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Functions1,626 in github.com/LuChengTHU/dpm-solver

⨍ Functions 1,626◇ Types & classes 286
↓ 81 callersMethodlog
(self, *args, level=INFO)
examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:376
↓ 77 callersFunctionexpand_dims
Expand the tensor `v` to the dim `dims`. Args: `v`: a jnp.DeviceArray with shape [N]. `dim`: a `int`. Returns: a
dpm_solver_jax.py:1168
↓ 77 callersFunctionexpand_dims
Expand the tensor `v` to the dim `dims`. Args: `v`: a jnp.DeviceArray with shape [N]. `dim`: a `int`. Returns: a
examples/score_sde_jax/dpm_solver.py:1168
↓ 43 callersMethodregister_buffer
(self, name, attr)
examples/stable-diffusion/ldm/models/diffusion/ddim.py:19
↓ 28 callersFunctionbatch_mul
(a, b)
examples/score_sde_jax/utils.py:41
↓ 26 callersFunctioninstantiate_from_config
(config)
examples/stable-diffusion/ldm/util.py:78
↓ 23 callersFunctionget_default_configs
()
examples/score_sde_pytorch/configs/default_cifar10_configs.py:5
↓ 23 callersFunctionget_default_configs
()
examples/score_sde_jax/configs/default_cifar10_configs.py:4
↓ 22 callersFunctionncsn_conv3x3
3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2.
examples/score_sde_jax/models/layers.py:77
↓ 21 callersFunctionmodel
(x, t_discrete)
examples/ddpm_and_guided-diffusion/functions/denoising.py:24
↓ 20 callersFunctionncsn_conv3x3
3x3 convolution with PyTorch initialization. Same as NCSNv1/NCSNv2.
examples/score_sde_pytorch/models/layers.py:108
↓ 19 callersMethodmarginal_lambda
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
dpm_solver_pytorch.py:148
↓ 19 callersMethodmarginal_lambda
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
examples/ddpm_and_guided-diffusion/dpm_solver/sampler.py:148
↓ 19 callersMethodmarginal_lambda
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
examples/score_sde_pytorch/dpm_solver.py:151
↓ 19 callersMethodmarginal_lambda
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
examples/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py:151
↓ 19 callersMethodmarginal_std
Compute sigma_t of a given continuous-time label t in [0, T].
dpm_solver_pytorch.py:142
↓ 19 callersMethodmarginal_std
Compute sigma_t of a given continuous-time label t in [0, T].
examples/ddpm_and_guided-diffusion/dpm_solver/sampler.py:142
↓ 19 callersMethodmarginal_std
Compute sigma_t of a given continuous-time label t in [0, T].
examples/score_sde_pytorch/dpm_solver.py:145
↓ 19 callersMethodmarginal_std
Compute sigma_t of a given continuous-time label t in [0, T].
examples/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py:145
↓ 18 callersMethod__init__
(self, in_dim, num_units, init_scale=0.1)
examples/score_sde_pytorch/models/layers.py:547
↓ 18 callersMethodmarginal_lambda
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
dpm_solver_jax.py:151
↓ 18 callersMethodmarginal_lambda
Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
examples/score_sde_jax/dpm_solver.py:151
↓ 18 callersMethodmarginal_std
Compute sigma_t of a given continuous-time label t in [0, T].
dpm_solver_jax.py:145
↓ 18 callersMethodmarginal_std
Compute sigma_t of a given continuous-time label t in [0, T].
examples/score_sde_jax/dpm_solver.py:145
↓ 18 callersFunctionnormalization
Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.
examples/stable-diffusion/ldm/modules/diffusionmodules/util.py:199
↓ 18 callersFunctiontqdm
(x)
examples/ddpm_and_guided-diffusion/evaluate/fid_score.py:49
↓ 16 callersFunctionexists
(val)
examples/stable-diffusion/ldm/modules/x_transformer.py:54
↓ 16 callersMethodload_state_dict
(self, state_dict)
examples/score_sde_pytorch/models/ema.py:95
↓ 16 callersMethodmarginal_log_mean_coeff
Compute log(alpha_t) of a given continuous-time label t in [0, T].
dpm_solver_jax.py:126
↓ 16 callersMethodmarginal_log_mean_coeff
Compute log(alpha_t) of a given continuous-time label t in [0, T].
dpm_solver_pytorch.py:127
↓ 16 callersMethodmarginal_log_mean_coeff
Compute log(alpha_t) of a given continuous-time label t in [0, T].
examples/ddpm_and_guided-diffusion/dpm_solver/sampler.py:127
↓ 16 callersMethodmarginal_log_mean_coeff
Compute log(alpha_t) of a given continuous-time label t in [0, T].
