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Functions4,150 in github.com/Adamdad/hash3D

⨍ Functions 4,150◇ Types & classes 914↳ Endpoints 2
↓ 2 callersFunctioncameraList_from_camInfos
(cam_infos, resolution_scale, args)
gaussiandreamer-hash3d/gaussiansplatting/utils/camera_utils.py:54
↓ 2 callersFunctionclamp
(v, T=0.0001)
gaussiandreamer-hash3d/threestudio/utils/ops.py:376
↓ 2 callersFunctioncleanup_and_exit
()
threestudio-hash3d/gradio_app.py:502
↓ 2 callersFunctioncolor_map_forward
(rgb)
eval_nvs.py:21
↓ 2 callersMethodcompute_activations
Compute image features for downstream evals. :param batches: a iterator over NHWC numpy arrays in [0, 255]. :return: a tuple
threestudio-hash3d/extern/ldm_zero123/modules/evaluate/adm_evaluator.py:170
↓ 2 callersFunctioncompute_stats
(feats: np.ndarray)
threestudio-hash3d/extern/ldm_zero123/modules/evaluate/torch_frechet_video_distance.py:36
↓ 2 callersMethodconfigure_pipe_token_merging
(self, pipe: StableDiffusionPipeline)
threestudio-hash3d/threestudio/models/guidance/stable_diffusion_unified_guidance.py:277
↓ 2 callersFunctionconv_1x1_bn
( inp, oup, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU )
gaussiandreamer-hash3d/threestudio/utils/GAN/mobilenet.py:23
↓ 2 callersFunctionconv_1x1_bn
( inp, oup, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU )
threestudio-hash3d/threestudio/utils/GAN/mobilenet.py:23
↓ 2 callersFunctionconv_attn_to_linear
(checkpoint)
gaussiandreamer-hash3d/scripts/convert_zero123_to_diffusers.py:859
↓ 2 callersFunctionconv_attn_to_linear
(checkpoint)
threestudio-hash3d/scripts/convert_zero123_to_diffusers.py:859
↓ 2 callersFunctionconvert_proj
(K, H, W, near, far)
gaussiandreamer-hash3d/threestudio/data/multiview.py:32
↓ 2 callersFunctionconvert_proj
(K, H, W, near, far)
threestudio-hash3d/threestudio/data/multiview.py:32
↓ 2 callersFunctioncount_flops_attn
A counter for the `thop` package to count the operations in an attention operation. Meant to be used like: macs, params = thop.pr
threestudio-hash3d/extern/ldm_zero123/modules/diffusionmodules/openaimodel.py:343
↓ 2 callersFunctioncpu_deep_copy_tuple
(input_tuple)
gaussiandreamer-hash3d/gaussiansplatting/submodules/diff-gaussian-rasterization/diff_gaussian_rasterization/__init__.py:17
↓ 2 callersMethodcreate_from_pcd
(self, pcd : BasicPointCloud, spatial_lr_scale : float = 1)
dreamgaussian-hash3d/gs_renderer.py:331
↓ 2 callersMethodcreate_from_pcd
(self, pcd : BasicPointCloud, spatial_lr_scale : float)
gaussiandreamer-hash3d/gaussiansplatting/scene/gaussian_model.py:124
↓ 2 callersFunctioncubic
(x)
threestudio-hash3d/extern/ldm_zero123/modules/image_degradation/utils_image.py:760
↓ 2 callersMethoddecode
(self, text)
threestudio-hash3d/extern/ldm_zero123/modules/encoders/modules.py:173
↓ 2 callersMethoddecode
(self, z)
threestudio-hash3d/extern/ldm_zero123/models/autoencoder.py:388
↓ 2 callersMethoddecode_latents
(self, latents)
dreamgaussian-hash3d/guidance/mvdream_utils.py:192
↓ 2 callersMethoddecode_latents
(self, latents)
dreamgaussian-hash3d/guidance/sd_utils.py:252
↓ 2 callersMethoddecode_latents
(self, latents)
dreamgaussian-hash3d/guidance/imagedream_utils.py:216
↓ 2 callersMethoddecode_latents
( self, latents: Float[Tensor, "B 4 H W"], latent_height: int = 64, latent_wid
threestudio-hash3d/threestudio/models/guidance/stable_diffusion_vsd_guidance.py:432
