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Functions1,211 in github.com/horseee/DeepCache

⨍ Functions 1,211◇ Types & classes 220

( self, *, num_tokens, max_seq_len, attn_layers,

experiments/ldm/ldm/modules/x_transformer.py:549

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

experiments/ldm/ldm/modules/attention.py:48

(self, dim, heads=4, dim_head=32)

experiments/ldm/ldm/modules/attention.py:81

(self, in_channels)

experiments/ldm/ldm/modules/attention.py:100

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

experiments/ldm/ldm/modules/attention.py:153

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

experiments/ldm/ldm/modules/attention.py:197

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

experiments/ldm/ldm/modules/attention.py:226

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

experiments/ldm/ldm/modules/diffusionmodules/openaimodel.py:37

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

experiments/ldm/ldm/modules/diffusionmodules/openaimodel.py:123

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

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

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

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

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

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

(self, n_heads)

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

(self, n_heads)

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

( self, image_size, in_channels, model_channels, out_channels,

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

( self, image_size, in_channels, model_channels, out_channels,

experiments/ldm/ldm/modules/diffusionmodules/openaimodel.py:770

(self, c_concat_config, c_crossattn_config)

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

(self, in_channels, with_conv)

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

(self, in_channels, with_conv)

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

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

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

(self, in_channels)

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

(self, in_channels)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

(self, parameters, deterministic=False)

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

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

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

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

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

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

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

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

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

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

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

experiments/ldm/ldm/modules/encoders/modules.py:142

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

experiments/ldm/ldm/modules/encoders/modules.py:174

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

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

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

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

(self, config=None)

experiments/ldm/ldm/data/imagenet.py:27

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

experiments/ldm/ldm/data/imagenet.py:145

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

experiments/ldm/ldm/data/imagenet.py:211

(self, **kwargs)

experiments/ldm/ldm/data/imagenet.py:376

(self, **kwargs)

experiments/ldm/ldm/data/imagenet.py:387

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

experiments/ldm/ldm/data/base.py:9

(self, **kwargs)

experiments/ldm/ldm/data/lsun.py:63

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

experiments/ldm/ldm/data/lsun.py:68

(self, **kwargs)

experiments/ldm/ldm/data/lsun.py:74

(self, flip_p=0.0, **kwargs)

experiments/ldm/ldm/data/lsun.py:79

(self, **kwargs)

experiments/ldm/ldm/data/lsun.py:85

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

experiments/ldm/ldm/data/lsun.py:90

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

experiments/ldm/ldm/models/autoencoder.py:265

(self, ddconfig, lossconfig, embed_dim, ck

experiments/ldm/ldm/models/autoencoder.py:286

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

experiments/ldm/ldm/models/autoencoder.py:427

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

experiments/ldm/ldm/models/diffusion/ddim.py:12

(self, diffusion_path, num_classes, ckpt_path=None,

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

(self, first_stage_config, cond_stage_config, num_timesteps

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

(self, diff_model_config, conditioning_key)

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

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

experiments/ldm/ldm/models/diffusion/ddpm.py:1465

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

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

experiments/ldm/ldm/data/base.py:22

experiments/ddpm/fid.py:89

experiments/ddpm/ddpm/datasets/ffhq.py:28

experiments/ddpm/ddpm/datasets/lsun.py:57

experiments/ddpm/ddpm/datasets/lsun.py:171

experiments/ddpm/ddpm/datasets/celeba.py:158

experiments/ldm/main.py:139

experiments/ldm/ldm/data/imagenet.py:39

experiments/ldm/ldm/data/imagenet.py:336

experiments/ldm/ldm/data/lsun.py:36

experiments/ddpm/ddpm/datasets/vision.py:75

experiments/ddpm/ddpm/datasets/__init__.py:24

Method__setattr__

(self, name: str, value: Any)

DeepCache/sdxl/pipeline_utils.py:549

Method__setattr__

(self, name: str, value: Any)

DeepCache/svd/pipeline_utils.py:597

Method__setattr__

(self, name: str, value: Any)

DeepCache/sd/pipeline_utils.py:550

Function_do_parallel_data_prefetch

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

experiments/ldm/ldm/util.py:96

Method_execution_device

r""" Returns the device on which the pipeline's models will be executed. After calling [`~DiffusionPipeline.enable_sequential_cpu_offl

DeepCache/sdxl/pipeline_utils.py:1210

Method_execution_device

r""" Returns the device on which the pipeline's models will be executed. After calling [`~DiffusionPipeline.enable_sequential_cpu_offl

DeepCache/svd/pipeline_utils.py:1371

Method_execution_device

r""" Returns the device on which the pipeline's models will be executed. After calling [`~DiffusionPipeline.enable_sequential_cpu_offl

DeepCache/sd/pipeline_utils.py:1210

(self, x, context=None)

experiments/ldm/ldm/modules/attention.py:211

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

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

Method_predict_dataloader

(self, shuffle=False)

experiments/ldm/main.py:231

Method_predict_eps_from_xstart

(self, x_t, t, pred_xstart)

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

experiments/ldm/ldm/data/imagenet.py:150

experiments/ldm/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

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

Method_remove_text_encoder_monkey_patch

DeepCache/sdxl/pipeline_stable_diffusion_xl.py:1029

Method_remove_text_encoder_monkey_patch

DeepCache/sdxl/pipeline_stable_diffusion_xl_img2img.py:1180

Method_rescale_annotations

(self, bboxes, crop_coordinates)

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

Method_set_gradient_checkpointing

(self, module, value=False)

DeepCache/sdxl/unet_2d_condition.py:735

Method_set_gradient_checkpointing

(self, module, value=False)

DeepCache/svd/unet_spatio_temporal_condition.py:323

Method_set_gradient_checkpointing

(self, module, value=False)

DeepCache/sd/unet_2d_condition.py:735

Method_test_dataloader

(self, shuffle=False)

experiments/ldm/main.py:218

Method_testtube

(self, pl_module, images, batch_idx, split)

experiments/ldm/main.py:310

Method_train_dataloader

experiments/ldm/main.py:197

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