Functions — github.com/JiangkaiWu/Promptus

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Functions645 in github.com/JiangkaiWu/Promptus

⨍ Functions 645◇ Types & classes 174↳ Endpoints 3

↓ 1 callersFunctionget_resizing_factor

( desired_shape: Tuple[int, int], current_shape: Tuple[int, int] )

scripts/demo/streamlit_helpers.py:787

↓ 1 callersFunctionget_sampler

(sampler_name, steps, discretization_config, guider_config, key=1)

scripts/demo/streamlit_helpers.py:360

↓ 1 callersMethodget_sigmas

(self, n, device)

sgm/modules/diffusionmodules/discretizer.py:24

↓ 1 callersFunctionget_timestep_embedding

This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches

sgm/modules/diffusionmodules/model.py:26

↓ 1 callersMethodget_trainable_autoencoder_parameters

(self)

sgm/modules/autoencoding/losses/discriminator_loss.py:88

↓ 1 callersMethodget_variables

(self, sigma, sigma_down)

sgm/modules/diffusionmodules/sampling.py:248

↓ 1 callersMethodget_variables

(self, sigma, next_sigma, previous_sigma=None)

sgm/modules/diffusionmodules/sampling.py:288

↓ 1 callersMethodidx_to_sigma

(self, idx)

sgm/modules/diffusionmodules/sigma_sampling.py:23

↓ 1 callersMethodidx_to_sigma

(self, idx: Union[torch.Tensor, int])

sgm/modules/diffusionmodules/denoiser.py:65

↓ 1 callersMethodinit_decoder

(self, config)

sgm/modules/autoencoding/losses/lpips.py:27

↓ 1 callersFunctioninit_embedder_options

(keys, init_dict, prompt=None, negative_prompt=None)

scripts/demo/turbo_demo.py:70

↓ 1 callersMethodinit_from_ckpt

( self, path: str, )

sgm/models/diffusion.py:85

↓ 1 callersFunctioninitial_model_load

(model)

scripts/demo/streamlit_helpers.py:70

↓ 1 callersMethodinner_training_step

( self, batch: dict, batch_idx: int, optimizer_idx: int = 0 )

sgm/models/autoencoder.py:221

↓ 1 callersMethodinstantiate_optimizer_from_config

(self, params, lr, cfg)

sgm/models/diffusion.py:212

↓ 1 callersFunctioninversion

( model, sampler, decoder, rank, interval, frame_path,

inversion.py:66

↓ 1 callersMethodkl

(self, other=None)

sgm/modules/distributions/distributions.py:43

↓ 1 callersFunctionlinear_multistep_coeff

(order, t, i, j, epsrel=1e-4)

sgm/modules/diffusionmodules/sampling_utils.py:7

↓ 1 callersMethodload_from_pretrained

(self, name="vgg_lpips")

sgm/modules/autoencoding/lpips/loss/lpips.py:28

↓ 1 callersFunctionload_img

(display=True, key=None, device="cuda")

scripts/demo/sampling.py:94

↓ 1 callersFunctionload_img

(path: str)

scripts/util/detection/nsfw_and_watermark_dectection.py:78

↓ 1 callersFunctionload_img_for_prediction

( W: int, H: int, display=True, key=None, device="cuda" )

scripts/demo/streamlit_helpers.py:821

↓ 1 callersFunctionload_model_from_config

(config, ckpt=None, verbose=True)

scripts/demo/streamlit_helpers.py:86

↓ 1 callersFunctionload_model_from_config

(config, ckpt, verbose=True, freeze=True)

sgm/util.py:202

↓ 1 callersMethodlog_conditionings

Defines heuristics to log different conditionings. These can be lists of strings (text-to-image), tensors, ints, ...