examples/score_sde_pytorch/dpm_solver.py:126
↓ 16 callersMethodmarginal_log_mean_coeff
Compute log(alpha_t) of a given continuous-time label t in [0, T].
examples/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py:126
↓ 16 callersMethodmarginal_log_mean_coeff
Compute log(alpha_t) of a given continuous-time label t in [0, T].
examples/score_sde_jax/dpm_solver.py:126
↓ 15 callersMethod__init__
(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, attn_resolutions, dropout=0.0, resam
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:217
↓ 15 callersFunctionconv_nd
Create a 1D, 2D, or 3D convolution module.
examples/stable-diffusion/ldm/modules/diffusionmodules/util.py:218
↓ 15 callersFunctionextract_into_tensor
(a, t, x_shape)
examples/stable-diffusion/ldm/modules/diffusionmodules/util.py:96
↓ 15 callersMethodmodel_fn
Convert the model to the noise prediction model or the data prediction model.
dpm_solver_jax.py:400
↓ 15 callersMethodmodel_fn
Convert the model to the noise prediction model or the data prediction model.
dpm_solver_pytorch.py:444
↓ 15 callersMethodmodel_fn
Convert the model to the noise prediction model or the data prediction model.
examples/ddpm_and_guided-diffusion/dpm_solver/sampler.py:444
↓ 15 callersMethodmodel_fn
Convert the model to the noise prediction model or the data prediction model.
examples/score_sde_pytorch/dpm_solver.py:452
↓ 15 callersMethodmodel_fn
Convert the model to the noise prediction model or the data prediction model.
examples/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py:452
↓ 15 callersMethodmodel_fn
Convert the model to the noise prediction model or the data prediction model.
examples/score_sde_jax/dpm_solver.py:400
↓ 14 callersFunctionconv_nd
Create a 1D, 2D, or 3D convolution module.
examples/ddpm_and_guided-diffusion/models/guided_diffusion/nn.py:22
↓ 14 callersMethoddecode
(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
examples/stable-diffusion/ldm/models/diffusion/ddim.py:223
↓ 14 callersMethoddecode_first_stage
(self, z, predict_cids=False, force_not_quantize=False)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:706
↓ 13 callersMethodregister_buffer
(self, name, attr)
examples/stable-diffusion/ldm/models/diffusion/plms.py:18
↓ 12 callersMethod__init__
(self, value, fn)
examples/stable-diffusion/ldm/modules/x_transformer.py:118
↓ 12 callersMethodq_sample
(self, x_start, t, noise=None)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:274
↓ 12 callersMethodupdate
Update currently maintained parameters. Call this every time the parameters are updated, such as the result of the `optimizer.step()` ca
examples/score_sde_pytorch/models/ema.py:32
↓ 11 callersMethodload_state_dict
(self, state_dict)
examples/ddpm_and_guided-diffusion/models/ema.py:48
↓ 10 callersFunctionconv_nd
Create a 1D, 2D, or 3D convolution module.
examples/ddpm_and_guided-diffusion/models/improved_ddpm/nn.py:22
↓ 10 callersFunctionnonlinearity
(x)
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:33
↓ 9 callersFunctionNormalize
(in_channels, num_groups=32)
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:38
↓ 9 callersMethod__init__
(self, channels, use_conv, dims=2, out_channels=None)
examples/ddpm_and_guided-diffusion/models/guided_diffusion/unet.py:91
↓ 9 callersMethod__init__
(self, channels, use_conv, dims=2, out_channels=None, padding=1)
examples/stable-diffusion/ldm/modules/diffusionmodules/openaimodel.py:100
↓ 9 callersFunctionexpand_dims
Expand the tensor `v` to the dim `dims`. Args: `v`: a PyTorch tensor with shape [N]. `dim`: a `int`. Returns: a
dpm_solver_pytorch.py:1295
↓ 9 callersFunctionget_current
()
examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:325
↓ 8 callersMethod__init__
(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, device="cuda",use_tokenizer=True,
examples/stable-diffusion/ldm/modules/encoders/modules.py:82
↓ 8 callersMethodapply_model
(self, x_noisy, t, cond, return_ids=False)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:891
↓ 8 callersFunctiondefault_init
The same initialization used in DDPM.
examples/score_sde_pytorch/models/layers.py:88
↓ 8 callersFunctiondefault_init
The same initialization used in DDPM.
