SYMBOL INDEX (535 symbols across 53 files) FILE: ADD/dnnlib/util.py class EasyDict (line 39) | class EasyDict(dict): method __getattr__ (line 42) | def __getattr__(self, name: str) -> Any: method __setattr__ (line 48) | def __setattr__(self, name: str, value: Any) -> None: method __delattr__ (line 51) | def __delattr__(self, name: str) -> None: class Logger (line 55) | class Logger(object): method __init__ (line 58) | def __init__(self, file_name: Optional[str] = None, file_mode: str = "... method __enter__ (line 71) | def __enter__(self) -> "Logger": method __exit__ (line 74) | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> N... method write (line 77) | def write(self, text: Union[str, bytes]) -> None: method flush (line 92) | def flush(self) -> None: method close (line 99) | def close(self) -> None: function set_cache_dir (line 119) | def set_cache_dir(path: str) -> None: function make_cache_dir_path (line 124) | def make_cache_dir_path(*paths: str) -> str: function format_time (line 139) | def format_time(seconds: Union[int, float]) -> str: function format_time_brief (line 153) | def format_time_brief(seconds: Union[int, float]) -> str: function ask_yes_no (line 167) | def ask_yes_no(question: str) -> bool: function tuple_product (line 177) | def tuple_product(t: Tuple) -> Any: function get_dtype_and_ctype (line 201) | def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: function is_pickleable (line 224) | def is_pickleable(obj: Any) -> bool: function get_module_from_obj_name (line 236) | def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, s... function get_obj_from_module (line 277) | def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: function get_obj_by_name (line 287) | def get_obj_by_name(name: str) -> Any: function call_func_by_name (line 293) | def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: function construct_class_by_name (line 301) | def construct_class_by_name(*args, class_name: str = None, **kwargs) -> ... function get_module_dir_by_obj_name (line 306) | def get_module_dir_by_obj_name(obj_name: str) -> str: function is_top_level_function (line 312) | def is_top_level_function(obj: Any) -> bool: function get_top_level_function_name (line 317) | def get_top_level_function_name(obj: Any) -> str: function list_dir_recursively_with_ignore (line 329) | def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] =... function copy_files_and_create_dirs (line 362) | def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: function is_url (line 378) | def is_url(obj: Any, allow_file_urls: bool = False) -> bool: function open_url (line 396) | def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, ve... FILE: ADD/layers/attention.py class Attention (line 36) | class Attention(nn.Module): method __init__ (line 37) | def __init__( method forward (line 56) | def forward(self, x: Tensor) -> Tensor: class MemEffAttention (line 72) | class MemEffAttention(Attention): method forward (line 73) | def forward(self, x: Tensor, attn_bias=None) -> Tensor: FILE: ADD/layers/block.py class Block (line 43) | class Block(nn.Module): method __init__ (line 44) | def __init__( method forward (line 89) | def forward(self, x: Tensor) -> Tensor: function drop_add_residual_stochastic_depth (line 117) | def drop_add_residual_stochastic_depth( function get_branges_scales (line 141) | def get_branges_scales(x, sample_drop_ratio=0.0): function add_residual (line 149) | def add_residual(x, brange, residual, residual_scale_factor, scaling_vec... function get_attn_bias_and_cat (line 164) | def get_attn_bias_and_cat(x_list, branges=None): function drop_add_residual_stochastic_depth_list (line 188) | def drop_add_residual_stochastic_depth_list( class NestedTensorBlock (line 211) | class NestedTensorBlock(Block): method forward_nested (line 212) | def forward_nested(self, x_list: List[Tensor]) -> List[Tensor]: method forward (line 252) | def forward(self, x_or_x_list): FILE: ADD/layers/dino_head.py class DINOHead (line 12) | class DINOHead(nn.Module): method __init__ (line 13) | def __init__( method _init_weights (line 30) | def _init_weights(self, m): method forward (line 36) | def forward(self, x): function _build_mlp (line 44) | def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=... FILE: ADD/layers/drop_path.py function drop_path (line 14) | def drop_path(x, drop_prob: float = 0.0, training: bool = False): class DropPath (line 26) | class DropPath(nn.Module): method __init__ (line 29) | def __init__(self, drop_prob=None): method forward (line 33) | def forward(self, x): FILE: ADD/layers/layer_scale.py class LayerScale (line 15) | class LayerScale(nn.Module): method __init__ (line 16) | def __init__( method forward (line 26) | def forward(self, x: Tensor) -> Tensor: FILE: ADD/layers/mlp.py class Mlp (line 16) | class Mlp(nn.Module): method __init__ (line 17) | def __init__( method forward (line 34) | def forward(self, x: Tensor) -> Tensor: FILE: ADD/layers/patch_embed.py function make_2tuple (line 16) | def make_2tuple(x): class PatchEmbed (line 25) | class PatchEmbed(nn.Module): method __init__ (line 37) | def __init__( method forward (line 68) | def forward(self, x: Tensor) -> Tensor: FILE: ADD/layers/swiglu_ffn.py class SwiGLUFFN (line 14) | class SwiGLUFFN(nn.Module): method __init__ (line 15) | def __init__( method forward (line 30) | def forward(self, x: Tensor) -> Tensor: class SwiGLUFFNFused (line 54) | class SwiGLUFFNFused(SwiGLU): method __init__ (line 55) | def __init__( FILE: ADD/models/discriminator.py class SpectralConv1d (line 31) | class SpectralConv1d(nn.Conv1d): method __init__ (line 32) | def __init__(self, *args, **kwargs): class BatchNormLocal (line 37) | class BatchNormLocal(nn.Module): method __init__ (line 38) | def __init__(self, num_features: int, affine: bool = True, virtual_bs:... method forward (line 48) | def forward(self, x: torch.Tensor) -> torch.Tensor: function make_block (line 66) | def make_block(channels: int, kernel_size: int) -> nn.Module: class DiscHead (line 80) | class DiscHead(nn.Module): method __init__ (line 81) | def __init__(self, channels: int, c_dim: int, cmap_dim: int = 64): method forward (line 98) | def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: class DINO (line 108) | class DINO(torch.nn.Module): method __init__ (line 109) | def __init__(self, hooks: list[int] = [2,5,8,11], hook_patch: bool = T... method forward (line 125) | def forward(self, x: torch.Tensor) -> torch.Tensor: class ProjectedDiscriminator (line 133) | class ProjectedDiscriminator(nn.Module): method __init__ (line 134) | def __init__(self, c_dim: int, diffaug: bool = True, p_crop: float = 0... method train (line 147) | def train(self, mode: bool = True): method eval (line 152) | def eval(self): method forward (line 155) | def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: FILE: ADD/models/vit.py function named_apply (line 26) | def named_apply(fn: Callable, module: nn.Module, name="", depth_first=Tr... class BlockChunk (line 37) | class BlockChunk(nn.ModuleList): method forward (line 38) | def forward(self, x): class DinoVisionTransformer (line 44) | class DinoVisionTransformer(nn.Module): method __init__ (line 45) | def __init__( method init_weights (line 172) | def init_weights(self): method interpolate_pos_encoding (line 179) | def interpolate_pos_encoding(self, x, w, h): method prepare_tokens_with_masks (line 209) | def prepare_tokens_with_masks(self, x, masks=None): method forward_features_list (line 230) | def forward_features_list(self, x_list, masks_list): method forward_features (line 250) | def forward_features(self, x, masks=None): method _get_intermediate_layers_not_chunked (line 268) | def _get_intermediate_layers_not_chunked(self, x, n=1): method _get_intermediate_layers_chunked (line 280) | def _get_intermediate_layers_chunked(self, x, n=1): method get_intermediate_layers (line 294) | def get_intermediate_layers( method forward (line 320) | def forward(self, *args, is_training=False, **kwargs): function init_weights_vit_timm (line 328) | def init_weights_vit_timm(module: nn.Module, name: str = ""): function vit_small (line 336) | def vit_small(patch_size=16, num_register_tokens=0, **kwargs): function vit_large (line 349) | def vit_large(patch_size=16, num_register_tokens=0, **kwargs): FILE: ADD/th_utils/custom_ops.py function _find_compiler_bindir (line 29) | def _find_compiler_bindir(): function _get_mangled_gpu_name (line 44) | def _get_mangled_gpu_name(): function get_plugin (line 59) | def get_plugin(module_name, sources, headers=None, source_dir=None, **bu... FILE: ADD/th_utils/misc.py function constant (line 22) | def constant(value, shape=None, dtype=None, device=None, memory_format=N... function nan_to_num (line 49) | def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): #... function suppress_tracer_warnings (line 71) | def suppress_tracer_warnings(): function assert_shape (line 82) | def assert_shape(tensor, ref_shape): function profiled_function (line 100) | def profiled_function(fn): class InfiniteSampler (line 111) | class InfiniteSampler(torch.utils.data.Sampler): method __init__ (line 112) | def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed... method __iter__ (line 125) | def __iter__(self): function spectral_to_cpu (line 146) | def spectral_to_cpu(model: torch.nn.Module): function get_children (line 154) | def get_children(model: torch.nn.Module): function params_and_buffers (line 167) | def params_and_buffers(module): function named_params_and_buffers (line 171) | def named_params_and_buffers(module): function copy_params_and_buffers (line 175) | def copy_params_and_buffers(src_module, dst_module, require_all=False): function ddp_sync (line 189) | def ddp_sync(module, sync): function check_ddp_consistency (line 200) | def check_ddp_consistency(module, ignore_regex=None): function print_module_summary (line 216) | def print_module_summary(module, inputs, max_nesting=3, skip_redundant=T... FILE: ADD/th_utils/ops/bias_act.cpp function has_same_layout (line 16) | static bool has_same_layout(torch::Tensor x, torch::Tensor y) function bias_act (line 32) | static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::T... function PYBIND11_MODULE (line 94) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) FILE: ADD/th_utils/ops/bias_act.h type bias_act_kernel_params (line 12) | struct bias_act_kernel_params FILE: ADD/th_utils/ops/bias_act.py function _init (line 38) | def _init(): function bias_act (line 52) | def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clam... function _bias_act_ref (line 91) | def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None,... function _bias_act_cuda (line 126) | def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): FILE: ADD/th_utils/ops/conv2d_gradfix.py function no_weight_gradients (line 27) | def no_weight_gradients(disable=True): function conv2d (line 37) | def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, gr... function conv_transpose2d (line 42) | def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, outp... function _should_use_custom_op (line 49) | def _should_use_custom_op(input): function _tuple_of_ints (line 60) | def _tuple_of_ints(xs, ndim): function _conv2d_gradfix (line 71) | def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_pad... FILE: ADD/th_utils/ops/conv2d_resample.py function _get_weight_shape (line 21) | def _get_weight_shape(w): function _conv2d_wrapper (line 29) | def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False... function conv2d_resample (line 46) | def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, fli... FILE: ADD/th_utils/ops/filtered_lrelu.cpp function filtered_lrelu (line 16) | static std::tuple filtered_lrelu( function filtered_lrelu_act (line 213) | static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor s... function PYBIND11_MODULE (line 294) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) FILE: ADD/th_utils/ops/filtered_lrelu.h type filtered_lrelu_kernel_params (line 14) | struct filtered_lrelu_kernel_params type filtered_lrelu_act_kernel_params (line 55) | struct filtered_lrelu_act_kernel_params type filtered_lrelu_kernel_spec (line 73) | struct filtered_lrelu_kernel_spec FILE: ADD/th_utils/ops/filtered_lrelu.py function _init (line 23) | def _init(): function _get_filter_size (line 35) | def _get_filter_size(f): function _parse_padding (line 42) | def _parse_padding(padding): function filtered_lrelu (line 56) | def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0,... function _filtered_lrelu_ref (line 121) | def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, paddi... function _filtered_lrelu_cuda (line 159) | def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope... FILE: ADD/th_utils/ops/fma.py function fma (line 15) | def fma(a, b, c): # => a * b + c class _FusedMultiplyAdd (line 20) | class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c method forward (line 22) | def forward(ctx, a, b, c): # pylint: disable=arguments-differ method backward (line 29) | def backward(ctx, dout): # pylint: disable=arguments-differ function _unbroadcast (line 49) | def _unbroadcast(x, shape): FILE: ADD/th_utils/ops/grid_sample_gradfix.py function grid_sample (line 28) | def grid_sample(input, grid): function _should_use_custom_op (line 35) | def _should_use_custom_op(): class _GridSample2dForward (line 40) | class _GridSample2dForward(torch.autograd.Function): method forward (line 42) | def forward(ctx, input, grid): method backward (line 50) | def backward(ctx, grad_output): class _GridSample2dBackward (line 57) | class _GridSample2dBackward(torch.autograd.Function): method forward (line 59) | def forward(ctx, grad_output, input, grid): method backward (line 70) | def backward(ctx, grad2_grad_input, grad2_grad_grid): FILE: ADD/th_utils/ops/upfirdn2d.cpp function upfirdn2d (line 16) | static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx... function PYBIND11_MODULE (line 102) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) FILE: ADD/th_utils/ops/upfirdn2d.h type upfirdn2d_kernel_params (line 14) | struct upfirdn2d_kernel_params type upfirdn2d_kernel_spec (line 45) | struct upfirdn2d_kernel_spec FILE: ADD/th_utils/ops/upfirdn2d.py function _init (line 23) | def _init(): function _parse_scaling (line 35) | def _parse_scaling(scaling): function _parse_padding (line 44) | def _parse_padding(padding): function _get_filter_size (line 55) | def _get_filter_size(f): function setup_filter (line 70) | def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_fil... function upfirdn2d (line 118) | def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, ... function _upfirdn2d_ref (line 167) | def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gai... function _upfirdn2d_cuda (line 217) | def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): function filter2d (line 277) | def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): function upsample2d (line 313) | def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='c... function downsample2d (line 352) | def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, imp... FILE: ADD/utils/util_net.py function calculate_parameters (line 12) | def calculate_parameters(net): function pad_input (line 18) | def pad_input(x, mod): function forward_chop (line 25) | def forward_chop(net, x, net_kwargs=None, scale=1, shave=10, min_size=16... function measure_time (line 68) | def measure_time(net, inputs, num_forward=100): function reload_model (line 86) | def reload_model(model, ckpt): function compute_hinge_loss (line 99) | def compute_hinge_loss(real_output, fake_output, x_start_, r1_lambda): function reload_model_ (line 125) | def reload_model_(model, ckpt): function reload_model_IDE (line 140) | def reload_model_IDE(model, ckpt): class EMA (line 151) | class EMA(): method __init__ (line 152) | def __init__(self, model, decay): method register (line 158) | def register(self): method update (line 163) | def update(self): method apply_shadow (line 170) | def apply_shadow(self): method restore (line 177) | def restore(self): FILE: dataloaders/paired_dataset_txt.py class PairedCaptionDataset (line 14) | class PairedCaptionDataset(data.Dataset): method __init__ (line 15) | def __init__( method tokenize_caption (line 37) | def tokenize_caption(self, caption=""): method __getitem__ (line 44) | def __getitem__(self, index): method __len__ (line 69) | def __len__(self): FILE: dataloaders/realesrgan.py function ordered_yaml (line 23) | def ordered_yaml(): function opt_parse (line 47) | def opt_parse(opt_path): class RealESRGAN_degradation (line 54) | class RealESRGAN_degradation(object): method __init__ (line 55) | def __init__(self, opt_path='', device='cpu'): method color_jitter_pt (line 88) | def color_jitter_pt(self, img, brightness, contrast, saturation, hue): method random_augment (line 108) | def random_augment(self, img_gt): method random_kernels (line 129) | def random_kernels(self): method degrade_process (line 191) | def degrade_process(self, img_gt, resize_bak=False): FILE: models/DiffAugment.py function DiffAugment (line 35) | def DiffAugment(x: torch.Tensor, policy: str = '', channels_first: bool ... function rand_brightness (line 48) | def rand_brightness(x: torch.Tensor) -> torch.Tensor: function rand_saturation (line 53) | def rand_saturation(x: torch.Tensor) -> torch.Tensor: function rand_contrast (line 59) | def rand_contrast(x: torch.Tensor) -> torch.Tensor: function rand_translation (line 65) | def rand_translation(x: torch.Tensor, ratio: float = 0.125) -> torch.Ten... function rand_cutout (line 81) | def rand_cutout(x: torch.Tensor, ratio: float = 0.2) -> torch.Tensor: function rand_resize (line 98) | def rand_resize(x: torch.Tensor, min_ratio: float = 0.8, max_ratio: floa... FILE: models/controlnet.py class ControlNetOutput (line 40) | class ControlNetOutput(BaseOutput): class ControlNetConditioningEmbedding (line 59) | class ControlNetConditioningEmbedding(nn.Module): method __init__ (line 69) | def __init__( method forward (line 91) | def forward(self, conditioning): class ControlNetModel (line 104) | class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMix... method __init__ (line 173) | def __init__( method from_unet (line 434) | def from_unet( method attn_processors (line 507) | def attn_processors(self) -> Dict[str, AttentionProcessor]: method set_attn_processor (line 531) | def set_attn_processor(self, processor: Union[AttentionProcessor, Dict... method set_default_attn_processor (line 566) | def set_default_attn_processor(self): method set_attention_slice (line 573) | def set_attention_slice(self, slice_size): method _set_gradient_checkpointing (line 638) | def _set_gradient_checkpointing(self, module, value=False): method forward (line 642) | def forward( function zero_module (line 845) | def zero_module(module): FILE: models/losses/contperceptual.py class LPIPSWithDiscriminator (line 9) | class LPIPSWithDiscriminator(ModelMixin, ConfigMixin, FromOriginalContro... method __init__ (line 10) | def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixello... method calculate_adaptive_weight (line 34) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): method forward (line 47) | def forward(self, inputs, reconstructions, optimizer_idx, FILE: models/losses/vqperceptual.py function hinge_d_loss_with_exemplar_weights (line 11) | def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): function adopt_weight (line 20) | def adopt_weight(weight, global_step, threshold=0, value=0.): function measure_perplexity (line 26) | def measure_perplexity(predicted_indices, n_embed): function l1 (line 35) | def l1(x, y): function l2 (line 39) | def l2(x, y): class VQLPIPSWithDiscriminator (line 43) | class VQLPIPSWithDiscriminator(nn.Module): method __init__ (line 44) | def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, method calculate_adaptive_weight (line 98) | def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): method forward (line 111) | def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, FILE: models/shared.py class ResidualBlock (line 20) | class ResidualBlock(nn.Module): method __init__ (line 21) | def __init__(self, fn: Callable): method forward (line 25) | def forward(self, x: torch.Tensor) -> torch.Tensor: class FullyConnectedLayer (line 29) | class FullyConnectedLayer(nn.Module): method __init__ (line 30) | def __init__( method forward (line 51) | def forward(self, x: torch.Tensor) -> torch.Tensor: method extra_repr (line 66) | def extra_repr(self) -> str: class MLP (line 70) | class MLP(nn.Module): method __init__ (line 71) | def __init__( method forward (line 91) | def forward(self, x: torch.Tensor) -> torch.Tensor: FILE: models/unet_2d_blocks.py function get_down_block (line 33) | def get_down_block( function get_up_block (line 230) | def get_up_block( class AutoencoderTinyBlock (line 431) | class AutoencoderTinyBlock(nn.Module): method __init__ (line 432) | def __init__(self, in_channels: int, out_channels: int, act_fn: str): method forward (line 449) | def forward(self, x): class UNetMidBlock2D (line 453) | class