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Functions235 in github.com/Damarcreative/rem-wm

⨍ Functions 235◇ Types & classes 75
Method__init__
(self, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels)
lama_cleaner/model/mat.py:159
Method__init__
(self, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels)
lama_cleaner/model/mat.py:192
Method__init__
(self, res, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_chan
lama_cleaner/model/mat.py:228
Method__init__
(self, z_dim, # Input latent (Z) dimensionality, 0 = no latent. c_dim, # C
lama_cleaner/model/mat.py:272
Method__init__
(self, in_channels, out_channels, activation)
lama_cleaner/model/mat.py:349
Method__init__
(self, in_channels, out_channels, activation)
lama_cleaner/model/mat.py:362
Method__init__
(self, c_dim, # Conditioning label (C) dimensionality. img_resolution, # I
lama_cleaner/model/mat.py:392
Method__init__
(self, in_channels, # Number of input channels. out_channels, # Number of
lama_cleaner/model/mat.py:502
Method__init__
(self, dim, window_size, num_heads, down_ratio=1, qkv_bias=True, qk_scale=None, attn_drop=0.,
lama_cleaner/model/mat.py:554
Method__init__
(self, dim, input_resolution, num_heads, down_ratio=1, window_size=7, shift_size=0, mlp_ratio
lama_cleaner/model/mat.py:622
Method__init__
(self, in_channels, out_channels, down=2)
lama_cleaner/model/mat.py:745
Method__init__
(self, in_channels, out_channels, up=2)
lama_cleaner/model/mat.py:770
Method__init__
(self, dim, input_resolution, depth, num_heads, window_size, down_ratio=1, mlp_ratio=2., qkv_
lama_cleaner/model/mat.py:812
Method__init__
(self, in_channels=3, dim=128, kernel_size=5, stride=1)
lama_cleaner/model/mat.py:862
Method__init__
(self, in_channels, out_channels, activation)
lama_cleaner/model/mat.py:875
Method__init__
(self, in_channels, out_channels, activation)
lama_cleaner/model/mat.py:896
Method__init__
(self, res_log2, img_channels, activation, patch_size=5, channels=16, drop_path_rate=0.1)
lama_cleaner/model/mat.py:932
Method__init__
(self, in_channels, out_channels, activation, drop_rate)
lama_cleaner/model/mat.py:957
Method__init__
(self, res_log2, activation, style_dim, use_noise, demodulate, img_channels)
lama_cleaner/model/mat.py:1066
Method__init__
(self, res, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels)
lama_cleaner/model/mat.py:1084
Method__init__
(self, img_channels, img_resolution=256, dim=180, w_dim=512, use_noise=False, demodulate=True,
lama_cleaner/model/mat.py:1124
Method__init__
(self, w_dim, # Intermediate latent (W) dimensionality. img_resolution, #
lama_cleaner/model/mat.py:1227
Method__init__
(self, z_dim, # Input latent (Z) dimensionality, 0 = no latent. c_dim, # C
lama_cleaner/model/mat.py:1290
Method__init__
(self, group_size, num_channels=1)
lama_cleaner/model/utils.py:477
Method__init__
(self, in_features, # Number of input features. out_features, # Number of
lama_cleaner/model/utils.py:502
Method__init__
(self, in_channels, # Number of input channels. cmap_dim, # Dimensionality
lama_cleaner/model/fcf.py:68
Method__init__
(self, in_channels, # Number of input channels, 0 = first block. tmp_channe
lama_cleaner/model/fcf.py:121
Method__init__
(self, c_dim, # Conditioning label (C) dimensionality. z_dim, # Input late
lama_cleaner/model/fcf.py:210
Method__init__
(self, in_channels, # Number of input channels. out_channels, # Number of
lama_cleaner/model/fcf.py:381
Method__init__
(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False)
lama_cleaner/model/fcf.py:441
Method__init__
(self, z_dim, # Output Latent (Z) dimensionality. resolution, # Resolution
lama_cleaner/model/fcf.py:459
Method__init__
(self, channel, reduction=16)
lama_cleaner/model/fcf.py:517
Method__init__
(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
lama_cleaner/model/fcf.py:537
Method__init__
(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **fu_kwargs)
lama_cleaner/model/fcf.py:604
Method__init__
(self, in_channels, out_channels, kernel_size, ratio_gin, ratio_gout, stride
lama_cleaner/model/fcf.py:718
Method__init__
(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1, spatial_transform
lama_cleaner/model/fcf.py:747
Method__init__
(self, dim, # Number of output/input channels. kernel_size, # Width and he
lama_cleaner/model/fcf.py:791
Method__init__
(self, dim, # Number of input/output channels. kernel_size=3, # Convolutio
lama_cleaner/model/fcf.py:830
Method__init__
(self, in_channels, # Number of input channels, 0 = first block. out_channe
lama_cleaner/model/fcf.py:848
Method__init__
(self, w_dim, # Intermediate latent (W) dimensionality. z_dim, # Output La
lama_cleaner/model/fcf.py:956
Method__init__
(self, z_dim, # Input latent (Z) dimensionality, 0 = no latent. c_dim, # C
lama_cleaner/model/fcf.py:1020
Method__init__
