import spaces import datetime import uuid from PIL import Image import numpy as np import cv2 from scipy.interpolate import interp1d, PchipInterpolator from packaging import version import torch import torchvision import gradio as gr # from moviepy.editor import * from diffusers.utils.import_utils import is_xformers_available from diffusers.utils import load_image, export_to_video, export_to_gif import os import sys sys.path.insert(0, os.getcwd()) from models_diffusers.controlnet_svd import ControlNetSVDModel from models_diffusers.unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel from pipelines.pipeline_stable_video_diffusion_interp_control import StableVideoDiffusionInterpControlPipeline from gradio_demo.utils_drag import * import warnings print("gr file", gr.__file__) from huggingface_hub import hf_hub_download, snapshot_download os.makedirs("checkpoints", exist_ok=True) snapshot_download( "wwen1997/framer_512x320", local_dir="checkpoints/framer_512x320", token=os.environ["TOKEN"], ) def get_args(): import argparse parser = argparse.ArgumentParser() parser.add_argument("--min_guidance_scale", type=float, default=1.0) parser.add_argument("--max_guidance_scale", type=float, default=3.0) parser.add_argument("--middle_max_guidance", type=int, default=0, choices=[0, 1]) parser.add_argument("--with_control", type=int, default=1, choices=[0, 1]) parser.add_argument("--controlnet_cond_scale", type=float, default=1.0) parser.add_argument( "--dataset", type=str, default='videoswap', ) parser.add_argument( "--model", type=str, default="checkpoints/framer_512x320", help="Path to model.", ) parser.add_argument("--output_dir", type=str, default="gradio_demo/outputs", help="Path to the output video.") parser.add_argument("--seed", type=int, default=42, help="random seed.") parser.add_argument("--noise_aug", type=float, default=0.02) parser.add_argument("--num_frames", type=int, default=14) parser.add_argument("--frame_interval", type=int, default=2) parser.add_argument("--width", type=int, default=512) parser.add_argument("--height", type=int, default=320) parser.add_argument( "--num_workers", type=int, default=8, help=( "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." ), ) args = parser.parse_args() return args args = get_args() ensure_dirname(args.output_dir) color_list = [] for i in range(20): color = np.concatenate([np.random.random(4)*255], axis=0) color_list.append(color) 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 = interp1d(t, x, kind='cubic') # fy = interp1d(t, y, kind='cubic') 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 gen_gaussian_heatmap(imgSize=200): circle_img = np.zeros((imgSize, imgSize), np.float32) circle_mask = cv2.circle(circle_img, (imgSize//2, imgSize//2), imgSize//2, 1, -1) isotropicGrayscaleImage = np.zeros((imgSize, imgSize), np.float32) for i in range(imgSize): for j in range(imgSize): isotropicGrayscaleImage[i, j] = 1 / 2 / np.pi / (40 ** 2) * np.exp( -1 / 2 * ((i - imgSize / 2) ** 2 / (40 ** 2) + (j - imgSize / 2) ** 2 / (40 ** 2))) isotropicGrayscaleImage = isotropicGrayscaleImage * circle_mask isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)).astype(np.float32) isotropicGrayscaleImage = (isotropicGrayscaleImage / np.max(isotropicGrayscaleImage)*255).astype(np.uint8) return isotropicGrayscaleImage def get_vis_image( target_size=(512 , 512), points=None, side=20, num_frames=14, # original_size=(512 , 512), args="", first_frame=None, is_mask = False, model_id=None, ): # images = [] vis_images = [] heatmap = gen_gaussian_heatmap() trajectory_list = [] radius_list = [] for index, point in enumerate(points): trajectories = [[int(i[0]), int(i[1])] for i in point] trajectory_list.append(trajectories) radius = 20 radius_list.append(radius) if len(trajectory_list) == 0: vis_images = [Image.fromarray(np.zeros(target_size, np.uint8)) for _ in range(num_frames)] return vis_images for idxx, point in enumerate(trajectory_list[0]): new_img = np.zeros(target_size, np.uint8) vis_img = new_img.copy() # ids_embedding = torch.zeros((target_size[0], target_size[1], 320)) if idxx >= args.num_frames: break # for cc, (mask, trajectory, radius) in enumerate(zip(mask_list, trajectory_list, radius_list)): for cc, (trajectory, radius) in enumerate(zip(trajectory_list, radius_list)): center_coordinate = trajectory[idxx] trajectory_ = trajectory[:idxx] side = min(radius, 50) y1 = max(center_coordinate[1] - side,0) y2 = min(center_coordinate[1] + side, target_size[0] - 1) x1 = max(center_coordinate[0] - side, 0) x2 = min(center_coordinate[0] + side, target_size[1] - 1) if x2-x1>3 and y2-y1>3: need_map = cv2.resize(heatmap, (x2-x1, y2-y1)) new_img[y1:y2, x1:x2] = need_map.copy() if cc >= 0: vis_img[y1:y2,x1:x2] = need_map.copy() if len(trajectory_) == 1: vis_img[trajectory_[0][1], trajectory_[0][0]] = 255 else: for itt in range(len(trajectory_)-1): cv2.line(vis_img, (trajectory_[itt][0], trajectory_[itt][1]), (trajectory_[itt+1][0], trajectory_[itt+1][1]), (255, 255, 255), 3) img = new_img # Ensure all images are in RGB format if len(img.shape) == 2: # Grayscale image img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) vis_img = cv2.cvtColor(vis_img, cv2.COLOR_GRAY2RGB) elif len(img.shape) == 3 and img.shape[2] == 3: # Color image in BGR format img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) vis_img = cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB) # Convert the numpy array to a PIL image # pil_img = Image.fromarray(img) # images.append(pil_img) vis_images.append(Image.fromarray(vis_img)) return vis_images def frames_to_video(frames_folder, output_video_path, fps=7): frame_files = os.listdir(frames_folder) # sort the frame files by their names frame_files = sorted(frame_files, key=lambda x: int(x.split(".")[0])) video = [] for frame_file in frame_files: frame_path = os.path.join(frames_folder, frame_file) frame = torchvision.io.read_image(frame_path) video.append(frame) video = torch.stack(video) video = rearrange(video, 'T C H W -> T H W C') torchvision.io.write_video(output_video_path, video, fps=fps) def save_gifs_side_by_side( batch_output, validation_control_images, output_folder, target_size=(512 , 512), duration=200, point_tracks=None, ): flattened_batch_output = batch_output def create_gif(image_list, gif_path, duration=100): pil_images = [validate_and_convert_image(img, target_size=target_size) for img in image_list] pil_images = [img for img in pil_images if img is not None] if pil_images: pil_images[0].save(gif_path, save_all=True, append_images=pil_images[1:], loop=0, duration=duration) # also save all the pil_images tmp_folder = gif_path.replace(".gif", "") print(tmp_folder) ensure_dirname(tmp_folder) tmp_frame_list = [] for idx, pil_image in enumerate(pil_images): tmp_frame_path = os.path.join(tmp_folder, f"{idx}.png") pil_image.save(tmp_frame_path) tmp_frame_list.append(tmp_frame_path) # also save as mp4 output_video_path = gif_path.replace(".gif", ".mp4") frames_to_video(tmp_folder, output_video_path, fps=7) # Creating GIFs for each image list timestamp = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") gif_paths = [] for idx, image_list in enumerate([validation_control_images, flattened_batch_output]): gif_path = os.path.join(output_folder.replace("vis_gif.gif", ""), f"temp_{idx}_{timestamp}.gif") create_gif(image_list, gif_path) gif_paths.append(gif_path) # also save the point_tracks assert point_tracks is not None point_tracks_path = gif_path.replace(".gif", ".npy") np.save(point_tracks_path, point_tracks.cpu().numpy()) # Function to combine GIFs side by side def