Functions — github.com/LuChengTHU/dpm-solver

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

⨍ Functions 1,626◇ Types & classes 286

Methodload_multi_files

(self, data_archive)

examples/stable-diffusion/scripts/knn2img.py:96

Functionload_training_state

(filepath, state)

examples/score_sde_jax/utils.py:45

Methodlog_images

(self, batch, only_inputs=False, **kwargs)

examples/stable-diffusion/ldm/models/autoencoder.py:401

Methodlog_images

(self, batch, N=8, *args, **kwargs)

examples/stable-diffusion/ldm/models/diffusion/classifier.py:238

Methodlog_images

(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None, qua

examples/stable-diffusion/ldm/models/diffusion/ddpm.py:1251

Methodlog_images

(self, batch, N=8, *args, **kwargs)

examples/stable-diffusion/ldm/models/diffusion/ddpm.py:1430

Functionlogkv_mean

The same as logkv(), but if called many times, values averaged.

examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:221

Functionlogkvs

Log a dictionary of key-value pairs

examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:228

Functionlogs2pil

(logs, keys=["sample"])

examples/stable-diffusion/scripts/sample_diffusion.py:36

Functionloop_body

(i, val)

examples/score_sde_jax/controllable_generation.py:82

Functionloop_body

(i, val)

examples/score_sde_jax/sampling.py:442

Methodloop_body

(step, val)

examples/score_sde_jax/sampling.py:297

Methodloop_body

(step, val)

examples/score_sde_jax/sampling.py:344

Functionmain

(argv)

examples/score_sde_pytorch/main.py:39

Functionmain

(argv)

examples/score_sde_jax/main.py:37

Functionmake_master_params

Copy model parameters into a (differently-shaped) list of full-precision parameters.

examples/ddpm_and_guided-diffusion/models/improved_ddpm/fp16_util.py:27

Methodmarginal_prob

(self, x, t)

examples/score_sde_pytorch/sde_lib.py:141

Methodmarginal_prob

(self, x, t)

examples/score_sde_pytorch/sde_lib.py:192

Methodmarginal_prob

(self, x, t)

examples/score_sde_pytorch/sde_lib.py:233

Methodmarginal_prob

(self, x, t)

examples/score_sde_jax/sde_lib.py:143

Methodmarginal_prob

(self, x, t)

examples/score_sde_jax/sde_lib.py:194

Methodmarginal_prob

(self, x, t)

examples/score_sde_jax/sde_lib.py:235

Functionmaster_params_to_model_params

Copy the master parameter data back into the model parameters.

examples/ddpm_and_guided-diffusion/models/improved_ddpm/fp16_util.py:50

Methodmaster_params_to_state_dict

(self, master_params)

examples/ddpm_and_guided-diffusion/models/guided_diffusion/fp16_util.py:227

Functionmax_neg_value

(t)

examples/stable-diffusion/ldm/modules/attention.py:25

Functionmean_flat

Take the mean over all non-batch dimensions.

examples/ddpm_and_guided-diffusion/models/guided_diffusion/nn.py:86

Functionmean_flat

Take the mean over all non-batch dimensions.

examples/ddpm_and_guided-diffusion/models/improved_ddpm/nn.py:85

Functionmean_flat

https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 Take the mean over all no

examples/stable-diffusion/ldm/util.py:63

Functionmkdir_and_rename

(path)

examples/stable-diffusion/ldm/modules/image_degradation/utils_image.py:166

Functionmkdirs

(paths)

examples/stable-diffusion/ldm/modules/image_degradation/utils_image.py:158

Functionmodcrop

(img_in, scale)

examples/stable-diffusion/ldm/modules/image_degradation/utils_image.py:494

Functionmodcrop_np

Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image

examples/stable-diffusion/ldm/modules/image_degradation/bsrgan_light.py:29

Functionmodcrop_np

Args: img: numpy image, WxH or WxHxC sf: scale factor Return: cropped image

examples/stable-diffusion/ldm/modules/image_degradation/bsrgan.py:29

Methodmode

(self)

examples/stable-diffusion/ldm/modules/distributions/distributions.py:9

Methodmode

(self)

examples/stable-diffusion/ldm/modules/distributions/distributions.py:61

Functionmodel_fn

The noise predicition model function that is used for DPM-Solver.

