SYMBOL INDEX (1216 symbols across 67 files) FILE: ToonCrafter/cldm/cldm.py class ControlledUnetModel (line 23) | class ControlledUnetModel(UNetModel): method forward (line 24) | def forward(self, x, timesteps, context=None, features_adapter=None, f... class ControlNet (line 90) | class ControlNet(nn.Module): method __init__ (line 91) | def __init__( method make_zero_conv (line 323) | def make_zero_conv(self, channels): method forward (line 326) | def forward(self, x, hint, timesteps, context, **kwargs): class ControlLDM (line 351) | class ControlLDM(LatentDiffusion): method __init__ (line 353) | def __init__(self, control_stage_config, control_key, only_mid_control... method get_input (line 361) | def get_input(self, batch, k, bs=None, *args, **kwargs): method apply_model (line 371) | def apply_model(self, x_noisy, t, cond, *args, **kwargs): method get_unconditional_conditioning (line 387) | def get_unconditional_conditioning(self, N): method log_images (line 391) | def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50,... method sample_log (line 452) | def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): method configure_optimizers (line 459) | def configure_optimizers(self): method low_vram_shift (line 468) | def low_vram_shift(self, is_diffusing): FILE: ToonCrafter/cldm/ddim_hacked.py class DDIMSampler (line 10) | class DDIMSampler(object): method __init__ (line 11) | def __init__(self, model, schedule="linear", **kwargs): method register_buffer (line 17) | def register_buffer(self, name, attr): method make_schedule (line 23) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi... method sample (line 55) | def sample(self, method ddim_sampling (line 123) | def ddim_sampling(self, cond, shape, method p_sample_ddim (line 181) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin... method encode (line 234) | def encode(self, x0, c, t_enc, use_original_steps=False, return_interm... method stochastic_encode (line 282) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): method decode (line 298) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale... FILE: ToonCrafter/cldm/hack.py function disable_verbosity (line 11) | def disable_verbosity(): function enable_sliced_attention (line 17) | def enable_sliced_attention(): function hack_everything (line 23) | def hack_everything(clip_skip=0): function _hacked_clip_forward (line 32) | def _hacked_clip_forward(self, text): function _hacked_sliced_attentin_forward (line 72) | def _hacked_sliced_attentin_forward(self, x, context=None, mask=None): FILE: ToonCrafter/cldm/logger.py class ImageLogger (line 11) | class ImageLogger(Callback): method __init__ (line 12) | def __init__(self, batch_frequency=2000, max_images=4, clamp=True, inc... method log_local (line 28) | def log_local(self, save_dir, split, images, global_step, current_epoc... method log_img (line 42) | def log_img(self, pl_module, batch, batch_idx, split="train"): method check_frequency (line 71) | def check_frequency(self, check_idx): method on_train_batch_end (line 74) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch... FILE: ToonCrafter/cldm/model.py function get_state_dict (line 8) | def get_state_dict(d): function load_state_dict (line 12) | def load_state_dict(ckpt_path, location='cpu'): function create_model (line 24) | def create_model(config_path): FILE: ToonCrafter/gradio_app.py function dynamicrafter_demo (line 16) | def dynamicrafter_demo(result_dir='./tmp/', res=512): function get_parser (line 67) | def get_parser(): FILE: ToonCrafter/ldm/data/util.py class AddMiDaS (line 6) | class AddMiDaS(object): method __init__ (line 7) | def __init__(self, model_type): method pt2np (line 11) | def pt2np(self, x): method np2pt (line 15) | def np2pt(self, x): method __call__ (line 19) | def __call__(self, sample): FILE: ToonCrafter/ldm/models/autoencoder.py class AutoencoderKL (line 13) | class AutoencoderKL(pl.LightningModule): method __init__ (line 14) | def __init__(self, method init_from_ckpt (line 52) | def init_from_ckpt(self, path, ignore_keys=list()): method ema_scope (line 64) | def ema_scope(self, context=None): method on_train_batch_end (line 78) | def on_train_batch_end(self, *args, **kwargs): method encode (line 82) | def encode(self, x): method decode (line 88) | def decode(self, z): method forward (line 93) | def forward(self, input, sample_posterior=True): method get_input (line 102) | def get_input(self, batch, k): method training_step (line 109) | def training_step(self, batch, batch_idx, optimizer_idx): method validation_step (line 130) | def validation_step(self, batch, batch_idx): method _validation_step (line 136) | def _validation_step(self, batch, batch_idx, postfix=""): method configure_optimizers (line 150) | def configure_optimizers(self): method get_last_layer (line 163) | def get_last_layer(self): method log_images (line 167) | def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs): method to_rgb (line 192) | def to_rgb(self, x): class IdentityFirstStage (line 201) | class IdentityFirstStage(torch.nn.Module): method __init__ (line 202) | def __init__(self, *args, vq_interface=False, **kwargs): method encode (line 206) | def encode(self, x, *args, **kwargs): method decode (line 209) | def decode(self, x, *args, **kwargs): method quantize (line 212) | def quantize(self, x, *args, **kwargs): method forward (line 217) | def forward(self, x, *args, **kwargs): FILE: ToonCrafter/ldm/models/diffusion/ddim.py class DDIMSampler (line 10) | class DDIMSampler(object): method __init__ (line 11) | def __init__(self, model, schedule="linear", **kwargs): method register_buffer (line 17) | def register_buffer(self, name, attr): method make_schedule (line 23) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi... method sample (line 55) | def sample(self, method ddim_sampling (line 123) | def ddim_sampling(self, cond, shape, method p_sample_ddim (line 181) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin... method encode (line 254) | def encode(self, x0, c, t_enc, use_original_steps=False, return_interm... method stochastic_encode (line 301) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): method decode (line 317) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale... FILE: ToonCrafter/ldm/models/diffusion/ddpm.py function disabled_train (line 36) | def disabled_train(self, mode=True): function uniform_on_device (line 42) | def uniform_on_device(r1, r2, shape, device): class DDPM (line 46) | class DDPM(pl.LightningModule): method __init__ (line 48) | def __init__(self, method register_schedule (line 138) | def register_schedule(self, given_betas=None, beta_schedule="linear", ... method ema_scope (line 195) | def ema_scope(self, context=None): method init_from_ckpt (line 210) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): method q_mean_variance (line 272) | def q_mean_variance(self, x_start, t): method predict_start_from_noise (line 284) | def predict_start_from_noise(self, x_t, t, noise): method predict_start_from_z_and_v (line 290) | def predict_start_from_z_and_v(self, x_t, t, v): method predict_eps_from_z_and_v (line 298) | def predict_eps_from_z_and_v(self, x_t, t, v): method q_posterior (line 304) | def q_posterior(self, x_start, x_t, t): method p_mean_variance (line 313) | def p_mean_variance(self, x, t, clip_denoised: bool): method p_sample (line 326) | def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): method p_sample_loop (line 335) | def p_sample_loop(self, shape, return_intermediates=False): method sample (line 350) | def sample(self, batch_size=16, return_intermediates=False): method q_sample (line 356) | def q_sample(self, x_start, t, noise=None): method get_v (line 361) | def get_v(self, x, noise, t): method get_loss (line 367) | def get_loss(self, pred, target, mean=True): method p_losses (line 382) | def p_losses(self, x_start, t, noise=None): method forward (line 413) | def forward(self, x, *args, **kwargs): method get_input (line 419) | def get_input(self, batch, k): method shared_step (line 427) | def shared_step(self, batch): method training_step (line 432) | def training_step(self, batch, batch_idx): method validation_step (line 457) | def validation_step(self, batch, batch_idx): method on_train_batch_end (line 465) | def on_train_batch_end(self, *args, **kwargs): method _get_rows_from_list (line 469) | def _get_rows_from_list(self, samples): method log_images (line 477) | def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=Non... method configure_optimizers (line 514) | def configure_optimizers(self): class LatentDiffusion (line 523) | class LatentDiffusion(DDPM): method __init__ (line 526) | def __init__(self, method make_cond_schedule (line 584) | def make_cond_schedule(self, ): method on_train_batch_start (line 591) | def on_train_batch_start(self, batch, batch_idx, dataloader_idx): method register_schedule (line 606) | def register_schedule(self, method instantiate_first_stage (line 615) | def instantiate_first_stage(self, config): method instantiate_cond_stage (line 622) | def instantiate_cond_stage(self, config): method _get_denoise_row_from_list (line 643) | def _get_denoise_row_from_list(self, samples, desc='', force_no_decode... method get_first_stage_encoding (line 655) | def get_first_stage_encoding(self, encoder_posterior): method get_learned_conditioning (line 664) | def get_learned_conditioning(self, c): method meshgrid (line 677) | def meshgrid(self, h, w): method delta_border (line 684) | def delta_border(self, h, w): method get_weighting (line 698) | def get_weighting(self, h, w, Ly, Lx, device): method get_fold_unfold (line 714) | def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo... method get_input (line 767) | def get_input(self, batch, k, return_first_stage_outputs=False, force_... method decode_first_stage (line 820) | def decode_first_stage(self, z, predict_cids=False, force_not_quantize... method encode_first_stage (line 831) | def encode_first_stage(self, x): method shared_step (line 834) | def shared_step(self, batch, **kwargs): method forward (line 839) | def forward(self, x, c, *args, **kwargs): method apply_model (line 850) | def apply_model(self, x_noisy, t, cond, return_ids=False): method _predict_eps_from_xstart (line 867) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart): method _prior_bpd (line 871) | def _prior_bpd(self, x_start): method p_losses (line 885) | def p_losses(self, x_start, cond, t, noise=None): method p_mean_variance (line 922) | def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codeboo... method p_sample (line 954) | def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, method progressive_denoising (line 985) | def progressive_denoising(self, cond, shape, verbose=True, callback=No... method p_sample_loop (line 1041) | def p_sample_loop(self, cond, shape, return_intermediates=False, method sample (line 1092) | def sample(self, cond, batch_size=16, return_intermediates=False, x_T=... method sample_log (line 1110) | def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): method get_unconditional_conditioning (line 1124) | def get_unconditional_conditioning(self, batch_size, null_label=None): method log_images (line 1149) | def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ... method configure_optimizers (line 1278) | def configure_optimizers(self): method to_rgb (line 1303) | def to_rgb(self, x): class DiffusionWrapper (line 1312) | class DiffusionWrapper(pl.LightningModule): method __init__ (line 1313) | def __init__(self, diff_model_config, conditioning_key): method forward (line 1320) | def forward(self, x, t, c_concat: list = None, c_crossattn: list = Non... class LatentUpscaleDiffusion (line 1354) | class LatentUpscaleDiffusion(LatentDiffusion): method __init__ (line 1355) | def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_... method instantiate_low_stage (line 1363) | def instantiate_low_stage(self, config): method get_input (line 1371) | def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False): method log_images (line 1393) | def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200,... class LatentFinetuneDiffusion (line 1492) | class LatentFinetuneDiffusion(LatentDiffusion): method __init__ (line 1498) | def __init__(self, method init_from_ckpt (line 1521) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): method log_images (line 1553) | def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200,... class LatentInpaintDiffusion (line 1634) | class LatentInpaintDiffusion(LatentFinetuneDiffusion): method __init__ (line 1641) | def __init__(self, method get_input (line 1651) | def get_input(self, batch, k, cond_key=None, bs=None, return_first_sta... method log_images (line 1677) | def log_images(self, *args, **kwargs): class LatentDepth2ImageDiffusion (line 1684) | class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion): method __init__ (line 1689) | def __init__(self, depth_stage_config, concat_keys=("midas_in",), *arg... method get_input (line 1695) | def get_input(self, batch, k, cond_key=None, bs=None, return_first_sta... method log_images (line 1728) | def log_images(self, *args, **kwargs): class LatentUpscaleFinetuneDiffusion (line 1737) | class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion): method __init__ (line 1741) | def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None, method instantiate_low_stage (line 1752) | def instantiate_low_stage(self, config): method get_input (line 1760) | def get_input(self, batch, k, cond_key=None, bs=None, return_first_sta... method log_images (line 1794) | def log_images(self, *args, **kwargs): FILE: ToonCrafter/ldm/models/diffusion/dpm_solver/dpm_solver.py class NoiseScheduleVP (line 7) | class NoiseScheduleVP: method __init__ (line 8) | def __init__( method marginal_log_mean_coeff (line 106) | def marginal_log_mean_coeff(self, t): method marginal_alpha (line 120) | def marginal_alpha(self, t): method marginal_std (line 126) | def marginal_std(self, t): method marginal_lambda (line 132) | def marginal_lambda(self, t): method inverse_lambda (line 140) | def inverse_lambda(self, lamb): function model_wrapper (line 161) | def model_wrapper( class DPM_Solver (line 319) | class DPM_Solver: method __init__ (line 320) | def __init__(self, model_fn, noise_schedule, predict_x0=False, thresho... method noise_prediction_fn (line 346) | def noise_prediction_fn(self, x, t): method data_prediction_fn (line 352) | def data_prediction_fn(self, x, t): method model_fn (line 367) | def model_fn(self, x, t): method get_time_steps (line 376) | def get_time_steps(self, skip_type, t_T, t_0, N, device): method get_orders_and_timesteps_for_singlestep_solver (line 405) | def get_orders_and_timesteps_for_singlestep_solver(self, steps, order,... method denoise_to_zero_fn (line 463) | def denoise_to_zero_fn(self, x, s): method dpm_solver_first_update (line 469) | def dpm_solver_first_update(self, x, s, t, model_s=None, return_interm... method singlestep_dpm_solver_second_update (line 515) | def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s... method singlestep_dpm_solver_third_update (line 599) | def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2... method multistep_dpm_solver_second_update (line 723) | def multistep_dpm_solver_second_update(self, x, model_prev_list, t_pre... method multistep_dpm_solver_third_update (line 780) | def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev... method singlestep_dpm_solver_update (line 827) | def singlestep_dpm_solver_update(self, x, s, t, order, return_intermed... method multistep_dpm_solver_update (line 855) | def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list,... method dpm_solver_adaptive (line 878) | def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.... method sample (line 939) | def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_... function interpolate_fn (line 1104) | def interpolate_fn(x, xp, yp): function expand_dims (line 1145) | def expand_dims(v, dims): FILE: ToonCrafter/ldm/models/diffusion/dpm_solver/sampler.py class DPMSolverSampler (line 13) | class DPMSolverSampler(object): method __init__ (line 14) | def __init__(self, model, **kwargs): method register_buffer (line 20) | def register_buffer(self, name, attr): method sample (line 27) | def sample(self, FILE: ToonCrafter/ldm/models/diffusion/plms.py class PLMSSampler (line 12) | class PLMSSampler(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 59) | def sample(self, method plms_sampling (line 118) | def plms_sampling(self, cond, shape, method p_sample_plms (line 178) | def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_origin... FILE: ToonCrafter/ldm/models/diffusion/sampling_util.py function append_dims (line 5) | def append_dims(x, target_dims): function norm_thresholding (line 14) | def norm_thresholding(x0, value): function spatial_norm_thresholding (line 19) | def spatial_norm_thresholding(x0, value): FILE: ToonCrafter/ldm/modules/attention.py function exists (line 23) | def exists(val): function uniq (line 27) | def uniq(arr): function default (line 31) | def default(val, d): function max_neg_value (line 37) | def max_neg_value(t): function init_ (line 41) | def init_(tensor): class GEGLU (line 49) | class GEGLU(nn.Module): method __init__ (line 50) | def __init__(self, dim_in, dim_out): method forward (line 54) | def forward(self, x): class FeedForward (line 59) | class FeedForward(nn.Module): method __init__ (line 60) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): method forward (line 75) | def forward(self, x): function zero_module (line 79) | def zero_module(module): function Normalize (line 88) | def Normalize(in_channels): class SpatialSelfAttention (line 92) | class SpatialSelfAttention(nn.Module): method __init__ (line 93) | def __init__(self, in_channels): method forward (line 119) | def forward(self, x): class CrossAttention (line 145) | class CrossAttention(nn.Module): method __init__ (line 146) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ... method forward (line 163) | def forward(self, x, context=None, mask=None): class MemoryEfficientCrossAttention (line 197) | class MemoryEfficientCrossAttention(nn.Module): method __init__ (line 199) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ... method forward (line 216) | def forward(self, x, context=None, mask=None): class BasicTransformerBlock (line 246) | class BasicTransformerBlock(nn.Module): method __init__ (line 251) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,... method forward (line 268) | def forward(self, x, context=None): method _forward (line 271) | def _forward(self, x, context=None): class SpatialTransformer (line 278) | class SpatialTransformer(nn.Module): method __init__ (line 287) | def __init__(self, in_channels, n_heads, d_head, method forward (line 321) | def forward(self, x, context=None): FILE: ToonCrafter/ldm/modules/diffusionmodules/model.py function get_timestep_embedding (line 20) | def get_timestep_embedding(timesteps, embedding_dim): function nonlinearity (line 41) | def nonlinearity(x): function Normalize (line 46) | def Normalize(in_channels, num_groups=32): class Upsample (line 50) | class Upsample(nn.Module): method __init__ (line 51) | def __init__(self, in_channels, with_conv): method forward (line 61) | def forward(self, x): class Downsample (line 68) | class Downsample(nn.Module): method __init__ (line 69) | def __init__(self, in_channels, with_conv): method forward (line 80) | def forward(self, x): class ResnetBlock (line 90) | class ResnetBlock(nn.Module): method __init__ (line 91) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa... method forward (line 129) | def forward(self, x, temb): class AttnBlock (line 152) | class AttnBlock(nn.Module): method __init__ (line 153) | def __init__(self, in_channels): method forward (line 179) | def forward(self, x): class MemoryEfficientAttnBlock (line 205) | class MemoryEfficientAttnBlock(nn.Module): method __init__ (line 212) | def __init__(self, in_channels): method forward (line 239) | def forward(self, x): class MemoryEfficientCrossAttentionWrapper (line 271) | class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): method forward (line 272) | def forward(self, x, context=None, mask=None): function make_attn (line 280) | def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): class Model (line 300) | class Model(nn.Module): method __init__ (line 301) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, method forward (line 400) | def