SYMBOL INDEX (2187 symbols across 192 files)

FILE: iopaint/__init__.py
  function fix_window_pytorch (line 21) | def fix_window_pytorch():
  function entry_point (line 49) | def entry_point():

FILE: iopaint/api.py
  function api_middleware (line 70) | def api_middleware(app: FastAPI):
  function diffuser_callback (line 138) | def diffuser_callback(pipe, step: int, timestep: int, callback_kwargs: D...
  class Api (line 148) | class Api:
    method __init__ (line 149) | def __init__(self, app: FastAPI, config: ApiConfig):
    method add_api_route (line 183) | def add_api_route(self, path: str, endpoint, **kwargs):
    method api_save_image (line 186) | def api_save_image(self, file: UploadFile):
    method api_current_model (line 205) | def api_current_model(self) -> ModelInfo:
    method api_switch_model (line 208) | def api_switch_model(self, req: SwitchModelRequest) -> ModelInfo:
    method api_switch_plugin_model (line 214) | def api_switch_plugin_model(self, req: SwitchPluginModelRequest):
    method api_server_config (line 225) | def api_server_config(self) -> ServerConfigResponse:
    method api_input_image (line 254) | def api_input_image(self) -> FileResponse:
    method api_geninfo (line 262) | def api_geninfo(self, file: UploadFile) -> GenInfoResponse:
    method api_inpaint (line 271) | def api_inpaint(self, req: InpaintRequest):
    method api_run_plugin_gen_image (line 306) | def api_run_plugin_gen_image(self, req: RunPluginRequest):
    method api_run_plugin_gen_mask (line 334) | def api_run_plugin_gen_mask(self, req: RunPluginRequest):
    method api_samplers (line 350) | def api_samplers(self) -> List[str]:
    method api_adjust_mask (line 353) | def api_adjust_mask(self, req: AdjustMaskRequest):
    method launch (line 358) | def launch(self):
    method _build_file_manager (line 367) | def _build_file_manager(self) -> Optional[FileManager]:
    method _build_plugins (line 381) | def _build_plugins(self) -> Dict[str, BasePlugin]:
    method _build_model_manager (line 400) | def _build_model_manager(self):

FILE: iopaint/batch_processing.py
  function glob_images (line 26) | def glob_images(path: Path) -> Dict[str, Path]:
  function batch_inpaint (line 38) | def batch_inpaint(

FILE: iopaint/benchmark.py
  function run_model (line 34) | def run_model(model, size):
  function benchmark (line 52) | def benchmark(model, times: int, empty_cache: bool):
  function get_args_parser (line 91) | def get_args_parser():

FILE: iopaint/cli.py
  function install_plugins_packages (line 20) | def install_plugins_packages():
  function download (line 27) | def download(
  function list_model (line 44) | def list_model(
  function run (line 60) | def run(
  function start (line 99) | def start(
  function start_web_config (line 234) | def start_web_config(

FILE: iopaint/download.py
  function cli_download_model (line 22) | def cli_download_model(model: str):
  function folder_name_to_show_name (line 44) | def folder_name_to_show_name(name: str) -> str:
  function get_sd_model_type (line 49) | def get_sd_model_type(model_abs_path: str) -> Optional[ModelType]:
  function get_sdxl_model_type (line 77) | def get_sdxl_model_type(model_abs_path: str) -> Optional[ModelType]:
  function scan_single_file_diffusion_models (line 108) | def scan_single_file_diffusion_models(cache_dir) -> List[ModelInfo]:
  function scan_inpaint_models (line 182) | def scan_inpaint_models(model_dir: Path) -> List[ModelInfo]:
  function scan_diffusers_models (line 200) | def scan_diffusers_models() -> List[ModelInfo]:
  function _scan_converted_diffusers_models (line 253) | def _scan_converted_diffusers_models(cache_dir) -> List[ModelInfo]:
  function scan_converted_diffusers_models (line 297) | def scan_converted_diffusers_models(cache_dir) -> List[ModelInfo]:
  function scan_models (line 307) | def scan_models() -> List[ModelInfo]:

