SYMBOL INDEX (701 symbols across 34 files) FILE: ldm/data/base.py class Txt2ImgIterableBaseDataset (line 5) | class Txt2ImgIterableBaseDataset(IterableDataset): method __init__ (line 9) | def __init__(self, num_records=0, valid_ids=None, size=256): method __len__ (line 18) | def __len__(self): method __iter__ (line 22) | def __iter__(self): FILE: ldm/data/imagenet.py function synset2idx (line 20) | def synset2idx(path_to_yaml="data/index_synset.yaml"): class ImageNetBase (line 26) | class ImageNetBase(Dataset): method __init__ (line 27) | def __init__(self, config=None): method __len__ (line 39) | def __len__(self): method __getitem__ (line 42) | def __getitem__(self, i): method _prepare (line 45) | def _prepare(self): method _filter_relpaths (line 48) | def _filter_relpaths(self, relpaths): method _prepare_synset_to_human (line 66) | def _prepare_synset_to_human(self): method _prepare_idx_to_synset (line 74) | def _prepare_idx_to_synset(self): method _prepare_human_to_integer_label (line 80) | def _prepare_human_to_integer_label(self): method _load (line 93) | def _load(self): class ImageNetTrain (line 134) | class ImageNetTrain(ImageNetBase): method __init__ (line 145) | def __init__(self, process_images=True, data_root=None, **kwargs): method _prepare (line 150) | def _prepare(self): class ImageNetValidation (line 197) | class ImageNetValidation(ImageNetBase): method __init__ (line 211) | def __init__(self, process_images=True, data_root=None, **kwargs): method _prepare (line 216) | def _prepare(self): class ImageNetSR (line 272) | class ImageNetSR(Dataset): method __init__ (line 273) | def __init__(self, size=None, method __len__ (line 336) | def __len__(self): method __getitem__ (line 339) | def __getitem__(self, i): class ImageNetSRTrain (line 375) | class ImageNetSRTrain(ImageNetSR): method __init__ (line 376) | def __init__(self, **kwargs): method get_base (line 379) | def get_base(self): class ImageNetSRValidation (line 386) | class ImageNetSRValidation(ImageNetSR): method __init__ (line 387) | def __init__(self, **kwargs): method get_base (line 390) | def get_base(self): FILE: ldm/data/lsun.py class LSUNBase (line 9) | class LSUNBase(Dataset): method __init__ (line 10) | def __init__(self, method __len__ (line 36) | def __len__(self): method __getitem__ (line 39) | def __getitem__(self, i): class LSUNChurchesTrain (line 62) | class LSUNChurchesTrain(LSUNBase): method __init__ (line 63) | def __init__(self, **kwargs): class LSUNChurchesValidation (line 67) | class LSUNChurchesValidation(LSUNBase): method __init__ (line 68) | def __init__(self, flip_p=0., **kwargs): class LSUNBedroomsTrain (line 73) | class LSUNBedroomsTrain(LSUNBase): method __init__ (line 74) | def __init__(self, **kwargs): class LSUNBedroomsValidation (line 78) | class LSUNBedroomsValidation(LSUNBase): method __init__ (line 79) | def __init__(self, flip_p=0.0, **kwargs): class LSUNCatsTrain (line 84) | class LSUNCatsTrain(LSUNBase): method __init__ (line 85) | def __init__(self, **kwargs): class LSUNCatsValidation (line 89) | class LSUNCatsValidation(LSUNBase): method __init__ (line 90) | def __init__(self, flip_p=0., **kwargs): FILE: ldm/lr_scheduler.py class LambdaWarmUpCosineScheduler (line 4) | class LambdaWarmUpCosineScheduler: method __init__ (line 8) | def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_... method schedule (line 17) | def schedule(self, n, **kwargs): method __call__ (line 32) | def __call__(self, n, **kwargs): class LambdaWarmUpCosineScheduler2 (line 36) | class LambdaWarmUpCosineScheduler2: method __init__ (line 41) | def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths... method find_in_interval (line 52) | def find_in_interval(self, n): method schedule (line 59) | def schedule(self, n, **kwargs): method __call__ (line 77) | def __call__(self, n, **kwargs): class LambdaLinearScheduler (line 81) | class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): method schedule (line 83) | def schedule(self, n, **kwargs): FILE: ldm/models/autoencoder.py class VQModel (line 14) | class VQModel(pl.LightningModule): method __init__ (line 15) | def __init__(self, method ema_scope (line 64) | def ema_scope(self, context=None): method init_from_ckpt (line 78) | def init_from_ckpt(self, path, ignore_keys=list()): method on_train_batch_end (line 92) | def on_train_batch_end(self, *args, **kwargs): method encode (line 96) | def encode(self, x): method encode_to_prequant (line 102) | def encode_to_prequant(self, x): method decode (line 107) | def decode(self, quant): method decode_code (line 112) | def decode_code(self, code_b): method forward (line 117) | def forward(self, input, return_pred_indices=False): method get_input (line 124) | def get_input(self, batch, k): method training_step (line 142) | def training_step(self, batch, batch_idx, optimizer_idx): method validation_step (line 164) | def validation_step(self, batch, batch_idx): method _validation_step (line 170) | def _validation_step(self, batch, batch_idx, suffix=""): method configure_optimizers (line 197) | def configure_optimizers(self): method get_last_layer (line 230) | def get_last_layer(self): method log_images (line 233) | def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): method to_rgb (line 255) | def to_rgb(self, x): class VQModelInterface (line 264) | class VQModelInterface(VQModel): method __init__ (line 265) | def __init__(self, embed_dim, *args, **kwargs): method encode (line 269) | def encode(self, x): method decode (line 274) | def decode(self, h, force_not_quantize=False): class AutoencoderKL (line 285) | class AutoencoderKL(pl.LightningModule): method __init__ (line 286) | def __init__(self, method init_from_ckpt (line 313) | def init_from_ckpt(self, path, ignore_keys=list()): method encode (line 324) | def encode(self, x): method decode (line 330) | def decode(self, z): method forward (line 335) | def forward(self, input, sample_posterior=True): method get_input (line 344) | def get_input(self, batch, k): method training_step (line 351) | def training_step(self, batch, batch_idx, optimizer_idx): method validation_step (line 372) | def validation_step(self, batch, batch_idx): method configure_optimizers (line 386) | def configure_optimizers(self): method get_last_layer (line 397) | def get_last_layer(self): method log_images (line 