument.
+            image_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated image embeddings.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
+                the corresponding scale as a list.
+            guess_mode (`bool`, *optional*, defaults to `False`):
+                The ControlNet encoder tries to recognize the content of the input image even if you remove all
+                prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
+            control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
+                The percentage of total steps at which the ControlNet starts applying.
+            control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The percentage of total steps at which the ControlNet stops applying.
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeine class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned containing the output images.
+        """
+
+        lpw = LongPromptWeight()
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+
+        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+        # align format for control guidance
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+        elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
+            mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
+        
+        # 0. set ip_adapter_scale
+        if ip_adapter_scale is not None:
+            self.set_ip_adapter_scale(ip_adapter_scale)
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+            controlnet_conditioning_scale,
+            control_guidance_start,
+            control_guidance_end,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
+            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
+
+        global_pool_conditions = (
+            controlnet.config.global_pool_conditions
+            if isinstance(controlnet, ControlNetModel)
+            else controlnet.nets[0].config.global_pool_conditions
+        )
+        guess_mode = guess_mode or global_pool_conditions
+
+        # 3.1 Encode input prompt
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = lpw.get_weighted_text_embeddings_sdxl(
+            pipe=self, 
+            prompt=prompt, 
+            neg_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        )
+        
+        # 3.2 Encode image prompt
+        prompt_image_emb = self._encode_prompt_image_emb(image_embeds, 
+                                                         device,
+                                                         num_images_per_prompt,
+                                                         self.unet.dtype,
+                                                         self.do_classifier_free_guidance)
+        
+        # 4. Prepare image
+        if isinstance(controlnet, ControlNetModel):
+            image = self.prepare_image(
+                image=image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=controlnet.dtype,
+                do_classifier_free_guidance=self.do_classifier_free_guidance,
+                guess_mode=guess_mode,
+            )
+            height, width = image.shape[-2:]
+        elif isinstance(controlnet, MultiControlNetModel):
+            images = []
+
+            for image_ in image:
+                image_ = self.prepare_image(
+                    image=image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=controlnet.dtype,
+                    do_classifier_free_guidance=self.do_classifier_free_guidance,
+                    guess_mode=guess_mode,
+                )
+
+                images.append(image_)
+
+            image = images
+            height, width = image[0].shape[-2:]
+        else:
+            assert False
+
+        # 4.1 Region control
+        if control_mask is not None:
+            mask_weight_image = control_mask
+            mask_weight_image = np.array(mask_weight_image)
+            mask_weight_image_tensor = torch.from_numpy(mask_weight_image).to(device=device, dtype=prompt_embeds.dtype)
+            mask_weight_image_tensor = mask_weight_image_tensor[:, :, 0] / 255.
+            mask_weight_image_tensor = mask_weight_image_tensor[None, None]
+            h, w = mask_weight_image_tensor.shape[-2:]
+            control_mask_wight_image_list = []
+            for scale in [8, 8, 8, 16, 16, 16, 32, 32, 32]:
+                scale_mask_weight_image_tensor = F.interpolate(
+                    mask_weight_image_tensor,(h // scale, w // scale), mode='bilinear')
+                control_mask_wight_image_list.append(scale_mask_weight_image_tensor)
+            region_mask = torch.from_numpy(np.array(control_mask)[:, :, 0]).to(self.unet.device, dtype=self.unet.dtype) / 255.
+            region_control.prompt_image_conditioning = [dict(region_mask=region_mask)]
+        else:
+            control_mask_wight_image_list = None
+            region_control.prompt_image_conditioning = [dict(region_mask=None)]
+
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+        self._num_timesteps = len(timesteps)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6.5 Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7.1 Create tensor stating which controlnets to keep
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end)
+            ]
+            controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
+
+        # 7.2 Prepare added time ids & embeddings
+        if isinstance(image, list):
+            original_size = original_size or image[0].shape[-2:]
+        else:
+            original_size = original_size or image.shape[-2:]
+        target_size = target_size or (height, width)
+
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        add_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+        encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        is_unet_compiled = is_compiled_module(self.unet)
+        is_controlnet_compiled = is_compiled_module(self.controlnet)
+        is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
+                
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # Relevant thread:
+                # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
+                if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
+                    torch._inductor.cudagraph_mark_step_begin()
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+
+                # controlnet(s) inference
+                if guess_mode and self.do_classifier_free_guidance:
+                    # Infer ControlNet only for the conditional batch.
+                    control_model_input = latents
+                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
+                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
+                    controlnet_added_cond_kwargs = {
+                        "text_embeds": add_text_embeds.chunk(2)[1],
+                        "time_ids": add_time_ids.chunk(2)[1],
+                    }
+                else:
+                    control_model_input = latent_model_input
+                    controlnet_prompt_embeds = prompt_embeds
+                    controlnet_added_cond_kwargs = added_cond_kwargs
+                
+                if isinstance(controlnet_keep[i], list):
+                    cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+                else:
+                    controlnet_cond_scale = controlnet_conditioning_scale
+                    if isinstance(controlnet_cond_scale, list):
+                        controlnet_cond_scale = controlnet_cond_scale[0]
+                    cond_scale = controlnet_cond_scale * controlnet_keep[i]
+
+                if isinstance(self.controlnet, MultiControlNetModel):
+                    down_block_res_samples_list, mid_block_res_sample_list = [], []
+                    for control_index in range(len(self.controlnet.nets)):
+                        controlnet = self.controlnet.nets[control_index]
+                        if control_index == 0:
+                            # assume fhe first controlnet is IdentityNet
+                            controlnet_prompt_embeds = prompt_image_emb
+                        else:
+                            controlnet_prompt_embeds = prompt_embeds
+                        down_block_res_samples, mid_block_res_sample = controlnet(control_model_input,
+                                                                                  t,
+                                                                                  encoder_hidden_states=controlnet_prompt_embeds,
+                                                                                  controlnet_cond=image[control_index],
+                                                                                  conditioning_scale=cond_scale[control_index],
+                                                                                  guess_mode=guess_mode,
+                                                                                  added_cond_kwargs=controlnet_added_cond_kwargs,
+                                                                                  return_dict=False)
+
+                        # controlnet mask
+                        if control_index == 0 and control_mask_wight_image_list is not None:
+                            down_block_res_samples = [
+                                down_block_res_sample * mask_weight
+                                for down_block_res_sample, mask_weight in zip(down_block_res_samples, control_mask_wight_image_list)
+                            ]
+                            mid_block_res_sample *= control_mask_wight_image_list[-1]
+
+                        down_block_res_samples_list.append(down_block_res_samples)
+                        mid_block_res_sample_list.append(mid_block_res_sample)
+
+                    mid_block_res_sample = torch.stack(mid_block_res_sample_list).sum(dim=0)
+                    down_block_res_samples = [torch.stack(down_block_res_samples).sum(dim=0) for down_block_res_samples in
+                                              zip(*down_block_res_samples_list)]
+                else:
+                    down_block_res_samples, mid_block_res_sample = self.controlnet(
+                        control_model_input,
+                        t,
+                        encoder_hidden_states=prompt_image_emb,
+                        controlnet_cond=image,
+                        conditioning_scale=cond_scale,
+                        guess_mode=guess_mode,
+                        added_cond_kwargs=controlnet_added_cond_kwargs,
+                        return_dict=False,
+                    )
+
+                    # controlnet mask
+                    if control_mask_wight_image_list is not None:
+                        down_block_res_samples = [
+                            down_block_res_sample * mask_weight
+                            for down_block_res_sample, mask_weight in zip(down_block_res_samples, control_mask_wight_image_list)
+                        ]
+                        mid_block_res_sample *= control_mask_wight_image_list[-1]
+
+                if guess_mode and self.do_classifier_free_guidance:
+                    # Infered ControlNet only for the conditional batch.
+                    # To apply the output of ControlNet to both the unconditional and conditional batches,
+                    # add 0 to the unconditional batch to keep it unchanged.
+                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=encoder_hidden_states,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+        
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)            
+        else:
+            image = latents
+
+        if not output_type == "latent":
+            # apply watermark if available
+            if self.watermark is not None:
+                image = self.watermark.apply_watermark(image)
+
+            image = self.image_processor.postprocess(image, output_type=output_type)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image,)
+
+        return StableDiffusionXLPipelineOutput(images=image)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..149c936b94d3728fa3e4c50290c53d751a167401
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,16 @@
+diffusers==0.25.1
+transformers==4.37.1
+accelerate
+safetensors
+einops
+onnxruntime
+spaces==0.19.4
+omegaconf
+peft
+huggingface-hub==0.20.2
+opencv-python
+insightface
+gradio
+controlnet_aux
+gdown
+peft
\ No newline at end of file
diff --git a/style_template.py b/style_template.py
new file mode 100644
index 0000000000000000000000000000000000000000..cab2a17625ecb97cc43a10d43bb733dd88187424
--- /dev/null
+++ b/style_template.py
@@ -0,0 +1,155 @@
+style_list = [
+    {
+        "name": "(No style)",
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+        
+    },
+    {
+        "name": "Spring Festival",
+        "prompt": "Flat illustration, a Chinese {prompt}, ancient style, wearing a red cloth, smile face, white skin, clean background, fireworks blooming, red lanterns",
+        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast, realistic, cropped, worst quality, missing fingers, extra digit, jpeg artifacts, signature, multiple, (lowres, low quality, worst quality:1.2)",
+    },
+    {
+        "name": "Watercolor",
+        "prompt": "watercolor painting, {prompt}. vibrant, beautiful, painterly, detailed, textural, artistic",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy",
+    },
+    {
+        "name": "Film Noir",
+        "prompt": "film noir style, ink sketch|vector, {prompt} highly detailed, sharp focus, ultra sharpness, monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Neon",
+        "prompt": "masterpiece painting, buildings in the backdrop, kaleidoscope, lilac orange blue cream fuchsia bright vivid gradient colors, the scene is cinematic, {prompt}, emotional realism, double exposure, watercolor ink pencil, graded wash, color layering, magic realism, figurative painting, intricate motifs, organic tracery, polished",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Jungle",
+        "prompt": 'waist-up "{prompt} in a Jungle" by Syd Mead, tangerine cold color palette, muted colors, detailed, 8k,photo r3al,dripping paint,3d toon style,3d style,Movie Still',
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Mars",
+        "prompt": "{prompt}, Post-apocalyptic. Mars Colony, Scavengers roam the wastelands searching for valuable resources, rovers, bright morning sunlight shining, (detailed) (intricate) (8k) (HDR) (cinematic lighting) (sharp focus)",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Vibrant Color",
+        "prompt": "vibrant colorful, ink sketch|vector|2d colors, at nightfall, sharp focus, {prompt}, highly detailed, sharp focus, the clouds,colorful,ultra sharpness",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Snow",
+        "prompt": "cinema 4d render, {prompt}, high contrast, vibrant and saturated, sico style, surrounded by magical glow,floating ice shards, snow crystals, cold, windy background, frozen natural landscape in background  cinematic atmosphere,highly detailed, sharp focus, intricate design, 3d, unreal engine, octane render, CG best quality, highres, photorealistic, dramatic lighting, artstation, concept art, cinematic, epic Steven Spielberg movie still, sharp focus, smoke, sparks, art by pascal blanche and greg rutkowski and repin, trending on artstation, hyperrealism painting, matte painting, 4k resolution",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
+    },
+    {
+        "name": "Line art",
+        "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics",
+        "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic",
+    },
+        {
+        "name": "Art Nouveau",
+        "prompt": "Art Nouveau style, {prompt}, organic forms, curvilinear lines, elegant, nature-inspired, intricate patterns, flowing designs, soft colors",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, brutalism, geometric, harsh lines, noisy",
+    },
+    {
+        "name": "Cubism",
+        "prompt": "cubist painting, {prompt}, abstract, geometric shapes, fragmented objects, multiple viewpoints, bold lines, reduced colors, Pablo Picasso inspired",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, smooth, flowing, noisy",
+    },
+    {
+        "name": "Minimalism",
+        "prompt": "minimalist art, {prompt}, simple, clean lines, monochrome, negative space, minimal detail, geometric shapes, modern, sophisticated",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, baroque, cluttered, over-detailed, noisy",
+    },
+    {
+        "name": "Baroque",
+        "prompt": "baroque style, {prompt}, grandeur, drama, contrast, rich colors, intense lighting, ornate, Caravaggio inspired, emotional, detailed",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, minimalist, flat, dull, noisy",
+    },
+    {
+        "name": "Expressionism",
+        "prompt": "expressionist art, {prompt}, emotional, distorted, exaggerated, vivid colors, dynamic brushstrokes, subjective perspective, impactful",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealism, bland, underwhelming, noisy",
+    },
+    {
+        "name": "Digital Glitch",
+        "prompt": "digital glitch art, {prompt}, corrupted image, tech-inspired, vibrant, surreal, abstract, pixelated, data moshing, futuristic",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, classical, realistic, smooth, noiseless",
+    },
+    {
+        "name": "Psychedelic",
+        "prompt": "psychedelic art, {prompt}, vivid colors, hallucinatory patterns, surreal, fluid shapes, trippy, 1960s style, kaleidoscopic, vibrant",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, monochrome, simplistic, noiseless",
+    },
+    {
+        "name": "Victorian",
+        "prompt": "Victorian style, {prompt}, elegant, ornate, romantic, historical, detailed patterns, rich textures, sophisticated, vintage",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, modern, minimalist, plain, noisy",
+    },
+    {
+        "name": "Graffiti",
+        "prompt": "graffiti art, {prompt}, urban, street style, bold colors, spray paint, tagging, hip-hop culture, dynamic, expressive, contemporary",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, classical, fine art, noiseless, smooth",
+    },
+    {
+        "name": "Ukiyo-e",
+        "prompt": "Ukiyo-e style, {prompt}, Japanese woodblock print, flat color areas, bold outlines, historical scenes, nature, Edo period, traditional",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, 3D, photorealistic, modern, noisy",
+    },
+        {
+        "name": "Retro Futurism",
+        "prompt": "retro futurism, {prompt}, vibrant colors, geometric shapes, streamline design, 1950s and 1960s style, optimistic, space-age, visionary, neon lighting, bold typography",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, medieval, deformed, glitch, blurry, noisy",
+    },
+    {
+        "name": "Steampunk",
+        "prompt": "steampunk style, {prompt}, Victorian era, industrial, mechanical gears, brass and copper, steam engines, intricate details, fantastical machines, sepia tones",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, neon, futuristic, blurry, noisy",
+    },
+    {
+        "name": "Cyberpunk",
+        "prompt": "cyberpunk aesthetic, {prompt}, neon lights, urban dystopia, futuristic, high-tech, low-life, cybernetics, dark and gritty, vivid colors, digital world",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, medieval, steampunk, blurry, noisy",
+    },
+    {
+        "name": "Impressionist",
+        "prompt": "impressionist style painting, {prompt}, vibrant, light brushstrokes, open composition, emphasis on light in its changing qualities, movement, ordinary subject matter",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy",
+    },
+    {
+        "name": "Art Deco",
+        "prompt": "art deco style, {prompt}, glamorous, elegant, functional, geometric patterns, bold colors, sleek lines, chrome, glass, shiny fabrics",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, rustic, medieval, deformed, glitch, noisy",
+    },
+    {
+        "name": "Fantasy",
+        "prompt": "fantasy style, {prompt}, mythical creatures, enchanted forests, magic elements, dreamlike landscapes, vibrant colors, detailed, imaginative",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy",
+    },
+    {
+        "name": "Gothic",
+        "prompt": "gothic style, {prompt}, dark and moody, gothic architecture, medieval elements, dramatic lighting, somber tones, intricate details, mysterious atmosphere",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, neon, futuristic, blurry, noisy",
+    },
+    {
+        "name": "Pop Art",
+        "prompt": "pop art style, {prompt}, bold colors, mass culture, comic style, ironic, whimsical, repetition of images, bright, high contrast",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, medieval, steampunk, blurry, noisy",
+    },
+    {
+        "name": "Surrealism",
+        "prompt": "surrealist style, {prompt}, dreamlike, bizarre, irrational, abstract, imaginative, distorted reality, vivid, unexpected juxtapositions",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy",
+    },
+    {
+        "name": "Abstract",
+        "prompt": "abstract style, {prompt}, non-representational, shapes, forms, colors, lines, dynamic, modern, expressive, non-figurative, bold",
+        "negative_prompt": "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy",
+    },
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
\ No newline at end of file
diff --git a/torchhub/README.md b/torchhub/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..eade757c3b0a25c350ba6bf3b5d2e6f048ad1de6
--- /dev/null
+++ b/torchhub/README.md
@@ -0,0 +1,3 @@
+# Local PyTorch Hub
+
+This directory is for loading the DINOv2 encoder locally in case of no Internet connection.
diff --git a/torchhub/facebookresearch_dinov2_main/CODE_OF_CONDUCT.md b/torchhub/facebookresearch_dinov2_main/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..3232ed665566ec047ce55a929db1581dbda266a1
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/CODE_OF_CONDUCT.md
@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <opensource-conduct@meta.com>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq
diff --git a/torchhub/facebookresearch_dinov2_main/CONTRIBUTING.md b/torchhub/facebookresearch_dinov2_main/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..afc89823fc90b920f0758f50e4d808df6a884a34
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/CONTRIBUTING.md
@@ -0,0 +1,31 @@
+# Contributing to DINOv2
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to DINOv2, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.
diff --git a/torchhub/facebookresearch_dinov2_main/LICENSE b/torchhub/facebookresearch_dinov2_main/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..a115f899f8d09ef3b1def4a16c7bae1a0bd50fbe
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/LICENSE
@@ -0,0 +1,400 @@
+
+Attribution-NonCommercial 4.0 International
+
+=======================================================================
+
+Creative Commons Corporation ("Creative Commons") is not a law firm and
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+
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diff --git a/torchhub/facebookresearch_dinov2_main/MODEL_CARD.md b/torchhub/facebookresearch_dinov2_main/MODEL_CARD.md
new file mode 100644
index 0000000000000000000000000000000000000000..5cd35748eb3c5d8f607f83ff068367a0102117c5
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/MODEL_CARD.md
@@ -0,0 +1,201 @@
+# Model Card for DINOv2-S/B/L/g
+
+These are Vision Transformer models trained following the method described in the paper:
+"DINOv2: Learning Robust Visual Features without Supervision"
+
+We provide 4 models: 1 ViT-g trained from scratch, and 3 ViT-S/B/L models distilled from the ViT-g.
+
+## Model Details
+The model takes an image as input and returns a class token and patch tokens.
+
+The embedding dimension is: 
+- 384 for ViT-S.
+- 768 for ViT-B.
+- 1024 for ViT-L.
+- 1536 for ViT-g.
+
+The models follow a Transformer architecture, with a patch size of 14.
+
+For a 224x224 image, this results in 1 class token + 256 patch tokens.
+
+The models can accept larger images provided the image shapes are multiples of the patch size (14). 
+If this condition is not verified, the model will crop to the closest smaller multiple of the patch size.
+
+### Model Description
+
+- **Developed by:** Meta AI
+- **Model type:** Vision Transformer
+- **License:** CC-BY-NC
+
+- **Repository:** https://github.com/facebookresearch/dinov2
+- **Paper:** https://arxiv.org/abs/2304.07193
+- **Demo:** https://dinov2.metademolab.com/
+
+## Uses
+
+The models are vision backbones providing multi-purpose features for downstream tasks.
+
+### Direct Use
+
+The models can be used without fine-tuning, with downstream classifiers as simple as linear layers, to obtain competitive results:
+- on depth estimation, semantic segmentation, using linear layers.
+- on image classification, using k-NN classifiers on the class token.
+- on image classification, with logistic regression classifiers applied on the class token.
+- on image classification, with a linear layer applied on the class token and the average of the patch tokens.
+- on image retrieval using nearest neighbors.
+
+### Downstream Use
+
+It is technically possible to perform fine-tuning on the models, for small gains (we measured +2% on ImageNet-1k classification). 
+We recommend keeping this as a very last step and only when necessary, as the features already provide good performance out-of-the-box.
+
+## Bias, Risks, and Limitations
+
+Despite improvements thanks to the training method not using annotations, we still observe significant biases in our models toward rich households from Western countries.
+
+### Recommendations
+
+We expect fine-tuning will increase the biases in the features produced by the model as they will be tuned to the fine-tuning labels.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import torch
+dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
+dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14')
+dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14')
+dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14')
+```
+
+## Training Details
+
+### Training Data
+
+- **Training data:** LVD-142M (see paper)
+- **Training regime:** fp16 using PyTorch-FSDP mixed-precision.
+
+### Training Procedure 
+
+- **Training objective:**
+  - DINO self-distillation loss with multi-crop
+  - iBOT masked-image modeling loss
+  - KoLeo regularization on [CLS] tokens
+- **Architectures:**
+  - ViT-S (21M params): Patch size 14, embedding dimension 384, 6 heads, MLP FFN
+  - ViT-B (86M params): Patch size 14, embedding dimension 768, 12 heads, MLP FFN
+  - ViT-L (0.3B params): Patch size 14, embedding dimension 1024, 16 heads, MLP FFN
+  - ViT-g (1.1B params): Patch size 14, embedding dimension 1536, 24 heads, SwiGLU FFN
+- **Distillation:**
+  - Distillation follows the standard DINOv2 pretraining procedure, except the teacher is a pretrained ViT-g, frozen.
+
+## Evaluation
+
+We refer users to the associated paper for the evaluation protocols.
+
+<table>
+  <tr>
+    <th>model</th>
+    <th colspan="3">ImageNet-1k</th>
+    <th>NYU-Depth v2</th>
+    <th>SUN-RGBD</th>
+    <th>ADE20k</th>
+    <th>iNaturalist 2018</th>
+    <th>Oxford-H</th>
+  </tr>
+  <tr>
+    <th rowspan="2">task</th>
+    <th>classif. (acc)</th>
+    <th>classif. (acc)</th>
+    <th>classif. V2 (acc)</th>
+    <th>depth (RMSE)</th>
+    <th>depth (RMSE)</th>
+    <th>segm. (mAP)</th>
+    <th>classif. (acc)</th>
+    <th>retrieval (mAP)</th>
+  </tr>
+  <tr>
+    <!-- <th>^</th> -->
+    <th>k-NN</th>
+    <th>linear</th>
+    <th>linear</th>
+    <th>linear<br />4 layers</th>
+    <th>NYU-D transfer</th>
+    <th>multiscale</th>
+    <th>linear</th>
+    <th>nearest neighbor</th>
+  </tr>
+  <tr>
+    <td>ViT-S/14</td>
+    <td align="right">79.0%</td>
+    <td align="right">81.1%</td>
+    <td align="right">70.8%</td> 
+    <td align="right">0.417</td> 
+    <td align="right">0.431</td> 
+    <td align="right">47.2</td> 
+    <td align="right">69.5%</td> 
+    <td align="right">43.2</td> 
+  </tr>
+  <tr>
+    <td>ViT-B/14</td>
+    <td align="right">82.1%</td>
+    <td align="right">84.5%</td>
+    <td align="right">74.9%</td>
+    <td align="right">0.362</td> 
+    <td align="right">0.400</td> 
+    <td align="right">51.3</td> 
+    <td align="right">76.3%</td> 
+    <td align="right">49.5</td> 
+  </tr>
+  <tr>
+    <td>ViT-L/14</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.3%</td>
+    <td align="right">77.6%</td>
+    <td align="right">0.333</td> 
+    <td align="right">0.396</td> 
+    <td align="right">53.1</td> 
+    <td align="right">79.8%</td> 
+    <td align="right">54.0</td> 
+  </tr>
+  <tr>
+    <td>ViT-g/14</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.5%</td>
+    <td align="right">78.4%</td>
+    <td align="right">0.298</td> 
+    <td align="right">0.362</td> 
+    <td align="right">53.0</td> 
+    <td align="right">81.6%</td> 
+    <td align="right">52.3</td> 
+  </tr>
+</table>
+
+## Environmental Impact
+
+- **Hardware Type:** Nvidia A100
+- **Hours used:** 22,000 for ViT-g, 4,500 for ViT-S distillation, 5,300 for ViT-B distillation, 8,000 for ViT-L distillation
+- **Cloud Provider:** Private infra
+- **Compute Region:** USA
+- **Carbon Emitted:** 7t CO2eq
+
+#### Hardware
+
+Nvidia A100 GPUs
+
+#### Software
+
+PyTorch 2.0,
+xFormers 0.0.18
+
+**BibTeX**
+
+```
+@misc{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
+  journal={arXiv:2304.07193},
+  year={2023}
+}
+```
diff --git a/torchhub/facebookresearch_dinov2_main/README.md b/torchhub/facebookresearch_dinov2_main/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..96453e567dee10148be83b5e92d91f347f8521d5
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/README.md
@@ -0,0 +1,277 @@
+# DINOv2: Learning Robust Visual Features without Supervision
+
+**[Meta AI Research, FAIR](https://ai.facebook.com/research/)**
+
+Maxime Oquab,
+Timothée Darcet,
+Théo Moutakanni,
+Huy V. Vo,
+Marc Szafraniec,
+Vasil Khalidov,
+Patrick Labatut,
+Armand Joulin,
+Piotr Bojanowski
+
+[[`Paper`](https://arxiv.org/abs/2304.07193)] [[`Blog`](https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/)] [[`Demo`](https://dinov2.metademolab.com)] [[`BibTeX`](#citing-dinov2)]
+
+PyTorch implementation and pretrained models for DINOv2. For details, see the paper: **[DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)**.
+
+DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. The models were pretrained on a dataset of 142 M images without using any labels or annotations.
+
+https://github.com/facebookresearch/dinov2/assets/60359573/f168823e-7922-415a-b429-578badf5c356
+
+<div align="center">
+  Visualization of the three first principal components of the patch features of all frames, mapped to RGB values.
+</div>
+
+## Pretrained models
+
+<table style="margin: auto">
+  <tr>
+    <th>model</th>
+    <th># of<br />params</th>
+    <th>ImageNet<br />k-NN</th>
+    <th>ImageNet<br />linear</th>
+    <th>download</th>
+  </tr>
+  <tr>
+    <td>ViT-S/14 distilled</td>
+    <td align="right">21 M</td>
+    <td align="right">79.0%</td>
+    <td align="right">81.1%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth">backbone only</a></td>
+  </tr>
+  <tr>
+    <td>ViT-B/14 distilled</td>
+    <td align="right">86 M</td>
+    <td align="right">82.1%</td>
+    <td align="right">84.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth">backbone only</a></td>
+  </tr>
+  <tr>
+    <td>ViT-L/14 distilled</td>
+    <td align="right">300 M</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.3%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth">backbone only</a></td>
+  </tr>
+  <tr>
+    <td>ViT-g/14</td>
+    <td align="right">1,100 M</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth">backbone only</a></td>
+  </tr>
+</table>
+
+### Pretrained models via PyTorch Hub
+
+Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install PyTorch (the only required dependency for loading the model). Installing PyTorch with CUDA support is strongly recommended.
+
+A corresponding [model card](MODEL_CARD.md) is included in the repository.
+
+```python
+import torch
+
+dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
+dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14')
+dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14')
+dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14')
+```
+
+## Installation
+
+The training and evaluation code requires PyTorch 2.0 and [xFormers](https://github.com/facebookresearch/xformers) 0.0.18 as well as a number of other 3rd party packages. Note that the code has only been tested with the specified versions and also expects a Linux environment. To setup all the required dependencies for training and evaluation, please follow the instructions below:
+
+*[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html)* **(Recommended)** - Clone the repository and then create and activate a `dinov2` conda environment using the provided environment definition:
+
+```shell
+conda env create -f conda.yaml
+conda activate dinov2
+```
+
+*[pip](https://pip.pypa.io/en/stable/getting-started/)* - Clone the repository and then use the provided `requirements.txt` to install the dependencies:
+
+```shell
+pip install -r requirements.txt
+```
+
+## Data preparation
+
+### ImageNet-1k
+
+The root directory of the dataset should hold the following contents:
+
+- `<ROOT>/test/ILSVRC2012_test_00000001.JPEG`
+- `<ROOT>/test/[..]`
+- `<ROOT>/test/ILSVRC2012_test_00100000.JPEG`
+- `<ROOT>/train/n01440764/n01440764_10026.JPEG`
+- `<ROOT>/train/[...]`
+- `<ROOT>/train/n15075141/n15075141_9993.JPEG`
+- `<ROOT>/val/n01440764/ILSVRC2012_val_00000293.JPEG`
+- `<ROOT>/val/[...]`
+- `<ROOT>/val/n15075141/ILSVRC2012_val_00049174.JPEG`
+- `<ROOT>/labels.txt`
+
+The provided dataset implementation expects a few additional metadata files to be present under the extra directory:
+
+- `<EXTRA>/class-ids-TRAIN.npy`
+- `<EXTRA>/class-ids-VAL.npy`
+- `<EXTRA>/class-names-TRAIN.npy`
+- `<EXTRA>/class-names-VAL.npy`
+- `<EXTRA>/entries-TEST.npy`
+- `<EXTRA>/entries-TRAIN.npy`
+- `<EXTRA>/entries-VAL.npy`
+
+These metadata files can be generated (once) with the following lines of Python code:
+
+```python
+from dinov2.data.datasets import ImageNet
+
+for split in ImageNet.Split:
+    dataset = ImageNet(split=split, root="<ROOT>", extra="<EXTRA>")
+    dataset.dump_extra()
+```
+
+Note that the root and extra directories do not have to be distinct directories.
+
+### ImageNet-22k
+
+Please adapt the [dataset class](dinov2/data/datasets/image_net_22k.py) to match your local setup.
+
+<br />
+
+:warning: To execute the commands provided in the next sections for training and evaluation, the `dinov2` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`.
+
+## Training
+
+### Fast setup: training DINOv2 ViT-L/16 on ImageNet-1k
+
+Run DINOv2 training on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit:
+
+```shell
+python dinov2/run/train/train.py \
+    --nodes 4 \
+    --config-file dinov2/configs/train/vitl16_short.yaml \
+    --output-dir <PATH/TO/OUTPUT/DIR> \
+    train.dataset_path=ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+Training time is approximately 1 day and the resulting checkpoint should reach 81.6% on k-NN eval and 82.9% on linear eval.
+
+The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation.
+
+### Long setup: training DINOv2 ViT-L/14 on ImageNet-22k
+
+Run DINOv2 training on 12 A100-80GB nodes (96 GPUs) in a SLURM cluster environment with submitit:
+
+```shell
+python dinov2/run/train/train.py \
+    --nodes 12 \
+    --config-file dinov2/configs/train/vitl14.yaml \
+    --output-dir <PATH/TO/OUTPUT/DIR> \
+    train.dataset_path=ImageNet22k:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+Training time is approximately 3.3 days and the resulting checkpoint should reach 82.0% on k-NN eval and 84.5% on linear eval.
+
+The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation.
+
+
+## Evaluation
+
+The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node:
+
+### k-NN classification on ImageNet-1k
+
+```shell
+python dinov2/run/eval/knn.py \
+    --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
+    --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
+    --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/knn \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+### Logistic regression classification on ImageNet-1k
+
+```shell
+python dinov2/run/eval/log_regression.py \
+    --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
+    --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
+    --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/logreg \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+### Linear classification with data augmentation on ImageNet-1k
+
+```shell
+python dinov2/run/eval/linear.py \
+    --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
+    --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
+    --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/linear \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+We release the weights from evaluating the different models:
+
+<table style="margin: auto">
+  <tr>
+    <th>model</th>
+    <th>ImageNet<br />top-1</th>
+    <th>linear evaluation</th>
+  </tr>
+  <tr>
+    <td>ViT-S/14 distilled</td>
+    <td align="right">81.1%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_linear_head.pth">linear head weights</a></td>
+  </tr>
+  <tr>
+    <td>ViT-B/14 distilled</td>
+    <td align="right">84.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_linear_head.pth">linear head weights</a></td>
+  </tr>
+  <tr>
+    <td>ViT-L/14 distilled</td>
+    <td align="right">86.3%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_linear_head.pth">linear head weights</a></td>
+  </tr>
+  <tr>
+    <td>ViT-g/14</td>
+    <td align="right">86.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_linear_head.pth">linear head weights</a></td>
+  </tr>
+</table>
+
+The performance of the provided pretrained model weights can be evaluated as follows on ImageNet-1k:
+
+```shell
+python dinov2/run/eval/linear.py \
+    --config-file dinov2/configs/eval/vitg14_pretrain.yaml \
+    --pretrained-weights https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+## License
+
+DINOv2 code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.
