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

⨍ Functions 1,626◇ Types & classes 286
Method__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:369
Method__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:463
Method__init__
(self, in_channels, out_channels, *args, **kwargs)
examples/stable-diffusion/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)
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:608
Method__init__
(self, factor, in_channels, mid_channels, out_channels, depth=2)
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:656
Method__init__
(self, in_channels, ch, resolution, out_ch, num_res_blocks, attn_resolutions, dropout=0.0, re
examples/stable-diffusion/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),
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:712
Method__init__
(self, in_size, out_size, in_channels, out_channels, ch_mult=2)
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:729
Method__init__
(self, in_channels=None, learned=False, mode="bilinear")
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:748
Method__init__
(self, ch_mult:list, in_channels, pretrained_model:nn.Module=None, reshape=F
examples/stable-diffusion/ldm/modules/diffusionmodules/model.py:772
Method__init__
(self, value)
examples/stable-diffusion/ldm/modules/distributions/distributions.py:14
Method__init__
(self, parameters, deterministic=False)
examples/stable-diffusion/ldm/modules/distributions/distributions.py:25
Method__init__
(self)
examples/stable-diffusion/ldm/modules/encoders/modules.py:13
Method__init__
(self, embed_dim, n_classes=1000, key='class')
examples/stable-diffusion/ldm/modules/encoders/modules.py:22
Method__init__
(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda")
examples/stable-diffusion/ldm/modules/encoders/modules.py:38
Method__init__
(self, device="cuda", vq_interface=True, max_length=77)
examples/stable-diffusion/ldm/modules/encoders/modules.py:55
Method__init__
(self, n_stages=1, method='bilinear', multiplier=0.5,
examples/stable-diffusion/ldm/modules/encoders/modules.py:107
Method__init__
(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77)
examples/stable-diffusion/ldm/modules/encoders/modules.py:139
Method__init__
(self, version='ViT-L/14', device="cuda", max_length=77, n_repeat=1, normalize=True)
examples/stable-diffusion/ldm/modules/encoders/modules.py:169
Method__init__
( self, model, jit=False, device='cuda' if torch.cuda.is_avail
examples/stable-diffusion/ldm/modules/encoders/modules.py:201
Method__init__
(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_chan
examples/stable-diffusion/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
examples/stable-diffusion/ldm/modules/losses/contperceptual.py:8
Method__init__
(self, config=None)
examples/stable-diffusion/ldm/data/imagenet.py:27
Method__init__
(self, process_images=True, data_root=None, **kwargs)
examples/stable-diffusion/ldm/data/imagenet.py:145
Method__init__
(self, process_images=True, data_root=None, **kwargs)
examples/stable-diffusion/ldm/data/imagenet.py:211
Method__init__
(self, **kwargs)
examples/stable-diffusion/ldm/data/imagenet.py:376
Method__init__
(self, **kwargs)
examples/stable-diffusion/ldm/data/imagenet.py:387
Method__init__
(self, num_records=0, valid_ids=None, size=256)
examples/stable-diffusion/ldm/data/base.py:9
Method__init__
(self, **kwargs)
examples/stable-diffusion/ldm/data/lsun.py:63
Method__init__
(self, flip_p=0., **kwargs)
examples/stable-diffusion/ldm/data/lsun.py:68
Method__init__
(self, **kwargs)
examples/stable-diffusion/ldm/data/lsun.py:74
Method__init__
(self, flip_p=0.0, **kwargs)
examples/stable-diffusion/ldm/data/lsun.py:79
Method__init__
(self, **kwargs)
examples/stable-diffusion/ldm/data/lsun.py:85
Method__init__
(self, flip_p=0., **kwargs)
examples/stable-diffusion/ldm/data/lsun.py:90
Method__init__
(self, embed_dim, *args, **kwargs)
examples/stable-diffusion/ldm/models/autoencoder.py:265
Method__init__
(self, ddconfig, lossconfig, embed_dim, ck
examples/stable-diffusion/ldm/models/autoencoder.py:286
Method__init__
(self, *args, vq_interface=False, **kwargs)
examples/stable-diffusion/ldm/models/autoencoder.py:427
Method__init__
(self, model, schedule="linear", **kwargs)
examples/stable-diffusion/ldm/models/diffusion/ddim.py:13
Method__init__
(self, diffusion_path, num_classes, ckpt_path=None,
examples/stable-diffusion/ldm/models/diffusion/classifier.py:30
Method__init__
(self, first_stage_config, cond_stage_config, num_timesteps
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:426
Method__init__
(self, diff_model_config, conditioning_key)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:1396
Method__init__
(self, cond_stage_key, *args, **kwargs)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:1426
Method__init__
(self, model, schedule="linear", **kwargs)
examples/stable-diffusion/ldm/models/diffusion/plms.py:12
Method__init__
Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewis
examples/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py:7
Method__init__
Construct a DPM-Solver. We support both DPM-Solver (`algorithm_type="dpmsolver"`) and DPM-Solver++ (`algorithm_type="dpmsolver++"`).