FILE: iopaint/file_manager/file_manager.py
  class FileManager (line 18) | class FileManager:
    method __init__ (line 19) | def __init__(self, app: FastAPI, input_dir: Path, mask_dir: Path, outp...
    method api_medias (line 36) | def api_medias(self, tab: MediaTab) -> List[MediasResponse]:
    method api_media_file (line 40) | def api_media_file(self, tab: MediaTab, filename: str) -> FileResponse:
    method api_media_thumbnail_file (line 45) | def api_media_thumbnail_file(
    method _get_dir (line 62) | def _get_dir(self, tab: MediaTab) -> Path:
    method _get_file (line 72) | def _get_file(self, tab: MediaTab, filename: str) -> Path:
    method thumbnail_directory (line 79) | def thumbnail_directory(self) -> Path:
    method _media_names (line 83) | def _media_names(directory: Path) -> List[MediasResponse]:
    method get_thumbnail (line 102) | def get_thumbnail(
    method get_raw_data (line 160) | def get_raw_data(self, image, **options):
    method colormode (line 171) | def colormode(image, colormode="RGB"):
    method background (line 185) | def background(original_image, color=0xFF):
    method _get_format (line 195) | def _get_format(self, image, **options):
    method _create_thumbnail (line 203) | def _create_thumbnail(self, image, size, crop="fit", background=None):

FILE: iopaint/file_manager/storage_backends.py
  class BaseStorageBackend (line 7) | class BaseStorageBackend(ABC):
    method __init__ (line 8) | def __init__(self, app=None):
    method read (line 12) | def read(self, filepath, mode="rb", **kwargs):
    method exists (line 16) | def exists(self, filepath):
    method save (line 20) | def save(self, filepath, data):
  class FilesystemStorageBackend (line 24) | class FilesystemStorageBackend(BaseStorageBackend):
    method read (line 25) | def read(self, filepath, mode="rb", **kwargs):
    method exists (line 29) | def exists(self, filepath):
    method save (line 32) | def save(self, filepath, data):

FILE: iopaint/file_manager/utils.py
  function generate_filename (line 8) | def generate_filename(directory: Path, original_filename, *options) -> str:
  function parse_size (line 17) | def parse_size(size):
  function aspect_to_string (line 42) | def aspect_to_string(size):
  function glob_img (line 52) | def glob_img(p: Union[Path, str], recursive: bool = False):

FILE: iopaint/helper.py
  function md5sum (line 19) | def md5sum(filename):
  function switch_mps_device (line 27) | def switch_mps_device(model_name, device):
  function get_cache_path_by_url (line 34) | def get_cache_path_by_url(url):
  function download_model (line 45) | def download_model(url, model_md5: str = None):
  function ceil_modulo (line 74) | def ceil_modulo(x, mod):
  function handle_error (line 80) | def handle_error(model_path, model_md5, e):
  function load_jit_model (line 101) | def load_jit_model(url_or_path, device, model_md5: str):
  function load_model (line 116) | def load_model(model: torch.nn.Module, url_or_path, device, model_md5):
  function numpy_to_bytes (line 133) | def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
  function pil_to_bytes (line 143) | def pil_to_bytes(pil_img, ext: str, quality: int = 95, infos={}) -> bytes:
  function load_img (line 158) | def load_img(img_bytes, gray: bool = False, return_info: bool = False):
  function norm_img (line 187) | def norm_img(np_img):
  function resize_max_size (line 195) | def resize_max_size(
  function pad_img_to_modulo (line 209) | def pad_img_to_modulo(
  function boxes_from_mask (line 246) | def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
  function only_keep_largest_contour (line 270) | def only_keep_largest_contour(mask: np.ndarray) -> List[np.ndarray]:
  function is_mac (line 296) | def is_mac():
  function get_image_ext (line 300) | def get_image_ext(img_bytes):
  function decode_base64_to_image (line 307) | def decode_base64_to_image(
  function encode_pil_to_base64 (line 341) | def encode_pil_to_base64(image: Image, quality: int, infos: Dict) -> bytes:
  function concat_alpha_channel (line 351) | def concat_alpha_channel(rgb_np_img, alpha_channel) -> np.ndarray:
  function adjust_mask (line 363) | def adjust_mask(mask: np.ndarray, kernel_size: int, operate):
  function gen_frontend_mask (line 393) | def gen_frontend_mask(bgr_or_gray_mask):

FILE: iopaint/installer.py
  function install (line 5) | def install(package):
  function install_plugins_package (line 9) | def install_plugins_package():