forward(self, x, t=None, context=None): method get_last_layer (line 448) | def get_last_layer(self): class Encoder (line 452) | class Encoder(nn.Module): method __init__ (line 453) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, method forward (line 518) | def forward(self, x): class Decoder (line 546) | class Decoder(nn.Module): method __init__ (line 547) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, method forward (line 619) | def forward(self, z): class SimpleDecoder (line 655) | class SimpleDecoder(nn.Module): method __init__ (line 656) | def __init__(self, in_channels, out_channels, *args, **kwargs): method forward (line 678) | def forward(self, x): class UpsampleDecoder (line 691) | class UpsampleDecoder(nn.Module): method __init__ (line 692) | def __init__(self, in_channels, out_channels, ch, num_res_blocks, reso... method forward (line 725) | def forward(self, x): class LatentRescaler (line 739) | class LatentRescaler(nn.Module): method __init__ (line 740) | def __init__(self, factor, in_channels, mid_channels, out_channels, de... method forward (line 764) | def forward(self, x): class MergedRescaleEncoder (line 776) | class MergedRescaleEncoder(nn.Module): method __init__ (line 777) | def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, method forward (line 789) | def forward(self, x): class MergedRescaleDecoder (line 795) | class MergedRescaleDecoder(nn.Module): method __init__ (line 796) | def __init__(self, z_channels, out_ch, resolution, num_res_blocks, att... method forward (line 806) | def forward(self, x): class Upsampler (line 812) | class Upsampler(nn.Module): method __init__ (line 813) | def __init__(self, in_size, out_size, in_channels, out_channels, ch_mu... method forward (line 825) | def forward(self, x): class Resize (line 831) | class Resize(nn.Module): method __init__ (line 832) | def __init__(self, in_channels=None, learned=False, mode="bilinear"): method forward (line 847) | def forward(self, x, scale_factor=1.0): FILE: ToonCrafter/ldm/modules/diffusionmodules/openaimodel.py function convert_module_to_f16 (line 23) | def convert_module_to_f16(x): function convert_module_to_f32 (line 26) | def convert_module_to_f32(x): class AttentionPool2d (line 31) | class AttentionPool2d(nn.Module): method __init__ (line 36) | def __init__( method forward (line 50) | def forward(self, x): class TimestepBlock (line 61) | class TimestepBlock(nn.Module): method forward (line 67) | def forward(self, x, emb): class TimestepEmbedSequential (line 73) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock): method forward (line 79) | def forward(self, x, emb, context=None, check=False): class Upsample (line 90) | class Upsample(nn.Module): method __init__ (line 99) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd... method forward (line 108) | def forward(self, x): class TransposedUpsample (line 120) | class TransposedUpsample(nn.Module): method __init__ (line 122) | def __init__(self, channels, out_channels=None, ks=5): method forward (line 129) | def forward(self,x): class Downsample (line 133) | class Downsample(nn.Module): method __init__ (line 142) | def __init__(self, channels, use_conv, dims=2, out_channels=None,paddi... method forward (line 157) | def forward(self, x): class ResBlock (line 162) | class ResBlock(TimestepBlock): method __init__ (line 178) | def __init__( method forward (line 242) | def forward(self, x, emb): method _forward (line 254) | def _forward(self, x, emb): class AttentionBlock (line 277) | class AttentionBlock(nn.Module): method __init__ (line 284) | def __init__( method forward (line 313) | def forward(self, x): method _forward (line 317) | def _forward(self, x): function count_flops_attn (line 326) | def count_flops_attn(model, _x, y): class QKVAttentionLegacy (line 346) | class QKVAttentionLegacy(nn.Module): method __init__ (line 351) | def __init__(self, n_heads): method forward (line 355) | def forward(self, qkv): method count_flops (line 374) | def count_flops(model, _x, y): class QKVAttention (line 378) | class QKVAttention(nn.Module): method __init__ (line 383) | def __init__(self, n_heads): method forward (line 387) | def forward(self, qkv): method count_flops (line 408) | def count_flops(model, _x, y): class UNetModel (line 412) | class UNetModel(nn.Module): method __init__ (line 442) | def __init__( method convert_to_fp16 (line 738) | def convert_to_fp16(self): method convert_to_fp32 (line 746) | def convert_to_fp32(self): method forward (line 754) | def forward(self, x, timesteps=None, context=None, y=None,**kwargs): FILE: ToonCrafter/ldm/modules/diffusionmodules/upscaling.py class AbstractLowScaleModel (line 10) | class AbstractLowScaleModel(nn.Module): method __init__ (line 12) | def __init__(self, noise_schedule_config=None): method register_schedule (line 17) | def register_schedule(self, beta_schedule="linear", timesteps=1000, method q_sample (line 44) | def q_sample(self, x_start, t, noise=None): method forward (line 49) | def forward(self, x): method decode (line 52) | def decode(self, x): class SimpleImageConcat (line 56) | class SimpleImageConcat(AbstractLowScaleModel): method __init__ (line 58) | def __init__(self): method forward (line 62) | def forward(self, x): class ImageConcatWithNoiseAugmentation (line 67) | class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): method __init__ (line 68) | def __init__(self, noise_schedule_config, max_noise_level=1000, to_cud... method forward (line 72) | def forward(self, x, noise_level=None): FILE: ToonCrafter/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 133) | def backward(ctx, *output_grads): function timestep_embedding (line 154) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal... function zero_module (line 177) | def zero_module(module): function scale_module (line 186) | def scale_module(module, scale): function mean_flat (line 195) | def mean_flat(tensor): function normalization (line 202) | def normalization(channels): class SiLU (line 212) | class SiLU(nn.Module): method forward (line 213) | def forward(self, x): class GroupNorm32 (line 217) | class GroupNorm32(nn.GroupNorm): method forward (line 218) | def forward(self, x): function conv_nd (line 221) | def conv_nd(dims, *args, **kwargs): function linear (line 234) | def linear(*args, **kwargs): function avg_pool_nd (line 241) | def avg_pool_nd(dims, *args, **kwargs): class HybridConditioner (line 254) | class HybridConditioner(nn.Module): method __init__ (line 256) | def __init__(self, c_concat_config, c_crossattn_config): method forward (line 261) | def forward(self, c_concat, c_crossattn): function noise_like (line 267) | def noise_like(shape, device, repeat=False): FILE: ToonCrafter/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: ToonCrafter/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 reset_num_updates (line 25) | def reset_num_updates(self): method forward (line 29) | def forward(self, model): method copy_to (line 50) | def copy_to(self, model): method store (line 59) | def store(self, parameters): method restore (line 68) | def restore(self, parameters): FILE: ToonCrafter/ldm/modules/encoders/modules.py class AbstractEncoder (line 11) | class AbstractEncoder(nn.Module): method __init__ (line 12) | def __init__(self): method encode (line 15) | def encode(self, *args, **kwargs): class IdentityEncoder (line 19) | class IdentityEncoder(AbstractEncoder): method encode (line 21) | def encode(self, x): class ClassEmbedder (line 25) | class ClassEmbedder(nn.Module): method __init__ (line 26) | def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): method forward (line 33) | def forward(self, batch, key=None, disable_dropout=False): method get_unconditional_conditioning (line 45) | def get_unconditional_conditioning(self, bs, device="cuda"): function disabled_train (line 52) | def disabled_train(self, mode=True): class FrozenT5Embedder (line 58) | class FrozenT5Embedder(AbstractEncoder): method __init__ (line 60) | def __init__(self, version="google/t5-v1_1-large", device="cuda", max_... method freeze (line 69) | def freeze(self): method forward (line 75) | def forward(self, text): method encode (line 84) | def encode(self, text): class FrozenCLIPEmbedder (line 88) | class FrozenCLIPEmbedder(AbstractEncoder): method __init__ (line 95) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu... method freeze (line 111) | def freeze(self): method forward (line 117) | def forward(self, text): method encode (line 130) | def encode(self, text): class FrozenOpenCLIPEmbedder (line 134) | class FrozenOpenCLIPEmbedder(AbstractEncoder): method __init__ (line 143) | def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", devic... method freeze (line 163) | def freeze(self): method forward (line 168) | def forward(self, text): method encode_with_transformer (line 173) | def encode_with_transformer(self, text): method text_transformer_forward (line 182) | def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): method encode (line 192) | def encode(self, text): class FrozenCLIPT5Encoder (line 196) | class FrozenCLIPT5Encoder(AbstractEncoder): method __init__ (line 197) | def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_ve... method encode (line 205) | def encode(self, text): method forward (line 208) | def forward(self, text): FILE: ToonCrafter/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: ToonCrafter/ldm/modules/image_degradation/bsrgan_light.py function modcrop_np (line 28) | def modcrop_np(img, sf): function analytic_kernel (line 48) | def analytic_kernel(k): function anisotropic_Gaussian (line 64) | def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6): function gm_blur_kernel (line 85) | def gm_blur_kernel(mean, cov, size=15): function shift_pixel (line 98) | def shift_pixel(x, sf, upper_left=True): function blur (line 127) | def blur(x, k): function gen_kernel (line 144) | def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]),... function fspecial_gaussian (line 186) | def fspecial_gaussian(hsize, sigma): function fspecial_laplacian (line 200) | def fspecial_laplacian(alpha): function fspecial (line 209) | def fspecial(filter_type, *args, **kwargs): function bicubic_degradation (line 227) | def bicubic_degradation(x, sf=3): function srmd_degradation (line 