diff --git a/NaRCan_model.py b/NaRCan_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c6accb1b5c1a5e1a483fcc58ed159ef4bafe82f
--- /dev/null
+++ b/NaRCan_model.py
@@ -0,0 +1,221 @@
+import torch
+from torch import nn
+import numpy as np
+import math
+# import tinycudann as tcnn
+
+
+class SineLayer(nn.Module):
+    # See paper sec. 3.2, final paragraph, and supplement Sec. 1.5 for discussion of omega_0.
+    
+    # If is_first=True, omega_0 is a frequency factor which simply multiplies the activations before the 
+    # nonlinearity. Different signals may require different omega_0 in the first layer - this is a 
+    # hyperparameter.
+    
+    # If is_first=False, then the weights will be divided by omega_0 so as to keep the magnitude of 
+    # activations constant, but boost gradients to the weight matrix (see supplement Sec. 1.5)
+    
+    def __init__(self, in_features, out_features, bias=True,
+                 is_first=False, omega_0=30):
+        super().__init__()
+        self.omega_0 = omega_0
+        self.is_first = is_first
+        
+        self.in_features = in_features
+        self.linear = nn.Linear(in_features, out_features, bias=bias)
+        
+        self.init_weights()
+    
+    def init_weights(self):
+        with torch.no_grad():
+            if self.is_first:
+                self.linear.weight.uniform_(-1 / self.in_features, 
+                                             1 / self.in_features)
+            else:
+                self.linear.weight.uniform_(-np.sqrt(6 / self.in_features) / self.omega_0, 
+                                             np.sqrt(6 / self.in_features) / self.omega_0)
+        
+    def forward(self, input):
+        return torch.sin(self.omega_0 * self.linear(input))
+    
+    def forward_with_intermediate(self, input): 
+        # For visualization of activation distributions
+        intermediate = self.omega_0 * self.linear(input)
+        return torch.sin(intermediate), intermediate
+    
+    
+class Siren(nn.Module):
+    def __init__(self, in_features, hidden_features, hidden_layers, out_features, outermost_linear=False, 
+                 first_omega_0=30, hidden_omega_0=30.):
+        super().__init__()
+        
+        self.net = []
+        self.net.append(SineLayer(in_features, hidden_features, 
+                                  is_first=True, omega_0=first_omega_0))
+
+        for i in range(hidden_layers):
+            self.net.append(SineLayer(hidden_features, hidden_features, 
+                                      is_first=False, omega_0=hidden_omega_0))
+
+        if outermost_linear:
+            final_linear = nn.Linear(hidden_features, out_features)
+            
+            with torch.no_grad():
+                final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, 
+                                              np.sqrt(6 / hidden_features) / hidden_omega_0)
+                
+            self.net.append(final_linear)
+        else:
+            self.net.append(SineLayer(hidden_features, out_features, 
+                                      is_first=False, omega_0=hidden_omega_0))
+        
+        self.net = nn.Sequential(*self.net)
+    
+    def forward(self, coords):
+        output = self.net(coords)
+        return output
+
+
+class Homography(nn.Module):
+    def __init__(self, in_features=1, hidden_features=256, hidden_layers=1):
+        super().__init__()
+        out_features = 8
+        
+        self.net = []
+        self.net.append(nn.Linear(in_features, hidden_features))
+        self.net.append(nn.ReLU(inplace=True))
+        for i in range(hidden_layers):
+            self.net.append(nn.Linear(hidden_features, hidden_features))
+            self.net.append(nn.ReLU(inplace=True))
+        self.net.append(nn.Linear(hidden_features, out_features))     
+        self.net = nn.Sequential(*self.net)
+        
+        self.init_weights()
+        
+    def init_weights(self):
+        with torch.no_grad():
+            self.net[-1].bias.copy_(torch.Tensor([1., 0., 0., 0., 1., 0., 0., 0.]))
+    
+    def forward(self, coords):
+        output = self.net(coords)
+        return output
+    
+
+class Annealed(nn.Module):
+    def __init__(self, in_channels, annealed_step, annealed_begin_step=0, identity=True):
+        """
+        Defines a function that embeds x to (x, sin(2^k x), cos(2^k x), ...)