401) | def log_images(self, batch, only_inputs=False, **kwargs): method to_rgb (line 417) | def to_rgb(self, x): class IdentityFirstStage (line 426) | class IdentityFirstStage(torch.nn.Module): method __init__ (line 427) | def __init__(self, *args, vq_interface=False, **kwargs): method encode (line 431) | def encode(self, x, *args, **kwargs): method decode (line 434) | def decode(self, x, *args, **kwargs): method quantize (line 437) | def quantize(self, x, *args, **kwargs): method forward (line 442) | def forward(self, x, *args, **kwargs): FILE: ldm/models/diffusion/classifier.py function disabled_train (line 22) | def disabled_train(self, mode=True): class NoisyLatentImageClassifier (line 28) | class NoisyLatentImageClassifier(pl.LightningModule): method __init__ (line 30) | def __init__(self, method init_from_ckpt (line 70) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): method load_diffusion (line 88) | def load_diffusion(self): method load_classifier (line 95) | def load_classifier(self, ckpt_path, pool): method get_x_noisy (line 110) | def get_x_noisy(self, x, t, noise=None): method forward (line 120) | def forward(self, x_noisy, t, *args, **kwargs): method get_input (line 124) | def get_input(self, batch, k): method get_conditioning (line 133) | def get_conditioning(self, batch, k=None): method compute_top_k (line 150) | def compute_top_k(self, logits, labels, k, reduction="mean"): method on_train_epoch_start (line 157) | def on_train_epoch_start(self): method write_logs (line 162) | def write_logs(self, loss, logits, targets): method shared_step (line 179) | def shared_step(self, batch, t=None): method training_step (line 198) | def training_step(self, batch, batch_idx): method reset_noise_accs (line 202) | def reset_noise_accs(self): method on_validation_start (line 206) | def on_validation_start(self): method validation_step (line 210) | def validation_step(self, batch, batch_idx): method configure_optimizers (line 220) | def configure_optimizers(self): method log_images (line 238) | def log_images(self, batch, N=8, *args, **kwargs): FILE: ldm/models/diffusion/ddim.py class DDIMSampler (line 12) | class DDIMSampler(object): method __init__ (line 13) | def __init__(self, model, schedule="linear", **kwargs): method register_buffer (line 19) | def register_buffer(self, name, attr): method make_schedule (line 25) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi... method sample (line 57) | def sample(self, method ddim_sampling (line 114) | def ddim_sampling(self, cond, shape, method p_sample_ddim (line 166) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin... method stochastic_encode (line 207) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): method decode (line 223) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale... FILE: ldm/models/diffusion/ddpm.py function disabled_train (line 34) | def disabled_train(self, mode=True): function uniform_on_device (line 40) | def uniform_on_device(r1, r2, shape, device): class DDPM (line 44) | class DDPM(pl.LightningModule): method __init__ (line 46) | def __init__(self, method register_schedule (line 117) | def register_schedule(self, given_betas=None, beta_schedule="linear", ... method ema_scope (line 172) | def ema_scope(self, context=None): method init_from_ckpt (line 186) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): method q_mean_variance (line 204) | def q_mean_variance(self, x_start, t): method predict_start_from_noise (line 216) | def predict_start_from_noise(self, x_t, t, noise): method q_posterior (line 222) | def q_posterior(self, x_start, x_t, t): method p_mean_variance (line 231) | def p_mean_variance(self, x, t, clip_denoised: bool): method p_sample (line 244) | def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): method p_sample_loop (line 253) | def p_sample_loop(self, shape, return_intermediates=False): method sample (line 268) | def sample(self, batch_size=16, return_intermediates=False): method q_sample (line 274) | def q_sample(self, x_start, t, noise=None): method get_loss (line 279) | def get_loss(self, pred, target, mean=True): method p_losses (line 294) | def p_losses(self, x_start, t, noise=None): method forward (line 323) | def forward(self, x, *args, **kwargs): method get_input (line 329) | def get_input(self, batch, k): method shared_step (line 337) | def shared_step(self, batch): method training_step (line 342) | def training_step(self, batch, batch_idx): method validation_step (line 358) | def validation_step(self, batch, batch_idx): method on_train_batch_end (line 366) | def on_train_batch_end(self, *args, **kwargs): method _get_rows_from_list (line 370) | def _get_rows_from_list(self, samples): method log_images (line 378) | def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=Non... method configure_optimizers (line 415) | def configure_optimizers(self): class LatentDiffusion (line 424) | class LatentDiffusion(DDPM): method __init__ (line 426) | def __init__(self, method make_cond_schedule (line 471) | def make_cond_schedule(self, ): method on_train_batch_start (line 478) | def on_train_batch_start(self, batch, batch_idx, dataloader_idx): method register_schedule (line 493) | def register_schedule(self, method instantiate_first_stage (line 502) | def instantiate_first_stage(self, config): method instantiate_cond_stage (line 509) | def instantiate_cond_stage(self, config): method _get_denoise_row_from_list (line 530) | def _get_denoise_row_from_list(self, samples, desc='', force_no_decode... method get_first_stage_encoding (line 542) | def get_first_stage_encoding(self, encoder_posterior): method get_learned_conditioning (line 551) | def get_learned_conditioning(self, c): method meshgrid (line 564) | def meshgrid(self, h, w): method delta_border (line 571) | def delta_border(self, h, w): method get_weighting (line 585) | def get_weighting(self, h, w, Ly, Lx, device): method get_fold_unfold (line 601) | def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo... method get_input (line 654) | def get_input(self, batch, k, return_first_stage_outputs=False, force_... method decode_first_stage (line 706) | def decode_first_stage(self, z, predict_cids=False, force_not_quantize... method differentiable_decode_first_stage (line 766) | def differentiable_decode_first_stage(self, z, predict_cids=False, for... method