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Functions380 in github.com/TencentARC/MotionCtrl

⨍ Functions 380◇ Types & classes 83
↓ 1 callersMethodinit_
(self)
lvdm/modules/x_transformer.py:31
↓ 1 callersMethodinit_
(self)
lvdm/modules/x_transformer.py:595
↓ 1 callersMethodinit_from_ckpt
(self, path, ignore_keys=list())
lvdm/models/autoencoder.py:82
↓ 1 callersMethodinit_test
(self,)
lvdm/models/autoencoder.py:53
↓ 1 callersMethodinstantiate_cond_stage
(self, config)
lvdm/models/ddpm3d.py:538
↓ 1 callersMethodinstantiate_first_stage
(self, config)
lvdm/models/ddpm3d.py:531
↓ 1 callersMethodinstantiate_pretrained
(self, config)
lvdm/modules/networks/ae_modules.py:819
↓ 1 callersFunctionlocal_spatial_temporal_attn_reshape
(x, window_size)
lvdm/modules/attention_temporal.py:674
↓ 1 callersFunctionlocal_spatial_temporal_attn_reshape_back
(x, window_size, b, h, w, t)
lvdm/modules/attention_temporal.py:684
↓ 1 callersFunctionmain
(args)
app.py:320
↓ 1 callersFunctionmake_beta_schedule
(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3)
lvdm/models/utils_diffusion.py:32
↓ 1 callersMethodmake_cond_schedule
(self, )
lvdm/models/ddpm3d.py:499
↓ 1 callersFunctionmake_ddim_sampling_parameters
(alphacums, ddim_timesteps, eta, verbose=True)
lvdm/models/utils_diffusion.py:74
↓ 1 callersFunctionmake_ddim_timesteps
(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True)
lvdm/models/utils_diffusion.py:57
↓ 1 callersMethodmake_schedule
(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True)
lvdm/models/samplers/ddim.py:26
↓ 1 callersFunctionmax_neg_value
(tensor)
lvdm/modules/x_transformer.py:82
↓ 1 callersFunctionmesh_grid
Generate the mesh grid, centering at zero. Args: kernel_size (int): Returns: xy (ndarray): with the shape (kernel_size, kernel
gradio_utils/flow_utils.py:18
↓ 1 callersFunctionmotion_segmentation
(args, image_dir, output_dir, traj_dir, skip_exists=False, keep_intermediate=False)
dataset/object_trajectories/run_particlesfm_obj_traj.py:87
↓ 1 callersFunctionmotionctrl_sample
( model, prompts, noise_shape, camera_poses=None, trajs=None,
main/evaluation/motionctrl_inference.py:123
↓ 1 callersFunctionnot_equals
(val)
lvdm/modules/x_transformer.py:70
↓ 1 callersMethodp_losses
(self, x_start, t, noise=None)
lvdm/models/ddpm3d.py:292
↓ 1 callersMethodp_losses
(self, x_start, cond, t, noise=None, **kwargs)
lvdm/models/ddpm3d.py:687
↓ 1 callersMethodp_mean_variance
(self, x, t, clip_denoised: bool)
lvdm/models/ddpm3d.py:229
↓ 1 callersMethodp_mean_variance
(self, x, c, t, clip_denoised: bool, return_x0=False, score_corrector=None, corrector_kwargs=None, **kwargs)
lvdm/models/ddpm3d.py:836
↓ 1 callersMethodp_sample
(self, x, t, clip_denoised=True, repeat_noise=False)
lvdm/models/ddpm3d.py:242
↓ 1 callersMethodp_sample
(self, x, c, t, clip_denoised=False, repeat_noise=False, return_x0=False, \ temperature=1., n
lvdm/models/ddpm3d.py:862
↓ 1 callersMethodp_sample_ddim
(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
lvdm/models/samplers/ddim.py:189