↓ 2 callersMethoddelta_border
:param h: height :param w: width :return: normalized distance to image border, wtith min distance = 0 at border and
threestudio-hash3d/extern/ldm_zero123/models/diffusion/ddpm.py:799
↓ 2 callersMethoddensification_postfix
(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation)
dreamgaussian-hash3d/gs_renderer.py:535
↓ 2 callersMethoddensification_postfix
(self, new_xyz, new_features_dc, new_features_rest, new_opacities, new_scaling, new_rotation)
gaussiandreamer-hash3d/gaussiansplatting/scene/gaussian_model.py:339
↓ 2 callersMethoddensify_and_prune
(self, max_grad, min_opacity, extent, max_screen_size)
dreamgaussian-hash3d/gs_renderer.py:597
↓ 2 callersMethoddensify_and_prune
(self, max_grad, min_opacity, extent, max_screen_size)
gaussiandreamer-hash3d/gaussiansplatting/scene/gaussian_model.py:399
↓ 2 callersMethoddisable_unet_class_embedding
(self, unet: UNet2DConditionModel)
threestudio-hash3d/threestudio/models/guidance/stable_diffusion_vsd_guidance.py:448
↓ 2 callersMethoddisable_unet_class_embedding
(self, unet: UNet2DConditionModel)
threestudio-hash3d/threestudio/models/guidance/stable_diffusion_unified_guidance.py:338
↓ 2 callersFunctiondot
(x, y)
dreamgaussian-hash3d/mesh.py:7
↓ 2 callersMethodema_scope
(self, context=None)
threestudio-hash3d/extern/ldm_zero123/models/autoencoder.py:72
↓ 2 callersMethodema_scope
(self, context=None)
threestudio-hash3d/extern/ldm_zero123/models/diffusion/ddpm.py:253
↓ 2 callersMethodenable_attention_slicing
r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compu
dreamgaussian-hash3d/DeepCache/sd/pipeline_utils.py:1783
↓ 2 callersMethodenable_sequential_cpu_offload
r""" Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state dicts of all `torch.nn
dreamgaussian-hash3d/DeepCache/sdxl/pipeline_utils.py:1297
↓ 2 callersMethodenable_sequential_cpu_offload
r""" Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state dicts of all `torch.nn
gaussiandreamer-hash3d/DeepCache/sdxl/pipeline_utils.py:1297
↓ 2 callersMethodenable_sequential_cpu_offload
r""" Offloads all models to CPU using 🤗 Accelerate, significantly reducing memory usage. When called, the state dicts of all `torch.nn
threestudio-hash3d/DeepCache/sdxl/pipeline_utils.py:1297
↓ 2 callersMethodencode_imgs
(self, imgs)
dreamgaussian-hash3d/guidance/mvdream_utils.py:197
↓ 2 callersMethodencode_imgs
(self, imgs)
dreamgaussian-hash3d/guidance/sd_utils.py:260
↓ 2 callersMethodencode_prompt
r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*):
dreamgaussian-hash3d/DeepCache/sd/pipeline_stable_diffusion.py:301
↓ 2 callersMethodevaluate_pr
Evaluate precision and recall efficiently. :param features_1: [N1 x D] feature vectors for reference batch. :param radii_1:
threestudio-hash3d/extern/ldm_zero123/modules/evaluate/adm_evaluator.py:370
↓ 2 callersFunctionexists
(val)
gaussiandreamer-hash3d/threestudio/utils/GAN/attention.py:12
↓ 2 callersFunctionexists
(val)
threestudio-hash3d/threestudio/utils/GAN/attention.py:12
↓ 2 callersMethodexport_mesh
(self, save_path)
dreamgaussian-hash3d/mesh_renderer.py:73
↓ 2 callersFunctionfetchPly
(path)
gaussiandreamer-hash3d/gaussiansplatting/scene/dataset_readers.py:107
↓ 2 callersMethodfind_in_interval
(self, n)
threestudio-hash3d/extern/ldm_zero123/lr_scheduler.py:76
↓ 2 callersFunctionfind_last_path
(path: str)
threestudio-hash3d/threestudio/utils/misc.py:143