239) | def srmd_degradation(x, k, sf=3): function dpsr_degradation (line 261) | def dpsr_degradation(x, k, sf=3): function classical_degradation (line 283) | def classical_degradation(x, k, sf=3): function add_sharpening (line 298) | def add_sharpening(img, weight=0.5, radius=50, threshold=10): function add_blur (line 324) | def add_blur(img, sf=4): function add_resize (line 342) | def add_resize(img, sf=4): function add_Gaussian_noise (line 372) | def add_Gaussian_noise(img, noise_level1=2, noise_level2=25): function add_speckle_noise (line 389) | def add_speckle_noise(img, noise_level1=2, noise_level2=25): function add_Poisson_noise (line 407) | def add_Poisson_noise(img): function add_JPEG_noise (line 421) | def add_JPEG_noise(img): function random_crop (line 430) | def random_crop(lq, hq, sf=4, lq_patchsize=64): function degradation_bsrgan (line 441) | def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None): function degradation_bsrgan_variant (line 533) | def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False): FILE: ToonCrafter/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: ToonCrafter/ldm/modules/midas/api.py function disabled_train (line 22) | def disabled_train(self, mode=True): function load_midas_transform (line 28) | def load_midas_transform(model_type): function load_model (line 73) | def load_model(model_type): class MiDaSInference (line 137) | class MiDaSInference(nn.Module): method __init__ (line 150) | def __init__(self, model_type): method forward (line 157) | def forward(self, x): FILE: ToonCrafter/ldm/modules/midas/midas/base_model.py class BaseModel (line 4) | class BaseModel(torch.nn.Module): method load (line 5) | def load(self, path): FILE: ToonCrafter/ldm/modules/midas/midas/blocks.py function _make_encoder (line 11) | def _make_encoder(backbone, features, use_pretrained, groups=1, expand=F... function _make_scratch (line 49) | def _make_scratch(in_shape, out_shape, groups=1, expand=False): function _make_pretrained_efficientnet_lite3 (line 78) | def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): function _make_efficientnet_backbone (line 88) | def _make_efficientnet_backbone(effnet): function _make_resnet_backbone (line 101) | def _make_resnet_backbone(resnet): function _make_pretrained_resnext101_wsl (line 114) | def _make_pretrained_resnext101_wsl(use_pretrained): class Interpolate (line 120) | class Interpolate(nn.Module): method __init__ (line 124) | def __init__(self, scale_factor, mode, align_corners=False): method forward (line 138) | def forward(self, x): class ResidualConvUnit (line 155) | class ResidualConvUnit(nn.Module): method __init__ (line 159) | def __init__(self, features): method forward (line 177) | def forward(self, x): class FeatureFusionBlock (line 194) | class FeatureFusionBlock(nn.Module): method __init__ (line 198) | def __init__(self, features): method forward (line 209) | def forward(self, *xs): class ResidualConvUnit_custom (line 231) | class ResidualConvUnit_custom(nn.Module): method __init__ (line 235) | def __init__(self, features, activation, bn): method forward (line 263) | def forward(self, x): class FeatureFusionBlock_custom (line 291) | class FeatureFusionBlock_custom(nn.Module): method __init__ (line 295) | def __init__(self, features, activation, deconv=False, bn=False, expan... method forward (line 320) | def forward(self, *xs): FILE: ToonCrafter/ldm/modules/midas/midas/dpt_depth.py function _make_fusion_block (line 15) | def _make_fusion_block(features, use_bn): class DPT (line 26) | class DPT(BaseModel): method __init__ (line 27) | def __init__( method forward (line 67) | def forward(self, x): class DPTDepthModel (line 88) | class DPTDepthModel(DPT): method __init__ (line 89) | def __init__(self, path=None, non_negative=True, **kwargs): method forward (line 107) | def forward(self, x): FILE: ToonCrafter/ldm/modules/midas/midas/midas_net.py class MidasNet (line 12) | class MidasNet(BaseModel): method __init__ (line 16) | def __init__(self, path=None, features=256, non_negative=True): method forward (line 49) | def forward(self, x): FILE: ToonCrafter/ldm/modules/midas/midas/midas_net_custom.py class MidasNet_small (line 12) | class MidasNet_small(BaseModel): method __init__ (line 16) | def __init__(self, path=None, features=64, backbone="efficientnet_lite... method forward (line 73) | def forward(self, x): function fuse_model (line 109) | def fuse_model(m): FILE: ToonCrafter/ldm/modules/midas/midas/transforms.py function apply_min_size (line 6) | def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AR... class Resize (line 48) | class Resize(object): method __init__ (line 52) | def __init__( method constrain_to_multiple_of (line 94) | def constrain_to_multiple_of(self, x, min_val=0, max_val=None): method get_size (line 105) | def get_size(self, width, height): method __call__ (line 162) | def __call__(self, sample): class NormalizeImage (line 197) | class NormalizeImage(object): method __init__ (line 201) | def __init__(self, mean, std): method __call__ (line 205) | def __call__(self, sample): class PrepareForNet (line 211) | class PrepareForNet(object): method __init__ (line 215) | def __init__(self): method __call__ (line 218) | def __call__(self, sample): FILE: ToonCrafter/ldm/modules/midas/midas/vit.py class Slice (line 9) | class Slice(nn.Module): method __init__ (line 10) | def __init__(self, start_index=1): method forward (line 14) | def forward(self, x): class AddReadout (line 18) | class AddReadout(nn.Module): method __init__ (line 19) | def __init__(self, start_index=1): method forward (line 23) | def forward(self, x): class ProjectReadout (line 31) | class ProjectReadout(nn.Module): method __init__ (line 32) | def __init__(self, in_features, start_index=1): method forward (line 38) | def forward(self, x): class Transpose (line 45) | class Transpose(nn.Module): method __init__ (line 46) | def __init__(self, dim0, dim1): method forward (line 51) | def forward(self, x): function forward_vit (line 56) | def forward_vit(pretrained, x): function _resize_pos_embed (line 100) | def _resize_pos_embed(self, posemb, gs_h, gs_w): function forward_flex (line 117) | def forward_flex(self, x): function get_activation (line 159) | def get_activation(name): function get_readout_oper (line 166) | def get_readout_oper(vit_features, features, use_readout, start_index=1): function _make_vit_b16_backbone (line 183) | def _make_vit_b16_backbone( function _make_pretrained_vitl16_384 (line 297) | def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=... function _make_pretrained_vitb16_384 (line 310) | def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=... function _make_pretrained_deitb16_384 (line 319) | def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks... function _make_pretrained_deitb16_distil_384 (line 328) | def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore"... function _make_vit_b_rn50_backbone (line 343) | def _make_vit_b_rn50_backbone( function _make_pretrained_vitb_rn50_384 (line 478) | def _make_pretrained_vitb_rn50_384( FILE: ToonCrafter/ldm/modules/midas/utils.py function read_pfm (line 9) | def read_pfm(path): function write_pfm (line 58) | def write_pfm(path, image, scale=1): function read_image (line 97) | def read_image(path): function resize_image (line 116) | def resize_image(img): function resize_depth (line 146) | def resize_depth(depth, width, height): function write_depth (line 165) | def write_depth(path, depth, bits=1): FILE: ToonCrafter/ldm/util.py function log_txt_as_img (line 11) | def log_txt_as_img(wh, xc, size=10): function ismap (line 35) | def ismap(x): function isimage (line 41) | def isimage(x): function exists (line 47) | def exists(x): function default (line 51) | def default(val, d): function mean_flat (line 57) | def mean_flat(tensor): function count_params (line 65) | def count_params(model, verbose=False): function instantiate_from_config (line 72) | def instantiate_from_config(config): function get_obj_from_str (line 82) | def get_obj_from_str(string, reload=False): class AdamWwithEMAandWings (line 90) | class AdamWwithEMAandWings(optim.Optimizer): method __init__ (line 92) | def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, #... method __setstate__ (line 113) | def __setstate__(self, state): method step (line 119) | def step(self, closure=None): FILE: ToonCrafter/lvdm/basics.py function disabled_train (line 14) | def disabled_train(self, mode=True): function zero_module (line 20) | def zero_module(module): function scale_module (line 29) | def scale_module(module, scale): function conv_nd (line 38) | def conv_nd(dims, *args, **kwargs): function linear (line 51) | def linear(*args, **kwargs): function avg_pool_nd (line 58) | def avg_pool_nd(dims, *args, **kwargs): function nonlinearity (line 71) | def nonlinearity(type='silu'): class GroupNormSpecific (line 78) | class GroupNormSpecific(nn.GroupNorm): method forward (line 79) | def forward(self, x): function normalization (line 83) | def normalization(channels, num_groups=32): class HybridConditioner (line 92) | class HybridConditioner(nn.Module): method __init__ (line 94) | def __init__(self, c_concat_config, c_crossattn_config): method forward (line 99) | def forward(self, c_concat, c_crossattn): FILE: ToonCrafter/lvdm/common.py function gather_data (line 8) | def gather_data(data, return_np=True): function autocast (line 16) | def autocast(f): function extract_into_tensor (line 25) | def extract_into_tensor(a, t, x_shape): function noise_like (line 31) | def noise_like(shape, device, repeat=False): function default (line 37) | def default(val, d): function exists (line 42) | def exists(val): function identity (line 45) | def identity(*args, **kwargs): function uniq (line 48) | def uniq(arr): function mean_flat (line 51) | def mean_flat(tensor): function ismap (line 57) | def ismap(x): function isimage (line 62) | def isimage(x): function max_neg_value (line 67) | def max_neg_value(t): function shape_to_str (line 70) | def shape_to_str(x): function init_ (line 74) | def init_(tensor): function checkpoint (line 81) | def checkpoint(func, inputs, params, flag): FILE: ToonCrafter/lvdm/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: ToonCrafter/lvdm/data/webvid.py