import torch from torch import nn from nsr.triplane import Triplane_fg_bg_plane # import timm from vit.vit_triplane import Triplane, ViTTriplaneDecomposed import argparse import inspect import dnnlib from guided_diffusion import dist_util from pdb import set_trace as st import vit.vision_transformer as vits from guided_diffusion import logger from .confnet import ConfNet from ldm.modules.diffusionmodules.model import Encoder, MVEncoder, MVEncoderGS, MVEncoderGSDynamicInp from ldm.modules.diffusionmodules.mv_unet import MVUNet, LGM_MVEncoder # from ldm.modules.diffusionmodules.openaimodel import MultiViewUNetModel_Encoder # * create pre-trained encoder & triplane / other nsr decoder class AE(torch.nn.Module): def __init__(self, encoder, decoder, img_size, encoder_cls_token, decoder_cls_token, preprocess, use_clip, dino_version='v1', clip_dtype=None, no_dim_up_mlp=False, dim_up_mlp_as_func=False, uvit_skip_encoder=False, confnet=None) -> None: super().__init__() self.encoder = encoder self.decoder = decoder self.img_size = img_size self.encoder_cls_token = encoder_cls_token self.decoder_cls_token = decoder_cls_token self.use_clip = use_clip self.dino_version = dino_version self.confnet = confnet if self.dino_version == 'v2': self.encoder.mask_token = None self.decoder.vit_decoder.mask_token = None if 'sd' not in self.dino_version: self.uvit_skip_encoder = uvit_skip_encoder if uvit_skip_encoder: logger.log( f'enables uvit: length of vit_encoder.blocks: {len(self.encoder.blocks)}' ) for blk in self.encoder.blocks[len(self.encoder.blocks) // 2:]: blk.skip_linear = nn.Linear(2 * self.encoder.embed_dim, self.encoder.embed_dim) # trunc_normal_(blk.skip_linear.weight, std=.02) nn.init.constant_(blk.skip_linear.weight, 0) if isinstance( blk.skip_linear, nn.Linear) and blk.skip_linear.bias is not None: nn.init.constant_(blk.skip_linear.bias, 0) else: logger.log(f'disable uvit') else: if 'dit' not in self.dino_version: # dino vit, not dit self.decoder.vit_decoder.cls_token = None self.decoder.vit_decoder.patch_embed.proj = nn.Identity() self.decoder.triplane_decoder.planes = None self.decoder.vit_decoder.mask_token = None if self.use_clip: self.clip_dtype = clip_dtype # torch.float16 else: if not no_dim_up_mlp and self.encoder.embed_dim != self.decoder.vit_decoder.embed_dim: self.dim_up_mlp = nn.Linear( self.encoder.embed_dim, self.decoder.vit_decoder.embed_dim) logger.log( f"dim_up_mlp: {self.encoder.embed_dim} -> {self.decoder.vit_decoder.embed_dim}, as_func: {self.dim_up_mlp_as_func}" ) else: logger.log('ignore dim_up_mlp: ', no_dim_up_mlp) self.preprocess = preprocess self.dim_up_mlp = None # CLIP/B-16 self.dim_up_mlp_as_func = dim_up_mlp_as_func # * remove certain components to make sure no unused parameters during DDP # self.decoder.vit_decoder.cls_token = nn.Identity() torch.cuda.empty_cache() # self.decoder.vit_decoder.patch_embed.proj.bias = nn.Identity() # self.decoder.vit_decoder.patch_embed.proj.weight = nn.Identity() # self.decoder.vit_decoder.patch_embed.proj.bias = nn.Identity() def encode(self, *args, **kwargs): if not self.use_clip: if self.dino_version == 'v1': latent = self.encode_dinov1(*args, **kwargs) elif self.dino_version == 'v2': if self.uvit_skip_encoder: latent = self.encode_dinov2_uvit(*args, **kwargs) else: latent = self.encode_dinov2(*args, **kwargs) else: latent = self.encoder(*args) else: latent = self.encode_clip(*args, **kwargs) return latent def encode_dinov1(self, x): # return self.encoder(img) x = self.encoder.prepare_tokens(x) for blk in self.encoder.blocks: x = blk(x) x = self.encoder.norm(x) if not self.encoder_cls_token: return x[:, 1:] return x def encode_dinov2(self, x): # return self.encoder(img) x = self.encoder.prepare_tokens_with_masks(x, masks=None) for blk in self.encoder.blocks: x = blk(x) x_norm = self.encoder.norm(x) if not self.encoder_cls_token: return x_norm[:, 1:] # else: # return x_norm[:, :1] # return { # "x_norm_clstoken": x_norm[:, 0], # "x_norm_patchtokens": x_norm[:, 1:], # } return x_norm def encode_dinov2_uvit(self, x): # return self.encoder(img) x = self.encoder.prepare_tokens_with_masks(x, masks=None) # for blk in self.encoder.blocks: # x = blk(x) skips = [x] # in blks for blk in self.encoder.blocks[0:len(self.encoder.blocks) // 2 - 1]: x = blk(x) # B 3 N C skips.append(x) # mid blks for blk in self.encoder.blocks[len(self.encoder.blocks) // 2 - 1:len(self.encoder.blocks) // 