examples/stable-diffusion/ldm/models/diffusion/dpm_solver/dpm_solver.py:346
Method__init__
(self, model, **kwargs)
examples/stable-diffusion/ldm/models/diffusion/dpm_solver/sampler.py:9
Method__init__
Create a wrapper class for the forward SDE (VP type). *** Update: We support discrete-time diffusion models by implementing a picewis
examples/score_sde_jax/dpm_solver.py:8
Method__init__
Construct a DPM-Solver. We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
examples/score_sde_jax/dpm_solver.py:351
Method__init__
(self)
examples/score_sde_jax/sde_lib.py:87
Method__init__
Construct a Variance Preserving SDE. Args: beta_min: value of beta(0) beta_max: value of beta(1) N: number of discretization st
examples/score_sde_jax/sde_lib.py:115
Method__init__
Construct the sub-VP SDE that excels at likelihoods. Args: beta_min: value of beta(0) beta_max: value of beta(1) N: number of d
examples/score_sde_jax/sde_lib.py:170
Method__init__
Construct a Variance Exploding SDE. Args: sigma_min: smallest sigma. sigma_max: largest sigma. N: number of discretization step
examples/score_sde_jax/sde_lib.py:211
Method__init__
(self, sde, score_fn, snr, n_steps)
examples/score_sde_jax/sampling.py:177
Method__init__
(self, sde, score_fn, probability_flow=False)
examples/score_sde_jax/sampling.py:202
Method__init__
(self, sde, score_fn, probability_flow=False)
examples/score_sde_jax/sampling.py:216
Method__init__
(self, sde, score_fn, probability_flow=False)
examples/score_sde_jax/sampling.py:231
Method__init__
(self, sde, score_fn, probability_flow=False)
examples/score_sde_jax/sampling.py:270
Method__init__
(self, sde, score_fn, snr, n_steps)
examples/score_sde_jax/sampling.py:279
Method__init__
(self, sde, score_fn, snr, n_steps)
examples/score_sde_jax/sampling.py:324
Method__init__
(self, sde, score_fn, snr, n_steps)
examples/score_sde_jax/sampling.py:362
Method__iter__
(self)
examples/stable-diffusion/ldm/data/base.py:22
Method__len__
(self)
examples/ddpm_and_guided-diffusion/evaluate/fid_score.py:79
Method__len__
(self)
examples/ddpm_and_guided-diffusion/datasets/ffhq.py:28
Method__len__
(self)
examples/ddpm_and_guided-diffusion/datasets/lsun.py:57
Method__len__
(self)
examples/ddpm_and_guided-diffusion/datasets/lsun.py:171
Method__len__
(self)
examples/ddpm_and_guided-diffusion/datasets/celeba.py:158
Method__len__
(self)
examples/stable-diffusion/main.py:139
Method__len__
(self)
examples/stable-diffusion/ldm/data/imagenet.py:39
Method__len__
(self)
examples/stable-diffusion/ldm/data/imagenet.py:336
Method__len__
(self)
examples/stable-diffusion/ldm/data/lsun.py:36
Method__repr__
(self)
examples/ddpm_and_guided-diffusion/datasets/vision.py:75
Method__repr__
(self)
examples/ddpm_and_guided-diffusion/datasets/__init__.py:24
Function_classifier_fn
(images)
examples/score_sde_pytorch/evaluation.py:77
Function_classifier_fn
(images)
examples/score_sde_jax/evaluation.py:77
