Functions — github.com/Qiukunpeng/Siamese-Diffusion

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Functions550 in github.com/Qiukunpeng/Siamese-Diffusion

⨍ Functions 550◇ Types & classes 97

↓ 1 callersMethodinstantiate_cond_stage

(self, config)

ldm/models/diffusion/ddpm.py:630

↓ 1 callersMethodinstantiate_first_stage

(self, config)

ldm/models/diffusion/ddpm.py:623

↓ 1 callersMethodinstantiate_low_stage

(self, config)

ldm/models/diffusion/ddpm.py:1371

↓ 1 callersMethodinstantiate_low_stage

(self, config)

ldm/models/diffusion/ddpm.py:1760

↓ 1 callersFunctionis_image_file

(filename)

ldm/modules/image_degradation/utils_image.py:29

↓ 1 callersFunctionload_midas_transform

(model_type)

ldm/modules/midas/api.py:28

↓ 1 callersFunctionload_model

(model_type)

ldm/modules/midas/api.py:73

↓ 1 callersMethodlog_img

(self, pl_module, batch, batch_idx, split="train")

cldm/logger.py:53

↓ 1 callersFunctionlog_local

(save_dir, images, batch_idx)

tutorial_inference.py:40

↓ 1 callersMethodlog_local

(self, save_dir, split, images, global_step, current_epoch, batch_idx)

cldm/logger.py:29

↓ 1 callersMethodmake_cond_schedule

(self, )

ldm/models/diffusion/ddpm.py:591

↓ 1 callersMethodmake_schedule

(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True)

ldm/models/diffusion/ddim.py:23

↓ 1 callersMethodmake_schedule

(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True)

ldm/models/diffusion/plms.py:25

↓ 1 callersMethodmake_schedule

(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True)

cldm/ddim_hacked.py:23

↓ 1 callersFunctionmean_flat

Take the mean over all non-batch dimensions.

ldm/modules/diffusionmodules/util.py:195

↓ 1 callersFunctionmodel_wrapper

Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discr

ldm/models/diffusion/dpm_solver/dpm_solver.py:161

↓ 1 callersMethodmultistep_dpm_solver_second_update

Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initial value at

ldm/models/diffusion/dpm_solver/dpm_solver.py:723

↓ 1 callersMethodmultistep_dpm_solver_third_update

Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initial value at

ldm/models/diffusion/dpm_solver/dpm_solver.py:780

↓ 1 callersFunctionnorm_thresholding

(x0, value)

ldm/models/diffusion/sampling_util.py:14

↓ 1 callersFunctionnormal_kl

source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the

ldm/modules/distributions/distributions.py:65

↓ 1 callersMethodp_losses

(self, x_start, t, noise=None)

ldm/models/diffusion/ddpm.py:389

↓ 1 callersMethodp_losses

(self, x_start, cond, t, noise=None)

ldm/models/diffusion/ddpm.py:893

↓ 1 callersMethodp_mean_variance

(self, x, t, clip_denoised: bool)

ldm/models/diffusion/ddpm.py:320

↓ 1 callersMethodp_mean_variance

(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,

ldm/models/diffusion/ddpm.py:930

↓ 1 callersMethodp_sample

(self, x, t, clip_denoised=True, repeat_noise=False)

ldm/models/diffusion/ddpm.py:333

↓ 1 callersMethodp_sample_loop

(self, shape, return_intermediates=False)

ldm/models/diffusion/ddpm.py:342

↓ 1 callersMethodp_sample_loop

(self, cond, shape, return_intermediates=False, x_T=None, verbose=True, callback=None,

ldm/models/diffusion/ddpm.py:1049

↓ 1 callersMethodp_sample_plms

(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,

ldm/models/diffusion/plms.py:178

↓ 1 callersFunctionpad

(x, p, i)

cldm/hack.py:50

↓ 1 callersFunctionpatches_from_image

(img, p_size=512, p_overlap=64, p_max=800)