class WebVid (line 13) | class WebVid(Dataset): method __init__ (line 25) | def __init__(self, method _load_metadata (line 72) | def _load_metadata(self): method _get_video_path (line 83) | def _get_video_path(self, sample): method __getitem__ (line 88) | def __getitem__(self, index): method __len__ (line 170) | def __len__(self): FILE: ToonCrafter/lvdm/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, noise=None): method kl (line 42) | def kl(self, other=None): method nll (line 56) | def nll(self, sample, dims=[1,2,3]): method mode (line 64) | def mode(self): function normal_kl (line 68) | def normal_kl(mean1, logvar1, mean2, logvar2): FILE: ToonCrafter/lvdm/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: ToonCrafter/lvdm/models/autoencoder.py class AutoencoderKL (line 15) | class AutoencoderKL(pl.LightningModule): method __init__ (line 16) | def __init__(self, method init_test (line 58) | def init_test(self,): method init_from_ckpt (line 87) | def init_from_ckpt(self, path, ignore_keys=list()): method encode (line 104) | def encode(self, x, return_hidden_states=False, **kwargs): method decode (line 116) | def decode(self, z, **kwargs): method forward (line 122) | def forward(self, input, sample_posterior=True, **additional_decode_kw... method _forward (line 127) | def _forward(self, input, sample_posterior=True, **additional_decode_k... method get_input (line 137) | def get_input(self, batch, k): method training_step (line 147) | def training_step(self, batch, batch_idx, optimizer_idx): method validation_step (line 168) | def validation_step(self, batch, batch_idx): method configure_optimizers (line 182) | def configure_optimizers(self): method get_last_layer (line 193) | def get_last_layer(self): method log_images (line 197) | def log_images(self, batch, only_inputs=False, **kwargs): method to_rgb (line 213) | def to_rgb(self, x): class IdentityFirstStage (line 222) | class IdentityFirstStage(torch.nn.Module): method __init__ (line 223) | def __init__(self, *args, vq_interface=False, **kwargs): method encode (line 227) | def encode(self, x, *args, **kwargs): method decode (line 230) | def decode(self, x, *args, **kwargs): method quantize (line 233) | def quantize(self, x, *args, **kwargs): method forward (line 238) | def forward(self, x, *args, **kwargs): class AutoencoderKL_Dualref (line 245) | class AutoencoderKL_Dualref(AutoencoderKL): method __init__ (line 246) | def __init__(self, method _forward (line 266) | def _forward(self, input, sample_posterior=True, **additional_decode_k... FILE: ToonCrafter/lvdm/models/autoencoder_dualref.py function nonlinearity (line 24) | def nonlinearity(x): function Normalize (line 29) | def Normalize(in_channels, num_groups=32): class ResnetBlock (line 35) | class ResnetBlock(nn.Module): method __init__ (line 36) | def __init__( method forward (line 72) | def forward(self, x, temb): class LinAttnBlock (line 95) | class LinAttnBlock(LinearAttention): method __init__ (line 98) | def __init__(self, in_channels): class AttnBlock (line 102) | class AttnBlock(nn.Module): method __init__ (line 103) | def __init__(self, in_channels): method attention (line 121) | def attention(self, h_: torch.Tensor) -> torch.Tensor: method forward (line 138) | def forward(self, x, **kwargs): class MemoryEfficientAttnBlock (line 145) | class MemoryEfficientAttnBlock(nn.Module): method __init__ (line 153) | def __init__(self, in_channels): method attention (line 172) | def attention(self, h_: torch.Tensor) -> torch.Tensor: method forward (line 202) | def forward(self, x, **kwargs): class CrossAttentionWrapper (line 209) | class CrossAttentionWrapper(CrossAttention): method forward (line 210) | def forward(self, x, context=None, mask=None, **unused_kwargs): class MemoryEfficientCrossAttentionWrapper (line 218) | class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention): method forward (line 219) | def forward(self, x, context=None, mask=None, **unused_kwargs): function make_attn (line 227) | def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): class CrossAttentionWrapperFusion (line 274) | class CrossAttentionWrapperFusion(CrossAttention): method __init__ (line 275) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ... method forward (line 282) | def forward(self, x, context=None, mask=None): method _forward (line 288) | def _forward( class MemoryEfficientCrossAttentionWrapperFusion (line 345) | class MemoryEfficientCrossAttentionWrapperFusion(MemoryEfficientCrossAtt... method __init__ (line 347) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ... method forward (line 353) | def forward(self, x, context=None, mask=None): method _forward (line 359) | def _forward( class Combiner (line 431) | class Combiner(nn.Module): method __init__ (line 432) | def __init__(self, ch) -> None: method forward (line 439) | def forward(self, x, context): method _forward (line 445) | def _forward(self, x, context): class Decoder (line 459) | class Decoder(nn.Module): method __init__ (line 460) | def __init__( method _make_attn (line 568) | def _make_attn(self) -> Callable: method _make_resblock (line 571) | def _make_resblock(self) -> Callable: method _make_conv (line 574) | def _make_conv(self) -> Callable: method get_last_layer (line 577) | def get_last_layer(self, **kwargs): method forward (line 580) | def forward(self, z, ref_context=None, **kwargs): class TimestepBlock (line 636) | class TimestepBlock(nn.Module): method forward (line 642) | def forward(self, x: torch.Tensor, emb: torch.Tensor): class ResBlock (line 648) | class ResBlock(TimestepBlock): method __init__ (line 664) | def __init__( method forward (line 754) | def forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: method _forward (line 766) | def _forward(self, x: torch.Tensor, emb: torch.Tensor) -> torch.Tensor: class VideoTransformerBlock (line 800) | class VideoTransformerBlock(nn.Module): method __init__ (line 806) | def __init__( method forward (line 888) | def forward( method _forward (line 896) | def _forward(self, x, context=None, timesteps=None): method get_last_layer (line 929) | def get_last_layer(self): function partialclass (line 939) | def partialclass(cls, *args, **kwargs): class VideoResBlock (line 947) | class VideoResBlock(ResnetBlock): method __init__ (line 948) | def __init__( method get_alpha (line 985) | def get_alpha(self, bs): method forward (line 993) | def forward(self, x, temb, skip_video=False, timesteps=None): class AE3DConv (line 1015) | class AE3DConv(torch.nn.Conv2d): method __init__ (line 1016) | def __init__(self, in_channels, out_channels, video_kernel_size=3, *ar... method forward (line 1030) | def forward(self, input, timesteps, skip_video=False): class VideoBlock (line 1039) | class VideoBlock(AttnBlock): method __init__ (line 1040) | def __init__( method forward (line 1071) | def forward(self, x, timesteps, skip_video=False): method get_alpha (line 1098) | def get_alpha( class MemoryEfficientVideoBlock (line 1109) | class MemoryEfficientVideoBlock(MemoryEfficientAttnBlock): method __init__ (line 1110) | def __init__( method forward (line 1141) | def forward(self, x, timesteps, skip_time_block=False): method get_alpha (line 1168) | def get_alpha( function make_time_attn (line 1179) | def make_time_attn( class Conv2DWrapper (line 1217) | class Conv2DWrapper(torch.nn.Conv2d): method forward (line 1218) | def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: class VideoDecoder (line 1222) | class VideoDecoder(Decoder): method __init__ (line 1225) | def __init__( method get_last_layer (line 1243) | def get_last_layer(self, skip_time_mix=False, **kwargs): method _make_attn (line 1253) | def _make_attn(self) -> Callable: method _make_conv (line 1263) | def _make_conv(self) -> Callable: method _make_resblock (line 1269) | def _make_resblock(self) -> Callable: FILE: ToonCrafter/lvdm/models/ddpm3d.py class DDPM (line 42) | class DDPM(pl.LightningModule): method __init__ (line 44) | def __init__(self, method register_schedule (line 125) | def register_schedule(self, given_betas=None, beta_schedule="linear", ... method ema_scope (line 191) | def ema_scope(self, context=None): method init_from_ckpt (line 205) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False): method q_mean_variance (line 223) | def q_mean_variance(self, x_start, t): method predict_start_from_noise (line 235) | def predict_start_from_noise(self, x_t, t, noise): method predict_start_from_z_and_v (line 241) | def predict_start_from_z_and_v(self, x_t, t, v): method predict_eps_from_z_and_v (line 249) | def predict_eps_from_z_and_v(self, x_t, t, v): method q_posterior (line 255) | def q_posterior(self, x_start, x_t, t): method p_mean_variance (line 264) | def p_mean_variance(self, x, t, clip_denoised: bool): method p_sample (line 277) | def p_sample(self, x, t, clip_denoised=True, repeat_noise=False): method p_sample_loop (line 286) | def p_sample_loop(self, shape, return_intermediates=False): method sample (line 301) | def sample(self, batch_size=16, return_intermediates=False): method q_sample (line 307) | def q_sample(self, x_start, t, noise=None): method get_v (line 312) | def get_v(self, x, noise, t): method get_loss (line 318) | def get_loss(self, pred, target, mean=True): method p_losses (line 333) | def p_losses(self, x_start, t, noise=None): method forward (line 364) | def forward(self, x, *args, **kwargs): method get_input (line 370) | def get_input(self, batch, k): method shared_step (line 380) | def shared_step(self, batch): method training_step (line 385) | def training_step(self, batch, batch_idx): method validation_step (line 401) | def validation_step(self, batch, batch_idx): method on_train_batch_end (line 409) | def on_train_batch_end(self, *args, **kwargs): method _get_rows_from_list (line 413) | def _get_rows_from_list(self, samples): method log_images (line 421) | def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=Non... method configure_optimizers (line 458) | def configure_optimizers(self): class LatentDiffusion (line 466) | class LatentDiffusion(DDPM): method __init__ (line 468) | def __init__(self, method make_cond_schedule (line 550) | def make_cond_schedule(self, ): method on_train_batch_start (line 557) | def