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Functions2,992 in github.com/brycedrennan/imaginAIry

⨍ Functions 2,992◇ Types & classes 731↳ Endpoints 37

imaginairy/modules/diffusion/openaimodel.py:173

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

imaginairy/modules/diffusion/openaimodel.py:258

imaginairy/modules/diffusion/openaimodel.py:328

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

imaginairy/modules/diffusion/openaimodel.py:372

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

imaginairy/modules/diffusion/openaimodel.py:404

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

imaginairy/modules/diffusion/openaimodel.py:812

imaginairy/modules/diffusion/upscaling.py:76

imaginairy/modules/diffusion/upscaling.py:89

(self, x, noise_level=None)

imaginairy/modules/diffusion/upscaling.py:99

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

imaginairy/modules/diffusion/ddpm.py:588

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

imaginairy/modules/diffusion/ddpm.py:1141

( self, x, t, c_concat: Optional[list] = None, c_crossattn: Optional[list] = None )

imaginairy/modules/diffusion/ddpm.py:1721

(ctx, run_function, length, *args)

imaginairy/modules/diffusion/util.py:157

imaginairy/modules/diffusion/util.py:250

(self, c_concat, c_crossattn)

imaginairy/modules/diffusion/util.py:293

imaginairy/modules/diffusion/model.py:66

imaginairy/modules/diffusion/model.py:83

(self, x, temb)

imaginairy/modules/diffusion/model.py:130

imaginairy/modules/diffusion/model.py:184

imaginairy/modules/diffusion/model.py:306

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

imaginairy/modules/diffusion/model.py:341

imaginairy/modules/diffusion/model.py:639

imaginairy/modules/diffusion/model.py:764

imaginairy/modules/diffusion/model.py:855

imaginairy/modules/diffusion/model.py:909

imaginairy/modules/diffusion/model.py:961

imaginairy/modules/diffusion/model.py:1017

imaginairy/modules/diffusion/model.py:1060

imaginairy/modules/diffusion/model.py:1092

(self, x, scale_factor=1.0)

imaginairy/modules/diffusion/model.py:1114

imaginairy/modules/midas/api.py:343

Forward pass. Args: x (tensor): input data (image) Returns: tensor: depth

imaginairy/modules/midas/midas/midas_net_custom.py:124

Forward pass. Args: x (tensor): input Returns: tensor: interpolated data

imaginairy/modules/midas/midas/blocks.py:241

Forward pass. Args: x (tensor): input Returns: tensor: output

imaginairy/modules/midas/midas/blocks.py:282

Forward pass. Returns: tensor: output

imaginairy/modules/midas/midas/blocks.py:313

Forward pass. Args: x (tensor): input Returns: tensor: output

imaginairy/modules/midas/midas/blocks.py:376

Forward pass. Returns: tensor: output

imaginairy/modules/midas/midas/blocks.py:451

Forward pass. Args: x (tensor): input data (image) Returns: tensor: depth

imaginairy/modules/midas/midas/midas_net.py:53

imaginairy/modules/midas/midas/dpt_depth.py:179

imaginairy/modules/midas/midas/backbones/utils.py:10

imaginairy/modules/midas/midas/backbones/utils.py:19

imaginairy/modules/midas/midas/backbones/utils.py:34

imaginairy/modules/midas/midas/backbones/utils.py:47

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

imaginairy/modules/sgm/video_attention.py:29

( self, x: torch.Tensor, context: torch.Tensor = None, timesteps: Optional[int

imaginairy/modules/sgm/video_attention.py:129

( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, time_context:

imaginairy/modules/sgm/video_attention.py:261

(self, x, batch)

imaginairy/modules/sgm/diffusion.py:170

imaginairy/modules/sgm/attention.py:94

imaginairy/modules/sgm/attention.py:114

imaginairy/modules/sgm/attention.py:141

(self, x: torch.Tensor)

imaginairy/modules/sgm/attention.py:181

( self, x, context=None, mask=None, additional_tokens=None, n_

imaginairy/modules/sgm/attention.py:238

( self, x, context=None, mask=None, additional_tokens=None, n_

imaginairy/modules/sgm/attention.py:313

( self, x, context=None, mask=None, additional_tokens=None, n_

imaginairy/modules/sgm/attention.py:418

( self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0 )

imaginairy/modules/sgm/attention.py:577

(self, x, context=None)

imaginairy/modules/sgm/attention.py:658

(self, x, context=None)

imaginairy/modules/sgm/attention.py:752

( self, x: torch.Tensor, context: Optional[torch.Tensor] = None, )

imaginairy/modules/sgm/attention.py:802

( self, x: torch.Tensor, **additional_decode_kwargs )

imaginairy/modules/sgm/autoencoder.py:226

( self, network: nn.Module, denoiser: Denoiser, conditioner: GeneralConditione

imaginairy/modules/sgm/diffusionmodules/loss.py:52

(self, x: th.Tensor)