sgm/models/diffusion.py:255

↓ 1 callersFunctionmeasure_perplexity

( predicted_indices: torch.Tensor, num_centroids: int )

sgm/modules/autoencoding/regularizers/base.py:29

↓ 1 callersMethodmode

(self)

sgm/modules/distributions/distributions.py:71

↓ 1 callersFunctionperform_save_locally

(save_path, samples)

scripts/demo/streamlit_helpers.py:197

↓ 1 callersMethodpool

(self, x, text)

sgm/modules/encoders/modules.py:471

↓ 1 callersMethodpossible_correction_step

( self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc )

sgm/modules/diffusionmodules/sampling.py:219

↓ 1 callersMethodpossibly_get_ucg_val

(self, embedder: AbstractEmbModel, batch: Dict)

sgm/modules/encoders/modules.py:111

↓ 1 callersMethodpossibly_quantize_c_noise

(self, c_noise: torch.Tensor)

sgm/modules/diffusionmodules/denoiser.py:20

↓ 1 callersMethodpossibly_quantize_sigma

(self, sigma: torch.Tensor)

sgm/modules/diffusionmodules/denoiser.py:17

↓ 1 callersMethodpreprocess

(self, x)

sgm/modules/encoders/modules.py:629

↓ 1 callersMethodq_sample

(self, x_start, t, noise=None)

sgm/modules/encoders/modules.py:896

↓ 1 callersFunctionquantize_tensor

(x, scale, zero_point, num_bits=8, signed=False)

quantization.py:36

↓ 1 callersMethodreconstruct

(self, bits)

scripts/demo/detect.py:31

↓ 1 callersMethodregister_schedule

( self, beta_schedule="linear", timesteps=1000, linear_start=1e-4, lin

sgm/modules/encoders/modules.py:848

↓ 1 callersMethodreverse

(self, output)

sgm/modules/autoencoding/lpips/util.py:107

↓ 1 callersMethodrun_decoder_engine

(self, latent)

realtime_demo.py:112

↓ 1 callersMethodrun_denoise_engine

(self, x, timesteps, context)

realtime_demo.py:100

↓ 1 callersFunctionrun_img2img

( state, version_dict, is_legacy=False, return_latents=False, filter=None, stage2stren

scripts/demo/sampling.py:162

↓ 1 callersFunctionrun_txt2img

( state, version, version_dict, is_legacy=False, return_latents=False, filter=None,

scripts/demo/sampling.py:112

↓ 1 callersFunctionsample

( model, sampler, prompt="A lush garden with oversized flowers and vibrant colors, inhabited by mi

scripts/demo/turbo.py:94

↓ 1 callersFunctionsample

( model, sampler, prompt="A scenic landscape with a sky filled with clouds above, a la

scripts/demo/turbo_demo.py:102

↓ 1 callersMethodsample

(self)

sgm/modules/distributions/distributions.py:17

↓ 1 callersMethodsample

(self)

sgm/modules/distributions/distributions.py:37

↓ 1 callersMethodsample

( self, cond: Dict, uc: Union[Dict, None] = None, batch_size: int = 16,

sgm/models/diffusion.py:238

↓ 1 callersMethodsampler_step

( self, old_denoised, previous_sigma, sigma, next_sigma, denoi

sgm/modules/diffusionmodules/sampling.py:310

↓ 1 callersFunctionsave_video_as_grid_and_mp4

( video_batch: torch.Tensor, save_path: str, T: int, fps: int = 5 )

scripts/demo/streamlit_helpers.py:852

↓ 1 callersMethodschedule

(self, n, **kwargs)

sgm/lr_scheduler.py:26

↓ 1 callersMethodschedule

(self, n, **kwargs)

sgm/lr_scheduler.py:83

↓ 1 callersMethodseeded_randn

(self, shape, seed)

realtime_demo.py:131

↓ 1 callersFunctionset_lowvram_mode

(mode)

scripts/demo/streamlit_helpers.py:65

↓ 1 callersMethodshared_step

(self, batch: Dict)

sgm/models/diffusion.py:158

↓ 1 callersFunctionspatial_average

(x, keepdim=True)

sgm/modules/autoencoding/lpips/loss/lpips.py:146

↓ 1 callersMethodtext_transformer_forward

(self, x: torch.Tensor, attn_mask=None)

sgm/modules/encoders/modules.py:479

↓ 1 callersMethodweight_update

(self, num_tokens)

sgm/modules/autoencoding/regularizers/quantize.py:345

FunctionTVLoss

(U, V)

scripts/demo/turbo_demo.py:414

FunctionVAE_encoder

( model, sampler, H=1024, W=1024, seed=0, filter=None, )

