Functions — github.com/DreamEditBenchTeam/DreamEdit

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Functions598 in github.com/DreamEditBenchTeam/DreamEdit

⨍ Functions 598◇ Types & classes 104

Method__init__

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

src/ldm/modules/diffusionmodules/openaimodel.py:143

Method__init__

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

src/ldm/modules/diffusionmodules/openaimodel.py:179

Method__init__

( self, channels, num_heads=1, num_head_channels=-1, use_checkpoint=Fa

src/ldm/modules/diffusionmodules/openaimodel.py:285

Method__init__

(self, n_heads)

src/ldm/modules/diffusionmodules/openaimodel.py:352

Method__init__

(self, n_heads)

src/ldm/modules/diffusionmodules/openaimodel.py:384

Method__init__

( self, image_size, in_channels, model_channels, out_channels,

src/ldm/modules/diffusionmodules/openaimodel.py:443

Method__init__

( self, image_size, in_channels, model_channels, out_channels,

src/ldm/modules/diffusionmodules/openaimodel.py:751

Method__init__

(self, c_concat_config, c_crossattn_config)

src/ldm/modules/diffusionmodules/util.py:253

Method__init__

(self, in_channels, with_conv)

src/ldm/modules/diffusionmodules/model.py:43

Method__init__

(self, in_channels, with_conv)

src/ldm/modules/diffusionmodules/model.py:61

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:83

Method__init__

(self, in_channels)

src/ldm/modules/diffusionmodules/model.py:146

Method__init__

(self, in_channels)

src/ldm/modules/diffusionmodules/model.py:151

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:369

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:463

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:572

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:608

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:656

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:693

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:712

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:729

Method__init__

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

src/ldm/modules/diffusionmodules/model.py:748

Method__init__

(self, ch_mult:list, in_channels, pretrained_model:nn.Module=None, reshape=F

src/ldm/modules/diffusionmodules/model.py:772

Method__init__

(self, value)

src/ldm/modules/distributions/distributions.py:14

Method__init__

(self, parameters, deterministic=False)

src/ldm/modules/distributions/distributions.py:25

Method__init__

(self)

src/ldm/modules/encoders/modules.py:13

Method__init__

(self, embed_dim, n_classes=1000, key='class')

src/ldm/modules/encoders/modules.py:22

Method__init__

(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda")

src/ldm/modules/encoders/modules.py:38

Method__init__

(self, device="cuda", vq_interface=True, max_length=77)

src/ldm/modules/encoders/modules.py:55

Method__init__

(self, n_stages=1, method='bilinear', multiplier=0.5,

src/ldm/modules/encoders/modules.py:107

Method__init__

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

src/ldm/modules/encoders/modules.py:139

Method__init__

(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True)

src/ldm/modules/encoders/modules.py:169

Method__init__

( self, model, jit=False, device='cuda' if torch.cuda.is_avail

src/ldm/modules/encoders/modules.py:201

Method__init__

(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_chan

src/ldm/modules/losses/vqperceptual.py:44

Method__init__

(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, d

src/ldm/modules/losses/contperceptual.py:8

Method__init__

(self, config=None)

src/ldm/data/imagenet.py:27

Method__init__

(self, process_images=True, data_root=None, **kwargs)

src/ldm/data/imagenet.py:145

Method__init__

(self, process_images=True, data_root=None, **kwargs)

src/ldm/data/imagenet.py:211

Method__init__

(self, **kwargs)

src/ldm/data/imagenet.py:376

Method__init__

(self, **kwargs)

src/ldm/data/imagenet.py:387

Method__init__

(self, num_records=0, valid_ids=None, size=256)

src/ldm/data/base.py:9

Method__init__

(self, **kwargs)

src/ldm/data/lsun.py:63

Method__init__

(self, flip_p=0., **kwargs)

src/ldm/data/lsun.py:68

Method__init__

(self, **kwargs)

src/ldm/data/lsun.py:74

Method__init__

(self, flip_p=0.0, **kwargs)

src/ldm/data/lsun.py:79

Method__init__

(self, **kwargs)

src/ldm/data/lsun.py:85

Method__init__

(self, flip_p=0., **kwargs)

src/ldm/data/lsun.py:90

Method__init__

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

src/ldm/models/autoencoder.py:265

Method__init__

(self, ddconfig, lossconfig, embed_dim, ck

src/ldm/models/autoencoder.py:286

Method__init__

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

src/ldm/models/autoencoder.py:427

Method__init__

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

src/ldm/models/diffusion/ddim.py:13

Method__init__

(self, diffusion_path, num_classes, ckpt_path=None,

src/ldm/models/diffusion/classifier.py:30

Method__init__

(self, first_stage_config, cond_stage_config, num_timesteps

src/ldm/models/diffusion/ddpm.py:426

Method__init__

(self, diff_model_config, conditioning_key)

src/ldm/models/diffusion/ddpm.py:1396

Method__init__

(self, cond_stage_key, *args, **kwargs)

src/ldm/models/diffusion/ddpm.py:1426

Method__init__

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

src/ldm/models/diffusion/plms.py:12

Method__init__

Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewis

src/ldm/models/diffusion/dpm_solver/dpm_solver.py:7

Method__init__

Construct a DPM-Solver. We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").