↓ 2 callersMethodforward
(self, hidden_states: torch.Tensor)
dreamgaussian-hash3d/DeepCache/sd/lora.py:217
↓ 2 callersMethodforward
(self, hidden_states: torch.Tensor)
gaussiandreamer-hash3d/DeepCache/sd/lora.py:217
↓ 2 callersMethodforward
(self, hidden_states: torch.Tensor)
threestudio-hash3d/DeepCache/sd/lora.py:217
↓ 2 callersMethodforward_control_unet
( self, latents: Float[Tensor, "..."], t: Float[Tensor, "..."], encoder_hidden
threestudio-hash3d/threestudio/models/guidance/controlnet_guidance.py:166
↓ 2 callersMethodforward_controlnet
( self, latents: Float[Tensor, "..."], t: Float[Tensor, "..."], image_cond: Fl
threestudio-hash3d/threestudio/models/guidance/controlnet_guidance.py:148
↓ 2 callersMethodforward_unet
( self, latents: Float[Tensor, "..."], t: Float[Tensor, "..."], encoder_hidden
threestudio-hash3d/threestudio/models/guidance/instructpix2pix_guidance.py:121
↓ 2 callersMethodfrechet_distance
Compute the Frechet distance between two sets of statistics.
threestudio-hash3d/extern/ldm_zero123/modules/evaluate/adm_evaluator.py:98
↓ 2 callersMethodfrom_pretrained
r""" Instantiate a PyTorch diffusion pipeline from pretrained pipeline weights. The pipeline is set in evaluation mode (`model.eval()
gaussiandreamer-hash3d/DeepCache/sd/pipeline_utils.py:769
↓ 2 callersFunctiongetNerfppNorm
(cam_info)
gaussiandreamer-hash3d/gaussiansplatting/scene/dataset_readers.py:45
↓ 2 callersMethodgetTestCameras
(self, scale=1.0)
gaussiandreamer-hash3d/gaussiansplatting/scene/__init__.py:92
↓ 2 callersMethodget_activated_density
( self, points: Float[Tensor, "*N Di"], density: Float[Tensor, "*N 1"] )
gaussiandreamer-hash3d/threestudio/models/geometry/implicit_volume.py:77
↓ 2 callersMethodget_activated_density
( self, points: Float[Tensor, "*N Di"], density: Float[Tensor, "*N 1"] )
threestudio-hash3d/threestudio/models/geometry/implicit_volume.py:80
↓ 2 callersMethodget_all_images
(self)
threestudio-hash3d/threestudio/data/co3d.py:524
↓ 2 callersMethodget_cam_embeddings
(self, elevation, azimuth, radius, default_elevation=0)
dreamgaussian-hash3d/guidance/zero123_utils.py:66
↓ 2 callersMethodget_cam_embeddings
(self, polar, azimuth, radius)
dreamgaussian-hash3d/guidance/zero123_cache_utils.py:90
↓ 2 callersFunctionget_ckpt_path
(name, root, check=False)
gaussiandreamer-hash3d/threestudio/utils/perceptual/utils.py:32
↓ 2 callersFunctionget_ckpt_path
(name, root, check=False)
threestudio-hash3d/threestudio/utils/perceptual/utils.py:32
↓ 2 callersMethodget_cond
( self, elevation: Float[Tensor, "B"], azimuth: Float[Tensor, "B"], camera_dis
threestudio-hash3d/threestudio/models/guidance/zero123_guidance.py:206
↓ 2 callersMethodget_conditioning
(self, batch, k=None)
threestudio-hash3d/extern/ldm_zero123/models/diffusion/classifier.py:164
↓ 2 callersFunctionget_current_status
(process, trial_dir, alive_path)
threestudio-hash3d/gradio_app.py:114
↓ 2 callersFunctionget_data_from_str
(input_str, nprc=None)
threestudio-hash3d/extern/ldm_zero123/modules/evaluate/torch_frechet_video_distance.py:137
↓ 2 callersMethodget_density_bias
(self, points: Float[Tensor, "*N Di"])
gaussiandreamer-hash3d/threestudio/models/geometry/volume_grid.py:48
↓ 2 callersMethodget_density_bias
(self, points: Float[Tensor, "*N Di"])
threestudio-hash3d/threestudio/models/geometry/volume_grid.py:48
↓ 2 callersMethodget_grayscale_image_