2]: x = blk(x) # B 3 N C # out blks for blk in self.encoder.blocks[len(self.encoder.blocks) // 2:]: x = x + blk.skip_linear(torch.cat( [x, skips.pop()], dim=-1)) # long skip connections in uvit x = blk(x) # B 3 N C x_norm = self.encoder.norm(x) if not self.decoder_cls_token: return x_norm[:, 1:] return x_norm def encode_clip(self, x): # * replace with CLIP encoding pipeline # return self.encoder(img) # x = x.dtype(self.clip_dtype) x = self.encoder.conv1(x) # shape = [*, width, grid, grid] x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] x = torch.cat([ self.encoder.class_embedding.to(x.dtype) + torch.zeros( x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x ], dim=1) # shape = [*, grid ** 2 + 1, width] x = x + self.encoder.positional_embedding.to(x.dtype) x = self.encoder.ln_pre(x) x = x.permute(1, 0, 2) # NLD -> LND x = self.encoder.transformer(x) x = x.permute(1, 0, 2) # LND -> NLD x = self.encoder.ln_post(x[:, 1:, :]) # * return the spatial tokens return x # x = self.ln_post(x[:, 0, :]) # * return the spatial tokens # if self.proj is not None: # x = x @ self.proj # return x def decode_wo_triplane(self, latent, c=None, img_size=None): if img_size is None: img_size = self.img_size if self.dim_up_mlp is not None: if not self.dim_up_mlp_as_func: latent = self.dim_up_mlp(latent) # return self.decoder.vit_decode(latent, img_size) else: return self.decoder.vit_decode( latent, img_size, dim_up_mlp=self.dim_up_mlp) # used in vae-ldm return self.decoder.vit_decode(latent, img_size, c=c) def decode(self, latent, c, img_size=None, return_raw_only=False): # if img_size is None: # img_size = self.img_size # if self.dim_up_mlp is not None: # latent = self.dim_up_mlp(latent) latent = self.decode_wo_triplane(latent, img_size=img_size, c=c) # return self.decoder.triplane_decode(latent, c, return_raw_only=return_raw_only) return self.decoder.triplane_decode(latent, c) def decode_after_vae_no_render( self, ret_dict, img_size=None, ): if img_size is None: img_size = self.img_size assert self.dim_up_mlp is None # if not self.dim_up_mlp_as_func: # latent = self.dim_up_mlp(latent) # return self.decoder.vit_decode(latent, img_size) latent = self.decoder.vit_decode_backbone(ret_dict, img_size) ret_dict = self.decoder.vit_decode_postprocess(latent, ret_dict) return ret_dict def decode_after_vae( self, # latent, ret_dict, # vae_dict c, img_size=None, return_raw_only=False): ret_dict = self.decode_after_vae_no_render(ret_dict, img_size) return self.decoder.triplane_decode(ret_dict, c) def decode_confmap(self, img): assert self.confnet is not None # https://github.com/elliottwu/unsup3d/blob/dc961410d61684561f19525c2f7e9ee6f4dacb91/unsup3d/model.py#L152 # conf_sigma_l1 = self.confnet(img) # Bx2xHxW return self.confnet(img) # Bx1xHxW def encode_decode(self, img, c, return_raw_only=False): latent = self.encode(img) pred = self.decode(latent, c, return_raw_only=return_raw_only) if self.confnet is not None: pred.update({ 'conf_sigma': self.decode_confmap(img) # 224x224 }) return pred def forward(self, img=None, c=None, latent=None, behaviour='enc_dec', coordinates=None, directions=None, return_raw_only=False, *args, **kwargs): """wrap all operations inside forward() for DDP use. """ if behaviour == 'enc_dec': pred = self.encode_decode(img, c, return_raw_only=return_raw_only) return pred elif behaviour == 'enc': latent = self.encode(img) return latent elif behaviour == 'dec': assert latent is not None pred: dict = self.decode(latent, c, self.img_size, return_raw_only=return_raw_only) return pred elif behaviour == 'dec_wo_triplane': assert latent is not None pred: dict = self.decode_wo_triplane(latent, self.img_size) return pred elif behaviour == 'enc_dec_wo_triplane': latent = self.encode(img) pred: dict = self.decode_wo_triplane(latent, img_size=self.img_size, c=c) return pred elif behaviour == 'encoder_vae': latent = self.encode(img) ret_dict = self.decoder.vae_reparameterization(latent, True) return ret_dict elif behaviour == 'decode_after_vae_no_render': pred: dict = self.decode_after_vae_no_render(latent, self.img_size) return pred elif behaviour == 'decode_after_vae': pred: dict = self.decode_after_vae(latent, c, self.img_size) return pred # elif behaviour == 'gaussian_dec': # assert latent is not None # pred: dict = self.decoder.triplane_decode( # latent, c, return_raw_only=return_raw_only, **kwargs) # # pred: dict = self.decoder.triplane_decode(latent, c) elif