FILE: iopaint/model/anytext/anytext_model.py
  class AnyText (line 11) | class AnyText(DiffusionInpaintModel):
    method download (line 17) | def download(local_files_only=False):
    method init_model (line 35) | def init_model(self, device, **kwargs):
    method forward (line 47) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/anytext/anytext_pipeline.py
  function check_limits (line 38) | def check_limits(tensor):
  class AnyTextPipeline (line 49) | class AnyTextPipeline:
    method __init__ (line 50) | def __init__(self, ckpt_path, font_path, device, use_fp16=True):
    method __call__ (line 72) | def __call__(
    method modify_prompt (line 338) | def modify_prompt(self, prompt):
    method separate_pos_imgs (line 364) | def separate_pos_imgs(self, img, sort_priority, gap=102):
    method find_polygon (line 379) | def find_polygon(self, image, min_rect=False):
    method arr2tensor (line 397) | def arr2tensor(self, arr, bs):

FILE: iopaint/model/anytext/cldm/cldm.py
  function count_parameters (line 30) | def count_parameters(model):
  class ControlledUnetModel (line 34) | class ControlledUnetModel(UNetModel):
    method forward (line 35) | def forward(self, x, timesteps=None, context=None, control=None, only_...
  class ControlNet (line 62) | class ControlNet(nn.Module):
    method __init__ (line 63) | def __init__(
    method make_zero_conv (line 317) | def make_zero_conv(self, channels):
    method forward (line 320) | def forward(self, x, hint, text_info, timesteps, context, **kwargs):
  class ControlLDM (line 352) | class ControlLDM(LatentDiffusion):
    method __init__ (line 354) | def __init__(self, control_stage_config, control_key, glyph_key, posit...
    method get_input (line 390) | def get_input(self, batch, k, bs=None, *args, **kwargs):
    method apply_model (line 442) | def apply_model(self, x_noisy, t, cond, *args, **kwargs):
    method instantiate_embedding_manager (line 455) | def instantiate_embedding_manager(self, config, embedder):
    method get_unconditional_conditioning (line 460) | def get_unconditional_conditioning(self, N):
    method get_learned_conditioning (line 463) | def get_learned_conditioning(self, c):
    method fill_caption (line 489) | def fill_caption(self, batch, place_holder='*'):
    method log_images (line 504) | def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50,...
    method sample_log (line 592) | def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
    method configure_optimizers (line 599) | def configure_optimizers(self):
    method low_vram_shift (line 620) | def low_vram_shift(self, is_diffusing):

FILE: iopaint/model/anytext/cldm/ddim_hacked.py
  class DDIMSampler (line 15) | class DDIMSampler(object):
    method __init__ (line 16) | def __init__(self, model, device, schedule="linear", **kwargs):
    method register_buffer (line 23) | def register_buffer(self, name, attr):
    method make_schedule (line 29) | def make_schedule(
    method sample (line 90) | def sample(
    method ddim_sampling (line 169) | def ddim_sampling(
    method p_sample_ddim (line 268) | def p_sample_ddim(
    method encode (line 352) | def encode(
    method stochastic_encode (line 429) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
    method decode (line 447) | def decode(

FILE: iopaint/model/anytext/cldm/embedding_manager.py
  function get_clip_token_for_string (line 11) | def get_clip_token_for_string(tokenizer, string):
  function get_bert_token_for_string (line 19) | def get_bert_token_for_string(tokenizer, string):
  function get_clip_vision_emb (line 26) | def get_clip_vision_emb(encoder, processor, img):
  function get_recog_emb (line 35) | def get_recog_emb(encoder, img_list):
  function pad_H (line 42) | def pad_H(x):
  class EncodeNet (line 49) | class EncodeNet(nn.Module):
    method __init__ (line 50) | def __init__(self, in_channels, out_channels):
    method forward (line 65) | def forward(self, x):
  class EmbeddingManager (line 75) | class EmbeddingManager(nn.Module):
    method __init__ (line 76) | def __init__(
    method encode_text (line 111) | def encode_text(self, text_info):
    method forward (line 146) | def forward(
    method embedding_parameters (line 164) | def embedding_parameters(self):

FILE: iopaint/model/anytext/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: iopaint/model/anytext/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 27) | def create_model(config_path, device, cond_stage_path=None, use_fp16=Fal...