↓ 1 callersMethodp_sample_loop
(self, shape, return_intermediates=False)
lvdm/models/ddpm3d.py:251
↓ 1 callersMethodp_sample_loop
(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None, \
lvdm/models/ddpm3d.py:884
↓ 1 callersFunctionparse_args
()
dataset/object_trajectories/run_particlesfm_obj_traj.py:163
↓ 1 callersFunctionparse_args
()
dataset/object_trajectories/prepare_webvideo_len32.py:19
↓ 1 callersFunctionpdf2
Calculate PDF of the bivariate Gaussian distribution. Args: sigma_matrix (ndarray): with the shape (2, 2) grid (ndarray): generate
gradio_utils/flow_utils.py:34
↓ 1 callersMethodpreprocess
(self, x)
lvdm/modules/encoders/condition2.py:158
↓ 1 callersMethodpreprocess
(self, x)
lvdm/modules/encoders/condition2.py:269
↓ 1 callersFunctionprocess_traj
(points, device='cpu')
gradio_utils/traj_utils.py:96
↓ 1 callersMethodquantize
(self, x, *args, **kwargs)
lvdm/models/autoencoder.py:215
↓ 1 callersFunctionread_points
(file, video_len=16, reverse=False)
gradio_utils/traj_utils.py:20
↓ 1 callersMethodrestore
Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the
lvdm/ema.py:64
↓ 1 callersFunctionrun_inference
(args, gpu_num, gpu_no)
main/evaluation/motionctrl_inference.py:192
↓ 1 callersMethodsample
(self, cond, batch_size=16, return_intermediates=False, x_T=None, \ verbose=True, timesteps=Non
lvdm/models/ddpm3d.py:931
↓ 1 callersMethodsample_log
(self, cond, batch_size, ddim, ddim_steps, **kwargs)
lvdm/models/ddpm3d.py:948
↓ 1 callersFunctionsave_images
(samples, savedir)
main/evaluation/motionctrl_inference.py:303
↓ 1 callersFunctionsave_results
(video, fps=10, out_dir=None)
app.py:293
↓ 1 callersFunctionsave_results
(samples, filename, savedir, fps=10)
main/evaluation/motionctrl_inference.py:102
↓ 1 callersMethodshared_step
(self, batch, random_uncond, **kwargs)
lvdm/models/ddpm3d.py:654
↓ 1 callersFunctionsigma_matrix2
Calculate the rotated sigma matrix (two dimensional matrix). Args: sig_x (float): sig_y (float): theta (float): Radian mea
gradio_utils/flow_utils.py:4
↓ 1 callersFunctionspatial_attn_reshape
(x)
lvdm/modules/attention_temporal.py:666
↓ 1 callersFunctionspatial_attn_reshape_back
(x,b,h)
lvdm/modules/attention_temporal.py:668
↓ 1 callersMethodstore
Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be
lvdm/ema.py:55
↓ 1 callersMethodtext_transformer_forward
(self, x: torch.Tensor, attn_mask=None)
lvdm/modules/encoders/condition2.py:227
FunctionTemporalTransformer_forward
(self, x, context=None, is_imgbatch=False)
motionctrl/lvdm_modified_modules.py:19
Method__init__
(self, omcm_config=None, pose_dim=12, context_dim=1024,
motionctrl/motionctrl.py:13
Method__init__
(self, model, decay=0.9999, use_num_upates=True)
lvdm/ema.py:6
Method__init__
(self, c_concat_config, c_crossattn_config)
lvdm/basics.py:98
Method__init__
(self, value)
lvdm/distributions.py:14
Method__init__
(self, parameters, deterministic=False)
lvdm/distributions.py:25
Method__init__
(self, dim, dim_out=None, mult=4, glu=False, dropout=0.)