Function_do_parallel_data_prefetch
(func, Q, data, idx, idx_to_fn=False)
examples/stable-diffusion/ldm/util.py:96
Method_forward
(self, x, emb)
examples/ddpm_and_guided-diffusion/models/guided_diffusion/unet.py:236
Method_forward
(self, x)
examples/ddpm_and_guided-diffusion/models/guided_diffusion/unet.py:299
Method_forward
(self, x, emb)
examples/ddpm_and_guided-diffusion/models/improved_ddpm/unet.py:184
Method_forward
(self, x)
examples/ddpm_and_guided-diffusion/models/improved_ddpm/unet.py:222
Method_forward
(self, x, context=None)
examples/stable-diffusion/ldm/modules/attention.py:211
Method_forward
(self, x, emb)
examples/stable-diffusion/ldm/modules/diffusionmodules/openaimodel.py:255
Method_forward
(self, x)
examples/stable-diffusion/ldm/modules/diffusionmodules/openaimodel.py:318
Method_predict_dataloader
(self, shuffle=False)
examples/stable-diffusion/main.py:231
Method_predict_eps_from_xstart
(self, x_t, t, pred_xstart)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:994
Method_prepare
(self)
examples/stable-diffusion/ldm/data/imagenet.py:150
Method_prepare
(self)
examples/stable-diffusion/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
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:998
Method_rescale_annotations
(self, bboxes, crop_coordinates)
examples/stable-diffusion/ldm/models/diffusion/ddpm.py:881
Method_test_dataloader
(self, shuffle=False)
examples/stable-diffusion/main.py:218
Method_testtube
(self, pl_module, images, batch_idx, split)
examples/stable-diffusion/main.py:310
Method_train_dataloader
(self)
examples/stable-diffusion/main.py:197
Method_val_dataloader
(self, shuffle=False)
examples/stable-diffusion/main.py:207
Functionadd_Poisson_noise
(img)
examples/stable-diffusion/ldm/modules/image_degradation/bsrgan_light.py:408
Methodadd_noise
Compute the noised input xt = alpha_t * x + sigma_t * noise. Args: x: A `torch.Tensor` with shape `(batch_size, *shape)
dpm_solver_pytorch.py:1012
Methodadd_noise
Compute the noised input xt = alpha_t * x + sigma_t * noise. Args: x: A `torch.Tensor` with shape `(batch_size, *shape)
examples/ddpm_and_guided-diffusion/dpm_solver/sampler.py:1012
Methodadd_noise
Compute the noised input xt = alpha_t * x + sigma_t * noise. Args: x: A `torch.Tensor` with shape `(batch_size, *shape)
examples/score_sde_pytorch/dpm_solver.py:1020
Functionadd_resize
(img, sf=4)
examples/stable-diffusion/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
examples/stable-diffusion/ldm/modules/image_degradation/bsrgan_light.py:299
Functionadd_speckle_noise
(img, noise_level1=2, noise_level2=25)
examples/stable-diffusion/ldm/modules/image_degradation/bsrgan_light.py:390
Functionanalytic_kernel
Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)
examples/stable-diffusion/ldm/modules/image_degradation/bsrgan_light.py:49
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