FILE: iopaint/model/anytext/cldm/recognizer.py
  function min_bounding_rect (line 16) | def min_bounding_rect(img):
  function create_predictor (line 43) | def create_predictor(model_dir=None, model_lang="ch", is_onnx=False):
  function _check_image_file (line 93) | def _check_image_file(path):
  function get_image_file_list (line 98) | def get_image_file_list(img_file):
  class TextRecognizer (line 115) | class TextRecognizer(object):
    method __init__ (line 116) | def __init__(self, args, predictor):
    method resize_norm_img (line 126) | def resize_norm_img(self, img, max_wh_ratio):
    method pred_imglist (line 151) | def pred_imglist(self, img_list, show_debug=False, is_ori=False):
    method get_char_dict (line 210) | def get_char_dict(self, character_dict_path):
    method get_text (line 221) | def get_text(self, order):
    method decode (line 225) | def decode(self, mat):
    method get_ctcloss (line 234) | def get_ctcloss(self, preds, gt_text, weight):
  function main (line 254) | def main():

FILE: iopaint/model/anytext/ldm/models/autoencoder.py
  class AutoencoderKL (line 12) | class AutoencoderKL(torch.nn.Module):
    method __init__ (line 13) | def __init__(self,
    method init_from_ckpt (line 51) | def init_from_ckpt(self, path, ignore_keys=list()):
    method ema_scope (line 63) | def ema_scope(self, context=None):
    method on_train_batch_end (line 77) | def on_train_batch_end(self, *args, **kwargs):
    method encode (line 81) | def encode(self, x):
    method decode (line 87) | def decode(self, z):
    method forward (line 92) | def forward(self, input, sample_posterior=True):
    method get_input (line 101) | def get_input(self, batch, k):
    method training_step (line 108) | def training_step(self, batch, batch_idx, optimizer_idx):
    method validation_step (line 129) | def validation_step(self, batch, batch_idx):
    method _validation_step (line 135) | def _validation_step(self, batch, batch_idx, postfix=""):
    method configure_optimizers (line 149) | def configure_optimizers(self):
    method get_last_layer (line 162) | def get_last_layer(self):
    method log_images (line 166) | def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
    method to_rgb (line 191) | def to_rgb(self, x):
  class IdentityFirstStage (line 200) | class IdentityFirstStage(torch.nn.Module):
    method __init__ (line 201) | def __init__(self, *args, vq_interface=False, **kwargs):
    method encode (line 205) | def encode(self, x, *args, **kwargs):
    method decode (line 208) | def decode(self, x, *args, **kwargs):
    method quantize (line 211) | def quantize(self, x, *args, **kwargs):
    method forward (line 216) | def forward(self, x, *args, **kwargs):

FILE: iopaint/model/anytext/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 124) | def ddim_sampling(self, cond, shape,
    method p_sample_ddim (line 185) | def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_origin...
    method encode (line 272) | def encode(self, x0, c, t_enc, use_original_steps=False, return_interm...
    method stochastic_encode (line 319) | def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
    method decode (line 335) | def decode(self, x_latent, cond, t_start, unconditional_guidance_scale...

