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Functions526 in github.com/bbaaii/DreamDiffusion

⨍ Functions 526◇ Types & classes 106

(self, embed_dim, *args, **kwargs)

code/dc_ldm/models/autoencoder.py:386

(self, ddconfig, lossconfig, embed_dim, ck

code/dc_ldm/models/autoencoder.py:407

(self, *args, vq_interface=False, **kwargs)

code/dc_ldm/models/autoencoder.py:549

(self, model, schedule="linear", **kwargs)

code/dc_ldm/models/diffusion/ddim.py:12

(self, diffusion_path, num_classes, ckpt_path=None,

code/dc_ldm/models/diffusion/classifier.py:30

(self, first_stage_config, cond_stage_config, num_timesteps_co

code/dc_ldm/models/diffusion/ddpm.py:574

(self, diff_model_config, conditioning_key)

code/dc_ldm/models/diffusion/ddpm.py:1561

(self, first_stage_config, cond_stage_config, num_timesteps_co

code/dc_ldm/models/diffusion/ddpm.py:1591

(self, model, schedule="linear", **kwargs)

code/dc_ldm/models/diffusion/plms.py:12

code/dataset.py:120

code/dataset.py:186

code/dataset.py:263

code/dataset.py:320

Function_do_parallel_data_prefetch

(func, Q, data, idx, idx_to_fn=False)

code/dc_ldm/util.py:95

(self, x, context=None)

code/dc_ldm/modules/attention.py:211

code/dc_ldm/modules/diffusionmodules/openaimodel.py:257

code/dc_ldm/modules/diffusionmodules/openaimodel.py:320

Method_init_weights

code/sc_mbm/mae_for_eeg.py:126

Method_init_weights

code/sc_mbm/mae_for_eeg.py:377

Method_predict_eps_from_xstart

(self, x_t, t, pred_xstart)

code/dc_ldm/models/diffusion/ddpm.py:1142

Method_prior_bpd

Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only dep

code/dc_ldm/models/diffusion/ddpm.py:1146

Method_rescale_annotations

(self, bboxes, crop_coordinates)

code/dc_ldm/models/diffusion/ddpm.py:1107

Functionadjust_learning_rate

Decay the learning rate with half-cycle cosine after warmup

code/sc_mbm/utils.py:71

Functionaugmentation

data: num_samples, num_voxels_padded return: data_aug: num_samples*aug_times, num_voxels_padded

code/dataset.py:55

Functionbetas_for_alpha_bar

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [

code/dc_ldm/modules/diffusionmodules/util.py:77

Functionchannel_first

code/dataset.py:92

Functionchannel_last

code/dataset.py:364

Functionchannel_last

code/eeg_ldm.py:38

Functionchannel_last

code/gen_eval_eeg.py:23

Functionclip_loss

(similarity: torch.Tensor)

code/dc_ldm/ldm_for_eeg.py:24

Methodconfigure_optimizers

code/dc_ldm/models/autoencoder.py:318

Methodconfigure_optimizers

code/dc_ldm/models/autoencoder.py:508

Methodconfigure_optimizers

code/dc_ldm/models/diffusion/classifier.py:220

Methodconfigure_optimizers

code/dc_ldm/models/diffusion/ddpm.py:563

Methodconfigure_optimizers

code/dc_ldm/models/diffusion/ddpm.py:1509

Methodconfigure_optimizers

code/dc_ldm/models/diffusion/ddpm.py:1676

Functionconvert_module_to_f16

code/dc_ldm/modules/diffusionmodules/openaimodel.py:26

Functionconvert_module_to_f32

code/dc_ldm/modules/diffusionmodules/openaimodel.py:29

Methodconvert_to_fp16

Convert the torso of the model to float16.

code/dc_ldm/modules/diffusionmodules/openaimodel.py:709

Methodconvert_to_fp16

Convert the torso of the model to float16.

code/dc_ldm/modules/diffusionmodules/openaimodel.py:943

Methodconvert_to_fp32

Convert the torso of the model to float32.

code/dc_ldm/modules/diffusionmodules/openaimodel.py:717

Methodconvert_to_fp32

Convert the torso of the model to float32.