+
+## Contributing
+
+See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
+
+## Citing DINOv2
+
+If you find this repository useful, please consider giving a star :star: and citation :t-rex::
+
+```
+@misc{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
+  journal={arXiv:2304.07193},
+  year={2023}
+}
+```
diff --git a/torchhub/facebookresearch_dinov2_main/__pycache__/hubconf.cpython-310.pyc b/torchhub/facebookresearch_dinov2_main/__pycache__/hubconf.cpython-310.pyc
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diff --git a/torchhub/facebookresearch_dinov2_main/__pycache__/vision_transformer.cpython-310.pyc b/torchhub/facebookresearch_dinov2_main/__pycache__/vision_transformer.cpython-310.pyc
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index 0000000000000000000000000000000000000000..47511d43bab8444989f1e0dfa9fce43782269ce8
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diff --git a/torchhub/facebookresearch_dinov2_main/conda.yaml b/torchhub/facebookresearch_dinov2_main/conda.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..35dfc30adc275da51b58ff2340dd1d53d2cb9250
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/conda.yaml
@@ -0,0 +1,22 @@
+name: dinov2
+channels:
+  - defaults
+  - pytorch
+  - nvidia
+  - xformers
+  - conda-forge
+dependencies:
+  - python=3.9
+  - pytorch::pytorch=2.0.0
+  - pytorch::pytorch-cuda=11.7.0
+  - pytorch::torchvision=0.15.0
+  - omegaconf
+  - torchmetrics=0.10.3
+  - fvcore
+  - iopath
+  - xformers::xformers=0.0.18
+  - pip
+  - pip:
+    - git+https://github.com/facebookincubator/submitit
+    - --extra-index-url https://pypi.nvidia.com
+    - cuml-cu11
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b4afb514783786adf76744f9b97f3e1db1d6081
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+__version__ = "0.0.1"
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/__pycache__/__init__.cpython-310.pyc b/torchhub/facebookresearch_dinov2_main/dinov2/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9e05b0071a4eb0682fe615ab70ac5ac600417132
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diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/configs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..033c35660450afec6612adb342c7c30e1ccd15ee
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/__init__.py
@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import pathlib
+
+from omegaconf import OmegaConf
+
+
+def load_config(config_name: str):
+    config_filename = config_name + ".yaml"
+    return OmegaConf.load(pathlib.Path(__file__).parent.resolve() / config_filename)
+
+
+dinov2_default_config = load_config("ssl_default_config")
+
+
+def load_and_merge_config(config_name: str):
+    default_config = OmegaConf.create(dinov2_default_config)
+    loaded_config = load_config(config_name)
+    return OmegaConf.merge(default_config, loaded_config)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitb14_pretrain.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitb14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..117d0f027ca26cd8ce6c010bb78d5a8fac42c70e
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitb14_pretrain.yaml
@@ -0,0 +1,6 @@
+student:
+  arch: vit_base
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitg14_pretrain.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitg14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a96dd5b117b4d59ee210b65037821f1b3e3f16e3
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitg14_pretrain.yaml
@@ -0,0 +1,7 @@
+student:
+  arch: vit_giant2
+  patch_size: 14
+  ffn_layer: swiglufused
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitl14_pretrain.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitl14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a984548bd034f762d455419d7193917fa462dd8
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitl14_pretrain.yaml
@@ -0,0 +1,6 @@
+student:
+  arch: vit_large
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vits14_pretrain.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vits14_pretrain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..afbdb4ba14f1c97130a25b579360f4d817cda495
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vits14_pretrain.yaml
@@ -0,0 +1,6 @@
+student:
+  arch: vit_small
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98
\ No newline at end of file
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/ssl_default_config.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/ssl_default_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a4ef04545ce9d6cc52b5179236008adc8a9bbda2
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/ssl_default_config.yaml
@@ -0,0 +1,115 @@
+MODEL:
+  WEIGHTS: ''
+compute_precision:
+  grad_scaler: true
+  teacher:
+    backbone:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    dino_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    ibot_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+  student:
+    backbone:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    dino_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp32
+        buffer_dtype: fp32
+    ibot_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp32
+        buffer_dtype: fp32
+dino:
+  loss_weight: 1.0
+  head_n_prototypes: 65536
+  head_bottleneck_dim: 256
+  head_nlayers: 3
+  head_hidden_dim: 2048
+  koleo_loss_weight: 0.1
+ibot:
+  loss_weight: 1.0
+  mask_sample_probability: 0.5
+  mask_ratio_min_max:
+  - 0.1
+  - 0.5
+  separate_head: false
+  head_n_prototypes: 65536
+  head_bottleneck_dim: 256
+  head_nlayers: 3
+  head_hidden_dim: 2048
+train:
+  batch_size_per_gpu: 64
+  dataset_path: ImageNet:split=TRAIN
+  output_dir: .
+  saveckp_freq: 20
+  seed: 0
+  num_workers: 10
+  OFFICIAL_EPOCH_LENGTH: 1250
+  cache_dataset: true
+  centering: "centering" # or "sinkhorn_knopp"
+student:
+  arch: vit_large
+  patch_size: 16
+  drop_path_rate: 0.3
+  layerscale: 1.0e-05
+  drop_path_uniform: true
+  pretrained_weights: ''
+  ffn_layer: "mlp"
+  block_chunks: 0
+  qkv_bias: true
+  proj_bias: true
+  ffn_bias: true
+teacher:
+  momentum_teacher: 0.992
+  final_momentum_teacher: 1
+  warmup_teacher_temp: 0.04
+  teacher_temp: 0.07
+  warmup_teacher_temp_epochs: 30
+optim:
+  epochs: 100
+  weight_decay: 0.04
+  weight_decay_end: 0.4
+  base_lr: 0.004  # learning rate for a batch size of 1024
+  lr: 0.  # will be set after applying scaling rule
+  warmup_epochs: 10
+  min_lr: 1.0e-06
+  clip_grad: 3.0
+  freeze_last_layer_epochs: 1
+  scaling_rule: sqrt_wrt_1024
+  patch_embed_lr_mult: 0.2
+  layerwise_decay: 0.9
+  adamw_beta1: 0.9
+  adamw_beta2: 0.999
+crops:
+  global_crops_scale:
+  - 0.32
+  - 1.0
+  local_crops_number: 8
+  local_crops_scale:
+  - 0.05
+  - 0.32
+  global_crops_size: 224
+  local_crops_size: 96
+evaluation:
+  eval_period_iterations: 12500
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitg14.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitg14.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d05cf0d59e07ac6e4a2b0f9bdcb6131d7c508962
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitg14.yaml
@@ -0,0 +1,26 @@
+dino:
+  head_n_prototypes: 131072
+  head_bottleneck_dim: 384
+ibot:
+  separate_head: true
+  head_n_prototypes: 131072
+train:
+  batch_size_per_gpu: 12
+  dataset_path: ImageNet22k
+  centering: sinkhorn_knopp
+student:
+  arch: vit_giant2
+  patch_size: 14
+  drop_path_rate: 0.4
+  ffn_layer: swiglufused
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.994
+optim:
+  epochs: 500
+  weight_decay_end: 0.2
+  base_lr: 2.0e-04  # learning rate for a batch size of 1024
+  warmup_epochs: 80
+  layerwise_decay: 1.0
+crops:
+  local_crops_size: 98
\ No newline at end of file
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl14.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl14.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d9b491dcc6a522c71328fc2933dd0501123c8f6b
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl14.yaml
@@ -0,0 +1,26 @@
+dino:
+  head_n_prototypes: 131072
+  head_bottleneck_dim: 384
+ibot:
+  separate_head: true
+  head_n_prototypes: 131072
+train:
+  batch_size_per_gpu: 32
+  dataset_path: ImageNet22k
+  centering: sinkhorn_knopp
+student:
+  arch: vit_large
+  patch_size: 14
+  drop_path_rate: 0.4
+  ffn_layer: swiglufused
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.994
+optim:
+  epochs: 500
+  weight_decay_end: 0.2
+  base_lr: 2.0e-04  # learning rate for a batch size of 1024
+  warmup_epochs: 80
+  layerwise_decay: 1.0
+crops:
+  local_crops_size: 98
\ No newline at end of file
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl16_short.yaml b/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl16_short.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3e7e72864c92175a1354142ac1d64da8070d1e5e
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl16_short.yaml
@@ -0,0 +1,6 @@
+# this corresponds to the default config
+train:
+  dataset_path: ImageNet:split=TRAIN
+  batch_size_per_gpu: 64
+student:
+  block_chunks: 4
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..357db5c542c5810391ba2bd45a60c13c01c3737a
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .adapters import DatasetWithEnumeratedTargets
+from .loaders import make_data_loader, make_dataset, SamplerType
+from .collate import collate_data_and_cast
+from .masking import MaskingGenerator
+from .augmentations import DataAugmentationDINO
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/adapters.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/adapters.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dcbc68e046f03460d5867f1388d5380d9c6f603
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/adapters.py
@@ -0,0 +1,29 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any, Tuple
+
+from torch.utils.data import Dataset
+
+
+class DatasetWithEnumeratedTargets(Dataset):
+    def __init__(self, dataset):
+        self._dataset = dataset
+
+    def get_image_data(self, index: int) -> bytes:
+        return self._dataset.get_image_data(index)
+
+    def get_target(self, index: int) -> Tuple[Any, int]:
+        target = self._dataset.get_target(index)
+        return (index, target)
+
+    def __getitem__(self, index: int) -> Tuple[Any, Tuple[Any, int]]:
+        image, target = self._dataset[index]
+        target = index if target is None else target
+        return image, (index, target)
+
+    def __len__(self) -> int:
+        return len(self._dataset)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/augmentations.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/augmentations.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ca28cb59a4de2566a6c9ef9c301cbbb4e54b5ee
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/augmentations.py
@@ -0,0 +1,119 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+from torchvision import transforms
+
+from .transforms import (
+    GaussianBlur,
+    make_normalize_transform,
+)
+
+
+logger = logging.getLogger("dinov2")
+
+
+class DataAugmentationDINO(object):
+    def __init__(
+        self,
+        global_crops_scale,
+        local_crops_scale,
+        local_crops_number,
+        global_crops_size=224,
+        local_crops_size=96,
+    ):
+        self.global_crops_scale = global_crops_scale
+        self.local_crops_scale = local_crops_scale
+        self.local_crops_number = local_crops_number
+        self.global_crops_size = global_crops_size
+        self.local_crops_size = local_crops_size
+
+        logger.info("###################################")
+        logger.info("Using data augmentation parameters:")
+        logger.info(f"global_crops_scale: {global_crops_scale}")
+        logger.info(f"local_crops_scale: {local_crops_scale}")
+        logger.info(f"local_crops_number: {local_crops_number}")
+        logger.info(f"global_crops_size: {global_crops_size}")
+        logger.info(f"local_crops_size: {local_crops_size}")
+        logger.info("###################################")
+
+        # random resized crop and flip
+        self.geometric_augmentation_global = transforms.Compose(
+            [
+                transforms.RandomResizedCrop(
+                    global_crops_size, scale=global_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
+                ),
+                transforms.RandomHorizontalFlip(p=0.5),
+            ]
+        )
+
+        self.geometric_augmentation_local = transforms.Compose(
+            [
+                transforms.RandomResizedCrop(
+                    local_crops_size, scale=local_crops_scale, interpolation=transforms.InterpolationMode.BICUBIC
+                ),
+                transforms.RandomHorizontalFlip(p=0.5),
+            ]
+        )
+
+        # color distorsions / blurring
+        color_jittering = transforms.Compose(
+            [
+                transforms.RandomApply(
+                    [transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)],
+                    p=0.8,
+                ),
+                transforms.RandomGrayscale(p=0.2),
+            ]
+        )
+
+        global_transfo1_extra = GaussianBlur(p=1.0)
+
+        global_transfo2_extra = transforms.Compose(
+            [
+                GaussianBlur(p=0.1),
+                transforms.RandomSolarize(threshold=128, p=0.2),
+            ]
+        )
+
+        local_transfo_extra = GaussianBlur(p=0.5)
+
+        # normalization
+        self.normalize = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                make_normalize_transform(),
+            ]
+        )
+
+        self.global_transfo1 = transforms.Compose([color_jittering, global_transfo1_extra, self.normalize])
+        self.global_transfo2 = transforms.Compose([color_jittering, global_transfo2_extra, self.normalize])
+        self.local_transfo = transforms.Compose([color_jittering, local_transfo_extra, self.normalize])
+
+    def __call__(self, image):
+        output = {}
+
+        # global crops:
+        im1_base = self.geometric_augmentation_global(image)
+        global_crop_1 = self.global_transfo1(im1_base)
+
+        im2_base = self.geometric_augmentation_global(image)
+        global_crop_2 = self.global_transfo2(im2_base)
+
+        output["global_crops"] = [global_crop_1, global_crop_2]
+
+        # global crops for teacher:
+        output["global_crops_teacher"] = [global_crop_1, global_crop_2]
+
+        # local crops:
+        local_crops = [
+            self.local_transfo(self.geometric_augmentation_local(image)) for _ in range(self.local_crops_number)
+        ]
+        output["local_crops"] = local_crops
+        output["offsets"] = ()
+
+        return output
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/collate.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/collate.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f0d98906808ed326dff4486d95b3ec04f8a5e75
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/collate.py
@@ -0,0 +1,50 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import random
+
+
+def collate_data_and_cast(samples_list, mask_ratio_tuple, mask_probability, dtype, n_tokens=None, mask_generator=None):
+    # dtype = torch.half  # TODO: Remove
+
+    n_global_crops = len(samples_list[0][0]["global_crops"])
+    n_local_crops = len(samples_list[0][0]["local_crops"])
+
+    collated_global_crops = torch.stack([s[0]["global_crops"][i] for i in range(n_global_crops) for s in samples_list])
+
+    collated_local_crops = torch.stack([s[0]["local_crops"][i] for i in range(n_local_crops) for s in samples_list])
+
+    B = len(collated_global_crops)
+    N = n_tokens
+    n_samples_masked = int(B * mask_probability)
+    probs = torch.linspace(*mask_ratio_tuple, n_samples_masked + 1)
+    upperbound = 0
+    masks_list = []
+    for i in range(0, n_samples_masked):
+        prob_min = probs[i]
+        prob_max = probs[i + 1]
+        masks_list.append(torch.BoolTensor(mask_generator(int(N * random.uniform(prob_min, prob_max)))))
+        upperbound += int(N * prob_max)
+    for i in range(n_samples_masked, B):
+        masks_list.append(torch.BoolTensor(mask_generator(0)))
+
+    random.shuffle(masks_list)
+
+    collated_masks = torch.stack(masks_list).flatten(1)
+    mask_indices_list = collated_masks.flatten().nonzero().flatten()
+
+    masks_weight = (1 / collated_masks.sum(-1).clamp(min=1.0)).unsqueeze(-1).expand_as(collated_masks)[collated_masks]
+
+    return {
+        "collated_global_crops": collated_global_crops.to(dtype),
+        "collated_local_crops": collated_local_crops.to(dtype),
+        "collated_masks": collated_masks,
+        "mask_indices_list": mask_indices_list,
+        "masks_weight": masks_weight,
+        "upperbound": upperbound,
+        "n_masked_patches": torch.full((1,), fill_value=mask_indices_list.shape[0], dtype=torch.long),
+    }
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b537aee8fe31d7e0fa06713d2cfe9233ff0ef60
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .image_net import ImageNet
+from .image_net_22k import ImageNet22k
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/decoders.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/decoders.py
new file mode 100644
index 0000000000000000000000000000000000000000..548720b3b9959b4949f71fb2dd5cf6af3d184066
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/decoders.py
@@ -0,0 +1,32 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from io import BytesIO
+from typing import Any
+
+from PIL import Image
+
+
+class Decoder:
+    def decode(self) -> Any:
+        raise NotImplementedError
+
+
+class ImageDataDecoder(Decoder):
+    def __init__(self, image_data: bytes) -> None:
+        self._image_data = image_data
+
+    def decode(self) -> Image:
+        f = BytesIO(self._image_data)
+        return Image.open(f).convert(mode="RGB")
+
+
+class TargetDecoder(Decoder):
+    def __init__(self, target: Any):
+        self._target = target
+
+    def decode(self) -> Any:
+        return self._target
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/extended.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/extended.py
new file mode 100644
index 0000000000000000000000000000000000000000..4da831e6ad275025ed55eaa490f780ecf6083f2c
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/extended.py
@@ -0,0 +1,39 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any, Tuple
+
+from torchvision.datasets import VisionDataset
+
+from .decoders import TargetDecoder, ImageDataDecoder
+
+
+class ExtendedVisionDataset(VisionDataset):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)  # type: ignore
+
+    def get_image_data(self, index: int) -> bytes:
+        raise NotImplementedError
+
+    def get_target(self, index: int) -> Any:
+        raise NotImplementedError
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        try:
+            image_data = self.get_image_data(index)
+            image = ImageDataDecoder(image_data).decode()
+        except Exception as e:
+            raise RuntimeError(f"can not read image for sample {index}") from e
+        target = self.get_target(index)
+        target = TargetDecoder(target).decode()
+
+        if self.transforms is not None:
+            image, target = self.transforms(image, target)
+
+        return image, target
+
+    def __len__(self) -> int:
+        raise NotImplementedError
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e1c384cc96ceb6afeb3e555d9b3e2a2c008c5d4
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net.py
@@ -0,0 +1,291 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import csv
+from enum import Enum
+import logging
+import os
+from typing import Callable, List, Optional, Tuple, Union
+
+import numpy as np
+
+from .extended import ExtendedVisionDataset
+
+
+logger = logging.getLogger("dinov2")
+_Target = int
+
+
+class _Split(Enum):
+    TRAIN = "train"
+    VAL = "val"
+    TEST = "test"  # NOTE: torchvision does not support the test split
+
+    @property
+    def length(self) -> int:
+        split_lengths = {
+            _Split.TRAIN: 1_281_167,
+            _Split.VAL: 50_000,
+            _Split.TEST: 100_000,
+        }
+        return split_lengths[self]
+
+    def get_dirname(self, class_id: Optional[str] = None) -> str:
+        return self.value if class_id is None else os.path.join(self.value, class_id)
+
+    def get_image_relpath(self, actual_index: int, class_id: Optional[str] = None) -> str:
+        dirname = self.get_dirname(class_id)
+        if self == _Split.TRAIN:
+            basename = f"{class_id}_{actual_index}"
+        else:  # self in (_Split.VAL, _Split.TEST):
+            basename = f"ILSVRC2012_{self.value}_{actual_index:08d}"
+        return os.path.join(dirname, basename + ".JPEG")
+
+    def parse_image_relpath(self, image_relpath: str) -> Tuple[str, int]:
+        assert self != _Split.TEST
+        dirname, filename = os.path.split(image_relpath)
+        class_id = os.path.split(dirname)[-1]
+        basename, _ = os.path.splitext(filename)
+        actual_index = int(basename.split("_")[-1])
+        return class_id, actual_index
+
+
+class ImageNet(ExtendedVisionDataset):
+    Target = Union[_Target]
+    Split = Union[_Split]
+
+    def __init__(
+        self,
+        *,
+        split: "ImageNet.Split",
+        root: str,
+        extra: str,
+        transforms: Optional[Callable] = None,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+    ) -> None:
+        super().__init__(root, transforms, transform, target_transform)
+        self._extra_root = extra
+        self._split = split
+
+        self._entries = None
+        self._class_ids = None
+        self._class_names = None
+
+    @property
+    def split(self) -> "ImageNet.Split":
+        return self._split
+
+    def _get_extra_full_path(self, extra_path: str) -> str:
+        return os.path.join(self._extra_root, extra_path)
+
+    def _load_extra(self, extra_path: str) -> np.ndarray:
+        extra_full_path = self._get_extra_full_path(extra_path)
+        return np.load(extra_full_path, mmap_mode="r")
+
+    def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
+        extra_full_path = self._get_extra_full_path(extra_path)
+        os.makedirs(self._extra_root, exist_ok=True)
+        np.save(extra_full_path, extra_array)
+
+    @property
+    def _entries_path(self) -> str:
+        return f"entries-{self._split.value.upper()}.npy"
+
+    @property
+    def _class_ids_path(self) -> str:
+        return f"class-ids-{self._split.value.upper()}.npy"
+
+    @property
+    def _class_names_path(self) -> str:
+        return f"class-names-{self._split.value.upper()}.npy"
+
+    def _get_entries(self) -> np.ndarray:
+        if self._entries is None:
+            self._entries = self._load_extra(self._entries_path)
+        assert self._entries is not None
+        return self._entries
+
+    def _get_class_ids(self) -> np.ndarray:
+        if self._split == _Split.TEST:
+            assert False, "Class IDs are not available in TEST split"
+        if self._class_ids is None:
+            self._class_ids = self._load_extra(self._class_ids_path)
+        assert self._class_ids is not None
+        return self._class_ids
+
+    def _get_class_names(self) -> np.ndarray:
+        if self._split == _Split.TEST:
+            assert False, "Class names are not available in TEST split"
+        if self._class_names is None:
+            self._class_names = self._load_extra(self._class_names_path)
+        assert self._class_names is not None
+        return self._class_names
+
+    def find_class_id(self, class_index: int) -> str:
+        class_ids = self._get_class_ids()
+        return str(class_ids[class_index])
+
+    def find_class_name(self, class_index: int) -> str:
+        class_names = self._get_class_names()
+        return str(class_names[class_index])
+
+    def get_image_data(self, index: int) -> bytes:
+        entries = self._get_entries()
+        actual_index = entries[index]["actual_index"]
+
+        class_id = self.get_class_id(index)
+
+        image_relpath = self.split.get_image_relpath(actual_index, class_id)
+        image_full_path = os.path.join(self.root, image_relpath)
+        with open(image_full_path, mode="rb") as f:
+            image_data = f.read()
+        return image_data
+
+    def get_target(self, index: int) -> Optional[Target]:
+        entries = self._get_entries()
+        class_index = entries[index]["class_index"]
+        return None if self.split == _Split.TEST else int(class_index)
+
+    def get_targets(self) -> Optional[np.ndarray]:
+        entries = self._get_entries()
+        return None if self.split == _Split.TEST else entries["class_index"]
+
+    def get_class_id(self, index: int) -> Optional[str]:
+        entries = self._get_entries()
+        class_id = entries[index]["class_id"]
+        return None if self.split == _Split.TEST else str(class_id)
+
+    def get_class_name(self, index: int) -> Optional[str]:
+        entries = self._get_entries()
+        class_name = entries[index]["class_name"]
+        return None if self.split == _Split.TEST else str(class_name)
+
+    def __len__(self) -> int:
+        entries = self._get_entries()
+        assert len(entries) == self.split.length
+        return len(entries)
+
+    def _load_labels(self, labels_path: str) -> List[Tuple[str, str]]:
+        labels_full_path = os.path.join(self.root, labels_path)
+        labels = []
+
+        try:
+            with open(labels_full_path, "r") as f:
+                reader = csv.reader(f)
+                for row in reader:
+                    class_id, class_name = row
+                    labels.append((class_id, class_name))
+        except OSError as e:
+            raise RuntimeError(f'can not read labels file "{labels_full_path}"') from e
+
+        return labels
+
+    def _dump_entries(self) -> None:
+        split = self.split
+        if split == ImageNet.Split.TEST:
+            dataset = None
+            sample_count = split.length
+            max_class_id_length, max_class_name_length = 0, 0
+        else:
+            labels_path = "labels.txt"
+            logger.info(f'loading labels from "{labels_path}"')
+            labels = self._load_labels(labels_path)
+
+            # NOTE: Using torchvision ImageFolder for consistency
+            from torchvision.datasets import ImageFolder
+
+            dataset_root = os.path.join(self.root, split.get_dirname())
+            dataset = ImageFolder(dataset_root)
+            sample_count = len(dataset)
+            max_class_id_length, max_class_name_length = -1, -1
+            for sample in dataset.samples:
+                _, class_index = sample
+                class_id, class_name = labels[class_index]
+                max_class_id_length = max(len(class_id), max_class_id_length)
+                max_class_name_length = max(len(class_name), max_class_name_length)
+
+        dtype = np.dtype(
+            [
+                ("actual_index", "<u4"),
+                ("class_index", "<u4"),
+                ("class_id", f"U{max_class_id_length}"),
+                ("class_name", f"U{max_class_name_length}"),
+            ]
+        )
+        entries_array = np.empty(sample_count, dtype=dtype)
+
+        if split == ImageNet.Split.TEST:
+            old_percent = -1
+            for index in range(sample_count):
+                percent = 100 * (index + 1) // sample_count
+                if percent > old_percent:
+                    logger.info(f"creating entries: {percent}%")
+                    old_percent = percent
+
+                actual_index = index + 1
+                class_index = np.uint32(-1)
+                class_id, class_name = "", ""
+                entries_array[index] = (actual_index, class_index, class_id, class_name)
+        else:
+            class_names = {class_id: class_name for class_id, class_name in labels}
+
+            assert dataset
+            old_percent = -1
+            for index in range(sample_count):
+                percent = 100 * (index + 1) // sample_count
+                if percent > old_percent:
+                    logger.info(f"creating entries: {percent}%")
+                    old_percent = percent
+
+                image_full_path, class_index = dataset.samples[index]
+                image_relpath = os.path.relpath(image_full_path, self.root)
+                class_id, actual_index = split.parse_image_relpath(image_relpath)
+                class_name = class_names[class_id]
+                entries_array[index] = (actual_index, class_index, class_id, class_name)
+
+        logger.info(f'saving entries to "{self._entries_path}"')
+        self._save_extra(entries_array, self._entries_path)
+
+    def _dump_class_ids_and_names(self) -> None:
+        split = self.split
+        if split == ImageNet.Split.TEST:
+            return
+
+        entries_array = self._load_extra(self._entries_path)
+
+        max_class_id_length, max_class_name_length, max_class_index = -1, -1, -1
+        for entry in entries_array:
+            class_index, class_id, class_name = (
+                entry["class_index"],
+                entry["class_id"],
+                entry["class_name"],
+            )
+            max_class_index = max(int(class_index), max_class_index)
+            max_class_id_length = max(len(str(class_id)), max_class_id_length)
+            max_class_name_length = max(len(str(class_name)), max_class_name_length)
+
+        class_count = max_class_index + 1
+        class_ids_array = np.empty(class_count, dtype=f"U{max_class_id_length}")
+        class_names_array = np.empty(class_count, dtype=f"U{max_class_name_length}")
+        for entry in entries_array:
+            class_index, class_id, class_name = (
+                entry["class_index"],
+                entry["class_id"],
+                entry["class_name"],
+            )
+            class_ids_array[class_index] = class_id
+            class_names_array[class_index] = class_name
+
+        logger.info(f'saving class IDs to "{self._class_ids_path}"')
+        self._save_extra(class_ids_array, self._class_ids_path)
+
+        logger.info(f'saving class names to "{self._class_names_path}"')
+        self._save_extra(class_names_array, self._class_names_path)
+
+    def dump_extra(self) -> None:
+        self._dump_entries()
+        self._dump_class_ids_and_names()
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net_22k.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net_22k.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c0bfd335a68b67e02c241f39b1ae06f9fbe1dd0
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/datasets/image_net_22k.py
@@ -0,0 +1,303 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+from enum import Enum
+from functools import lru_cache
+from gzip import GzipFile
+from io import BytesIO
+from mmap import ACCESS_READ, mmap
+import os
+from typing import Any, Callable, List, Optional, Set, Tuple
+import warnings
+
+import numpy as np
+
+from .extended import ExtendedVisionDataset
+
+
+_Labels = int
+
+_DEFAULT_MMAP_CACHE_SIZE = 16  # Warning: This can exhaust file descriptors
+
+
+@dataclass
+class _ClassEntry:
+    block_offset: int
+    maybe_filename: Optional[str] = None
+
+
+@dataclass
+class _Entry:
+    class_index: int  # noqa: E701
+    start_offset: int
+    end_offset: int
+    filename: str
+
+
+class _Split(Enum):
+    TRAIN = "train"
+    VAL = "val"
+
+    @property
+    def length(self) -> int:
+        return {
+            _Split.TRAIN: 11_797_647,
+            _Split.VAL: 561_050,
+        }[self]
+
+    def entries_path(self):
+        return f"imagenet21kp_{self.value}.txt"
+
+
+def _get_tarball_path(class_id: str) -> str:
+    return f"{class_id}.tar"
+
+
+def _make_mmap_tarball(tarballs_root: str, mmap_cache_size: int):
+    @lru_cache(maxsize=mmap_cache_size)
+    def _mmap_tarball(class_id: str) -> mmap:
+        tarball_path = _get_tarball_path(class_id)
+        tarball_full_path = os.path.join(tarballs_root, tarball_path)
+        with open(tarball_full_path) as f:
+            return mmap(fileno=f.fileno(), length=0, access=ACCESS_READ)
+
+    return _mmap_tarball
+
+
+class ImageNet22k(ExtendedVisionDataset):
+    _GZIPPED_INDICES: Set[int] = {
+        841_545,
+        1_304_131,
+        2_437_921,
+        2_672_079,
+        2_795_676,
+        2_969_786,
+        6_902_965,
+        6_903_550,
+        6_903_628,
+        7_432_557,
+        7_432_589,
+        7_813_809,
+        8_329_633,
+        10_296_990,
+        10_417_652,
+        10_492_265,
+        10_598_078,
+        10_782_398,
+        10_902_612,
+        11_203_736,
+        11_342_890,
+        11_397_596,
+        11_589_762,
+        11_705_103,
+        12_936_875,
+        13_289_782,
+    }
+    Labels = _Labels
+
+    def __init__(
+        self,
+        *,
+        root: str,
+        extra: str,
+        transforms: Optional[Callable] = None,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        mmap_cache_size: int = _DEFAULT_MMAP_CACHE_SIZE,
+    ) -> None:
+        super().__init__(root, transforms, transform, target_transform)
+        self._extra_root = extra
+
+        entries_path = self._get_entries_path(root)
+        self._entries = self._load_extra(entries_path)
+
+        class_ids_path = self._get_class_ids_path(root)
+        self._class_ids = self._load_extra(class_ids_path)
+
+        self._gzipped_indices = ImageNet22k._GZIPPED_INDICES
+        self._mmap_tarball = _make_mmap_tarball(self._tarballs_root, mmap_cache_size)
+
+    def _get_entries_path(self, root: Optional[str] = None) -> str:
+        return "entries.npy"
+
+    def _get_class_ids_path(self, root: Optional[str] = None) -> str:
+        return "class-ids.npy"
+
+    def _find_class_ids(self, path: str) -> List[str]:
+        class_ids = []
+
+        with os.scandir(path) as entries:
+            for entry in entries:
+                root, ext = os.path.splitext(entry.name)
+                if ext != ".tar":
+                    continue
+                class_ids.append(root)
+
+        return sorted(class_ids)
+
+    def _load_entries_class_ids(self, root: Optional[str] = None) -> Tuple[List[_Entry], List[str]]:
+        root = self.get_root(root)
+        entries: List[_Entry] = []
+        class_ids = self._find_class_ids(root)
+
+        for class_index, class_id in enumerate(class_ids):
+            path = os.path.join(root, "blocks", f"{class_id}.log")
+            class_entries = []
+
+            try:
+                with open(path) as f:
+                    for line in f:
+                        line = line.rstrip()
+                        block, filename = line.split(":")
+                        block_offset = int(block[6:])
+                        filename = filename[1:]
+
+                        maybe_filename = None
+                        if filename != "** Block of NULs **":
+                            maybe_filename = filename
+                            _, ext = os.path.splitext(filename)
+                            # assert ext == ".JPEG"
+
+                        class_entry = _ClassEntry(block_offset, maybe_filename)
+                        class_entries.append(class_entry)
+            except OSError as e:
+                raise RuntimeError(f'can not read blocks file "{path}"') from e
+
+            assert class_entries[-1].maybe_filename is None
+
+            for class_entry1, class_entry2 in zip(class_entries, class_entries[1:]):
+                assert class_entry1.block_offset <= class_entry2.block_offset
+                start_offset = 512 * class_entry1.block_offset
+                end_offset = 512 * class_entry2.block_offset
+                assert class_entry1.maybe_filename is not None
+                filename = class_entry1.maybe_filename
+                entry = _Entry(class_index, start_offset, end_offset, filename)
+                # Skip invalid image files (PIL throws UnidentifiedImageError)
+                if filename == "n06470073_47249.JPEG":
+                    continue
+                entries.append(entry)
+
+        return entries, class_ids
+
+    def _load_extra(self, extra_path: str) -> np.ndarray:
+        extra_root = self._extra_root
+        extra_full_path = os.path.join(extra_root, extra_path)
+        return np.load(extra_full_path, mmap_mode="r")
+
+    def _save_extra(self, extra_array: np.ndarray, extra_path: str) -> None:
+        extra_root = self._extra_root
+        extra_full_path = os.path.join(extra_root, extra_path)
+        os.makedirs(extra_root, exist_ok=True)
+        np.save(extra_full_path, extra_array)
+
+    @property
+    def _tarballs_root(self) -> str:
+        return self.root
+
+    def find_class_id(self, class_index: int) -> str:
+        return str(self._class_ids[class_index])
+
+    def get_image_data(self, index: int) -> bytes:
+        entry = self._entries[index]
+        class_id = entry["class_id"]
+        class_mmap = self._mmap_tarball(class_id)
+
+        start_offset, end_offset = entry["start_offset"], entry["end_offset"]
+        try:
+            mapped_data = class_mmap[start_offset:end_offset]
+            data = mapped_data[512:]  # Skip entry header block
+
+            if len(data) >= 2 and tuple(data[:2]) == (0x1F, 0x8B):
+                assert index in self._gzipped_indices, f"unexpected gzip header for sample {index}"
+                with GzipFile(fileobj=BytesIO(data)) as g:
+                    data = g.read()
+        except Exception as e:
+            raise RuntimeError(f"can not retrieve image data for sample {index} " f'from "{class_id}" tarball') from e
+
+        return data
+
+    def get_target(self, index: int) -> Any:
+        return int(self._entries[index]["class_index"])
+
+    def get_targets(self) -> np.ndarray:
+        return self._entries["class_index"]
+
+    def get_class_id(self, index: int) -> str:
+        return str(self._entries[index]["class_id"])
+
+    def get_class_ids(self) -> np.ndarray:
+        return self._entries["class_id"]
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            return super().__getitem__(index)
+
+    def __len__(self) -> int:
+        return len(self._entries)
+
+    def _dump_entries(self, *args, **kwargs) -> None:
+        entries, class_ids = self._load_entries_class_ids(*args, **kwargs)
+
+        max_class_id_length, max_filename_length, max_class_index = -1, -1, -1
+        for entry in entries:
+            class_id = class_ids[entry.class_index]
+            max_class_index = max(entry.class_index, max_class_index)
+            max_class_id_length = max(len(class_id), max_class_id_length)
+            max_filename_length = max(len(entry.filename), max_filename_length)
+
+        dtype = np.dtype(
+            [
+                ("class_index", "<u4"),
+                ("class_id", f"U{max_class_id_length}"),
+                ("start_offset", "<u4"),
+                ("end_offset", "<u4"),
+                ("filename", f"U{max_filename_length}"),
+            ]
+        )
+        sample_count = len(entries)
+        entries_array = np.empty(sample_count, dtype=dtype)
+        for i, entry in enumerate(entries):
+            class_index = entry.class_index
+            class_id = class_ids[class_index]
+            start_offset = entry.start_offset
+            end_offset = entry.end_offset
+            filename = entry.filename
+            entries_array[i] = (
+                class_index,
+                class_id,
+                start_offset,
+                end_offset,
+                filename,
+            )
+
+        entries_path = self._get_entries_path(*args, **kwargs)
+        self._save_extra(entries_array, entries_path)
+
+    def _dump_class_ids(self, *args, **kwargs) -> None:
+        entries_path = self._get_entries_path(*args, **kwargs)
+        entries_array = self._load_extra(entries_path)
+
+        max_class_id_length, max_class_index = -1, -1
+        for entry in entries_array:
+            class_index, class_id = entry["class_index"], entry["class_id"]
+            max_class_index = max(int(class_index), max_class_index)
+            max_class_id_length = max(len(str(class_id)), max_class_id_length)
+
+        class_ids_array = np.empty(max_class_index + 1, dtype=f"U{max_class_id_length}")
+        for entry in entries_array:
+            class_index, class_id = entry["class_index"], entry["class_id"]
+            class_ids_array[class_index] = class_id
+        class_ids_path = self._get_class_ids_path(*args, **kwargs)
+        self._save_extra(class_ids_array, class_ids_path)
+
+    def _dump_extra(self, *args, **kwargs) -> None:
+        self._dump_entries(*args, *kwargs)
+        self._dump_class_ids(*args, *kwargs)
+
+    def dump_extra(self, root: Optional[str] = None) -> None:
+        return self._dump_extra(root)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/loaders.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fb6f25a0a3c3251b803f48d0a515aa0b9591226
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/loaders.py
@@ -0,0 +1,223 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from enum import Enum
+from typing import Any, Callable, List, Optional, TypeVar
+
+import torch
+from torch.utils.data import Sampler
+
+from .datasets import ImageNet, ImageNet22k
+from .samplers import EpochSampler, InfiniteSampler, ShardedInfiniteSampler
+
+
+logger = logging.getLogger("dinov2")
+
+
+class SamplerType(Enum):
+    DISTRIBUTED = 0
+    EPOCH = 1
+    INFINITE = 2
+    SHARDED_INFINITE = 3
+    SHARDED_INFINITE_NEW = 4
+
+
+def _make_bool_str(b: bool) -> str:
+    return "yes" if b else "no"
+
+
+def _make_sample_transform(image_transform: Optional[Callable] = None, target_transform: Optional[Callable] = None):
+    def transform(sample):
+        image, target = sample
+        if image_transform is not None:
+            image = image_transform(image)
+        if target_transform is not None:
+            target = target_transform(target)
+        return image, target
+
+    return transform
+
+
+def _parse_dataset_str(dataset_str: str):
+    tokens = dataset_str.split(":")
+
+    name = tokens[0]
+    kwargs = {}
+
+    for token in tokens[1:]:
+        key, value = token.split("=")
+        assert key in ("root", "extra", "split")
+        kwargs[key] = value
+
+    if name == "ImageNet":
+        class_ = ImageNet
+        if "split" in kwargs:
+            kwargs["split"] = ImageNet.Split[kwargs["split"]]
+    elif name == "ImageNet22k":
+        class_ = ImageNet22k
+    else:
+        raise ValueError(f'Unsupported dataset "{name}"')
+
+    return class_, kwargs
+
+
+def make_dataset(
+    *,
+    dataset_str: str,
+    transform: Optional[Callable] = None,
+    target_transform: Optional[Callable] = None,
+):
+    """
+    Creates a dataset with the specified parameters.