combine_gifs_side_by_side(gif_paths, output_path): print(gif_paths) gifs = [Image.open(gif) for gif in gif_paths] # Assuming all gifs have the same frame count and duration frames = [] for frame_idx in range(gifs[-1].n_frames): combined_frame = None for gif in gifs: if frame_idx >= gif.n_frames: gif.seek(gif.n_frames - 1) else: gif.seek(frame_idx) if combined_frame is None: combined_frame = gif.copy() else: combined_frame = get_concat_h(combined_frame, gif.copy(), gap=10) frames.append(combined_frame) if output_path.endswith(".mp4"): video = [torchvision.transforms.functional.pil_to_tensor(frame) for frame in frames] video = torch.stack(video) video = rearrange(video, 'T C H W -> T H W C') torchvision.io.write_video(output_path, video, fps=7) print(f"Saved video to {output_path}") else: frames[0].save(output_path, save_all=True, append_images=frames[1:], loop=0, duration=duration) # Helper function to concatenate images horizontally def get_concat_h(im1, im2, gap=10): # # img first, heatmap second # im1, im2 = im2, im1 dst = Image.new('RGB', (im1.width + im2.width + gap, max(im1.height, im2.height)), (255, 255, 255)) dst.paste(im1, (0, 0)) dst.paste(im2, (im1.width + gap, 0)) return dst # Helper function to concatenate images vertically def get_concat_v(im1, im2): dst = Image.new('RGB', (max(im1.width, im2.width), im1.height + im2.height)) dst.paste(im1, (0, 0)) dst.paste(im2, (0, im1.height)) return dst # Combine the GIFs into a single file combined_gif_path = output_folder combine_gifs_side_by_side(gif_paths, combined_gif_path) combined_gif_path_v = gif_path.replace(".gif", "_v.mp4") ensure_dirname(combined_gif_path_v.replace(".mp4", "")) combine_gifs_side_by_side(gif_paths, combined_gif_path_v) # # Clean up temporary GIFs # for gif_path in gif_paths: # os.remove(gif_path) return combined_gif_path # Define functions def validate_and_convert_image(image, target_size=(512 , 512)): if image is None: print("Encountered a None image") return None if isinstance(image, torch.Tensor): # Convert PyTorch tensor to PIL Image if image.ndim == 3 and image.shape[0] in [1, 3]: # Check for CxHxW format if image.shape[0] == 1: # Convert single-channel grayscale to RGB image = image.repeat(3, 1, 1) image = image.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy() image = Image.fromarray(image) else: print(f"Invalid image tensor shape: {image.shape}") return None elif isinstance(image, Image.Image): # Resize PIL Image image = image.resize(target_size) else: print("Image is not a PIL Image or a PyTorch tensor") return None return image class Drag: def __init__(self, device, args, height, width, model_length, dtype=torch.float16, use_sift=False): self.device = device self.dtype = dtype unet = UNetSpatioTemporalConditionModel.from_pretrained( os.path.join(args.model, "unet"), torch_dtype=torch.float16, low_cpu_mem_usage=True, custom_resume=True, ) unet = unet.to(device, dtype) controlnet = ControlNetSVDModel.from_pretrained( os.path.join(args.model, "controlnet"), ) controlnet = controlnet.to(device, dtype) if is_xformers_available(): import xformers xformers_version = version.parse(xformers.__version__) unet.enable_xformers_memory_efficient_attention() # controlnet.enable_xformers_memory_efficient_attention() else: raise ValueError( "xformers is not available. Make sure it is installed correctly") pipe = StableVideoDiffusionInterpControlPipeline.from_pretrained( "stabilityai/stable-video-diffusion-img2vid-xt", unet=unet, controlnet=controlnet, low_cpu_mem_usage=False, torch_dtype=torch.float16, variant="fp16", local_files_only=True, ) pipe.to(device) self.pipeline = pipe # self.pipeline.enable_model_cpu_offload() self.height = height self.width = width self.args = args self.model_length = model_length self.use_sift = use_sift def