behaviour == 'triplane_dec': assert latent is not None pred: dict = self.decoder.triplane_decode( latent, c, return_raw_only=return_raw_only, **kwargs) # pred: dict = self.decoder.triplane_decode(latent, c) elif behaviour == 'triplane_decode_grid': assert latent is not None pred: dict = self.decoder.triplane_decode_grid( latent, **kwargs) # pred: dict = self.decoder.triplane_decode(latent, c) elif behaviour == 'vit_postprocess_triplane_dec': assert latent is not None latent = self.decoder.vit_decode_postprocess( latent) # translate spatial token from vit-decoder into 2D pred: dict = self.decoder.triplane_decode( latent, c) # render with triplane elif behaviour == 'triplane_renderer': assert latent is not None pred: dict = self.decoder.triplane_renderer( latent, coordinates, directions) # elif behaviour == 'triplane_SR': # assert latent is not None # pred: dict = self.decoder.triplane_renderer( # latent, coordinates, directions) elif behaviour == 'get_rendering_kwargs': pred = self.decoder.triplane_decoder.rendering_kwargs return pred class AE_CLIPEncoder(AE): def __init__(self, encoder, decoder, img_size, cls_token) -> None: super().__init__(encoder, decoder, img_size, cls_token) class AE_with_Diffusion(torch.nn.Module): def __init__(self, auto_encoder, denoise_model) -> None: super().__init__() self.auto_encoder = auto_encoder self.denoise_model = denoise_model # simply for easy MPTrainer manipulation def forward(self, img, c, behaviour='enc_dec', latent=None, *args, **kwargs): # wrap auto_encoder and denoising model inside a single forward function to use DDP (only forward supported) and MPTrainer (single model) easier if behaviour == 'enc_dec': pred = self.auto_encoder(img, c) return pred elif behaviour == 'enc': latent = self.auto_encoder.encode(img) if self.auto_encoder.dim_up_mlp is not None: latent = self.auto_encoder.dim_up_mlp(latent) return latent elif behaviour == 'dec': assert latent is not None pred: dict = self.auto_encoder.decode(latent, c, self.img_size) return pred elif behaviour == 'denoise': assert latent is not None pred: dict = self.denoise_model(*args, **kwargs) return pred def eg3d_options_default(): opts = dnnlib.EasyDict( dict( cbase=32768, cmax=512, map_depth=2, g_class_name='nsr.triplane.TriPlaneGenerator', # TODO g_num_fp16_res=0, )) return opts def rendering_options_defaults(opts): rendering_options = { # 'image_resolution': c.training_set_kwargs.resolution, 'image_resolution': 256, 'disparity_space_sampling': False, 'clamp_mode': 'softplus', 'c_gen_conditioning_zero': True, # if true, fill generator pose conditioning label with dummy zero vector # 'gpc_reg_prob': opts.gpc_reg_prob if opts.gen_pose_cond else None, 'c_scale': opts.c_scale, # mutliplier for generator pose conditioning label 'superresolution_noise_mode': 'none', 'density_reg': opts.density_reg, # strength of density regularization 'density_reg_p_dist': opts. density_reg_p_dist, # distance at which to sample perturbed points for density regularization 'reg_type': opts. reg_type, # for experimenting with variations on density regularization 'decoder_lr_mul': 1, # opts.decoder_lr_mul, # learning rate multiplier for decoder 'decoder_activation': 'sigmoid', 'sr_antialias': True, 'return_triplane_features': False, # for DDF supervision 'return_sampling_details_flag': False, # * shape default sr # 'superresolution_module': 'nsr.superresolution.SuperresolutionHybrid4X', # 'superresolution_module': # 'utils.torch_utils.components.PixelUnshuffleUpsample', 'superresolution_module': 'utils.torch_utils.components.NearestConvSR', } if opts.cfg == 'ffhq': rendering_options.update({ 'superresolution_module': 'nsr.superresolution.SuperresolutionHybrid8XDC', 'focal': 2985.29 / 700, 'depth_resolution': 48 - 0, # number of uniform samples to take per ray. 'depth_resolution_importance': 48 - 0, # number of importance samples to take per ray. 'bg_depth_resolution': 16, # 4/14 in stylenerf, https://github.com/facebookresearch/StyleNeRF/blob/7f5610a058f27fcc360c6b972181983d7df794cb/conf/model/stylenerf_ffhq.yaml#L48 'ray_start': 2.25, # near point along each ray to start taking samples. 'ray_end': 3.3, # far point along each ray to stop taking samples. 'box_warp': 1, # the side-length of the bounding box spanned by the tri-planes; box_warp=1 means [-0.5, -0.5, -0.5] -> [0.5, 0.5, 0.5]. 'avg_camera_radius': 2.7, # used only in the visualizer to specify camera orbit radius. 