+
+    Args:
+        dataset_str: A dataset string description (e.g. ImageNet:split=TRAIN).
+        transform: A transform to apply to images.
+        target_transform: A transform to apply to targets.
+
+    Returns:
+        The created dataset.
+    """
+    logger.info(f'using dataset: "{dataset_str}"')
+
+    class_, kwargs = _parse_dataset_str(dataset_str)
+    dataset = class_(transform=transform, target_transform=target_transform, **kwargs)
+
+    logger.info(f"# of dataset samples: {len(dataset):,d}")
+
+    # Aggregated datasets do not expose (yet) these attributes, so add them.
+    if not hasattr(dataset, "transform"):
+        setattr(dataset, "transform", transform)
+    if not hasattr(dataset, "target_transform"):
+        setattr(dataset, "target_transform", target_transform)
+
+    return dataset
+
+
+def _make_sampler(
+    *,
+    dataset,
+    type: Optional[SamplerType] = None,
+    shuffle: bool = False,
+    seed: int = 0,
+    size: int = -1,
+    advance: int = 0,
+) -> Optional[Sampler]:
+    sample_count = len(dataset)
+
+    if type == SamplerType.INFINITE:
+        logger.info("sampler: infinite")
+        if size > 0:
+            raise ValueError("sampler size > 0 is invalid")
+        return InfiniteSampler(
+            sample_count=sample_count,
+            shuffle=shuffle,
+            seed=seed,
+            advance=advance,
+        )
+    elif type in (SamplerType.SHARDED_INFINITE, SamplerType.SHARDED_INFINITE_NEW):
+        logger.info("sampler: sharded infinite")
+        if size > 0:
+            raise ValueError("sampler size > 0 is invalid")
+        # TODO: Remove support for old shuffling
+        use_new_shuffle_tensor_slice = type == SamplerType.SHARDED_INFINITE_NEW
+        return ShardedInfiniteSampler(
+            sample_count=sample_count,
+            shuffle=shuffle,
+            seed=seed,
+            advance=advance,
+            use_new_shuffle_tensor_slice=use_new_shuffle_tensor_slice,
+        )
+    elif type == SamplerType.EPOCH:
+        logger.info("sampler: epoch")
+        if advance > 0:
+            raise NotImplementedError("sampler advance > 0 is not supported")
+        size = size if size > 0 else sample_count
+        logger.info(f"# of samples / epoch: {size:,d}")
+        return EpochSampler(
+            size=size,
+            sample_count=sample_count,
+            shuffle=shuffle,
+            seed=seed,
+        )
+    elif type == SamplerType.DISTRIBUTED:
+        logger.info("sampler: distributed")
+        if size > 0:
+            raise ValueError("sampler size > 0 is invalid")
+        if advance > 0:
+            raise ValueError("sampler advance > 0 is invalid")
+        return torch.utils.data.DistributedSampler(
+            dataset=dataset,
+            shuffle=shuffle,
+            seed=seed,
+            drop_last=False,
+        )
+
+    logger.info("sampler: none")
+    return None
+
+
+T = TypeVar("T")
+
+
+def make_data_loader(
+    *,
+    dataset,
+    batch_size: int,
+    num_workers: int,
+    shuffle: bool = True,
+    seed: int = 0,
+    sampler_type: Optional[SamplerType] = SamplerType.INFINITE,
+    sampler_size: int = -1,
+    sampler_advance: int = 0,
+    drop_last: bool = True,
+    persistent_workers: bool = False,
+    collate_fn: Optional[Callable[[List[T]], Any]] = None,
+):
+    """
+    Creates a data loader with the specified parameters.
+
+    Args:
+        dataset: A dataset (third party, LaViDa or WebDataset).
+        batch_size: The size of batches to generate.
+        num_workers: The number of workers to use.
+        shuffle: Whether to shuffle samples.
+        seed: The random seed to use.
+        sampler_type: Which sampler to use: EPOCH, INFINITE, SHARDED_INFINITE, SHARDED_INFINITE_NEW, DISTRIBUTED or None.
+        sampler_size: The number of images per epoch (when applicable) or -1 for the entire dataset.
+        sampler_advance: How many samples to skip (when applicable).
+        drop_last: Whether the last non-full batch of data should be dropped.
+        persistent_workers: maintain the workers Dataset instances alive after a dataset has been consumed once.
+        collate_fn: Function that performs batch collation
+    """
+
+    sampler = _make_sampler(
+        dataset=dataset,
+        type=sampler_type,
+        shuffle=shuffle,
+        seed=seed,
+        size=sampler_size,
+        advance=sampler_advance,
+    )
+
+    logger.info("using PyTorch data loader")
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        sampler=sampler,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        pin_memory=True,
+        drop_last=drop_last,
+        persistent_workers=persistent_workers,
+        collate_fn=collate_fn,
+    )
+
+    try:
+        logger.info(f"# of batches: {len(data_loader):,d}")
+    except TypeError:  # data loader has no length
+        logger.info("infinite data loader")
+    return data_loader
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/masking.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/masking.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc3c72648c3e440dcdb284366b98d2df12ad1272
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/masking.py
@@ -0,0 +1,87 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import random
+import math
+import numpy as np
+
+
+class MaskingGenerator:
+    def __init__(
+        self,
+        input_size,
+        num_masking_patches=None,
+        min_num_patches=4,
+        max_num_patches=None,
+        min_aspect=0.3,
+        max_aspect=None,
+    ):
+        if not isinstance(input_size, tuple):
+            input_size = (input_size,) * 2
+        self.height, self.width = input_size
+
+        self.num_patches = self.height * self.width
+        self.num_masking_patches = num_masking_patches
+
+        self.min_num_patches = min_num_patches
+        self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches
+
+        max_aspect = max_aspect or 1 / min_aspect
+        self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
+
+    def __repr__(self):
+        repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
+            self.height,
+            self.width,
+            self.min_num_patches,
+            self.max_num_patches,
+            self.num_masking_patches,
+            self.log_aspect_ratio[0],
+            self.log_aspect_ratio[1],
+        )
+        return repr_str
+
+    def get_shape(self):
+        return self.height, self.width
+
+    def _mask(self, mask, max_mask_patches):
+        delta = 0
+        for _ in range(10):
+            target_area = random.uniform(self.min_num_patches, max_mask_patches)
+            aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
+            h = int(round(math.sqrt(target_area * aspect_ratio)))
+            w = int(round(math.sqrt(target_area / aspect_ratio)))
+            if w < self.width and h < self.height:
+                top = random.randint(0, self.height - h)
+                left = random.randint(0, self.width - w)
+
+                num_masked = mask[top : top + h, left : left + w].sum()
+                # Overlap
+                if 0 < h * w - num_masked <= max_mask_patches:
+                    for i in range(top, top + h):
+                        for j in range(left, left + w):
+                            if mask[i, j] == 0:
+                                mask[i, j] = 1
+                                delta += 1
+
+                if delta > 0:
+                    break
+        return delta
+
+    def __call__(self, num_masking_patches=0):
+        mask = np.zeros(shape=self.get_shape(), dtype=bool)
+        mask_count = 0
+        while mask_count < num_masking_patches:
+            max_mask_patches = num_masking_patches - mask_count
+            max_mask_patches = min(max_mask_patches, self.max_num_patches)
+
+            delta = self._mask(mask, max_mask_patches)
+            if delta == 0:
+                break
+            else:
+                mask_count += delta
+
+        return mask
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/samplers.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e356edf603a33ce2d18a388fd799694e22d1980f
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/samplers.py
@@ -0,0 +1,230 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import itertools
+from typing import Any, Optional
+import warnings
+
+import numpy as np
+import torch
+from torch.utils.data.sampler import Sampler
+
+import dinov2.distributed as distributed
+
+
+class EpochSampler(Sampler):
+    def __init__(
+        self,
+        *,
+        size: int,
+        sample_count: int,
+        shuffle: bool = False,
+        seed: int = 0,
+        start: Optional[int] = None,
+        step: Optional[int] = None,
+    ):
+        self._size = size
+        self._sample_count = sample_count
+        self._shuffle = shuffle
+        self._seed = seed
+        self._start = distributed.get_global_rank() if start is None else start
+        self._step = distributed.get_global_size() if step is None else step
+        self._epoch = 0
+
+    def __iter__(self):
+        count = (self._size + self._sample_count - 1) // self._sample_count
+        tiled_indices = np.tile(np.arange(self._sample_count), count)
+        if self._shuffle:
+            seed = self._seed * self._epoch if self._seed != 0 else self._epoch
+            rng = np.random.default_rng(seed)
+            iterable = rng.choice(tiled_indices, self._size, replace=False)
+        else:
+            iterable = tiled_indices[: self._size]
+
+        yield from itertools.islice(iterable, self._start, None, self._step)
+
+    def __len__(self):
+        return (self._size - self._start + self._step - 1) // self._step
+
+    def set_epoch(self, epoch):
+        self._epoch = epoch
+
+
+def _get_numpy_dtype(size: int) -> Any:
+    return np.int32 if size <= 2**31 else np.int64
+
+
+def _get_torch_dtype(size: int) -> Any:
+    return torch.int32 if size <= 2**31 else torch.int64
+
+
+def _generate_randperm_indices(*, size: int, generator: torch.Generator):
+    """Generate the indices of a random permutation."""
+    dtype = _get_torch_dtype(size)
+    # This is actually matching PyTorch's CPU implementation, see: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/TensorFactories.cpp#L900-L921
+    perm = torch.arange(size, dtype=dtype)
+    for i in range(size):
+        j = torch.randint(i, size, size=(1,), generator=generator).item()
+
+        # Always swap even if no-op
+        value = perm[j].item()
+        perm[j] = perm[i].item()
+        perm[i] = value
+        yield value
+
+
+class InfiniteSampler(Sampler):
+    def __init__(
+        self,
+        *,
+        sample_count: int,
+        shuffle: bool = False,
+        seed: int = 0,
+        start: Optional[int] = None,
+        step: Optional[int] = None,
+        advance: int = 0,
+    ):
+        self._sample_count = sample_count
+        self._seed = seed
+        self._shuffle = shuffle
+        self._start = distributed.get_global_rank() if start is None else start
+        self._step = distributed.get_global_size() if step is None else step
+        self._advance = advance
+
+    def __iter__(self):
+        if self._shuffle:
+            iterator = self._shuffled_iterator()
+        else:
+            iterator = self._iterator()
+
+        yield from itertools.islice(iterator, self._advance, None)
+
+    def _iterator(self):
+        assert not self._shuffle
+
+        while True:
+            iterable = range(self._sample_count)
+            yield from itertools.islice(iterable, self._start, None, self._step)
+
+    def _shuffled_iterator(self):
+        assert self._shuffle
+
+        # Instantiate a generator here (rather than in the ctor) to keep the class
+        # picklable (requirement of mp.spawn)
+        generator = torch.Generator().manual_seed(self._seed)
+
+        while True:
+            iterable = _generate_randperm_indices(size=self._sample_count, generator=generator)
+            yield from itertools.islice(iterable, self._start, None, self._step)
+
+
+# The following function is somewhat equivalent to _new_shuffle_tensor_slice below,
+# but avoids a full in-place random permutation generation.
+def _shuffle_tensor_slice(
+    *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
+) -> np.ndarray:
+    stop = len(tensor)
+    count = stop // step
+    drop_count = stop - step * count
+    if drop_count:
+        warnings.warn(f"# of dropped samples: {drop_count}")
+
+    dtype = _get_numpy_dtype(stop)
+    result = np.empty(count, dtype=dtype)
+
+    for i in range(count):
+        j = torch.randint(0, i + 1, size=(1,), generator=generator).item() if i > 0 else 0
+
+        result[i] = result[j]
+        result[j] = tensor[start + i * step].item()
+
+    return result
+
+
+def _new_shuffle_tensor_slice(
+    *, tensor: torch.Tensor, start: int = 0, step: int = 1, generator: torch.Generator
+) -> np.ndarray:
+    stop = len(tensor)
+    count = stop // step
+    dtype = torch.int64  # Needed for using randperm result as indices
+    count = stop // step
+    drop_count = stop - step * count
+    if drop_count:
+        warnings.warn(f"# of dropped samples: {drop_count}")
+    indices = torch.randperm(count, dtype=dtype, generator=generator)
+    return tensor[start::step][indices].numpy()
+
+
+def _make_seed(seed: int, start: int, iter_count: int) -> int:
+    # NOTE: Tried a few variants (including iter_count << 32), this one worked best.
+    return seed + start + (iter_count << 24)
+
+
+class ShardedInfiniteSampler(Sampler):
+    def __init__(
+        self,
+        *,
+        sample_count: int,
+        shuffle: bool = False,
+        seed: int = 0,
+        start: Optional[int] = None,
+        step: Optional[int] = None,
+        advance: int = 0,
+        use_new_shuffle_tensor_slice: bool = False,
+    ):
+        self._sample_count = sample_count
+        self._seed = seed
+        self._shuffle = shuffle
+        self._start = distributed.get_global_rank() if start is None else start
+        self._step = distributed.get_global_size() if step is None else step
+        self._advance = advance
+        self._iter_count = 0
+        self._shuffle_tensor_slice_fn = (
+            _new_shuffle_tensor_slice if use_new_shuffle_tensor_slice else _shuffle_tensor_slice
+        )
+
+    def __iter__(self):
+        iter_count = self._advance // self._sample_count
+        if iter_count > 0:
+            self._advance -= iter_count * self._sample_count
+            self._iter_count += iter_count
+
+        if self._shuffle:
+            iterator = self._shuffled_iterator()
+        else:
+            iterator = self._iterator()
+
+        yield from itertools.islice(iterator, self._advance, None)
+
+    def _iterator(self):
+        assert not self._shuffle
+
+        while True:
+            iterable = range(self._sample_count)
+            yield from itertools.islice(iterable, self._start, None, self._step)
+
+    def _shuffled_iterator(self):
+        assert self._shuffle
+
+        # Instantiate a generator here (rather than in the ctor) to be keep the class
+        # picklable (requirement of mp.spawn)
+        generator = torch.Generator()
+
+        # Always shuffle everything first
+        generator.manual_seed(self._seed)
+        dtype = _get_torch_dtype(self._sample_count)
+        perm = torch.randperm(self._sample_count, dtype=dtype, generator=generator)
+
+        while True:
+            # Re-seed on each iteration to allow skipping whole permutations
+            seed = _make_seed(self._seed, self._start, self._iter_count)
+            generator.manual_seed(seed)
+
+            iterable = self._shuffle_tensor_slice_fn(
+                tensor=perm, start=self._start, step=self._step, generator=generator
+            )
+            yield from iterable
+            self._iter_count += 1
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/data/transforms.py b/torchhub/facebookresearch_dinov2_main/dinov2/data/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1bc4cbd1a459a9f44314806cf9ccedea112ab14
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/data/transforms.py
@@ -0,0 +1,92 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Sequence
+
+import torch
+from torchvision import transforms
+
+
+class GaussianBlur(transforms.RandomApply):
+    """
+    Apply Gaussian Blur to the PIL image.
+    """
+
+    def __init__(self, *, p: float = 0.5, radius_min: float = 0.1, radius_max: float = 2.0):
+        # NOTE: torchvision is applying 1 - probability to return the original image
+        keep_p = 1 - p
+        transform = transforms.GaussianBlur(kernel_size=9, sigma=(radius_min, radius_max))
+        super().__init__(transforms=[transform], p=keep_p)
+
+
+class MaybeToTensor(transforms.ToTensor):
+    """
+    Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor.
+    """
+
+    def __call__(self, pic):
+        """
+        Args:
+            pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor.
+        Returns:
+            Tensor: Converted image.
+        """
+        if isinstance(pic, torch.Tensor):
+            return pic
+        return super().__call__(pic)
+
+
+# Use timm's names
+IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
+
+
+def make_normalize_transform(
+    mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
+    std: Sequence[float] = IMAGENET_DEFAULT_STD,
+) -> transforms.Normalize:
+    return transforms.Normalize(mean=mean, std=std)
+
+
+# This roughly matches torchvision's preset for classification training:
+#   https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L6-L44
+def make_classification_train_transform(
+    *,
+    crop_size: int = 224,
+    interpolation=transforms.InterpolationMode.BICUBIC,
+    hflip_prob: float = 0.5,
+    mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
+    std: Sequence[float] = IMAGENET_DEFAULT_STD,
+):
+    transforms_list = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)]
+    if hflip_prob > 0.0:
+        transforms_list.append(transforms.RandomHorizontalFlip(hflip_prob))
+    transforms_list.extend(
+        [
+            MaybeToTensor(),
+            make_normalize_transform(mean=mean, std=std),
+        ]
+    )
+    return transforms.Compose(transforms_list)
+
+
+# This matches (roughly) torchvision's preset for classification evaluation:
+#   https://github.com/pytorch/vision/blob/main/references/classification/presets.py#L47-L69
+def make_classification_eval_transform(
+    *,
+    resize_size: int = 256,
+    interpolation=transforms.InterpolationMode.BICUBIC,
+    crop_size: int = 224,
+    mean: Sequence[float] = IMAGENET_DEFAULT_MEAN,
+    std: Sequence[float] = IMAGENET_DEFAULT_STD,
+) -> transforms.Compose:
+    transforms_list = [
+        transforms.Resize(resize_size, interpolation=interpolation),
+        transforms.CenterCrop(crop_size),
+        MaybeToTensor(),
+        make_normalize_transform(mean=mean, std=std),
+    ]
+    return transforms.Compose(transforms_list)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/distributed/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ccd663f33d5a21ad1f9d25db7bd378ec52aeac2
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/distributed/__init__.py
@@ -0,0 +1,271 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import random
+import re
+import socket
+from typing import Dict, List
+
+import torch
+import torch.distributed as dist
+
+_LOCAL_RANK = -1
+_LOCAL_WORLD_SIZE = -1
+
+
+def is_enabled() -> bool:
+    """
+    Returns:
+        True if distributed training is enabled
+    """
+    return dist.is_available() and dist.is_initialized()
+
+
+def get_global_size() -> int:
+    """
+    Returns:
+        The number of processes in the process group
+    """
+    return dist.get_world_size() if is_enabled() else 1
+
+
+def get_global_rank() -> int:
+    """
+    Returns:
+        The rank of the current process within the global process group.
+    """
+    return dist.get_rank() if is_enabled() else 0
+
+
+def get_local_rank() -> int:
+    """
+    Returns:
+        The rank of the current process within the local (per-machine) process group.
+    """
+    if not is_enabled():
+        return 0
+    assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
+    return _LOCAL_RANK
+
+
+def get_local_size() -> int:
+    """
+    Returns:
+        The size of the per-machine process group,
+        i.e. the number of processes per machine.
+    """
+    if not is_enabled():
+        return 1
+    assert 0 <= _LOCAL_RANK < _LOCAL_WORLD_SIZE
+    return _LOCAL_WORLD_SIZE
+
+
+def is_main_process() -> bool:
+    """
+    Returns:
+        True if the current process is the main one.
+    """
+    return get_global_rank() == 0
+
+
+def _restrict_print_to_main_process() -> None:
+    """
+    This function disables printing when not in the main process
+    """
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_main_process() or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def _get_master_port(seed: int = 0) -> int:
+    MIN_MASTER_PORT, MAX_MASTER_PORT = (20_000, 60_000)
+
+    master_port_str = os.environ.get("MASTER_PORT")
+    if master_port_str is None:
+        rng = random.Random(seed)
+        return rng.randint(MIN_MASTER_PORT, MAX_MASTER_PORT)
+
+    return int(master_port_str)
+
+
+def _get_available_port() -> int:
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        # A "" host address means INADDR_ANY i.e. binding to all interfaces.
+        # Note this is not compatible with IPv6.
+        s.bind(("", 0))
+        port = s.getsockname()[1]
+        return port
+
+
+_TORCH_DISTRIBUTED_ENV_VARS = (
+    "MASTER_ADDR",
+    "MASTER_PORT",
+    "RANK",
+    "WORLD_SIZE",
+    "LOCAL_RANK",
+    "LOCAL_WORLD_SIZE",
+)
+
+
+def _collect_env_vars() -> Dict[str, str]:
+    return {env_var: os.environ[env_var] for env_var in _TORCH_DISTRIBUTED_ENV_VARS if env_var in os.environ}
+
+
+def _is_slurm_job_process() -> bool:
+    return "SLURM_JOB_ID" in os.environ
+
+
+def _parse_slurm_node_list(s: str) -> List[str]:
+    nodes = []
+    # Extract "hostname", "hostname[1-2,3,4-5]," substrings
+    p = re.compile(r"(([^\[]+)(?:\[([^\]]+)\])?),?")
+    for m in p.finditer(s):
+        prefix, suffixes = s[m.start(2) : m.end(2)], s[m.start(3) : m.end(3)]
+        for suffix in suffixes.split(","):
+            span = suffix.split("-")
+            if len(span) == 1:
+                nodes.append(prefix + suffix)
+            else:
+                width = len(span[0])
+                start, end = int(span[0]), int(span[1]) + 1
+                nodes.extend([prefix + f"{i:0{width}}" for i in range(start, end)])
+    return nodes
+
+
+def _check_env_variable(key: str, new_value: str):
+    # Only check for difference with preset environment variables
+    if key in os.environ and os.environ[key] != new_value:
+        raise RuntimeError(f"Cannot export environment variables as {key} is already set")
+
+
+class _TorchDistributedEnvironment:
+    def __init__(self):
+        self.master_addr = "127.0.0.1"
+        self.master_port = 0
+        self.rank = -1
+        self.world_size = -1
+        self.local_rank = -1
+        self.local_world_size = -1
+
+        if _is_slurm_job_process():
+            return self._set_from_slurm_env()
+
+        env_vars = _collect_env_vars()
+        if not env_vars:
+            # Environment is not set
+            pass
+        elif len(env_vars) == len(_TORCH_DISTRIBUTED_ENV_VARS):
+            # Environment is fully set
+            return self._set_from_preset_env()
+        else:
+            # Environment is partially set
+            collected_env_vars = ", ".join(env_vars.keys())
+            raise RuntimeError(f"Partially set environment: {collected_env_vars}")
+
+        if torch.cuda.device_count() > 0:
+            return self._set_from_local()
+
+        raise RuntimeError("Can't initialize PyTorch distributed environment")
+
+    # Slurm job created with sbatch, submitit, etc...
+    def _set_from_slurm_env(self):
+        # logger.info("Initialization from Slurm environment")
+        job_id = int(os.environ["SLURM_JOB_ID"])
+        node_count = int(os.environ["SLURM_JOB_NUM_NODES"])
+        nodes = _parse_slurm_node_list(os.environ["SLURM_JOB_NODELIST"])
+        assert len(nodes) == node_count
+
+        self.master_addr = nodes[0]
+        self.master_port = _get_master_port(seed=job_id)
+        self.rank = int(os.environ["SLURM_PROCID"])
+        self.world_size = int(os.environ["SLURM_NTASKS"])
+        assert self.rank < self.world_size
+        self.local_rank = int(os.environ["SLURM_LOCALID"])
+        self.local_world_size = self.world_size // node_count
+        assert self.local_rank < self.local_world_size
+
+    # Single node job with preset environment (i.e. torchrun)
+    def _set_from_preset_env(self):
+        # logger.info("Initialization from preset environment")
+        self.master_addr = os.environ["MASTER_ADDR"]
+        self.master_port = os.environ["MASTER_PORT"]
+        self.rank = int(os.environ["RANK"])
+        self.world_size = int(os.environ["WORLD_SIZE"])
+        assert self.rank < self.world_size
+        self.local_rank = int(os.environ["LOCAL_RANK"])
+        self.local_world_size = int(os.environ["LOCAL_WORLD_SIZE"])
+        assert self.local_rank < self.local_world_size
+
+    # Single node and GPU job (i.e. local script run)
+    def _set_from_local(self):
+        # logger.info("Initialization from local")
+        self.master_addr = "127.0.0.1"
+        self.master_port = _get_available_port()
+        self.rank = 0
+        self.world_size = 1
+        self.local_rank = 0
+        self.local_world_size = 1
+
+    def export(self, *, overwrite: bool) -> "_TorchDistributedEnvironment":
+        # See the "Environment variable initialization" section from
+        # https://pytorch.org/docs/stable/distributed.html for the complete list of
+        # environment variables required for the env:// initialization method.
+        env_vars = {
+            "MASTER_ADDR": self.master_addr,
+            "MASTER_PORT": str(self.master_port),
+            "RANK": str(self.rank),
+            "WORLD_SIZE": str(self.world_size),
+            "LOCAL_RANK": str(self.local_rank),
+            "LOCAL_WORLD_SIZE": str(self.local_world_size),
+        }
+        if not overwrite:
+            for k, v in env_vars.items():
+                _check_env_variable(k, v)
+
+        os.environ.update(env_vars)
+        return self
+
+
+def enable(*, set_cuda_current_device: bool = True, overwrite: bool = False, allow_nccl_timeout: bool = False):
+    """Enable distributed mode
+
+    Args:
+        set_cuda_current_device: If True, call torch.cuda.set_device() to set the
+            current PyTorch CUDA device to the one matching the local rank.
+        overwrite: If True, overwrites already set variables. Else fails.
+    """
+
+    global _LOCAL_RANK, _LOCAL_WORLD_SIZE
+    if _LOCAL_RANK >= 0 or _LOCAL_WORLD_SIZE >= 0:
+        raise RuntimeError("Distributed mode has already been enabled")
+    torch_env = _TorchDistributedEnvironment()
+    torch_env.export(overwrite=overwrite)
+
+    if set_cuda_current_device:
+        torch.cuda.set_device(torch_env.local_rank)
+
+    if allow_nccl_timeout:
+        # This allows to use torch distributed timeout in a NCCL backend
+        key, value = "NCCL_ASYNC_ERROR_HANDLING", "1"
+        if not overwrite:
+            _check_env_variable(key, value)
+        os.environ[key] = value
+
+    dist.init_process_group(backend="nccl")
+    dist.barrier()
+
+    # Finalize setup
+    _LOCAL_RANK = torch_env.local_rank
+    _LOCAL_WORLD_SIZE = torch_env.local_world_size
+    _restrict_print_to_main_process()
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/eval/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/eval/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0952fcc3f57e34b3747962e9ebd6fc57aeea63fa
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/eval/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/eval/knn.py b/torchhub/facebookresearch_dinov2_main/dinov2/eval/knn.py
new file mode 100644
index 0000000000000000000000000000000000000000..02ee261348e9871b10bfc40b7283b4f6205cba18
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/eval/knn.py
@@ -0,0 +1,405 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+from functools import partial
+import json
+import logging
+import os
+import sys
+from typing import List, Optional
+
+import torch
+from torch.nn.functional import one_hot, softmax
+
+import dinov2.distributed as distributed
+from dinov2.data import SamplerType, make_data_loader, make_dataset
+from dinov2.data.transforms import make_classification_eval_transform
+from dinov2.eval.metrics import AccuracyAveraging, build_topk_accuracy_metric
+from dinov2.eval.setup import get_args_parser as get_setup_args_parser
+from dinov2.eval.setup import setup_and_build_model
+from dinov2.eval.utils import ModelWithNormalize, evaluate, extract_features
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parents = parents or []
+    setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
+    parents = [setup_args_parser]
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--train-dataset",
+        dest="train_dataset_str",
+        type=str,
+        help="Training dataset",
+    )
+    parser.add_argument(
+        "--val-dataset",
+        dest="val_dataset_str",
+        type=str,
+        help="Validation dataset",
+    )
+    parser.add_argument(
+        "--nb_knn",
+        nargs="+",
+        type=int,
+        help="Number of NN to use. 20 is usually working the best.",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        help="Temperature used in the voting coefficient",
+    )
+    parser.add_argument(
+        "--gather-on-cpu",
+        action="store_true",
+        help="Whether to gather the train features on cpu, slower"
+        "but useful to avoid OOM for large datasets (e.g. ImageNet22k).",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        help="Batch size.",
+    )
+    parser.add_argument(
+        "--n-per-class-list",
+        nargs="+",
+        type=int,
+        help="Number to take per class",
+    )
+    parser.add_argument(
+        "--n-tries",
+        type=int,
+        help="Number of tries",
+    )
+    parser.set_defaults(
+        train_dataset_str="ImageNet:split=TRAIN",
+        val_dataset_str="ImageNet:split=VAL",
+        nb_knn=[10, 20, 100, 200],
+        temperature=0.07,
+        batch_size=256,
+        n_per_class_list=[-1],
+        n_tries=1,
+    )
+    return parser
+
+
+class KnnModule(torch.nn.Module):
+    """
+    Gets knn of test features from all processes on a chunk of the train features
+
+    Each rank gets a chunk of the train features as well as a chunk of the test features.
+    In `compute_neighbors`, for each rank one after the other, its chunk of test features
+    is sent to all devices, partial knns are computed with each chunk of train features
+    then collated back on the original device.