run(self, first_frame_path, last_frame_path, tracking_points, controlnet_cond_scale, motion_bucket_id): original_width, original_height = 512, 320 # TODO # load_image image = Image.open(first_frame_path).convert('RGB') width, height = image.size image = image.resize((self.width, self.height)) image_end = Image.open(last_frame_path).convert('RGB') image_end = image_end.resize((self.width, self.height)) input_all_points = tracking_points.constructor_args['value'] sift_track_update = False anchor_points_flag = None if (len(input_all_points) == 0) and self.use_sift: sift_track_update = True controlnet_cond_scale = 0.5 from models_diffusers.sift_match import sift_match from models_diffusers.sift_match import interpolate_trajectory as sift_interpolate_trajectory output_file_sift = os.path.join(args.output_dir, "sift.png") # (f, topk, 2), f=2 (before interpolation) pred_tracks = sift_match( image, image_end, thr=0.5, topk=5, method="random", output_path=output_file_sift, ) if pred_tracks is not None: # interpolate the tracks, following draganything gradio demo pred_tracks = sift_interpolate_trajectory(pred_tracks, num_frames=self.model_length) anchor_points_flag = torch.zeros((self.model_length, pred_tracks.shape[1])).to(pred_tracks.device) anchor_points_flag[0] = 1 anchor_points_flag[-1] = 1 pred_tracks = pred_tracks.permute(1, 0, 2) # (num_points, num_frames, 2) else: 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 ] # a list of num_tracks tuples, each tuple contains a track with several points, represented as (x, y) # in image w & h scale for idx, splited_track in enumerate(resized_all_points): if len(splited_track) == 0: warnings.warn("running without point trajectory control") continue if len(splited_track) == 1: # stationary point displacement_point = tuple([splited_track[0][0] + 1, splited_track[0][1] + 1]) splited_track = tuple([splited_track[0], displacement_point]) # interpolate the track splited_track = interpolate_trajectory(splited_track, self.model_length) splited_track = splited_track[:self.model_length] resized_all_points[idx] = splited_track pred_tracks = torch.tensor(resized_all_points) # (num_points, num_frames, 2) vis_images = get_vis_image( target_size=(self.args.height, self.args.width), points=pred_tracks, num_frames=self.model_length, ) if len(pred_tracks.shape) != 3: print("pred_tracks.shape", pred_tracks.shape) with_control = False controlnet_cond_scale = 0.0 else: with_control = True pred_tracks = pred_tracks.permute(1, 0, 2).to(self.device, self.dtype) # (num_frames, num_points, 2) point_embedding = None video_frames = self.pipeline( image, image_end, # trajectory control with_control=with_control, point_tracks=pred_tracks, point_embedding=point_embedding, with_id_feature=False, controlnet_cond_scale=controlnet_cond_scale, # others num_frames=14, width=width, height=height, # decode_chunk_size=8, # generator=generator, motion_bucket_id=motion_bucket_id, fps=7, num_inference_steps=30, # track sift_track_update=sift_track_update, anchor_points_flag=anchor_points_flag, ).frames[0] vis_images = [cv2.applyColorMap(np.array(img).astype(np.uint8), cv2.COLORMAP_JET) for img in vis_images] vis_images = [cv2.cvtColor(np.array(img).astype(np.uint8), cv2.COLOR_BGR2RGB) for img in vis_images] vis_images = [Image.fromarray(img) for img in vis_images] # video_frames = [img for sublist in video_frames for img in sublist] val_save_dir = os.path.join(args.output_dir, "vis_gif.gif") save_gifs_side_by_side( video_frames, vis_images[:self.model_length], val_save_dir, target_size=(self.width, self.height), duration=110, point_tracks=pred_tracks, ) return val_save_dir with gr.Blocks() as demo: gr.Markdown("""

Framer: Interactive Frame Interpolation


""") gr.Markdown("""Gradio Demo for Framer: Interactive Frame Interpolation.