examples/score_sde_jax/models/layers.py:60
↓ 8 callersMethodema_scope
(self, context=None)
examples/stable-diffusion/ldm/models/autoencoder.py:64
↓ 8 callersFunctionget_default_configs
()
examples/score_sde_pytorch/configs/default_lsun_configs.py:5
↓ 8 callersFunctionget_default_configs
()
examples/score_sde_jax/configs/default_lsun_configs.py:4
↓ 8 callersMethodget_learned_conditioning
(self, c)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:551
↓ 8 callersMethodmarginal_prob
Parameters to determine the marginal distribution of the SDE, $p_t(x)$.
examples/score_sde_jax/sde_lib.py:32
↓ 7 callersMethod__init__
(self, num_features, bias=True)
examples/score_sde_pytorch/models/normalization.py:150
↓ 7 callersMethodema_scope
(self, context=None)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:172
↓ 7 callersMethodencode
(self, x)
examples/stable-diffusion/ldm/models/autoencoder.py:96
↓ 7 callersFunctionmake_attn
(in_channels, attn_type="vanilla")
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:205
↓ 7 callersFunctionnormalization
Make a standard normalization layer. :param channels: number of input channels. :return: an nn.Module for normalization.
examples/ddpm_and_guided-diffusion/models/guided_diffusion/nn.py:93
↓ 7 callersMethodsample
(self)
examples/stable-diffusion/ldm/modules/distributions/distributions.py:17
↓ 7 callersFunctionscore_fn
(x, t, rng=None)
examples/score_sde_jax/models/utils.py:214
↓ 7 callersMethodstate_dict
(self)
examples/ddpm_and_guided-diffusion/models/ema.py:45
↓ 6 callersMethod__init__
(self, sde, score_fn, probability_flow=False)
examples/score_sde_pytorch/sampling.py:146
↓ 6 callersMethod__init__
(self, dim1, dim2, method='cat')
examples/score_sde_pytorch/models/layerspp.py:47
↓ 6 callersMethod__init__
(self, dim_in, dim_out)
examples/stable-diffusion/ldm/modules/attention.py:38
↓ 6 callersMethod__init__
(self, txt_file, data_root, size=None, int
examples/stable-diffusion/ldm/data/lsun.py:10
↓ 6 callersMethod__init__
(self, sde, score_fn, probability_flow=False)
examples/score_sde_jax/sampling.py:151
↓ 6 callersMethod_compute_cond_module
(self, module, x)
examples/score_sde_pytorch/models/ncsnv2.py:381
↓ 6 callersFunctionadd_JPEG_noise
(img)
examples/stable-diffusion/ldm/modules/image_degradation/bsrgan.py:418
↓ 6 callersFunctionadd_blur
(img, sf=4)
examples/stable-diffusion/ldm/modules/image_degradation/bsrgan.py:325
↓ 6 callersMethodclose
(self)
examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:391
↓ 6 callersFunctioncompute_alpha
(beta, t)
examples/ddpm_and_guided-diffusion/functions/denoising.py:5
↓ 6 callersFunctiondefault
(val, d)
examples/stable-diffusion/ldm/modules/x_transformer.py:58
↓ 6 callersFunctionget_act
Get activation functions from the config file.
examples/score_sde_pytorch/models/layers.py:29
↓ 6 callersFunctionget_act
Get activation functions from the config file.
examples/score_sde_jax/models/layers.py:30
↓ 6 callersFunctionget_default_configs
()
examples/score_sde_pytorch/configs/default_celeba_configs.py:5
↓ 6 callersFunctionget_default_configs
()
examples/score_sde_jax/configs/default_celeba_configs.py:4
↓ 6 callersFunctionlog
Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:247
↓ 6 callersMethodlogkv_mean
(self, key, val)
examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:350
↓ 6 callersMethodmarginal_prob
Parameters to determine the marginal distribution of the SDE, $p_t(x)$.
examples/score_sde_pytorch/sde_lib.py:30
↓ 6 callersMethodmeshgrid
(self, h, w)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:564
↓ 6 callersMethodprior_sampling
Generate one sample from the prior distribution, $p_T(x)$.
examples/score_sde_jax/sde_lib.py:37
↓ 6 callersMethodsample
(self, batch_size=16, return_intermediates=False)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:268
↓ 5 callersMethod__init__
(self, channels, use_conv, dims=2)
examples/ddpm_and_guided-diffusion/models/improved_ddpm/unet.py:60
↓ 5 callersMethod_compute_cond_module
(self, module, x)
examples/score_sde_pytorch/models/ncsnv2.py:279
↓ 5 callersFunction_shape
(x, dim)
examples/score_sde_pytorch/models/up_or_down_sampling.py:191
↓ 5 callersFunctionddpm_conv3x3
3x3 convolution with DDPM initialization.
examples/score_sde_pytorch/models/layers.py:118
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