dpm_solver_jax.py:320

Methodmodel_fn

(x, t, **model_kwargs)

examples/ddpm_and_guided-diffusion/runners/diffusion.py:563

Functionmodel_grads_to_master_grads

Copy the gradients from the model parameters into the master parameters from make_master_params().

examples/ddpm_and_guided-diffusion/models/improved_ddpm/fp16_util.py:40

Functionmodel_wrapper

Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discr

dpm_solver_jax.py:178

Functionmodel_wrapper

Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discr

dpm_solver_pytorch.py:170

Functionmodel_wrapper

Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discr

examples/score_sde_pytorch/dpm_solver.py:178

Functionmodel_wrapper

Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discr

examples/score_sde_jax/dpm_solver.py:178

Methodmultistep_loop_fn

(step, val)

dpm_solver_jax.py:1080

Methodmultistep_loop_fn

(step, val)

examples/score_sde_jax/dpm_solver.py:1080

Functionnaive_downsample_2d

(x, factor=2)

examples/score_sde_pytorch/models/up_or_down_sampling.py:66

Functionnaive_downsample_2d

(x, factor=2)

examples/score_sde_jax/models/up_or_down_sampling.py:83

Functionnaive_upsample_2d

(x, factor=2)

examples/score_sde_pytorch/models/up_or_down_sampling.py:59

Functionnaive_upsample_2d

(x, factor=2)

examples/score_sde_jax/models/up_or_down_sampling.py:76

Functionncsn_conv1x1

1x1 convolution. Same as NCSNv1/v2.

examples/score_sde_pytorch/models/layers.py:44

Methodnext_step

(inputs)

dpm_solver_jax.py:948

Methodnext_step

(inputs)

examples/score_sde_jax/dpm_solver.py:948

Methodnll

(self, sample, dims=[1,2,3])

examples/stable-diffusion/ldm/modules/distributions/distributions.py:53

Functionnoise_estimation_loss

(model, x0: torch.Tensor, t: torch.LongTensor,

examples/ddpm_and_guided-diffusion/functions/losses.py:4

Functionnoise_fn

(x, t)

examples/score_sde_pytorch/models/utils.py:144

Functionnoise_fn

(x, t, rng=None)

examples/score_sde_jax/models/utils.py:179

Functionode_func

(t, x)

examples/score_sde_pytorch/likelihood.py:91

Functionode_func

(t, x)

examples/score_sde_pytorch/sampling.py:483

Functionode_func

(t, x)

examples/score_sde_jax/likelihood.py:100

Functionode_func

(t, x)

examples/score_sde_jax/sampling.py:515

Functionode_sampler

The probability flow ODE sampler with black-box ODE solver. Args: model: A score model. z: If present, generate samples from latent c

examples/score_sde_pytorch/sampling.py:466

Functionode_sampler

The probability flow ODE sampler with black-box ODE solver. Args: prng: An array of random state. The leading dimension equals the number o

examples/score_sde_jax/sampling.py:496

Methodon_keyboard_interrupt

(self, trainer, pl_module)

examples/stable-diffusion/main.py:251

Methodon_pretrain_routine_start

(self, trainer, pl_module)

examples/stable-diffusion/main.py:257

Methodon_train_batch_end

(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)

examples/stable-diffusion/main.py:383

Methodon_train_batch_end

(self, *args, **kwargs)

examples/stable-diffusion/ldm/models/autoencoder.py:92

Methodon_train_batch_end

(self, *args, **kwargs)

examples/stable-diffusion/ldm/models/diffusion/ddpm.py:366

Methodon_train_batch_start

(self, batch, batch_idx, dataloader_idx)

examples/stable-diffusion/ldm/models/diffusion/ddpm.py:478

Methodon_train_epoch_end

(self, trainer, pl_module, outputs)

examples/stable-diffusion/main.py:403

Methodon_train_epoch_start

(self, trainer, pl_module)

examples/stable-diffusion/main.py:397

Methodon_train_epoch_start

(self)

examples/stable-diffusion/ldm/models/diffusion/classifier.py:157

Methodon_validation_batch_end

(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)

examples/stable-diffusion/main.py:387

Methodon_validation_start

(self)

examples/stable-diffusion/ldm/models/diffusion/classifier.py:206

Functionoptimization_manager

Returns an optimize_fn based on `config`.