+    """
+
+    def __init__(self, train_features, train_labels, nb_knn, T, device, num_classes=1000):
+        super().__init__()
+
+        self.global_rank = distributed.get_global_rank()
+        self.global_size = distributed.get_global_size()
+
+        self.device = device
+        self.train_features_rank_T = train_features.chunk(self.global_size)[self.global_rank].T.to(self.device)
+        self.candidates = train_labels.chunk(self.global_size)[self.global_rank].view(1, -1).to(self.device)
+
+        self.nb_knn = nb_knn
+        self.max_k = max(self.nb_knn)
+        self.T = T
+        self.num_classes = num_classes
+
+    def _get_knn_sims_and_labels(self, similarity, train_labels):
+        topk_sims, indices = similarity.topk(self.max_k, largest=True, sorted=True)
+        neighbors_labels = torch.gather(train_labels, 1, indices)
+        return topk_sims, neighbors_labels
+
+    def _similarity_for_rank(self, features_rank, source_rank):
+        # Send the features from `source_rank` to all ranks
+        broadcast_shape = torch.tensor(features_rank.shape).to(self.device)
+        torch.distributed.broadcast(broadcast_shape, source_rank)
+
+        broadcasted = features_rank
+        if self.global_rank != source_rank:
+            broadcasted = torch.zeros(*broadcast_shape, dtype=features_rank.dtype, device=self.device)
+        torch.distributed.broadcast(broadcasted, source_rank)
+
+        # Compute the neighbors for `source_rank` among `train_features_rank_T`
+        similarity_rank = torch.mm(broadcasted, self.train_features_rank_T)
+        candidate_labels = self.candidates.expand(len(similarity_rank), -1)
+        return self._get_knn_sims_and_labels(similarity_rank, candidate_labels)
+
+    def _gather_all_knn_for_rank(self, topk_sims, neighbors_labels, target_rank):
+        # Gather all neighbors for `target_rank`
+        topk_sims_rank = retrieved_rank = None
+        if self.global_rank == target_rank:
+            topk_sims_rank = [torch.zeros_like(topk_sims) for _ in range(self.global_size)]
+            retrieved_rank = [torch.zeros_like(neighbors_labels) for _ in range(self.global_size)]
+
+        torch.distributed.gather(topk_sims, topk_sims_rank, dst=target_rank)
+        torch.distributed.gather(neighbors_labels, retrieved_rank, dst=target_rank)
+
+        if self.global_rank == target_rank:
+            # Perform a second top-k on the k * global_size retrieved neighbors
+            topk_sims_rank = torch.cat(topk_sims_rank, dim=1)
+            retrieved_rank = torch.cat(retrieved_rank, dim=1)
+            results = self._get_knn_sims_and_labels(topk_sims_rank, retrieved_rank)
+            return results
+        return None
+
+    def compute_neighbors(self, features_rank):
+        for rank in range(self.global_size):
+            topk_sims, neighbors_labels = self._similarity_for_rank(features_rank, rank)
+            results = self._gather_all_knn_for_rank(topk_sims, neighbors_labels, rank)
+            if results is not None:
+                topk_sims_rank, neighbors_labels_rank = results
+        return topk_sims_rank, neighbors_labels_rank
+
+    def forward(self, features_rank):
+        """
+        Compute the results on all values of `self.nb_knn` neighbors from the full `self.max_k`
+        """
+        assert all(k <= self.max_k for k in self.nb_knn)
+
+        topk_sims, neighbors_labels = self.compute_neighbors(features_rank)
+        batch_size = neighbors_labels.shape[0]
+        topk_sims_transform = softmax(topk_sims / self.T, 1)
+        matmul = torch.mul(
+            one_hot(neighbors_labels, num_classes=self.num_classes),
+            topk_sims_transform.view(batch_size, -1, 1),
+        )
+        probas_for_k = {k: torch.sum(matmul[:, :k, :], 1) for k in self.nb_knn}
+        return probas_for_k
+
+
+class DictKeysModule(torch.nn.Module):
+    def __init__(self, keys):
+        super().__init__()
+        self.keys = keys
+
+    def forward(self, features_dict, targets):
+        for k in self.keys:
+            features_dict = features_dict[k]
+        return {"preds": features_dict, "target": targets}
+
+
+def create_module_dict(*, module, n_per_class_list, n_tries, nb_knn, train_features, train_labels):
+    modules = {}
+    mapping = create_class_indices_mapping(train_labels)
+    for npc in n_per_class_list:
+        if npc < 0:  # Only one try needed when using the full data
+            full_module = module(
+                train_features=train_features,
+                train_labels=train_labels,
+                nb_knn=nb_knn,
+            )
+            modules["full"] = ModuleDictWithForward({"1": full_module})
+            continue
+        all_tries = {}
+        for t in range(n_tries):
+            final_indices = filter_train(mapping, npc, seed=t)
+            k_list = list(set(nb_knn + [npc]))
+            k_list = sorted([el for el in k_list if el <= npc])
+            all_tries[str(t)] = module(
+                train_features=train_features[final_indices],
+                train_labels=train_labels[final_indices],
+                nb_knn=k_list,
+            )
+        modules[f"{npc} per class"] = ModuleDictWithForward(all_tries)
+
+    return ModuleDictWithForward(modules)
+
+
+def filter_train(mapping, n_per_class, seed):
+    torch.manual_seed(seed)
+    final_indices = []
+    for k in mapping.keys():
+        index = torch.randperm(len(mapping[k]))[:n_per_class]
+        final_indices.append(mapping[k][index])
+    return torch.cat(final_indices).squeeze()
+
+
+def create_class_indices_mapping(labels):
+    unique_labels, inverse = torch.unique(labels, return_inverse=True)
+    mapping = {unique_labels[i]: (inverse == i).nonzero() for i in range(len(unique_labels))}
+    return mapping
+
+
+class ModuleDictWithForward(torch.nn.ModuleDict):
+    def forward(self, *args, **kwargs):
+        return {k: module(*args, **kwargs) for k, module in self._modules.items()}
+
+
+def eval_knn(
+    model,
+    train_dataset,
+    val_dataset,
+    accuracy_averaging,
+    nb_knn,
+    temperature,
+    batch_size,
+    num_workers,
+    gather_on_cpu,
+    n_per_class_list=[-1],
+    n_tries=1,
+):
+    model = ModelWithNormalize(model)
+
+    logger.info("Extracting features for train set...")
+    train_features, train_labels = extract_features(
+        model, train_dataset, batch_size, num_workers, gather_on_cpu=gather_on_cpu
+    )
+    logger.info(f"Train features created, shape {train_features.shape}.")
+
+    val_dataloader = make_data_loader(
+        dataset=val_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        sampler_type=SamplerType.DISTRIBUTED,
+        drop_last=False,
+        shuffle=False,
+        persistent_workers=True,
+    )
+    num_classes = train_labels.max() + 1
+    metric_collection = build_topk_accuracy_metric(accuracy_averaging, num_classes=num_classes)
+
+    device = torch.cuda.current_device()
+    partial_module = partial(KnnModule, T=temperature, device=device, num_classes=num_classes)
+    knn_module_dict = create_module_dict(
+        module=partial_module,
+        n_per_class_list=n_per_class_list,
+        n_tries=n_tries,
+        nb_knn=nb_knn,
+        train_features=train_features,
+        train_labels=train_labels,
+    )
+    postprocessors, metrics = {}, {}
+    for n_per_class, knn_module in knn_module_dict.items():
+        for t, knn_try in knn_module.items():
+            postprocessors = {
+                **postprocessors,
+                **{(n_per_class, t, k): DictKeysModule([n_per_class, t, k]) for k in knn_try.nb_knn},
+            }
+            metrics = {**metrics, **{(n_per_class, t, k): metric_collection.clone() for k in knn_try.nb_knn}}
+    model_with_knn = torch.nn.Sequential(model, knn_module_dict)
+
+    # ============ evaluation ... ============
+    logger.info("Start the k-NN classification.")
+    _, results_dict = evaluate(model_with_knn, val_dataloader, postprocessors, metrics, device)
+
+    # Averaging the results over the n tries for each value of n_per_class
+    for n_per_class, knn_module in knn_module_dict.items():
+        first_try = list(knn_module.keys())[0]
+        k_list = knn_module[first_try].nb_knn
+        for k in k_list:
+            keys = results_dict[(n_per_class, first_try, k)].keys()  # keys are e.g. `top-1` and `top-5`
+            results_dict[(n_per_class, k)] = {
+                key: torch.mean(torch.stack([results_dict[(n_per_class, t, k)][key] for t in knn_module.keys()]))
+                for key in keys
+            }
+            for t in knn_module.keys():
+                del results_dict[(n_per_class, t, k)]
+
+    return results_dict
+
+
+def eval_knn_with_model(
+    model,
+    output_dir,
+    train_dataset_str="ImageNet:split=TRAIN",
+    val_dataset_str="ImageNet:split=VAL",
+    nb_knn=(10, 20, 100, 200),
+    temperature=0.07,
+    autocast_dtype=torch.float,
+    accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY,
+    transform=None,
+    gather_on_cpu=False,
+    batch_size=256,
+    num_workers=5,
+    n_per_class_list=[-1],
+    n_tries=1,
+):
+    transform = transform or make_classification_eval_transform()
+
+    train_dataset = make_dataset(
+        dataset_str=train_dataset_str,
+        transform=transform,
+    )
+    val_dataset = make_dataset(
+        dataset_str=val_dataset_str,
+        transform=transform,
+    )
+
+    with torch.cuda.amp.autocast(dtype=autocast_dtype):
+        results_dict_knn = eval_knn(
+            model=model,
+            train_dataset=train_dataset,
+            val_dataset=val_dataset,
+            accuracy_averaging=accuracy_averaging,
+            nb_knn=nb_knn,
+            temperature=temperature,
+            batch_size=batch_size,
+            num_workers=num_workers,
+            gather_on_cpu=gather_on_cpu,
+            n_per_class_list=n_per_class_list,
+            n_tries=n_tries,
+        )
+
+    results_dict = {}
+    if distributed.is_main_process():
+        for knn_ in results_dict_knn.keys():
+            top1 = results_dict_knn[knn_]["top-1"].item() * 100.0
+            top5 = results_dict_knn[knn_]["top-5"].item() * 100.0
+            results_dict[f"{knn_} Top 1"] = top1
+            results_dict[f"{knn_} Top 5"] = top5
+            logger.info(f"{knn_} classifier result: Top1: {top1:.2f} Top5: {top5:.2f}")
+
+    metrics_file_path = os.path.join(output_dir, "results_eval_knn.json")
+    with open(metrics_file_path, "a") as f:
+        for k, v in results_dict.items():
+            f.write(json.dumps({k: v}) + "\n")
+
+    if distributed.is_enabled():
+        torch.distributed.barrier()
+    return results_dict
+
+
+def main(args):
+    model, autocast_dtype = setup_and_build_model(args)
+    eval_knn_with_model(
+        model=model,
+        output_dir=args.output_dir,
+        train_dataset_str=args.train_dataset_str,
+        val_dataset_str=args.val_dataset_str,
+        nb_knn=args.nb_knn,
+        temperature=args.temperature,
+        autocast_dtype=autocast_dtype,
+        accuracy_averaging=AccuracyAveraging.MEAN_ACCURACY,
+        transform=None,
+        gather_on_cpu=args.gather_on_cpu,
+        batch_size=args.batch_size,
+        num_workers=5,
+        n_per_class_list=args.n_per_class_list,
+        n_tries=args.n_tries,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    description = "DINOv2 k-NN evaluation"
+    args_parser = get_args_parser(description=description)
+    args = args_parser.parse_args()
+    sys.exit(main(args))
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/eval/linear.py b/torchhub/facebookresearch_dinov2_main/dinov2/eval/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d8202606999c0c01353904d8b02d2ff3509fef9
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/eval/linear.py
@@ -0,0 +1,626 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+from functools import partial
+import json
+import logging
+import os
+import sys
+from typing import List, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.nn.parallel import DistributedDataParallel
+from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer
+
+from dinov2.data import SamplerType, make_data_loader, make_dataset
+from dinov2.data.transforms import make_classification_eval_transform, make_classification_train_transform
+import dinov2.distributed as distributed
+from dinov2.eval.metrics import MetricType, build_metric
+from dinov2.eval.setup import get_args_parser as get_setup_args_parser
+from dinov2.eval.setup import setup_and_build_model
+from dinov2.eval.utils import ModelWithIntermediateLayers, evaluate
+from dinov2.logging import MetricLogger
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parents = parents or []
+    setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
+    parents = [setup_args_parser]
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--train-dataset",
+        dest="train_dataset_str",
+        type=str,
+        help="Training dataset",
+    )
+    parser.add_argument(
+        "--val-dataset",
+        dest="val_dataset_str",
+        type=str,
+        help="Validation dataset",
+    )
+    parser.add_argument(
+        "--test-datasets",
+        dest="test_dataset_strs",
+        type=str,
+        nargs="+",
+        help="Test datasets, none to reuse the validation dataset",
+    )
+    parser.add_argument(
+        "--epochs",
+        type=int,
+        help="Number of training epochs",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        help="Batch Size (per GPU)",
+    )
+    parser.add_argument(
+        "--num-workers",
+        type=int,
+        help="Number de Workers",
+    )
+    parser.add_argument(
+        "--epoch-length",
+        type=int,
+        help="Length of an epoch in number of iterations",
+    )
+    parser.add_argument(
+        "--save-checkpoint-frequency",
+        type=int,
+        help="Number of epochs between two named checkpoint saves.",
+    )
+    parser.add_argument(
+        "--eval-period-iterations",
+        type=int,
+        help="Number of iterations between two evaluations.",
+    )
+    parser.add_argument(
+        "--learning-rates",
+        nargs="+",
+        type=float,
+        help="Learning rates to grid search.",
+    )
+    parser.add_argument(
+        "--no-resume",
+        action="store_true",
+        help="Whether to not resume from existing checkpoints",
+    )
+    parser.add_argument(
+        "--val-metric-type",
+        type=MetricType,
+        choices=list(MetricType),
+        help="Validation metric",
+    )
+    parser.add_argument(
+        "--test-metric-types",
+        type=MetricType,
+        choices=list(MetricType),
+        nargs="+",
+        help="Evaluation metric",
+    )
+    parser.add_argument(
+        "--classifier-fpath",
+        type=str,
+        help="Path to a file containing pretrained linear classifiers",
+    )
+    parser.add_argument(
+        "--val-class-mapping-fpath",
+        type=str,
+        help="Path to a file containing a mapping to adjust classifier outputs",
+    )
+    parser.add_argument(
+        "--test-class-mapping-fpaths",
+        nargs="+",
+        type=str,
+        help="Path to a file containing a mapping to adjust classifier outputs",
+    )
+    parser.set_defaults(
+        train_dataset_str="ImageNet:split=TRAIN",
+        val_dataset_str="ImageNet:split=VAL",
+        test_dataset_strs=None,
+        epochs=10,
+        batch_size=128,
+        num_workers=8,
+        epoch_length=1250,
+        save_checkpoint_frequency=20,
+        eval_period_iterations=1250,
+        learning_rates=[1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4, 1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2, 0.1],
+        val_metric_type=MetricType.MEAN_ACCURACY,
+        test_metric_types=None,
+        classifier_fpath=None,
+        val_class_mapping_fpath=None,
+        test_class_mapping_fpaths=[None],
+    )
+    return parser
+
+
+def has_ddp_wrapper(m: nn.Module) -> bool:
+    return isinstance(m, DistributedDataParallel)
+
+
+def remove_ddp_wrapper(m: nn.Module) -> nn.Module:
+    return m.module if has_ddp_wrapper(m) else m
+
+
+def _pad_and_collate(batch):
+    maxlen = max(len(targets) for image, targets in batch)
+    padded_batch = [
+        (image, np.pad(targets, (0, maxlen - len(targets)), constant_values=-1)) for image, targets in batch
+    ]
+    return torch.utils.data.default_collate(padded_batch)
+
+
+def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool):
+    intermediate_output = x_tokens_list[-use_n_blocks:]
+    output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1)
+    if use_avgpool:
+        output = torch.cat(
+            (
+                output,
+                torch.mean(intermediate_output[-1][0], dim=1),  # patch tokens
+            ),
+            dim=-1,
+        )
+        output = output.reshape(output.shape[0], -1)
+    return output.float()
+
+
+class LinearClassifier(nn.Module):
+    """Linear layer to train on top of frozen features"""
+
+    def __init__(self, out_dim, use_n_blocks, use_avgpool, num_classes=1000):
+        super().__init__()
+        self.out_dim = out_dim
+        self.use_n_blocks = use_n_blocks
+        self.use_avgpool = use_avgpool
+        self.num_classes = num_classes
+        self.linear = nn.Linear(out_dim, num_classes)
+        self.linear.weight.data.normal_(mean=0.0, std=0.01)
+        self.linear.bias.data.zero_()
+
+    def forward(self, x_tokens_list):
+        output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool)
+        return self.linear(output)
+
+
+class AllClassifiers(nn.Module):
+    def __init__(self, classifiers_dict):
+        super().__init__()
+        self.classifiers_dict = nn.ModuleDict()
+        self.classifiers_dict.update(classifiers_dict)
+
+    def forward(self, inputs):
+        return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()}
+
+    def __len__(self):
+        return len(self.classifiers_dict)
+
+
+class LinearPostprocessor(nn.Module):
+    def __init__(self, linear_classifier, class_mapping=None):
+        super().__init__()
+        self.linear_classifier = linear_classifier
+        self.register_buffer("class_mapping", None if class_mapping is None else torch.LongTensor(class_mapping))
+
+    def forward(self, samples, targets):
+        preds = self.linear_classifier(samples)
+        return {
+            "preds": preds[:, self.class_mapping] if self.class_mapping is not None else preds,
+            "target": targets,
+        }
+
+
+def scale_lr(learning_rates, batch_size):
+    return learning_rates * (batch_size * distributed.get_global_size()) / 256.0
+
+
+def setup_linear_classifiers(sample_output, n_last_blocks_list, learning_rates, batch_size, num_classes=1000):
+    linear_classifiers_dict = nn.ModuleDict()
+    optim_param_groups = []
+    for n in n_last_blocks_list:
+        for avgpool in [False, True]:
+            for _lr in learning_rates:
+                lr = scale_lr(_lr, batch_size)
+                out_dim = create_linear_input(sample_output, use_n_blocks=n, use_avgpool=avgpool).shape[1]
+                linear_classifier = LinearClassifier(
+                    out_dim, use_n_blocks=n, use_avgpool=avgpool, num_classes=num_classes
+                )
+                linear_classifier = linear_classifier.cuda()
+                linear_classifiers_dict[
+                    f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}".replace(".", "_")
+                ] = linear_classifier
+                optim_param_groups.append({"params": linear_classifier.parameters(), "lr": lr})
+
+    linear_classifiers = AllClassifiers(linear_classifiers_dict)
+    if distributed.is_enabled():
+        linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers)
+
+    return linear_classifiers, optim_param_groups
+
+
+@torch.no_grad()
+def evaluate_linear_classifiers(
+    feature_model,
+    linear_classifiers,
+    data_loader,
+    metric_type,
+    metrics_file_path,
+    training_num_classes,
+    iteration,
+    prefixstring="",
+    class_mapping=None,
+    best_classifier_on_val=None,
+):
+    logger.info("running validation !")
+
+    num_classes = len(class_mapping) if class_mapping is not None else training_num_classes
+    metric = build_metric(metric_type, num_classes=num_classes)
+    postprocessors = {k: LinearPostprocessor(v, class_mapping) for k, v in linear_classifiers.classifiers_dict.items()}
+    metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict}
+
+    _, results_dict_temp = evaluate(
+        feature_model,
+        data_loader,
+        postprocessors,
+        metrics,
+        torch.cuda.current_device(),
+    )
+
+    logger.info("")
+    results_dict = {}
+    max_accuracy = 0
+    best_classifier = ""
+    for i, (classifier_string, metric) in enumerate(results_dict_temp.items()):
+        logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}")
+        if (
+            best_classifier_on_val is None and metric["top-1"].item() > max_accuracy
+        ) or classifier_string == best_classifier_on_val:
+            max_accuracy = metric["top-1"].item()
+            best_classifier = classifier_string
+
+    results_dict["best_classifier"] = {"name": best_classifier, "accuracy": max_accuracy}
+
+    logger.info(f"best classifier: {results_dict['best_classifier']}")
+
+    if distributed.is_main_process():
+        with open(metrics_file_path, "a") as f:
+            f.write(f"iter: {iteration}\n")
+            for k, v in results_dict.items():
+                f.write(json.dumps({k: v}) + "\n")
+            f.write("\n")
+
+    return results_dict
+
+
+def eval_linear(
+    *,
+    feature_model,
+    linear_classifiers,
+    train_data_loader,
+    val_data_loader,
+    metrics_file_path,
+    optimizer,
+    scheduler,
+    output_dir,
+    max_iter,
+    checkpoint_period,  # In number of iter, creates a new file every period
+    running_checkpoint_period,  # Period to update main checkpoint file
+    eval_period,
+    metric_type,
+    training_num_classes,
+    resume=True,
+    classifier_fpath=None,
+    val_class_mapping=None,
+):
+    checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler)
+    start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1
+
+    periodic_checkpointer = PeriodicCheckpointer(checkpointer, checkpoint_period, max_iter=max_iter)
+    iteration = start_iter
+    logger.info("Starting training from iteration {}".format(start_iter))
+    metric_logger = MetricLogger(delimiter="  ")
+    header = "Training"
+
+    for data, labels in metric_logger.log_every(
+        train_data_loader,
+        10,
+        header,
+        max_iter,
+        start_iter,
+    ):
+        data = data.cuda(non_blocking=True)
+        labels = labels.cuda(non_blocking=True)
+
+        features = feature_model(data)
+        outputs = linear_classifiers(features)
+
+        losses = {f"loss_{k}": nn.CrossEntropyLoss()(v, labels) for k, v in outputs.items()}
+        loss = sum(losses.values())
+
+        # compute the gradients
+        optimizer.zero_grad()
+        loss.backward()
+
+        # step
+        optimizer.step()
+        scheduler.step()
+
+        # log
+        if iteration % 10 == 0:
+            torch.cuda.synchronize()
+            metric_logger.update(loss=loss.item())
+            metric_logger.update(lr=optimizer.param_groups[0]["lr"])
+            print("lr", optimizer.param_groups[0]["lr"])
+
+        if iteration - start_iter > 5:
+            if iteration % running_checkpoint_period == 0:
+                torch.cuda.synchronize()
+                if distributed.is_main_process():
+                    logger.info("Checkpointing running_checkpoint")
+                    periodic_checkpointer.save("running_checkpoint_linear_eval", iteration=iteration)
+                torch.cuda.synchronize()
+        periodic_checkpointer.step(iteration)
+
+        if eval_period > 0 and (iteration + 1) % eval_period == 0 and iteration != max_iter - 1:
+            _ = evaluate_linear_classifiers(
+                feature_model=feature_model,
+                linear_classifiers=remove_ddp_wrapper(linear_classifiers),
+                data_loader=val_data_loader,
+                metrics_file_path=metrics_file_path,
+                prefixstring=f"ITER: {iteration}",
+                metric_type=metric_type,
+                training_num_classes=training_num_classes,
+                iteration=iteration,
+                class_mapping=val_class_mapping,
+            )
+            torch.cuda.synchronize()
+
+        iteration = iteration + 1
+
+    val_results_dict = evaluate_linear_classifiers(
+        feature_model=feature_model,
+        linear_classifiers=remove_ddp_wrapper(linear_classifiers),
+        data_loader=val_data_loader,
+        metrics_file_path=metrics_file_path,
+        metric_type=metric_type,
+        training_num_classes=training_num_classes,
+        iteration=iteration,
+        class_mapping=val_class_mapping,
+    )
+    return val_results_dict, feature_model, linear_classifiers, iteration
+
+
+def make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type):
+    test_dataset = make_dataset(
+        dataset_str=test_dataset_str,
+        transform=make_classification_eval_transform(),
+    )
+    test_data_loader = make_data_loader(
+        dataset=test_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        sampler_type=SamplerType.DISTRIBUTED,
+        drop_last=False,
+        shuffle=False,
+        persistent_workers=False,
+        collate_fn=_pad_and_collate if metric_type == MetricType.IMAGENET_REAL_ACCURACY else None,
+    )
+    return test_data_loader
+
+
+def test_on_datasets(
+    feature_model,
+    linear_classifiers,
+    test_dataset_strs,
+    batch_size,
+    num_workers,
+    test_metric_types,
+    metrics_file_path,
+    training_num_classes,
+    iteration,
+    best_classifier_on_val,
+    prefixstring="",
+    test_class_mappings=[None],
+):
+    results_dict = {}
+    for test_dataset_str, class_mapping, metric_type in zip(test_dataset_strs, test_class_mappings, test_metric_types):
+        logger.info(f"Testing on {test_dataset_str}")
+        test_data_loader = make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type)
+        dataset_results_dict = evaluate_linear_classifiers(
+            feature_model,
+            remove_ddp_wrapper(linear_classifiers),
+            test_data_loader,
+            metric_type,
+            metrics_file_path,
+            training_num_classes,
+            iteration,
+            prefixstring="",
+            class_mapping=class_mapping,
+            best_classifier_on_val=best_classifier_on_val,
+        )
+        results_dict[f"{test_dataset_str}_accuracy"] = 100.0 * dataset_results_dict["best_classifier"]["accuracy"]
+    return results_dict
+
+
+def run_eval_linear(
+    model,
+    output_dir,
+    train_dataset_str,
+    val_dataset_str,
+    batch_size,
+    epochs,
+    epoch_length,
+    num_workers,
+    save_checkpoint_frequency,
+    eval_period_iterations,
+    learning_rates,
+    autocast_dtype,
+    test_dataset_strs=None,
+    resume=True,
+    classifier_fpath=None,
+    val_class_mapping_fpath=None,
+    test_class_mapping_fpaths=[None],
+    val_metric_type=MetricType.MEAN_ACCURACY,
+    test_metric_types=None,
+):
+    seed = 0
+
+    if test_dataset_strs is None:
+        test_dataset_strs = [val_dataset_str]
+    if test_metric_types is None:
+        test_metric_types = [val_metric_type] * len(test_dataset_strs)
+    else:
+        assert len(test_metric_types) == len(test_dataset_strs)
+    assert len(test_dataset_strs) == len(test_class_mapping_fpaths)
+
+    train_transform = make_classification_train_transform()
+    train_dataset = make_dataset(
+        dataset_str=train_dataset_str,
+        transform=train_transform,
+    )
+    training_num_classes = len(torch.unique(torch.Tensor(train_dataset.get_targets().astype(int))))
+    sampler_type = SamplerType.SHARDED_INFINITE
+    # sampler_type = SamplerType.INFINITE
+
+    n_last_blocks_list = [1, 4]
+    n_last_blocks = max(n_last_blocks_list)
+    autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype)
+    feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx)
+    sample_output = feature_model(train_dataset[0][0].unsqueeze(0).cuda())
+
+    linear_classifiers, optim_param_groups = setup_linear_classifiers(
+        sample_output,
+        n_last_blocks_list,
+        learning_rates,
+        batch_size,
+        training_num_classes,
+    )
+
+    optimizer = torch.optim.SGD(optim_param_groups, momentum=0.9, weight_decay=0)
+    max_iter = epochs * epoch_length
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, max_iter, eta_min=0)
+    checkpointer = Checkpointer(linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler)
+    start_iter = checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get("iteration", -1) + 1
+    train_data_loader = make_data_loader(
+        dataset=train_dataset,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        shuffle=True,
+        seed=seed,
+        sampler_type=sampler_type,
+        sampler_advance=start_iter,
+        drop_last=True,
+        persistent_workers=True,
+    )
+    val_data_loader = make_eval_data_loader(val_dataset_str, batch_size, num_workers, val_metric_type)
+
+    checkpoint_period = save_checkpoint_frequency * epoch_length
+
+    if val_class_mapping_fpath is not None:
+        logger.info(f"Using class mapping from {val_class_mapping_fpath}")
+        val_class_mapping = np.load(val_class_mapping_fpath)
+    else:
+        val_class_mapping = None
+
+    test_class_mappings = []
+    for class_mapping_fpath in test_class_mapping_fpaths:
+        if class_mapping_fpath is not None and class_mapping_fpath != "None":
+            logger.info(f"Using class mapping from {class_mapping_fpath}")
+            class_mapping = np.load(class_mapping_fpath)
+        else:
+            class_mapping = None
+        test_class_mappings.append(class_mapping)
+
+    metrics_file_path = os.path.join(output_dir, "results_eval_linear.json")
+    val_results_dict, feature_model, linear_classifiers, iteration = eval_linear(
+        feature_model=feature_model,
+        linear_classifiers=linear_classifiers,
+        train_data_loader=train_data_loader,
+        val_data_loader=val_data_loader,
+        metrics_file_path=metrics_file_path,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        output_dir=output_dir,
+        max_iter=max_iter,
+        checkpoint_period=checkpoint_period,
+        running_checkpoint_period=epoch_length,
+        eval_period=eval_period_iterations,
+        metric_type=val_metric_type,
+        training_num_classes=training_num_classes,
+        resume=resume,
+        val_class_mapping=val_class_mapping,
+        classifier_fpath=classifier_fpath,
+    )
+    results_dict = {}
+    if len(test_dataset_strs) > 1 or test_dataset_strs[0] != val_dataset_str:
+        results_dict = test_on_datasets(
+            feature_model,
+            linear_classifiers,
+            test_dataset_strs,
+            batch_size,
+            0,  # num_workers,
+            test_metric_types,
+            metrics_file_path,
+            training_num_classes,
+            iteration,
+            val_results_dict["best_classifier"]["name"],
+            prefixstring="",
+            test_class_mappings=test_class_mappings,
+        )
+    results_dict["best_classifier"] = val_results_dict["best_classifier"]["name"]
+    results_dict[f"{val_dataset_str}_accuracy"] = 100.0 * val_results_dict["best_classifier"]["accuracy"]
+    logger.info("Test Results Dict " + str(results_dict))
+
+    return results_dict
+
+
+def main(args):
+    model, autocast_dtype = setup_and_build_model(args)
+    run_eval_linear(
+        model=model,
+        output_dir=args.output_dir,
+        train_dataset_str=args.train_dataset_str,
+        val_dataset_str=args.val_dataset_str,
+        test_dataset_strs=args.test_dataset_strs,
+        batch_size=args.batch_size,
+        epochs=args.epochs,
+        epoch_length=args.epoch_length,
+        num_workers=args.num_workers,
+        save_checkpoint_frequency=args.save_checkpoint_frequency,
+        eval_period_iterations=args.eval_period_iterations,
+        learning_rates=args.learning_rates,
+        autocast_dtype=autocast_dtype,
+        resume=not args.no_resume,
+        classifier_fpath=args.classifier_fpath,
+        val_metric_type=args.val_metric_type,
+        test_metric_types=args.test_metric_types,
+        val_class_mapping_fpath=args.val_class_mapping_fpath,
+        test_class_mapping_fpaths=args.test_class_mapping_fpaths,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    description = "DINOv2 linear evaluation"
+    args_parser = get_args_parser(description=description)
+    args = args_parser.parse_args()
+    sys.exit(main(args))
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/eval/log_regression.py b/torchhub/facebookresearch_dinov2_main/dinov2/eval/log_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e6ede2b616208cb49c7af67d58c8e6e4afb60e1
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/eval/log_regression.py
@@ -0,0 +1,445 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import gc
+import logging
+import sys
+import time
+from typing import List, Optional
+
+from cuml.linear_model import LogisticRegression
+import torch
+import torch.backends.cudnn as cudnn
+import torch.distributed
+from torch import nn
+from torch.utils.data import TensorDataset
+from torchmetrics import MetricTracker
+
+from dinov2.data import make_dataset
+from dinov2.data.transforms import make_classification_eval_transform
+from dinov2.distributed import get_global_rank, get_global_size
+from dinov2.eval.metrics import MetricType, build_metric
+from dinov2.eval.setup import get_args_parser as get_setup_args_parser
+from dinov2.eval.setup import setup_and_build_model
+from dinov2.eval.utils import evaluate, extract_features
+from dinov2.utils.dtype import as_torch_dtype
+
+
+logger = logging.getLogger("dinov2")
+
+DEFAULT_MAX_ITER = 1_000
+C_POWER_RANGE = torch.linspace(-6, 5, 45)
+_CPU_DEVICE = torch.device("cpu")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parents = parents or []
+    setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
+    parents = [setup_args_parser]
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--train-dataset",
+        dest="train_dataset_str",
+        type=str,
+        help="Training dataset",
+    )
+    parser.add_argument(
+        "--val-dataset",
+        dest="val_dataset_str",
+        type=str,
+        help="Validation dataset",
+    )
+    parser.add_argument(
+        "--finetune-dataset-str",
+        dest="finetune_dataset_str",
+        type=str,
+        help="Fine-tuning dataset",
+    )
+    parser.add_argument(
+        "--finetune-on-val",
+        action="store_true",
+        help="If there is no finetune dataset, whether to choose the "
+        "hyperparameters on the val set instead of 10%% of the train dataset",
+    )
+    parser.add_argument(
+        "--metric-type",
+        type=MetricType,
+        choices=list(MetricType),
+        help="Metric type",
+    )
+    parser.add_argument(
+        "--train-features-device",
+        type=str,
+        help="Device to gather train features (cpu, cuda, cuda:0, etc.), default: %(default)s",
+    )
+    parser.add_argument(
+        "--train-dtype",
+        type=str,
+        help="Data type to convert the train features to (default: %(default)s)",
+    )
+    parser.add_argument(
+        "--max-train-iters",
+        type=int,
+        help="Maximum number of train iterations (default: %(default)s)",
+    )
+    parser.set_defaults(
+        train_dataset_str="ImageNet:split=TRAIN",
+        val_dataset_str="ImageNet:split=VAL",
+        finetune_dataset_str=None,
+        metric_type=MetricType.MEAN_ACCURACY,
+        train_features_device="cpu",
+        train_dtype="float64",
+        max_train_iters=DEFAULT_MAX_ITER,
+        finetune_on_val=False,
+    )
+    return parser
+
+
+class LogRegModule(nn.Module):
+    def __init__(
+        self,
+        C,
+        max_iter=DEFAULT_MAX_ITER,
+        dtype=torch.float64,
+        device=_CPU_DEVICE,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.device = device
+        self.estimator = LogisticRegression(
+            penalty="l2",
+            C=C,
+            max_iter=max_iter,
+            output_type="numpy",
+            tol=1e-12,
+            linesearch_max_iter=50,
+        )
+
+    def forward(self, samples, targets):
+        samples_device = samples.device
+        samples = samples.to(dtype=self.dtype, device=self.device)
+        if self.device == _CPU_DEVICE:
+            samples = samples.numpy()
+        probas = self.estimator.predict_proba(samples)
+        return {"preds": torch.from_numpy(probas).to(samples_device), "target": targets}
+
+    def fit(self, train_features, train_labels):
+        train_features = train_features.to(dtype=self.dtype, device=self.device)
+        train_labels = train_labels.to(dtype=self.dtype, device=self.device)
+        if self.device == _CPU_DEVICE:
+            # both cuML and sklearn only work with numpy arrays on CPU
+            train_features = train_features.numpy()
+            train_labels = train_labels.numpy()
+        self.estimator.fit(train_features, train_labels)
+
+
+def evaluate_model(*, logreg_model, logreg_metric, test_data_loader, device):
+    postprocessors = {"metrics": logreg_model}
+    metrics = {"metrics": logreg_metric}
+    return evaluate(nn.Identity(), test_data_loader, postprocessors, metrics, device)
+
+
+def train_for_C(*, C, max_iter, train_features, train_labels, dtype=torch.float64, device=_CPU_DEVICE):
+    logreg_model = LogRegModule(C, max_iter=max_iter, dtype=dtype, device=device)
+    logreg_model.fit(train_features, train_labels)
+    return logreg_model
+
+
+def train_and_evaluate(
+    *,
+    C,
+    max_iter,
+    train_features,
+    train_labels,
+    logreg_metric,
+    test_data_loader,
+    train_dtype=torch.float64,
+    train_features_device,
+    eval_device,
+):
+    logreg_model = train_for_C(
+        C=C,
+        max_iter=max_iter,
+        train_features=train_features,
+        train_labels=train_labels,
+        dtype=train_dtype,
+        device=train_features_device,
+    )
+    return evaluate_model(
+        logreg_model=logreg_model,
+        logreg_metric=logreg_metric,
+        test_data_loader=test_data_loader,
+        device=eval_device,
+    )
+
+
+def sweep_C_values(
+    *,
+    train_features,
+    train_labels,
+    test_data_loader,
+    metric_type,
+    num_classes,
+    train_dtype=torch.float64,
+    train_features_device=_CPU_DEVICE,
+    max_train_iters=DEFAULT_MAX_ITER,
+):
+    if metric_type == MetricType.PER_CLASS_ACCURACY:
+        # If we want to output per-class accuracy, we select the hyperparameters with mean per class
+        metric_type = MetricType.MEAN_PER_CLASS_ACCURACY
+    logreg_metric = build_metric(metric_type, num_classes=num_classes)
+    metric_tracker = MetricTracker(logreg_metric, maximize=True)
+    ALL_C = 10**C_POWER_RANGE
+    logreg_models = {}
+
+    train_features = train_features.to(dtype=train_dtype, device=train_features_device)
+    train_labels = train_labels.to(device=train_features_device)
+
+    for i in range(get_global_rank(), len(ALL_C), get_global_size()):
+        C = ALL_C[i].item()
+        logger.info(
+            f"Training for C = {C:.5f}, dtype={train_dtype}, "
+            f"features: {train_features.shape}, {train_features.dtype}, "
+            f"labels: {train_labels.shape}, {train_labels.dtype}"
+        )
+        logreg_models[C] = train_for_C(
+            C=C,
+            max_iter=max_train_iters,
+            train_features=train_features,
+            train_labels=train_labels,
+            dtype=train_dtype,
+            device=train_features_device,
+        )
+
+    gather_list = [None for _ in range(get_global_size())]
+    torch.distributed.all_gather_object(gather_list, logreg_models)
+
+    logreg_models_gathered = {}
+    for logreg_dict in gather_list:
+        logreg_models_gathered.update(logreg_dict)
+
+    for i in range(len(ALL_C)):
+        metric_tracker.increment()
+        C = ALL_C[i].item()
+        evals = evaluate_model(
+            logreg_model=logreg_models_gathered[C],
+            logreg_metric=metric_tracker,
+            test_data_loader=test_data_loader,
+            device=torch.cuda.current_device(),
+        )
+        logger.info(f"Trained for C = {C:.5f}, accuracies = {evals}")
+
+        best_stats, which_epoch = metric_tracker.best_metric(return_step=True)
+        best_stats_100 = {k: 100.0 * v for k, v in best_stats.items()}
+        if which_epoch["top-1"] == i:
+            best_C = C
+    logger.info(f"Sweep best {best_stats_100}, best C = {best_C:.6f}")
+
+    return best_stats, best_C
+
+
+def eval_log_regression(
+    *,
+    model,
+    train_dataset,
+    val_dataset,
+    finetune_dataset,
+    metric_type,
+    batch_size,
+    num_workers,
+    finetune_on_val=False,
+    train_dtype=torch.float64,
+    train_features_device=_CPU_DEVICE,
+    max_train_iters=DEFAULT_MAX_ITER,
+):
+    """
+    Implements the "standard" process for log regression evaluation:
+    The value of C is chosen by training on train_dataset and evaluating on
+    finetune_dataset. Then, the final model is trained on a concatenation of
+    train_dataset and finetune_dataset, and is evaluated on val_dataset.