src/ldm/models/diffusion/dpm_solver/dpm_solver.py:352

Method__init__

(self, model, **kwargs)

src/ldm/models/diffusion/dpm_solver/sampler.py:9

Method__iter__

(self)

src/ldm/data/base.py:22

Method__len__

(self)

src/ldm/data/imagenet.py:39

Method__len__

(self)

src/ldm/data/imagenet.py:336

Method__len__

(self)

src/ldm/data/lsun.py:36

Function_do_parallel_data_prefetch

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

src/ldm/util.py:96

Method_forward

(self, x, context=None)

src/ldm/modules/attention.py:211

Method_forward

(self, x, emb)

src/ldm/modules/diffusionmodules/openaimodel.py:255

Method_forward

(self, x)

src/ldm/modules/diffusionmodules/openaimodel.py:318

Method_predict_eps_from_xstart

(self, x_t, t, pred_xstart)

src/ldm/models/diffusion/ddpm.py:994

Method_prepare

(self)

src/ldm/data/imagenet.py:150

Method_prepare

(self)

src/ldm/data/imagenet.py:216

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

src/ldm/models/diffusion/ddpm.py:998

Method_rescale_annotations

(self, bboxes, crop_coordinates)

src/ldm/models/diffusion/ddpm.py:881

Functionadd_Poisson_noise

(img)

src/ldm/modules/image_degradation/bsrgan_light.py:408

Functionadd_resize

(img, sf=4)

src/ldm/modules/image_degradation/bsrgan_light.py:343

Functionadd_sharpening

USM sharpening. borrowed from real-ESRGAN Input image: I; Blurry image: B. 1. K = I + weight * (I - B) 2. Mask = 1 if abs(I - B) > thresho

src/ldm/modules/image_degradation/bsrgan_light.py:299

Functionadd_speckle_noise

(img, noise_level1=2, noise_level2=25)

src/ldm/modules/image_degradation/bsrgan_light.py:390

Functionanalytic_kernel

Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)

src/ldm/modules/image_degradation/bsrgan_light.py:49

Functionanalytic_kernel

Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)

src/ldm/modules/image_degradation/bsrgan.py:49

Functionaugment_img_np3

(img, mode=0)

src/ldm/modules/image_degradation/utils_image.py:441

Functionaugment_img_tensor

Kai Zhang (github: https://github.com/cszn)

src/ldm/modules/image_degradation/utils_image.py:422

Functionaugment_img_tensor4

Kai Zhang (github: https://github.com/cszn)

src/ldm/modules/image_degradation/utils_image.py:401

Functionaugment_imgs

(img_list, hflip=True, rot=True)

src/ldm/modules/image_degradation/utils_image.py:469

Methodbackward

(ctx, *output_grads)

src/ldm/modules/diffusionmodules/util.py:131

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 = [

src/ldm/modules/diffusionmodules/util.py:77

Functionblur

x: image, NxcxHxW k: kernel, Nx1xhxw

src/ldm/modules/image_degradation/bsrgan_light.py:128

Functionblur

x: image, NxcxHxW k: kernel, Nx1xhxw

src/ldm/modules/image_degradation/bsrgan.py:128

Functioncalculate_psnr

(img1, img2, border=0)

src/ldm/modules/image_degradation/utils_image.py:621

Functioncalculate_ssim

calculate SSIM the same outputs as MATLAB's img1, img2: [0, 255]

src/ldm/modules/image_degradation/utils_image.py:642

Functionchannel_convert

(in_c, tar_type, img_list)

src/ldm/modules/image_degradation/utils_image.py:597

Functioncheck_format

(source_path)

src/utils/path_finder.py:13

Functioncheck_is_image

(source_path)

src/utils/path_finder.py:25

Functionclassical_degradation

blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsa

src/ldm/modules/image_degradation/bsrgan_light.py:284

Functionclassical_degradation

blur + downsampling Args: x: HxWxC image, [0, 1]/[0, 255] k: hxw, double sf: down-scale factor Return: downsa

src/ldm/modules/image_degradation/bsrgan.py:284

Functioncompute_l2_distance

(image_features, image_features2)

src/metrics/distances.py:11

Methodconfigure_optimizers

(self)

src/ldm/models/autoencoder.py:197

Methodconfigure_optimizers

(self)

src/ldm/models/autoencoder.py:386

Methodconfigure_optimizers

(self)

src/ldm/models/diffusion/classifier.py:220

Methodconfigure_optimizers

(self)

src/ldm/models/diffusion/ddpm.py:415

Methodconfigure_optimizers

(self)

src/ldm/models/diffusion/ddpm.py:1361

Functionconvert_module_to_f16

(x)

src/ldm/modules/diffusionmodules/openaimodel.py:24

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