UNetMidBlock2D(nn.Module): method __init__ (line 454) | def __init__( method forward (line 534) | def forward(self, hidden_states, temb=None): class UNetMidBlock2DCrossAttn (line 544) | class UNetMidBlock2DCrossAttn(nn.Module): method __init__ (line 545) | def __init__( method forward (line 636) | def forward( class UNetMidBlock2DSimpleCrossAttn (line 689) | class UNetMidBlock2DSimpleCrossAttn(nn.Module): method __init__ (line 690) | def __init__( method forward (line 774) | def forward( class AttnDownBlock2D (line 812) | class AttnDownBlock2D(nn.Module): method __init__ (line 813) | def __init__( method forward (line 904) | def forward(self, hidden_states, temb=None, upsample_size=None): class CrossAttnDownBlock2D (line 924) | class CrossAttnDownBlock2D(nn.Module): method __init__ (line 925) | def __init__( method forward (line 1017) | def forward( class DownBlock2D (line 1084) | class DownBlock2D(nn.Module): method __init__ (line 1085) | def __init__( method forward (line 1136) | def forward(self, hidden_states, temb=None): class DownEncoderBlock2D (line 1170) | class DownEncoderBlock2D(nn.Module): method __init__ (line 1171) | def __init__( method forward (line 1219) | def forward(self, hidden_states): class AttnDownEncoderBlock2D (line 1230) | class AttnDownEncoderBlock2D(nn.Module): method __init__ (line 1231) | def __init__( method forward (line 1302) | def forward(self, hidden_states): class AttnSkipDownBlock2D (line 1314) | class AttnSkipDownBlock2D(nn.Module): method __init__ (line 1315) | def __init__( method forward (line 1395) | def forward(self, hidden_states, temb=None, skip_sample=None): class SkipDownBlock2D (line 1415) | class SkipDownBlock2D(nn.Module): method __init__ (line 1416) | def __init__( method forward (line 1475) | def forward(self, hidden_states, temb=None, skip_sample=None): class ResnetDownsampleBlock2D (line 1494) | class ResnetDownsampleBlock2D(nn.Module): method __init__ (line 1495) | def __init__( method forward (line 1558) | def forward(self, hidden_states, temb=None): class SimpleCrossAttnDownBlock2D (line 1592) | class SimpleCrossAttnDownBlock2D(nn.Module): method __init__ (line 1593) | def __init__( method forward (line 1687) | def forward( class KDownBlock2D (line 1750) | class KDownBlock2D(nn.Module): method __init__ (line 1751) | def __init__( method forward (line 1796) | def forward(self, hidden_states, temb=None): class KCrossAttnDownBlock2D (line 1828) | class KCrossAttnDownBlock2D(nn.Module): method __init__ (line 1829) | def __init__( method forward (line 1893) | def forward( class AttnUpBlock2D (line 1954) | class AttnUpBlock2D(nn.Module): method __init__ (line 1955) | def __init__( method forward (line 2043) | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, u... class CrossAttnUpBlock2D (line 2063) | class CrossAttnUpBlock2D(nn.Module): method __init__ (line 2064) | def __init__( method forward (line 2152) | def forward( class UpBlock2D (line 2215) | class UpBlock2D(nn.Module): method __init__ (line 2216) | def __init__( method forward (line 2263) | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, u... class UpDecoderBlock2D (line 2296) | class UpDecoderBlock2D(nn.Module): method __init__ (line 2297) | def __init__( method forward (line 2340) | def forward(self, hidden_states, temb=None): class AttnUpDecoderBlock2D (line 2351) | class AttnUpDecoderBlock2D(nn.Module): method __init__ (line 2352) | def __init__( method forward (line 2419) | def forward(self, hidden_states, temb=None): class AttnSkipUpBlock2D (line 2431) | class AttnSkipUpBlock2D(nn.Module): method __init__ (line 2432) | def __init__( method forward (line 2522) | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, s... class SkipUpBlock2D (line 2550) | class SkipUpBlock2D(nn.Module): method __init__ (line 2551) | def __init__( method forward (line 2619) | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, s... class ResnetUpsampleBlock2D (line 2645) | class ResnetUpsampleBlock2D(nn.Module): method __init__ (line 2646) | def __init__( method forward (line 2712) | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, u... class SimpleCrossAttnUpBlock2D (line 2745) | class SimpleCrossAttnUpBlock2D(nn.Module): method __init__ (line 2746) | def __init__( method forward (line 2842) | def forward( class KUpBlock2D (line 2908) | class KUpBlock2D(nn.Module): method __init__ (line 2909) | def __init__( method forward (line 2956) | def forward(self, hidden_states, res_hidden_states_tuple, temb=None, u... class KCrossAttnUpBlock2D (line 2988) | class KCrossAttnUpBlock2D(nn.Module): method __init__ (line 2989) | def __init__( method forward (line 3072) | def forward( class KAttentionBlock (line 3133) | class KAttentionBlock(nn.Module): method __init__ (line 3150) | def __init__( method _to_3d (line 3193) | def _to_3d(self, hidden_states, height, weight): method _to_4d (line 3196) | def _to_4d(self, hidden_states, height, weight): method forward (line 3199) | def forward( FILE: models/unet_2d_condition.py class UNet2DConditionOutput (line 53) | class UNet2DConditionOutput(BaseOutput): class UNet2DConditionModel (line 65) | class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoade... method __init__ (line 156) | def __init__( method attn_processors (line 579) | def