content: import tqdm
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  import comfy.utils
  
  import nerfacc
  import nvdiffrast.torch as dr
  
  from kiui.mesh_utils import clean_mesh, decimate_mesh
  from kiui.mesh_utils import laplacian_smooth_loss, normal_consistency
  from kiui.op import inverse_sigmoid
  from kiui.cam import orbit_camera, get_perspective
  from kiui.nn import MLP, trunc_exp
  
  from mesh_processer.mesh import Mesh
  from mesh_processer.mesh_utils import marching_cubes_density_to_mesh, color_func_to_albedo
  from LGM.core.options import Options
  from LGM.core.gs import GaussianRenderer
  from LGM.core.utils import get_rays
  
  # Triple renderer of gaussians, gaussian, and diso mesh.
  # gaussian --> nerf --> mesh
  class GSConverterNeRFMarchingCubes(nn.Module):
      def __init__(self, opt: Options, gs_ply):
          super().__init__()
          from kiui.gridencoder import GridEncoder
  
          self.opt = opt
          self.device = torch.device("cuda")
  
          # gs renderer
          self.tan_half_fov = np.tan(0.5 * np.deg2rad(opt.fovy))
          self.proj_matrix = torch.zeros(4, 4, dtype=torch.float32, device=self.device)
          self.proj_matrix[0, 0] = 1 / self.tan_half_fov
          self.proj_matrix[1, 1] = 1 / self.tan_half_fov
          self.proj_matrix[2, 2] = (opt.zfar + opt.znear) / (opt.zfar - opt.znear)
          self.proj_matrix[3, 2] = - (opt.zfar * opt.znear) / (opt.zfar - opt.znear)
          self.proj_matrix[2, 3] = 1
  
          self.gs_renderer = GaussianRenderer(opt)
  
          self.gaussians = self.gs_renderer.create_from_ply(gs_ply).to(self.device)
  
          # nerf renderer
          if not self.opt.force_cuda_rast:
              self.glctx = dr.RasterizeGLContext()
          else:
              self.glctx = dr.RasterizeCudaContext()
          
          self.step = 0
          self.render_step_size = 5e-3
          self.aabb = torch.tensor([-1.0, -1.0, -1.0, 1.0, 1.0, 1.0], device=self.device)
          self.estimator = nerfacc.OccGridEstimator(roi_aabb=self.aabb, resolution=64, levels=1)
  
          self.encoder_density = GridEncoder(num_levels=12) # VMEncoder(output_dim=16, mode='sum')
          self.encoder = GridEncoder(num_levels=12)
          self.mlp_density = MLP(self.encoder_density.output_dim, 1, 32, 2, bias=False)
          self.mlp = MLP(self.encoder.output_dim, 3, 32, 2, bias=False)
  
          # mesh renderer
          self.proj = torch.from_numpy(get_perspective(self.opt.fovy)).float().to(self.device)
          self.v = self.f = None
          self.vt = self.ft = None
          self.deform = None
          self.albedo = None
          
         
      @torch.no_grad()
      def render_gs(self, pose):
      
          cam_poses = torch.from_numpy(pose).unsqueeze(0).to(self.device)
          cam_poses[:, :3, 1:3] *= -1 # invert up & forward direction
          
          # cameras needed by gaussian rasterizer
          cam_view = torch.inverse(cam_poses).transpose(1, 2) # [V, 4, 4]
          cam_view_proj = cam_view @ self.proj_matrix # [V, 4, 4]
          cam_pos = - cam_poses[:, :3, 3] # [V, 3]
          
          out = self.gs_renderer.render(self.gaussians.unsqueeze(0), cam_view.unsqueeze(0), cam_view_proj.unsqueeze(0), cam_pos.unsqueeze(0))
          image = out['image'].squeeze(1).squeeze(0) # [C, H, W]
          alpha = out['alpha'].squeeze(2).squeeze(1).squeeze(0) # [H, W]
  
          return image, alpha
  
      def get_density(self, xs):
          # xs: [..., 3]
          xs = xs.to(self.device)
          prefix = xs.shape[:-1]
          xs = xs.view(-1, 3)
          feats = self.encoder_density(xs)
          density = trunc_exp(self.mlp_density(feats))
          density = density.view(*prefix, 1)
          return density
      
      def render_nerf(self, pose):
          
          pose = torch.from_numpy(pose.astype(np.float32)).to(self.device)
          
