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Functions1,051 in github.com/Ground-A-Video/Ground-A-Video

⨍ Functions 1,051◇ Types & classes 244
Method__init__
(self, in_channels, with_conv)
ldm/modules/diffusionmodules/model.py:43
Method__init__
(self, in_channels, with_conv)
ldm/modules/diffusionmodules/model.py:61
Method__init__
(self, *, in_channels, out_channels=None, conv_shortcut=False, dropout, temb_channels=512)
ldm/modules/diffusionmodules/model.py:83
Method__init__
(self, in_channels)
ldm/modules/diffusionmodules/model.py:146
Method__init__
(self, in_channels)
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
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
ldm/modules/diffusionmodules/model.py:463
Method__init__
(self, in_channels, out_channels, *args, **kwargs)
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)
ldm/modules/diffusionmodules/model.py:608
Method__init__
(self, factor, in_channels, mid_channels, out_channels, depth=2)
ldm/modules/diffusionmodules/model.py:656
Method__init__
(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, re
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),
ldm/modules/diffusionmodules/model.py:712
Method__init__
(self, in_size, out_size, in_channels, out_channels, ch_mult=2)
ldm/modules/diffusionmodules/model.py:729
Method__init__
(self, in_channels=None, learned=False, mode="bilinear")
ldm/modules/diffusionmodules/model.py:748
Method__init__
(self, ch_mult:list, in_channels, pretrained_model:nn.Module=None, reshape=F
ldm/modules/diffusionmodules/model.py:772
Method__init__
( self, inplanes: int, planes: int, stride: int = 1, downsample: Optio
ldm/modules/diffusionmodules/resnet.py:41
Method__init__
( self, inplanes: int, planes: int, stride: int = 1, downsample: Optio
ldm/modules/diffusionmodules/resnet.py:96
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_backup.py:13
Method__init__
(self, embed_dim, n_classes=1000, key='class')
ldm/modules/encoders/modules_backup.py:22
Method__init__
(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda")
ldm/modules/encoders/modules_backup.py:38
Method__init__
(self, device="cuda", vq_interface=True, max_length=77)
ldm/modules/encoders/modules_backup.py:55
Method__init__
(self, n_stages=1, method='bilinear', multiplier=0.5,
ldm/modules/encoders/modules_backup.py:107
Method__init__
(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77)
ldm/modules/encoders/modules_backup.py:139
Method__init__
(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True)
ldm/modules/encoders/modules_backup.py:169
Method__init__
( self, model, jit=False, device='cuda' if torch.cuda.is_avail
ldm/modules/encoders/modules_backup.py:201
Method__init__
(self)
ldm/modules/encoders/modules.py:13
Method__init__
(self, embed_dim, n_classes=1000, key='class')
ldm/modules/encoders/modules.py:22
Method__init__
(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda")
ldm/modules/encoders/modules.py:38
Method__init__
(self, device="cuda", vq_interface=True, max_length=77)
ldm/modules/encoders/modules.py:55
Method__init__
(self, n_stages=1, method='bilinear', multiplier=0.5,
ldm/modules/encoders/modules.py:114
Method__init__
(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77)
ldm/modules/encoders/modules.py:146
Method__init__
(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True)
ldm/modules/encoders/modules.py:180
Method__init__
( self, model, jit=False, device='cuda' if torch.cuda.is_avail
ldm/modules/encoders/modules.py:212
Method__init__
(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_chan
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
ldm/modules/losses/contperceptual.py:8
Method__init__
(self, config=None)
ldm/data/imagenet.py:27
Method__init__
(self, process_images=True, data_root=None, **kwargs)
ldm/data/imagenet.py:145
Method__init__
(self, process_images=True, data_root=None, **kwargs)
ldm/data/imagenet.py:211
Method__init__
(self, **kwargs)
ldm/data/imagenet.py:376
Method__init__
(self, **kwargs)
ldm/data/imagenet.py:387
Method__init__
(self, num_records=0, valid_ids=None, size=256)
ldm/data/base.py:9
Method__init__
(self, **kwargs)
ldm/data/lsun.py:63
Method__init__
(self, flip_p=0., **kwargs)
ldm/data/lsun.py:68
Method__init__
(self, **kwargs)
ldm/data/lsun.py:74
Method__init__
(self, flip_p=0.0, **kwargs)
ldm/data/lsun.py:79
Method__init__
(self, **kwargs)
ldm/data/lsun.py:85
Method__init__
(self, flip_p=0., **kwargs)
ldm/data/lsun.py:90
Method__init__
(self, ddconfig, embed_dim, scale_factor=1
ldm/models/autoencoder.py:13
Method__init__
(self, diffusion, model, controlnet, schedule="linear", alpha_generator_func=None, set_alpha_scale=None, dtype
ldm/models/diffusion/ddim.py:10
Method__init__
(self, diffusion_path, num_classes, ckpt_path=None,
ldm/models/diffusion/classifier.py:30