'avg_camera_pivot': [ 0, 0, 0.2 ], # used only in the visualizer to control center of camera rotation. 'superresolution_noise_mode': 'random', }) elif opts.cfg == 'afhq': rendering_options.update({ 'superresolution_module': 'nsr.superresolution.SuperresolutionHybrid8X', 'superresolution_noise_mode': 'random', 'focal': 4.2647, 'depth_resolution': 48, 'depth_resolution_importance': 48, 'ray_start': 2.25, 'ray_end': 3.3, 'box_warp': 1, 'avg_camera_radius': 2.7, 'avg_camera_pivot': [0, 0, -0.06], }) elif opts.cfg == 'shapenet': # TODO, lies in a sphere rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, # * radius 1.2 setting, newly rendered images 'ray_start': 0.2, 'ray_end': 2.2, # 'ray_start': opts.ray_start, # 'ray_end': opts.ray_end, 'box_warp': 2, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'eg3d_shapenet_aug_resolution': rendering_options.update({ 'depth_resolution': 80, 'depth_resolution_importance': 80, 'ray_start': 0.1, 'ray_end': 1.9, # 2.6/1.7*1.2 'box_warp': 1.1, 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair': rendering_options.update({ 'depth_resolution': 96, 'depth_resolution_importance': 96, 'ray_start': 0.1, 'ray_end': 1.9, # 2.6/1.7*1.2 'box_warp': 1.1, 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair_128': rendering_options.update({ 'depth_resolution': 128, 'depth_resolution_importance': 128, 'ray_start': 0.1, 'ray_end': 1.9, # 2.6/1.7*1.2 'box_warp': 1.1, 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair_64': rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, 'ray_start': 0.1, 'ray_end': 1.9, # 2.6/1.7*1.2 'box_warp': 1.1, 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'srn_shapenet_aug_resolution_chair_128': rendering_options.update({ 'depth_resolution': 128, 'depth_resolution_importance': 128, 'ray_start': 1.25, 'ray_end': 2.75, 'box_warp': 1.5, 'white_back': True, 'avg_camera_radius': 2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'eg3d_shapenet_aug_resolution_chair_128_residualSR': rendering_options.update({ 'depth_resolution': 128, 'depth_resolution_importance': 128, 'ray_start': 0.1, 'ray_end': 1.9, # 2.6/1.7*1.2 'box_warp': 1.1, 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], 'superresolution_module': 'utils.torch_utils.components.NearestConvSR_Residual', }) elif opts.cfg == 'shapenet_tuneray': # TODO, lies in a sphere rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'shapenet_tuneray_aug_resolution': # to differentiate hwc rendering_options.update({ 'depth_resolution': 80, 'depth_resolution_importance': 80, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'shapenet_tuneray_aug_resolution_64': # to differentiate hwc rendering_options.update({ 'depth_resolution': 128, 'depth_resolution_importance': 128, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_96': # to differentiate hwc rendering_options.update({ 'depth_resolution': 96, 'depth_resolution_importance': 96, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) # ! default version elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_96_nearestSR': # to differentiate hwc rendering_options.update({ 'depth_resolution': 96, 'depth_resolution_importance': 96, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], 'superresolution_module': 'utils.torch_utils.components.NearestConvSR', }) # ! 64+64, since ssdnerf adopts this setting elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_64_nearestSR': # to differentiate hwc rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], 'superresolution_module': 'utils.torch_utils.components.NearestConvSR', }) # ! 64+64+patch, since ssdnerf adopts this setting elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_64_nearestSR_patch': # to differentiate hwc rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], 'superresolution_module': 'utils.torch_utils.components.NearestConvSR', # patch configs 'PatchRaySampler': True, # 'patch_rendering_resolution': 32, # 'patch_rendering_resolution': 48, 'patch_rendering_resolution': opts.patch_rendering_resolution, }) elif opts.cfg == 'objverse_tuneray_aug_resolution_64_64_nearestSR': # to differentiate hwc rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, # 'auto', 'ray_end': opts.ray_end, # 'auto', 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? # 2, 'white_back': True, 'avg_camera_radius': 1.946, # ? 