+        in_channels: number of input channels (3 for both xyz and direction)
+        """
+        super(Annealed, self).__init__()
+        self.N_freqs = 16
+        self.in_channels = in_channels
+        self.annealed = True
+        self.annealed_step = annealed_step
+        self.annealed_begin_step = annealed_begin_step
+
+        self.index = torch.linspace(0, self.N_freqs - 1, self.N_freqs)
+        self.identity = identity
+
+        self.index_2 = self.index.view(-1, 1).repeat(1, 2).view(-1)
+
+    def forward(self, x_embed, step):
+        """
+        Embeds x to (x, sin(2^k x), cos(2^k x), ...)
+        Different from the paper, "x" is also in the output
+        See https://github.com/bmild/nerf/issues/12
+
+        Inputs:
+            x: (B, self.in_channels)
+
+        Outputs:
+            out: (B, self.out_channels)
+        """
+        use_PE = False
+
+        if self.annealed_begin_step == 0:
+            # calculate the w for each freq bands
+            alpha = self.N_freqs * step / float(self.annealed_step)
+        else:
+            if step <= self.annealed_begin_step:
+                alpha = 0
+            else:
+                alpha = (self.N_freqs) * (step - self.annealed_begin_step) / float(
+                    self.annealed_step)
+
+        w = (1 - torch.cos(math.pi * torch.clamp(alpha * torch.ones_like(self.index_2) - self.index_2, 0, 1))) / 2
+        
+        if use_PE:
+            w[16:] = w[:16]
+
+        out = x_embed * w.to(x_embed.device)
+
+        return out
+    
+
+class BARF_PE(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.encoder = tcnn.Encoding(n_input_dims=2,
+                                     encoding_config=config["positional encoding"])
+        self.decoder = tcnn.Network(n_input_dims=self.encoder.n_output_dims +
+                                    2,
+                                    n_output_dims=3,
+                                    network_config=config["BARF network"])
+
+    def forward(self, x, step=0, aneal_func=None):
+        input = x
+        input = self.encoder(input)
+        if aneal_func is not None:
+            input = torch.cat([x, aneal_func(input,step)], dim=-1)
+        else:
+            input = torch.cat([x, input], dim=-1)
+        
+        weight = torch.ones(input.shape[-1], device=input.device).cuda()
+        x = self.decoder(weight * input)
+        return x
+    
+
+class Deform_Hash3d(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.encoder = tcnn.Encoding(n_input_dims=3,
+                                     encoding_config=config["encoding_deform3d"])
+        self.decoder = nn.Sequential(nn.Linear(self.encoder.n_output_dims + 3, 256),
+                                     nn.ReLU(), 
+                                     nn.Linear(256, 256), 
+                                     nn.ReLU(),
+                                     nn.Linear(256, 256), 
+                                     nn.ReLU(),
+                                     nn.Linear(256, 256), 
+                                     nn.ReLU(),
+                                     nn.Linear(256, 256), 
+                                     nn.ReLU(),
+                                     nn.Linear(256, 256),
+                                     nn.ReLU(),
+                                     nn.Linear(256, 2)
+                                     )
+
+    def forward(self, x, step=0, aneal_func=None):
+        input = x
+        input = self.encoder(input)
+        if aneal_func is not None:
+            input = torch.cat([x, aneal_func(input,step)], dim=-1)
+        else:
+            input = torch.cat([x, input], dim=-1)
+
+        weight = torch.ones(input.shape[-1], device=input.device).cuda()
+        x = self.decoder(weight * input) / 5
+
+        return x
+
+
+class Deform_Hash3d_Warp(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.Deform_Hash3d = Deform_Hash3d(config)
+
+    def forward(self, xyt_norm, step=0,aneal_func=None):
+        x = self.Deform_Hash3d(xyt_norm,step=step, aneal_func=aneal_func)
+
+        return x
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0a1592f40bc5fe9cf61d9e205011f3fc41be7f6
--- /dev/null
+++ b/app.py
@@ -0,0 +1,362 @@
+import gradio as gr
+import numpy as np
+import torch