ldm/modules/image_degradation/utils_image.py:93

↓ 1 callersMethodplms_sampling

(self, cond, shape, x_T=None, ddim_use_original_steps=False, cal

ldm/models/diffusion/plms.py:118

↓ 1 callersMethodpt2np

(self, x)

ldm/data/util.py:11

↓ 1 callersMethodq_mean_variance

Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of d

ldm/models/diffusion/ddpm.py:279

↓ 1 callersMethodq_sample

(self, x_start, t, noise=None)

ldm/modules/diffusionmodules/upscaling.py:44

↓ 1 callersFunctionrandom_crop

(lq, hq, sf=4, lq_patchsize=64)

ldm/modules/image_degradation/bsrgan_light.py:430

↓ 1 callersMethodregister_buffer

(self, name, attr)

ldm/models/diffusion/dpm_solver/sampler.py:20

↓ 1 callersMethodregister_schedule

(self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2,

ldm/modules/diffusionmodules/upscaling.py:17

↓ 1 callersMethodsample

(self, S, batch_size, shape, conditioning=None

ldm/models/diffusion/ddim.py:55

↓ 1 callersMethodsample

(self, cond, batch_size=16, return_intermediates=False, x_T=None, verbose=True, timesteps=None

ldm/models/diffusion/ddpm.py:1100

↓ 1 callersMethodsample

Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. ========================================

ldm/models/diffusion/dpm_solver/dpm_solver.py:939

↓ 1 callersMethodsample

(self, S, batch_size, shape, conditioning=None

cldm/ddim_hacked.py:55

↓ 1 callersMethodsinglestep_dpm_solver_update

Singlestep DPM-Solver with the order `order` from time `s` to time `t`. Args: x: A pytorch tensor. The initial value a

ldm/models/diffusion/dpm_solver/dpm_solver.py:827

↓ 1 callersFunctionsplit

(x)

cldm/hack.py:47

↓ 1 callersMethodtext_transformer_forward

(self, x: torch.Tensor, attn_mask = None)

ldm/modules/encoders/modules.py:184

↓ 1 callersFunctiontokenize

(t)

cldm/hack.py:37

↓ 1 callersMethodtransform

(self, size=384)

tutorial_dataset_sample.py:51

↓ 1 callersFunctiontransformer_encode

(t)

cldm/hack.py:40

↓ 1 callersFunctionwrite_pfm

Write pfm file. Args: path (str): pathto file image (array): data scale (int, optional): Scale. Defaults to 1.

ldm/modules/midas/utils.py:58

Method__call__

(self, sample)

ldm/modules/midas/midas/transforms.py:162

Method__call__

(self, sample)

ldm/modules/midas/midas/transforms.py:205

Method__call__

(self, sample)

ldm/modules/midas/midas/transforms.py:218

tutorial_dataset_sample.py:22

Method__getitem__

(self, idx)

tutorial_dataset.py:22

Method__init__

(self)

tutorial_dataset_sample.py:12

Method__init__

(self)

tutorial_dataset.py:12

Method__init__

AdamW that saves EMA versions of the parameters.

ldm/util.py:92

Method__init__

(self, model, decay=0.9999, use_num_upates=True)

ldm/modules/ema.py:6

Method__init__

(self, dim, dim_out=None, mult=4, glu=False, dropout=0.)

ldm/modules/attention.py:60

Method__init__

(self, in_channels)

ldm/modules/attention.py:93

Method__init__

(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.)