encode_first_stage (line 826) | def encode_first_stage(self, x): method shared_step (line 865) | def shared_step(self, batch, **kwargs): method forward (line 870) | def forward(self, x, c, *args, **kwargs): method _rescale_annotations (line 881) | def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: mov... method apply_model (line 891) | def apply_model(self, x_noisy, t, cond, return_ids=False): method _predict_eps_from_xstart (line 994) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart): method _prior_bpd (line 998) | def _prior_bpd(self, x_start): method p_losses (line 1012) | def p_losses(self, x_start, cond, t, noise=None): method p_mean_variance (line 1047) | def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codeboo... method p_sample (line 1079) | def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, method progressive_denoising (line 1110) | def progressive_denoising(self, cond, shape, verbose=True, callback=No... method p_sample_loop (line 1166) | def p_sample_loop(self, cond, shape, return_intermediates=False, method sample (line 1217) | def sample(self, cond, batch_size=16, return_intermediates=False, x_T=... method sample_log (line 1235) | def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs): method log_images (line 1251) | def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200,... method configure_optimizers (line 1361) | def configure_optimizers(self): method to_rgb (line 1386) | def to_rgb(self, x): class DiffusionWrapper (line 1395) | class DiffusionWrapper(pl.LightningModule): method __init__ (line 1396) | def __init__(self, diff_model_config, conditioning_key): method forward (line 1402) | def forward(self, x, t, c_concat: list = None, c_crossattn: list = None): class Layout2ImgDiffusion (line 1424) | class Layout2ImgDiffusion(LatentDiffusion): method __init__ (line 1426) | def __init__(self, cond_stage_key, *args, **kwargs): method log_images (line 1430) | def log_images(self, batch, N=8, *args, **kwargs): FILE: ldm/models/diffusion/dpm_solver/dpm_solver.py class NoiseScheduleVP (line 6) | class NoiseScheduleVP: method __init__ (line 7) | def __init__( method marginal_log_mean_coeff (line 125) | def marginal_log_mean_coeff(self, t): method marginal_alpha (line 138) | def marginal_alpha(self, t): method marginal_std (line 144) | def marginal_std(self, t): method marginal_lambda (line 150) | def marginal_lambda(self, t): method inverse_lambda (line 158) | def inverse_lambda(self, lamb): function model_wrapper (line 177) | def model_wrapper( class DPM_Solver (line 351) | class DPM_Solver: method __init__ (line 352) | def __init__(self, model_fn, noise_schedule, predict_x0=False, thresho... method noise_prediction_fn (line 380) | def noise_prediction_fn(self, x, t): method data_prediction_fn (line 386) | def data_prediction_fn(self, x, t): method model_fn (line 401) | def model_fn(self, x, t): method get_time_steps (line 410) | def get_time_steps(self, skip_type, t_T, t_0, N, device): method get_orders_and_timesteps_for_singlestep_solver (line 439) | def get_orders_and_timesteps_for_singlestep_solver(self, steps, order,... method denoise_to_zero_fn (line 498) | def denoise_to_zero_fn(self, x, s): method dpm_solver_first_update (line 504) | def dpm_solver_first_update(self, x, s, t, model_s=None, return_interm... method singlestep_dpm_solver_second_update (line 551) | def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s... method singlestep_dpm_solver_third_update (line 633) | def singlestep_dpm_solver_third_update(self, x, s, t, r1=1./3., r2=2./... method multistep_dpm_solver_second_update (line 755) | def multistep_dpm_solver_second_update(self, x, model_prev_list, t_pre... method multistep_dpm_solver_third_update (line 812) | def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev... method singlestep_dpm_solver_update (line 859) | def singlestep_dpm_solver_update(self, x, s, t, order, return_intermed... method multistep_dpm_solver_update (line 885) | def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list,... method dpm_solver_adaptive (line 909) | def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.... method sample (line 965) | def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_... function interpolate_fn (line 1132) | def interpolate_fn(x, xp, yp): function expand_dims (line 1174) | def expand_dims(v, dims): FILE: ldm/models/diffusion/dpm_solver/sampler.py class DPMSolverSampler (line 8) | class DPMSolverSampler(object): method __init__ (line 9) | def __init__(self, model, **kwargs): method register_buffer (line 15) | def register_buffer(self, name, attr): method sample (line 22) | def sample(self, FILE: ldm/models/diffusion/plms.py class PLMSSampler (line 11) | class PLMSSampler(object): method __init__ (line 12) | def __init__(self, model, schedule="linear", **kwargs): method register_buffer (line 18) | def register_buffer(self, name, attr): method make_schedule (line 24) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi... method sample (line 58) | def sample(self, method plms_sampling (line 115) | def plms_sampling(self, cond, shape, method p_sample_plms (line 173) | def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_origin... FILE: ldm/modules/attention.py function exists (line 11) | def exists(val): function uniq (line 15) | def uniq(arr): function default (line 19) | def default(val, d): function max_neg_value (line 25) | def max_neg_value(t): function init_ (line 29) | def init_(tensor): class GEGLU (line 37) | class GEGLU(nn.Module): method __init__ (line 38) | def __init__(self, dim_in, dim_out): method forward (line 42) | def forward(self, x): class FeedForward (line 47) | class FeedForward(nn.Module): method __init__ (line 48) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): method forward (line 63) | def forward(self, x): function zero_module (line 67) | def zero_module(module): function Normalize (line 76) | def Normalize(in_channels): class LinearAttention (line 80) | class LinearAttention(nn.Module): method __init__ (line 81) | def __init__(self, dim, heads=4, dim_head=32): method forward (line 88) | def forward(self, x): class SpatialSelfAttention (line 99) | class SpatialSelfAttention(nn.Module): method __init__ (line 100) | def __init__(self, in_channels): method forward (line 126) | def