scripts/demo/turbo_demo.py:1299

scripts/demo/turbo.py:57

Method__call__

Detects the number of matching bits the predefined watermark with one or multiple images. Images should be in cv2 format, e.g. h x w

scripts/demo/detect.py:83

Method__call__

(self, *args, **kwargs)

scripts/demo/discretization.py:18

Method__call__

(self, *args, **kwargs)

scripts/demo/discretization.py:45

Method__call__

(self, x)

scripts/demo/turbo_demo.py:65

Method__call__

(self, images: torch.Tensor)

scripts/util/detection/nsfw_and_watermark_dectection.py:58

Method__call__

(self, n, **kwargs)

sgm/lr_scheduler.py:47

Method__call__

(self, n, **kwargs)

sgm/lr_scheduler.py:109

Method__call__

(self, sigma: torch.Tensor)

sgm/modules/diffusionmodules/loss_weighting.py:8

Method__call__

(self, sigma: torch.Tensor)

sgm/modules/diffusionmodules/loss_weighting.py:13

Method__call__

(self, sigma: torch.Tensor)

sgm/modules/diffusionmodules/loss_weighting.py:21

Method__call__

(self, sigma: torch.Tensor)

sgm/modules/diffusionmodules/loss_weighting.py:31

Method__call__

(self, sigma)

sgm/modules/diffusionmodules/denoiser_weighting.py:5

Method__call__

(self, sigma)

sgm/modules/diffusionmodules/denoiser_weighting.py:13

Method__call__

(self, sigma)

sgm/modules/diffusionmodules/denoiser_weighting.py:23

Method__call__

( self, sigma: torch.Tensor )

sgm/modules/diffusionmodules/denoiser_scaling.py:9

Method__call__

( self, sigma: torch.Tensor )

sgm/modules/diffusionmodules/denoiser_scaling.py:19

Method__call__

( self, sigma: torch.Tensor )

sgm/modules/diffusionmodules/denoiser_scaling.py:30

Method__call__

( self, sigma: torch.Tensor )

sgm/modules/diffusionmodules/denoiser_scaling.py:41

Method__call__

( self, sigma: torch.Tensor )

sgm/modules/diffusionmodules/denoiser_scaling.py:52

Method__call__

(self, n, do_append_zero=True, device="cpu", flip=False)

sgm/modules/diffusionmodules/discretizer.py:18

Method__call__

(self, denoiser, x, cond, uc=None, num_steps=None)

sgm/modules/diffusionmodules/sampling.py:109

Method__call__

(self, denoiser, x, cond, uc=None, num_steps=None)

sgm/modules/diffusionmodules/sampling.py:155

Method__call__

(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs)

sgm/modules/diffusionmodules/sampling.py:184

Method__call__

(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs)

sgm/modules/diffusionmodules/sampling.py:344

Method__call__

(self, n_samples, rand=None)

sgm/modules/diffusionmodules/sigma_sampling.py:11

Method__call__

(self, n_samples, rand=None)

sgm/modules/diffusionmodules/sigma_sampling.py:26

Method__call__

(self, x: torch.Tensor, sigma: float)

sgm/modules/diffusionmodules/guiders.py:15

Method__call__

(self, x: torch.Tensor, sigma: torch.Tensor)

sgm/modules/diffusionmodules/guiders.py:28

Method__call__

(self, x: torch.Tensor, sigma: float)

sgm/modules/diffusionmodules/guiders.py:46

Method__call__

(self, x: torch.Tensor, sigma: torch.Tensor)

sgm/modules/diffusionmodules/guiders.py:78

Method__call__

Adds a predefined watermark to the input image Args: image: ([N,] B, RGB, H, W) in range [0, 1] Returns:

sgm/inference/helpers.py:23

Method__call__

(self, *args, **kwargs)

sgm/inference/helpers.py:89

Method__del__

(self)

tensorrt_acceleration/utilities.py:94

Method__getitem__

(self, i)

sgm/data/cifar10.py:12

(self, n_sample_steps=1, *args, **kwargs)

inversion.py:32

Method__init__

(self, n_sample_steps=1, *args, **kwargs)

generation.py:36

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