+    If there is no finetune_dataset, the value of C is the one that yields
+    the best results on a random 10% subset of the train dataset
+    """
+
+    start = time.time()
+
+    train_features, train_labels = extract_features(
+        model, train_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
+    )
+    val_features, val_labels = extract_features(
+        model, val_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
+    )
+    val_data_loader = torch.utils.data.DataLoader(
+        TensorDataset(val_features, val_labels),
+        batch_size=batch_size,
+        drop_last=False,
+        num_workers=0,
+        persistent_workers=False,
+    )
+
+    if finetune_dataset is None and finetune_on_val:
+        logger.info("Choosing hyperparameters on the val dataset")
+        finetune_features, finetune_labels = val_features, val_labels
+    elif finetune_dataset is None and not finetune_on_val:
+        logger.info("Choosing hyperparameters on 10% of the train dataset")
+        torch.manual_seed(0)
+        indices = torch.randperm(len(train_features), device=train_features.device)
+        finetune_index = indices[: len(train_features) // 10]
+        train_index = indices[len(train_features) // 10 :]
+        finetune_features, finetune_labels = train_features[finetune_index], train_labels[finetune_index]
+        train_features, train_labels = train_features[train_index], train_labels[train_index]
+    else:
+        logger.info("Choosing hyperparameters on the finetune dataset")
+        finetune_features, finetune_labels = extract_features(
+            model, finetune_dataset, batch_size, num_workers, gather_on_cpu=(train_features_device == _CPU_DEVICE)
+        )
+    # release the model - free GPU memory
+    del model
+    gc.collect()
+    torch.cuda.empty_cache()
+    finetune_data_loader = torch.utils.data.DataLoader(
+        TensorDataset(finetune_features, finetune_labels),
+        batch_size=batch_size,
+        drop_last=False,
+    )
+
+    if len(train_labels.shape) > 1:
+        num_classes = train_labels.shape[1]
+    else:
+        num_classes = train_labels.max() + 1
+
+    logger.info("Using cuML for logistic regression")
+
+    best_stats, best_C = sweep_C_values(
+        train_features=train_features,
+        train_labels=train_labels,
+        test_data_loader=finetune_data_loader,
+        metric_type=metric_type,
+        num_classes=num_classes,
+        train_dtype=train_dtype,
+        train_features_device=train_features_device,
+        max_train_iters=max_train_iters,
+    )
+
+    if not finetune_on_val:
+        logger.info("Best parameter found, concatenating features")
+        train_features = torch.cat((train_features, finetune_features))
+        train_labels = torch.cat((train_labels, finetune_labels))
+
+    logger.info("Training final model")
+    logreg_metric = build_metric(metric_type, num_classes=num_classes)
+    evals = train_and_evaluate(
+        C=best_C,
+        max_iter=max_train_iters,
+        train_features=train_features,
+        train_labels=train_labels,
+        logreg_metric=logreg_metric.clone(),
+        test_data_loader=val_data_loader,
+        eval_device=torch.cuda.current_device(),
+        train_dtype=train_dtype,
+        train_features_device=train_features_device,
+    )
+
+    best_stats = evals[1]["metrics"]
+
+    best_stats["best_C"] = best_C
+
+    logger.info(f"Log regression evaluation done in {int(time.time() - start)}s")
+    return best_stats
+
+
+def eval_log_regression_with_model(
+    model,
+    train_dataset_str="ImageNet:split=TRAIN",
+    val_dataset_str="ImageNet:split=VAL",
+    finetune_dataset_str=None,
+    autocast_dtype=torch.float,
+    finetune_on_val=False,
+    metric_type=MetricType.MEAN_ACCURACY,
+    train_dtype=torch.float64,
+    train_features_device=_CPU_DEVICE,
+    max_train_iters=DEFAULT_MAX_ITER,
+):
+    cudnn.benchmark = True
+
+    transform = make_classification_eval_transform(resize_size=224)
+    target_transform = None
+
+    train_dataset = make_dataset(dataset_str=train_dataset_str, transform=transform, target_transform=target_transform)
+    val_dataset = make_dataset(dataset_str=val_dataset_str, transform=transform, target_transform=target_transform)
+    if finetune_dataset_str is not None:
+        finetune_dataset = make_dataset(
+            dataset_str=finetune_dataset_str, transform=transform, target_transform=target_transform
+        )
+    else:
+        finetune_dataset = None
+
+    with torch.cuda.amp.autocast(dtype=autocast_dtype):
+        results_dict_logreg = eval_log_regression(
+            model=model,
+            train_dataset=train_dataset,
+            val_dataset=val_dataset,
+            finetune_dataset=finetune_dataset,
+            metric_type=metric_type,
+            batch_size=256,
+            num_workers=0,  # 5,
+            finetune_on_val=finetune_on_val,
+            train_dtype=train_dtype,
+            train_features_device=train_features_device,
+            max_train_iters=max_train_iters,
+        )
+
+    results_dict = {
+        "top-1": results_dict_logreg["top-1"].cpu().numpy() * 100.0,
+        "top-5": results_dict_logreg.get("top-5", torch.tensor(0.0)).cpu().numpy() * 100.0,
+        "best_C": results_dict_logreg["best_C"],
+    }
+    logger.info(
+        "\n".join(
+            [
+                "Training of the supervised logistic regression on frozen features completed.\n"
+                "Top-1 test accuracy: {acc:.1f}".format(acc=results_dict["top-1"]),
+                "Top-5 test accuracy: {acc:.1f}".format(acc=results_dict["top-5"]),
+                "obtained for C = {c:.6f}".format(c=results_dict["best_C"]),
+            ]
+        )
+    )
+
+    torch.distributed.barrier()
+    return results_dict
+
+
+def main(args):
+    model, autocast_dtype = setup_and_build_model(args)
+    eval_log_regression_with_model(
+        model=model,
+        train_dataset_str=args.train_dataset_str,
+        val_dataset_str=args.val_dataset_str,
+        finetune_dataset_str=args.finetune_dataset_str,
+        autocast_dtype=autocast_dtype,
+        finetune_on_val=args.finetune_on_val,
+        metric_type=args.metric_type,
+        train_dtype=as_torch_dtype(args.train_dtype),
+        train_features_device=torch.device(args.train_features_device),
+        max_train_iters=args.max_train_iters,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    description = "DINOv2 logistic regression evaluation"
+    args_parser = get_args_parser(description=description)
+    args = args_parser.parse_args()
+    sys.exit(main(args))
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/eval/metrics.py b/torchhub/facebookresearch_dinov2_main/dinov2/eval/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..80bf88da224e749dd6b3dd4b2bd90ec99eaec34e
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/eval/metrics.py
@@ -0,0 +1,114 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+import logging
+from typing import Any, Dict, Optional
+
+import torch
+from torch import Tensor
+from torchmetrics import Metric, MetricCollection
+from torchmetrics.classification import MulticlassAccuracy
+from torchmetrics.utilities.data import dim_zero_cat, select_topk
+
+
+logger = logging.getLogger("dinov2")
+
+
+class MetricType(Enum):
+    MEAN_ACCURACY = "mean_accuracy"
+    MEAN_PER_CLASS_ACCURACY = "mean_per_class_accuracy"
+    PER_CLASS_ACCURACY = "per_class_accuracy"
+    IMAGENET_REAL_ACCURACY = "imagenet_real_accuracy"
+
+    @property
+    def accuracy_averaging(self):
+        return getattr(AccuracyAveraging, self.name, None)
+
+    def __str__(self):
+        return self.value
+
+
+class AccuracyAveraging(Enum):
+    MEAN_ACCURACY = "micro"
+    MEAN_PER_CLASS_ACCURACY = "macro"
+    PER_CLASS_ACCURACY = "none"
+
+    def __str__(self):
+        return self.value
+
+
+def build_metric(metric_type: MetricType, *, num_classes: int, ks: Optional[tuple] = None):
+    if metric_type.accuracy_averaging is not None:
+        return build_topk_accuracy_metric(
+            average_type=metric_type.accuracy_averaging,
+            num_classes=num_classes,
+            ks=(1, 5) if ks is None else ks,
+        )
+    elif metric_type == MetricType.IMAGENET_REAL_ACCURACY:
+        return build_topk_imagenet_real_accuracy_metric(
+            num_classes=num_classes,
+            ks=(1, 5) if ks is None else ks,
+        )
+
+    raise ValueError(f"Unknown metric type {metric_type}")
+
+
+def build_topk_accuracy_metric(average_type: AccuracyAveraging, num_classes: int, ks: tuple = (1, 5)):
+    metrics: Dict[str, Metric] = {
+        f"top-{k}": MulticlassAccuracy(top_k=k, num_classes=int(num_classes), average=average_type.value) for k in ks
+    }
+    return MetricCollection(metrics)
+
+
+def build_topk_imagenet_real_accuracy_metric(num_classes: int, ks: tuple = (1, 5)):
+    metrics: Dict[str, Metric] = {f"top-{k}": ImageNetReaLAccuracy(top_k=k, num_classes=int(num_classes)) for k in ks}
+    return MetricCollection(metrics)
+
+
+class ImageNetReaLAccuracy(Metric):
+    is_differentiable: bool = False
+    higher_is_better: Optional[bool] = None
+    full_state_update: bool = False
+
+    def __init__(
+        self,
+        num_classes: int,
+        top_k: int = 1,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.num_classes = num_classes
+        self.top_k = top_k
+        self.add_state("tp", [], dist_reduce_fx="cat")
+
+    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        # preds [B, D]
+        # target [B, A]
+        # preds_oh [B, D] with 0 and 1
+        # select top K highest probabilities, use one hot representation
+        preds_oh = select_topk(preds, self.top_k)
+        # target_oh [B, D + 1] with 0 and 1
+        target_oh = torch.zeros((preds_oh.shape[0], preds_oh.shape[1] + 1), device=target.device, dtype=torch.int32)
+        target = target.long()
+        # for undefined targets (-1) use a fake value `num_classes`
+        target[target == -1] = self.num_classes
+        # fill targets, use one hot representation
+        target_oh.scatter_(1, target, 1)
+        # target_oh [B, D] (remove the fake target at index `num_classes`)
+        target_oh = target_oh[:, :-1]
+        # tp [B] with 0 and 1
+        tp = (preds_oh * target_oh == 1).sum(dim=1)
+        # at least one match between prediction and target
+        tp.clip_(max=1)
+        # ignore instances where no targets are defined
+        mask = target_oh.sum(dim=1) > 0
+        tp = tp[mask]
+        self.tp.append(tp)  # type: ignore
+
+    def compute(self) -> Tensor:
+        tp = dim_zero_cat(self.tp)  # type: ignore
+        return tp.float().mean()
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/eval/setup.py b/torchhub/facebookresearch_dinov2_main/dinov2/eval/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7fadc2b63b994f569c8def82a43ed08ccd15b33
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/eval/setup.py
@@ -0,0 +1,76 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+from typing import Any, List, Optional, Tuple
+
+import torch
+import torch.backends.cudnn as cudnn
+
+from dinov2.models import build_model_from_cfg
+from dinov2.utils.config import setup
+import dinov2.utils.utils as dinov2_utils
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+):
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents or [],
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--config-file",
+        type=str,
+        help="Model configuration file",
+    )
+    parser.add_argument(
+        "--pretrained-weights",
+        type=str,
+        help="Pretrained model weights",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default="",
+        type=str,
+        help="Output directory to write results and logs",
+    )
+    parser.add_argument(
+        "--opts",
+        help="Extra configuration options",
+        default=[],
+        nargs="+",
+    )
+    return parser
+
+
+def get_autocast_dtype(config):
+    teacher_dtype_str = config.compute_precision.teacher.backbone.mixed_precision.param_dtype
+    if teacher_dtype_str == "fp16":
+        return torch.half
+    elif teacher_dtype_str == "bf16":
+        return torch.bfloat16
+    else:
+        return torch.float
+
+
+def build_model_for_eval(config, pretrained_weights):
+    model, _ = build_model_from_cfg(config, only_teacher=True)
+    dinov2_utils.load_pretrained_weights(model, pretrained_weights, "teacher")
+    model.eval()
+    model.cuda()
+    return model
+
+
+def setup_and_build_model(args) -> Tuple[Any, torch.dtype]:
+    cudnn.benchmark = True
+    config = setup(args)
+    model = build_model_for_eval(config, args.pretrained_weights)
+    autocast_dtype = get_autocast_dtype(config)
+    return model, autocast_dtype
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/eval/utils.py b/torchhub/facebookresearch_dinov2_main/dinov2/eval/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2f7e34f41ba6a0b911023e0c5375eef21f426fa
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/eval/utils.py
@@ -0,0 +1,147 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from typing import Dict, Optional
+
+import torch
+from torch import nn
+from torchmetrics import MetricCollection
+
+from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader
+import dinov2.distributed as distributed
+from dinov2.logging import MetricLogger
+
+
+logger = logging.getLogger("dinov2")
+
+
+class ModelWithNormalize(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, samples):
+        return nn.functional.normalize(self.model(samples), dim=1, p=2)
+
+
+class ModelWithIntermediateLayers(nn.Module):
+    def __init__(self, feature_model, n_last_blocks, autocast_ctx):
+        super().__init__()
+        self.feature_model = feature_model
+        self.feature_model.eval()
+        self.n_last_blocks = n_last_blocks
+        self.autocast_ctx = autocast_ctx
+
+    def forward(self, images):
+        with torch.inference_mode():
+            with self.autocast_ctx():
+                features = self.feature_model.get_intermediate_layers(
+                    images, self.n_last_blocks, return_class_token=True
+                )
+        return features
+
+
+@torch.inference_mode()
+def evaluate(
+    model: nn.Module,
+    data_loader,
+    postprocessors: Dict[str, nn.Module],
+    metrics: Dict[str, MetricCollection],
+    device: torch.device,
+    criterion: Optional[nn.Module] = None,
+):
+    model.eval()
+    if criterion is not None:
+        criterion.eval()
+
+    for metric in metrics.values():
+        metric = metric.to(device)
+
+    metric_logger = MetricLogger(delimiter="  ")
+    header = "Test:"
+
+    for samples, targets, *_ in metric_logger.log_every(data_loader, 10, header):
+        outputs = model(samples.to(device))
+        targets = targets.to(device)
+
+        if criterion is not None:
+            loss = criterion(outputs, targets)
+            metric_logger.update(loss=loss.item())
+
+        for k, metric in metrics.items():
+            metric_inputs = postprocessors[k](outputs, targets)
+            metric.update(**metric_inputs)
+
+    metric_logger.synchronize_between_processes()
+    logger.info(f"Averaged stats: {metric_logger}")
+
+    stats = {k: metric.compute() for k, metric in metrics.items()}
+    metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+    return metric_logger_stats, stats
+
+
+def all_gather_and_flatten(tensor_rank):
+    tensor_all_ranks = torch.empty(
+        distributed.get_global_size(),
+        *tensor_rank.shape,
+        dtype=tensor_rank.dtype,
+        device=tensor_rank.device,
+    )
+    tensor_list = list(tensor_all_ranks.unbind(0))
+    torch.distributed.all_gather(tensor_list, tensor_rank.contiguous())
+    return tensor_all_ranks.flatten(end_dim=1)
+
+
+def extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=False):
+    dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset)
+    sample_count = len(dataset_with_enumerated_targets)
+    data_loader = make_data_loader(
+        dataset=dataset_with_enumerated_targets,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        sampler_type=SamplerType.DISTRIBUTED,
+        drop_last=False,
+        shuffle=False,
+    )
+    return extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu)
+
+
+@torch.inference_mode()
+def extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu=False):
+    gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda")
+    metric_logger = MetricLogger(delimiter="  ")
+    features, all_labels = None, None
+    for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10):
+        samples = samples.cuda(non_blocking=True)
+        labels_rank = labels_rank.cuda(non_blocking=True)
+        index = index.cuda(non_blocking=True)
+        features_rank = model(samples).float()
+
+        # init storage feature matrix
+        if features is None:
+            features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device)
+            labels_shape = list(labels_rank.shape)
+            labels_shape[0] = sample_count
+            all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device)
+            logger.info(f"Storing features into tensor of shape {features.shape}")
+
+        # share indexes, features and labels between processes
+        index_all = all_gather_and_flatten(index).to(gather_device)
+        features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device)
+        labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device)
+
+        # update storage feature matrix
+        if len(index_all) > 0:
+            features.index_copy_(0, index_all, features_all_ranks)
+            all_labels.index_copy_(0, index_all, labels_all_ranks)
+
+    logger.info(f"Features shape: {tuple(features.shape)}")
+    logger.info(f"Labels shape: {tuple(all_labels.shape)}")
+
+    assert torch.all(all_labels > -1)
+
+    return features, all_labels
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/fsdp/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/fsdp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..71d20397611619e6a02ea07f5305d650ffef2a51
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/fsdp/__init__.py
@@ -0,0 +1,158 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+from typing import Any
+
+import torch
+import dinov2.distributed as distributed
+from functools import partial
+from fvcore.common.checkpoint import Checkpointer
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+from torch.distributed.fsdp import ShardingStrategy
+from torch.distributed.fsdp import MixedPrecision
+from torch.distributed.fsdp import StateDictType
+from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
+from torch.distributed.fsdp.wrap import ModuleWrapPolicy
+from torch.distributed.fsdp._runtime_utils import _reshard
+
+
+def get_fsdp_wrapper(model_cfg, modules_to_wrap=set()):
+    sharding_strategy_dict = {
+        "NO_SHARD": ShardingStrategy.NO_SHARD,
+        "SHARD_GRAD_OP": ShardingStrategy.SHARD_GRAD_OP,
+        "FULL_SHARD": ShardingStrategy.FULL_SHARD,
+    }
+
+    dtype_dict = {
+        "fp32": torch.float32,
+        "fp16": torch.float16,
+        "bf16": torch.bfloat16,
+    }
+
+    mixed_precision_config = MixedPrecision(
+        param_dtype=dtype_dict[model_cfg.mixed_precision.param_dtype],
+        reduce_dtype=dtype_dict[model_cfg.mixed_precision.reduce_dtype],
+        buffer_dtype=dtype_dict[model_cfg.mixed_precision.buffer_dtype],
+    )
+
+    sharding_strategy_config = sharding_strategy_dict[model_cfg.sharding_strategy]
+
+    local_rank = distributed.get_local_rank()
+
+    fsdp_wrapper = partial(
+        FSDP,
+        sharding_strategy=sharding_strategy_config,
+        mixed_precision=mixed_precision_config,
+        device_id=local_rank,
+        sync_module_states=True,
+        use_orig_params=True,
+        auto_wrap_policy=ModuleWrapPolicy(modules_to_wrap),
+    )
+    return fsdp_wrapper
+
+
+def is_fsdp(x):
+    return isinstance(x, FSDP)
+
+
+def is_sharded_fsdp(x):
+    return is_fsdp(x) and x.sharding_strategy is not ShardingStrategy.NO_SHARD
+
+
+def free_if_fsdp(x):
+    if is_sharded_fsdp(x):
+        handles = x._handles
+        true_list = [True for h in handles]
+        _reshard(x, handles, true_list)
+
+
+def get_fsdp_modules(x):
+    return FSDP.fsdp_modules(x)
+
+
+def reshard_fsdp_model(x):
+    for m in get_fsdp_modules(x):
+        free_if_fsdp(m)
+
+
+def rankstr():
+    return f"rank_{distributed.get_global_rank()}"
+
+
+class FSDPCheckpointer(Checkpointer):
+    def save(self, name: str, **kwargs: Any) -> None:
+        """
+        Dump model and checkpointables to a file.
+
+        Args:
+            name (str): name of the file.
+            kwargs (dict): extra arbitrary data to save.
+        """
+        if not self.save_dir or not self.save_to_disk:
+            return
+
+        data = {}
+        with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT):
+            data["model"] = self.model.state_dict()
+
+        # data["model"] = self.model.state_dict()
+        for key, obj in self.checkpointables.items():
+            data[key] = obj.state_dict()
+        data.update(kwargs)
+
+        basename = f"{name}.{rankstr()}.pth"
+        save_file = os.path.join(self.save_dir, basename)
+        assert os.path.basename(save_file) == basename, basename
+        self.logger.info("Saving checkpoint to {}".format(save_file))
+        with self.path_manager.open(save_file, "wb") as f:
+            torch.save(data, f)
+        self.tag_last_checkpoint(basename)
+
+    def load(self, *args, **kwargs):
+        with FSDP.state_dict_type(self.model, StateDictType.LOCAL_STATE_DICT):
+            return super().load(*args, **kwargs)
+
+    def has_checkpoint(self) -> bool:
+        """
+        Returns:
+            bool: whether a checkpoint exists in the target directory.
+        """
+        save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
+        return self.path_manager.exists(save_file)
+
+    def get_checkpoint_file(self) -> str:
+        """
+        Returns:
+            str: The latest checkpoint file in target directory.
+        """
+        save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
+        try:
+            with self.path_manager.open(save_file, "r") as f:
+                last_saved = f.read().strip()
+        except IOError:
+            # if file doesn't exist, maybe because it has just been
+            # deleted by a separate process
+            return ""
+        # pyre-fixme[6]: For 2nd param expected `Union[PathLike[str], str]` but got
+        #  `Union[bytes, str]`.
+        return os.path.join(self.save_dir, last_saved)
+
+    def tag_last_checkpoint(self, last_filename_basename: str) -> None:
+        """
+        Tag the last checkpoint.
+
+        Args:
+            last_filename_basename (str): the basename of the last filename.