forward(self, x): class CrossAttention (line 152) | class CrossAttention(nn.Module): method __init__ (line 153) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ... method forward (line 170) | def forward(self, x, context=None, mask=None): class BasicTransformerBlock (line 196) | class BasicTransformerBlock(nn.Module): method __init__ (line 197) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,... method forward (line 208) | def forward(self, x, context=None): method _forward (line 211) | def _forward(self, x, context=None): class SpatialTransformer (line 218) | class SpatialTransformer(nn.Module): method __init__ (line 226) | def __init__(self, in_channels, n_heads, d_head, method forward (line 250) | def forward(self, x, context=None): FILE: ldm/modules/diffusionmodules/model.py function get_timestep_embedding (line 12) | def get_timestep_embedding(timesteps, embedding_dim): function nonlinearity (line 33) | def nonlinearity(x): function Normalize (line 38) | def Normalize(in_channels, num_groups=32): class Upsample (line 42) | class Upsample(nn.Module): method __init__ (line 43) | def __init__(self, in_channels, with_conv): method forward (line 53) | def forward(self, x): class Downsample (line 60) | class Downsample(nn.Module): method __init__ (line 61) | def __init__(self, in_channels, with_conv): method forward (line 72) | def forward(self, x): class ResnetBlock (line 82) | class ResnetBlock(nn.Module): method __init__ (line 83) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa... method forward (line 121) | def forward(self, x, temb): class LinAttnBlock (line 144) | class LinAttnBlock(LinearAttention): method __init__ (line 146) | def __init__(self, in_channels): class AttnBlock (line 150) | class AttnBlock(nn.Module): method __init__ (line 151) | def __init__(self, in_channels): method forward (line 178) | def forward(self, x): function make_attn (line 205) | def make_attn(in_channels, attn_type="vanilla"): class Model (line 216) | class Model(nn.Module): method __init__ (line 217) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, method forward (line 316) | def forward(self, x, t=None, context=None): method get_last_layer (line 364) | def get_last_layer(self): class Encoder (line 368) | class Encoder(nn.Module): method __init__ (line 369) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, method forward (line 434) | def forward(self, x): class Decoder (line 462) | class Decoder(nn.Module): method __init__ (line 463) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, method forward (line 535) | def forward(self, z): class SimpleDecoder (line 571) | class SimpleDecoder(nn.Module): method __init__ (line 572) | def __init__(self, in_channels, out_channels, *args, **kwargs): method forward (line 594) | def forward(self, x): class UpsampleDecoder (line 607) | class UpsampleDecoder(nn.Module): method __init__ (line 608) | def __init__(self, in_channels, out_channels, ch, num_res_blocks, reso... method forward (line 641) | def forward(self, x): class LatentRescaler (line 655) | class LatentRescaler(nn.Module): method __init__ (line 656) | def __init__(self, factor, in_channels, mid_channels, out_channels, de... method forward (line 680) | def forward(self, x): class MergedRescaleEncoder (line 692) | class MergedRescaleEncoder(nn.Module): method __init__ (line 693) | def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, method forward (line 705) | def forward(self, x): class MergedRescaleDecoder (line 711) | class MergedRescaleDecoder(nn.Module): method __init__ (line 712) | def __init__(self, z_channels, out_ch, resolution, num_res_blocks, att... method forward (line 722) | def forward(self, x): class Upsampler (line 728) | class Upsampler(nn.Module): method __init__ (line 729) | def __init__(self, in_size, out_size, in_channels, out_channels, ch_mu... method forward (line 741) | def forward(self, x): class Resize (line 747) | class Resize(nn.Module): method __init__ (line 748) | def __init__(self, in_channels=None, learned=False, mode="bilinear"): method forward (line 763) | def forward(self, x, scale_factor=1.0): class FirstStagePostProcessor (line 770) | class FirstStagePostProcessor(nn.Module): method __init__ (line 772) | def __init__(self, ch_mult:list, in_channels, method instantiate_pretrained (line 807) | def instantiate_pretrained(self, config): method encode_with_pretrained (line 816) | def encode_with_pretrained(self,x): method forward (line 822) | def forward(self,x): FILE: ldm/modules/diffusionmodules/openaimodel.py function convert_module_to_f16 (line 24) | def convert_module_to_f16(x): function convert_module_to_f32 (line 27) | def convert_module_to_f32(x): class AttentionPool2d (line 32) | class AttentionPool2d(nn.Module): method __init__ (line 37) | def __init__( method forward (line 51) | def forward(self, x): class TimestepBlock (line 62) | class TimestepBlock(nn.Module): method forward (line 68) | def forward(self, x, emb): class TimestepEmbedSequential (line 74) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock): method forward (line 80) | def forward(self, x, emb, context=None): class Upsample (line 91) | class Upsample(nn.Module): method __init__ (line 100) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd... method forward (line 109) | def forward(self, x): class TransposedUpsample (line 121) | class TransposedUpsample(nn.Module): method __init__ (line 123) | def __init__(self, channels, out_channels=None, ks=5): method forward (line 130) | def forward(self,x): class Downsample (line 134) | class Downsample(nn.Module): method __init__ (line 143) | def __init__(self, channels, use_conv, dims=2, out_channels=None,paddi... method forward (line 158) | def forward(self, x): class ResBlock (line 163) | class ResBlock(TimestepBlock): method __init__ (line 179) | def __init__( method forward (line 243) | def forward(self, x, emb): method _forward (line 255) | def _forward(self, x, emb): class AttentionBlock (line 278) | class AttentionBlock(nn.Module): method __init__ (line 285) | def __init__( method forward (line 314) | def forward(self, x): method _forward (line 318) | def _forward(self, x): function count_flops_attn (line 327) | def count_flops_attn(model, _x, y): class QKVAttentionLegacy (line 347) | class QKVAttentionLegacy(nn.Module): method __init__ (line 352) | def __init__(self, n_heads): method forward (line 356) | def forward(self, qkv): method count_flops (line 375) | def count_flops(model, _x, y): class QKVAttention (line 379) | class QKVAttention(nn.Module): method __init__ (line 384) | def __init__(self, n_heads): method forward (line 388) | def forward(self, qkv): method count_flops (line 409) | def count_flops(model, _x, y): class UNetModel (line 413) | class UNetModel(nn.Module): method __init__ (line 443) | def __init__( method convert_to_fp16 (line 694) | def convert_to_fp16(self): method convert_to_fp32 (line 702) | def convert_to_fp32(self): method forward (line 710) | def forward(self, x, timesteps=None, context=None, y=None,**kwargs): class EncoderUNetModel (line 745) | class EncoderUNetModel(nn.Module): method __init__ (line 751) | def __init__( method convert_to_fp16 (line 924) | def convert_to_fp16(self): method convert_to_fp32 (line 931) | def convert_to_fp32(self): method forward (line 938) | def forward(self, x, timesteps): FILE: ldm/modules/diffusionmodules/util.py function make_beta_schedule (line 21) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e... function make_ddim_timesteps (line 46) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_... function make_ddim_sampling_parameters (line 63) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos... function betas_for_alpha_bar (line 77) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9... function extract_into_tensor (line 96) | def extract_into_tensor(a, t, x_shape): function checkpoint (line 102) | def checkpoint(func, inputs, params, flag): class CheckpointFunction (line 119) | class CheckpointFunction(torch.autograd.Function): method forward (line 121) | def forward(ctx, run_function, length, *args): method backward (line 131) | def backward(ctx, *output_grads): function timestep_embedding (line 151) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal... function zero_module (line 174) | def zero_module(module): function scale_module (line 183) | def scale_module(module, scale): function mean_flat (line 192) | def mean_flat(tensor): function normalization (line 199) | def normalization(channels): class SiLU (line 209) | class SiLU(nn.Module): method forward (line 210) | def forward(self, x): class GroupNorm32 (line 214) | class GroupNorm32(nn.GroupNorm): method forward (line 215) | def forward(self, x): function conv_nd (line 218) | def conv_nd(dims, *args, **kwargs): function linear (line 231) | def linear(*args, **kwargs): function avg_pool_nd (line 238) | def avg_pool_nd(dims, *args, **kwargs): class HybridConditioner (line 251) | class HybridConditioner(nn.Module): method __init__ (line 253) | def __init__(self, c_concat_config, c_crossattn_config): method forward (line 258) | def forward(self, c_concat, c_crossattn): function noise_like (line 264) | def noise_like(shape, device, repeat=False): FILE: ldm/modules/distributions/distributions.py class AbstractDistribution (line 5) | class AbstractDistribution: method sample (line 6) | def sample(self): method mode (line 9) | def mode(self): class DiracDistribution (line 13) | class DiracDistribution(AbstractDistribution): method __init__ (line 14) | def __init__(self, value): method sample (line 17) | def sample(self): method mode (line 20) | def mode(self): class DiagonalGaussianDistribution (line 24) | class DiagonalGaussianDistribution(object): method __init__ (line 25) | def __init__(self, parameters, deterministic=False): method sample (line 35) | def sample(self): method kl (line 39) | def kl(self, other=None): method nll (line 53) | def nll(self, sample, dims=[1,2,3]): method mode (line 61) | def mode(self): function normal_kl (line 65) | def normal_kl(mean1, logvar1, mean2, logvar2): FILE: ldm/modules/ema.py class LitEma (line 5) | class LitEma(nn.Module): method __init__ (line 6) | def __init__(self, model, decay=0.9999, use_num_upates=True): method forward (line 25) | def forward(self,model): method copy_to (line 46) | def copy_to(self, model): method store (line 55) | def store(self, parameters): method restore (line 64) | def restore(self, parameters): FILE: ldm/modules/encoders/modules.py class AbstractEncoder (line 12) | class AbstractEncoder(nn.Module): method __init__ (line 13) | def __init__(self): method encode (line 16) | def encode(self, *args, **kwargs): class ClassEmbedder (line 21) | class ClassEmbedder(nn.Module): method __init__ (line 22) | def __init__(self, embed_dim, n_classes=1000, key='class'): method forward (line 27) | def forward(self, batch, key=None): class TransformerEmbedder (line 36) | class TransformerEmbedder(AbstractEncoder): method __init__ (line 38) | def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, devic... method forward (line 44) | def forward(self, tokens): method encode (line 49) | def encode(self, x): class BERTTokenizer (line 53) | class BERTTokenizer(AbstractEncoder): method __init__ (line 55) | def __init__(self, device="cuda", vq_interface=True, max_length=77): method forward (line 63) | def forward(self, text): method encode (line 70) | def encode(self, text): method decode (line 76) | def decode(self, text): class BERTEmbedder (line 80) | class BERTEmbedder(AbstractEncoder): method __init__ (line 82) | def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, method forward (line 93) | def forward(self, text): method encode (line 101) | def encode(self, text): class SpatialRescaler (line 106) | class SpatialRescaler(nn.Module): method __init__ (line 107) | def __init__(self, method forward (line 125) | def forward(self,x): method encode (line 134) | def encode(self, x): class FrozenCLIPEmbedder (line 137) | class FrozenCLIPEmbedder(AbstractEncoder): method __init__ (line 139) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu... method freeze (line 147) | def freeze(self): method forward (line 152) | def forward(self, text): method encode (line 161) | def encode(self, text): class FrozenCLIPTextEmbedder (line 165) | class FrozenCLIPTextEmbedder(nn.Module): method __init__ (line 169) | def __init__(self, version='ViT-L/14', device="cuda", max_length=77, n... method freeze (line 177) | def freeze(self): method forward (line 182) | def forward(self, text): method encode (line 189) | def encode(self, text): class FrozenClipImageEmbedder (line 197) | class