code/dc_ldm/modules/diffusionmodules/openaimodel.py:950

Methodcount_flops

code/dc_ldm/modules/diffusionmodules/openaimodel.py:377

Methodcount_flops

code/dc_ldm/modules/diffusionmodules/openaimodel.py:411

code/dc_ldm/modules/encoders/modules.py:77

(self, h, force_not_quantize=False)

code/dc_ldm/models/autoencoder.py:395

(self, x, *args, **kwargs)

code/dc_ldm/models/autoencoder.py:556

Methoddecode_code

code/dc_ldm/models/autoencoder.py:233

Methoddifferentiable_decode_first_stage

(self, z, predict_cids=False, force_not_quantize=False)

code/dc_ldm/models/diffusion/ddpm.py:964

Functiondisabled_train

Overwrite model.train with this function to make sure train/eval mode does not change anymore.

code/dc_ldm/models/diffusion/classifier.py:22

Functiondisabled_train

Overwrite model.train with this function to make sure train/eval mode does not change anymore.

code/dc_ldm/models/diffusion/ddpm.py:37

(self, *args, **kwargs)

code/dc_ldm/modules/encoders/modules.py:17

code/dc_ldm/modules/encoders/modules.py:50

code/dc_ldm/modules/encoders/modules.py:71

code/dc_ldm/modules/encoders/modules.py:135

code/dc_ldm/modules/encoders/modules.py:164

code/dc_ldm/modules/encoders/modules.py:194

code/dc_ldm/modules/encoders/modules.py:222

code/dc_ldm/models/autoencoder.py:390

(self, x, *args, **kwargs)

code/dc_ldm/models/autoencoder.py:553

Methodencode_to_prequant

code/dc_ldm/models/autoencoder.py:223

(self, trainers, dataset, test_dataset, bs1, lr1, output_path, config=None)

code/dc_ldm/ldm_for_eeg.py:272

Functionfmri_transform

(x, sparse_rate=0.2)

code/eeg_ldm.py:112

Functionfmri_transform

(x, sparse_rate=0.2)

code/stageA1_eeg_pretrain.py:97

(self, x, **kwargs)

code/sc_mbm/mae_for_eeg.py:24

(self, imgs, img_features=None, valid_idx=None, mask_ratio=0.75)

code/sc_mbm/mae_for_eeg.py:306

code/sc_mbm/mae_for_eeg.py:410

code/sc_mbm/mae_for_eeg.py:434

code/sc_mbm/mae_for_eeg.py:447

code/dc_ldm/ldm_for_eeg.py:66

code/dc_ldm/modules/ema.py:25

code/dc_ldm/modules/x_transformer.py:34

(self, x, seq_dim=1, offset=0)

code/dc_ldm/modules/x_transformer.py:45

(self, x, **kwargs)

code/dc_ldm/modules/x_transformer.py:123

(self, x, **kwargs)

code/dc_ldm/modules/x_transformer.py:134

code/dc_ldm/modules/x_transformer.py:146

code/dc_ldm/modules/x_transformer.py:158

(self, x, residual)

code/dc_ldm/modules/x_transformer.py:164

(self, x, residual)

code/dc_ldm/modules/x_transformer.py:173

code/dc_ldm/modules/x_transformer.py:189

code/dc_ldm/modules/x_transformer.py:210

( self, x, context=None, mask=None, context_mask=N

code/dc_ldm/modules/x_transformer.py:268

( self, x, context=None, mask=None, context_mask=N

code/dc_ldm/modules/x_transformer.py:481

( self, x, return_embeddings=False, mask=None, ret

code/dc_ldm/modules/x_transformer.py:598

code/dc_ldm/modules/attention.py:42

code/dc_ldm/modules/attention.py:63

code/dc_ldm/modules/attention.py:88

code/dc_ldm/modules/attention.py:126

(self, x, context=None, mask=None)

code/dc_ldm/modules/attention.py:170

(self, x, context=None)

code/dc_ldm/modules/attention.py:208

(self, x, context=None)

code/dc_ldm/modules/attention.py:250

code/dc_ldm/modules/diffusionmodules/openaimodel.py:53

Apply the module to `x` given `emb` timestep embeddings.

code/dc_ldm/modules/diffusionmodules/openaimodel.py:70

(self, x, emb, context=None)

code/dc_ldm/modules/diffusionmodules/openaimodel.py:82

code/dc_ldm/modules/diffusionmodules/openaimodel.py:111

code/dc_ldm/modules/diffusionmodules/openaimodel.py:132

code/dc_ldm/modules/diffusionmodules/openaimodel.py:160

Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb:

code/dc_ldm/modules/diffusionmodules/openaimodel.py:245

code/dc_ldm/modules/diffusionmodules/openaimodel.py:316

Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor afte

code/dc_ldm/modules/diffusionmodules/openaimodel.py:358

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