+        """
+        if distributed.is_enabled():
+            torch.distributed.barrier()
+        save_file = os.path.join(self.save_dir, f"last_checkpoint.{rankstr()}")
+        with self.path_manager.open(save_file, "w") as f:
+            f.write(last_filename_basename)  # pyre-ignore
+
+
+ShardedGradScaler = ShardedGradScaler
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..31f196aacac5be8a7c537a3dfa8f97084671b466
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .dino_head import DINOHead
+from .mlp import Mlp
+from .patch_embed import PatchEmbed
+from .swiglu_ffn import SwiGLUFFN, SwiGLUFFNFused
+from .block import NestedTensorBlock
+from .attention import MemEffAttention
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diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/attention.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f9b0c94b40967dfdff4f261c127cbd21328c905
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/attention.py
@@ -0,0 +1,81 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+import logging
+
+from torch import Tensor
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import memory_efficient_attention, unbind, fmha
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+    ) -> None:
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim, bias=proj_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+        attn = q @ k.transpose(-2, -1)
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class MemEffAttention(Attention):
+    def forward(self, x: Tensor, attn_bias=None) -> Tensor:
+        if not XFORMERS_AVAILABLE:
+            assert attn_bias is None, "xFormers is required for nested tensors usage"
+            return super().forward(x)
+
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+
+        q, k, v = unbind(qkv, 2)
+
+        x = memory_efficient_attention(q, k, v, attn_bias=attn_bias)
+        x = x.reshape([B, N, C])
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/block.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/block.py
new file mode 100644
index 0000000000000000000000000000000000000000..25488f57cc0ad3c692f86b62555f6668e2a66db1
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/block.py
@@ -0,0 +1,252 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+import logging
+from typing import Callable, List, Any, Tuple, Dict
+
+import torch
+from torch import nn, Tensor
+
+from .attention import Attention, MemEffAttention
+from .drop_path import DropPath
+from .layer_scale import LayerScale
+from .mlp import Mlp
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import fmha
+    from xformers.ops import scaled_index_add, index_select_cat
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    logger.warning("xFormers not available")
+    XFORMERS_AVAILABLE = False
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        proj_bias: bool = True,
+        ffn_bias: bool = True,
+        drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values=None,
+        drop_path: float = 0.0,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
+        attn_class: Callable[..., nn.Module] = Attention,
+        ffn_layer: Callable[..., nn.Module] = Mlp,
+    ) -> None:
+        super().__init__()
+        # print(f"biases: qkv: {qkv_bias}, proj: {proj_bias}, ffn: {ffn_bias}")
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_class(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            proj_bias=proj_bias,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+        self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = ffn_layer(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+            bias=ffn_bias,
+        )
+        self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.sample_drop_ratio = drop_path
+
+    def forward(self, x: Tensor) -> Tensor:
+        def attn_residual_func(x: Tensor) -> Tensor:
+            return self.ls1(self.attn(self.norm1(x)))
+
+        def ffn_residual_func(x: Tensor) -> Tensor:
+            return self.ls2(self.mlp(self.norm2(x)))
+
+        if self.training and self.sample_drop_ratio > 0.1:
+            # the overhead is compensated only for a drop path rate larger than 0.1
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+            x = drop_add_residual_stochastic_depth(
+                x,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+            )
+        elif self.training and self.sample_drop_ratio > 0.0:
+            x = x + self.drop_path1(attn_residual_func(x))
+            x = x + self.drop_path1(ffn_residual_func(x))  # FIXME: drop_path2
+        else:
+            x = x + attn_residual_func(x)
+            x = x + ffn_residual_func(x)
+        return x
+
+
+def drop_add_residual_stochastic_depth(
+    x: Tensor,
+    residual_func: Callable[[Tensor], Tensor],
+    sample_drop_ratio: float = 0.0,
+) -> Tensor:
+    # 1) extract subset using permutation
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    x_subset = x[brange]
+
+    # 2) apply residual_func to get residual
+    residual = residual_func(x_subset)
+
+    x_flat = x.flatten(1)
+    residual = residual.flatten(1)
+
+    residual_scale_factor = b / sample_subset_size
+
+    # 3) add the residual
+    x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    return x_plus_residual.view_as(x)
+
+
+def get_branges_scales(x, sample_drop_ratio=0.0):
+    b, n, d = x.shape
+    sample_subset_size = max(int(b * (1 - sample_drop_ratio)), 1)
+    brange = (torch.randperm(b, device=x.device))[:sample_subset_size]
+    residual_scale_factor = b / sample_subset_size
+    return brange, residual_scale_factor
+
+
+def add_residual(x, brange, residual, residual_scale_factor, scaling_vector=None):
+    if scaling_vector is None:
+        x_flat = x.flatten(1)
+        residual = residual.flatten(1)
+        x_plus_residual = torch.index_add(x_flat, 0, brange, residual.to(dtype=x.dtype), alpha=residual_scale_factor)
+    else:
+        x_plus_residual = scaled_index_add(
+            x, brange, residual.to(dtype=x.dtype), scaling=scaling_vector, alpha=residual_scale_factor
+        )
+    return x_plus_residual
+
+
+attn_bias_cache: Dict[Tuple, Any] = {}
+
+
+def get_attn_bias_and_cat(x_list, branges=None):
+    """
+    this will perform the index select, cat the tensors, and provide the attn_bias from cache
+    """
+    batch_sizes = [b.shape[0] for b in branges] if branges is not None else [x.shape[0] for x in x_list]
+    all_shapes = tuple((b, x.shape[1]) for b, x in zip(batch_sizes, x_list))
+    if all_shapes not in attn_bias_cache.keys():
+        seqlens = []
+        for b, x in zip(batch_sizes, x_list):
+            for _ in range(b):
+                seqlens.append(x.shape[1])
+        attn_bias = fmha.BlockDiagonalMask.from_seqlens(seqlens)
+        attn_bias._batch_sizes = batch_sizes
+        attn_bias_cache[all_shapes] = attn_bias
+
+    if branges is not None:
+        cat_tensors = index_select_cat([x.flatten(1) for x in x_list], branges).view(1, -1, x_list[0].shape[-1])
+    else:
+        tensors_bs1 = tuple(x.reshape([1, -1, *x.shape[2:]]) for x in x_list)
+        cat_tensors = torch.cat(tensors_bs1, dim=1)
+
+    return attn_bias_cache[all_shapes], cat_tensors
+
+
+def drop_add_residual_stochastic_depth_list(
+    x_list: List[Tensor],
+    residual_func: Callable[[Tensor, Any], Tensor],
+    sample_drop_ratio: float = 0.0,
+    scaling_vector=None,
+) -> Tensor:
+    # 1) generate random set of indices for dropping samples in the batch
+    branges_scales = [get_branges_scales(x, sample_drop_ratio=sample_drop_ratio) for x in x_list]
+    branges = [s[0] for s in branges_scales]
+    residual_scale_factors = [s[1] for s in branges_scales]
+
+    # 2) get attention bias and index+concat the tensors
+    attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
+
+    # 3) apply residual_func to get residual, and split the result
+    residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
+
+    outputs = []
+    for x, brange, residual, residual_scale_factor in zip(x_list, branges, residual_list, residual_scale_factors):
+        outputs.append(add_residual(x, brange, residual, residual_scale_factor, scaling_vector).view_as(x))
+    return outputs
+
+
+class NestedTensorBlock(Block):
+    def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]:
+        """
+        x_list contains a list of tensors to nest together and run
+        """
+        assert isinstance(self.attn, MemEffAttention)
+
+        if self.training and self.sample_drop_ratio > 0.0:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.attn(self.norm1(x), attn_bias=attn_bias)
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.mlp(self.norm2(x))
+
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=attn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls1.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            x_list = drop_add_residual_stochastic_depth_list(
+                x_list,
+                residual_func=ffn_residual_func,
+                sample_drop_ratio=self.sample_drop_ratio,
+                scaling_vector=self.ls2.gamma if isinstance(self.ls1, LayerScale) else None,
+            )
+            return x_list
+        else:
+
+            def attn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls1(self.attn(self.norm1(x), attn_bias=attn_bias))
+
+            def ffn_residual_func(x: Tensor, attn_bias=None) -> Tensor:
+                return self.ls2(self.mlp(self.norm2(x)))
+
+            attn_bias, x = get_attn_bias_and_cat(x_list)
+            x = x + attn_residual_func(x, attn_bias=attn_bias)
+            x = x + ffn_residual_func(x)
+            return attn_bias.split(x)
+
+    def forward(self, x_or_x_list):
+        if isinstance(x_or_x_list, Tensor):
+            return super().forward(x_or_x_list)
+        elif isinstance(x_or_x_list, list):
+            assert XFORMERS_AVAILABLE, "Please install xFormers for nested tensors usage"
+            return self.forward_nested(x_or_x_list)
+        else:
+            raise AssertionError
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/dino_head.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/dino_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..7212db92a4fd8d4c7230e284e551a0234e9d8623
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/dino_head.py
@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+from torch.nn.init import trunc_normal_
+from torch.nn.utils import weight_norm
+
+
+class DINOHead(nn.Module):
+    def __init__(
+        self,
+        in_dim,
+        out_dim,
+        use_bn=False,
+        nlayers=3,
+        hidden_dim=2048,
+        bottleneck_dim=256,
+        mlp_bias=True,
+    ):
+        super().__init__()
+        nlayers = max(nlayers, 1)
+        self.mlp = _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=hidden_dim, use_bn=use_bn, bias=mlp_bias)
+        self.apply(self._init_weights)
+        self.last_layer = weight_norm(nn.Linear(bottleneck_dim, out_dim, bias=False))
+        self.last_layer.weight_g.data.fill_(1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        x = self.mlp(x)
+        eps = 1e-6 if x.dtype == torch.float16 else 1e-12
+        x = nn.functional.normalize(x, dim=-1, p=2, eps=eps)
+        x = self.last_layer(x)
+        return x
+
+
+def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True):
+    if nlayers == 1:
+        return nn.Linear(in_dim, bottleneck_dim, bias=bias)
+    else:
+        layers = [nn.Linear(in_dim, hidden_dim, bias=bias)]
+        if use_bn:
+            layers.append(nn.BatchNorm1d(hidden_dim))
+        layers.append(nn.GELU())
+        for _ in range(nlayers - 2):
+            layers.append(nn.Linear(hidden_dim, hidden_dim, bias=bias))
+            if use_bn:
+                layers.append(nn.BatchNorm1d(hidden_dim))
+            layers.append(nn.GELU())
+        layers.append(nn.Linear(hidden_dim, bottleneck_dim, bias=bias))
+        return nn.Sequential(*layers)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/drop_path.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/drop_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..af05625984dd14682cc96a63bf0c97bab1f123b1
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/drop_path.py
@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/drop.py
+
+
+from torch import nn
+
+
+def drop_path(x, drop_prob: float = 0.0, training: bool = False):
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0:
+        random_tensor.div_(keep_prob)
+    output = x * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/layer_scale.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/layer_scale.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca5daa52bd81d3581adeb2198ea5b7dba2a3aea1
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/layer_scale.py
@@ -0,0 +1,28 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Modified from: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L103-L110
+
+from typing import Union
+
+import torch
+from torch import Tensor
+from torch import nn
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: Union[float, Tensor] = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/mlp.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/mlp.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e4b315f972f9a9f54aef1e4ef4e81b52976f018
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/mlp.py
@@ -0,0 +1,41 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/mlp.py
+
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = nn.GELU,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..574abe41175568d700a389b8b96d1ba554914779
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/patch_embed.py
@@ -0,0 +1,89 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py
+
+from typing import Callable, Optional, Tuple, Union
+
+from torch import Tensor
+import torch.nn as nn
+
+
+def make_2tuple(x):
+    if isinstance(x, tuple):
+        assert len(x) == 2
+        return x
+
+    assert isinstance(x, int)
+    return (x, x)
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D image to patch embedding: (B,C,H,W) -> (B,N,D)
+
+    Args:
+        img_size: Image size.
+        patch_size: Patch token size.
+        in_chans: Number of input image channels.
+        embed_dim: Number of linear projection output channels.
+        norm_layer: Normalization layer.
+    """
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        embed_dim: int = 768,
+        norm_layer: Optional[Callable] = None,
+        flatten_embedding: bool = True,
+    ) -> None:
+        super().__init__()
+
+        image_HW = make_2tuple(img_size)
+        patch_HW = make_2tuple(patch_size)
+        patch_grid_size = (
+            image_HW[0] // patch_HW[0],
+            image_HW[1] // patch_HW[1],
+        )
+
+        self.img_size = image_HW
+        self.patch_size = patch_HW
+        self.patches_resolution = patch_grid_size
+        self.num_patches = patch_grid_size[0] * patch_grid_size[1]
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.flatten_embedding = flatten_embedding
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x: Tensor) -> Tensor:
+        _, _, H, W = x.shape
+        patch_H, patch_W = self.patch_size
+
+        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
+        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"
+
+        x = self.proj(x)  # B C H W
+        H, W = x.size(2), x.size(3)
+        x = x.flatten(2).transpose(1, 2)  # B HW C
+        x = self.norm(x)
+        if not self.flatten_embedding:
+            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
+        return x
+
+    def flops(self) -> float:
+        Ho, Wo = self.patches_resolution
+        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
+        if self.norm is not None:
+            flops += Ho * Wo * self.embed_dim
+        return flops
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/layers/swiglu_ffn.py b/torchhub/facebookresearch_dinov2_main/dinov2/layers/swiglu_ffn.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3324b266fb0a50ccf8c3a0ede2ae10ac4dfa03e
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/layers/swiglu_ffn.py
@@ -0,0 +1,63 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Callable, Optional
+
+from torch import Tensor, nn
+import torch.nn.functional as F
+
+
+class SwiGLUFFN(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.w12 = nn.Linear(in_features, 2 * hidden_features, bias=bias)
+        self.w3 = nn.Linear(hidden_features, out_features, bias=bias)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x12 = self.w12(x)
+        x1, x2 = x12.chunk(2, dim=-1)
+        hidden = F.silu(x1) * x2
+        return self.w3(hidden)
+
+
+try:
+    from xformers.ops import SwiGLU
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    SwiGLU = SwiGLUFFN
+    XFORMERS_AVAILABLE = False
+
+
+class SwiGLUFFNFused(SwiGLU):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: Optional[int] = None,
+        out_features: Optional[int] = None,
+        act_layer: Callable[..., nn.Module] = None,
+        drop: float = 0.0,
+        bias: bool = True,
+    ) -> None:
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+        super().__init__(
+            in_features=in_features,
+            hidden_features=hidden_features,
+            out_features=out_features,
+            bias=bias,
+        )
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/logging/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/logging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e80dadb2d57056e9f6f4989cd24a3c7e26fee23f
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/logging/__init__.py
@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import functools
+import logging
+import os
+import sys
+from typing import Optional
+
+import dinov2.distributed as distributed
+from .helpers import MetricLogger, SmoothedValue
+
+
+# So that calling _configure_logger multiple times won't add many handlers
+@functools.lru_cache()
+def _configure_logger(
+    name: Optional[str] = None,
+    *,
+    level: int = logging.DEBUG,
+    output: Optional[str] = None,
+):
+    """
+    Configure a logger.
+
+    Adapted from Detectron2.
+
+    Args:
+        name: The name of the logger to configure.
+        level: The logging level to use.
+        output: A file name or a directory to save log. If None, will not save log file.
+            If ends with ".txt" or ".log", assumed to be a file name.
+            Otherwise, logs will be saved to `output/log.txt`.
+
+    Returns:
+        The configured logger.
+    """
+
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    logger.propagate = False
+
+    # Loosely match Google glog format:
+    #   [IWEF]yyyymmdd hh:mm:ss.uuuuuu threadid file:line] msg
+    # but use a shorter timestamp and include the logger name:
+    #   [IWEF]yyyymmdd hh:mm:ss logger threadid file:line] msg
+    fmt_prefix = "%(levelname).1s%(asctime)s %(process)s %(name)s %(filename)s:%(lineno)s] "
+    fmt_message = "%(message)s"
+    fmt = fmt_prefix + fmt_message
+    datefmt = "%Y%m%d %H:%M:%S"
+    formatter = logging.Formatter(fmt=fmt, datefmt=datefmt)
+
+    # stdout logging for main worker only
+    if distributed.is_main_process():
+        handler = logging.StreamHandler(stream=sys.stdout)
+        handler.setLevel(logging.DEBUG)
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+
+    # file logging for all workers
+    if output:
+        if os.path.splitext(output)[-1] in (".txt", ".log"):
+            filename = output
+        else:
+            filename = os.path.join(output, "logs", "log.txt")
+
+        if not distributed.is_main_process():
+            global_rank = distributed.get_global_rank()
+            filename = filename + ".rank{}".format(global_rank)
+
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+
+        handler = logging.StreamHandler(open(filename, "a"))
+        handler.setLevel(logging.DEBUG)
+        handler.setFormatter(formatter)
+        logger.addHandler(handler)
+
+    return logger
+
+
+def setup_logging(
+    output: Optional[str] = None,
+    *,
+    name: Optional[str] = None,
+    level: int = logging.DEBUG,
+    capture_warnings: bool = True,
+) -> None:
+    """
+    Setup logging.
+
+    Args:
+        output: A file name or a directory to save log files. If None, log
+            files will not be saved. If output ends with ".txt" or ".log", it
+            is assumed to be a file name.
+            Otherwise, logs will be saved to `output/log.txt`.
+        name: The name of the logger to configure, by default the root logger.
+        level: The logging level to use.
+        capture_warnings: Whether warnings should be captured as logs.
+    """
+    logging.captureWarnings(capture_warnings)
+    _configure_logger(name, level=level, output=output)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/logging/helpers.py b/torchhub/facebookresearch_dinov2_main/dinov2/logging/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..16d643500d2ee10ffea5916aad07f9b9d7c0af6d
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/logging/helpers.py
@@ -0,0 +1,195 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections import defaultdict, deque
+import datetime
+import json
+import logging
+import time
+
+import torch
+
+import dinov2.distributed as distributed
+
+
+logger = logging.getLogger("dinov2")
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t", output_file=None):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+        self.output_file = output_file
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            loss_str.append("{}: {}".format(name, str(meter)))
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def dump_in_output_file(self, iteration, iter_time, data_time):
+        if self.output_file is None or not distributed.is_main_process():
+            return
+        dict_to_dump = dict(
+            iteration=iteration,
+            iter_time=iter_time,
+            data_time=data_time,
+        )
+        dict_to_dump.update({k: v.median for k, v in self.meters.items()})
+        with open(self.output_file, "a") as f:
+            f.write(json.dumps(dict_to_dump) + "\n")
+        pass
+
+    def log_every(self, iterable, print_freq, header=None, n_iterations=None, start_iteration=0):
+        i = start_iteration
+        if not header:
+            header = ""
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt="{avg:.6f}")
+        data_time = SmoothedValue(fmt="{avg:.6f}")
+
+        if n_iterations is None:
+            n_iterations = len(iterable)
+
+        space_fmt = ":" + str(len(str(n_iterations))) + "d"
+
+        log_list = [
+            header,
+            "[{0" + space_fmt + "}/{1}]",
+            "eta: {eta}",
+            "{meters}",
+            "time: {time}",
+            "data: {data}",
+        ]
+        if torch.cuda.is_available():
+            log_list += ["max mem: {memory:.0f}"]
+
+        log_msg = self.delimiter.join(log_list)
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == n_iterations - 1:
+                self.dump_in_output_file(iteration=i, iter_time=iter_time.avg, data_time=data_time.avg)
+                eta_seconds = iter_time.global_avg * (n_iterations - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    logger.info(
+                        log_msg.format(
+                            i,
+                            n_iterations,
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB,
+                        )
+                    )
+                else:
+                    logger.info(
+                        log_msg.format(
+                            i,
+                            n_iterations,
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                        )
+                    )
+            i += 1
+            end = time.time()
+            if i >= n_iterations:
+                break
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        logger.info("{} Total time: {} ({:.6f} s / it)".format(header, total_time_str, total_time / n_iterations))
+
+
+class SmoothedValue:
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, num=1):
+        self.deque.append(value)
+        self.count += num
+        self.total += value * num
+
+    def synchronize_between_processes(self):
+        """
+        Distributed synchronization of the metric
+        Warning: does not synchronize the deque!
+        """
+        if not distributed.is_enabled():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
+        torch.distributed.barrier()
+        torch.distributed.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        return self.total / self.count
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median,
+            avg=self.avg,
+            global_avg=self.global_avg,
+            max=self.max,
+            value=self.value,
+        )
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/loss/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/loss/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..477b71b28259bf97b806df3f3d2f392dded866d6
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/loss/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .dino_clstoken_loss import DINOLoss
+from .ibot_patch_loss import iBOTPatchLoss
+from .koleo_loss import KoLeoLoss
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/loss/dino_clstoken_loss.py b/torchhub/facebookresearch_dinov2_main/dinov2/loss/dino_clstoken_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f33897efb1084e6c1c14ae00bc93ab332c61074
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/loss/dino_clstoken_loss.py
@@ -0,0 +1,100 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import nn
+
+
+class DINOLoss(nn.Module):
+    def __init__(
+        self,
+        out_dim,
+        student_temp=0.1,
+        center_momentum=0.9,
+    ):
+        super().__init__()
+        self.student_temp = student_temp
+        self.center_momentum = center_momentum
+        self.register_buffer("center", torch.zeros(1, out_dim))
+        self.updated = True
+        self.reduce_handle = None
+        self.len_teacher_output = None
+        self.async_batch_center = None
+
+    @torch.no_grad()
+    def softmax_center_teacher(self, teacher_output, teacher_temp):
+        self.apply_center_update()
+        # teacher centering and sharpening
+        return F.softmax((teacher_output - self.center) / teacher_temp, dim=-1)
+
+    @torch.no_grad()
+    def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_iterations=3):
+        teacher_output = teacher_output.float()
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        Q = torch.exp(teacher_output / teacher_temp).t()  # Q is K-by-B for consistency with notations from our paper
+        B = Q.shape[1] * world_size  # number of samples to assign
+        K = Q.shape[0]  # how many prototypes
+
+        # make the matrix sums to 1
+        sum_Q = torch.sum(Q)
+        if dist.is_initialized():
+            dist.all_reduce(sum_Q)
+        Q /= sum_Q
+
+        for it in range(n_iterations):
+            # normalize each row: total weight per prototype must be 1/K
+            sum_of_rows = torch.sum(Q, dim=1, keepdim=True)
+            if dist.is_initialized():
+                dist.all_reduce(sum_of_rows)
+            Q /= sum_of_rows
+            Q /= K
+
+            # normalize each column: total weight per sample must be 1/B
+            Q /= torch.sum(Q, dim=0, keepdim=True)
+            Q /= B
+
+        Q *= B  # the columns must sum to 1 so that Q is an assignment
+        return Q.t()
+
+    def forward(self, student_output_list, teacher_out_softmaxed_centered_list):
+        """
+        Cross-entropy between softmax outputs of the teacher and student networks.
+        """
+        # TODO: Use cross_entropy_distribution here
+        total_loss = 0
+        for s in student_output_list:
+            lsm = F.log_softmax(s / self.student_temp, dim=-1)
+            for t in teacher_out_softmaxed_centered_list:
+                loss = torch.sum(t * lsm, dim=-1)
+                total_loss -= loss.mean()
+        return total_loss
+
+    @torch.no_grad()
+    def update_center(self, teacher_output):
+        self.reduce_center_update(teacher_output)
+
+    @torch.no_grad()
+    def reduce_center_update(self, teacher_output):
+        self.updated = False
+        self.len_teacher_output = len(teacher_output)
+        self.async_batch_center = torch.sum(teacher_output, dim=0, keepdim=True)
+        if dist.is_initialized():
+            self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True)
+
+    @torch.no_grad()
+    def apply_center_update(self):
+        if self.updated is False:
+            world_size = dist.get_world_size() if dist.is_initialized() else 1
+
+            if self.reduce_handle is not None:
+                self.reduce_handle.wait()
+            _t = self.async_batch_center / (self.len_teacher_output * world_size)
+
+            self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum)
+
+            self.updated = True
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/loss/ibot_patch_loss.py b/torchhub/facebookresearch_dinov2_main/dinov2/loss/ibot_patch_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..16bc5cf634d661f1fa337304273f60dcd43c79c3
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/loss/ibot_patch_loss.py
@@ -0,0 +1,152 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import nn
+
+import logging
+
+
+logger = logging.getLogger("dinov2")
+
+
+try:
+    from xformers.ops import cross_entropy
+
+    def lossfunc(t, s, temp):
+        s = s.float()
+        t = t.float()
+        if s.ndim == 2:
+            return -cross_entropy(s.unsqueeze(0), t.unsqueeze(0), temp, bw_inplace=True).squeeze(0)
+        elif s.ndim == 3:
+            return -cross_entropy(s, t, temp, bw_inplace=True)
+
+except ImportError:
+
+    def lossfunc(t, s, temp):
+        return torch.sum(t * F.log_softmax(s / temp, dim=-1), dim=-1)
+
+
+class iBOTPatchLoss(nn.Module):
+    def __init__(self, patch_out_dim, student_temp=0.1, center_momentum=0.9):
+        super().__init__()
+        self.student_temp = student_temp
+        self.center_momentum = center_momentum
+        self.register_buffer("center", torch.zeros(1, 1, patch_out_dim))
+        self.updated = True
+        self.reduce_handle = None
+        self.len_teacher_patch_tokens = None
+        self.async_batch_center = None
+
+    @torch.no_grad()
+    def softmax_center_teacher(self, teacher_patch_tokens, teacher_temp):
+        self.apply_center_update()
+        # teacher centering and sharpening
+        #
+        # WARNING:
+        #   as self.center is a float32, everything gets casted to float32 afterwards
+        #
+        # teacher_patch_tokens = teacher_patch_tokens.float()
+        # return F.softmax((teacher_patch_tokens.sub_(self.center.to(teacher_patch_tokens.dtype))).mul_(1 / teacher_temp), dim=-1)
+
+        return F.softmax((teacher_patch_tokens - self.center) / teacher_temp, dim=-1)
+
+        # this is experimental, keep everything in float16 and let's see what happens:
+        # return F.softmax((teacher_patch_tokens.sub_(self.center)) / teacher_temp, dim=-1)
+
+    @torch.no_grad()
+    def sinkhorn_knopp_teacher(self, teacher_output, teacher_temp, n_masked_patches_tensor, n_iterations=3):
+        teacher_output = teacher_output.float()
+        # world_size = dist.get_world_size() if dist.is_initialized() else 1
+        Q = torch.exp(teacher_output / teacher_temp).t()  # Q is K-by-B for consistency with notations from our paper
+        # B = Q.shape[1] * world_size # number of samples to assign
+        B = n_masked_patches_tensor
+        dist.all_reduce(B)
+        K = Q.shape[0]  # how many prototypes
+
+        # make the matrix sums to 1
+        sum_Q = torch.sum(Q)
+        if dist.is_initialized():
+            dist.all_reduce(sum_Q)
+        Q /= sum_Q
+
+        for it in range(n_iterations):
+            # normalize each row: total weight per prototype must be 1/K
+            sum_of_rows = torch.sum(Q, dim=1, keepdim=True)
+            if dist.is_initialized():
+                dist.all_reduce(sum_of_rows)
+            Q /= sum_of_rows
+            Q /= K
+
+            # normalize each column: total weight per sample must be 1/B
+            Q /= torch.sum(Q, dim=0, keepdim=True)
+            Q /= B
+
+        Q *= B  # the columns must sum to 1 so that Q is an assignment
+        return Q.t()
+
+    def forward(self, student_patch_tokens, teacher_patch_tokens, student_masks_flat):
+        """
+        Cross-entropy between softmax outputs of the teacher and student networks.
+        student_patch_tokens: (B, N, D) tensor
+        teacher_patch_tokens: (B, N, D) tensor
+        student_masks_flat: (B, N) tensor
+        """
+        t = teacher_patch_tokens
+        s = student_patch_tokens
+        loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1)
+        loss = torch.sum(loss * student_masks_flat.float(), dim=-1) / student_masks_flat.sum(dim=-1).clamp(min=1.0)
+        return -loss.mean()
+
+    def forward_masked(
+        self,
+        student_patch_tokens_masked,
+        teacher_patch_tokens_masked,
+        student_masks_flat,
+        n_masked_patches=None,
+        masks_weight=None,
+    ):
+        t = teacher_patch_tokens_masked
+        s = student_patch_tokens_masked
+        # loss = torch.sum(t * F.log_softmax(s / self.student_temp, dim=-1), dim=-1)
+        loss = lossfunc(t, s, self.student_temp)
+        if masks_weight is None:
+            masks_weight = (
+                (1 / student_masks_flat.sum(-1).clamp(min=1.0))
+                .unsqueeze(-1)
+                .expand_as(student_masks_flat)[student_masks_flat]
+            )
+        if n_masked_patches is not None:
+            loss = loss[:n_masked_patches]
+        loss = loss * masks_weight
+        return -loss.sum() / student_masks_flat.shape[0]
+
+    @torch.no_grad()
+    def update_center(self, teacher_patch_tokens):
+        self.reduce_center_update(teacher_patch_tokens)
+
+    @torch.no_grad()
+    def reduce_center_update(self, teacher_patch_tokens):
+        self.updated = False
+        self.len_teacher_patch_tokens = len(teacher_patch_tokens)
+        self.async_batch_center = torch.sum(teacher_patch_tokens.mean(1), dim=0, keepdim=True)
+        if dist.is_initialized():
+            self.reduce_handle = dist.all_reduce(self.async_batch_center, async_op=True)
+
+    @torch.no_grad()
+    def apply_center_update(self):
+        if self.updated is False:
+            world_size = dist.get_world_size() if dist.is_initialized() else 1
+
+            if self.reduce_handle is not None:
+                self.reduce_handle.wait()
+            _t = self.async_batch_center / (self.len_teacher_patch_tokens * world_size)
+
+            self.center = self.center * self.center_momentum + _t * (1 - self.center_momentum)
+
+            self.updated = True
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/loss/koleo_loss.py b/torchhub/facebookresearch_dinov2_main/dinov2/loss/koleo_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..e776d0426bb029cf48f25b0c94077720bc8421c4
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/loss/koleo_loss.py
@@ -0,0 +1,49 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# import torch.distributed as dist
+
+
+logger = logging.getLogger("dinov2")
+
+
+class KoLeoLoss(nn.Module):
+    """Kozachenko-Leonenko entropic loss regularizer from Sablayrolles et al. - 2018 - Spreading vectors for similarity search"""
+
+    def __init__(self):
+        super().__init__()
+        self.pdist = nn.PairwiseDistance(2, eps=1e-8)
+
+    def pairwise_NNs_inner(self, x):
+        """
+        Pairwise nearest neighbors for L2-normalized vectors.
+        Uses Torch rather than Faiss to remain on GPU.
+        """
+        # parwise dot products (= inverse distance)
+        dots = torch.mm(x, x.t())
+        n = x.shape[0]
+        dots.view(-1)[:: (n + 1)].fill_(-1)  # Trick to fill diagonal with -1
+        # max inner prod -> min distance
+        _, I = torch.max(dots, dim=1)  # noqa: E741
+        return I
+
+    def forward(self, student_output, eps=1e-8):
+        """
+        Args:
+            student_output (BxD): backbone output of student
+        """
+        with torch.cuda.amp.autocast(enabled=False):
+            student_output = F.normalize(student_output, eps=eps, p=2, dim=-1)
+            I = self.pairwise_NNs_inner(student_output)  # noqa: E741
+            distances = self.pdist(student_output, student_output[I])  # BxD, BxD -> B
+            loss = -torch.log(distances + eps).mean()
+        return loss
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/models/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e5a1f3832464f898752e57e865760e9864613cb
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/models/__init__.py
@@ -0,0 +1,41 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+from . import vision_transformer as vits
+
+
+logger = logging.getLogger("dinov2")
+
+
+def build_model(args, only_teacher=False, img_size=224):
+    args.arch = args.arch.removesuffix("_memeff")
+    if "vit" in args.arch:
+        vit_kwargs = dict(
+            img_size=img_size,
+            patch_size=args.patch_size,
+            init_values=args.layerscale,
+            ffn_layer=args.ffn_layer,
+            block_chunks=args.block_chunks,
+            qkv_bias=args.qkv_bias,
+            proj_bias=args.proj_bias,
+            ffn_bias=args.ffn_bias,
+        )
+        teacher = vits.__dict__[args.arch](**vit_kwargs)
+        if only_teacher:
+            return teacher, teacher.embed_dim
+        student = vits.__dict__[args.arch](
+            **vit_kwargs,
+            drop_path_rate=args.drop_path_rate,
+            drop_path_uniform=args.drop_path_uniform,
+        )
+        embed_dim = student.embed_dim
+    return student, teacher, embed_dim
+
+
+def build_model_from_cfg(cfg, only_teacher=False):
+    return build_model(cfg.student, only_teacher=only_teacher, img_size=cfg.crops.global_crops_size)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/models/vision_transformer.py b/torchhub/facebookresearch_dinov2_main/dinov2/models/vision_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..18e159a986336af813c8f0e505b946f42cd83e47
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/models/vision_transformer.py
@@ -0,0 +1,358 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        w0, h0 = w0 + 0.1, h0 + 0.1
+
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
+            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
+            mode="bicubic",
+        )
+
+        assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None):
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        x = x + self.interpolate_pos_encoding(x, w, h)
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list):
+        x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_patchtokens": x_norm[:, 1:],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None):
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks)
+
+        x = self.prepare_tokens_with_masks(x, masks)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_patchtokens": x_norm[:, 1:],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1:] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, *args, is_training=False, **kwargs):
+        ret = self.forward_features(*args, **kwargs)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        **kwargs,
+    )
+    return model
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/run/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/run/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0952fcc3f57e34b3747962e9ebd6fc57aeea63fa
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/run/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/knn.py b/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/knn.py
new file mode 100644
index 0000000000000000000000000000000000000000..15d674b78b0629aa0f041c2426c894925469a0e8
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/knn.py
@@ -0,0 +1,60 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.eval.knn import get_args_parser as get_knn_args_parser
+from dinov2.logging import setup_logging
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Evaluator:
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.eval.knn import main as knn_main
+
+        self._setup_args()
+        knn_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 k-NN evaluation"
+    knn_args_parser = get_knn_args_parser(add_help=False)
+    parents = [knn_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Evaluator, args, name="dinov2:knn")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/linear.py b/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8c264762ac6bb82a3622c74e1e683ea5c6be437
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/linear.py
@@ -0,0 +1,60 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.eval.linear import get_args_parser as get_linear_args_parser
+from dinov2.logging import setup_logging
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Evaluator:
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.eval.linear import main as linear_main
+
+        self._setup_args()
+        linear_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 linear evaluation"
+    linear_args_parser = get_linear_args_parser(add_help=False)
+    parents = [linear_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Evaluator, args, name="dinov2:linear")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/log_regression.py b/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/log_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d3d5a5742792fc8d4ca3b39c15c47e8aa349bc7
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/run/eval/log_regression.py
@@ -0,0 +1,60 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.eval.log_regression import get_args_parser as get_log_regression_args_parser
+from dinov2.logging import setup_logging
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Evaluator:
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.eval.log_regression import main as log_regression_main
+
+        self._setup_args()
+        log_regression_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 logistic evaluation"
+    log_regression_args_parser = get_log_regression_args_parser(add_help=False)
+    parents = [log_regression_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Evaluator, args, name="dinov2:logreg")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/run/submit.py b/torchhub/facebookresearch_dinov2_main/dinov2/run/submit.py
new file mode 100644
index 0000000000000000000000000000000000000000..68140f3d6d93dc67ccd7c45fe712eb15483d1ad6
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/run/submit.py
@@ -0,0 +1,123 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import logging
+import os
+from pathlib import Path
+from typing import List, Optional
+
+import submitit
+
+from dinov2.utils.cluster import (
+    get_slurm_executor_parameters,
+    get_slurm_partition,
+    get_user_checkpoint_path,
+)
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(
+    description: Optional[str] = None,
+    parents: Optional[List[argparse.ArgumentParser]] = None,
+    add_help: bool = True,
+) -> argparse.ArgumentParser:
+    parents = parents or []
+    slurm_partition = get_slurm_partition()
+    parser = argparse.ArgumentParser(
+        description=description,
+        parents=parents,
+        add_help=add_help,
+    )
+    parser.add_argument(
+        "--ngpus",
+        "--gpus",
+        "--gpus-per-node",
+        default=8,
+        type=int,
+        help="Number of GPUs to request on each node",
+    )
+    parser.add_argument(
+        "--nodes",
+        "--nnodes",
+        default=2,
+        type=int,
+        help="Number of nodes to request",
+    )
+    parser.add_argument(
+        "--timeout",
+        default=2800,
+        type=int,
+        help="Duration of the job",
+    )
+    parser.add_argument(
+        "--partition",
+        default=slurm_partition,
+        type=str,
+        help="Partition where to submit",
+    )
+    parser.add_argument(
+        "--use-volta32",
+        action="store_true",
+        help="Request V100-32GB GPUs",
+    )
+    parser.add_argument(
+        "--comment",
+        default="",
+        type=str,
+        help="Comment to pass to scheduler, e.g. priority message",
+    )
+    parser.add_argument(
+        "--exclude",
+        default="",
+        type=str,
+        help="Nodes to exclude",
+    )
+    return parser
+
+
+def get_shared_folder() -> Path:
+    user_checkpoint_path = get_user_checkpoint_path()
+    if user_checkpoint_path is None:
+        raise RuntimeError("Path to user checkpoint cannot be determined")
+    path = user_checkpoint_path / "experiments"
+    path.mkdir(exist_ok=True)
+    return path
+
+
+def submit_jobs(task_class, args, name: str):
+    if not args.output_dir:
+        args.output_dir = str(get_shared_folder() / "%j")
+
+    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
+    executor = submitit.AutoExecutor(folder=args.output_dir, slurm_max_num_timeout=30)
+
+    kwargs = {}
+    if args.use_volta32:
+        kwargs["slurm_constraint"] = "volta32gb"
+    if args.comment:
+        kwargs["slurm_comment"] = args.comment
+    if args.exclude:
+        kwargs["slurm_exclude"] = args.exclude
+
+    executor_params = get_slurm_executor_parameters(
+        nodes=args.nodes,
+        num_gpus_per_node=args.ngpus,
+        timeout_min=args.timeout,  # max is 60 * 72
+        slurm_signal_delay_s=120,
+        slurm_partition=args.partition,
+        **kwargs,
+    )
+    executor.update_parameters(name=name, **executor_params)
+
+    task = task_class(args)
+    job = executor.submit(task)
+
+    logger.info(f"Submitted job_id: {job.job_id}")
+    str_output_dir = os.path.abspath(args.output_dir).replace("%j", str(job.job_id))
+    logger.info(f"Logs and checkpoints will be saved at: {str_output_dir}")
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/run/train/train.py b/torchhub/facebookresearch_dinov2_main/dinov2/run/train/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..24716f2a314820a4cc15289fe0cb13ad52cf343c
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/run/train/train.py
@@ -0,0 +1,60 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import sys
+
+from dinov2.logging import setup_logging
+from dinov2.train import get_args_parser as get_train_args_parser
+from dinov2.run.submit import get_args_parser, submit_jobs
+
+
+logger = logging.getLogger("dinov2")
+
+
+class Trainer(object):
+    def __init__(self, args):
+        self.args = args
+
+    def __call__(self):
+        from dinov2.train import main as train_main
+
+        self._setup_args()
+        train_main(self.args)
+
+    def checkpoint(self):
+        import submitit
+
+        logger.info(f"Requeuing {self.args}")
+        empty = type(self)(self.args)
+        return submitit.helpers.DelayedSubmission(empty)
+
+    def _setup_args(self):
+        import submitit
+
+        job_env = submitit.JobEnvironment()
+        self.args.output_dir = self.args.output_dir.replace("%j", str(job_env.job_id))
+        logger.info(f"Process group: {job_env.num_tasks} tasks, rank: {job_env.global_rank}")
+        logger.info(f"Args: {self.args}")
+
+
+def main():
+    description = "Submitit launcher for DINOv2 training"
+    train_args_parser = get_train_args_parser(add_help=False)
+    parents = [train_args_parser]
+    args_parser = get_args_parser(description=description, parents=parents)
+    args = args_parser.parse_args()
+
+    setup_logging()
+
+    assert os.path.exists(args.config_file), "Configuration file does not exist!"