lvdm/modules/attention_temporal.py:42
Method__init__
(self, num_units, max_relative_position)
lvdm/modules/attention_temporal.py:69
Method__init__
(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout
lvdm/modules/attention_temporal.py:91
Method__init__
(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., sa_shared_kv=False, sha
lvdm/modules/attention_temporal.py:271
Method__init__
(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0)
lvdm/modules/attention_temporal.py:356
Method__init__
(self, # Spatial Stuff dim, n_heads, d_head, dropout=0.,
lvdm/modules/attention_temporal.py:366
Method__init__
( self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None,
lvdm/modules/attention_temporal.py:700
Method__init__
version 1: guided_diffusion implemented version version 2: remove args input argument
lvdm/modules/attention_temporal.py:773
Method__init__
(self, n_heads)
lvdm/modules/attention_temporal.py:885
Method__init__
(self, n_heads)
lvdm/modules/attention_temporal.py:921
Method__init__
(self, dim, max_seq_len)
lvdm/modules/x_transformer.py:26
Method__init__
(self, dim)
lvdm/modules/x_transformer.py:40
Method__init__
(self, fn)
lvdm/modules/x_transformer.py:129
Method__init__
(self, dim, eps=1e-5)
lvdm/modules/x_transformer.py:140
Method__init__
(self, dim, eps=1e-8)
lvdm/modules/x_transformer.py:152
Method__init__
(self, dim)
lvdm/modules/x_transformer.py:169
Method__init__
(self, dim_in, dim_out)
lvdm/modules/x_transformer.py:185
Method__init__
(self, dim, dim_out=None, mult=4, glu=False, dropout=0.)
lvdm/modules/x_transformer.py:195
Method__init__
( self, dim, dim_head=DEFAULT_DIM_HEAD, heads=8, c
lvdm/modules/x_transformer.py:216
Method__init__
( self, dim, depth, heads=8, causal=False,
lvdm/modules/x_transformer.py:371
Method__init__
(self, **kwargs)
lvdm/modules/x_transformer.py:542
Method__init__
( self, *, num_tokens, max_seq_len, attn_layers,
lvdm/modules/x_transformer.py:549
Method__init__
(self, num_units, max_relative_position)
lvdm/modules/attention.py:23
Method__init__
(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., relative_position=Fals
lvdm/modules/attention.py:46
Method__init__
(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, dis
lvdm/modules/attention.py:136
Method__init__
(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, use_checkpoint=Tr
lvdm/modules/attention.py:181
Method__init__
(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None, use_checkpoint=Tr
lvdm/modules/attention.py:235
Method__init__
(self, dim, dim_out=None, mult=4, glu=False, dropout=0.)
lvdm/modules/attention.py:340
Method__init__
(self, dim, heads=4, dim_head=32)
lvdm/modules/attention.py:360
Method__init__
(self, in_channels)
lvdm/modules/attention.py:379
Method__init__
(self, in_channels)
lvdm/modules/networks/ae_modules.py:24
Method__init__
(self, in_channels)
lvdm/modules/networks/ae_modules.py:29
Method__init__
(self, in_channels, with_conv)
lvdm/modules/networks/ae_modules.py:93
Method__init__
(self, in_channels, with_conv)
lvdm/modules/networks/ae_modules.py:114
Method__init__
(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512)
lvdm/modules/networks/ae_modules.py:154
Method__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
lvdm/modules/networks/ae_modules.py:367
Method__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
lvdm/modules/networks/ae_modules.py:469
Method__init__
(self, in_channels, out_channels, *args, **kwargs)
lvdm/modules/networks/ae_modules.py:584
Method__init__
(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2,2), dropout=0.0)
lvdm/modules/networks/ae_modules.py:620
Method__init__
(self, factor, in_channels, mid_channels, out_channels, depth=2)
lvdm/modules/networks/ae_modules.py:668
Method__init__
(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, re
lvdm/modules/networks/ae_modules.py:705
Method__init__
(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
lvdm/modules/networks/ae_modules.py:724
Method__init__
(self, in_size, out_size, in_channels, out_channels, ch_mult=2)
lvdm/modules/networks/ae_modules.py:741
Method__init__
(self, in_channels=None, learned=False, mode="bilinear")
lvdm/modules/networks/ae_modules.py:760
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