on_train_batch_start(self, batch, batch_idx, dataloader_idx=None): method register_schedule (line 574) | def register_schedule(self, given_betas=None, beta_schedule="linear", ... method instantiate_first_stage (line 582) | def instantiate_first_stage(self, config): method instantiate_cond_stage (line 589) | def instantiate_cond_stage(self, config): method get_learned_conditioning (line 600) | def get_learned_conditioning(self, c): method get_first_stage_encoding (line 613) | def get_first_stage_encoding(self, encoder_posterior, noise=None): method encode_first_stage (line 623) | def encode_first_stage(self, x): method decode_core (line 648) | def decode_core(self, z, **kwargs): method decode_first_stage (line 692) | def decode_first_stage(self, z, **kwargs): method differentiable_decode_first_stage (line 696) | def differentiable_decode_first_stage(self, z, **kwargs): method get_batch_input (line 700) | def get_batch_input(self, batch, random_uncond, return_first_stage_out... method forward (line 733) | def forward(self, x, c, **kwargs): method shared_step (line 739) | def shared_step(self, batch, random_uncond, **kwargs): method apply_model (line 745) | def apply_model(self, x_noisy, t, cond, **kwargs): method p_losses (line 762) | def p_losses(self, x_start, cond, t, noise=None, **kwargs): method training_step (line 808) | def training_step(self, batch, batch_idx): method _get_denoise_row_from_list (line 822) | def _get_denoise_row_from_list(self, samples, desc=''): method log_images (line 847) | def log_images(self, batch, sample=True, ddim_steps=200, ddim_eta=1., ... method p_mean_variance (line 902) | def p_mean_variance(self, x, c, t, clip_denoised: bool, return_x0=Fals... method p_sample (line 928) | def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False, r... method p_sample_loop (line 950) | def p_sample_loop(self, cond, shape, return_intermediates=False, x_T=N... method sample (line 997) | def sample(self, cond, batch_size=16, return_intermediates=False, x_T=... method sample_log (line 1014) | def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs): method configure_schedulers (line 1025) | def configure_schedulers(self, optimizer): class LatentVisualDiffusion (line 1051) | class LatentVisualDiffusion(LatentDiffusion): method __init__ (line 1052) | def __init__(self, img_cond_stage_config, image_proj_stage_config, fre... method _init_img_ctx_projector (line 1058) | def _init_img_ctx_projector(self, config, trainable): method _init_embedder (line 1066) | def _init_embedder(self, config, freeze=True): method shared_step (line 1074) | def shared_step(self, batch, random_uncond, **kwargs): method get_batch_input (line 1080) | def get_batch_input(self, batch, random_uncond, return_first_stage_out... method log_images (line 1147) | def log_images(self, batch, sample=True, ddim_steps=50, ddim_eta=1., p... method configure_optimizers (line 1218) | def configure_optimizers(self): class DiffusionWrapper (line 1253) | class DiffusionWrapper(pl.LightningModule): method __init__ (line 1254) | def __init__(self, diff_model_config, conditioning_key): method forward (line 1259) | def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, FILE: ToonCrafter/lvdm/models/samplers/ddim.py class DDIMSampler (line 10) | class DDIMSampler(object): method __init__ (line 11) | 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 60) | def sample(self, method ddim_sampling (line 135) | def ddim_sampling(self, cond, shape, method p_sample_ddim (line 206) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin... method decode (line 282) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale... method stochastic_encode (line 305) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): FILE: ToonCrafter/lvdm/models/samplers/ddim_multiplecond.py class DDIMSampler (line 10) | class DDIMSampler(object): method __init__ (line 11) | 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 60) | def sample(self, method ddim_sampling (line 138) | def ddim_sampling(self, cond, shape, method p_sample_ddim (line 211) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin... method decode (line 288) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale... method stochastic_encode (line 310) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): FILE: ToonCrafter/lvdm/models/utils_diffusion.py function timestep_embedding (line 8) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal... function make_beta_schedule (line 31) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e... function make_ddim_timesteps (line 56) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_... function make_ddim_sampling_parameters (line 79) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos... function betas_for_alpha_bar (line 94) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9... function rescale_zero_terminal_snr (line 112) | def rescale_zero_terminal_snr(betas): function rescale_noise_cfg (line 147) | def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): FILE: ToonCrafter/lvdm/modules/attention.py class RelativePosition (line 20) | class RelativePosition(nn.Module): method __init__ (line 23) | def __init__(self, num_units, max_relative_position): method forward (line 30) | def forward(self, length_q, length_k): class CrossAttention (line 42) | class CrossAttention(nn.Module): method __init__ (line 44) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ... method forward (line 81) | def forward(self, x, context=None, mask=None): method efficient_forward (line 146) | def efficient_forward(self, x, context=None, mask=None): class BasicTransformerBlock (line 212) | class BasicTransformerBlock(nn.Module): method __init__ (line 214) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,... method forward (line 231) | def forward(self, x, context=None, mask=None, **kwargs): method _forward (line 242) | def _forward(self, x, context=None, mask=None): class SpatialTransformer (line 249) | class SpatialTransformer(nn.Module): method __init__ (line 259) | def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., ... method forward (line 294) | def forward(self, x, context=None, **kwargs): class TemporalTransformer (line 313) | class TemporalTransformer(nn.Module): method __init__ (line 320) | def __init__(self, in_channels, n_heads, d_head, depth=1, dropout=0., ... method forward (line 365) | def forward(self, x, context=None): class GEGLU (line 415) | class GEGLU(nn.Module): method __init__ (line 416) | def __init__(self, dim_in, dim_out): method forward (line 420) | def forward(self, x): class FeedForward (line 425) | class FeedForward(nn.Module): method __init__ (line 426) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): method forward (line 441) | def forward(self, x): class LinearAttention (line 445) | class LinearAttention(nn.Module): method __init__ (line 446) | def __init__(self, dim, heads=4, dim_head=32): method forward (line 453) | def forward(self, x): class SpatialSelfAttention (line 464) | class SpatialSelfAttention(nn.Module): method __init__ (line 465) | def __init__(self, in_channels): method forward (line 491) | def forward(self, x): FILE: ToonCrafter/lvdm/modules/attention_svd.py function exists (line 61) | def exists(val): function uniq (line 65) | def uniq(arr): function default (line 69) | def default(val, d): function max_neg_value (line 75) | def max_neg_value(t): function init_ (line 79) | def init_(tensor): class GEGLU (line 87) | class GEGLU(nn.Module): method __init__ (line 88) | def __init__(self, dim_in, dim_out): method forward (line 92) | def forward(self, x): class FeedForward (line 97) | class FeedForward(nn.Module): method __init__ (line 98) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0): method forward (line 112) | def forward(self, x): function zero_module (line 116) | def zero_module(module): function Normalize (line 125) | def Normalize(in_channels): class LinearAttention (line 131) | class LinearAttention(nn.Module): method __init__ (line 132) | def __init__(self, dim, heads=4, dim_head=32): method forward (line 139) | def forward(self, x): class SelfAttention (line 154) | class SelfAttention(nn.Module): method __init__ (line 157) | def __init__( method forward (line 182) | def forward(self, x: torch.Tensor) -> torch.Tensor: class SpatialSelfAttention (line 211) | class SpatialSelfAttention(nn.Module): method __init__ (line 212) | def __init__(self, in_channels): method forward (line 230) | def forward(self, x): class CrossAttention (line 256) | class CrossAttention(nn.Module): method __init__ (line 257) | def __init__( method forward (line 282) | def forward( class MemoryEfficientCrossAttention (line 348) | class MemoryEfficientCrossAttention(nn.Module): method __init__ (line 350) | def __init__( method forward (line 374) | def forward( class BasicTransformerBlock (line 457) | class BasicTransformerBlock(nn.Module): method __init__ (line 463) | def __init__( method forward (line 528) | def forward( method _forward (line 552) | def _forward( class BasicTransformerSingleLayerBlock (line 576) | class BasicTransformerSingleLayerBlock(nn.Module): method __init__ (line 583) | def __init__( method forward (line 609) | def forward(self, x, context=None): method _forward (line 614) | def _forward(self, x, context=None): class SpatialTransformer (line 620) | class SpatialTransformer(nn.Module): method __init__ (line 630) | def __init__( method forward (line 703) | def forward(self, x, context=None): class SimpleTransformer (line 727) | class SimpleTransformer(nn.Module): method __init__ (line 728) | def __init__( method forward (line 753) | def forward( FILE: ToonCrafter/lvdm/modules/encoders/condition.