attn_processors(self) -> Dict[str, AttentionProcessor]: method set_attn_processor (line 602) | def set_attn_processor(self, processor: Union[AttentionProcessor, Dict... method set_default_attn_processor (line 636) | def set_default_attn_processor(self): method set_attention_slice (line 642) | def set_attention_slice(self, slice_size): method _set_gradient_checkpointing (line 707) | def _set_gradient_checkpointing(self, module, value=False): method forward (line 711) | def forward( method from_pretrained_orig (line 1025) | def from_pretrained_orig(cls, pretrained_model_path, subfolder=None, *... method from_pretrained_safetensor (line 1059) | def from_pretrained_safetensor(cls, pretrained_model_path, subfolder=N... FILE: models/vit_utils.py class AddReadout (line 36) | class AddReadout(nn.Module): method __init__ (line 37) | def __init__(self, start_index: bool = 1): method forward (line 41) | def forward(self, x: torch.Tensor) -> torch.Tensor: class Transpose (line 49) | class Transpose(nn.Module): method __init__ (line 50) | def __init__(self, dim0: int, dim1: int): method forward (line 55) | def forward(self, x: torch.Tensor) -> torch.Tensor: function forward_vit (line 60) | def forward_vit(pretrained: nn.Module, x: torch.Tensor) -> dict: function _resize_pos_embed (line 66) | def _resize_pos_embed(self, posemb: torch.Tensor, gs_h: int, gs_w: int) ... function forward_flex (line 83) | def forward_flex(self, x: torch.Tensor) -> torch.Tensor: function get_activation (line 111) | def get_activation(name: str) -> Callable: function make_sd_backbone (line 117) | def make_sd_backbone( function make_vit_backbone (line 150) | def make_vit_backbone( FILE: myutils/devices.py function has_mps (line 12) | def has_mps() -> bool: function get_cuda_device_string (line 19) | def get_cuda_device_string(): function get_optimal_device_name (line 23) | def get_optimal_device_name(): function get_optimal_device (line 33) | def get_optimal_device(): function get_device_for (line 37) | def get_device_for(task): function torch_gc (line 41) | def torch_gc(): function enable_tf32 (line 52) | def enable_tf32(): function cond_cast_unet (line 75) | def cond_cast_unet(input): function cond_cast_float (line 79) | def cond_cast_float(input): function randn (line 83) | def randn(seed, shape): function randn_without_seed (line 88) | def randn_without_seed(shape): function autocast (line 92) | def autocast(disable=False): function without_autocast (line 99) | def without_autocast(disable=False): class NansException (line 103) | class NansException(Exception): function test_for_nans (line 107) | def test_for_nans(x, where): function first_time_calculation (line 126) | def first_time_calculation(): FILE: myutils/img_util.py function save_videos_grid (line 12) | def save_videos_grid(videos, path=None, rescale=True, n_rows=4, fps=8, d... function convert_image_to_fn (line 32) | def convert_image_to_fn(img_type, minsize, image, eps=0.02): FILE: myutils/misc.py function rand_name (line 9) | def rand_name(length=8, suffix=''): function cycle (line 17) | def cycle(dl): function exists (line 22) | def exists(x): function identity (line 25) | def identity(x): function load_dreambooth_lora (line 28) | def load_dreambooth_lora(unet, vae=None, model_path=None, alpha=1.0, mod... FILE: myutils/vaehook.py function get_recommend_encoder_tile_size (line 83) | def get_recommend_encoder_tile_size(): function get_recommend_decoder_tile_size (line 100) | def get_recommend_decoder_tile_size(): function inplace_nonlinearity (line 130) | def inplace_nonlinearity(x): function attn_forward_new (line 137) | def attn_forward_new(self, h_): function attn_forward (line 173) | def attn_forward(self, h_): function xformer_attn_forward (line 199) | def xformer_attn_forward(self, h_): function attn2task (line 230) | def attn2task(task_queue, net): function resblock2task (line 248) | def resblock2task(queue, block): function build_sampling (line 279) | def build_sampling(task_queue, net, is_decoder): function build_task_queue (line 336) | def build_task_queue(net, is_decoder): function clone_task_queue (line 366) | def clone_task_queue(task_queue): function get_var_mean (line 375) | def get_var_mean(input, num_groups, eps=1e-6): function custom_group_norm (line 388) | def custom_group_norm(input, num_groups, mean, var, weight=None, bias=No... function crop_valid_region (line 420) | def crop_valid_region(x, input_bbox, target_bbox, is_decoder): function perfcount (line 436) | def perfcount(fn): class GroupNormParam (line 463) | class GroupNormParam: method __init__ (line 464) | def __init__(self): method add_tile (line 471) | def add_tile(self, tile, layer): method summary (line 493) | def summary(self): method from_tile (line 515) | def from_tile(tile, norm): class VAEHook (line 541) | class VAEHook: method __init__ (line 542) | def __init__(self, net, tile_size, is_decoder, fast_decoder, fast_enco... method __call__ (line 552) | def __call__(self, x): method get_best_tile_size (line 566) | def get_best_tile_size(self, lowerbound, upperbound): method split_tiles (line 581) | def split_tiles(self, h, w): method estimate_group_norm (line 641) | def estimate_group_norm(self, z, task_queue, color_fix): method vae_tile_forward (line 685) | def vae_tile_forward(self, z): FILE: myutils/wavelet_color_fix.py function adain_color_fix (line 14) | def adain_color_fix(target: Image, source: Image): function wavelet_color_fix (line 29) | def wavelet_color_fix(target: Image, source: Image): function calc_mean_std (line 44) | def calc_mean_std(feat: Tensor, eps=1e-5): function adaptive_instance_normalization (line 59) | def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tens... function wavelet_blur (line 73) | def wavelet_blur(image: Tensor, radius: int): function wavelet_decomposition (line 94) | def wavelet_decomposition(image: Tensor, levels=5): function wavelet_reconstruction (line 108) | def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor): FILE: pipelines/pipeline_ccsr.py class StableDiffusionControlNetPipeline (line 104) | class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInvers... method __init__ (line 140) | def __init__( method _init_tiled_vae (line 188) | def _init_tiled_vae(self, method enable_vae_slicing (line 211) | def enable_vae_slicing(self): method disable_vae_slicing (line 221) | def disable_vae_slicing(self): method enable_vae_tiling (line 229) | def enable_vae_tiling(self): method disable_vae_tiling (line 239) | def disable_vae_tiling(self): method enable_sequential_cpu_offload (line 246) | def enable_sequential_cpu_offload(self, gpu_id=0): method enable_model_cpu_offload (line 267) | def enable_model_cpu_offload(self, gpu_id=0): method _execution_device (line 297) | def _execution_device(self): method _encode_prompt (line 315) | def _encode_prompt( method run_safety_checker (line 462) | def run_safety_checker(self, image, device, dtype): method decode_latents (line 477) | def decode_latents(self, latents): method prepare_extra_step_kwargs (line 491) | def prepare_extra_step_kwargs(self, generator, eta): method check_inputs (line 509) | def check_inputs( method check_image (line 625) | def check_image(self, image, prompt, prompt_embeds): method prepare_image (line 657) | def prepare_image( method prepare_latents (line 710) | def prepare_latents(self, batch_size, num_channels_latents, height, wi... method _default_height_width (line 727) | def _default_height_width(self, height, width, image): method save_pretrained (line 753) | def save_pretrained( method previous_timestep (line 764) | def previous_timestep(self, timestep): method predict_start_from_noise (line 779) | def predict_start_from_noise(self, sample, t, model_output): method _sliding_windows (line 808) | def _sliding_windows(self,h: int, w: int, tile_size: int, tile_stride:... method _prepare_controlnet_inputs (line 824) | def _prepare_controlnet_inputs(self, latent_model_input, latents, prom... method _predict_noise (line 829) | def _predict_noise(self, latent_model_input, t, image, prompt_embeds, ... method _unet_predict (line 836) | def _unet_predict(self, latent_model_input, t, image, prompt_embeds, c... method _tile_predict (line 851) | def _tile_predict(self, latent_model_input, t, image, prompt_embeds, c... method _initial_step (line 881) | def _initial_step(self, do_classifier_free_guidance, latents, t, times... method _postprocess_latents (line 893) | def _postprocess_latents(self, latents, output_type, do_denormalize): method gaussian_weights (line 902) | def gaussian_weights(self, tile_width: int, tile_height: int, nbatches... method __call__ (line 921) | def __call__( FILE: scripts/get_path.py function write_png_paths (line 3) | def write_png_paths(folder_path, txt_path): FILE: test_ccsr_tile.py function load_pipeline (line 35) | def load_pipeline(args, accelerator, enable_xformers_memory_efficient_at... function main (line 103) | def main(args, enable_xformers_memory_efficient_attention=True,): FILE: train_ccsr_stage1.py function image_grid (line 66) | def image_grid(imgs, rows, cols): function log_validation (line 77) | def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args,... function import_model_class_from_model_name_or_path (line 176) | def import_model_class_from_model_name_or_path(pretrained_model_name_or_... function save_model_card (line 196) | def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_... function parse_args (line 233) | def parse_args(input_args=None): function previous_timestep (line 561) | def previous_timestep(timestep): function predict_start_from_noise (line 576) | def predict_start_from_noise(sample, t, model_output): function save_model_hook (line 680) | def save_model_hook(models, weights, output_dir): function load_model_hook (line 688) | def load_model_hook(models, input_dir): FILE: train_ccsr_stage2.py function image_grid (line 72) | def image_grid(imgs, rows, cols): function log_validation (line 83) | def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args,... function import_model_class_from_model_name_or_path (line 182) | def import_model_class_from_model_name_or_path(pretrained_model_name_or_... function save_model_card (line 202) | def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_... function parse_args (line 239) | def parse_args(input_args=None): function previous_timestep (line 583) | def previous_timestep(timestep): function predict_start_from_noise (line 598) | def predict_start_from_noise(sample, t, model_output): function save_model_hook (line 727) | def