+    submit_jobs(Trainer, args, name="dinov2:train")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/train/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0b66d17aa547ed5560e75a03f5c1587da2d4fd7
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/train/__init__.py
@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .train import get_args_parser, main
+from .ssl_meta_arch import SSLMetaArch
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/train/ssl_meta_arch.py b/torchhub/facebookresearch_dinov2_main/dinov2/train/ssl_meta_arch.py
new file mode 100644
index 0000000000000000000000000000000000000000..86d0c2413f9abc61953d0e12b43a5a843d97d244
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/train/ssl_meta_arch.py
@@ -0,0 +1,403 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from functools import partial
+import logging
+
+import torch
+from torch import nn
+
+from dinov2.loss import DINOLoss, iBOTPatchLoss, KoLeoLoss
+from dinov2.models import build_model_from_cfg
+from dinov2.layers import DINOHead
+from dinov2.utils.utils import has_batchnorms
+from dinov2.utils.param_groups import get_params_groups_with_decay, fuse_params_groups
+from dinov2.fsdp import get_fsdp_wrapper, ShardedGradScaler, get_fsdp_modules, reshard_fsdp_model
+
+from dinov2.models.vision_transformer import BlockChunk
+
+try:
+    from xformers.ops import fmha
+
+    XFORMERS_AVAILABLE = True
+except ImportError:
+    XFORMERS_AVAILABLE = False
+assert XFORMERS_AVAILABLE, "xFormers is required for DINOv2 training"
+
+
+logger = logging.getLogger("dinov2")
+
+
+class SSLMetaArch(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.cfg = cfg
+        self.fp16_scaler = ShardedGradScaler() if cfg.compute_precision.grad_scaler else None
+
+        student_model_dict = dict()
+        teacher_model_dict = dict()
+
+        student_backbone, teacher_backbone, embed_dim = build_model_from_cfg(cfg)
+        student_model_dict["backbone"] = student_backbone
+        teacher_model_dict["backbone"] = teacher_backbone
+        logger.info(f"OPTIONS -- architecture : embed_dim: {embed_dim}")
+
+        if cfg.student.pretrained_weights:
+            chkpt = torch.load(cfg.student.pretrained_weights)
+            logger.info(f"OPTIONS -- pretrained weights: loading from {cfg.student.pretrained_weights}")
+            student_backbone.load_state_dict(chkpt["model"], strict=False)
+
+        self.embed_dim = embed_dim
+        self.dino_out_dim = cfg.dino.head_n_prototypes
+
+        self.do_dino = cfg.dino.loss_weight > 0
+        self.do_koleo = cfg.dino.koleo_loss_weight > 0
+        self.do_ibot = cfg.ibot.loss_weight > 0
+        self.ibot_separate_head = cfg.ibot.separate_head
+
+        logger.info("OPTIONS -- DINO")
+        if self.do_dino:
+            logger.info(f"OPTIONS -- DINO -- loss_weight: {cfg.dino.loss_weight}")
+            logger.info(f"OPTIONS -- DINO -- head_n_prototypes: {cfg.dino.head_n_prototypes}")
+            logger.info(f"OPTIONS -- DINO -- head_bottleneck_dim: {cfg.dino.head_bottleneck_dim}")
+            logger.info(f"OPTIONS -- DINO -- head_hidden_dim: {cfg.dino.head_hidden_dim}")
+            self.dino_loss_weight = cfg.dino.loss_weight
+            dino_head = partial(
+                DINOHead,
+                in_dim=embed_dim,
+                out_dim=cfg.dino.head_n_prototypes,
+                hidden_dim=cfg.dino.head_hidden_dim,
+                bottleneck_dim=cfg.dino.head_bottleneck_dim,
+                nlayers=cfg.dino.head_nlayers,
+            )
+            self.dino_loss = DINOLoss(self.dino_out_dim)
+            if self.do_koleo:
+                logger.info("OPTIONS -- DINO -- applying KOLEO regularization")
+                self.koleo_loss = KoLeoLoss()
+
+        else:
+            logger.info("OPTIONS -- DINO -- not using DINO")
+
+        if self.do_dino or self.do_ibot:
+            student_model_dict["dino_head"] = dino_head()
+            teacher_model_dict["dino_head"] = dino_head()
+
+        logger.info("OPTIONS -- IBOT")
+        logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}")
+        logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_ratio_tuple: {cfg.ibot.mask_ratio_min_max}")
+        logger.info(f"OPTIONS -- IBOT masking -- ibot_mask_sample_probability: {cfg.ibot.mask_sample_probability}")
+        if self.do_ibot:
+            self.ibot_loss_weight = cfg.ibot.loss_weight
+            assert max(cfg.ibot.mask_ratio_min_max) > 0, "please provide a positive mask ratio tuple for ibot"
+            assert cfg.ibot.mask_sample_probability > 0, "please provide a positive mask probability for ibot"
+            self.ibot_out_dim = cfg.ibot.head_n_prototypes if self.ibot_separate_head else cfg.dino.head_n_prototypes
+            self.ibot_patch_loss = iBOTPatchLoss(self.ibot_out_dim)
+            if self.ibot_separate_head:
+                logger.info(f"OPTIONS -- IBOT -- loss_weight: {cfg.ibot.loss_weight}")
+                logger.info(f"OPTIONS -- IBOT -- head_n_prototypes: {cfg.ibot.head_n_prototypes}")
+                logger.info(f"OPTIONS -- IBOT -- head_bottleneck_dim: {cfg.ibot.head_bottleneck_dim}")
+                logger.info(f"OPTIONS -- IBOT -- head_hidden_dim: {cfg.ibot.head_hidden_dim}")
+                ibot_head = partial(
+                    DINOHead,
+                    in_dim=embed_dim,
+                    out_dim=cfg.ibot.head_n_prototypes,
+                    hidden_dim=cfg.ibot.head_hidden_dim,
+                    bottleneck_dim=cfg.ibot.head_bottleneck_dim,
+                    nlayers=cfg.ibot.head_nlayers,
+                )
+                student_model_dict["ibot_head"] = ibot_head()
+                teacher_model_dict["ibot_head"] = ibot_head()
+            else:
+                logger.info("OPTIONS -- IBOT -- head shared with DINO")
+
+        self.need_to_synchronize_fsdp_streams = True
+
+        self.student = nn.ModuleDict(student_model_dict)
+        self.teacher = nn.ModuleDict(teacher_model_dict)
+
+        # there is no backpropagation through the teacher, so no need for gradients
+        for p in self.teacher.parameters():
+            p.requires_grad = False
+        logger.info(f"Student and Teacher are built: they are both {cfg.student.arch} network.")
+
+    def forward(self, inputs):
+        raise NotImplementedError
+
+    def backprop_loss(self, loss):
+        if self.fp16_scaler is not None:
+            self.fp16_scaler.scale(loss).backward()
+        else:
+            loss.backward()
+
+    def forward_backward(self, images, teacher_temp):
+        n_global_crops = 2
+        assert n_global_crops == 2
+        n_local_crops = self.cfg.crops.local_crops_number
+
+        global_crops = images["collated_global_crops"].cuda(non_blocking=True)
+        local_crops = images["collated_local_crops"].cuda(non_blocking=True)
+
+        masks = images["collated_masks"].cuda(non_blocking=True)
+        mask_indices_list = images["mask_indices_list"].cuda(non_blocking=True)
+        n_masked_patches_tensor = images["n_masked_patches"].cuda(non_blocking=True)
+        n_masked_patches = mask_indices_list.shape[0]
+        upperbound = images["upperbound"]
+        masks_weight = images["masks_weight"].cuda(non_blocking=True)
+
+        n_local_crops_loss_terms = max(n_local_crops * n_global_crops, 1)
+        n_global_crops_loss_terms = (n_global_crops - 1) * n_global_crops
+
+        do_dino = self.do_dino
+        do_ibot = self.do_ibot
+
+        # loss scales
+        ibot_loss_scale = 1.0 / n_global_crops
+
+        # teacher output
+        @torch.no_grad()
+        def get_teacher_output():
+            x, n_global_crops_teacher = global_crops, n_global_crops
+            teacher_backbone_output_dict = self.teacher.backbone(x, is_training=True)
+            teacher_cls_tokens = teacher_backbone_output_dict["x_norm_clstoken"]
+            teacher_cls_tokens = teacher_cls_tokens.chunk(n_global_crops_teacher)
+            # watch out: these are chunked and cat'd in reverse so A is matched to B in the global crops dino loss
+            teacher_cls_tokens = torch.cat((teacher_cls_tokens[1], teacher_cls_tokens[0]))
+            ibot_teacher_patch_tokens = teacher_backbone_output_dict["x_norm_patchtokens"]
+            _dim = ibot_teacher_patch_tokens.shape[-1]
+            n_cls_tokens = teacher_cls_tokens.shape[0]
+
+            if do_ibot and not self.ibot_separate_head:
+                buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound + n_cls_tokens, _dim)
+                buffer_tensor_teacher[:n_cls_tokens].copy_(teacher_cls_tokens)
+                torch.index_select(
+                    ibot_teacher_patch_tokens.flatten(0, 1),
+                    dim=0,
+                    index=mask_indices_list,
+                    out=buffer_tensor_teacher[n_cls_tokens : n_cls_tokens + n_masked_patches],
+                )
+                tokens_after_head = self.teacher.dino_head(buffer_tensor_teacher)
+                teacher_cls_tokens_after_head = tokens_after_head[:n_cls_tokens]
+                masked_teacher_patch_tokens_after_head = tokens_after_head[
+                    n_cls_tokens : n_cls_tokens + n_masked_patches
+                ]
+            elif do_ibot and self.ibot_separate_head:
+                buffer_tensor_teacher = ibot_teacher_patch_tokens.new_zeros(upperbound, _dim)
+                torch.index_select(
+                    ibot_teacher_patch_tokens.flatten(0, 1),
+                    dim=0,
+                    index=mask_indices_list,
+                    out=buffer_tensor_teacher[:n_masked_patches],
+                )
+                teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens)
+                masked_teacher_patch_tokens_after_head = self.teacher.ibot_head(buffer_tensor_teacher)[
+                    :n_masked_patches
+                ]
+            else:
+                teacher_cls_tokens_after_head = self.teacher.dino_head(teacher_cls_tokens)
+                masked_teacher_ibot_softmaxed_centered = None
+
+            if self.cfg.train.centering == "centering":
+                teacher_dino_softmaxed_centered_list = self.dino_loss.softmax_center_teacher(
+                    teacher_cls_tokens_after_head, teacher_temp=teacher_temp
+                ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:])
+                self.dino_loss.update_center(teacher_cls_tokens_after_head)
+                if do_ibot:
+                    masked_teacher_patch_tokens_after_head = masked_teacher_patch_tokens_after_head.unsqueeze(0)
+                    masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.softmax_center_teacher(
+                        masked_teacher_patch_tokens_after_head[:, :n_masked_patches], teacher_temp=teacher_temp
+                    )
+                    masked_teacher_ibot_softmaxed_centered = masked_teacher_ibot_softmaxed_centered.squeeze(0)
+                    self.ibot_patch_loss.update_center(masked_teacher_patch_tokens_after_head[:n_masked_patches])
+
+            elif self.cfg.train.centering == "sinkhorn_knopp":
+                teacher_dino_softmaxed_centered_list = self.dino_loss.sinkhorn_knopp_teacher(
+                    teacher_cls_tokens_after_head, teacher_temp=teacher_temp
+                ).view(n_global_crops_teacher, -1, *teacher_cls_tokens_after_head.shape[1:])
+
+                if do_ibot:
+                    masked_teacher_ibot_softmaxed_centered = self.ibot_patch_loss.sinkhorn_knopp_teacher(
+                        masked_teacher_patch_tokens_after_head,
+                        teacher_temp=teacher_temp,
+                        n_masked_patches_tensor=n_masked_patches_tensor,
+                    )
+
+            else:
+                raise NotImplementedError
+
+            return teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered
+
+        teacher_dino_softmaxed_centered_list, masked_teacher_ibot_softmaxed_centered = get_teacher_output()
+        reshard_fsdp_model(self.teacher)
+
+        loss_dict = {}
+
+        loss_accumulator = 0  # for backprop
+        student_global_backbone_output_dict, student_local_backbone_output_dict = self.student.backbone(
+            [global_crops, local_crops], masks=[masks, None], is_training=True
+        )
+
+        inputs_for_student_head_list = []
+
+        # 1a: local crops cls tokens
+        student_local_cls_tokens = student_local_backbone_output_dict["x_norm_clstoken"]
+        inputs_for_student_head_list.append(student_local_cls_tokens.unsqueeze(0))
+
+        # 1b: global crops cls tokens
+        student_global_cls_tokens = student_global_backbone_output_dict["x_norm_clstoken"]
+        inputs_for_student_head_list.append(student_global_cls_tokens.unsqueeze(0))
+
+        # 1c: global crops patch tokens
+        if do_ibot:
+            _dim = student_global_backbone_output_dict["x_norm_clstoken"].shape[-1]
+            ibot_student_patch_tokens = student_global_backbone_output_dict["x_norm_patchtokens"]
+            buffer_tensor_patch_tokens = ibot_student_patch_tokens.new_zeros(upperbound, _dim)
+            buffer_tensor_patch_tokens[:n_masked_patches].copy_(
+                torch.index_select(ibot_student_patch_tokens.flatten(0, 1), dim=0, index=mask_indices_list)
+            )
+            if not self.ibot_separate_head:
+                inputs_for_student_head_list.append(buffer_tensor_patch_tokens.unsqueeze(0))
+            else:
+                student_global_masked_patch_tokens_after_head = self.student.ibot_head(buffer_tensor_patch_tokens)[
+                    :n_masked_patches
+                ]
+
+        # 2: run
+        _attn_bias, cat_inputs = fmha.BlockDiagonalMask.from_tensor_list(inputs_for_student_head_list)
+        outputs_list = _attn_bias.split(self.student.dino_head(cat_inputs))
+
+        # 3a: local crops cls tokens
+        student_local_cls_tokens_after_head = outputs_list.pop(0).squeeze(0)
+
+        # 3b: global crops cls tokens
+        student_global_cls_tokens_after_head = outputs_list.pop(0).squeeze(0)
+
+        # 3c: global crops patch tokens
+        if do_ibot and not self.ibot_separate_head:
+            student_global_masked_patch_tokens_after_head = outputs_list.pop(0).squeeze(0)[:n_masked_patches]
+
+        if n_local_crops > 0:
+            dino_local_crops_loss = self.dino_loss(
+                student_output_list=student_local_cls_tokens_after_head.chunk(n_local_crops),
+                teacher_out_softmaxed_centered_list=teacher_dino_softmaxed_centered_list,
+            ) / (n_global_crops_loss_terms + n_local_crops_loss_terms)
+
+            # store for display
+            loss_dict["dino_local_crops_loss"] = dino_local_crops_loss
+
+            # accumulate loss
+            loss_accumulator += self.dino_loss_weight * dino_local_crops_loss
+
+        # process global crops
+        loss_scales = 2  # this is here since we process global crops together
+
+        if do_dino:
+            # compute loss
+            dino_global_crops_loss = (
+                self.dino_loss(
+                    student_output_list=[student_global_cls_tokens_after_head],
+                    teacher_out_softmaxed_centered_list=[
+                        teacher_dino_softmaxed_centered_list.flatten(0, 1)
+                    ],  # these were chunked and stacked in reverse so A is matched to B
+                )
+                * loss_scales
+                / (n_global_crops_loss_terms + n_local_crops_loss_terms)
+            )
+
+            loss_dict["dino_global_crops_loss"] = dino_global_crops_loss
+
+            # accumulate loss
+            loss_accumulator += self.dino_loss_weight * dino_global_crops_loss
+
+            student_cls_tokens = student_global_cls_tokens
+
+            if self.do_koleo:
+                koleo_loss = self.cfg.dino.koleo_loss_weight * sum(
+                    self.koleo_loss(p) for p in student_cls_tokens.chunk(2)
+                )  # we don't apply koleo loss between cls tokens of a same image
+                loss_accumulator += koleo_loss
+                loss_dict["koleo_loss"] = (
+                    koleo_loss / loss_scales
+                )  # this is to display the same losses as before but we can remove eventually
+
+        if do_ibot:
+            # compute loss
+            ibot_patch_loss = (
+                self.ibot_patch_loss.forward_masked(
+                    student_global_masked_patch_tokens_after_head,
+                    masked_teacher_ibot_softmaxed_centered,
+                    student_masks_flat=masks,
+                    n_masked_patches=n_masked_patches,
+                    masks_weight=masks_weight,
+                )
+                * loss_scales
+                * ibot_loss_scale
+            )
+
+            # store for display
+            loss_dict["ibot_loss"] = ibot_patch_loss / 2
+
+            # accumulate loss
+            loss_accumulator += self.ibot_loss_weight * ibot_patch_loss
+
+        self.backprop_loss(loss_accumulator)
+
+        self.fsdp_synchronize_streams()
+
+        return loss_dict
+
+    def fsdp_synchronize_streams(self):
+        if self.need_to_synchronize_fsdp_streams:
+            torch.cuda.synchronize()
+            self.student.dino_head._streams = (
+                self.teacher.dino_head._streams
+            ) = self.student.backbone._streams = self.teacher.backbone._streams
+            self.need_to_synchronize_fsdp_streams = False
+
+    def update_teacher(self, m):
+        student_param_list = []
+        teacher_param_list = []
+        with torch.no_grad():
+            for k in self.student.keys():
+                for ms, mt in zip(get_fsdp_modules(self.student[k]), get_fsdp_modules(self.teacher[k])):
+                    student_param_list += ms.params
+                    teacher_param_list += mt.params
+            torch._foreach_mul_(teacher_param_list, m)
+            torch._foreach_add_(teacher_param_list, student_param_list, alpha=1 - m)
+
+    def train(self):
+        super().train()
+        self.teacher.eval()
+
+    def get_maybe_fused_params_for_submodel(self, m):
+        params_groups = get_params_groups_with_decay(
+            model=m,
+            lr_decay_rate=self.cfg.optim.layerwise_decay,
+            patch_embed_lr_mult=self.cfg.optim.patch_embed_lr_mult,
+        )
+        fused_params_groups = fuse_params_groups(params_groups)
+        logger.info("fusing param groups")
+
+        for g in fused_params_groups:
+            g["foreach"] = True
+        return fused_params_groups
+
+    def get_params_groups(self):
+        all_params_groups = []
+        for m in self.student.values():
+            all_params_groups += self.get_maybe_fused_params_for_submodel(m)
+        return all_params_groups
+
+    def prepare_for_distributed_training(self):
+        logger.info("DISTRIBUTED FSDP -- preparing model for distributed training")
+        if has_batchnorms(self.student):
+            raise NotImplementedError
+        # below will synchronize all student subnetworks across gpus:
+        for k, v in self.student.items():
+            self.teacher[k].load_state_dict(self.student[k].state_dict())
+            student_model_cfg = self.cfg.compute_precision.student[k]
+            self.student[k] = get_fsdp_wrapper(student_model_cfg, modules_to_wrap={BlockChunk})(self.student[k])
+            teacher_model_cfg = self.cfg.compute_precision.teacher[k]
+            self.teacher[k] = get_fsdp_wrapper(teacher_model_cfg, modules_to_wrap={BlockChunk})(self.teacher[k])
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/train/train.py b/torchhub/facebookresearch_dinov2_main/dinov2/train/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..5279b9c4317e56b5c0a9c39f7bf9bf56b04a1f8b
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/train/train.py
@@ -0,0 +1,319 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import logging
+import math
+import os
+from functools import partial
+
+from fvcore.common.checkpoint import PeriodicCheckpointer
+import torch
+
+from dinov2.data import SamplerType, make_data_loader, make_dataset
+from dinov2.data import collate_data_and_cast, DataAugmentationDINO, MaskingGenerator
+import dinov2.distributed as distributed
+from dinov2.fsdp import FSDPCheckpointer
+from dinov2.logging import MetricLogger
+from dinov2.utils.config import setup
+from dinov2.utils.utils import CosineScheduler
+
+from dinov2.train.ssl_meta_arch import SSLMetaArch
+
+
+torch.backends.cuda.matmul.allow_tf32 = True  # PyTorch 1.12 sets this to False by default
+logger = logging.getLogger("dinov2")
+
+
+def get_args_parser(add_help: bool = True):
+    parser = argparse.ArgumentParser("DINOv2 training", add_help=add_help)
+    parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
+    parser.add_argument(
+        "--no-resume",
+        action="store_true",
+        help="Whether to not attempt to resume from the checkpoint directory. ",
+    )
+    parser.add_argument("--eval-only", action="store_true", help="perform evaluation only")
+    parser.add_argument("--eval", type=str, default="", help="Eval type to perform")
+    parser.add_argument(
+        "opts",
+        help="""
+Modify config options at the end of the command. For Yacs configs, use
+space-separated "PATH.KEY VALUE" pairs.
+For python-based LazyConfig, use "path.key=value".
+        """.strip(),
+        default=None,
+        nargs=argparse.REMAINDER,
+    )
+    parser.add_argument(
+        "--output-dir",
+        "--output_dir",
+        default="",
+        type=str,
+        help="Output directory to save logs and checkpoints",
+    )
+
+    return parser
+
+
+def build_optimizer(cfg, params_groups):
+    return torch.optim.AdamW(params_groups, betas=(cfg.optim.adamw_beta1, cfg.optim.adamw_beta2))
+
+
+def build_schedulers(cfg):
+    OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH
+    lr = dict(
+        base_value=cfg.optim["lr"],
+        final_value=cfg.optim["min_lr"],
+        total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH,
+        warmup_iters=cfg.optim["warmup_epochs"] * OFFICIAL_EPOCH_LENGTH,
+        start_warmup_value=0,
+    )
+    wd = dict(
+        base_value=cfg.optim["weight_decay"],
+        final_value=cfg.optim["weight_decay_end"],
+        total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH,
+    )
+    momentum = dict(
+        base_value=cfg.teacher["momentum_teacher"],
+        final_value=cfg.teacher["final_momentum_teacher"],
+        total_iters=cfg.optim["epochs"] * OFFICIAL_EPOCH_LENGTH,
+    )
+    teacher_temp = dict(
+        base_value=cfg.teacher["teacher_temp"],
+        final_value=cfg.teacher["teacher_temp"],
+        total_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH,
+        warmup_iters=cfg.teacher["warmup_teacher_temp_epochs"] * OFFICIAL_EPOCH_LENGTH,
+        start_warmup_value=cfg.teacher["warmup_teacher_temp"],
+    )
+
+    lr_schedule = CosineScheduler(**lr)
+    wd_schedule = CosineScheduler(**wd)
+    momentum_schedule = CosineScheduler(**momentum)
+    teacher_temp_schedule = CosineScheduler(**teacher_temp)
+    last_layer_lr_schedule = CosineScheduler(**lr)
+
+    last_layer_lr_schedule.schedule[
+        : cfg.optim["freeze_last_layer_epochs"] * OFFICIAL_EPOCH_LENGTH
+    ] = 0  # mimicking the original schedules
+
+    logger.info("Schedulers ready.")
+
+    return (
+        lr_schedule,
+        wd_schedule,
+        momentum_schedule,
+        teacher_temp_schedule,
+        last_layer_lr_schedule,
+    )
+
+
+def apply_optim_scheduler(optimizer, lr, wd, last_layer_lr):
+    for param_group in optimizer.param_groups:
+        is_last_layer = param_group["is_last_layer"]
+        lr_multiplier = param_group["lr_multiplier"]
+        wd_multiplier = param_group["wd_multiplier"]
+        param_group["weight_decay"] = wd * wd_multiplier
+        param_group["lr"] = (last_layer_lr if is_last_layer else lr) * lr_multiplier
+
+
+def do_test(cfg, model, iteration):
+    new_state_dict = model.teacher.state_dict()
+
+    if distributed.is_main_process():
+        iterstring = str(iteration)
+        eval_dir = os.path.join(cfg.train.output_dir, "eval", iterstring)
+        os.makedirs(eval_dir, exist_ok=True)
+        # save teacher checkpoint
+        teacher_ckp_path = os.path.join(eval_dir, "teacher_checkpoint.pth")
+        torch.save({"teacher": new_state_dict}, teacher_ckp_path)
+
+
+def do_train(cfg, model, resume=False):
+    model.train()
+    inputs_dtype = torch.half
+    fp16_scaler = model.fp16_scaler  # for mixed precision training
+
+    # setup optimizer
+
+    optimizer = build_optimizer(cfg, model.get_params_groups())
+    (
+        lr_schedule,
+        wd_schedule,
+        momentum_schedule,
+        teacher_temp_schedule,
+        last_layer_lr_schedule,
+    ) = build_schedulers(cfg)
+
+    # checkpointer
+    checkpointer = FSDPCheckpointer(model, cfg.train.output_dir, optimizer=optimizer, save_to_disk=True)
+
+    start_iter = checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume).get("iteration", -1) + 1
+
+    OFFICIAL_EPOCH_LENGTH = cfg.train.OFFICIAL_EPOCH_LENGTH
+    max_iter = cfg.optim.epochs * OFFICIAL_EPOCH_LENGTH
+
+    periodic_checkpointer = PeriodicCheckpointer(
+        checkpointer,
+        period=3 * OFFICIAL_EPOCH_LENGTH,
+        max_iter=max_iter,
+        max_to_keep=3,
+    )
+
+    # setup data preprocessing
+
+    img_size = cfg.crops.global_crops_size
+    patch_size = cfg.student.patch_size
+    n_tokens = (img_size // patch_size) ** 2
+    mask_generator = MaskingGenerator(
+        input_size=(img_size // patch_size, img_size // patch_size),
+        max_num_patches=0.5 * img_size // patch_size * img_size // patch_size,
+    )
+
+    data_transform = DataAugmentationDINO(
+        cfg.crops.global_crops_scale,
+        cfg.crops.local_crops_scale,
+        cfg.crops.local_crops_number,
+        global_crops_size=cfg.crops.global_crops_size,
+        local_crops_size=cfg.crops.local_crops_size,
+    )
+
+    collate_fn = partial(
+        collate_data_and_cast,
+        mask_ratio_tuple=cfg.ibot.mask_ratio_min_max,
+        mask_probability=cfg.ibot.mask_sample_probability,
+        n_tokens=n_tokens,
+        mask_generator=mask_generator,
+        dtype=inputs_dtype,
+    )
+
+    # setup data loader
+
+    dataset = make_dataset(
+        dataset_str=cfg.train.dataset_path,
+        transform=data_transform,
+        target_transform=lambda _: (),
+    )
+    # sampler_type = SamplerType.INFINITE
+    sampler_type = SamplerType.SHARDED_INFINITE
+    data_loader = make_data_loader(
+        dataset=dataset,
+        batch_size=cfg.train.batch_size_per_gpu,
+        num_workers=cfg.train.num_workers,
+        shuffle=True,
+        seed=start_iter,  # TODO: Fix this -- cfg.train.seed
+        sampler_type=sampler_type,
+        sampler_advance=0,  # TODO(qas): fix this -- start_iter * cfg.train.batch_size_per_gpu,
+        drop_last=True,
+        collate_fn=collate_fn,
+    )
+
+    # training loop
+
+    iteration = start_iter
+
+    logger.info("Starting training from iteration {}".format(start_iter))
+    metrics_file = os.path.join(cfg.train.output_dir, "training_metrics.json")
+    metric_logger = MetricLogger(delimiter="  ", output_file=metrics_file)
+    header = "Training"
+
+    for data in metric_logger.log_every(
+        data_loader,
+        10,
+        header,
+        max_iter,
+        start_iter,
+    ):
+        current_batch_size = data["collated_global_crops"].shape[0] / 2
+        if iteration > max_iter:
+            return
+
+        # apply schedules
+
+        lr = lr_schedule[iteration]
+        wd = wd_schedule[iteration]
+        mom = momentum_schedule[iteration]
+        teacher_temp = teacher_temp_schedule[iteration]
+        last_layer_lr = last_layer_lr_schedule[iteration]
+        apply_optim_scheduler(optimizer, lr, wd, last_layer_lr)
+
+        # compute losses
+
+        optimizer.zero_grad(set_to_none=True)
+        loss_dict = model.forward_backward(data, teacher_temp=teacher_temp)
+
+        # clip gradients
+
+        if fp16_scaler is not None:
+            if cfg.optim.clip_grad:
+                fp16_scaler.unscale_(optimizer)
+                for v in model.student.values():
+                    v.clip_grad_norm_(cfg.optim.clip_grad)
+            fp16_scaler.step(optimizer)
+            fp16_scaler.update()
+        else:
+            if cfg.optim.clip_grad:
+                for v in model.student.values():
+                    v.clip_grad_norm_(cfg.optim.clip_grad)
+            optimizer.step()
+
+        # perform teacher EMA update
+
+        model.update_teacher(mom)
+
+        # logging
+
+        if distributed.get_global_size() > 1:
+            for v in loss_dict.values():
+                torch.distributed.all_reduce(v)
+        loss_dict_reduced = {k: v.item() / distributed.get_global_size() for k, v in loss_dict.items()}
+
+        if math.isnan(sum(loss_dict_reduced.values())):
+            logger.info("NaN detected")
+            raise AssertionError
+        losses_reduced = sum(loss for loss in loss_dict_reduced.values())
+
+        metric_logger.update(lr=lr)
+        metric_logger.update(wd=wd)
+        metric_logger.update(mom=mom)
+        metric_logger.update(last_layer_lr=last_layer_lr)
+        metric_logger.update(current_batch_size=current_batch_size)
+        metric_logger.update(total_loss=losses_reduced, **loss_dict_reduced)
+
+        # checkpointing and testing
+
+        if cfg.evaluation.eval_period_iterations > 0 and (iteration + 1) % cfg.evaluation.eval_period_iterations == 0:
+            do_test(cfg, model, f"training_{iteration}")
+            torch.cuda.synchronize()
+        periodic_checkpointer.step(iteration)
+
+        iteration = iteration + 1
+    metric_logger.synchronize_between_processes()
+    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
+
+
+def main(args):
+    cfg = setup(args)
+
+    model = SSLMetaArch(cfg).to(torch.device("cuda"))
+    model.prepare_for_distributed_training()
+
+    logger.info("Model:\n{}".format(model))
+    if args.eval_only:
+        iteration = (
+            FSDPCheckpointer(model, save_dir=cfg.train.output_dir)
+            .resume_or_load(cfg.MODEL.WEIGHTS, resume=not args.no_resume)
+            .get("iteration", -1)
+            + 1
+        )
+        return do_test(cfg, model, f"manual_{iteration}")
+
+    do_train(cfg, model, resume=not args.no_resume)
+
+
+if __name__ == "__main__":
+    args = get_args_parser(add_help=True).parse_args()
+    main(args)
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/utils/__init__.py b/torchhub/facebookresearch_dinov2_main/dinov2/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0952fcc3f57e34b3747962e9ebd6fc57aeea63fa
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/utils/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/utils/cluster.py b/torchhub/facebookresearch_dinov2_main/dinov2/utils/cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d98c05d68aa6e9dc165df3db06bd70d999b3fda
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/utils/cluster.py
@@ -0,0 +1,96 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+import os
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+
+class ClusterType(Enum):
+    AWS = "aws"
+    FAIR = "fair"
+    RSC = "rsc"
+
+
+def _guess_cluster_type() -> ClusterType:
+    uname = os.uname()
+    if uname.sysname == "Linux":
+        if uname.release.endswith("-aws"):
+            # Linux kernel versions on AWS instances are of the form "5.4.0-1051-aws"
+            return ClusterType.AWS
+        elif uname.nodename.startswith("rsc"):
+            # Linux kernel versions on RSC instances are standard ones but hostnames start with "rsc"
+            return ClusterType.RSC
+
+    return ClusterType.FAIR
+
+
+def get_cluster_type(cluster_type: Optional[ClusterType] = None) -> Optional[ClusterType]:
+    if cluster_type is None:
+        return _guess_cluster_type()
+
+    return cluster_type
+
+
+def get_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type is None:
+        return None
+
+    CHECKPOINT_DIRNAMES = {
+        ClusterType.AWS: "checkpoints",
+        ClusterType.FAIR: "checkpoint",
+        ClusterType.RSC: "checkpoint/dino",
+    }
+    return Path("/") / CHECKPOINT_DIRNAMES[cluster_type]
+
+
+def get_user_checkpoint_path(cluster_type: Optional[ClusterType] = None) -> Optional[Path]:
+    checkpoint_path = get_checkpoint_path(cluster_type)
+    if checkpoint_path is None:
+        return None
+
+    username = os.environ.get("USER")
+    assert username is not None
+    return checkpoint_path / username
+
+
+def get_slurm_partition(cluster_type: Optional[ClusterType] = None) -> Optional[str]:
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type is None:
+        return None
+
+    SLURM_PARTITIONS = {
+        ClusterType.AWS: "learnlab",
+        ClusterType.FAIR: "learnlab",
+        ClusterType.RSC: "learn",
+    }
+    return SLURM_PARTITIONS[cluster_type]
+
+
+def get_slurm_executor_parameters(
+    nodes: int, num_gpus_per_node: int, cluster_type: Optional[ClusterType] = None, **kwargs
+) -> Dict[str, Any]:
+    # create default parameters
+    params = {
+        "mem_gb": 0,  # Requests all memory on a node, see https://slurm.schedmd.com/sbatch.html
+        "gpus_per_node": num_gpus_per_node,
+        "tasks_per_node": num_gpus_per_node,  # one task per GPU
+        "cpus_per_task": 10,
+        "nodes": nodes,
+        "slurm_partition": get_slurm_partition(cluster_type),
+    }
+    # apply cluster-specific adjustments
+    cluster_type = get_cluster_type(cluster_type)
+    if cluster_type == ClusterType.AWS:
+        params["cpus_per_task"] = 12
+        del params["mem_gb"]
+    elif cluster_type == ClusterType.RSC:
+        params["cpus_per_task"] = 12
+    # set additional parameters / apply overrides
+    params.update(kwargs)
+    return params
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/utils/config.py b/torchhub/facebookresearch_dinov2_main/dinov2/utils/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3763a8b0808ad45cbbfc1dcb00d52b00113f9ad
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/utils/config.py
@@ -0,0 +1,73 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import logging
+import os
+
+from omegaconf import OmegaConf
+
+import dinov2.distributed as distributed
+from dinov2.logging import setup_logging
+from dinov2.utils import utils
+from dinov2.configs import dinov2_default_config
+
+
+logger = logging.getLogger("dinov2")
+
+
+def apply_scaling_rules_to_cfg(cfg):  # to fix
+    if cfg.optim.scaling_rule == "sqrt_wrt_1024":
+        base_lr = cfg.optim.base_lr
+        cfg.optim.lr = base_lr
+        cfg.optim.lr *= math.sqrt(cfg.train.batch_size_per_gpu * distributed.get_global_size() / 1024.0)
+        logger.info(f"sqrt scaling learning rate; base: {base_lr}, new: {cfg.optim.lr}")
+    else:
+        raise NotImplementedError
+    return cfg
+
+
+def write_config(cfg, output_dir, name="config.yaml"):
+    logger.info(OmegaConf.to_yaml(cfg))
+    saved_cfg_path = os.path.join(output_dir, name)
+    with open(saved_cfg_path, "w") as f:
+        OmegaConf.save(config=cfg, f=f)
+    return saved_cfg_path
+
+
+def get_cfg_from_args(args):
+    args.output_dir = os.path.abspath(args.output_dir)
+    args.opts += [f"train.output_dir={args.output_dir}"]
+    default_cfg = OmegaConf.create(dinov2_default_config)
+    cfg = OmegaConf.load(args.config_file)
+    cfg = OmegaConf.merge(default_cfg, cfg, OmegaConf.from_cli(args.opts))
+    return cfg
+
+
+def default_setup(args):
+    distributed.enable(overwrite=True)
+    seed = getattr(args, "seed", 0)
+    rank = distributed.get_global_rank()
+
+    global logger
+    setup_logging(output=args.output_dir, level=logging.INFO)
+    logger = logging.getLogger("dinov2")
+
+    utils.fix_random_seeds(seed + rank)
+    logger.info("git:\n  {}\n".format(utils.get_sha()))
+    logger.info("\n".join("%s: %s" % (k, str(v)) for k, v in sorted(dict(vars(args)).items())))
+
+
+def setup(args):
+    """
+    Create configs and perform basic setups.