+import cv2
+import os
+import imageio
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from controlnet_aux import LineartDetector
+from functools import partial
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from torchvision.transforms import Compose, ToTensor, Normalize, Resize
+
+from NaRCan_model import Homography, Siren
+from util import get_mgrid, apply_homography, jacobian, VideoFitting, TestVideoFitting
+
+
+
+def get_example():
+    case = [
+        [
+            'examples/bear.mp4',     
+        ],
+        [
+            'examples/boat.mp4',     
+        ],
+        [
+            'examples/woman-drink.mp4',     
+        ],
+        [
+            'examples/corgi.mp4',     
+        ],
+        [
+            'examples/yacht.mp4',     
+        ],
+        [
+            'examples/koolshooters.mp4',     
+        ],
+        [
+            'examples/overlook-the-ocean.mp4',     
+        ],
+        [
+            'examples/rotate.mp4',
+        ],
+        [
+            'examples/shark-ocean.mp4',     
+        ],
+        [
+            'examples/surf.mp4',     
+        ],
+        [
+            'examples/cactus.mp4',     
+        ],
+        [
+            'examples/gold-fish.mp4',
+        ]
+    ]
+    return case
+
+
+def set_default_prompt(video_name):
+    video_to_prompt = {
+        'bear.mp4': 'bear, Van Gogh Style',
+        'boat.mp4': 'a burning boat sails on lava',
+        'cactus.mp4': 'cactus, made of paper',
+        'corgi.mp4': 'a hellhound',
+        'gold-fish.mp4': 'Goldfish in the Milky Way',
+        'koolshooters.mp4': 'Avatar',
+        'overlook-the-ocean.mp4': 'ocean, pixel style',
+        'rotate.mp4': 'turbine engine',
+        'shark-ocean.mp4': 'A sleek shark, cartoon style',
+        'surf.mp4': 'Sailing, The background is a large white cloud, sketch style',
+        'woman-drink.mp4': 'a drinking zombie',
+        'yacht.mp4': 'yacht, cyberpunk style',
+    }
+    return video_to_prompt.get(video_name, '')
+
+
+def update_prompt(input_video):
+    video_name = input_video.split('/')[-1]
+    return set_default_prompt(video_name)
+
+
+# Map videos to corresponding images
+video_to_image = {
+    'bear.mp4': ['canonical/bear.png', 'pth_file/bear', 'examples_frames/bear'],
+    'boat.mp4': ['canonical/boat.png', 'pth_file/boat', 'examples_frames/boat'],
+    'cactus.mp4': ['canonical/cactus.png', 'pth_file/cactus', 'examples_frames/cactus'],
+    'corgi.mp4': ['canonical/corgi.png', 'pth_file/corgi', 'examples_frames/corgi'],
+    'gold-fish.mp4': ['canonical/gold-fish.png', 'pth_file/gold-fish', 'examples_frames/gold-fish'],
+    'koolshooters.mp4': ['canonical/koolshooters.png', 'pth_file/koolshooters', 'examples_frames/koolshooters'],
+    'overlook-the-ocean.mp4': ['canonical/overlook-the-ocean.png', 'pth_file/overlook-the-ocean', 'examples_frames/overlook-the-ocean'],
+    'rotate.mp4': ['canonical/rotate.png', 'pth_file/rotate', 'examples_frames/rotate'],
+    'shark-ocean.mp4': ['canonical/shark-ocean.png', 'pth_file/shark-ocean', 'examples_frames/shark-ocean'],
+    'surf.mp4': ['canonical/surf.png', 'pth_file/surf', 'examples_frames/surf'],
+    'woman-drink.mp4': ['canonical/woman-drink.png', 'pth_file/woman-drink', 'examples_frames/woman-drink'],
+    'yacht.mp4': ['canonical/yacht.png', 'pth_file/yacht', 'examples_frames/yacht'],
+}
+
+
+def images_to_video(image_list, output_path, fps=10):
+    # Convert PIL Images to numpy arrays
+    frames = [np.array(img).astype(np.uint8) for img in image_list]
+    frames = frames[:20]
+
+    # Create video writer
+    writer = imageio.get_writer(output_path, fps=fps, codec='libx264')
+
+    for frame in frames:
+        writer.append_data(frame)
+
+    writer.close()
+
+
+def NaRCan_make_video(edit_canonical, pth_path, frames_path):
+    # load NaRCan model
+    checkpoint_g_old = torch.load(os.path.join(pth_path, "homography_g.pth"))
+    checkpoint_g = torch.load(os.path.join(pth_path, "mlp_g.pth"))
+    g_old = Homography(hidden_features=256, hidden_layers=2).cuda()
+    g = Siren(in_features=3, out_features=2, hidden_features=256,
+              hidden_layers=5, outermost_linear=True).cuda()
+    
+    g_old.load_state_dict(checkpoint_g_old)
+    g.load_state_dict(checkpoint_g)
+
+    g_old.eval()