Method__init__
(self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3)
ldm/models/diffusion/ddpm.py:12
Method__init__
(self, *args, **kwargs)
ldm/models/diffusion/ldm.py:12
Method__iter__
(self)
annotator/zoe/zoedepth/data/data_mono.py:208
Method__iter__
(self)
ldm/data/base.py:22
Method__len__
(self)
annotator/zoe/zoedepth/data/vkitti.py:132
Method__len__
(self)
annotator/zoe/zoedepth/data/vkitti2.py:168
Method__len__
(self)
annotator/zoe/zoedepth/data/diode.py:117
Method__len__
(self)
annotator/zoe/zoedepth/data/data_mono.py:211
Method__len__
(self)
annotator/zoe/zoedepth/data/data_mono.py:509
Method__len__
(self)
annotator/zoe/zoedepth/data/diml_outdoor_test.py:105
Method__len__
(self)
annotator/zoe/zoedepth/data/sun_rgbd_loader.py:100
Method__len__
(self)
annotator/zoe/zoedepth/data/hypersim.py:132
Method__len__
(self)
annotator/zoe/zoedepth/data/ddad.py:111
Method__len__
(self)
annotator/zoe/zoedepth/data/ibims.py:75
Method__len__
(self)
annotator/zoe/zoedepth/data/diml_indoor_test.py:116
Method__len__
(self)
ldm/data/imagenet.py:39
Method__len__
(self)
ldm/data/imagenet.py:336
Method__len__
(self)
ldm/data/lsun.py:36
Method__setattr__
(self, name, value)
annotator/zoe/zoedepth/utils/easydict/__init__.py:134
Method_dequantize_depth
Inverse of quantization depth : NCHW -> N1HW
annotator/zoe/zoedepth/trainers/loss.py:211
Function_get_rel_pos_bias
Modification of timm.models.beit.py: Attention._get_rel_pos_bias to support arbitrary window sizes.
annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/beit.py:29
Method_init_weights
(self, m)
ldm/modules/diffusionmodules/convnext.py:103
Method_prepare
(self)
ldm/data/imagenet.py:150
Method_prepare
(self)
ldm/data/imagenet.py:216
Function_resize_pos_embed
(self, posemb, gs_h, gs_w)
annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/vit.py:16
Method_set_gradient_checkpointing
(self, module, value=False)
controlnet/unet_3d_condition.py:276
Method_set_gradient_checkpointing
(self, module, value=False)
controlnet/controlnet.py:420
Method_set_gradient_checkpointing
(self, module, value=False)
controlnet/controlnet_3d.py:398
Functionadd_Poisson_noise
(img)
ldm/modules/image_degradation/bsrgan_light.py:408
Functionadd_additional_channels
state_dict should be just from unet model, not the entire SD or GLIGEN
convert_ckpt.py:5
Functionadd_resize
(img, sf=4)
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
ldm/modules/image_degradation/bsrgan_light.py:299
Functionadd_speckle_noise
(img, noise_level1=2, noise_level2=25)
ldm/modules/image_degradation/bsrgan_light.py:390
Functionalpha_generator
length is total timestpes needed for sampling. type should be a list containing three values which sum should be 1 It means the per
inference.py:33
Functionanalytic_kernel
Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)
ldm/modules/image_degradation/bsrgan_light.py:49
Functionanalytic_kernel
Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)
ldm/modules/image_degradation/bsrgan.py:49
Functionapply_min_size
Rezise the sample to ensure the given size. Keeps aspect ratio. Args: sample (dict): sample size (tuple): image size Returns
annotator/zoe/zoedepth/models/base_models/midas_repo/tf/transforms.py:6
Functionapply_min_size
Rezise the sample to ensure the given size. Keeps aspect ratio. Args: sample (dict): sample size (tuple): image size Returns
annotator/zoe/zoedepth/models/base_models/midas_repo/midas/transforms.py:6
Methodapply_temporal_attention
(self, hidden_states, timestep, clip_length)
controlnet/attention.py:276
Functionattention_forward
Modification of timm.models.beit.py: Attention.forward to support arbitrary window sizes.
annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/beit.py:65
Methodattn_processors
r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with
controlnet/controlnet.py:303
Functionaugment_img_np3
(img, mode=0)
ldm/modules/image_degradation/utils_image.py:441
Functionaugment_img_tensor
Kai Zhang (github: https://github.com/cszn)
ldm/modules/image_degradation/utils_image.py:422
Functionaugment_img_tensor4
Kai Zhang (github: https://github.com/cszn)
ldm/modules/image_degradation/utils_image.py:401
Functionaugment_imgs
(img_list, hflip=True, rot=True)
ldm/modules/image_degradation/utils_image.py:469
Functionbeit_forward_features
Modification of timm.models.beit.py: Beit.forward_features to support arbitrary window sizes.
annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/beit.py:108
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 = [
ldm/modules/diffusionmodules/util.py:86
Functionblock_forward
Modification of timm.models.beit.py: Block.forward to support arbitrary window sizes.
annotator/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/beit.py:94
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