+    """
+    cfg = get_cfg_from_args(args)
+    os.makedirs(args.output_dir, exist_ok=True)
+    default_setup(args)
+    apply_scaling_rules_to_cfg(cfg)
+    write_config(cfg, args.output_dir)
+    return cfg
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/utils/dtype.py b/torchhub/facebookresearch_dinov2_main/dinov2/utils/dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..cef122b25ff3533e004799a1d977f63eb213fee0
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/utils/dtype.py
@@ -0,0 +1,38 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from typing import Dict, Union
+
+import numpy as np
+import torch
+
+
+TypeSpec = Union[str, np.dtype, torch.dtype]
+
+
+_NUMPY_TO_TORCH_DTYPE: Dict[np.dtype, torch.dtype] = {
+    np.dtype("bool"): torch.bool,
+    np.dtype("uint8"): torch.uint8,
+    np.dtype("int8"): torch.int8,
+    np.dtype("int16"): torch.int16,
+    np.dtype("int32"): torch.int32,
+    np.dtype("int64"): torch.int64,
+    np.dtype("float16"): torch.float16,
+    np.dtype("float32"): torch.float32,
+    np.dtype("float64"): torch.float64,
+    np.dtype("complex64"): torch.complex64,
+    np.dtype("complex128"): torch.complex128,
+}
+
+
+def as_torch_dtype(dtype: TypeSpec) -> torch.dtype:
+    if isinstance(dtype, torch.dtype):
+        return dtype
+    if isinstance(dtype, str):
+        dtype = np.dtype(dtype)
+    assert isinstance(dtype, np.dtype), f"Expected an instance of nunpy dtype, got {type(dtype)}"
+    return _NUMPY_TO_TORCH_DTYPE[dtype]
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/utils/param_groups.py b/torchhub/facebookresearch_dinov2_main/dinov2/utils/param_groups.py
new file mode 100644
index 0000000000000000000000000000000000000000..d707e70cc11591858d4166410d6ed80621cd49ff
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/utils/param_groups.py
@@ -0,0 +1,94 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections import defaultdict
+import logging
+
+
+logger = logging.getLogger("dinov2")
+
+
+def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12, force_is_backbone=False, chunked_blocks=False):
+    """
+    Calculate lr decay rate for different ViT blocks.
+    Args:
+        name (string): parameter name.
+        lr_decay_rate (float): base lr decay rate.
+        num_layers (int): number of ViT blocks.
+    Returns:
+        lr decay rate for the given parameter.
+    """
+    layer_id = num_layers + 1
+    if name.startswith("backbone") or force_is_backbone:
+        if ".pos_embed" in name or ".patch_embed" in name or ".mask_token" in name or ".cls_token" in name:
+            layer_id = 0
+        elif force_is_backbone and (
+            "pos_embed" in name or "patch_embed" in name or "mask_token" in name or "cls_token" in name
+        ):
+            layer_id = 0
+        elif ".blocks." in name and ".residual." not in name:
+            layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1
+        elif chunked_blocks and "blocks." in name and "residual." not in name:
+            layer_id = int(name[name.find("blocks.") :].split(".")[2]) + 1
+        elif "blocks." in name and "residual." not in name:
+            layer_id = int(name[name.find("blocks.") :].split(".")[1]) + 1
+
+    return lr_decay_rate ** (num_layers + 1 - layer_id)
+
+
+def get_params_groups_with_decay(model, lr_decay_rate=1.0, patch_embed_lr_mult=1.0):
+    chunked_blocks = False
+    if hasattr(model, "n_blocks"):
+        logger.info("chunked fsdp")
+        n_blocks = model.n_blocks
+        chunked_blocks = model.chunked_blocks
+    elif hasattr(model, "blocks"):
+        logger.info("first code branch")
+        n_blocks = len(model.blocks)
+    elif hasattr(model, "backbone"):
+        logger.info("second code branch")
+        n_blocks = len(model.backbone.blocks)
+    else:
+        logger.info("else code branch")
+        n_blocks = 0
+    all_param_groups = []
+
+    for name, param in model.named_parameters():
+        name = name.replace("_fsdp_wrapped_module.", "")
+        if not param.requires_grad:
+            continue
+        decay_rate = get_vit_lr_decay_rate(
+            name, lr_decay_rate, num_layers=n_blocks, force_is_backbone=n_blocks > 0, chunked_blocks=chunked_blocks
+        )
+        d = {"params": param, "is_last_layer": False, "lr_multiplier": decay_rate, "wd_multiplier": 1.0, "name": name}
+
+        if "last_layer" in name:
+            d.update({"is_last_layer": True})
+
+        if name.endswith(".bias") or "norm" in name or "gamma" in name:
+            d.update({"wd_multiplier": 0.0})
+
+        if "patch_embed" in name:
+            d.update({"lr_multiplier": d["lr_multiplier"] * patch_embed_lr_mult})
+
+        all_param_groups.append(d)
+        logger.info(f"""{name}: lr_multiplier: {d["lr_multiplier"]}, wd_multiplier: {d["wd_multiplier"]}""")
+
+    return all_param_groups
+
+
+def fuse_params_groups(all_params_groups, keys=("lr_multiplier", "wd_multiplier", "is_last_layer")):
+    fused_params_groups = defaultdict(lambda: {"params": []})
+    for d in all_params_groups:
+        identifier = ""
+        for k in keys:
+            identifier += k + str(d[k]) + "_"
+
+        for k in keys:
+            fused_params_groups[identifier][k] = d[k]
+        fused_params_groups[identifier]["params"].append(d["params"])
+
+    return fused_params_groups.values()
diff --git a/torchhub/facebookresearch_dinov2_main/dinov2/utils/utils.py b/torchhub/facebookresearch_dinov2_main/dinov2/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..53e63eb427f6d5396c8dc153ab07e825c72b68b4
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/dinov2/utils/utils.py
@@ -0,0 +1,96 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import random
+import subprocess
+from urllib.parse import urlparse
+
+import numpy as np
+import torch
+from torch import nn
+
+
+logger = logging.getLogger("dinov2")
+
+
+def load_pretrained_weights(model, pretrained_weights, checkpoint_key):
+    if urlparse(pretrained_weights).scheme:  # If it looks like an URL
+        state_dict = torch.hub.load_state_dict_from_url(pretrained_weights, map_location="cpu")
+    else:
+        state_dict = torch.load(pretrained_weights, map_location="cpu")
+    if checkpoint_key is not None and checkpoint_key in state_dict:
+        logger.info(f"Take key {checkpoint_key} in provided checkpoint dict")
+        state_dict = state_dict[checkpoint_key]
+    # remove `module.` prefix
+    state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
+    # remove `backbone.` prefix induced by multicrop wrapper
+    state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
+    msg = model.load_state_dict(state_dict, strict=False)
+    logger.info("Pretrained weights found at {} and loaded with msg: {}".format(pretrained_weights, msg))
+
+
+def fix_random_seeds(seed=31):
+    """
+    Fix random seeds.
+    """
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def get_sha():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command):
+        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
+
+    sha = "N/A"
+    diff = "clean"
+    branch = "N/A"
+    try:
+        sha = _run(["git", "rev-parse", "HEAD"])
+        subprocess.check_output(["git", "diff"], cwd=cwd)
+        diff = _run(["git", "diff-index", "HEAD"])
+        diff = "has uncommitted changes" if diff else "clean"
+        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+    except Exception:
+        pass
+    message = f"sha: {sha}, status: {diff}, branch: {branch}"
+    return message
+
+
+class CosineScheduler(object):
+    def __init__(self, base_value, final_value, total_iters, warmup_iters=0, start_warmup_value=0, freeze_iters=0):
+        super().__init__()
+        self.final_value = final_value
+        self.total_iters = total_iters
+
+        freeze_schedule = np.zeros((freeze_iters))
+
+        warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
+
+        iters = np.arange(total_iters - warmup_iters - freeze_iters)
+        schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters)))
+        self.schedule = np.concatenate((freeze_schedule, warmup_schedule, schedule))
+
+        assert len(self.schedule) == self.total_iters
+
+    def __getitem__(self, it):
+        if it >= self.total_iters:
+            return self.final_value
+        else:
+            return self.schedule[it]
+
+
+def has_batchnorms(model):
+    bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
+    for name, module in model.named_modules():
+        if isinstance(module, bn_types):
+            return True
+    return False
diff --git a/torchhub/facebookresearch_dinov2_main/hubconf.py b/torchhub/facebookresearch_dinov2_main/hubconf.py
new file mode 100644
index 0000000000000000000000000000000000000000..b36b42cd2136182ea956d8be785cf492418163d8
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/hubconf.py
@@ -0,0 +1,162 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+from enum import Enum
+from typing import Union
+
+import torch
+
+_DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
+
+
+def _make_dinov2_model_name(arch_name: str, patch_size: int, num_register_tokens: int = 0) -> str:
+    compact_arch_name = arch_name.replace("_", "")[:4]
+    registers_suffix = f"_reg{num_register_tokens}" if num_register_tokens else ""
+    return f"dinov2_{compact_arch_name}{patch_size}{registers_suffix}"
+
+
+class Weights(Enum):
+    LVD142M = "LVD142M"
+
+
+def _make_dinov2_model(
+    *,
+    arch_name: str = "vit_large",
+    img_size: int = 518,
+    patch_size: int = 14,
+    init_values: float = 1.0,
+    ffn_layer: str = "mlp",
+    block_chunks: int = 0,
+    num_register_tokens: int = 0,
+    interpolate_antialias: bool = False,
+    interpolate_offset: float = 0.1,
+    pretrained: bool = True,
+    weights: Union[Weights, str] = Weights.LVD142M,
+    **kwargs,
+):
+    import vision_transformer as vits
+
+    if isinstance(weights, str):
+        try:
+            weights = Weights[weights]
+        except KeyError:
+            raise AssertionError(f"Unsupported weights: {weights}")
+
+    model_base_name = _make_dinov2_model_name(arch_name, patch_size)
+    vit_kwargs = dict(
+        img_size=img_size,
+        patch_size=patch_size,
+        init_values=init_values,
+        ffn_layer=ffn_layer,
+        block_chunks=block_chunks,
+        num_register_tokens=num_register_tokens,
+        interpolate_antialias=interpolate_antialias,
+        interpolate_offset=interpolate_offset,
+    )
+    vit_kwargs.update(**kwargs)
+    model = vits.__dict__[arch_name](**vit_kwargs)
+
+    if pretrained:
+        model_full_name = _make_dinov2_model_name(arch_name, patch_size, num_register_tokens)
+        url = _DINOV2_BASE_URL + f"/{model_base_name}/{model_full_name}_pretrain.pth"
+        state_dict = torch.hub.load_state_dict_from_url(url, map_location="cpu")
+        model.load_state_dict(state_dict, strict=True)
+
+    return model
+
+
+def dinov2_vits14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitb14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitl14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)
+
+
+def dinov2_vitg14(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        **kwargs,
+    )
+
+
+def dinov2_vits14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-S/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_small",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitb14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-B/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_base",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitl14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-L/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_large",
+        pretrained=pretrained,
+        weights=weights,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
+
+
+def dinov2_vitg14_reg(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.LVD142M, **kwargs):
+    """
+    DINOv2 ViT-g/14 model with registers (optionally) pretrained on the LVD-142M dataset.
+    """
+    return _make_dinov2_model(
+        arch_name="vit_giant2",
+        ffn_layer="swiglufused",
+        weights=weights,
+        pretrained=pretrained,
+        num_register_tokens=4,
+        interpolate_antialias=True,
+        interpolate_offset=0.0,
+        **kwargs,
+    )
diff --git a/torchhub/facebookresearch_dinov2_main/pyproject.toml b/torchhub/facebookresearch_dinov2_main/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..da67abd8ceabe6d427a96e5d9d4f04b25aebcd32
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/pyproject.toml
@@ -0,0 +1,29 @@
+[tool.black]
+line-length = 120
+
+[tool.pylint.master]
+persistent = false
+score = false
+
+[tool.pylint.messages_control]
+disable = "all"
+enable = [
+  "miscellaneous",
+  "similarities",
+]
+
+[tool.pylint.similarities]
+ignore-comments = true
+ignore-docstrings = true
+ignore-imports = true
+min-similarity-lines = 8
+
+[tool.pylint.reports]
+reports = false
+
+[tool.pylint.miscellaneous]
+notes = [
+  "FIXME",
+  "XXX",
+  "TODO",
+]
diff --git a/torchhub/facebookresearch_dinov2_main/requirements-dev.txt b/torchhub/facebookresearch_dinov2_main/requirements-dev.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5cad34c34cde3a182b616d68b168588827eb9b7c
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/requirements-dev.txt
@@ -0,0 +1,3 @@
+black==22.6.0
+flake8==5.0.4
+pylint==2.15.0
diff --git a/torchhub/facebookresearch_dinov2_main/requirements.txt b/torchhub/facebookresearch_dinov2_main/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..04c159c443b89330ff3c84257c41b011f9791257
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/requirements.txt
@@ -0,0 +1,11 @@
+--extra-index-url https://download.pytorch.org/whl/cu117
+torch==2.0.0
+torchvision==0.15.0
+omegaconf
+torchmetrics==0.10.3
+fvcore
+iopath
+xformers==0.0.18
+submitit
+--extra-index-url https://pypi.nvidia.com
+cuml-cu11
diff --git a/torchhub/facebookresearch_dinov2_main/scripts/lint.sh b/torchhub/facebookresearch_dinov2_main/scripts/lint.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b91acaf762c4be3a0c9d2a162210bfebfaacba08
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/scripts/lint.sh
@@ -0,0 +1,28 @@
+#!/bin/sh
+
+if [ -n "$1" ]; then
+  echo "linting \"$1\""
+fi
+
+echo "running black"
+if [ -n "$1" ]; then
+  black "$1"
+else
+  black dinov2
+fi
+
+echo "running flake8"
+if [ -n "$1" ]; then
+  flake8 "$1"
+else
+  flake8
+fi
+
+echo "running pylint"
+if [ -n "$1" ]; then
+  pylint "$1"
+else
+  pylint dinov2
+fi
+
+exit 0
diff --git a/torchhub/facebookresearch_dinov2_main/setup.cfg b/torchhub/facebookresearch_dinov2_main/setup.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..3cac0c045434cde205eebe91fd5a2c35a1226b4b
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/setup.cfg
@@ -0,0 +1,7 @@
+[flake8]
+max-line-length = 120
+ignore = E203,E501,W503
+per-file-ignores =
+  __init__.py:F401
+exclude =
+    venv
diff --git a/torchhub/facebookresearch_dinov2_main/setup.py b/torchhub/facebookresearch_dinov2_main/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..001987cfeef6c5fe3469ea09cd4698352fa90939
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/setup.py
@@ -0,0 +1,87 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from pathlib import Path
+import re
+from typing import List, Tuple
+
+from setuptools import setup, find_packages
+
+
+NAME = "dinov2"
+DESCRIPTION = "PyTorch code and models for the DINOv2 self-supervised learning method."
+
+URL = "https://github.com/facebookresearch/dinov2"
+AUTHOR = "FAIR"
+REQUIRES_PYTHON = ">=3.9.0"
+HERE = Path(__file__).parent
+
+
+try:
+    with open(HERE / "README.md", encoding="utf-8") as f:
+        long_description = "\n" + f.read()
+except FileNotFoundError:
+    long_description = DESCRIPTION
+
+
+def get_requirements(path: str = HERE / "requirements.txt") -> Tuple[List[str], List[str]]:
+    requirements = []
+    extra_indices = []
+    with open(path) as f:
+        for line in f.readlines():
+            line = line.rstrip("\r\n")
+            if line.startswith("--extra-index-url "):
+                extra_indices.append(line[18:])
+                continue
+            requirements.append(line)
+    return requirements, extra_indices
+
+
+def get_package_version() -> str:
+    with open(HERE / "dinov2/__init__.py") as f:
+        result = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", f.read(), re.M)
+        if result:
+            return result.group(1)
+    raise RuntimeError("Can't get package version")
+
+
+requirements, extra_indices = get_requirements()
+version = get_package_version()
+dev_requirements, _ = get_requirements(HERE / "requirements-dev.txt")
+
+
+setup(
+    name=NAME,
+    version=version,
+    description=DESCRIPTION,
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    author=AUTHOR,
+    python_requires=REQUIRES_PYTHON,
+    url=URL,
+    packages=find_packages(),
+    package_data={
+        "": ["*.yaml"],
+    },
+    install_requires=requirements,
+    dependency_links=extra_indices,
+    extras_require={
+        "dev": dev_requirements,
+    },
+    install_package_data=True,
+    license="CC-BY-NC",
+    license_files=("LICENSE",),
+    classifiers=[
+        # Trove classifiers: https://github.com/pypa/trove-classifiers/blob/main/src/trove_classifiers/__init__.py
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Science/Research",
+        "License :: Other/Proprietary License",
+        "Programming Language :: Python :: 3.9",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+    ],
+)
diff --git a/torchhub/facebookresearch_dinov2_main/utils.py b/torchhub/facebookresearch_dinov2_main/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c6641404093652d5a2f19b4cf283d976ec39e64
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/utils.py
@@ -0,0 +1,39 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+import itertools
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+_DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
+
+
+def _make_dinov2_model_name(arch_name: str, patch_size: int, num_register_tokens: int = 0) -> str:
+    compact_arch_name = arch_name.replace("_", "")[:4]
+    registers_suffix = f"_reg{num_register_tokens}" if num_register_tokens else ""
+    return f"dinov2_{compact_arch_name}{patch_size}{registers_suffix}"
+
+
+class CenterPadding(nn.Module):
+    def __init__(self, multiple):
+        super().__init__()
+        self.multiple = multiple
+
+    def _get_pad(self, size):
+        new_size = math.ceil(size / self.multiple) * self.multiple
+        pad_size = new_size - size
+        pad_size_left = pad_size // 2
+        pad_size_right = pad_size - pad_size_left
+        return pad_size_left, pad_size_right
+
+    @torch.inference_mode()
+    def forward(self, x):
+        pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))
+        output = F.pad(x, pads)
+        return output
diff --git a/torchhub/facebookresearch_dinov2_main/vision_transformer.py b/torchhub/facebookresearch_dinov2_main/vision_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..121318f9c77a69a4467888cce44e49549e9954c0
--- /dev/null
+++ b/torchhub/facebookresearch_dinov2_main/vision_transformer.py
@@ -0,0 +1,395 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+#
+# This source code is licensed under the Apache License, Version 2.0
+# found in the LICENSE file in the root directory of this source tree.
+
+# References:
+#   https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
+#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
+
+from functools import partial
+import math
+import logging
+from typing import Sequence, Tuple, Union, Callable
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from torch.nn.init import trunc_normal_
+
+from dinov2.layers import Mlp, PatchEmbed, SwiGLUFFNFused, MemEffAttention, NestedTensorBlock as Block
+
+
+logger = logging.getLogger("dinov2")
+
+
+def named_apply(fn: Callable, module: nn.Module, name="", depth_first=True, include_root=False) -> nn.Module:
+    if not depth_first and include_root:
+        fn(module=module, name=name)
+    for child_name, child_module in module.named_children():
+        child_name = ".".join((name, child_name)) if name else child_name
+        named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
+    if depth_first and include_root:
+        fn(module=module, name=name)
+    return module
+
+
+class BlockChunk(nn.ModuleList):
+    def forward(self, x):
+        for b in self:
+            x = b(x)
+        return x
+
+
+class DinoVisionTransformer(nn.Module):
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        ffn_bias=True,
+        proj_bias=True,
+        drop_path_rate=0.0,
+        drop_path_uniform=False,
+        init_values=None,  # for layerscale: None or 0 => no layerscale
+        embed_layer=PatchEmbed,
+        act_layer=nn.GELU,
+        block_fn=Block,
+        ffn_layer="mlp",
+        block_chunks=1,
+        num_register_tokens=0,
+        interpolate_antialias=False,
+        interpolate_offset=0.1,
+    ):
+        """
+        Args:
+            img_size (int, tuple): input image size
+            patch_size (int, tuple): patch size
+            in_chans (int): number of input channels
+            embed_dim (int): embedding dimension
+            depth (int): depth of transformer
+            num_heads (int): number of attention heads
+            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
+            qkv_bias (bool): enable bias for qkv if True
+            proj_bias (bool): enable bias for proj in attn if True
+            ffn_bias (bool): enable bias for ffn if True
+            drop_path_rate (float): stochastic depth rate
+            drop_path_uniform (bool): apply uniform drop rate across blocks
+            weight_init (str): weight init scheme
+            init_values (float): layer-scale init values
+            embed_layer (nn.Module): patch embedding layer
+            act_layer (nn.Module): MLP activation layer
+            block_fn (nn.Module): transformer block class
+            ffn_layer (str): "mlp", "swiglu", "swiglufused" or "identity"
+            block_chunks: (int) split block sequence into block_chunks units for FSDP wrap
+            num_register_tokens: (int) number of extra cls tokens (so-called "registers")
+            interpolate_antialias: (str) flag to apply anti-aliasing when interpolating positional embeddings
+            interpolate_offset: (float) work-around offset to apply when interpolating positional embeddings
+        """
+        super().__init__()
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+        self.num_tokens = 1
+        self.n_blocks = depth
+        self.num_heads = num_heads
+        self.patch_size = patch_size
+        self.num_register_tokens = num_register_tokens
+        self.interpolate_antialias = interpolate_antialias
+        self.interpolate_offset = interpolate_offset
+
+        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
+        assert num_register_tokens >= 0
+        self.register_tokens = (
+            nn.Parameter(torch.zeros(1, num_register_tokens, embed_dim)) if num_register_tokens else None
+        )
+
+        if drop_path_uniform is True:
+            dpr = [drop_path_rate] * depth
+        else:
+            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+
+        if ffn_layer == "mlp":
+            logger.info("using MLP layer as FFN")
+            ffn_layer = Mlp
+        elif ffn_layer == "swiglufused" or ffn_layer == "swiglu":
+            logger.info("using SwiGLU layer as FFN")
+            ffn_layer = SwiGLUFFNFused
+        elif ffn_layer == "identity":
+            logger.info("using Identity layer as FFN")
+
+            def f(*args, **kwargs):
+                return nn.Identity()
+
+            ffn_layer = f
+        else:
+            raise NotImplementedError
+
+        blocks_list = [
+            block_fn(
+                dim=embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                proj_bias=proj_bias,
+                ffn_bias=ffn_bias,
+                drop_path=dpr[i],
+                norm_layer=norm_layer,
+                act_layer=act_layer,
+                ffn_layer=ffn_layer,
+                init_values=init_values,
+            )
+            for i in range(depth)
+        ]
+        if block_chunks > 0:
+            self.chunked_blocks = True
+            chunked_blocks = []
+            chunksize = depth // block_chunks
+            for i in range(0, depth, chunksize):
+                # this is to keep the block index consistent if we chunk the block list
+                chunked_blocks.append([nn.Identity()] * i + blocks_list[i : i + chunksize])
+            self.blocks = nn.ModuleList([BlockChunk(p) for p in chunked_blocks])
+        else:
+            self.chunked_blocks = False
+            self.blocks = nn.ModuleList(blocks_list)
+
+        self.norm = norm_layer(embed_dim)
+        self.head = nn.Identity()
+
+        self.mask_token = nn.Parameter(torch.zeros(1, embed_dim))
+
+        self.init_weights()
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+        nn.init.normal_(self.cls_token, std=1e-6)
+        if self.register_tokens is not None:
+            nn.init.normal_(self.register_tokens, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    def interpolate_pos_encoding(self, x, w, h):
+        previous_dtype = x.dtype
+        npatch = x.shape[1] - 1
+        N = self.pos_embed.shape[1] - 1
+        if npatch == N and w == h:
+            return self.pos_embed
+        pos_embed = self.pos_embed.float()
+        class_pos_embed = pos_embed[:, 0]
+        patch_pos_embed = pos_embed[:, 1:]
+        dim = x.shape[-1]
+        w0 = w // self.patch_size
+        h0 = h // self.patch_size
+        # we add a small number to avoid floating point error in the interpolation
+        # see discussion at https://github.com/facebookresearch/dino/issues/8
+        # DINOv2 with register modify the interpolate_offset from 0.1 to 0.0
+        w0, h0 = w0 + self.interpolate_offset, h0 + self.interpolate_offset
+        # w0, h0 = w0 + 0.1, h0 + 0.1
+        
+        sqrt_N = math.sqrt(N)
+        sx, sy = float(w0) / sqrt_N, float(h0) / sqrt_N
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed.reshape(1, int(sqrt_N), int(sqrt_N), dim).permute(0, 3, 1, 2),
+            scale_factor=(sx, sy),
+            # (int(w0), int(h0)), # to solve the upsampling shape issue
+            mode="bicubic",
+            antialias=self.interpolate_antialias
+        )
+        
+        assert int(w0) == patch_pos_embed.shape[-2]
+        assert int(h0) == patch_pos_embed.shape[-1]
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1).to(previous_dtype)
+
+    def prepare_tokens_with_masks(self, x, masks=None):
+        B, nc, w, h = x.shape
+        x = self.patch_embed(x)
+        if masks is not None:
+            x = torch.where(masks.unsqueeze(-1), self.mask_token.to(x.dtype).unsqueeze(0), x)
+
+        x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
+        x = x + self.interpolate_pos_encoding(x, w, h)
+
+        if self.register_tokens is not None:
+            x = torch.cat(
+                (
+                    x[:, :1],
+                    self.register_tokens.expand(x.shape[0], -1, -1),
+                    x[:, 1:],
+                ),
+                dim=1,
+            )
+
+        return x
+
+    def forward_features_list(self, x_list, masks_list):
+        x = [self.prepare_tokens_with_masks(x, masks) for x, masks in zip(x_list, masks_list)]
+        for blk in self.blocks:
+            x = blk(x)
+
+        all_x = x
+        output = []
+        for x, masks in zip(all_x, masks_list):
+            x_norm = self.norm(x)
+            output.append(
+                {
+                    "x_norm_clstoken": x_norm[:, 0],
+                    "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+                    "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+                    "x_prenorm": x,
+                    "masks": masks,
+                }
+            )
+        return output
+
+    def forward_features(self, x, masks=None):
+        if isinstance(x, list):
+            return self.forward_features_list(x, masks)
+
+        x = self.prepare_tokens_with_masks(x, masks)
+
+        for blk in self.blocks:
+            x = blk(x)
+
+        x_norm = self.norm(x)
+        return {
+            "x_norm_clstoken": x_norm[:, 0],
+            "x_norm_regtokens": x_norm[:, 1 : self.num_register_tokens + 1],
+            "x_norm_patchtokens": x_norm[:, self.num_register_tokens + 1 :],
+            "x_prenorm": x,
+            "masks": masks,
+        }
+
+    def _get_intermediate_layers_not_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        # If n is an int, take the n last blocks. If it's a list, take them
+        output, total_block_len = [], len(self.blocks)
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in blocks_to_take:
+                output.append(x)
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def _get_intermediate_layers_chunked(self, x, n=1):
+        x = self.prepare_tokens_with_masks(x)
+        output, i, total_block_len = [], 0, len(self.blocks[-1])
+        # If n is an int, take the n last blocks. If it's a list, take them
+        blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
+        for block_chunk in self.blocks:
+            for blk in block_chunk[i:]:  # Passing the nn.Identity()
+                x = blk(x)
+                if i in blocks_to_take:
+                    output.append(x)
+                i += 1
+        assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
+        return output
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,  # Layers or n last layers to take
+        reshape: bool = False,
+        return_class_token: bool = False,
+        norm=True,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        if self.chunked_blocks:
+            outputs = self._get_intermediate_layers_chunked(x, n)
+        else:
+            outputs = self._get_intermediate_layers_not_chunked(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        class_tokens = [out[:, 0] for out in outputs]
+        outputs = [out[:, 1 + self.num_register_tokens:] for out in outputs]
+        if reshape:
+            B, _, w, h = x.shape
+            outputs = [
+                out.reshape(B, w // self.patch_size, h // self.patch_size, -1).permute(0, 3, 1, 2).contiguous()
+                for out in outputs
+            ]
+        if return_class_token:
+            return tuple(zip(outputs, class_tokens))
+        return tuple(outputs)
+
+    def forward(self, *args, is_training=False, **kwargs):
+        ret = self.forward_features(*args, **kwargs)
+        if is_training:
+            return ret
+        else:
+            return self.head(ret["x_norm_clstoken"])
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = ""):
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+
+
+def vit_small(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=384,
+        depth=12,
+        num_heads=6,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_base(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_large(patch_size=16, num_register_tokens=0, **kwargs):
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1024,
+        depth=24,
+        num_heads=16,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model
+
+
+def vit_giant2(patch_size=16, num_register_tokens=0, **kwargs):
+    """
+    Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64
+    """
+    model = DinoVisionTransformer(
+        patch_size=patch_size,
+        embed_dim=1536,
+        depth=40,
+        num_heads=24,
+        mlp_ratio=4,
+        block_fn=partial(Block, attn_class=MemEffAttention),
+        num_register_tokens=num_register_tokens,
+        **kwargs,
+    )
+    return model