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 IdentityEncoder (line 20) | class IdentityEncoder(AbstractEncoder): method encode (line 21) | def encode(self, x): class ClassEmbedder (line 25) | class ClassEmbedder(nn.Module): method __init__ (line 26) | def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1): method forward (line 33) | def forward(self, batch, key=None, disable_dropout=False): method get_unconditional_conditioning (line 45) | def get_unconditional_conditioning(self, bs, device="cuda"): function disabled_train (line 52) | def disabled_train(self, mode=True): function get_available_devices (line 58) | def get_available_devices(): function get_device (line 68) | def get_device(device): class FrozenT5Embedder (line 75) | class FrozenT5Embedder(AbstractEncoder): method __init__ (line 78) | def __init__(self, version="google/t5-v1_1-large", device="cuda", max_... method freeze (line 88) | def freeze(self): method forward (line 94) | def forward(self, text): method encode (line 103) | def encode(self, text): class FrozenCLIPEmbedder (line 107) | class FrozenCLIPEmbedder(AbstractEncoder): method __init__ (line 115) | def __init__(self, version="openai/clip-vit-large-patch14", device="cu... method freeze (line 131) | def freeze(self): method forward (line 137) | def forward(self, text): method encode (line 150) | def encode(self, text): class ClipImageEmbedder (line 154) | class ClipImageEmbedder(nn.Module): method __init__ (line 155) | def __init__( method preprocess (line 173) | def preprocess(self, x): method forward (line 183) | def forward(self, x, no_dropout=False): class FrozenOpenCLIPEmbedder (line 192) | class FrozenOpenCLIPEmbedder(AbstractEncoder): method __init__ (line 202) | def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", devic... method freeze (line 223) | def freeze(self): method forward (line 228) | def forward(self, text): method encode_with_transformer (line 233) | def encode_with_transformer(self, text): method text_transformer_forward (line 242) | def text_transformer_forward(self, x: torch.Tensor, attn_mask=None): method encode (line 252) | def encode(self, text): class FrozenOpenCLIPImageEmbedder (line 256) | class FrozenOpenCLIPImageEmbedder(AbstractEncoder): method __init__ (line 261) | def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", devic... method preprocess (line 285) | def preprocess(self, x): method freeze (line 295) | def freeze(self): method forward (line 301) | def forward(self, image, no_dropout=False): method encode_with_vision_transformer (line 307) | def encode_with_vision_transformer(self, img): method encode (line 312) | def encode(self, text): class FrozenOpenCLIPImageEmbedderV2 (line 316) | class FrozenOpenCLIPImageEmbedderV2(AbstractEncoder): method __init__ (line 321) | def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", devic... method preprocess (line 343) | def preprocess(self, x): method freeze (line 353) | def freeze(self): method forward (line 358) | def forward(self, image, no_dropout=False): method encode_with_vision_transformer (line 363) | def encode_with_vision_transformer(self, x): class FrozenCLIPT5Encoder (line 396) | class FrozenCLIPT5Encoder(AbstractEncoder): method __init__ (line 397) | def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_ve... method encode (line 405) | def encode(self, text): method forward (line 408) | def forward(self, text): FILE: ToonCrafter/lvdm/modules/encoders/resampler.py class ImageProjModel (line 9) | class ImageProjModel(nn.Module): method __init__ (line 11) | def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024,... method forward (line 18) | def forward(self, image_embeds): function FeedForward (line 27) | def FeedForward(dim, mult=4): function reshape_tensor (line 37) | def reshape_tensor(x, heads): class PerceiverAttention (line 48) | class PerceiverAttention(nn.Module): method __init__ (line 49) | def __init__(self, *, dim, dim_head=64, heads=8): method forward (line 64) | def forward(self, x, latents): class Resampler (line 96) | class Resampler(nn.Module): method __init__ (line 97) | def __init__( method forward (line 134) | def forward(self, x): FILE: ToonCrafter/lvdm/modules/networks/ae_modules.py function nonlinearity (line 13) | def nonlinearity(x): function Normalize (line 18) | def Normalize(in_channels, num_groups=32): class LinAttnBlock (line 22) | class LinAttnBlock(LinearAttention): method __init__ (line 25) | def __init__(self, in_channels): class AttnBlock (line 29) | class AttnBlock(nn.Module): method __init__ (line 30) | def __init__(self, in_channels): method forward (line 56) | def forward(self, x): function make_attn (line 84) | def make_attn(in_channels, attn_type="vanilla"): class Downsample (line 95) | class Downsample(nn.Module): method __init__ (line 96) | def __init__(self, in_channels, with_conv): method forward (line 108) | def forward(self, x): class Upsample (line 118) | class Upsample(nn.Module): method __init__ (line 119) | def __init__(self, in_channels, with_conv): method forward (line 130) | def forward(self, x): function get_timestep_embedding (line 137) | def get_timestep_embedding(timesteps, embedding_dim): class ResnetBlock (line 158) | class ResnetBlock(nn.Module): method __init__ (line 159) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa... method forward (line 197) | def forward(self, x, temb): class Model (line 220) | class Model(nn.Module): method __init__ (line 221) | def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, method forward (line 321) | def forward(self, x, t=None, context=None): method get_last_layer (line 369) | def get_last_layer(self): class Encoder (line 373) | class Encoder(nn.Module): method __init__ (line 374) | def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, method forward (line 440) | def forward(self, x, return_hidden_states=False): class Decoder (line 486) | class Decoder(nn.Module): method __init__ (line 487) | def __init__(self, *, ch, out_ch, ch_mult=(1, 2, 4, 8), num_res_blocks, method forward (line 560) | def forward(self, z): class SimpleDecoder (line 602) | class SimpleDecoder(nn.Module): method __init__ (line 603) | def __init__(self, in_channels, out_channels, *args, **kwargs): method forward (line 625) | def forward(self, x): class UpsampleDecoder (line 638) | class UpsampleDecoder(nn.Module): method __init__ (line 639) | def __init__(self, in_channels, out_channels, ch, num_res_blocks, reso... method forward (line 672) | def forward(self, x): class LatentRescaler (line 686) | class LatentRescaler(nn.Module): method __init__ (line 687) | def __init__(self, factor, in_channels, mid_channels, out_channels, de... method forward (line 711) | def forward(self, x): class MergedRescaleEncoder (line 723) | class MergedRescaleEncoder(nn.Module): method __init__ (line 724) | def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks, method forward (line 736) | def forward(self, x): class MergedRescaleDecoder (line 742) | class MergedRescaleDecoder(nn.Module): method __init__ (line 743) | def __init__(self, z_channels, out_ch, resolution, num_res_blocks, att... method forward (line 753) | def forward(self, x): class Upsampler (line 759) | class Upsampler(nn.Module): method __init__ (line 760) | def __init__(self, in_size, out_size, in_channels, out_channels, ch_mu... method forward (line 772) | def forward(self, x): class Resize (line 778) | class Resize(nn.Module): method __init__ (line 779) | def __init__(self, in_channels=None, learned=False, mode="bilinear"): method forward (line 794) | def forward(self, x, scale_factor=1.0): class FirstStagePostProcessor (line 802) | class FirstStagePostProcessor(nn.Module): method __init__ (line 804) | def __init__(self, ch_mult: list, in_channels, method instantiate_pretrained (line 838) | def instantiate_pretrained(self, config): method encode_with_pretrained (line 846) | def encode_with_pretrained(self, x): method forward (line 852) | def forward(self, x): FILE: ToonCrafter/lvdm/modules/networks/openaimodel3d.py class TimestepBlock (line 19) | class TimestepBlock(nn.Module): method forward (line 24) | def forward(self, x, emb): class TimestepEmbedSequential (line 30) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock): method forward (line 36) | def forward(self, x, emb, context=None, batch_size=None): class Downsample (line 51) | class Downsample(nn.Module): method __init__ (line 60) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd... method forward (line 75) | def forward(self, x): class Upsample (line 80) | class Upsample(nn.Module): method __init__ (line 89) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd... method forward (line 98) | def forward(self, x): class ResBlock (line 109) | class ResBlock(TimestepBlock): method __init__ (line 126) | def __init__( method forward (line 197) | def forward(self, x, emb, batch_size=None): method _forward (line 210) | def _forward(self, x, emb, batch_size=None): class TemporalConvBlock (line 239) | class TemporalConvBlock(nn.Module): method __init__ (line 243) | def __init__(self, in_channels, out_channels=None, dropout=0.0, spatia... method forward (line 272) | def forward(self, x): class UNetModel (line 281) | class UNetModel(nn.Module): method __init__ (line 311) | def __init__(self, method forward (line 548) | def forward(self, x, timesteps, context=None, features_adapter=None, f... FILE: ToonCrafter/lvdm/modules/x_transformer.py class AbsolutePositionalEmbedding (line 24) | class AbsolutePositionalEmbedding(nn.Module): method __init__ (line 25) | def __init__(self, dim, max_seq_len): method init_ (line 30) | def init_(self): method forward (line 33) | def forward(self, x): class FixedPositionalEmbedding (line 38) | class FixedPositionalEmbedding(nn.Module): method __init__ (line 39) | def __init__(self, dim): method forward (line 44) | def forward(self, x, seq_dim=1, offset=0): function exists (line 53) | def exists(val): function default (line 57) | def default(val, d): function always (line 63) | def always(val): function not_equals (line 69) | def not_equals(val): function equals (line 75) | def equals(val): function max_neg_value (line 81) | def max_neg_value(tensor): function pick_and_pop (line 87) | def pick_and_pop(keys, d): function group_dict_by_key (line 92) | def group_dict_by_key(cond, d): function string_begins_with (line 101) | def string_begins_with(prefix, str): function group_by_key_prefix (line 105) | def group_by_key_prefix(prefix, d): function groupby_prefix_and_trim (line 109) | def groupby_prefix_and_trim(prefix, d): class Scale (line 116) | class Scale(nn.Module): method __init__ (line 117) | def __init__(self, value, fn): method forward (line 122) | def forward(self, x, **kwargs): class Rezero (line 127) | class Rezero(nn.Module): method __init__ (line 128) | def __init__(self, fn): method forward (line 