          # get rays
          resolution = self.opt.output_size
          rays_o, rays_d = get_rays(pose, resolution, resolution, self.opt.fovy)
          hw = rays_o.shape[0] * rays_o.shape[1]
          rays_o = rays_o.view(hw, 3)
          rays_d = rays_d.view(hw, 3)
          
          # update occ grid
          if self.training:
              def occ_eval_fn(xs):
                  sigmas = self.get_density(xs)
                  return self.render_step_size * sigmas
              
              self.estimator.update_every_n_steps(self.step, occ_eval_fn=occ_eval_fn, occ_thre=0.01, n=8)
              self.step += 1
  
          # render
          def sigma_fn(t_starts, t_ends, ray_indices):
              t_origins = rays_o[ray_indices]
              t_dirs = rays_d[ray_indices]
              xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
              sigmas = self.get_density(xs)
              return sigmas.squeeze(-1)
  
          with torch.no_grad():
              ray_indices, t_starts, t_ends = self.estimator.sampling(
                  rays_o,
                  rays_d,
                  sigma_fn=sigma_fn,
                  near_plane=0.01,
                  far_plane=100,
                  render_step_size=self.render_step_size,
                  stratified=self.training,
                  cone_angle=0,
              )
  
          t_origins = rays_o[ray_indices]
          t_dirs = rays_d[ray_indices]
          xs = t_origins + t_dirs * (t_starts + t_ends)[:, None] / 2.0
          sigmas = self.get_density(xs).squeeze(-1)
          rgbs = torch.sigmoid(self.mlp(self.encoder(xs)))
  
          n_rays=rays_o.shape[0]
          weights, trans, alphas = nerfacc.render_weight_from_density(t_starts, t_ends, sigmas, ray_indices=ray_indices, n_rays=n_rays)
          color = nerfacc.accumulate_along_rays(weights, values=rgbs, ray_indices=ray_indices, n_rays=n_rays)
          alpha = nerfacc.accumulate_along_rays(weights, values=None, ray_indices=ray_indices, n_rays=n_rays)
  
          color = color + 1 * (1.0 - alpha)
  
          color = color.view(resolution, resolution, 3).clamp(0, 1).permute(2, 0, 1).contiguous()
          alpha = alpha.view(resolution, resolution).clamp(0, 1).contiguous()
          
          return color, alpha
  
      def fit_nerf(self, iters=512, resolution=128):
  
          self.opt.output_size = resolution
  
          optimizer = torch.optim.Adam([
              {'params': self.encoder_density.parameters(), 'lr': 1e-2},
              {'params': self.encoder.parameters(), 'lr': 1e-2},
              {'params': self.mlp_density.parameters(), 'lr': 1e-3},
              {'params': self.mlp.parameters(), 'lr': 1e-3},
          ])
  
          print(f"[INFO] fitting nerf...")
          imgs, alphas = [], []
          
          comfy_pbar = comfy.utils.ProgressBar(iters)
          pbar = tqdm.trange(iters)
          for i in pbar:
  
              ver = np.random.randint(-45, 45)
              hor = np.random.randint(-180, 180)
              rad = np.random.uniform(1.5, 3.0)
              
              pose = orbit_camera(ver, hor, rad)
              
              image_gt, alpha_gt = self.render_gs(pose)
              imgs.append(image_gt.permute(1, 2, 0).unsqueeze(0))
              alphas.append(alpha_gt.unsqueeze(0))
              image_pred, alpha_pred = self.render_nerf(pose)
  
              # if i % 200 == 0:
              #     kiui.vis.plot_image(image_gt, alpha_gt, image_pred, alpha_pred)
              
              loss_mse = F.mse_loss(image_pred, image_gt) + 0.1 * F.mse_loss(alpha_pred, alpha_gt)
              loss = loss_mse #+ 0.1 * self.encoder_density.tv_loss() #+ 0.0001 * self.encoder_density.density_loss()
  
              loss.backward()
              self.encoder_density.grad_total_variation(1e-8)
          
              optimizer.step()
              optimizer.zero_grad()
  
              pbar.set_description(f"MSE = {loss_mse.item():.6f}")
              comfy_pbar.update_absolute(i + 1)
          
          print(f"[INFO] finished fitting nerf!")
          