+    g.eval()
+
+    transform = Compose([
+        Resize(512),
+        ToTensor(),
+        Normalize(torch.Tensor([0.5, 0.5, 0.5]), torch.Tensor([0.5, 0.5, 0.5]))
+    ])
+    v = TestVideoFitting(frames_path, transform)
+    videoloader = DataLoader(v, batch_size=1, pin_memory=True, num_workers=0)
+
+    model_input, ground_truth = next(iter(videoloader))
+    model_input, ground_truth = model_input[0].cuda(), ground_truth[0].cuda()
+
+    myoutput = None
+    data_len = len(os.listdir(frames_path))
+
+    with torch.no_grad():
+        batch_size = (v.H * v.W)
+        for step in range(data_len):
+            start = (step * batch_size) % len(model_input)
+            end = min(start + batch_size, len(model_input))
+
+            # get the deformation
+            xy, t = model_input[start:end, :-1], model_input[start:end, [-1]]
+            xyt = model_input[start:end]
+            h_old = apply_homography(xy, g_old(t))
+            h = g(xyt)
+            xy_ = h_old + h
+
+            # use canonical to reconstruct
+            w, h = v.W, v.H
+            canonical_img = np.array(edit_canonical.convert('RGB'))
+            canonical_img = torch.from_numpy(canonical_img).float().cuda()
+            h_c, w_c = canonical_img.shape[:2]
+            grid_new = xy_.clone()
+            grid_new[..., 1] = xy_[..., 0] / 1.5
+            grid_new[..., 0] = xy_[..., 1] / 2.0
+
+            if len(canonical_img.shape) == 3:
+                canonical_img = canonical_img.unsqueeze(0)
+            results = torch.nn.functional.grid_sample(
+                canonical_img.permute(0, 3, 1, 2),
+                grid_new.unsqueeze(1).unsqueeze(0),
+                mode='bilinear',
+                padding_mode='border')
+            o = results.squeeze().permute(1,0)
+
+            if step == 0:
+                myoutput = o
+            
+            else:
+                myoutput = torch.cat([myoutput, o])
+
+    myoutput = myoutput.reshape(512, 512, data_len, 3).permute(2, 0, 1, 3).clone().detach().cpu().numpy().astype(np.float32)
+    # myoutput = np.clip(myoutput, -1, 1) * 0.5 + 0.5
+
+    for i in range(len(myoutput)):
+        myoutput[i] = Image.fromarray(np.uint8(myoutput[i])).resize((512, 512)) #854, 480
+
+    edit_video_path = f'NaRCan_fps_10.mp4'
+    images_to_video(myoutput, edit_video_path)
+    
+    return edit_video_path
+
+
+def edit_with_pnp(input_video, prompt, num_steps, guidance_scale, seed, n_prompt, control_type="Lineart"):
+    video_name = input_video.split('/')[-1]
+    if video_name in video_to_image:
+        image_path = video_to_image[video_name][0]
+        pth_path = video_to_image[video_name][1]
+        frames_path = video_to_image[video_name][2]
+    else:
+        return None
+
+    if control_type == "Lineart":
+        # Load the control net model for lineart
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_lineart", torch_dtype=torch.float16)
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        )
+        pipe.to("cuda")
+        # lineart
+        processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
+        processor_partial = partial(processor, coarse=False)
+        size_ = 768
+        canonical_image = Image.open(image_path)
+        ori_size = canonical_image.size
+        image = processor_partial(canonical_image.resize((size_, size_)), detect_resolution=size_, image_resolution=size_)
+        image = image.resize(ori_size, resample=Image.BILINEAR)
+        
+        generator = torch.manual_seed(seed) if seed != -1 else None
+        output_images = pipe(
+            prompt=prompt,
+            image=image,
+            num_inference_steps=num_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=n_prompt,
+            generator=generator
+        ).images
+        # output_images[0] = output_images[0].resize(ori_size, resample=Image.BILINEAR)
+    
+    else:
+        # Load the control net model for canny
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16)
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+        )
+        pipe.to("cuda")
+        # canny
+        canonical_image = cv2.imread(image_path)
+        canonical_image = cv2.cvtColor(canonical_image, cv2.COLOR_BGR2RGB)
+        image = cv2.cvtColor(canonical_image, cv2.COLOR_RGB2GRAY)