Github Repo can be found at https://github.com/aim-uofa/Framer
The template is inspired by DragAnything.""") gr.Image(label="Framer: Interactive Frame Interpolation", value="assets/demos.gif", height=432, width=768) gr.Markdown("""## Usage:
1. Upload images
  1.1 Upload the start image via the "Upload Start Image" button.
  1.2. Upload the end image via the "Upload End Image" button.
2. (Optional) Draw some drags.
  2.1. Click "Add Drag Trajectory" to add the motion trajectory.
  2.2. You can click several points on either start or end image to forms a path.
  2.3. Click "Delete last drag" to delete the whole lastest path.
  2.4. Click "Delete last step" to delete the lastest clicked control point.
3. Interpolate the images (according the path) with a click on "Run" button.
""") # device, args, height, width, model_length Framer = Drag("cuda", args, 320, 512, 14) first_frame_path = gr.State() last_frame_path = gr.State() tracking_points = gr.State([]) def reset_states(first_frame_path, last_frame_path, tracking_points): first_frame_path = gr.State() last_frame_path = gr.State() tracking_points = gr.State([]) return first_frame_path, last_frame_path, tracking_points def preprocess_image(image): image_pil = image2pil(image.name) raw_w, raw_h = image_pil.size # resize_ratio = max(512 / raw_w, 320 / raw_h) # image_pil = image_pil.resize((int(raw_w * resize_ratio), int(raw_h * resize_ratio)), Image.BILINEAR) # image_pil = transforms.CenterCrop((320, 512))(image_pil.convert('RGB')) image_pil = image_pil.resize((512, 320), Image.BILINEAR) first_frame_path = os.path.join(args.output_dir, f"first_frame_{str(uuid.uuid4())[:4]}.png") image_pil.save(first_frame_path) return first_frame_path, first_frame_path, gr.State([]) def preprocess_image_end(image_end): image_end_pil = image2pil(image_end.name) raw_w, raw_h = image_end_pil.size # resize_ratio = max(512 / raw_w, 320 / raw_h) # image_end_pil = image_end_pil.resize((int(raw_w * resize_ratio), int(raw_h * resize_ratio)), Image.BILINEAR) # image_end_pil = transforms.CenterCrop((320, 512))(image_end_pil.convert('RGB')) image_end_pil = image_end_pil.resize((512, 320), Image.BILINEAR) last_frame_path = os.path.join(args.output_dir, f"last_frame_{str(uuid.uuid4())[:4]}.png") image_end_pil.save(last_frame_path) return last_frame_path, last_frame_path, gr.State([]) def add_drag(tracking_points): tracking_points.constructor_args['value'].append([]) return tracking_points def delete_last_drag(tracking_points, first_frame_path, last_frame_path): tracking_points.constructor_args['value'].pop() transparent_background = Image.open(first_frame_path).convert('RGBA') transparent_background_end = Image.open(last_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) trajectory_map_end = Image.alpha_composite(transparent_background_end, transparent_layer) return tracking_points, trajectory_map, trajectory_map_end def delete_last_step(tracking_points, first_frame_path, last_frame_path): tracking_points.constructor_args['value'][-1].pop() transparent_background = Image.open(first_frame_path).convert('RGBA') transparent_background_end = Image.open(last_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) trajectory_map_end = Image.alpha_composite(transparent_background_end, transparent_layer) return tracking_points, trajectory_map, trajectory_map_end def add_tracking_points(tracking_points, first_frame_path, last_frame_path, evt: gr.SelectData): # SelectData is a subclass of EventData print(f"You selected {evt.value} at {evt.index} from {evt.target}") tracking_points.constructor_args['value'][-1].append(evt.index) transparent_background = Image.open(first_frame_path).convert('RGBA') transparent_background_end = Image.open(last_frame_path).convert('RGBA') w, h = transparent_background.size transparent_layer = 0 for idx, track in enumerate(tracking_points.constructor_args['value']): # mask = cv2.imread( # os.path.join(args.output_dir, f"mask_{idx+1}.jpg") # ) mask = np.zeros((320, 512, 3)) color = color_list[idx+1] transparent_layer = mask[:, :, 0].reshape(h, w, 1) * color.reshape(1, 1, -1) + transparent_layer 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)) alpha_coef = 0.99 im2_data = transparent_layer.getdata() new_im2_data = [(r, g, b, int(a * alpha_coef)) for r, g, b, a in im2_data] transparent_layer.putdata(new_im2_data) trajectory_map = Image.alpha_composite(transparent_background, transparent_layer) trajectory_map_end = Image.alpha_composite(transparent_background_end, transparent_layer) return tracking_points, trajectory_map, trajectory_map_end with gr.Row(): with gr.Column(scale=1): image_upload_button = gr.UploadButton(label="Upload Start Image", file_types=["image"]) image_end_upload_button = gr.UploadButton(label="Upload End Image", file_types=["image"]) # select_area_button = gr.Button(value="Select Area with SAM") add_drag_button = gr.Button(value="Add New Drag Trajectory") reset_button = gr.Button(value="Reset") run_button = gr.Button(value="Run") delete_last_drag_button = gr.Button(value="Delete last drag") delete_last_step_button = gr.Button(value="Delete last step") with gr.Column(scale=7): with gr.Row(): with gr.Column(scale=6): input_image = gr.Image( label="start frame", interactive=True, height=320, width=512, ) with gr.Column(scale=6): input_image_end = gr.Image( label="end frame", interactive=True, height=320, width=512, ) with gr.Row(): with gr.Column(scale=1): controlnet_cond_scale = gr.Slider( label='Control Scale', minimum=0.0, maximum=10, step=0.1, value=1.0, ) motion_bucket_id = gr.Slider( label='Motion Bucket', minimum=1, maximum=180, step=1, value=100, ) with gr.Column(scale=5): output_video = gr.Image( label="Output Video", height=320, width=1152, ) with gr.Row(): gr.Markdown(""" ## Citation ```bibtex @article{wang2024framer, title={Framer: Interactive Frame Interpolation}, author={Wang, Wen and Wang, Qiuyu and Zheng, Kecheng and Ouyang, Hao and Chen, Zhekai and Gong, Biao and Chen, Hao and Shen, Yujun and Shen, Chunhua}, journal={arXiv preprint https://arxiv.org/abs/2410.18978}, year={2024} } ``` """) image_upload_button.upload(preprocess_image, image_upload_button, [input_image, first_frame_path, tracking_points]) image_end_upload_button.upload(preprocess_image_end, image_end_upload_button, [input_image_end, last_frame_path, tracking_points]) add_drag_button.click(add_drag, tracking_points, [tracking_points, ]) delete_last_drag_button.click(delete_last_drag, [tracking_points, first_frame_path, last_frame_path], [tracking_points, input_image, input_image_end]) delete_last_step_button.click(delete_last_step, [tracking_points, first_frame_path, last_frame_path], [tracking_points, input_image, input_image_end]) reset_button.click(reset_states, [first_frame_path, last_frame_path, tracking_points], [first_frame_path, last_frame_path, tracking_points]) input_image.select(add_tracking_points, [tracking_points, first_frame_path, last_frame_path], [tracking_points, input_image, input_image_end]) input_image_end.select(add_tracking_points, [tracking_points, first_frame_path, last_frame_path], [tracking_points, input_image, input_image_end]) run_button.click(Framer.run, [first_frame_path, last_frame_path, tracking_points, controlnet_cond_scale, motion_bucket_id], output_video) demo.queue().launch()