          return torch.cat(imgs, dim=0), torch.cat(alphas, dim=0)
      
      def render_mesh(self, pose):
  
          h = w = self.opt.output_size
  
          v = self.v + self.deform
          f = self.f
  
          pose = torch.from_numpy(pose.astype(np.float32)).to(v.device)
  
          # get v_clip and render rgb
          v_cam = torch.matmul(F.pad(v, pad=(0, 1), mode='constant', value=1.0), torch.inverse(pose).T).float().unsqueeze(0)
          v_clip = v_cam @ self.proj.T
  
          rast, rast_db = dr.rasterize(self.glctx, v_clip, f, (h, w))
  
          alpha = torch.clamp(rast[..., -1:], 0, 1).contiguous() # [1, H, W, 1]
          alpha = dr.antialias(alpha, rast, v_clip, f).clamp(0, 1).squeeze(-1).squeeze(0) # [H, W] important to enable gradients!
          
          if self.albedo is None:
              xyzs, _ = dr.interpolate(v.unsqueeze(0), rast, f) # [1, H, W, 3]
              xyzs = xyzs.view(-1, 3)
              mask = (alpha > 0).view(-1)
              image = torch.zeros_like(xyzs, dtype=torch.float32)
              if mask.any():
                  masked_albedo = torch.sigmoid(self.mlp(self.encoder(xyzs[mask].detach(), bound=1)))
                  image[mask] = masked_albedo.float()
          else:
              texc, texc_db = dr.interpolate(self.vt.unsqueeze(0), rast, self.ft, rast_db=rast_db, diff_attrs='all')
              image = torch.sigmoid(dr.texture(self.albedo.unsqueeze(0), texc, uv_da=texc_db)) # [1, H, W, 3]
  
          image = image.view(1, h, w, 3)
          # image = dr.antialias(image, rast, v_clip, f).clamp(0, 1)
          image = image.squeeze(0).permute(2, 0, 1).contiguous() # [3, H, W]
          image = alpha * image + (1 - alpha)
  
          return image, alpha
  
      def fit_mesh(self, iters=2048, remesh_after_n_iteration=512, resolution=512, grid_size=256, S=128, density_thresh=10, decimate_target=5e4):
  
          self.opt.output_size = resolution
          
          vertices, triangles = marching_cubes_density_to_mesh(self.get_density, grid_size, S, density_thresh, decimate_target)
          
          self.v = torch.from_numpy(vertices).contiguous().float().to(self.device)
          self.f = torch.from_numpy(triangles).contiguous().int().to(self.device)
          self.deform = nn.Parameter(torch.zeros_like(self.v)).to(self.device)
          
          # Here coarse mesh already resemble the target shape closely
          #mcube_mesh = Mesh(v=self.v, f=self.f, albedo=None, device=self.device)
          #mcube_mesh.auto_normal()
          #mcube_mesh.auto_uv()
  
          # fit mesh from gs
          lr_factor = 1
          optimizer = torch.optim.Adam([
              {'params': self.encoder.parameters(), 'lr': 1e-3 * lr_factor},
              {'params': self.mlp.parameters(), 'lr': 1e-3 * lr_factor},
              {'params': self.deform, 'lr': 1e-4},
          ])
  
          print(f"[INFO] fitting mesh...")
          comfy_pbar = comfy.utils.ProgressBar(iters)
          pbar = tqdm.trange(iters)
          for i in pbar:
  
              ver = np.random.randint(-10, 10)
              hor = np.random.randint(-180, 180)
              rad = self.opt.cam_radius # np.random.uniform(1, 2)
  
              pose = orbit_camera(ver, hor, rad)
              
              image_gt, alpha_gt = self.render_gs(pose)
              image_pred, alpha_pred = self.render_mesh(pose)
  
              loss_mse = F.mse_loss(image_pred, image_gt) + 0.1 * F.mse_loss(alpha_pred, alpha_gt)
              loss_lap = laplacian_smooth_loss(self.v + self.deform, self.f)
              loss_normal = normal_consistency(self.v + self.deform, self.f)
              loss_offsets = (self.deform ** 2).sum(-1).mean()
              loss = loss_mse + 0.001 * loss_normal + 0.1 * loss_offsets * loss_lap * 0.01
  
              loss.backward()
  
              optimizer.step()
              optimizer.zero_grad()
  
