init

app.py

@@ -89,6 +89,7 @@ def get_sparseflow_and_mask_forward(
     return s_flow, mask
 
 
+@spaces.GPU(duration=200)
 def init_models(pretrained_model_name_or_path, resume_from_checkpoint, weight_dtype, device='cuda', enable_xformers_memory_efficient_attention=False, allow_tf32=False):
 
     from models.unet_spatio_temporal_condition_controlnet import UNetSpatioTemporalConditionControlNetModel
@@ -214,24 +215,45 @@ def visualize_drag_v2(background_image_path, splited_tracks, width, height):
     return trajectory_maps, transparent_layer
 
 
-class Drag:
-    @spaces.GPU(duration=200)
-    def __init__(self, height, width):
+with gr.Blocks() as demo:
+    gr.Markdown("""<h1 align="center">MOFA-Video</h1><br>""")
 
-        svd_ckpt = "ckpts/stable-video-diffusion-img2vid-xt-1-1"
-        mofa_ckpt = "ckpts/controlnet"
+    gr.Markdown("""Official Gradio Demo for <a href='https://myniuuu.github.io/MOFA_Video'><b>MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model</b></a>.<br>""")
 
-        self.pipeline, self.cmp = init_models(
-            svd_ckpt, 
-            mofa_ckpt, 
-            weight_dtype=torch.float16, 
-            device='cuda'
-        )
+    gr.Markdown(
+        """
+        During the inference, kindly follow these instructions:
+        <br>
+        1. Use the "Upload Image" button to upload an image. Avoid dragging the image directly into the window. <br>
+        2. Proceed to draw trajectories: <br>
+            2.1. Click "Add Trajectory" first, then select points on the "Add Trajectory Here" image. The first click sets the starting point. Click multiple points to create a non-linear trajectory. To add a new trajectory, click "Add Trajectory" again and select points on the image. Avoid clicking the "Add Trajectory" button multiple times without clicking points in the image to add the trajectory, as this can lead to errors. <br>
+            2.2. After adding each trajectory, an optical flow image will be displayed automatically. Use it as a reference to adjust the trajectory for desired effects (e.g., area, intensity). <br>
+            2.3. To delete the latest trajectory, click "Delete Last Trajectory." <br>
+            2.4. Choose the Control Scale in the bar. This determines the control intensity. Setting it to 0 means no control (pure generation result of SVD itself), while setting it to 1 results in the strongest control (which will not lead to good results in most cases because of twisting artifacts). A preset value of 0.6 is recommended for most cases. <br>
+            2.5. To use the motion brush for restraining the control area of the trajectory, click to add masks on the "Add Motion Brush Here" image. The motion brush restricts the optical flow area derived from the trajectory whose starting point is within the motion brush. The displayed optical flow image will change correspondingly. Adjust the motion brush radius using the "Motion Brush Radius" bar. <br>
+        3. Click the "Run" button to animate the image according to the path. <br>
+        """
+    )
+
+    height, width = 512, 512
+    svd_ckpt = "ckpts/stable-video-diffusion-img2vid-xt-1-1"
+    mofa_ckpt = "ckpts/controlnet"
+
+    pipeline, cmp = init_models(
+        svd_ckpt, 
+        mofa_ckpt, 
+        weight_dtype=torch.float16, 
+        device='cuda'
+    )
 
-        self.height = height
-        self.width = width
+    first_frame_path = gr.State()
+    tracking_points = gr.State([])
+    motion_brush_points = gr.State([])
+    motion_brush_mask = gr.State()
+    motion_brush_viz = gr.State()
+    inference_batch_size = gr.State(1)
 
-    def get_cmp_flow(self, frames, sparse_optical_flow, mask, brush_mask=None):
+    def get_cmp_flow(frames, sparse_optical_flow, mask, brush_mask=None):
 
         '''
             frames: [b, 13, 3, 384, 384] (0, 1) tensor
@@ -244,7 +266,7 @@ class Drag:
         frames = frames.flatten(0, 1)  # [b*13, 3, 256, 256]
         sparse_optical_flow = sparse_optical_flow.flatten(0, 1)  # [b*13, 2, 256, 256]
         mask = mask.flatten(0, 1)  # [b*13, 2, 256, 256]
-        cmp_flow = self.cmp.run(frames, sparse_optical_flow, mask)  # [b*13, 2, 256, 256]
+        cmp_flow = cmp.run(frames, sparse_optical_flow, mask)  # [b*13, 2, 256, 256]
 
         if brush_mask is not None:
             brush_mask = torch.from_numpy(brush_mask) / 255.
@@ -256,19 +278,19 @@ class Drag:
         return cmp_flow
     
 
-    def get_flow(self, pixel_values_384, sparse_optical_flow_384, mask_384, motion_brush_mask=None):
+    def get_flow(pixel_values_384, sparse_optical_flow_384, mask_384, motion_brush_mask=None):
 
         fb, fl, fc, _, _ = pixel_values_384.shape
 
-        controlnet_flow = self.get_cmp_flow(
+        controlnet_flow = get_cmp_flow(
             pixel_values_384[:, 0:1, :, :, :].repeat(1, fl, 1, 1, 1), 
             sparse_optical_flow_384, 
             mask_384, motion_brush_mask
         )
 
-        if self.height != 384 or self.width != 384:
-            scales = [self.height / 384, self.width / 384]
-            controlnet_flow = F.interpolate(controlnet_flow.flatten(0, 1), (self.height, self.width), mode='nearest').reshape(fb, fl, 2, self.height, self.width)
+        if height != 384 or width != 384:
+            scales = [height / 384, width / 384]
+            controlnet_flow = F.interpolate(controlnet_flow.flatten(0, 1), (height, width), mode='nearest').reshape(fb, fl, 2, height, width)
             controlnet_flow[:, :, 0] *= scales[1]
             controlnet_flow[:, :, 1] *= scales[0]
         
@@ -276,7 +298,7 @@ class Drag:
     
 
     @torch.no_grad()
-    def forward_sample(self, input_drag_384_inmask, input_drag_384_outmask, input_first_frame, input_mask_384_inmask, input_mask_384_outmask, in_mask_flag, out_mask_flag, motion_brush_mask=None, ctrl_scale=1., outputs=dict()):
+    def forward_sample(input_drag_384_inmask, input_drag_384_outmask, input_first_frame, input_mask_384_inmask, input_mask_384_outmask, in_mask_flag, out_mask_flag, motion_brush_mask=None, ctrl_scale=1., outputs=dict()):
         '''
             input_drag: [1, 13, 320, 576, 2]
             input_drag_384: [1, 13, 384, 384, 2]
@@ -308,29 +330,29 @@ class Drag:
         input_first_frame_384 = input_first_frame_384.to('cuda', dtype=torch.float16)
 
         if in_mask_flag:
-            flow_inmask = self.get_flow(
+            flow_inmask = get_flow(
                 input_first_frame_384, 
                 input_drag_384_inmask, mask_384_inmask, motion_brush_mask
             )
         else:
             fb, fl = mask_384_inmask.shape[:2]
-            flow_inmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+            flow_inmask = torch.zeros(fb, fl, 2, height, width).to('cuda', dtype=torch.float16)
 
         if out_mask_flag:
-            flow_outmask = self.get_flow(
+            flow_outmask = get_flow(
                 input_first_frame_384, 
                 input_drag_384_outmask, mask_384_outmask
             )
         else:
             fb, fl = mask_384_outmask.shape[:2]
-            flow_outmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+            flow_outmask = torch.zeros(fb, fl, 2, height, width).to('cuda', dtype=torch.float16)
         
         inmask_no_zero = (flow_inmask != 0).all(dim=2)
         inmask_no_zero = inmask_no_zero.unsqueeze(2).expand_as(flow_inmask)
 
         controlnet_flow = torch.where(inmask_no_zero, flow_inmask, flow_outmask)
 