(self, img, data_range, cmap)
gaussiandreamer-hash3d/threestudio/utils/saving.py:179
↓ 2 callersMethodget_grayscale_image_
(self, img, data_range, cmap)
threestudio-hash3d/threestudio/utils/saving.py:179
↓ 2 callersMethodget_image_text_embeds
(self, image, prompts, negative_prompts)
dreamgaussian-hash3d/guidance/imagedream_utils.py:46
↓ 2 callersMethodget_img_embeds
( self, img: Float[Tensor, "B 3 256 256"], )
threestudio-hash3d/threestudio/models/guidance/zero123_guidance.py:174
↓ 2 callersMethodget_input
(self, batch, k)
threestudio-hash3d/extern/ldm_zero123/models/diffusion/classifier.py:155
↓ 2 callersMethodget_input
( self, batch, k, return_first_stage_outputs=False, force_c_encode=Fal
threestudio-hash3d/extern/ldm_zero123/models/diffusion/ddpm.py:921
↓ 2 callersMethodget_params
(self)
dreamgaussian-hash3d/mesh_renderer.py:61
↓ 2 callersMethodget_save_dir
(self)
gaussiandreamer-hash3d/threestudio/utils/saving.py:29
↓ 2 callersMethodget_uv_image_
(self, img, data_format, data_range, cmap)
gaussiandreamer-hash3d/threestudio/utils/saving.py:143
↓ 2 callersMethodget_uv_image_
(self, img, data_format, data_range, cmap)
threestudio-hash3d/threestudio/utils/saving.py:143
↓ 2 callersFunctiongroup_dict_by_key
(cond, d)
threestudio-hash3d/extern/ldm_zero123/modules/x_transformer.py:96
↓ 2 callersFunctiongroupby_prefix_and_trim
(prefix, d)
threestudio-hash3d/extern/ldm_zero123/modules/x_transformer.py:113
↓ 2 callersMethodguidance_evaluation_save
(self, comp_rgb, guidance_eval_out)
threestudio-hash3d/threestudio/systems/base.py:344
↓ 2 callersMethodinit_from_ckpt
(self, path, ignore_keys=list(), only_model=False)
threestudio-hash3d/extern/ldm_zero123/models/diffusion/ddpm.py:268
↓ 2 callersMethodinitialize
(self, input)
gaussiandreamer-hash3d/threestudio/utils/GAN/discriminator.py:25
↓ 2 callersMethodinitialize
(self, input)
threestudio-hash3d/threestudio/utils/GAN/discriminator.py:25
↓ 2 callersMethodinitialize_shape
(self)
threestudio-hash3d/threestudio/models/geometry/implicit_sdf.py:91
↓ 2 callersFunctioninverse_sigmoid
(x)
dreamgaussian-hash3d/gs_renderer.py:22
↓ 2 callersFunctioninverse_sigmoid
(x)
gaussiandreamer-hash3d/gaussiansplatting/utils/general_utils.py:18
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
dreamgaussian-hash3d/DeepCache/zero123/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
dreamgaussian-hash3d/DeepCache/sdxl/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
dreamgaussian-hash3d/DeepCache/sd/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
gaussiandreamer-hash3d/DeepCache/zero123/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
gaussiandreamer-hash3d/DeepCache/sdxl/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
gaussiandreamer-hash3d/DeepCache/sd/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
threestudio-hash3d/DeepCache/zero123/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
threestudio-hash3d/DeepCache/sdxl/pipeline_utils.py:138
↓ 2 callersFunctionis_safetensors_compatible
Checking for safetensors compatibility: - By default, all models are saved with the default pytorch serialization, so we use the list of defa
threestudio-hash3d/DeepCache/sd/pipeline_utils.py:138
↓ 2 callersMethodisosurface
(self)
gaussiandreamer-hash3d/threestudio/models/geometry/tetrahedra_sdf_grid.py:237
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