'avg_camera_pivot': [0, 0, 0], 'superresolution_module': 'utils.torch_utils.components.NearestConvSR', # patch configs # 'PatchRaySampler': False, # 'patch_rendering_resolution': 32, # 'patch_rendering_resolution': 48, # 'patch_rendering_resolution': opts.patch_rendering_resolution, }) elif opts.cfg == 'objverse_tuneray_aug_resolution_64_64_auto': # to differentiate hwc rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, # * radius 1.2 setting, newly rendered images 'ray_start': 'auto', 'ray_end': 'auto', 'box_warp': 0.9, 'white_back': True, 'radius_range': [1.5,2], # 'z_near': 1.5-0.45, # radius in [1.5, 2], https://github.com/modelscope/richdreamer/issues/12#issuecomment-1897734616 # 'z_far': 2.0+0.45, 'sampler_bbox_min': -0.45, 'sampler_bbox_max': 0.45, # 'avg_camera_pivot': [0, 0, 0], # not used 'filter_out_of_bbox': True, # 'superresolution_module': # 'utils.torch_utils.components.NearestConvSR', # patch configs 'PatchRaySampler': True, # 'patch_rendering_resolution': 32, # 'patch_rendering_resolution': 48, 'patch_rendering_resolution': opts.patch_rendering_resolution, }) rendering_options['z_near'] = rendering_options['radius_range'][0]+rendering_options['sampler_bbox_min'] rendering_options['z_far'] = rendering_options['radius_range'][1]+rendering_options['sampler_bbox_max'] elif opts.cfg == 'objverse_tuneray_aug_resolution_128_128_auto': # to differentiate hwc rendering_options.update({ 'depth_resolution': 128, 'depth_resolution_importance': 128, # * radius 1.2 setting, newly rendered images 'ray_start': 'auto', 'ray_end': 'auto', 'box_warp': 0.9, 'white_back': True, 'radius_range': [1.5,2], # 'z_near': 1.5-0.45, # radius in [1.5, 2], https://github.com/modelscope/richdreamer/issues/12#issuecomment-1897734616 # 'z_far': 2.0+0.45, 'sampler_bbox_min': -0.45, 'sampler_bbox_max': 0.45, # 'avg_camera_pivot': [0, 0, 0], # not used 'filter_out_of_bbox': True, # 'superresolution_module': # 'utils.torch_utils.components.NearestConvSR', # patch configs 'PatchRaySampler': True, # 'patch_rendering_resolution': 32, # 'patch_rendering_resolution': 48, 'patch_rendering_resolution': opts.patch_rendering_resolution, }) rendering_options['z_near'] = rendering_options['radius_range'][0]+rendering_options['sampler_bbox_min'] rendering_options['z_far'] = rendering_options['radius_range'][1]+rendering_options['sampler_bbox_max'] elif opts.cfg == 'objverse_tuneray_aug_resolution_96_96_auto': # to differentiate hwc rendering_options.update({ 'depth_resolution': 96, 'depth_resolution_importance': 96, # * radius 1.2 setting, newly rendered images 'ray_start': 'auto', 'ray_end': 'auto', 'box_warp': 0.9, 'white_back': True, 'radius_range': [1.5,2], 'sampler_bbox_min': -0.45, 'sampler_bbox_max': 0.45, 'filter_out_of_bbox': True, 'PatchRaySampler': True, 'patch_rendering_resolution': opts.patch_rendering_resolution, }) rendering_options['z_near'] = rendering_options['radius_range'][0]+rendering_options['sampler_bbox_min'] rendering_options['z_far'] = rendering_options['radius_range'][1]+rendering_options['sampler_bbox_max'] elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_96_nearestResidualSR': # to differentiate hwc rendering_options.update({ 'depth_resolution': 96, 'depth_resolution_importance': 96, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], 'superresolution_module': 'utils.torch_utils.components.NearestConvSR_Residual', }) elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_64_nearestResidualSR': # to differentiate hwc rendering_options.update({ 'depth_resolution': 64, 'depth_resolution_importance': 64, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], 'superresolution_module': 'utils.torch_utils.components.NearestConvSR_Residual', }) elif opts.cfg == 'shapenet_tuneray_aug_resolution_64_104': # to differentiate hwc rendering_options.update({ 'depth_resolution': 104, 'depth_resolution_importance': 104, # * radius 1.2 setting, newly rendered images 'ray_start': opts.ray_start, 'ray_end': opts.ray_end, 'box_warp': opts.ray_end - opts.ray_start, # TODO, how to set this value? 