-        val_output = self.pipeline(
+        val_output = pipeline(
             input_first_frame_pil, 
             input_first_frame_pil,
             controlnet_flow, 
@@ -369,16 +391,16 @@ class Drag:
     
     @spaces.GPU
     @torch.no_grad()
-    def get_cmp_flow_from_tracking_points(self, tracking_points, motion_brush_mask, first_frame_path):
+    def get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path):
 
-        original_width, original_height = self.width, self.height
+        original_width, original_height = width, height
 
         input_all_points = tracking_points.constructor_args['value']
 
         if len(input_all_points) == 0 or len(input_all_points[-1]) == 1:
             return np.uint8(np.ones((original_width, original_height, 3))*255)
         
-        resized_all_points = [tuple([tuple([int(e1[0]*self.width/original_width), int(e1[1]*self.height/original_height)]) for e1 in e]) for e in input_all_points]
+        resized_all_points = [tuple([tuple([int(e1[0]*width/original_width), int(e1[1]*height/original_height)]) for e1 in e]) for e in input_all_points]
         resized_all_points_384 = [tuple([tuple([int(e1[0]*384/original_width), int(e1[1]*384/original_height)]) for e1 in e]) for e in input_all_points]
 
         new_resized_all_points = []
@@ -456,22 +478,22 @@ class Drag:
         input_first_frame_384 = input_first_frame_384.to('cuda', dtype=torch.float16)
 
         if in_mask_flag:
-            flow_inmask = self.get_flow(
+            flow_inmask = get_flow(
                 input_first_frame_384, 
                 input_drag_384_inmask, mask_384_inmask, motion_brush_mask_384
             )
         else:
             fb, fl = mask_384_inmask.shape[:2]
-            flow_inmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+            flow_inmask = torch.zeros(fb, fl, 2, height, width).to('cuda', dtype=torch.float16)
 
         if out_mask_flag:
-            flow_outmask = self.get_flow(
+            flow_outmask = get_flow(
                 input_first_frame_384, 
                 input_drag_384_outmask, mask_384_outmask
             )
         else:
             fb, fl = mask_384_outmask.shape[:2]
-            flow_outmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+            flow_outmask = torch.zeros(fb, fl, 2, height, width).to('cuda', dtype=torch.float16)
         
         inmask_no_zero = (flow_inmask != 0).all(dim=2)
         inmask_no_zero = inmask_no_zero.unsqueeze(2).expand_as(flow_inmask)
@@ -484,12 +506,12 @@ class Drag:
         return viz_esti_flows
     
     @spaces.GPU(duration=200)
-    def run(self, first_frame_path, tracking_points, inference_batch_size, motion_brush_mask, motion_brush_viz, ctrl_scale):
+    def run(first_frame_path, tracking_points, inference_batch_size, motion_brush_mask, motion_brush_viz, ctrl_scale):
         
-        original_width, original_height = self.width, self.height
+        original_width, original_height = width, height
 
         input_all_points = tracking_points.constructor_args['value']
-        resized_all_points = [tuple([tuple([int(e1[0]*self.width/original_width), int(e1[1]*self.height/original_height)]) for e1 in e]) for e in input_all_points]
+        resized_all_points = [tuple([tuple([int(e1[0]*width/original_width), int(e1[1]*height/original_height)]) for e1 in e]) for e in input_all_points]
         resized_all_points_384 = [tuple([tuple([int(e1[0]*384/original_width), int(e1[1]*384/original_height)]) for e1 in e]) for e in input_all_points]
 
         new_resized_all_points = []
@@ -542,9 +564,9 @@ class Drag:
         id = base.split('_')[0]
         
         image_pil = image2pil(first_frame_path)
-        image_pil = image_pil.resize((self.width, self.height), Image.BILINEAR).convert('RGB')
+        image_pil = image_pil.resize((width, height), Image.BILINEAR).convert('RGB')
         
-        visualized_drag, _ = visualize_drag_v2(first_frame_path, resized_all_points, self.width, self.height)
+        visualized_drag, _ = visualize_drag_v2(first_frame_path, resized_all_points, width, height)
 
         motion_brush_viz_pil = Image.fromarray(motion_brush_viz.astype(np.uint8)).convert('RGBA')
         visualized_drag = visualized_drag[0].convert('RGBA')
@@ -567,7 +589,7 @@ class Drag:
                 first_frames = outputs['logits_imgs'][:, -1]
             
 
-            outputs = self.forward_sample(
+            outputs = forward_sample(
                 input_drag_384_inmask.to('cuda'), 
                 input_drag_384_outmask.to('cuda'), 
                 first_frames.to('cuda'),
@@ -630,43 +652,13 @@ class Drag:
 
         return hint_path, outputs_path, flows_path, outputs_mp4_path, flows_mp4_path
 
-
-with gr.Blocks() as demo:
-    gr.Markdown("""<h1 align="center">MOFA-Video</h1><br>""")
-
-    gr.Markdown("""Official Gradio Demo for <a href='https://myniuuu.github.io/MOFA_Video'><b>MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model</b></a>.<br>""")
-
-    gr.Markdown(
-        """
-        During the inference, kindly follow these instructions:
-        <br>
-        1. Use the "Upload Image" button to upload an image. Avoid dragging the image directly into the window. <br>
-        2. Proceed to draw trajectories: <br>
-            2.1. Click "Add Trajectory" first, then select points on the "Add Trajectory Here" image. The first click sets the starting point. Click multiple points to create a non-linear trajectory. To add a new trajectory, click "Add Trajectory" again and select points on the image. Avoid clicking the "Add Trajectory" button multiple times without clicking points in the image to add the trajectory, as this can lead to errors. <br>
-            2.2. After adding each trajectory, an optical flow image will be displayed automatically. Use it as a reference to adjust the trajectory for desired effects (e.g., area, intensity). <br>
-            2.3. To delete the latest trajectory, click "Delete Last Trajectory." <br>
-            2.4. Choose the Control Scale in the bar. This determines the control intensity. Setting it to 0 means no control (pure generation result of SVD itself), while setting it to 1 results in the strongest control (which will not lead to good results in most cases because of twisting artifacts). A preset value of 0.6 is recommended for most cases. <br>
-            2.5. To use the motion brush for restraining the control area of the trajectory, click to add masks on the "Add Motion Brush Here" image. The motion brush restricts the optical flow area derived from the trajectory whose starting point is within the motion brush. The displayed optical flow image will change correspondingly. Adjust the motion brush radius using the "Motion Brush Radius" bar. <br>
-        3. Click the "Run" button to animate the image according to the path. <br>
-        """
-    )
-
-    target_size = 512
-    DragNUWA_net = Drag(target_size, target_size)
-    first_frame_path = gr.State()
-    tracking_points = gr.State([])
-    motion_brush_points = gr.State([])
-    motion_brush_mask = gr.State()
-    motion_brush_viz = gr.State()
-    inference_batch_size = gr.State(1)
-
     def preprocess_image(image):
 
         image_pil = image2pil(image.name)
         raw_w, raw_h = image_pil.size
 
         max_edge = min(raw_w, raw_h)
-        resize_ratio = target_size / max_edge
+        resize_ratio = width / max_edge
 
         image_pil = image_pil.resize((round(raw_w * resize_ratio), round(raw_h * resize_ratio)), Image.BILINEAR)
 