              # remesh periodically
              if i > 0 and i % remesh_after_n_iteration == 0:
                  vertices = (self.v + self.deform).detach().cpu().numpy()
                  triangles = self.f.detach().cpu().numpy()
                  vertices, triangles = clean_mesh(vertices, triangles, remesh=True, remesh_size=0.01)
                  if triangles.shape[0] > decimate_target:
                      vertices, triangles = decimate_mesh(vertices, triangles, decimate_target, optimalplacement=False)
                  self.v = torch.from_numpy(vertices).contiguous().float().to(self.device)
                  self.f = torch.from_numpy(triangles).contiguous().int().to(self.device)
                  self.deform = nn.Parameter(torch.zeros_like(self.v)).to(self.device)
                  lr_factor *= 0.5
                  optimizer = torch.optim.Adam([
                      {'params': self.encoder.parameters(), 'lr': 1e-3 * lr_factor},
                      {'params': self.mlp.parameters(), 'lr': 1e-3 * lr_factor},
                      {'params': self.deform, 'lr': 1e-4},
                  ])
  
              pbar.set_description(f"MSE = {loss_mse.item():.6f}")
              comfy_pbar.update_absolute(i + 1)
          
          # last clean
          vertices = (self.v + self.deform).detach().cpu().numpy()
          triangles = self.f.detach().cpu().numpy()
          vertices, triangles = clean_mesh(vertices, triangles, remesh=False)
          self.v = torch.from_numpy(vertices).contiguous().float().to(self.device)
          self.f = torch.from_numpy(triangles).contiguous().int().to(self.device)
          self.deform = nn.Parameter(torch.zeros_like(self.v).to(self.device))
          
          print(f"[INFO] finished fitting mesh!")
  
      def get_color(self, xs):
          return torch.sigmoid(self.mlp(self.encoder(xs.to(self.device))))
      
      # uv mesh refine
      def fit_mesh_uv(self, iters=512, resolution=512, texture_resolution=1024, padding=2):
  
          self.opt.output_size = resolution
  
          # unwrap uv
          print(f"[INFO] uv unwrapping...")
          mesh = Mesh(v=self.v, f=self.f, albedo=None, device=self.device)
          mesh.auto_normal()
          mesh.auto_uv()
  
          self.vt = mesh.vt
          self.ft = mesh.ft
          
          # get albedo map from NeRF
          albedo = color_func_to_albedo(mesh, self.get_color, texture_resolution, device=self.device, glctx=self.glctx)
  
          # optimize texture
          self.albedo = nn.Parameter(inverse_sigmoid(albedo)).to(self.device)
          
          optimizer = torch.optim.Adam([
              {'params': self.albedo, 'lr': 1e-3},
          ])
  
          print(f"[INFO] fitting mesh texture...")
          comfy_pbar = comfy.utils.ProgressBar(iters)
          pbar = tqdm.trange(iters)
          for i in pbar:
  
              # shrink to front view as we care more about it...
              ver = np.random.randint(-5, 5)
              hor = np.random.randint(-15, 15)
              rad = self.opt.cam_radius # np.random.uniform(1, 2)
              
              pose = orbit_camera(ver, hor, rad)
              
              image_gt, alpha_gt = self.render_gs(pose)
              image_pred, alpha_pred = self.render_mesh(pose)
  
              loss_mse = F.mse_loss(image_pred, image_gt)
              loss = loss_mse
  
              loss.backward()
  
              optimizer.step()
              optimizer.zero_grad()
  
              pbar.set_description(f"MSE = {loss_mse.item():.6f}")
              comfy_pbar.update_absolute(i + 1)
          
          print(f"[INFO] finished fitting mesh texture!")
  
  
      @torch.no_grad()
      def get_mesh(self):
          mesh = Mesh(v=self.v, f=self.f, vt=self.vt, ft=self.ft, albedo=torch.sigmoid(self.albedo), device=self.device)
          mesh.auto_normal()
          return mesh
  