save_model_hook(models, weights, output_dir): function load_model_hook (line 737) | def load_model_hook(models, input_dir): FILE: train_controlnet.py function image_grid (line 66) | def image_grid(imgs, rows, cols): function log_validation (line 77) | def log_validation(vae, text_encoder, tokenizer, unet, controlnet, args,... function import_model_class_from_model_name_or_path (line 176) | def import_model_class_from_model_name_or_path(pretrained_model_name_or_... function save_model_card (line 196) | def save_model_card(repo_id: str, image_logs=None, base_model=str, repo_... function parse_args (line 233) | def parse_args(input_args=None): function previous_timestep (line 563) | def previous_timestep(timestep): function predict_start_from_noise (line 578) | def predict_start_from_noise(sample, t, model_output): function save_model_hook (line 681) | def save_model_hook(models, weights, output_dir): function load_model_hook (line 689) | def load_model_hook(models, input_dir): FILE: utils/devices.py function has_mps (line 12) | def has_mps() -> bool: function get_cuda_device_string (line 19) | def get_cuda_device_string(): function get_optimal_device_name (line 23) | def get_optimal_device_name(): function get_optimal_device (line 33) | def get_optimal_device(): function get_device_for (line 37) | def get_device_for(task): function torch_gc (line 41) | def torch_gc(): function enable_tf32 (line 52) | def enable_tf32(): function cond_cast_unet (line 75) | def cond_cast_unet(input): function cond_cast_float (line 79) | def cond_cast_float(input): function randn (line 83) | def randn(seed, shape): function randn_without_seed (line 88) | def randn_without_seed(shape): function autocast (line 92) | def autocast(disable=False): function without_autocast (line 99) | def without_autocast(disable=False): class NansException (line 103) | class NansException(Exception): function test_for_nans (line 107) | def test_for_nans(x, where): function first_time_calculation (line 126) | def first_time_calculation(): FILE: utils/img_util.py function save_videos_grid (line 12) | def save_videos_grid(videos, path=None, rescale=True, n_rows=4, fps=8, d... function convert_image_to_fn (line 32) | def convert_image_to_fn(img_type, minsize, image, eps=0.02): FILE: utils/misc.py function rand_name (line 9) | def rand_name(length=8, suffix=''): function cycle (line 17) | def cycle(dl): function exists (line 22) | def exists(x): function identity (line 25) | def identity(x): function load_dreambooth_lora (line 28) | def load_dreambooth_lora(unet, vae=None, model_path=None, alpha=1.0, mod... FILE: utils/vaehook.py function get_recommend_encoder_tile_size (line 82) | def get_recommend_encoder_tile_size(): function get_recommend_decoder_tile_size (line 99) | def get_recommend_decoder_tile_size(): function inplace_nonlinearity (line 129) | def inplace_nonlinearity(x): function attn_forward_new (line 136) | def attn_forward_new(self, h_): function attn_forward (line 172) | def attn_forward(self, h_): function xformer_attn_forward (line 198) | def xformer_attn_forward(self, h_): function attn2task (line 229) | def attn2task(task_queue, net): function resblock2task (line 247) | def resblock2task(queue, block): function build_sampling (line 278) | def build_sampling(task_queue, net, is_decoder): function build_task_queue (line 329) | def build_task_queue(net, is_decoder): function clone_task_queue (line 359) | def clone_task_queue(task_queue): function get_var_mean (line 368) | def get_var_mean(input, num_groups, eps=1e-6): function custom_group_norm (line 381) | def custom_group_norm(input, num_groups, mean, var, weight=None, bias=No... function crop_valid_region (line 413) | def crop_valid_region(x, input_bbox, target_bbox, is_decoder): function perfcount (line 429) | def perfcount(fn): class GroupNormParam (line 456) | class GroupNormParam: method __init__ (line 457) | def __init__(self): method add_tile (line 464) | def add_tile(self, tile, layer): method summary (line 486) | def summary(self): method from_tile (line 508) | def from_tile(tile, norm): class VAEHook (line 534) | class VAEHook: method __init__ (line 535) | def __init__(self, net, tile_size, is_decoder, fast_decoder, fast_enco... method __call__ (line 545) | def __call__(self, x): method get_best_tile_size (line 565) | def get_best_tile_size(self, lowerbound, upperbound): method split_tiles (line 580) | def split_tiles(self, h, w): method estimate_group_norm (line 640) | def estimate_group_norm(self, z, task_queue, color_fix): method vae_tile_forward (line 684) | def vae_tile_forward(self, z): FILE: utils/wavelet_color_fix.py function adain_color_fix (line 14) | def adain_color_fix(target: Image, source: Image): function wavelet_color_fix (line 29) | def wavelet_color_fix(target: Image, source: Image): function calc_mean_std (line 44) | def calc_mean_std(feat: Tensor, eps=1e-5): function adaptive_instance_normalization (line 59) | def adaptive_instance_normalization(content_feat:Tensor, style_feat:Tens... function wavelet_blur (line 73) | def wavelet_blur(image: Tensor, radius: int): function wavelet_decomposition (line 94) | def wavelet_decomposition(image: Tensor, levels=5): function wavelet_reconstruction (line 108) | def wavelet_reconstruction(content_feat:Tensor, style_feat:Tensor):