@@ -676,8 +668,8 @@ with gr.Blocks() as demo:
 
         image_pil = transforms.CenterCrop((crop_h, crop_w))(image_pil.convert('RGB'))
 
-        DragNUWA_net.width = crop_w
-        DragNUWA_net.height = crop_h
+        width = crop_w
+        height = crop_h
 
         id = str(time.time()).split('.')[0]
         os.makedirs(os.path.join(output_dir_video, str(id)), exist_ok=True)
@@ -722,7 +714,7 @@ with gr.Blocks() as demo:
         transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8))
         trajectory_map = Image.alpha_composite(transparent_background, transparent_layer)
 
-        viz_flow = DragNUWA_net.get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
+        viz_flow = get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
 
         return tracking_points, trajectory_map, viz_flow
     
@@ -742,7 +734,7 @@ with gr.Blocks() as demo:
         transparent_layer_pil = Image.fromarray(transparent_layer.astype(np.uint8))
         motion_map = Image.alpha_composite(transparent_background, transparent_layer_pil)
 
-        viz_flow = DragNUWA_net.get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
+        viz_flow = get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
 
         return motion_brush_mask, transparent_layer, motion_map, viz_flow
 
@@ -778,7 +770,7 @@ with gr.Blocks() as demo:
         transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8))
         trajectory_map = Image.alpha_composite(transparent_background, transparent_layer)
 
-        viz_flow = DragNUWA_net.get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
+        viz_flow = get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
 
         return tracking_points, trajectory_map, viz_flow
 
@@ -833,6 +825,6 @@ with gr.Blocks() as demo:
 
     input_image_mask.select(add_motion_brushes, [motion_brush_points, motion_brush_mask, motion_brush_viz, first_frame_path, brush_radius, tracking_points], [motion_brush_mask, motion_brush_viz, input_image_mask, viz_flow])
 
-    run_button.click(DragNUWA_net.run, [first_frame_path, tracking_points, inference_batch_size, motion_brush_mask, motion_brush_viz, ctrl_scale], [hint_image, output_video, output_flow, output_video_mp4, output_flow_mp4])
+    run_button.click(run, [first_frame_path, tracking_points, inference_batch_size, motion_brush_mask, motion_brush_viz, ctrl_scale], [hint_image, output_video, output_flow, output_video_mp4, output_flow_mp4])
 
     demo.launch()