(self, z_dim, # Input latent (Z) dimensionality. c_dim, # Conditioning lab
lama_cleaner/model/fcf.py:1096
Method__init__
Args: device:
lama_cleaner/model/base.py:17
Method__init__
( self, device, timesteps=1000, beta_schedule="linear", linear_start=0
lama_cleaner/model/ldm.py:43
Method__init__
( self, diffusion_model, device, cond_stage_key="image", cond_stage_tr
lama_cleaner/model/ldm.py:168
Method__init__
Args: device:
lama_cleaner/model/zits.py:209
Method__init__
(self, pixel_values)
lama_cleaner/model/sd.py:43
Method__init__
(self, **kwargs)
lama_cleaner/model/sd.py:51
Method__setattr__
(self, name: str, value: Any)
lama_cleaner/model/utils.py:110
Methodbackward
(ctx, dout)
lama_cleaner/model/fcf.py:291
Methoddisable_attention_slicing
r""" Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go back to computing
lama_cleaner/model/sd_pipeline.py:110
Methodforward
Input image and output image have same size image: [H, W, C] RGB mask: [H, W, 1] return: BGR IMAGE
lama_cleaner/model/opencv2.py:17
Methodforward
Input image and output image have same size image: [H, W, C] RGB mask: [H, W] return: BGR IMAGE
lama_cleaner/model/lama.py:43
Methodforward
(self, x, style)
lama_cleaner/model/mat.py:45
Methodforward
(self, x, style, noise_mode='random', gain=1)
lama_cleaner/model/mat.py:99
Methodforward
(self, x, style, skip=None)
lama_cleaner/model/mat.py:142
Methodforward
(self, x, ws, gs, E_features, noise_mode='random')
lama_cleaner/model/mat.py:180
Methodforward
(self, x, ws, gs, E_features, noise_mode='random')
lama_cleaner/model/mat.py:215
Methodforward
(self, x, img, ws, gs, E_features, noise_mode='random')
lama_cleaner/model/mat.py:259
Methodforward
(self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False)
lama_cleaner/model/mat.py:311
Methodforward
(self, x)
lama_cleaner/model/mat.py:357
Methodforward
(self, x)
lama_cleaner/model/mat.py:382
Methodforward
(self, images_in, masks_in, c)
lama_cleaner/model/mat.py:437
Methodforward
(self, x)
lama_cleaner/model/mat.py:464
Methodforward
(self, x, mask=None)
lama_cleaner/model/mat.py:523
Methodforward
Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh
lama_cleaner/model/mat.py:571
Methodforward
(self, x, x_size, mask=None)
lama_cleaner/model/mat.py:679
Methodforward
(self, x, x_size, mask=None)
lama_cleaner/model/mat.py:755
Methodforward
(self, x, x_size, mask=None)
lama_cleaner/model/mat.py:780
Methodforward
(self, x, x_size, mask=None)
lama_cleaner/model/mat.py:843
Methodforward
(self, x, mask)
lama_cleaner/model/mat.py:868
Methodforward
(self, x)
lama_cleaner/model/mat.py:888
Methodforward
(self, x)
lama_cleaner/model/mat.py:911
Methodforward
(self, x)
lama_cleaner/model/mat.py:946
Methodforward
(self, x)
lama_cleaner/model/mat.py:974
Methodforward
(self, x, ws, gs, E_features, noise_mode='random')
lama_cleaner/model/mat.py:1074
Methodforward
(self, x, img, style, skip, noise_mode='random')
lama_cleaner/model/mat.py:1114
Methodforward
(self, images_in, masks_in, ws, noise_mode='random')
lama_cleaner/model/mat.py:1180
Methodforward
(self, images_in, masks_in, ws, noise_mode='random', return_stg1=False)
lama_cleaner/model/mat.py:1258
Methodforward
(self, images_in, masks_in, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False,
lama_cleaner/model/mat.py:1316
Methodforward
Input images and output images have same size images: [H, W, C] RGB masks: [H, W] mask area == 255 return: BGR IMAGE
lama_cleaner/model/mat.py:1423
Methodforward
(self, x)
lama_cleaner/model/utils.py:482
Methodforward
(self, x)
lama_cleaner/model/utils.py:518
Methodforward
(self, x, gain=1)
lama_cleaner/model/utils.py:701
Methodforward
(self, x, cmap, force_fp32=False)
lama_cleaner/model/fcf.py:97
Methodforward
(self, x, img, force_fp32=False)
lama_cleaner/model/fcf.py:177
Methodforward
(self, img, c, **block_kwargs)
lama_cleaner/model/fcf.py:258
Methodforward
(ctx, a, b, c)
lama_cleaner/model/fcf.py:284
Methodforward
(self, x, w, noise_mode='none', fused_modconv=True, gain=1)
lama_cleaner/model/fcf.py:413
Methodforward
(self, x, w, fused_modconv=True)
lama_cleaner/model/fcf.py:451
Methodforward
(self, x, ws, feats, img, force_fp32=False)
lama_cleaner/model/fcf.py:481
Methodforward
(self, x)
lama_cleaner/model/fcf.py:527
Methodforward
(self, x)
lama_cleaner/model/fcf.py:561
Methodforward
(self, x)
lama_cleaner/model/fcf.py:628
Methodforward
(self, x, fname=None)
lama_cleaner/model/fcf.py:691
Methodforward
(self, x, fname=None)
lama_cleaner/model/fcf.py:739
Methodforward
(self, x, fname=None)
lama_cleaner/model/fcf.py:762
Methodforward
(self, x)
lama_cleaner/model/fcf.py:781
Methodforward
(self, gen_ft, mask, fname=None)
lama_cleaner/model/fcf.py:816
Methodforward
(self, gen_ft, mask, fname=None)
lama_cleaner/model/fcf.py:842
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