+        image = cv2.Canny(image, 100, 200)
+        image = image[:, :, None]
+        image = np.concatenate([image, image, image], axis=2)
+        image = Image.fromarray(image)
+        
+        generator = torch.manual_seed(seed) if seed != -1 else None
+        output_images = pipe(
+            prompt=prompt,
+            image=image,
+            num_inference_steps=num_steps,
+            guidance_scale=guidance_scale,
+            negative_prompt=n_prompt,
+            generator=generator
+        ).images
+    
+    edit_video_path = NaRCan_make_video(output_images[0], pth_path, frames_path)
+
+    # Here we return the first output image as the result
+    return edit_video_path
+
+
+########
+# demo #
+########
+
+
+intro = """
+<div style="text-align:center">
+<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
+   NaRCan - <small>Natural Refined Canonical Image</small>
+</h1>
+<span>[<a target="_blank" href="https://koi953215.github.io/NaRCan_page/">Project page</a>], [<a target="_blank" href="https://huggingface.co/papers/2406.06523">Paper</a>]</span>
+<div style="display:flex; justify-content: center;margin-top: 0.5em">Each edit takes ~10 sec </div>
+</div>
+"""
+
+
+
+with gr.Blocks(css="style.css") as demo:
+    
+    gr.HTML(intro)
+    frames = gr.State()
+    inverted_latents = gr.State()
+    latents = gr.State()
+    zs = gr.State()
+    do_inversion = gr.State(value=True)
+
+    with gr.Row():
+        input_video = gr.Video(label="Input Video", interactive=False, elem_id="input_video", value='examples/bear.mp4')
+        output_video = gr.Video(label="Edited Video", interactive=False, elem_id="output_video")
+        input_video.style(height=365, width=365)
+        output_video.style(height=365, width=365)
+
+
+    with gr.Row():
+            prompt = gr.Textbox(
+                            label="Describe your edited video",
+                            max_lines=1, 
+                            value="bear, Van Gogh Style"
+                            # placeholder="bear, Van Gogh Style"
+                        )
+    
+               
+    with gr.Row():
+        run_button = gr.Button("Edit your video!", visible=True)
+
+    max_images = 12
+    default_num_images = 3
+    with gr.Accordion('Advanced options', open=False):
+        control_type = gr.Dropdown(
+            ["Canny", "Lineart"], 
+            label="Control Type", 
+            info="Canny or Lineart",
+            value="Lineart"
+        )
+        num_steps = gr.Slider(label='Steps',
+                                minimum=1,
+                                maximum=100,
+                                value=20,
+                                step=1)
+        guidance_scale = gr.Slider(label='Guidance Scale',
+                                    minimum=0.1,
+                                    maximum=30.0,
+                                    value=9.0,
+                                    step=0.1)
+        seed = gr.Slider(label='Seed',
+                            minimum=-1,
+                            maximum=2147483647,
+                            step=1,
+                            randomize=True)
+        n_prompt = gr.Textbox(
+            label='Negative Prompt',
+            value=""
+        )
+                    
+    input_video.change(
+        fn = update_prompt,
+        inputs = [input_video],
+        outputs = [prompt],
+        queue = False)
+    
+    run_button.click(fn = edit_with_pnp,
+                     inputs = [input_video, 
+                               prompt, 
+                               num_steps, 
+                               guidance_scale, 
+                               seed, 
+                               n_prompt,
+                               control_type,
+                               ],
+                                 outputs = [output_video]
+                                )
+
+    gr.Examples(
+        examples=get_example(),
+        label='Examples',
+        inputs=[input_video],
+        outputs=[output_video],
+        examples_per_page=8
+    )
+
+demo.queue()
+
+demo.launch(share=True)
\ No newline at end of file
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diff --git a/canonical/boat.png b/canonical/boat.png