oldapp.py

@@ -0,0 +1,838 @@
+import gradio as gr
+import spaces
+import numpy as np
+import cv2
+import os
+from PIL import Image, ImageFilter
+import uuid
+from scipy.interpolate import interp1d, PchipInterpolator
+import torchvision
+# from utils import *
+import time
+from tqdm import tqdm
+import imageio
+
+import torch
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms as transforms
+from einops import rearrange, repeat
+
+from packaging import version
+
+from accelerate.utils import set_seed
+from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
+
+from diffusers import AutoencoderKLTemporalDecoder, EulerDiscreteScheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+
+from utils.flow_viz import flow_to_image
+from utils.utils import split_filename, image2arr, image2pil, ensure_dirname
+
+
+output_dir_video = "./outputs/videos"
+output_dir_frame = "./outputs/frames"
+
+
+ensure_dirname(output_dir_video)
+ensure_dirname(output_dir_frame)
+
+# os.system('nvcc -V')
+
+
+def divide_points_afterinterpolate(resized_all_points, motion_brush_mask):
+    k = resized_all_points.shape[0]
+    starts = resized_all_points[:, 0]  # [K, 2]
+
+    in_masks = []
+    out_masks = []
+
+    for i in range(k):
+        x, y = int(starts[i][1]), int(starts[i][0])
+        if motion_brush_mask[x][y] == 255:
+            in_masks.append(resized_all_points[i])
+        else:
+            out_masks.append(resized_all_points[i])
+    
+    in_masks = np.array(in_masks)
+    out_masks = np.array(out_masks)
+
+    return in_masks, out_masks
+    
+
+def get_sparseflow_and_mask_forward(
+        resized_all_points, 
+        n_steps, H, W, 
+        is_backward_flow=False
+    ):
+
+    K = resized_all_points.shape[0]
+
+    starts = resized_all_points[:, 0]  # [K, 2]
+
+    interpolated_ends = resized_all_points[:, 1:]
+
+    s_flow = np.zeros((K, n_steps, H, W, 2))
+    mask = np.zeros((K, n_steps, H, W))
+
+    for k in range(K):
+        for i in range(n_steps):
+            start, end = starts[k], interpolated_ends[k][i]
+            flow = np.int64(end - start) * (-1 if is_backward_flow is True else 1)
+            s_flow[k][i][int(start[1]), int(start[0])] = flow
+            mask[k][i][int(start[1]), int(start[0])] = 1
+
+    s_flow = np.sum(s_flow, axis=0)
+    mask = np.sum(mask, axis=0)
+
+    return s_flow, mask
+
+
+def init_models(pretrained_model_name_or_path, resume_from_checkpoint, weight_dtype, device='cuda', enable_xformers_memory_efficient_attention=False, allow_tf32=False):
+
+    from models.unet_spatio_temporal_condition_controlnet import UNetSpatioTemporalConditionControlNetModel
+    from pipeline.pipeline import FlowControlNetPipeline
+    from models.svdxt_featureflow_forward_controlnet_s2d_fixcmp_norefine import FlowControlNet, CMP_demo
+
+    print('start loading models...')
+    # Load scheduler, tokenizer and models.
+    image_encoder = CLIPVisionModelWithProjection.from_pretrained(
+        pretrained_model_name_or_path, subfolder="image_encoder", revision=None, variant="fp16"
+    )
+    vae = AutoencoderKLTemporalDecoder.from_pretrained(
+        pretrained_model_name_or_path, subfolder="vae", revision=None, variant="fp16")
+    unet = UNetSpatioTemporalConditionControlNetModel.from_pretrained(
+        pretrained_model_name_or_path,
+        subfolder="unet",
+        low_cpu_mem_usage=True,
+        variant="fp16",
+    )
+
+    controlnet = FlowControlNet.from_pretrained(resume_from_checkpoint)
+
+    cmp = CMP_demo(
+        './models/cmp/experiments/semiauto_annot/resnet50_vip+mpii_liteflow/config.yaml',
+        42000
+    ).to(device)
+    cmp.requires_grad_(False)
+    
+    # Freeze vae and image_encoder
+    vae.requires_grad_(False)
+    image_encoder.requires_grad_(False)
+    unet.requires_grad_(False)
+    controlnet.requires_grad_(False)
+
+    # Move image_encoder and vae to gpu and cast to weight_dtype
+    image_encoder.to(device, dtype=weight_dtype)
+    vae.to(device, dtype=weight_dtype)
+    unet.to(device, dtype=weight_dtype)
+    controlnet.to(device, dtype=weight_dtype)
+
+    if enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = version.parse(xformers.__version__)
+            if xformers_version == version.parse("0.0.16"):
+                print(
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                )
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError(
+                "xformers is not available. Make sure it is installed correctly")
+
+    if allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+    
+    pipeline = FlowControlNetPipeline.from_pretrained(
+        pretrained_model_name_or_path,
+        unet=unet,
+        controlnet=controlnet,
+        image_encoder=image_encoder,
+        vae=vae,
+        torch_dtype=weight_dtype,
+    )
+    pipeline = pipeline.to(device)
+
+    print('models loaded.')
+
+    return pipeline, cmp
+
+
+def interpolate_trajectory(points, n_points):
+    x = [point[0] for point in points]
+    y = [point[1] for point in points]
+
+    t = np.linspace(0, 1, len(points))
+
+    fx = PchipInterpolator(t, x)
+    fy = PchipInterpolator(t, y)
+
+    new_t = np.linspace(0, 1, n_points)
+
+    new_x = fx(new_t)
+    new_y = fy(new_t)
+    new_points = list(zip(new_x, new_y))
+
+    return new_points
+
+
+def visualize_drag_v2(background_image_path, splited_tracks, width, height):
+    trajectory_maps = []
+    
+    background_image = Image.open(background_image_path).convert('RGBA')
+    background_image = background_image.resize((width, height))
+    w, h = background_image.size
+    transparent_background = np.array(background_image)
+    transparent_background[:, :, -1] = 128
+    transparent_background = Image.fromarray(transparent_background)
+
+    # Create a transparent layer with the same size as the background image
+    transparent_layer = np.zeros((h, w, 4))
+    for splited_track in splited_tracks:
+        if len(splited_track) > 1:
+            splited_track = interpolate_trajectory(splited_track, 16)
+            splited_track = splited_track[:16]
+            for i in range(len(splited_track)-1):
+                start_point = (int(splited_track[i][0]), int(splited_track[i][1]))
+                end_point = (int(splited_track[i+1][0]), int(splited_track[i+1][1]))
+                vx = end_point[0] - start_point[0]
+                vy = end_point[1] - start_point[1]
+                arrow_length = np.sqrt(vx**2 + vy**2)
+                if i == len(splited_track)-2:
+                    cv2.arrowedLine(transparent_layer, start_point, end_point, (255, 0, 0, 192), 2, tipLength=8 / arrow_length)
+                else:
+                    cv2.line(transparent_layer, start_point, end_point, (255, 0, 0, 192), 2)
+        else:
+            cv2.circle(transparent_layer, (int(splited_track[0][0]), int(splited_track[0][1])), 2, (255, 0, 0, 192), -1)
+
+    transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8))
+    trajectory_map = Image.alpha_composite(transparent_background, transparent_layer)
+    trajectory_maps.append(trajectory_map)
+    return trajectory_maps, transparent_layer
+
+
+class Drag:
+    @spaces.GPU(duration=200)
+    def __init__(self, height, width):
+
+        svd_ckpt = "ckpts/stable-video-diffusion-img2vid-xt-1-1"
+        mofa_ckpt = "ckpts/controlnet"
+
+        self.pipeline, self.cmp = init_models(
+            svd_ckpt, 
+            mofa_ckpt, 
+            weight_dtype=torch.float16, 