FrozenClipImageEmbedder(nn.Module): method __init__ (line 201) | def __init__( method preprocess (line 216) | def preprocess(self, x): method forward (line 226) | def forward(self, x): FILE: ldm/modules/image_degradation/bsrgan.py function modcrop_np (line 29) | def modcrop_np(img, sf): function analytic_kernel (line 49) | def analytic_kernel(k): function anisotropic_Gaussian (line 65) | def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): function gm_blur_kernel (line 86) | def gm_blur_kernel(mean, cov, size=15): function shift_pixel (line 99) | def shift_pixel(x, sf, upper_left=True): function blur (line 128) | def blur(x, k): function gen_kernel (line 145) | def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]),... function fspecial_gaussian (line 187) | def fspecial_gaussian(hsize, sigma): function fspecial_laplacian (line 201) | def fspecial_laplacian(alpha): function fspecial (line 210) | def fspecial(filter_type, *args, **kwargs): function bicubic_degradation (line 228) | def bicubic_degradation(x, sf=3): function srmd_degradation (line 240) | def srmd_degradation(x, k, sf=3): function dpsr_degradation (line 262) | def dpsr_degradation(x, k, sf=3): function classical_degradation (line 284) | def classical_degradation(x, k, sf=3): function add_sharpening (line 299) | def add_sharpening(img, weight=0.5, radius=50, threshold=10): function add_blur (line 325) | def add_blur(img, sf=4): function add_resize (line 339) | def add_resize(img, sf=4): function add_Gaussian_noise (line 369) | def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): function add_speckle_noise (line 386) | def add_speckle_noise(img, noise_level1=2, noise_level2=25): function add_Poisson_noise (line 404) | def add_Poisson_noise(img): function add_JPEG_noise (line 418) | def add_JPEG_noise(img): function random_crop (line 427) | def random_crop(lq, hq, sf=4, lq_patchsize=64): function degradation_bsrgan (line 438) | def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): function degradation_bsrgan_variant (line 530) | def degradation_bsrgan_variant(image, sf=4, isp_model=None): function degradation_bsrgan_plus (line 617) | def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True,... FILE: ldm/modules/image_degradation/bsrgan_light.py function modcrop_np (line 29) | def modcrop_np(img, sf): function analytic_kernel (line 49) | def analytic_kernel(k): function anisotropic_Gaussian (line 65) | def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): function gm_blur_kernel (line 86) | def gm_blur_kernel(mean, cov, size=15): function shift_pixel (line 99) | def shift_pixel(x, sf, upper_left=True): function blur (line 128) | def blur(x, k): function gen_kernel (line 145) | def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]),... function fspecial_gaussian (line 187) | def fspecial_gaussian(hsize, sigma): function fspecial_laplacian (line 201) | def fspecial_laplacian(alpha): function fspecial (line 210) | def fspecial(filter_type, *args, **kwargs): function bicubic_degradation (line 228) | def bicubic_degradation(x, sf=3): function srmd_degradation (line 240) | def srmd_degradation(x, k, sf=3): function dpsr_degradation (line 262) | def dpsr_degradation(x, k, sf=3): function classical_degradation (line 284) | def classical_degradation(x, k, sf=3): function add_sharpening (line 299) | def add_sharpening(img, weight=0.5, radius=50, threshold=10): function add_blur (line 325) | def add_blur(img, sf=4): function add_resize (line 343) | def add_resize(img, sf=4): function add_Gaussian_noise (line 373) | def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): function add_speckle_noise (line 390) | def add_speckle_noise(img, noise_level1=2, noise_level2=25): function add_Poisson_noise (line 408) | def add_Poisson_noise(img): function add_JPEG_noise (line 422) | def add_JPEG_noise(img): function random_crop (line 431) | def random_crop(lq, hq, sf=4, lq_patchsize=64): function degradation_bsrgan (line 442) | def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): function degradation_bsrgan_variant (line 534) | def degradation_bsrgan_variant(image, sf=4, isp_model=None): FILE: ldm/modules/image_degradation/utils_image.py function is_image_file (line 29) | def is_image_file(filename): function get_timestamp (line 33) | def get_timestamp(): function imshow (line 37) | def imshow(x, title=None, cbar=False, figsize=None): function surf (line 47) | def surf(Z, cmap='rainbow', figsize=None): function get_image_paths (line 67) | def get_image_paths(dataroot): function _get_paths_from_images (line 74) | def _get_paths_from_images(path): function patches_from_image (line 93) | def patches_from_image(img, p_size=512, p_overlap=64, p_max=800): function imssave (line 112) | def imssave(imgs, img_path): function split_imageset (line 125) | def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_si... function mkdir (line 153) | def mkdir(path): function mkdirs (line 158) | def mkdirs(paths): function mkdir_and_rename (line 166) | def mkdir_and_rename(path): function imread_uint (line 185) | def imread_uint(path, n_channels=3): function imsave (line 203) | def imsave(img, img_path): function imwrite (line 209) | def imwrite(img, img_path): function read_img (line 220) | def read_img(path): function uint2single (line 249) | def uint2single(img): function single2uint (line 254) | def single2uint(img): function uint162single (line 259) | def uint162single(img): function single2uint16 (line 264) | def single2uint16(img): function uint2tensor4 (line 275) | def uint2tensor4(img): function uint2tensor3 (line 282) | def uint2tensor3(img): function tensor2uint (line 289) | def tensor2uint(img): function single2tensor3 (line 302) | def single2tensor3(img): function single2tensor4 (line 307) | def single2tensor4(img): function tensor2single (line 312) | def tensor2single(img): function tensor2single3 (line 320) | def tensor2single3(img): function single2tensor5 (line 329) | def single2tensor5(img): function single32tensor5 (line 333) | def single32tensor5(img): function single42tensor4 (line 337) | def single42tensor4(img): function tensor2img (line 342) | def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)): function augment_img (line 380) | def augment_img(img, mode=0): function augment_img_tensor4 (line 401) | def augment_img_tensor4(img, mode=0): function