ldm/modules/attention.py:146

Method__init__

(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0)

ldm/modules/attention.py:199

Method__init__

(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, d

ldm/modules/attention.py:251

Method__init__

(self, in_channels, n_heads, d_head, depth=1, dropout=0., context_dim=None,

ldm/modules/attention.py:287

Method__init__

( self, spacial_dim: int, embed_dim: int, num_heads_channels: int,

ldm/modules/diffusionmodules/openaimodel.py:39

Method__init__

(self, channels, out_channels=None, ks=5)

ldm/modules/diffusionmodules/openaimodel.py:124

Method__init__

(self, channels, use_conv, dims=2, out_channels=None,padding=1)

ldm/modules/diffusionmodules/openaimodel.py:144

Method__init__

( self, channels, emb_channels, dropout, out_channels=None,

ldm/modules/diffusionmodules/openaimodel.py:180

Method__init__

( self, channels, num_heads=1, num_head_channels=-1, use_checkpoi

ldm/modules/diffusionmodules/openaimodel.py:286

Method__init__

(self, n_heads)

ldm/modules/diffusionmodules/openaimodel.py:353

Method__init__

(self, n_heads)

ldm/modules/diffusionmodules/openaimodel.py:385

Method__init__

( self, image_size, in_channels, model_channels, out_channels,

ldm/modules/diffusionmodules/openaimodel.py:444

Method__init__

(self, noise_schedule_config=None)

ldm/modules/diffusionmodules/upscaling.py:12

Method__init__

(self, noise_schedule_config, max_noise_level=1000, to_cuda=False)

ldm/modules/diffusionmodules/upscaling.py:68

Method__init__

(self, c_concat_config, c_crossattn_config)

ldm/modules/diffusionmodules/util.py:256

Method__init__

(self, in_channels, with_conv)

ldm/modules/diffusionmodules/model.py:58

Method__init__

(self, in_channels, with_conv)

ldm/modules/diffusionmodules/model.py:76

Method__init__

(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512)

ldm/modules/diffusionmodules/model.py:98

Method__init__

(self, in_channels)

ldm/modules/diffusionmodules/model.py:160

Method__init__

(self, in_channels)

ldm/modules/diffusionmodules/model.py:219

Method__init__

(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resa

ldm/modules/diffusionmodules/model.py:460

Method__init__

(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resa

ldm/modules/diffusionmodules/model.py:556

Method__init__

(self, in_channels, out_channels, *args, **kwargs)

ldm/modules/diffusionmodules/model.py:665

Method__init__

(self, in_channels, out_channels, ch, num_res_blocks, resolution, ch_mult=(2,2), dropout=0.0

ldm/modules/diffusionmodules/model.py:701

Method__init__

(self, factor, in_channels, mid_channels, out_channels, depth=2)

ldm/modules/diffusionmodules/model.py:749

Method__init__

(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, r

ldm/modules/diffusionmodules/model.py:786

Method__init__

(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),

ldm/modules/diffusionmodules/model.py:805

Method__init__

(self, in_size, out_size, in_channels, out_channels, ch_mult=2)

ldm/modules/diffusionmodules/model.py:822

Method__init__

(self, in_channels=None, learned=False, mode="bilinear")

ldm/modules/diffusionmodules/model.py:841

Method__init__

(self, value)

ldm/modules/distributions/distributions.py:14

Method__init__

(self, parameters, deterministic=False)

ldm/modules/distributions/distributions.py:25

Method__init__

(self)

ldm/modules/encoders/modules.py:12

Method__init__

(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True)

ldm/modules/encoders/modules.py:60

Method__init__

(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77, freeze=True, l

ldm/modules/encoders/modules.py:95

Method__init__

(self, arch="ViT-H-14", version="/opt/data/private/QiuKunpeng/Diffusion/ControlNet/models/CL

ldm/modules/encoders/modules.py:143

Method__init__

(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",

ldm/modules/encoders/modules.py:199

Method__init__

(self, model_type)

ldm/modules/midas/api.py:150

Method__init__

(self, start_index=1)

ldm/modules/midas/midas/vit.py:19

Method__init__

(self, in_features, start_index=1)

ldm/modules/midas/midas/vit.py:32

Method__init__

(self, dim0, dim1)

ldm/modules/midas/midas/vit.py:46

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