from __future__ import annotations import numpy as np import torch from einops import rearrange import comfy.model_management import nodes from comfy_api.v3 import io CAMERA_DICT = { "base_T_norm": 1.5, "base_angle": np.pi / 3, "Static": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 0.0, 0.0]}, "Pan Up": {"angle": [0.0, 0.0, 0.0], "T": [0.0, -1.0, 0.0]}, "Pan Down": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 1.0, 0.0]}, "Pan Left": {"angle": [0.0, 0.0, 0.0], "T": [-1.0, 0.0, 0.0]}, "Pan Right": {"angle": [0.0, 0.0, 0.0], "T": [1.0, 0.0, 0.0]}, "Zoom In": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 0.0, 2.0]}, "Zoom Out": {"angle": [0.0, 0.0, 0.0], "T": [0.0, 0.0, -2.0]}, "Anti Clockwise (ACW)": {"angle": [0.0, 0.0, -1.0], "T": [0.0, 0.0, 0.0]}, "ClockWise (CW)": {"angle": [0.0, 0.0, 1.0], "T": [0.0, 0.0, 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:]] return np.array(ret_poses, dtype=np.float32) """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] return rearrange(plucker_embedding, "b f c h w -> b f h w c")[0] class Camera: """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(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="WanCameraEmbedding_V3", category="camera", inputs=[ io.Combo.Input( "camera_pose", options=[ "Static", "Pan Up", "Pan Down", "Pan Left", "Pan Right", "Zoom In", "Zoom Out", "Anti Clockwise (ACW)", "ClockWise (CW)", ], default="Static", ), io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16), io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4), io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True), io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True), io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True), io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True), io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True), ], outputs=[ io.WanCameraEmbedding.Output(display_name="camera_embedding"), io.Int.Output(display_name="width"), io.Int.Output(display_name="height"), io.Int.Output(display_name="length"), ], ) @classmethod def execute(cls, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5) -> io.NodeOutput: """ 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 io.NodeOutput(control_camera_video, width, height, length) NODES_LIST = [ WanCameraEmbedding, ]