+            device='cuda'
+        )
+
+        self.height = height
+        self.width = width
+
+    def get_cmp_flow(self, frames, sparse_optical_flow, mask, brush_mask=None):
+
+        '''
+            frames: [b, 13, 3, 384, 384] (0, 1) tensor
+            sparse_optical_flow: [b, 13, 2, 384, 384] (-384, 384) tensor
+            mask: [b, 13, 2, 384, 384] {0, 1} tensor
+        '''
+
+        b, t, c, h, w = frames.shape
+        assert h == 384 and w == 384
+        frames = frames.flatten(0, 1)  # [b*13, 3, 256, 256]
+        sparse_optical_flow = sparse_optical_flow.flatten(0, 1)  # [b*13, 2, 256, 256]
+        mask = mask.flatten(0, 1)  # [b*13, 2, 256, 256]
+        cmp_flow = self.cmp.run(frames, sparse_optical_flow, mask)  # [b*13, 2, 256, 256]
+
+        if brush_mask is not None:
+            brush_mask = torch.from_numpy(brush_mask) / 255.
+            brush_mask = brush_mask.to(cmp_flow.device, dtype=cmp_flow.dtype)
+            brush_mask = brush_mask.unsqueeze(0).unsqueeze(0)
+            cmp_flow = cmp_flow * brush_mask
+
+        cmp_flow = cmp_flow.reshape(b, t, 2, h, w)
+        return cmp_flow
+    
+
+    def get_flow(self, pixel_values_384, sparse_optical_flow_384, mask_384, motion_brush_mask=None):
+
+        fb, fl, fc, _, _ = pixel_values_384.shape
+
+        controlnet_flow = self.get_cmp_flow(
+            pixel_values_384[:, 0:1, :, :, :].repeat(1, fl, 1, 1, 1), 
+            sparse_optical_flow_384, 
+            mask_384, motion_brush_mask
+        )
+
+        if self.height != 384 or self.width != 384:
+            scales = [self.height / 384, self.width / 384]
+            controlnet_flow = F.interpolate(controlnet_flow.flatten(0, 1), (self.height, self.width), mode='nearest').reshape(fb, fl, 2, self.height, self.width)
+            controlnet_flow[:, :, 0] *= scales[1]
+            controlnet_flow[:, :, 1] *= scales[0]
+        
+        return controlnet_flow
+    
+
+    @torch.no_grad()
+    def forward_sample(self, input_drag_384_inmask, input_drag_384_outmask, input_first_frame, input_mask_384_inmask, input_mask_384_outmask, in_mask_flag, out_mask_flag, motion_brush_mask=None, ctrl_scale=1., outputs=dict()):
+        '''
+            input_drag: [1, 13, 320, 576, 2]
+            input_drag_384: [1, 13, 384, 384, 2]
+            input_first_frame: [1, 3, 320, 576]
+        '''
+
+        seed = 42
+        num_frames = 25
+        
+        set_seed(seed)
+
+        input_first_frame_384 = F.interpolate(input_first_frame, (384, 384))
+        input_first_frame_384 = input_first_frame_384.repeat(num_frames - 1, 1, 1, 1).unsqueeze(0)
+        input_first_frame_pil = Image.fromarray(np.uint8(input_first_frame[0].cpu().permute(1, 2, 0)*255))
+        height, width = input_first_frame.shape[-2:]
+
+        input_drag_384_inmask = input_drag_384_inmask.permute(0, 1, 4, 2, 3)  # [1, 13, 2, 384, 384]
+        mask_384_inmask = input_mask_384_inmask.unsqueeze(2).repeat(1, 1, 2, 1, 1)  # [1, 13, 2, 384, 384]
+        input_drag_384_outmask = input_drag_384_outmask.permute(0, 1, 4, 2, 3)  # [1, 13, 2, 384, 384]
+        mask_384_outmask = input_mask_384_outmask.unsqueeze(2).repeat(1, 1, 2, 1, 1)  # [1, 13, 2, 384, 384]
+        
+        print('start diffusion process...')
+
+        input_drag_384_inmask = input_drag_384_inmask.to('cuda', dtype=torch.float16)
+        mask_384_inmask = mask_384_inmask.to('cuda', dtype=torch.float16)
+        input_drag_384_outmask = input_drag_384_outmask.to('cuda', dtype=torch.float16)
+        mask_384_outmask = mask_384_outmask.to('cuda', dtype=torch.float16)
+
+        input_first_frame_384 = input_first_frame_384.to('cuda', dtype=torch.float16)
+
+        if in_mask_flag:
+            flow_inmask = self.get_flow(
+                input_first_frame_384, 
+                input_drag_384_inmask, mask_384_inmask, motion_brush_mask
+            )
+        else:
+            fb, fl = mask_384_inmask.shape[:2]
+            flow_inmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+
+        if out_mask_flag:
+            flow_outmask = self.get_flow(
+                input_first_frame_384, 
+                input_drag_384_outmask, mask_384_outmask
+            )
+        else:
+            fb, fl = mask_384_outmask.shape[:2]
+            flow_outmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+        
+        inmask_no_zero = (flow_inmask != 0).all(dim=2)
+        inmask_no_zero = inmask_no_zero.unsqueeze(2).expand_as(flow_inmask)
+
+        controlnet_flow = torch.where(inmask_no_zero, flow_inmask, flow_outmask)
+
+        val_output = self.pipeline(
+            input_first_frame_pil, 
+            input_first_frame_pil,
+            controlnet_flow, 
+            height=height,
+            width=width,
+            num_frames=num_frames,
+            decode_chunk_size=8,
+            motion_bucket_id=127,
+            fps=7,
+            noise_aug_strength=0.02,
+            controlnet_cond_scale=ctrl_scale, 
+        )
+
+        video_frames, estimated_flow = val_output.frames[0], val_output.controlnet_flow
+
+        for i in range(num_frames):
+            img = video_frames[i]
+            video_frames[i] = np.array(img)
+        video_frames = torch.from_numpy(np.array(video_frames)).cuda().permute(0, 3, 1, 2).unsqueeze(0) / 255.
+
+        print(video_frames.shape)
+
+        viz_esti_flows = []
+        for i in range(estimated_flow.shape[1]):
+            temp_flow = estimated_flow[0][i].permute(1, 2, 0)
+            viz_esti_flows.append(flow_to_image(temp_flow))
+        viz_esti_flows = [np.uint8(np.ones_like(viz_esti_flows[-1]) * 255)] + viz_esti_flows
+        viz_esti_flows = np.stack(viz_esti_flows)  # [t-1, h, w, c]
+
+        total_nps = viz_esti_flows
+
+        outputs['logits_imgs'] = video_frames
+        outputs['flows'] = torch.from_numpy(total_nps).cuda().permute(0, 3, 1, 2).unsqueeze(0) / 255.
+
+        return outputs
+    
+    @spaces.GPU
+    @torch.no_grad()
+    def get_cmp_flow_from_tracking_points(self, tracking_points, motion_brush_mask, first_frame_path):
+
+        original_width, original_height = self.width, self.height
+
+        input_all_points = tracking_points.constructor_args['value']
+
+        if len(input_all_points) == 0 or len(input_all_points[-1]) == 1:
+            return np.uint8(np.ones((original_width, original_height, 3))*255)
+        
+        resized_all_points = [tuple([tuple([int(e1[0]*self.width/original_width), int(e1[1]*self.height/original_height)]) for e1 in e]) for e in input_all_points]
+        resized_all_points_384 = [tuple([tuple([int(e1[0]*384/original_width), int(e1[1]*384/original_height)]) for e1 in e]) for e in input_all_points]
+
+        new_resized_all_points = []
+        new_resized_all_points_384 = []
+        for tnum in range(len(resized_all_points)):
+            new_resized_all_points.append(interpolate_trajectory(input_all_points[tnum], 25))
+            new_resized_all_points_384.append(interpolate_trajectory(resized_all_points_384[tnum], 25))
+
+        resized_all_points = np.array(new_resized_all_points)
+        resized_all_points_384 = np.array(new_resized_all_points_384)
+
+        motion_brush_mask_384 = cv2.resize(motion_brush_mask, (384, 384), cv2.INTER_NEAREST)
+
+        resized_all_points_384_inmask, resized_all_points_384_outmask = \
+            divide_points_afterinterpolate(resized_all_points_384, motion_brush_mask_384)
+
+        in_mask_flag = False
