import nodes import torch import numpy as np from einops import rearrange import comfy.model_management MAX_RESOLUTION = nodes.MAX_RESOLUTION CAMERA_DICT = { "base_T_norm": 1.5, "base_angle": np.pi/3, "Static": { "angle":[0., 0., 0.], "T":[0., 0., 0.]}, "Pan Up": { "angle":[0., 0., 0.], "T":[0., -1., 0.]}, "Pan Down": { "angle":[0., 0., 0.], "T":[0.,1.,0.]}, "Pan Left": { "angle":[0., 0., 0.], "T":[-1.,0.,0.]}, "Pan Right": { "angle":[0., 0., 0.], "T": [1.,0.,0.]}, "Zoom In": { "angle":[0., 0., 0.], "T": [0.,0.,2.]}, "Zoom Out": { "angle":[0., 0., 0.], "T": [0.,0.,-2.]}, "Anti Clockwise (ACW)": { "angle": [0., 0., -1.], "T":[0., 0., 0.]}, "ClockWise (CW)": { "angle": [0., 0., 1.], "T":[0., 0., 0.]}, } def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'): def get_relative_pose(cam_params): """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params] abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params] cam_to_origin = 0 target_cam_c2w = np.array([ [1, 0, 0, 0], [0, 1, 0, -cam_to_origin], [0, 0, 1, 0], [0, 0, 0, 1] ]) abs2rel = target_cam_c2w @ abs_w2cs[0] ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]] ret_poses = np.array(ret_poses, dtype=np.float32) return ret_poses """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ cam_params = [Camera(cam_param) for cam_param in cam_params] sample_wh_ratio = width / height pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed if pose_wh_ratio > sample_wh_ratio: resized_ori_w = height * pose_wh_ratio for cam_param in cam_params: cam_param.fx = resized_ori_w * cam_param.fx / width else: resized_ori_h = width / pose_wh_ratio for cam_param in cam_params: cam_param.fy = resized_ori_h * cam_param.fy / height intrinsic = np.asarray([[cam_param.fx * width, cam_param.fy * height, cam_param.cx * width, cam_param.cy * height] for cam_param in cam_params], dtype=np.float32) K = torch.as_tensor(intrinsic)[None] # [1, 1, 4] c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4] plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W plucker_embedding = plucker_embedding[None] plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] return plucker_embedding class Camera(object): """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ def __init__(self, entry): fx, fy, cx, cy = entry[1:5] self.fx = fx self.fy = fy self.cx = cx self.cy = cy c2w_mat = np.array(entry[7:]).reshape(4, 4) self.c2w_mat = c2w_mat self.w2c_mat = np.linalg.inv(c2w_mat) def ray_condition(K, c2w, H, W, device): """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py """ # c2w: B, V, 4, 4 # K: B, V, 4 B = K.shape[0] j, i = torch.meshgrid( torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype), torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype), indexing='ij' ) i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW] fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1 zs = torch.ones_like(i) # [B, HxW] xs = (i - cx) / fx * zs ys = (j - cy) / fy * zs zs = zs.expand_as(ys) directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3 directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3 rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW rays_o = c2w[..., :3, 3] # B, V, 3 rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW # c2w @ dirctions rays_dxo = torch.cross(rays_o, rays_d) plucker = torch.cat([rays_dxo, rays_d], dim=-1) plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6 # plucker = plucker.permute(0, 1, 4, 2, 3) return plucker def get_camera_motion(angle, T, speed, n=81): def compute_R_form_rad_angle(angles): theta_x, theta_y, theta_z = angles Rx = np.array([[1, 0, 0], [0, np.cos(theta_x), -np.sin(theta_x)], [0, np.sin(theta_x), np.cos(theta_x)]]) Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)], [0, 1, 0], [-np.sin(theta_y), 0, np.cos(theta_y)]]) Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0], [np.sin(theta_z), np.cos(theta_z), 0], [0, 0, 1]]) R = np.dot(Rz, np.dot(Ry, Rx)) return R RT = [] for i in range(n): _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle R = compute_R_form_rad_angle(_angle) _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1)) _RT = np.concatenate([R,_T], axis=1) RT.append(_RT) RT = np.stack(RT) return RT class WanCameraEmbedding: @classmethod def INPUT_TYPES(cls): return { "required": { "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}), "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}), "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}), "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}), }, "optional":{ "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}), "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}), "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}), } } RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT") RETURN_NAMES = ("camera_embedding","width","height","length") FUNCTION = "run" CATEGORY = "camera" def run(self, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5): """ Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021) Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py """ motion_list = [camera_pose] speed = speed angle = np.array(CAMERA_DICT[motion_list[0]]["angle"]) T = np.array(CAMERA_DICT[motion_list[0]]["T"]) RT = get_camera_motion(angle, T, speed, length) trajs=[] for cp in RT.tolist(): traj=[fx,fy,cx,cy,0,0] traj.extend(cp[0]) traj.extend(cp[1]) traj.extend(cp[2]) traj.extend([0,0,0,1]) trajs.append(traj) cam_params = np.array([[float(x) for x in pose] for pose in trajs]) cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1) control_camera_video = process_pose_params(cam_params, width=width, height=height) control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device()) control_camera_video = torch.concat( [ torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), control_camera_video[:, :, 1:] ], dim=2 ).transpose(1, 2) # Reshape, transpose, and view into desired shape b, f, c, h, w = control_camera_video.shape control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) return (control_camera_video, width, height, length) NODE_CLASS_MAPPINGS = { "WanCameraEmbedding": WanCameraEmbedding, }