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diff --git a/canonical/cactus.png b/canonical/cactus.png
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diff --git a/canonical/corgi.png b/canonical/corgi.png
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+++ b/pth_file/shark-ocean/mlp_g.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6e4f269d0238e4a533ea128b53714fd48a85e2fa1ee39d651f6128cad63aac2
+size 1325931
diff --git a/pth_file/surf/homography_g.pth b/pth_file/surf/homography_g.pth
new file mode 100644
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--- /dev/null
+++ b/pth_file/surf/homography_g.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d7f112d7f4d3cc4f38a1ffa736e1bccd1bd4a0e15fad7b0c76b9c8ab07c07ce
+size 539531
diff --git a/pth_file/surf/mlp_g.pth b/pth_file/surf/mlp_g.pth
new file mode 100644
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--- /dev/null
+++ b/pth_file/surf/mlp_g.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7fc7b21d0ec4a5b583107948bb40d5b0a1eb2df9a23ed2694bdbe075167fdfca
+size 1325931
diff --git a/pth_file/woman-drink/homography_g.pth b/pth_file/woman-drink/homography_g.pth
new file mode 100644
index 0000000000000000000000000000000000000000..5126adff2f33b936d1e22974c0ed1312fc44c543
--- /dev/null
+++ b/pth_file/woman-drink/homography_g.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:63825d4c3da91a8f085806fcfa610266c224bee875cb891f8851b0d9f8673f75
+size 539531
diff --git a/pth_file/woman-drink/mlp_g.pth b/pth_file/woman-drink/mlp_g.pth
new file mode 100644
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--- /dev/null
+++ b/pth_file/woman-drink/mlp_g.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e2e49b7a8b2b1b48a686afe7f94d00e40c152a95c0ca225dbd6141a3403ed4f1
+size 1325931
diff --git a/pth_file/yacht/homography_g.pth b/pth_file/yacht/homography_g.pth
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--- /dev/null
+++ b/pth_file/yacht/homography_g.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64a42d59de2c5c7a87bde2ef9a49285bb587ac2ccbc4c86db510eef3ed63eebb
+size 539531
diff --git a/pth_file/yacht/mlp_g.pth b/pth_file/yacht/mlp_g.pth
new file mode 100644
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--- /dev/null
+++ b/pth_file/yacht/mlp_g.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c3abd0931266cb06ca832e3a0a6140dc64e747c2b21f3541568dfc5e9bb28b5
+size 1325931
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0e121d38ae500743a2c5cd402f6f0d091a845ec8
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,11 @@
+gradio==3.38.0
+numpy
+torch==2.3.1
+opencv-python==4.10.0.82
+imageio==2.34.1
+imageio-ffmpeg==0.5.1
+pillow==10.3.0
+diffusers==0.29.0
+controlnet-aux==0.0.9
+functools
+torchvision==0.18.1
\ No newline at end of file
diff --git a/style.css b/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..f934522dd2ff6ce95110be534a71181c252c0bf9
--- /dev/null
+++ b/style.css
@@ -0,0 +1,91 @@
+/*
+This CSS file is modified from:
+https://huggingface.co/spaces/DeepFloyd/IF/blob/main/style.css
+*/
+
+h1 {
+  text-align: center;
+}
+
+.gradio-container {
+  font-family: 'IBM Plex Sans', sans-serif;
+}
+
+.gr-button {
+  color: white;
+  border-color: black;
+  background: black;
+}
+
+input[type='range'] {
+  accent-color: black;
+}
+
+.dark input[type='range'] {
+  accent-color: #dfdfdf;
+}
+
+.container {
+  max-width: 730px;
+  margin: auto;
+}
+
+.gr-button:focus {
+  border-color: rgb(147 197 253 / var(--tw-border-opacity));
+  outline: none;
+  box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
+  --tw-border-opacity: 1;
+  --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
+  --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
+  --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
+  --tw-ring-opacity: .5;
+}
+
+.gr-form {
+  flex: 1 1 50%;
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+#prompt-container {
+  gap: 0;
+}
+
+#prompt-text-input,
+#negative-prompt-text-input {
+  padding: .45rem 0.625rem
+}
+
+/* #component-16 {
+  border-top-width: 1px !important;
+  margin-top: 1em
+} */
+
+.image_duplication {
+  position: absolute;
+  width: 100px;
+  left: 50px
+}
+
+#component-0 {
+  max-width: 730px;
+  margin: auto;
+  padding-top: 1.5rem;
+}
+
+#share-btn-container {