+        out_mask_flag = False
+        
+        if resized_all_points_384_inmask.shape[0] != 0:
+            in_mask_flag = True
+            input_drag_384_inmask, input_mask_384_inmask = \
+                get_sparseflow_and_mask_forward(
+                    resized_all_points_384_inmask, 
+                    25 - 1, 384, 384
+                )
+        else:
+            input_drag_384_inmask, input_mask_384_inmask = \
+                np.zeros((25 - 1, 384, 384, 2)), \
+                    np.zeros((25 - 1, 384, 384))
+        
+        if resized_all_points_384_outmask.shape[0] != 0:
+            out_mask_flag = True
+            input_drag_384_outmask, input_mask_384_outmask = \
+                get_sparseflow_and_mask_forward(
+                    resized_all_points_384_outmask, 
+                    25 - 1, 384, 384
+                )
+        else:
+            input_drag_384_outmask, input_mask_384_outmask = \
+                np.zeros((25 - 1, 384, 384, 2)), \
+                    np.zeros((25 - 1, 384, 384))
+
+        input_drag_384_inmask = torch.from_numpy(input_drag_384_inmask).unsqueeze(0).to('cuda')  # [1, 13, h, w, 2]
+        input_mask_384_inmask = torch.from_numpy(input_mask_384_inmask).unsqueeze(0).to('cuda')  # [1, 13, h, w]
+        input_drag_384_outmask = torch.from_numpy(input_drag_384_outmask).unsqueeze(0).to('cuda')  # [1, 13, h, w, 2]
+        input_mask_384_outmask = torch.from_numpy(input_mask_384_outmask).unsqueeze(0).to('cuda')  # [1, 13, h, w]
+
+        first_frames_transform = transforms.Compose([
+            lambda x: Image.fromarray(x),
+            transforms.ToTensor(),
+        ])
+
+        input_first_frame = image2arr(first_frame_path)
+        input_first_frame = repeat(first_frames_transform(input_first_frame), 'c h w -> b c h w', b=1).to('cuda')
+
+        seed = 42
+        num_frames = 25
+        
+        set_seed(seed)
+
+        input_first_frame_384 = F.interpolate(input_first_frame, (384, 384))
+        input_first_frame_384 = input_first_frame_384.repeat(num_frames - 1, 1, 1, 1).unsqueeze(0)
+
+        input_drag_384_inmask = input_drag_384_inmask.permute(0, 1, 4, 2, 3)  # [1, 13, 2, 384, 384]
+        mask_384_inmask = input_mask_384_inmask.unsqueeze(2).repeat(1, 1, 2, 1, 1)  # [1, 13, 2, 384, 384]
+        input_drag_384_outmask = input_drag_384_outmask.permute(0, 1, 4, 2, 3)  # [1, 13, 2, 384, 384]
+        mask_384_outmask = input_mask_384_outmask.unsqueeze(2).repeat(1, 1, 2, 1, 1)  # [1, 13, 2, 384, 384]
+
+        input_drag_384_inmask = input_drag_384_inmask.to('cuda', dtype=torch.float16)
+        mask_384_inmask = mask_384_inmask.to('cuda', dtype=torch.float16)
+        input_drag_384_outmask = input_drag_384_outmask.to('cuda', dtype=torch.float16)
+        mask_384_outmask = mask_384_outmask.to('cuda', dtype=torch.float16)
+
+        input_first_frame_384 = input_first_frame_384.to('cuda', dtype=torch.float16)
+
+        if in_mask_flag:
+            flow_inmask = self.get_flow(
+                input_first_frame_384, 
+                input_drag_384_inmask, mask_384_inmask, motion_brush_mask_384
+            )
+        else:
+            fb, fl = mask_384_inmask.shape[:2]
+            flow_inmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+
+        if out_mask_flag:
+            flow_outmask = self.get_flow(
+                input_first_frame_384, 
+                input_drag_384_outmask, mask_384_outmask
+            )
+        else:
+            fb, fl = mask_384_outmask.shape[:2]
+            flow_outmask = torch.zeros(fb, fl, 2, self.height, self.width).to('cuda', dtype=torch.float16)
+        
+        inmask_no_zero = (flow_inmask != 0).all(dim=2)
+        inmask_no_zero = inmask_no_zero.unsqueeze(2).expand_as(flow_inmask)
+
+        controlnet_flow = torch.where(inmask_no_zero, flow_inmask, flow_outmask)
+
+        controlnet_flow = controlnet_flow[0, -1].permute(1, 2, 0)
+        viz_esti_flows = flow_to_image(controlnet_flow)  # [h, w, c]
+
+        return viz_esti_flows
+    
+    @spaces.GPU(duration=200)
+    def run(self, first_frame_path, tracking_points, inference_batch_size, motion_brush_mask, motion_brush_viz, ctrl_scale):
+        
+        original_width, original_height = self.width, self.height
+
+        input_all_points = tracking_points.constructor_args['value']
+        resized_all_points = [tuple([tuple([int(e1[0]*self.width/original_width), int(e1[1]*self.height/original_height)]) for e1 in e]) for e in input_all_points]
+        resized_all_points_384 = [tuple([tuple([int(e1[0]*384/original_width), int(e1[1]*384/original_height)]) for e1 in e]) for e in input_all_points]
+
+        new_resized_all_points = []
+        new_resized_all_points_384 = []
+        for tnum in range(len(resized_all_points)):
+            new_resized_all_points.append(interpolate_trajectory(input_all_points[tnum], 25))
+            new_resized_all_points_384.append(interpolate_trajectory(resized_all_points_384[tnum], 25))
+
+        resized_all_points = np.array(new_resized_all_points)
+        resized_all_points_384 = np.array(new_resized_all_points_384)
+
+        motion_brush_mask_384 = cv2.resize(motion_brush_mask, (384, 384), cv2.INTER_NEAREST)
+
+        resized_all_points_384_inmask, resized_all_points_384_outmask = \
+            divide_points_afterinterpolate(resized_all_points_384, motion_brush_mask_384)
+
+        in_mask_flag = False
+        out_mask_flag = False
+        
+        if resized_all_points_384_inmask.shape[0] != 0:
+            in_mask_flag = True
+            input_drag_384_inmask, input_mask_384_inmask = \
+                get_sparseflow_and_mask_forward(
+                    resized_all_points_384_inmask, 
+                    25 - 1, 384, 384
+                )
+        else:
+            input_drag_384_inmask, input_mask_384_inmask = \
+                np.zeros((25 - 1, 384, 384, 2)), \
+                    np.zeros((25 - 1, 384, 384))
+        
+        if resized_all_points_384_outmask.shape[0] != 0:
+            out_mask_flag = True
+            input_drag_384_outmask, input_mask_384_outmask = \
+                get_sparseflow_and_mask_forward(
+                    resized_all_points_384_outmask, 
+                    25 - 1, 384, 384
+                )
+        else:
+            input_drag_384_outmask, input_mask_384_outmask = \
+                np.zeros((25 - 1, 384, 384, 2)), \
+                    np.zeros((25 - 1, 384, 384))
+
+        input_drag_384_inmask = torch.from_numpy(input_drag_384_inmask).unsqueeze(0)  # [1, 13, h, w, 2]
+        input_mask_384_inmask = torch.from_numpy(input_mask_384_inmask).unsqueeze(0)  # [1, 13, h, w]
+        input_drag_384_outmask = torch.from_numpy(input_drag_384_outmask).unsqueeze(0)  # [1, 13, h, w, 2]
+        input_mask_384_outmask = torch.from_numpy(input_mask_384_outmask).unsqueeze(0)  # [1, 13, h, w]
+
+        dir, base, ext = split_filename(first_frame_path)
+        id = base.split('_')[0]
+        
+        image_pil = image2pil(first_frame_path)
+        image_pil = image_pil.resize((self.width, self.height), Image.BILINEAR).convert('RGB')
+        
+        visualized_drag, _ = visualize_drag_v2(first_frame_path, resized_all_points, self.width, self.height)
+
+        motion_brush_viz_pil = Image.fromarray(motion_brush_viz.astype(np.uint8)).convert('RGBA')
+        visualized_drag = visualized_drag[0].convert('RGBA')
+        visualized_drag_brush = Image.alpha_composite(motion_brush_viz_pil, visualized_drag)