'white_back': True, 'avg_camera_radius': 1.2, 'avg_camera_pivot': [0, 0, 0], }) rendering_options.update({'return_sampling_details_flag': True}) rendering_options.update({'return_sampling_details_flag': True}) return rendering_options def model_encoder_defaults(): return dict( use_clip=False, arch_encoder="vits", arch_decoder="vits", load_pretrain_encoder=False, encoder_lr=1e-5, encoder_weight_decay= 0.001, # https://github.com/google-research/vision_transformer no_dim_up_mlp=False, dim_up_mlp_as_func=False, decoder_load_pretrained=True, uvit_skip_encoder=False, # vae ldm vae_p=1, ldm_z_channels=4, ldm_embed_dim=4, use_conf_map=False, # sd E, lite version by default sd_E_ch=64, z_channels=3*4, sd_E_num_res_blocks=1, num_frames=4, # vit_decoder arch_dit_decoder='DiT2-B/2', return_all_dit_layers=False, # sd D # sd_D_ch=32, # sd_D_res_blocks=1, # sd_D_res_blocks=1, lrm_decoder=False, plane_n=3, gs_rendering=False, ) def triplane_decoder_defaults(): opts = dict( triplane_fg_bg=False, cfg='shapenet', density_reg=0.25, density_reg_p_dist=0.004, reg_type='l1', triplane_decoder_lr=0.0025, # follow eg3d G lr super_resolution_lr=0.0025, # triplane_decoder_wd=0.1, c_scale=1, nsr_lr=0.02, triplane_size=224, decoder_in_chans=32, triplane_in_chans=-1, decoder_output_dim=3, out_chans=96, c_dim=25, # Conditioning label (C) dimensionality. # ray_start=0.2, # ray_end=2.2, ray_start=0.6, # shapenet default ray_end=1.8, rendering_kwargs={}, sr_training=False, bcg_synthesis=False, # from panohead bcg_synthesis_kwargs={}, # G_kwargs.copy() # image_size=128, # raw 3D rendering output resolution. patch_rendering_resolution=45, ) # else: # assert False, "Need to specify config" # opts = dict(opts) # opts.pop('cfg') return opts def vit_decoder_defaults(): res = dict( vit_decoder_lr=1e-5, # follow eg3d G lr vit_decoder_wd=0.001, ) return res def nsr_decoder_defaults(): res = { 'decomposed': False, } # TODO, add defaults for all nsr res.update(triplane_decoder_defaults()) # triplane by default now res.update(vit_decoder_defaults()) # type: ignore return res def loss_defaults(): opt = dict( color_criterion='mse', l2_lambda=1.0, lpips_lambda=0., lpips_delay_iter=0, sr_delay_iter=0, # kl_anneal=0, kl_anneal=False, latent_lambda=0., latent_criterion='mse', kl_lambda=0.0, # kl_anneal=False, ssim_lambda=0., l1_lambda=0., id_lambda=0.0, depth_lambda=0.0, # TODO alpha_lambda=0.0, # TODO fg_mse=False, bg_lamdba=0.0, density_reg=0.0, # tvloss in eg3d density_reg_p_dist=0.004, # 'density regularization strength.' density_reg_every=4, # lazy density reg # 3D supervision, ffhq/afhq eg3d warm up shape_uniform_lambda=0.005, shape_importance_lambda=0.01, shape_depth_lambda=0., # gan loss rec_cvD_lambda=0.01, nvs_cvD_lambda=0.025, patchgan_disc_factor=0.01, patchgan_disc_g_weight=0.2, # r1_gamma=1.0, # ffhq default value for eg3d sds_lamdba=1.0, nvs_D_lr_mul=1, # compared with 1e-4 cano_D_lr_mul=1, # compared with 1e-4 # lsgm loss ce_balanced_kl=1., p_eps_lambda=1, # symmetric loss symmetry_loss=False, depth_smoothness_lambda=0.0, ce_lambda=1.0, negative_entropy_lambda=1.0, grad_clip=False, online_mask=False, # in unsup3d ) return opt def dataset_defaults(): res = dict( use_lmdb=False, use_wds=False, use_lmdb_compressed=True, compile=False, interval=1, objv_dataset=False, decode_encode_img_only=False, load_wds_diff=False, load_wds_latent=False, eval_load_wds_instance=True, shards_lst="", eval_shards_lst="", mv_input=False, duplicate_sample=True, orthog_duplicate=False, split_chunk_input=False, # split=8 per chunk load_real=False, four_view_for_latent=False, single_view_for_i23d=False, shuffle_across_cls=False, load_extra_36_view=False, mv_latent_dir='', append_depth=False, plucker_embedding=False, gs_cam_format=False, split_chunk_size=8, ) return res def encoder_and_nsr_defaults(): """ Defaults for image training. """ # ViT configs res = dict( dino_version='v1', encoder_in_channels=3, img_size=[224], patch_size=16, # ViT-S/16 in_chans=384, num_classes=0, embed_dim=384, # Check ViT encoder dim depth=6, num_heads=16, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0.1, attn_drop_rate=0., drop_path_rate=0., norm_layer='nn.LayerNorm', # img_resolution=128, # Output resolution. cls_token=False, # image_size=128, # rendered output resolution. # img_channels=3, # Number of output color channels. encoder_cls_token=False, decoder_cls_token=False, sr_kwargs={}, sr_ratio=2, # sd configs ) # Triplane configs res.update(model_encoder_defaults()) res.update(nsr_decoder_defaults()) res.update( ae_classname='vit.vit_triplane.ViTTriplaneDecomposed') # if add SR return res def create_3DAE_model( arch_encoder, arch_decoder, dino_version='v1', img_size=[224], patch_size=16, in_chans=384, num_classes=0, embed_dim=1024, # Check ViT encoder dim