+    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; margin-left: auto;
+}
+#share-btn {
+    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
+}
+#share-btn * {
+    all: unset;
+}
+#share-btn-container div:nth-child(-n+2){
+    width: auto !important;
+    min-height: 0px !important;
+}
+#share-btn-container .wrap {
+    display: none !important;
+}
\ No newline at end of file
diff --git a/util.py b/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e88824ec1c0c92cd30efee13b727b62e060d63e
--- /dev/null
+++ b/util.py
@@ -0,0 +1,233 @@
+import os
+import numpy as np
+from PIL import Image
+
+import torch
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+
+def get_mgrid(sidelen, vmin=-1, vmax=1):
+    if type(vmin) is not list:
+        vmin = [vmin for _ in range(len(sidelen))]
+    if type(vmax) is not list:
+        vmax = [vmax for _ in range(len(sidelen))]
+    tensors = tuple([torch.linspace(vmin[i], vmax[i], steps=sidelen[i]) for i in range(len(sidelen))])
+    mgrid = torch.stack(torch.meshgrid(*tensors), dim=-1)
+    mgrid = mgrid.reshape(-1, len(sidelen))
+    return mgrid
+
+def apply_homography(x, h):
+    h = torch.cat([h, torch.ones_like(h[:, [0]])], -1)
+    h = h.view(-1, 3, 3)
+    x = torch.cat([x, torch.ones_like(x[:, 0]).unsqueeze(-1)], -1).unsqueeze(-1)
+    o = torch.bmm(h, x).squeeze(-1)
+    o = o[:, :-1] / o[:, [-1]]
+    return o
+
+def jacobian(y, x):
+    B, N = y.shape
+    jacobian = list()
+    for i in range(N):
+        v = torch.zeros_like(y)
+        v[:, i] = 1.
+        dy_i_dx = torch.autograd.grad(y,
+                                      x,
+                                      grad_outputs=v,
+                                      retain_graph=True,
+                                      create_graph=True)[0]  # shape [B, N]
+        jacobian.append(dy_i_dx)
+    jacobian = torch.stack(jacobian, dim=1).requires_grad_()
+    return jacobian
+
+def overlap_mix(img1, img2, img_order, overlap_num):
+    w1 = np.linspace(0, 1, overlap_num)[::-1]
+    w2 = 1 - w1
+    return w1[img_order] * img1 + w2[img_order] * img2
+
+
+class VideoFitting(Dataset):
+    def __init__(self, path, transform=None):
+        super().__init__()
+
+        self.path = path
+        if transform is None:
+            self.transform = ToTensor()
+        else:
+            self.transform = transform
+
+        self.video = self.get_video_tensor()
+        self.num_frames, _, self.H, self.W = self.video.size()
+        self.pixels = self.video.permute(2, 3, 0, 1).contiguous().view(-1, 3)
+        self.coords = get_mgrid([self.H, self.W, self.num_frames])
+
+        shuffle = torch.randperm(len(self.pixels))
+        self.pixels = self.pixels[shuffle]
+        self.coords = self.coords[shuffle]
+
+    def get_video_tensor(self):
+        frames = sorted(os.listdir(self.path))
+        video = []
+        for i in range(len(frames)):
+            img = Image.open(os.path.join(self.path, frames[i]))
+            img = self.transform(img)
+            video.append(img)
+        return torch.stack(video, 0)
+
+    def __len__(self):
+        return 1
+
+    def __getitem__(self, idx):    
+        if idx > 0: raise IndexError
+            
+        return self.coords, self.pixels
+    
+
+class TestVideoFitting(Dataset):
+    def __init__(self, path, transform=None):
+        super().__init__()
+
+        self.path = path
+        if transform is None:
+            self.transform = ToTensor()
+        else:
+            self.transform = transform
+
+        self.video = self.get_video_tensor()
+        self.num_frames, _, self.H, self.W = self.video.size()
+        self.pixels = self.video.permute(2, 3, 0, 1).contiguous().view(-1, 3)
+        self.coords = get_mgrid([self.H, self.W, self.num_frames])
+
+    def get_video_tensor(self):
+        frames = sorted(os.listdir(self.path))
+        video = []
+        for i in range(len(frames)):
+            img = Image.open(os.path.join(self.path, frames[i]))
+            img = self.transform(img)
+            video.append(img)
+        return torch.stack(video, 0)
+
+    def __len__(self):
+        return 1
+
+    def __getitem__(self, idx):    
+        if idx > 0: raise IndexError
+            
+        return self.coords, self.pixels