augment_img_tensor (line 422) | def augment_img_tensor(img, mode=0): function augment_img_np3 (line 441) | def augment_img_np3(img, mode=0): function augment_imgs (line 469) | def augment_imgs(img_list, hflip=True, rot=True): function modcrop (line 494) | def modcrop(img_in, scale): function shave (line 510) | def shave(img_in, border=0): function rgb2ycbcr (line 529) | def rgb2ycbcr(img, only_y=True): function ycbcr2rgb (line 553) | def ycbcr2rgb(img): function bgr2ycbcr (line 573) | def bgr2ycbcr(img, only_y=True): function channel_convert (line 597) | def channel_convert(in_c, tar_type, img_list): function calculate_psnr (line 621) | def calculate_psnr(img1, img2, border=0): function calculate_ssim (line 642) | def calculate_ssim(img1, img2, border=0): function ssim (line 669) | def ssim(img1, img2): function cubic (line 700) | def cubic(x): function calculate_weights_indices (line 708) | def calculate_weights_indices(in_length, out_length, scale, kernel, kern... function imresize (line 766) | def imresize(img, scale, antialiasing=True): function imresize_np (line 839) | def imresize_np(img, scale, antialiasing=True): FILE: ldm/modules/losses/contperceptual.py class LPIPSWithDiscriminator (line 7) | class LPIPSWithDiscriminator(nn.Module): method __init__ (line 8) | def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixello... method calculate_adaptive_weight (line 32) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): method forward (line 45) | def forward(self, inputs, reconstructions, posteriors, optimizer_idx, FILE: ldm/modules/losses/vqperceptual.py function hinge_d_loss_with_exemplar_weights (line 11) | def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): function adopt_weight (line 20) | def adopt_weight(weight, global_step, threshold=0, value=0.): function measure_perplexity (line 26) | def measure_perplexity(predicted_indices, n_embed): function l1 (line 35) | def l1(x, y): function l2 (line 39) | def l2(x, y): class VQLPIPSWithDiscriminator (line 43) | class VQLPIPSWithDiscriminator(nn.Module): method __init__ (line 44) | def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, method calculate_adaptive_weight (line 85) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): method forward (line 98) | def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, FILE: ldm/modules/x_transformer.py class AbsolutePositionalEmbedding (line 25) | class AbsolutePositionalEmbedding(nn.Module): method __init__ (line 26) | def __init__(self, dim, max_seq_len): method init_ (line 31) | def init_(self): method forward (line 34) | def forward(self, x): class FixedPositionalEmbedding (line 39) | class FixedPositionalEmbedding(nn.Module): method __init__ (line 40) | def __init__(self, dim): method forward (line 45) | def forward(self, x, seq_dim=1, offset=0): function exists (line 54) | def exists(val): function default (line 58) | def default(val, d): function always (line 64) | def always(val): function not_equals (line 70) | def not_equals(val): function equals (line 76) | def equals(val): function max_neg_value (line 82) | def max_neg_value(tensor): function pick_and_pop (line 88) | def pick_and_pop(keys, d): function group_dict_by_key (line 93) | def group_dict_by_key(cond, d): function string_begins_with (line 102) | def string_begins_with(prefix, str): function group_by_key_prefix (line 106) | def group_by_key_prefix(prefix, d): function groupby_prefix_and_trim (line 110) | def groupby_prefix_and_trim(prefix, d): class Scale (line 117) | class Scale(nn.Module): method __init__ (line 118) | def __init__(self, value, fn): method forward (line 123) | def forward(self, x, **kwargs): class Rezero (line 128) | class Rezero(nn.Module): method __init__ (line 129) | def __init__(self, fn): method forward (line 134) | def forward(self, x, **kwargs): class ScaleNorm (line 139) | class ScaleNorm(nn.Module): method __init__ (line 140) | def __init__(self, dim, eps=1e-5): method forward (line 146) | def forward(self, x): class RMSNorm (line 151) | class RMSNorm(nn.Module): method __init__ (line 152) | def __init__(self, dim, eps=1e-8): method forward (line 158) | def forward(self, x): class Residual (line 163) | class Residual(nn.Module): method forward (line 164) | def forward(self, x, residual): class GRUGating (line 168) | class GRUGating(nn.Module): method __init__ (line 169) | def __init__(self, dim): method forward (line 173) | def forward(self, x, residual): class GEGLU (line 184) | class GEGLU(nn.Module): method __init__ (line 185) | def __init__(self, dim_in, dim_out): method forward (line 189) | def forward(self, x): class FeedForward (line 194) | class FeedForward(nn.Module): method __init__ (line 195) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): method forward (line 210) | def forward(self, x): class Attention (line 215) | class Attention(nn.Module): method __init__ (line 216) | def __init__( method forward (line 268) | def forward( class AttentionLayers (line 370) | class AttentionLayers(nn.Module): method __init__ (line 371) | def __init__( method forward (line 481) | def forward( class Encoder (line 541) | class Encoder(AttentionLayers): method __init__ (line 542) | def __init__(self, **kwargs): class TransformerWrapper (line 548) | class TransformerWrapper(nn.Module): method __init__ (line 549) | def __init__( method init_ (line 595) | def init_(self): method forward (line 598) | def forward( FILE: ldm/util.py function log_txt_as_img (line 17) | def log_txt_as_img(wh, xc, size=10): function ismap (line 41) | def ismap(x): function isimage (line 47) | def isimage(x): function exists (line 53) | def exists(x): function default (line 57) | def default(val, d): function mean_flat (line 63) | def mean_flat(tensor): function count_params (line 71) | def count_params(model, verbose=False): function instantiate_from_config (line 78) | def instantiate_from_config(config): function get_obj_from_str (line 88) | def get_obj_from_str(string, reload=False): function _do_parallel_data_prefetch (line 96) | def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False): function parallel_data_prefetch (line 108) | def parallel_data_prefetch( FILE: main.py function get_parser (line 24) | def get_parser(**parser_kwargs): function nondefault_trainer_args (line 126) | def nondefault_trainer_args(opt): class