depth=6, num_heads=16, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0.1, attn_drop_rate=0., drop_path_rate=0., # norm_layer=nn.LayerNorm, norm_layer='nn.LayerNorm', out_chans=96, decoder_in_chans=32, triplane_in_chans=-1, decoder_output_dim=32, encoder_cls_token=False, decoder_cls_token=False, c_dim=25, # Conditioning label (C) dimensionality. image_size=128, # Output resolution. img_channels=3, # Number of output color channels. rendering_kwargs={}, load_pretrain_encoder=False, decomposed=True, triplane_size=224, ae_classname='ViTTriplaneDecomposed', use_clip=False, sr_kwargs={}, sr_ratio=2, no_dim_up_mlp=False, dim_up_mlp_as_func=False, decoder_load_pretrained=True, uvit_skip_encoder=False, bcg_synthesis_kwargs={}, # decoder params vae_p=1, ldm_z_channels=4, ldm_embed_dim=4, use_conf_map=False, triplane_fg_bg=False, encoder_in_channels=3, sd_E_ch=64, z_channels=3*4, sd_E_num_res_blocks=1, num_frames=6, arch_dit_decoder='DiT2-B/2', lrm_decoder=False, gs_rendering=False, return_all_dit_layers=False, *args, **kwargs): # TODO, check pre-trained ViT encoder cfgs preprocess = None clip_dtype = None if load_pretrain_encoder: if not use_clip: if dino_version == 'v1': encoder = torch.hub.load( 'facebookresearch/dino:main', 'dino_{}{}'.format(arch_encoder, patch_size)) logger.log( f'loaded pre-trained dino v1 ViT-S{patch_size} encoder ckpt' ) elif dino_version == 'v2': encoder = torch.hub.load( 'facebookresearch/dinov2', 'dinov2_{}{}'.format(arch_encoder, patch_size)) logger.log( f'loaded pre-trained dino v2 {arch_encoder}{patch_size} encoder ckpt' ) elif 'sd' in dino_version: # just for compat if 'mv' in dino_version: if 'lgm' in dino_version: encoder_cls = MVUNet( input_size=256, up_channels=(1024, 1024, 512, 256, 128), # one more decoder up_attention=(True, True, True, False, False), splat_size=128, output_size= 512, # render & supervise Gaussians at a higher resolution. batch_size=8, num_views=8, gradient_accumulation_steps=1, # mixed_precision='bf16', ) elif 'gs' in dino_version: encoder_cls = MVEncoder else: encoder_cls = MVEncoder else: encoder_cls = Encoder encoder = encoder_cls( # mono input double_z=True, resolution=256, in_channels=encoder_in_channels, # ch=128, ch=64, # ! fit in the memory # ch_mult=[1,2,4,4], # num_res_blocks=2, ch_mult=[1, 2, 4, 4], num_res_blocks=1, dropout=0.0, attn_resolutions=[], out_ch=3, # unused z_channels=4 * 3, ) # stable diffusion encoder else: raise NotImplementedError() else: import clip model, preprocess = clip.load("ViT-B/16", device=dist_util.dev()) model.float() # convert weight to float32 clip_dtype = model.dtype encoder = getattr( model, 'visual') # only use the CLIP visual encoder here encoder.requires_grad_(False) logger.log( f'loaded pre-trained CLIP ViT-B{patch_size} encoder, fixed.') elif 'sd' in dino_version: attn_kwargs = {} if 'mv' in dino_version: if 'lgm' in dino_version: encoder = LGM_MVEncoder( in_channels=9, # input_size=256, up_channels=(1024, 1024, 512, 256, 128), # one more decoder up_attention=(True, True, True, False, False), ) else: if 'dynaInp' in dino_version: encoder_cls = MVEncoderGSDynamicInp else: encoder_cls = MVEncoder attn_kwargs = { 'n_heads': 8, 'd_head': 64, } else: encoder_cls = Encoder if 'lgm' not in dino_version: # TODO, for compat now # st() encoder = encoder_cls( double_z=True, resolution=256, in_channels=encoder_in_channels, # ch=128, # ch=64, # ! fit in the memory ch=sd_E_ch, # ch_mult=[1,2,4,4], # num_res_blocks=2, ch_mult=[1, 2, 4, 4], # num_res_blocks=1, num_res_blocks=sd_E_num_res_blocks, num_frames=num_frames, dropout=0.0, attn_resolutions=[], out_ch=3, # unused z_channels=z_channels, # 4 * 3 attn_kwargs=attn_kwargs, ) # stable diffusion encoder else: encoder = vits.__dict__[arch_encoder]( patch_size=patch_size, drop_path_rate=drop_path_rate, # stochastic depth img_size=img_size) # assert decomposed # if decomposed: if triplane_in_chans == -1: triplane_in_chans = decoder_in_chans # if triplane_fg_bg: # triplane_renderer_cls = Triplane_fg_bg_plane # else: triplane_renderer_cls = Triplane # triplane_decoder = Triplane( triplane_decoder = triplane_renderer_cls( c_dim, # Conditioning label (C) dimensionality. image_size, # Output resolution. img_channels, # Number of output color channels. rendering_kwargs=rendering_kwargs, out_chans=out_chans, # create_triplane=True, # compatability, remove later