+        
+        first_frames_transform = transforms.Compose([
+                        lambda x: Image.fromarray(x),
+                        transforms.ToTensor(),
+                    ])
+        
+        outputs = None
+        ouput_video_list = []
+        ouput_flow_list = []
+        num_inference = 1
+        for i in tqdm(range(num_inference)):
+            if not outputs:
+                first_frames = image2arr(first_frame_path)
+                first_frames = repeat(first_frames_transform(first_frames), 'c h w -> b c h w', b=inference_batch_size).to('cuda')
+            else:
+                first_frames = outputs['logits_imgs'][:, -1]
+            
+
+            outputs = self.forward_sample(
+                input_drag_384_inmask.to('cuda'), 
+                input_drag_384_outmask.to('cuda'), 
+                first_frames.to('cuda'),
+                input_mask_384_inmask.to('cuda'),
+                input_mask_384_outmask.to('cuda'),
+                in_mask_flag,
+                out_mask_flag, 
+                motion_brush_mask_384,
+                ctrl_scale)
+
+            ouput_video_list.append(outputs['logits_imgs'])
+            ouput_flow_list.append(outputs['flows'])
+
+        hint_path = os.path.join(output_dir_video, str(id), f'{id}_hint.png')
+        visualized_drag_brush.save(hint_path)
+        
+        for i in range(inference_batch_size):
+            output_tensor = [ouput_video_list[0][i]]
+            flow_tensor = [ouput_flow_list[0][i]]
+            output_tensor = torch.cat(output_tensor, dim=0)
+            flow_tensor = torch.cat(flow_tensor, dim=0)
+            
+            outputs_path = os.path.join(output_dir_video, str(id), f's{ctrl_scale}', f'{id}_output.gif')
+            flows_path = os.path.join(output_dir_video, str(id), f's{ctrl_scale}', f'{id}_flow.gif')
+
+            outputs_mp4_path = os.path.join(output_dir_video, str(id), f's{ctrl_scale}', f'{id}_output.mp4')
+            flows_mp4_path = os.path.join(output_dir_video, str(id), f's{ctrl_scale}', f'{id}_flow.mp4')
+
+            outputs_frames_path = os.path.join(output_dir_frame, str(id), f's{ctrl_scale}', f'{id}_output')
+            flows_frames_path = os.path.join(output_dir_frame, str(id), f's{ctrl_scale}', f'{id}_flow')
+
+            os.makedirs(os.path.join(output_dir_video, str(id), f's{ctrl_scale}'), exist_ok=True)
+            os.makedirs(os.path.join(outputs_frames_path), exist_ok=True)
+            os.makedirs(os.path.join(flows_frames_path), exist_ok=True)
+
+            print(output_tensor.shape)
+
+            output_RGB = output_tensor.permute(0, 2, 3, 1).mul(255).cpu().numpy()
+            flow_RGB = flow_tensor.permute(0, 2, 3, 1).mul(255).cpu().numpy()
+
+            torchvision.io.write_video(
+                outputs_mp4_path, 
+                output_RGB, 
+                fps=20, video_codec='h264', options={'crf': '10'}
+            )
+
+            torchvision.io.write_video(
+                flows_mp4_path, 
+                flow_RGB, 
+                fps=20, video_codec='h264', options={'crf': '10'}
+            )
+
+            imageio.mimsave(outputs_path, np.uint8(output_RGB), fps=20, loop=0)
+
+            imageio.mimsave(flows_path, np.uint8(flow_RGB), fps=20, loop=0)
+
+            for f in range(output_RGB.shape[0]):
+                Image.fromarray(np.uint8(output_RGB[f])).save(os.path.join(outputs_frames_path, f'{str(f).zfill(3)}.png'))
+                Image.fromarray(np.uint8(flow_RGB[f])).save(os.path.join(flows_frames_path, f'{str(f).zfill(3)}.png'))
+
+        return hint_path, outputs_path, flows_path, outputs_mp4_path, flows_mp4_path
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""<h1 align="center">MOFA-Video</h1><br>""")
+
+    gr.Markdown("""Official Gradio Demo for <a href='https://myniuuu.github.io/MOFA_Video'><b>MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model</b></a>.<br>""")
+
+    gr.Markdown(
+        """
+        During the inference, kindly follow these instructions:
+        <br>
+        1. Use the "Upload Image" button to upload an image. Avoid dragging the image directly into the window. <br>
+        2. Proceed to draw trajectories: <br>
+            2.1. Click "Add Trajectory" first, then select points on the "Add Trajectory Here" image. The first click sets the starting point. Click multiple points to create a non-linear trajectory. To add a new trajectory, click "Add Trajectory" again and select points on the image. Avoid clicking the "Add Trajectory" button multiple times without clicking points in the image to add the trajectory, as this can lead to errors. <br>
+            2.2. After adding each trajectory, an optical flow image will be displayed automatically. Use it as a reference to adjust the trajectory for desired effects (e.g., area, intensity). <br>
+            2.3. To delete the latest trajectory, click "Delete Last Trajectory." <br>
+            2.4. Choose the Control Scale in the bar. This determines the control intensity. Setting it to 0 means no control (pure generation result of SVD itself), while setting it to 1 results in the strongest control (which will not lead to good results in most cases because of twisting artifacts). A preset value of 0.6 is recommended for most cases. <br>
+            2.5. To use the motion brush for restraining the control area of the trajectory, click to add masks on the "Add Motion Brush Here" image. The motion brush restricts the optical flow area derived from the trajectory whose starting point is within the motion brush. The displayed optical flow image will change correspondingly. Adjust the motion brush radius using the "Motion Brush Radius" bar. <br>
+        3. Click the "Run" button to animate the image according to the path. <br>
+        """
+    )
+
+    target_size = 512
+    DragNUWA_net = Drag(target_size, target_size)
+    first_frame_path = gr.State()
+    tracking_points = gr.State([])
+    motion_brush_points = gr.State([])
+    motion_brush_mask = gr.State()
+    motion_brush_viz = gr.State()
+    inference_batch_size = gr.State(1)
+
+    def preprocess_image(image):
+
+        image_pil = image2pil(image.name)
+        raw_w, raw_h = image_pil.size
+
+        max_edge = min(raw_w, raw_h)
+        resize_ratio = target_size / max_edge
+
+        image_pil = image_pil.resize((round(raw_w * resize_ratio), round(raw_h * resize_ratio)), Image.BILINEAR)
+
+        new_w, new_h = image_pil.size
+        crop_w = new_w - (new_w % 64)
+        crop_h = new_h - (new_h % 64)
+
+        image_pil = transforms.CenterCrop((crop_h, crop_w))(image_pil.convert('RGB'))
+
+        DragNUWA_net.width = crop_w
+        DragNUWA_net.height = crop_h
+
+        id = str(time.time()).split('.')[0]
+        os.makedirs(os.path.join(output_dir_video, str(id)), exist_ok=True)
+        os.makedirs(os.path.join(output_dir_frame, str(id)), exist_ok=True)
+
+        first_frame_path = os.path.join(output_dir_video, str(id), f"{id}_input.png")
+        image_pil.save(first_frame_path)
+
+        return first_frame_path, first_frame_path, first_frame_path, gr.State([]), gr.State([]), np.zeros((crop_h, crop_w)), np.zeros((crop_h, crop_w, 4))
+
+    def add_drag(tracking_points):
+        if len(tracking_points.constructor_args['value']) != 0 and tracking_points.constructor_args['value'][-1] == []:
+            return tracking_points