WrappedDataset (line 133) | class WrappedDataset(Dataset): method __init__ (line 136) | def __init__(self, dataset): method __len__ (line 139) | def __len__(self): method __getitem__ (line 142) | def __getitem__(self, idx): function worker_init_fn (line 146) | def worker_init_fn(_): class DataModuleFromConfig (line 162) | class DataModuleFromConfig(pl.LightningDataModule): method __init__ (line 163) | def __init__(self, batch_size, train=None, validation=None, test=None,... method prepare_data (line 185) | def prepare_data(self): method setup (line 189) | def setup(self, stage=None): method _train_dataloader (line 197) | def _train_dataloader(self): method _val_dataloader (line 207) | def _val_dataloader(self, shuffle=False): method _test_dataloader (line 218) | def _test_dataloader(self, shuffle=False): method _predict_dataloader (line 231) | def _predict_dataloader(self, shuffle=False): class SetupCallback (line 240) | class SetupCallback(Callback): method __init__ (line 241) | def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, light... method on_keyboard_interrupt (line 251) | def on_keyboard_interrupt(self, trainer, pl_module): method on_pretrain_routine_start (line 257) | def on_pretrain_routine_start(self, trainer, pl_module): class ImageLogger (line 289) | class ImageLogger(Callback): method __init__ (line 290) | def __init__(self, batch_frequency, max_images, clamp=True, increase_l... method _testtube (line 310) | def _testtube(self, pl_module, images, batch_idx, split): method log_local (line 321) | def log_local(self, save_dir, split, images, method log_img (line 340) | def log_img(self, pl_module, batch, batch_idx, split="train"): method check_frequency (line 372) | def check_frequency(self, check_idx): method on_train_batch_end (line 383) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch... method on_validation_batch_end (line 387) | def on_validation_batch_end(self, trainer, pl_module, outputs, batch, ... class CUDACallback (line 395) | class CUDACallback(Callback): method on_train_epoch_start (line 397) | def on_train_epoch_start(self, trainer, pl_module): method on_train_epoch_end (line 403) | def on_train_epoch_end(self, trainer, pl_module, outputs): function melk (line 697) | def melk(*args, **kwargs): function divein (line 705) | def divein(*args, **kwargs): FILE: notebook_helpers.py function download_models (line 19) | def download_models(mode): function load_model_from_config (line 40) | def load_model_from_config(config, ckpt): function get_model (line 52) | def get_model(mode): function get_custom_cond (line 59) | def get_custom_cond(mode): function get_cond_options (line 85) | def get_cond_options(mode): function select_cond_path (line 92) | def select_cond_path(mode): function get_cond (line 107) | def get_cond(mode, selected_path): function visualize_cond_img (line 127) | def visualize_cond_img(path): function run (line 131) | def run(model, selected_path, task, custom_steps, resize_enabled=False, ... function convsample_ddim (line 188) | def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, n... function make_convolutional_sample (line 208) | def make_convolutional_sample(batch, model, mode="vanilla", custom_steps... FILE: scripts/img2img.py function chunk (line 23) | def chunk(it, size): function load_model_from_config (line 28) | def load_model_from_config(config, ckpt, verbose=False): function load_img (line 48) | def load_img(path): function main (line 60) | def main(): FILE: scripts/inpaint.py function make_batch (line 11) | def make_batch(image, mask, device): FILE: scripts/knn2img.py function chunk (line 36) | def chunk(it, size): function load_model_from_config (line 41) | def load_model_from_config(config, ckpt, verbose=False): class Searcher (line 61) | class Searcher(object): method __init__ (line 62) | def __init__(self, database, retriever_version='ViT-L/14'): method train_searcher (line 75) | def train_searcher(self, k, method load_single_file (line 91) | def load_single_file(self, saved_embeddings): method load_multi_files (line 96) | def load_multi_files(self, data_archive): method load_database (line 104) | def load_database(self): method load_retriever (line 123) | def load_retriever(self, version='ViT-L/14', ): method load_searcher (line 130) | def load_searcher(self): method search (line 135) | def search(self, x, k): method __call__ (line 163) | def __call__(self, x, n): FILE: scripts/sample_diffusion.py function custom_to_pil (line 15) | def custom_to_pil(x): function custom_to_np (line 27) | def custom_to_np(x): function logs2pil (line 36) | def logs2pil(logs, keys=["sample"]): function convsample (line 54) | def convsample(model, shape, return_intermediates=True, function convsample_ddim (line 69) | def convsample_ddim(model, steps, shape, eta=1.0 function make_convolutional_sample (line 79) | def make_convolutional_sample(model, batch_size, vanilla=False, custom_s... function run (line 108) | def run(model, logdir, batch_size=50, vanilla=False, custom_steps=None, ... function save_logs (line 143) | def save_logs(logs, path, n_saved=0, key="sample", np_path=None): function get_parser (line 162) | def get_parser(): function load_model_from_config (line 220) | def load_model_from_config(config, sd): function load_model (line 228) | def load_model(config, ckpt, gpu, eval_mode): FILE: scripts/tests/test_watermark.py function testit (line 6) | def testit(img_path): FILE: scripts/train_searcher.py function search_bruteforce (line 12) | def search_bruteforce(searcher): function search_partioned_ah (line 16) | def search_partioned_ah(searcher, dims_per_block, aiq_threshold, reorder_k, function search_ah (line 24) | def search_ah(searcher, dims_per_block, aiq_threshold, reorder_k): function load_datapool (line 28) | def load_datapool(dpath): function train_searcher (line 62) | def train_searcher(opt, FILE: scripts/txt2img.py function chunk (line 32) | def chunk(it, size): function numpy_to_pil (line 37) | def numpy_to_pil(images): function load_model_from_config (line 49) | def load_model_from_config(config, ckpt, verbose=False): function put_watermark (line 69) | def put_watermark(img, wm_encoder=None): function load_replacement (line 77) | def load_replacement(x): function check_safety (line 88) | def check_safety(x_image): function main (line 98) | def main():