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:53

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

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:70

( self, x: th.Tensor, emb: th.Tensor, context: Optional[th.Tensor] = None,

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:82

(self, x: th.Tensor)

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:149

(self, x: th.Tensor)

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:214

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

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:326

(self, x: th.Tensor, **kwargs)

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:403

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

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:424

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

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:452

(self, t: th.Tensor)

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:478

Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

imaginairy/modules/sgm/diffusionmodules/openaimodel.py:822

( ctx, run_function, length_tensors, length_non_tensors, tensor_keys,

imaginairy/modules/sgm/diffusionmodules/util.py:80

(ctx, run_function, length, *args)

imaginairy/modules/sgm/diffusionmodules/util.py:166

imaginairy/modules/sgm/diffusionmodules/util.py:268

( self, x_spatial: torch.Tensor, x_temporal: torch.Tensor, image_only_indicato

imaginairy/modules/sgm/diffusionmodules/util.py:354

( self, x: th.Tensor, emb: th.Tensor, num_video_frames: int, image_onl

imaginairy/modules/sgm/diffusionmodules/video_model.py:81

( self, network: nn.Module, input_tensor: torch.Tensor, sigma: torch.Tensor,

imaginairy/modules/sgm/diffusionmodules/denoiser.py:28

(self, *args, **kwargs)

imaginairy/modules/sgm/diffusionmodules/wrappers.py:21

( self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs )

imaginairy/modules/sgm/diffusionmodules/wrappers.py:26

imaginairy/modules/sgm/diffusionmodules/model.py:69

imaginairy/modules/sgm/diffusionmodules/model.py:86

(self, x, temb)

imaginairy/modules/sgm/diffusionmodules/model.py:133

(self, x, **kwargs)

imaginairy/modules/sgm/diffusionmodules/model.py:199

(self, x, **kwargs)

imaginairy/modules/sgm/diffusionmodules/model.py:263

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

imaginairy/modules/sgm/diffusionmodules/model.py:271

imaginairy/modules/sgm/diffusionmodules/model.py:584

(self, z, **kwargs)

imaginairy/modules/sgm/diffusionmodules/model.py:723

(self, x, temb, skip_video=False, timesteps=None)

imaginairy/modules/sgm/autoencoding/temporal_ae.py:71

(self, x, timesteps, skip_video=False)

imaginairy/modules/sgm/autoencoding/temporal_ae.py:150

(self, x, timesteps, skip_time_block=False)

imaginairy/modules/sgm/autoencoding/temporal_ae.py:222

(self, input_tensor: torch.Tensor, **kwargs)

imaginairy/modules/sgm/autoencoding/temporal_ae.py:298

(self, z: torch.Tensor)

imaginairy/modules/sgm/autoencoding/regularizers/base.py:15

(self, z: torch.Tensor)

imaginairy/modules/sgm/autoencoding/regularizers/base.py:24

(self, z: torch.Tensor)

imaginairy/modules/sgm/autoencoding/regularizers/__init__.py:20

( self, z: torch.Tensor, temp: Optional[float] = None, return_logits: bool = False )

imaginairy/modules/sgm/autoencoding/regularizers/quantize.py:121

( self, z: torch.Tensor, )

imaginairy/modules/sgm/autoencoding/regularizers/quantize.py:236

(self, z: torch.Tensor)

imaginairy/modules/sgm/autoencoding/regularizers/quantize.py:398

(self, z: torch.Tensor)

imaginairy/modules/sgm/autoencoding/regularizers/quantize.py:466

(self, input_tensor, reverse=False)

imaginairy/modules/sgm/autoencoding/lpips/util.py:84

Standard forward.

imaginairy/modules/sgm/autoencoding/lpips/model/model.py:85

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