examples/score_sde_pytorch/losses.py:38

Functionoptimization_manager

Returns an optimize_fn based on `config`.

examples/score_sde_jax/losses.py:40

Methodoptimize

(self, opt: th.optim.Optimizer)

examples/ddpm_and_guided-diffusion/models/guided_diffusion/fp16_util.py:183

Functionpc_colorizer

Colorize gray-scale images using Predictor-Corrector (PC) sampler. Args: model: A score model. gray_scale_img: A minibatch of gray-sc

examples/score_sde_pytorch/controllable_generation.py:156

Functionpc_colorizer

Colorize gray-scale images using Predictor-Corrector (PC) sampler. Args: rng: A JAX random state. state: A `flax.struct.dataclass` ob

examples/score_sde_jax/controllable_generation.py:176

Functionpc_conditional_sampler

Generate class-conditional samples with Predictor-Corrector (PC) samplers. Args: rng: A JAX random state. score_state: A `flax.struct

examples/score_sde_jax/controllable_generation.py:270

Functionpc_inpainter

Predictor-Corrector (PC) sampler for image inpainting. Args: model: A score model. data: A PyTorch tensor that represents a mini-batc

examples/score_sde_pytorch/controllable_generation.py:59

Functionpc_inpainter

Predictor-Corrector (PC) sampler for image inpainting. Args: rng: A JAX random state. state: A `flax.struct.dataclass` object that co

examples/score_sde_jax/controllable_generation.py:64

Functionpc_sampler

The PC sampler funciton. Args: model: A score model. Returns: Samples, number of function evaluations.

examples/score_sde_pytorch/sampling.py:407

Functionpc_sampler

The PC sampler funciton. Args: rng: A JAX random state state: A `flax.struct.dataclass` object that represents the training state of

examples/score_sde_jax/sampling.py:428

Functionpick_and_pop

(keys, d)

examples/stable-diffusion/ldm/modules/x_transformer.py:88

Functionpreprocess_fn

(d)

examples/score_sde_pytorch/datasets.py:151

Functionpreprocess_fn

(d)

examples/score_sde_jax/datasets.py:160

Methodprior_logp

(self, z)

examples/score_sde_pytorch/sde_lib.py:150

Methodprior_logp

(self, z)

examples/score_sde_pytorch/sde_lib.py:201

Methodprior_logp

(self, z)

examples/score_sde_pytorch/sde_lib.py:241

Methodprior_logp

Compute log-density of the prior distribution. Useful for computing the log-likelihood via probability flow ODE. Args: z: latent code

examples/score_sde_jax/sde_lib.py:42

Methodprior_logp

(self, z)

examples/score_sde_jax/sde_lib.py:152

Methodprior_logp

(self, z)

examples/score_sde_jax/sde_lib.py:203

Methodprior_logp

(self, z)

examples/score_sde_jax/sde_lib.py:243

Methodprior_sampling

(self, shape)

examples/score_sde_pytorch/sde_lib.py:147

Methodprior_sampling

(self, shape)

examples/score_sde_pytorch/sde_lib.py:198

Methodprior_sampling

(self, shape)

examples/score_sde_pytorch/sde_lib.py:238

Methodprior_sampling

(self, rng, shape)

examples/score_sde_jax/sde_lib.py:149

Methodprior_sampling

(self, rng, shape)

examples/score_sde_jax/sde_lib.py:200

Methodprior_sampling

(self, rng, shape)

examples/score_sde_jax/sde_lib.py:240

Functionprob_fn

(data)

examples/score_sde_jax/models/utils.py:322

Functionprofile

Usage: @profile("my_func") def my_func(): code

examples/ddpm_and_guided-diffusion/models/guided_diffusion/logger.py:303

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