+    
+
+class GroupVideoFitting(Dataset):
+    def __init__(self, path, mask_path, transform=None, mask_transform=None):
+        super().__init__()
+
+        self.path = path
+        self.mask_path = mask_path
+        if transform is None:
+            self.transform = ToTensor()
+        else:
+            self.transform = transform
+        
+        if mask_transform is None:
+            self.mask_transform = ToTensor()
+        else:
+            self.mask_transform = mask_transform
+
+        self.video = self.get_video_tensor()
+        self.mask = self.get_mask_tensor()
+        self.num_frames, _, self.H, self.W = self.video.size()
+        self.pixels = self.video.permute(2, 3, 0, 1).contiguous().view(-1, 3)
+        self.mask_pixels = self.mask.permute(2, 3, 0, 1).contiguous().view(-1, 1)
+        self.coords = get_mgrid([self.H, self.W, self.num_frames])
+
+        shuffle = torch.randperm(len(self.pixels))
+        self.pixels = self.pixels[shuffle]
+        self.coords = self.coords[shuffle]
+        self.mask_pixels = self.mask_pixels[shuffle]
+
+    def get_video_tensor(self):
+        frames = sorted(os.listdir(self.path))
+        video = []
+        for i in range(len(frames)):
+            img = Image.open(os.path.join(self.path, frames[i]))
+            img = self.transform(img)
+            video.append(img)
+        return torch.stack(video, 0)
+    
+    def get_mask_tensor(self):
+        masks = sorted(os.listdir(self.mask_path))
+        all_mask = []
+        for i in range(len(masks)):
+            mask = Image.open(os.path.join(self.mask_path, masks[i]))
+            mask = self.mask_transform(mask)
+            all_mask.append(mask)
+        return torch.stack(all_mask, 0)
+
+    def __len__(self):
+        return 1
+
+    def __getitem__(self, idx):    
+        if idx > 0: raise IndexError
+            
+        return self.coords, self.pixels, self.mask_pixels
+    
+
+class TestGroupVideoFitting(Dataset):
+    def __init__(self, path, mask_path, back_mask_path, transform=None, mask_transform=None):
+        super().__init__()
+
+        self.path = path
+        self.mask_path = mask_path
+        self.back_mask_path = back_mask_path
+        if transform is None:
+            self.transform = ToTensor()
+        else:
+            self.transform = transform
+        
+        if mask_transform is None:
+            self.mask_transform = ToTensor()
+        else:
+            self.mask_transform = mask_transform
+
+        self.video = self.get_video_tensor()
+        self.mask = self.get_mask_tensor()
+        self.back_mask = self.get_back_mask_tensor()
+        self.num_frames, _, self.H, self.W = self.video.size()
+        self.pixels = self.video.permute(2, 3, 0, 1).contiguous().view(-1, 3)
+        self.mask_pixels = self.mask.permute(2, 3, 0, 1).contiguous().view(-1, 1)
+        self.back_mask_pixels = self.back_mask.permute(2, 3, 0, 1).contiguous().view(-1, 1)
+        self.coords = get_mgrid([self.H, self.W, self.num_frames])
+
+    def get_video_tensor(self):
+        frames = sorted(os.listdir(self.path))
+        video = []
+        for i in range(len(frames)):
+            img = Image.open(os.path.join(self.path, frames[i]))
+            img = self.transform(img)
+            video.append(img)
+        return torch.stack(video, 0)
+    
+    def get_mask_tensor(self):
+        masks = sorted(os.listdir(self.mask_path))
+        all_mask = []
+        for i in range(len(masks)):
+            mask = Image.open(os.path.join(self.mask_path, masks[i]))
+            mask = self.mask_transform(mask)
+            all_mask.append(mask)
+        return torch.stack(all_mask, 0)
+    
+    def get_back_mask_tensor(self):
+        masks = sorted(os.listdir(self.back_mask_path))
+        all_mask = []
+        for i in range(len(masks)):
+            mask = Image.open(os.path.join(self.back_mask_path, masks[i]))
+            mask = self.mask_transform(mask)
+            all_mask.append(mask)
+        return torch.stack(all_mask, 0)
+
+    def __len__(self):
+        return 1
+
+    def __getitem__(self, idx):    
+        if idx > 0: raise IndexError
+            
+        return self.coords, self.pixels, self.mask_pixels, self.back_mask_pixels
\ No newline at end of file