triplane_size=triplane_size, decoder_in_chans=triplane_in_chans, decoder_output_dim=decoder_output_dim, sr_kwargs=sr_kwargs, bcg_synthesis_kwargs=bcg_synthesis_kwargs, lrm_decoder=lrm_decoder) if load_pretrain_encoder: if dino_version == 'v1': vit_decoder = torch.hub.load( 'facebookresearch/dino:main', 'dino_{}{}'.format(arch_decoder, patch_size)) logger.log( 'loaded pre-trained decoder', "facebookresearch/dino:main', 'dino_{}{}".format( arch_decoder, patch_size)) else: vit_decoder = torch.hub.load( 'facebookresearch/dinov2', # 'dinov2_{}{}'.format(arch_decoder, patch_size)) 'dinov2_{}{}'.format(arch_decoder, patch_size), pretrained=decoder_load_pretrained) logger.log( 'loaded pre-trained decoder', "facebookresearch/dinov2', 'dinov2_{}{}".format( arch_decoder, patch_size), 'pretrianed=', decoder_load_pretrained) elif 'dit' in dino_version: from dit.dit_decoder import DiT2_models vit_decoder = DiT2_models[arch_dit_decoder]( input_size=16, num_classes=0, learn_sigma=False, in_channels=embed_dim, mixed_prediction=False, context_dim=None, # add CLIP text embedding roll_out=True, plane_n=4 if 'gs' in dino_version else 3, return_all_layers=return_all_dit_layers, ) else: # has bug on global token, to fix vit_decoder = vits.__dict__[arch_decoder]( patch_size=patch_size, drop_path_rate=drop_path_rate, # stochastic depth img_size=img_size) # decoder = ViTTriplaneDecomposed(vit_decoder, triplane_decoder) # if True: decoder_kwargs = dict( class_name=ae_classname, vit_decoder=vit_decoder, triplane_decoder=triplane_decoder, # encoder_cls_token=encoder_cls_token, cls_token=decoder_cls_token, sr_ratio=sr_ratio, vae_p=vae_p, ldm_z_channels=ldm_z_channels, ldm_embed_dim=ldm_embed_dim, ) decoder = dnnlib.util.construct_class_by_name(**decoder_kwargs) # if return_encoder_decoder: # return encoder, decoder, img_size[0], cls_token # else: if use_conf_map: confnet = ConfNet(cin=3, cout=1, nf=64, zdim=128) else: confnet = None auto_encoder = AE( encoder, decoder, img_size[0], encoder_cls_token, decoder_cls_token, preprocess, use_clip, dino_version, clip_dtype, no_dim_up_mlp=no_dim_up_mlp, dim_up_mlp_as_func=dim_up_mlp_as_func, uvit_skip_encoder=uvit_skip_encoder, confnet=confnet, ) logger.log(auto_encoder) torch.cuda.empty_cache() return auto_encoder # def create_3DAE_Diffusion_model( # arch_encoder, # arch_decoder, # img_size=[224], # patch_size=16, # in_chans=384, # num_classes=0, # embed_dim=1024, # Check ViT encoder dim # depth=6, # num_heads=16, # mlp_ratio=4., # qkv_bias=False, # qk_scale=None, # drop_rate=0.1, # attn_drop_rate=0., # drop_path_rate=0., # # norm_layer=nn.LayerNorm, # norm_layer='nn.LayerNorm', # out_chans=96, # decoder_in_chans=32, # decoder_output_dim=32, # cls_token=False, # c_dim=25, # Conditioning label (C) dimensionality. # img_resolution=128, # Output resolution. # img_channels=3, # Number of output color channels. # rendering_kwargs={}, # load_pretrain_encoder=False, # decomposed=True, # triplane_size=224, # ae_classname='ViTTriplaneDecomposed', # # return_encoder_decoder=False, # *args, # **kwargs # ): # # TODO, check pre-trained ViT encoder cfgs # encoder, decoder, img_size, cls_token = create_3DAE_model( # arch_encoder, # arch_decoder, # img_size, # patch_size, # in_chans, # num_classes, # embed_dim, # Check ViT encoder dim # depth, # num_heads, # mlp_ratio, # qkv_bias, # qk_scale, # drop_rate, # attn_drop_rate, # drop_path_rate, # # norm_layer=nn.LayerNorm, # norm_layer, # out_chans=96, # decoder_in_chans=32, # decoder_output_dim=32, # cls_token=False, # c_dim=25, # Conditioning label (C) dimensionality. # img_resolution=128, # Output resolution. # img_channels=3, # Number of output color channels. # rendering_kwargs={}, # load_pretrain_encoder=False, # decomposed=True, # triplane_size=224, # ae_classname='ViTTriplaneDecomposed', # return_encoder_decoder=False, # *args, # **kwargs # ) # type: ignore def create_Triplane( c_dim=25, # Conditioning label (C) dimensionality. img_resolution=128, # Output resolution. img_channels=3, # Number of output color channels. rendering_kwargs={}, decoder_output_dim=32, *args, **kwargs): decoder = Triplane( c_dim, # Conditioning label (C) dimensionality. img_resolution, # Output resolution. img_channels, # Number of output color channels. # TODO, replace with c rendering_kwargs=rendering_kwargs, create_triplane=True, decoder_output_dim=decoder_output_dim) return decoder def DiT_defaults(): return { 'dit_model': "DiT-B/16", 'vae': "ema" # dit_model="DiT-XL/2", # dit_patch_size=8, }