133) | def forward(self, x, **kwargs): class ScaleNorm (line 138) | class ScaleNorm(nn.Module): method __init__ (line 139) | def __init__(self, dim, eps=1e-5): method forward (line 145) | def forward(self, x): class RMSNorm (line 150) | class RMSNorm(nn.Module): method __init__ (line 151) | def __init__(self, dim, eps=1e-8): method forward (line 157) | def forward(self, x): class Residual (line 162) | class Residual(nn.Module): method forward (line 163) | def forward(self, x, residual): class GRUGating (line 167) | class GRUGating(nn.Module): method __init__ (line 168) | def __init__(self, dim): method forward (line 172) | def forward(self, x, residual): class GEGLU (line 183) | class GEGLU(nn.Module): method __init__ (line 184) | def __init__(self, dim_in, dim_out): method forward (line 188) | def forward(self, x): class FeedForward (line 193) | class FeedForward(nn.Module): method __init__ (line 194) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): method forward (line 209) | def forward(self, x): class Attention (line 214) | class Attention(nn.Module): method __init__ (line 215) | def __init__( method forward (line 267) | def forward( class AttentionLayers (line 369) | class AttentionLayers(nn.Module): method __init__ (line 370) | def __init__( method forward (line 480) | def forward( class Encoder (line 540) | class Encoder(AttentionLayers): method __init__ (line 541) | def __init__(self, **kwargs): class TransformerWrapper (line 547) | class TransformerWrapper(nn.Module): method __init__ (line 548) | def __init__( method init_ (line 594) | def init_(self): method forward (line 597) | def forward( FILE: ToonCrafter/main/callbacks.py class ImageLogger (line 15) | class ImageLogger(Callback): method __init__ (line 16) | def __init__(self, batch_frequency, max_images=8, clamp=True, rescale=... method log_to_tensorboard (line 31) | def log_to_tensorboard(self, pl_module, batch_logs, filename, split, s... method log_batch_imgs (line 58) | def log_batch_imgs(self, pl_module, batch, batch_idx, split="train"): method on_train_batch_end (line 90) | def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch... method on_validation_batch_end (line 94) | def on_validation_batch_end(self, trainer, pl_module, outputs, batch, ... class CUDACallback (line 104) | class CUDACallback(Callback): method on_train_epoch_start (line 106) | def on_train_epoch_start(self, trainer, pl_module): method on_train_epoch_end (line 117) | def on_train_epoch_end(self, trainer, pl_module): FILE: ToonCrafter/main/trainer.py function get_parser (line 14) | def get_parser(**parser_kwargs): function get_nondefault_trainer_args (line 33) | def get_nondefault_trainer_args(args): function melk (line 129) | def melk(*args, **kwargs): function divein (line 136) | def divein(*args, **kwargs): FILE: ToonCrafter/main/utils_data.py function worker_init_fn (line 15) | def worker_init_fn(_): class WrappedDataset (line 31) | class WrappedDataset(Dataset): method __init__ (line 34) | def __init__(self, dataset): method __len__ (line 37) | def __len__(self): method __getitem__ (line 40) | def __getitem__(self, idx): class DataModuleFromConfig (line 44) | class DataModuleFromConfig(pl.LightningDataModule): method __init__ (line 45) | def __init__(self, batch_size, train=None, validation=None, test=None,... method prepare_data (line 72) | def prepare_data(self): method setup (line 75) | def setup(self, stage=None): method _train_dataloader (line 81) | def _train_dataloader(self): method _val_dataloader (line 93) | def _val_dataloader(self, shuffle=False): method _test_dataloader (line 106) | def _test_dataloader(self, shuffle=False): method _predict_dataloader (line 128) | def _predict_dataloader(self, shuffle=False): FILE: ToonCrafter/main/utils_train.py function init_workspace (line 9) | def init_workspace(name, logdir, model_config, lightning_config, rank=0): function check_config_attribute (line 28) | def check_config_attribute(config, name): function get_trainer_callbacks (line 35) | def get_trainer_callbacks(lightning_config, config, logdir, ckptdir, log... function get_trainer_logger (line 99) | def get_trainer_logger(lightning_config, logdir, on_debug): function get_trainer_strategy (line 125) | def get_trainer_strategy(lightning_config): function load_checkpoints (line 138) | def load_checkpoints(model, model_cfg): function set_logger (line 162) | def set_logger(logfile, name='mainlogger'): FILE: ToonCrafter/scripts/evaluation/ddp_wrapper.py function setup_dist (line 8) | def setup_dist(local_rank): function get_dist_info (line 15) | def get_dist_info(): FILE: ToonCrafter/scripts/evaluation/funcs.py function batch_ddim_sampling (line 17) | def batch_ddim_sampling(model: LatentDiffusion, cond, noise_shape, n_sam... function get_filelist (line 99) | def get_filelist(data_dir, ext='*'): function get_dirlist (line 105) | def get_dirlist(path): function load_model_checkpoint (line 117) | def load_model_checkpoint(model, ckpt): function load_prompts (line 155) | def load_prompts(prompt_file): function load_video_batch (line 166) | def load_video_batch(filepath_list, frame_stride, video_size=(256, 256),... function load_image_batch (line 208) | def load_image_batch(filepath_list, image_size=(256, 256)): function save_videos (line 232) | def save_videos(batch_tensors, savedir, filenames, fps=10): function get_latent_z (line 247) | def get_latent_z(model, videos): FILE: ToonCrafter/scripts/evaluation/inference.py function get_filelist (line 19) | def get_filelist(data_dir, postfixes): function load_model_checkpoint (line 27) | def load_model_checkpoint(model, ckpt): function load_prompts (line 54) | def load_prompts(prompt_file): function load_data_prompts (line 64) | def load_data_prompts(data_dir, video_size=(256,256), video_frames=16, i... function save_results (line 109) | def save_results(prompt, samples, filename, fakedir, fps=8, loop=False): function save_results_seperate (line 135) | def save_results_seperate(prompt, samples, filename, fakedir, fps=10, lo... function get_latent_z (line 157) | def get_latent_z(model, videos): function get_latent_z_with_hidden_states (line 164) | def get_latent_z_with_hidden_states(model, videos): function image_guided_synthesis (line 180) | def image_guided_synthesis(model, prompts, videos, noise_shape, n_sample... function run_inference (line 277) | def run_inference(args, gpu_num, gpu_no): function get_parser (line 344) | def get_parser(): FILE: ToonCrafter/scripts/gradio/i2v_test.py class Image2Video (line 13) | class Image2Video(): method __init__ (line 14) | def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') ... method get_image (line 37) | def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, f... method download_model (line 94) | def download_model(self): FILE: ToonCrafter/scripts/gradio/i2v_test_application.py class Image2Video (line 13) | class Image2Video(): method __init__ (line 14) | def __init__(self,result_dir='./tmp/',gpu_num=1,resolution='256_256') ... method get_image (line 38) | def get_image(self, image, prompt, steps=50, cfg_scale=7.5, eta=1.0, f... method download_model (line 117) | def download_model(self): method get_latent_z_with_hidden_states (line 127) | def get_latent_z_with_hidden_states(self, model, videos): FILE: ToonCrafter/utils/save_video.py function frames_to_mp4 (line 14) | def frames_to_mp4(frame_dir,output_path,fps): function tensor_to_mp4 (line 27) | def tensor_to_mp4(video, savepath, fps, rescale=True, nrow=None): function tensor2videogrids (line 44) | def tensor2videogrids(video, root, filename, fps, rescale=True, clamp=Tr... function log_local (line 62) | def log_local(batch_logs, save_dir, filename, save_fps=10, rescale=True): function prepare_to_log (line 120) | def prepare_to_log(batch_logs, max_images=100000, clamp=True): function fill_with_black_squares (line 140) | def fill_with_black_squares(video, desired_len: int) -> Tensor: function load_num_videos (line 150) | def load_num_videos(data_path, num_videos): function npz_to_video_grid (line 163) | def npz_to_video_grid(data_path, out_path, num_frames, fps, num_videos=N... FILE: ToonCrafter/utils/utils.py function count_params (line 9) | def count_params(model, verbose=False): function check_istarget (line 16) | def check_istarget(name, para_list): function instantiate_from_config (line 28) | def instantiate_from_config(config): function get_obj_from_str (line 38) | def get_obj_from_str(string, reload=False): function load_npz_from_dir (line 46) | def load_npz_from_dir(data_dir): function load_npz_from_paths (line 52) | def load_npz_from_paths(data_paths): function resize_numpy_image (line 58) | def resize_numpy_image(image, max_resolution=512 * 512, resize_short_edg... function setup_dist (line 71) | def setup_dist(args): FILE: __init__.py function instantiate_from_config (line 36) | def instantiate_from_config(config): function get_obj_from_str (line 46) | def get_obj_from_str(string, reload=False): function get_state_dict (line 54) | def get_state_dict(d): function load_state_dict (line 58) | def load_state_dict(ckpt_path, location='cpu'): function get_models (line 71) | def get_models(root: Path = ROOT.joinpath("checkpoints"), ignoreed: tupl... class ToonCrafterNode (line 83) | class ToonCrafterNode: method INPUT_TYPES (line 86) | def INPUT_TYPES(s): method init (line 111) | def init(self, ckpt_name="", result_dir=ROOT.joinpath("tmp/"), gpu_num... method optional_autocast (line 144) | def optional_autocast(device): method get_image (line 152) | def get_image(self, image: torch.Tensor, ckpt_name, vram_opt_strategy,... method save_videos (line 266) | def save_videos(self, batch_tensors, savedir, filenames, fps=10): method download_model (line 281) | def download_model(self): method get_latent_z_with_hidden_states (line 291) | def get_latent_z_with_hidden_states(self, model, videos): class ToonCrafterWithSketch (line 308) | class ToonCrafterWithSketch(ToonCrafterNode): method INPUT_TYPES (line 311) | def INPUT_TYPES(s): method init (line 337) | def init(self, ckpt_name="", result_dir=ROOT.joinpath("tmp/"), gpu_num... method get_image (line 380) | def get_image(self, image: torch.Tensor, ckpt_name, vram_opt_strategy,... FILE: pre_run.py function run (line 9) | def run():