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