+        tracking_points.constructor_args['value'].append([])
+        return tracking_points
+
+    def add_mask(motion_brush_points):
+        motion_brush_points.constructor_args['value'].append([])
+        return motion_brush_points
+    
+    def delete_last_drag(tracking_points, first_frame_path, motion_brush_mask):
+        if len(tracking_points.constructor_args['value']) > 0:
+            tracking_points.constructor_args['value'].pop()
+        transparent_background = Image.open(first_frame_path).convert('RGBA')
+        w, h = transparent_background.size
+        transparent_layer = np.zeros((h, w, 4))
+        for track in tracking_points.constructor_args['value']:
+            if len(track) > 1:
+                for i in range(len(track)-1):
+                    start_point = track[i]
+                    end_point = track[i+1]
+                    vx = end_point[0] - start_point[0]
+                    vy = end_point[1] - start_point[1]
+                    arrow_length = np.sqrt(vx**2 + vy**2)
+                    if i == len(track)-2:
+                        cv2.arrowedLine(transparent_layer, tuple(start_point), tuple(end_point), (255, 0, 0, 255), 2, tipLength=8 / arrow_length)
+                    else:
+                        cv2.line(transparent_layer, tuple(start_point), tuple(end_point), (255, 0, 0, 255), 2,)
+            else:
+                cv2.circle(transparent_layer, tuple(track[0]), 5, (255, 0, 0, 255), -1)
+
+        transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8))
+        trajectory_map = Image.alpha_composite(transparent_background, transparent_layer)
+
+        viz_flow = DragNUWA_net.get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
+
+        return tracking_points, trajectory_map, viz_flow
+    
+    def add_motion_brushes(motion_brush_points, motion_brush_mask, transparent_layer, first_frame_path, radius, tracking_points, evt: gr.SelectData):
+        
+        transparent_background = Image.open(first_frame_path).convert('RGBA')
+        w, h = transparent_background.size
+
+        motion_points = motion_brush_points.constructor_args['value']
+        motion_points.append(evt.index)
+
+        x, y = evt.index
+
+        cv2.circle(motion_brush_mask, (x, y), radius, 255, -1)
+        cv2.circle(transparent_layer, (x, y), radius, (0, 0, 255, 255), -1)
+        
+        transparent_layer_pil = Image.fromarray(transparent_layer.astype(np.uint8))
+        motion_map = Image.alpha_composite(transparent_background, transparent_layer_pil)
+
+        viz_flow = DragNUWA_net.get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
+
+        return motion_brush_mask, transparent_layer, motion_map, viz_flow
+
+    def add_tracking_points(tracking_points, first_frame_path, motion_brush_mask, evt: gr.SelectData):
+
+        print(f"You selected {evt.value} at {evt.index} from {evt.target}")
+        
+        if len(tracking_points.constructor_args['value']) == 0:
+            tracking_points.constructor_args['value'].append([])
+            
+        tracking_points.constructor_args['value'][-1].append(evt.index)
+
+        # print(tracking_points.constructor_args['value'])
+
+        transparent_background = Image.open(first_frame_path).convert('RGBA')
+        w, h = transparent_background.size
+        transparent_layer = np.zeros((h, w, 4))
+        for track in tracking_points.constructor_args['value']:
+            if len(track) > 1:
+                for i in range(len(track)-1):
+                    start_point = track[i]
+                    end_point = track[i+1]
+                    vx = end_point[0] - start_point[0]
+                    vy = end_point[1] - start_point[1]
+                    arrow_length = np.sqrt(vx**2 + vy**2)
+                    if i == len(track)-2:
+                        cv2.arrowedLine(transparent_layer, tuple(start_point), tuple(end_point), (255, 0, 0, 255), 2, tipLength=8 / arrow_length)
+                    else:
+                        cv2.line(transparent_layer, tuple(start_point), tuple(end_point), (255, 0, 0, 255), 2,)
+            else:
+                cv2.circle(transparent_layer, tuple(track[0]), 3, (255, 0, 0, 255), -1)
+
+        transparent_layer = Image.fromarray(transparent_layer.astype(np.uint8))
+        trajectory_map = Image.alpha_composite(transparent_background, transparent_layer)
+
+        viz_flow = DragNUWA_net.get_cmp_flow_from_tracking_points(tracking_points, motion_brush_mask, first_frame_path)
+
+        return tracking_points, trajectory_map, viz_flow
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            image_upload_button = gr.UploadButton(label="Upload Image",file_types=["image"])
+            add_drag_button = gr.Button(value="Add Trajectory")
+            run_button = gr.Button(value="Run")
+            delete_last_drag_button = gr.Button(value="Delete Last Trajectory")
+            brush_radius = gr.Slider(label='Motion Brush Radius', 
+                                             minimum=1, 
+                                             maximum=100, 
+                                             step=1, 
+                                             value=10)
+            ctrl_scale = gr.Slider(label='Control Scale', 
+                                             minimum=0, 
+                                             maximum=1., 
+                                             step=0.01, 
+                                             value=0.6)
+
+        with gr.Column(scale=5):
+            input_image = gr.Image(label="Add Trajectory Here",
+                                interactive=True)
+        with gr.Column(scale=5):
+            input_image_mask = gr.Image(label="Add Motion Brush Here",
+                                interactive=True)
+             
+    with gr.Row():   
+        with gr.Column(scale=6):
+            viz_flow = gr.Image(label="Visualized Flow")
+        with gr.Column(scale=6):
+            hint_image = gr.Image(label="Visualized Hint Image")
+    with gr.Row():
+        with gr.Column(scale=6):
+            output_video = gr.Image(label="Output Video")
+        with gr.Column(scale=6):
+            output_flow = gr.Image(label="Output Flow")
+    
+    with gr.Row():
+        with gr.Column(scale=6):
+            output_video_mp4 = gr.Video(label="Output Video mp4")
+        with gr.Column(scale=6):
+            output_flow_mp4 = gr.Video(label="Output Flow mp4")
+    
+    image_upload_button.upload(preprocess_image, image_upload_button, [input_image, input_image_mask, first_frame_path, tracking_points, motion_brush_points, motion_brush_mask, motion_brush_viz])
+
+    add_drag_button.click(add_drag, tracking_points, tracking_points)
+
+    delete_last_drag_button.click(delete_last_drag, [tracking_points, first_frame_path, motion_brush_mask], [tracking_points, input_image, viz_flow])
+
+    input_image.select(add_tracking_points, [tracking_points, first_frame_path, motion_brush_mask], [tracking_points, input_image, viz_flow])
+
+    input_image_mask.select(add_motion_brushes, [motion_brush_points, motion_brush_mask, motion_brush_viz, first_frame_path, brush_radius, tracking_points], [motion_brush_mask, motion_brush_viz, input_image_mask, viz_flow])
+
+    run_button.click(DragNUWA_net.run, [first_frame_path, tracking_points, inference_batch_size, motion_brush_mask, motion_brush_viz, ctrl_scale], [hint_image, output_video, output_flow, output_video_mp4, output_flow_mp4])
+
+    demo.launch()