FILE: iopaint/model/anytext/ldm/models/diffusion/ddpm.py
  function print_grad (line 47) | def print_grad(grad):
  function disabled_train (line 64) | def disabled_train(self, mode=True):
  function uniform_on_device (line 70) | def uniform_on_device(r1, r2, shape, device):
  class DDPM (line 74) | class DDPM(torch.nn.Module):
    method __init__ (line 76) | def __init__(
    method register_schedule (line 186) | def register_schedule(
    method ema_scope (line 291) | def ema_scope(self, context=None):
    method init_from_ckpt (line 306) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
    method q_mean_variance (line 379) | def q_mean_variance(self, x_start, t):
    method predict_start_from_noise (line 393) | def predict_start_from_noise(self, x_t, t, noise):
    method predict_start_from_z_and_v (line 400) | def predict_start_from_z_and_v(self, x_t, t, v):
    method predict_eps_from_z_and_v (line 408) | def predict_eps_from_z_and_v(self, x_t, t, v):
    method q_posterior (line 415) | def q_posterior(self, x_start, x_t, t):
    method p_mean_variance (line 426) | def p_mean_variance(self, x, t, clip_denoised: bool):
    method p_sample (line 441) | def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
    method p_sample_loop (line 452) | def p_sample_loop(self, shape, return_intermediates=False):
    method sample (line 474) | def sample(self, batch_size=16, return_intermediates=False):
    method q_sample (line 482) | def q_sample(self, x_start, t, noise=None):
    method get_v (line 490) | def get_v(self, x, noise, t):
    method get_loss (line 496) | def get_loss(self, pred, target, mean=True):
    method p_losses (line 511) | def p_losses(self, x_start, t, noise=None):
    method forward (line 544) | def forward(self, x, *args, **kwargs):
    method get_input (line 552) | def get_input(self, batch, k):
    method shared_step (line 560) | def shared_step(self, batch):
    method training_step (line 565) | def training_step(self, batch, batch_idx):
    method validation_step (line 599) | def validation_step(self, batch, batch_idx):
    method on_train_batch_end (line 611) | def on_train_batch_end(self, *args, **kwargs):
    method _get_rows_from_list (line 615) | def _get_rows_from_list(self, samples):
    method log_images (line 623) | def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=Non...
    method configure_optimizers (line 662) | def configure_optimizers(self):
  class LatentDiffusion (line 671) | class LatentDiffusion(DDPM):
    method __init__ (line 674) | def __init__(
    method make_cond_schedule (line 741) | def make_cond_schedule(
    method on_train_batch_start (line 755) | def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
    method register_schedule (line 778) | def register_schedule(
    method instantiate_first_stage (line 795) | def instantiate_first_stage(self, config):
    method instantiate_cond_stage (line 802) | def instantiate_cond_stage(self, config):
    method _get_denoise_row_from_list (line 823) | def _get_denoise_row_from_list(
    method get_first_stage_encoding (line 840) | def get_first_stage_encoding(self, encoder_posterior):
    method get_learned_conditioning (line 851) | def get_learned_conditioning(self, c):
    method meshgrid (line 866) | def meshgrid(self, h, w):
    method delta_border (line 873) | def delta_border(self, h, w):
    method get_weighting (line 889) | def get_weighting(self, h, w, Ly, Lx, device):
    method get_fold_unfold (line 910) | def get_fold_unfold(
    method get_input (line 995) | def get_input(
    method decode_first_stage (line 1068) | def decode_first_stage(self, z, predict_cids=False, force_not_quantize...
    method decode_first_stage_grad (line 1078) | def decode_first_stage_grad(self, z, predict_cids=False, force_not_qua...
    method encode_first_stage (line 1089) | def encode_first_stage(self, x):
    method shared_step (line 1092) | def shared_step(self, batch, **kwargs):
    method forward (line 1097) | def forward(self, x, c, *args, **kwargs):
    method apply_model (line 1111) | def apply_model(self, x_noisy, t, cond, return_ids=False):
    method _predict_eps_from_xstart (line 1130) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
    method _prior_bpd (line 1136) | def _prior_bpd(self, x_start):
    method p_mean_variance (line 1152) | def p_mean_variance(
    method p_sample (line 1198) | def p_sample(
    method progressive_denoising (line 1252) | def progressive_denoising(
    method p_sample_loop (line 1346) | def p_sample_loop(
    method sample (line 1417) | def sample(
    method sample_log (line 1460) | def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
    method get_unconditional_conditioning (line 1476) | def get_unconditional_conditioning(self, batch_size, null_label=None):
    method log_images (line 1503) | def log_images(
    method configure_optimizers (line 1694) | def configure_optimizers(self):
    method to_rgb (line 1720) | def to_rgb(self, x):
  class DiffusionWrapper (line 1729) | class DiffusionWrapper(torch.nn.Module):
    method __init__ (line 1730) | def __init__(self, diff_model_config, conditioning_key):
    method forward (line 1747) | def forward(
  class LatentUpscaleDiffusion (line 1783) | class LatentUpscaleDiffusion(LatentDiffusion):
    method __init__ (line 1784) | def __init__(
    method instantiate_low_stage (line 1799) | def instantiate_low_stage(self, config):
    method get_input (line 1807) | def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
    method log_images (line 1835) | def log_images(
  class LatentFinetuneDiffusion (line 1975) | class LatentFinetuneDiffusion(LatentDiffusion):
    method __init__ (line 1981) | def __init__(
    method init_from_ckpt (line 2008) | def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
    method log_images (line 2046) | def log_images(
  class LatentInpaintDiffusion (line 2164) | class LatentInpaintDiffusion(LatentFinetuneDiffusion):
    method __init__ (line 2171) | def __init__(
    method get_input (line 2183) | def get_input(
    method log_images (line 2223) | def log_images(self, *args, **kwargs):
  class LatentDepth2ImageDiffusion (line 2233) | class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
    method __init__ (line 2238) | def __init__(self, depth_stage_config, concat_keys=("midas_in",), *arg...
    method get_input (line 2244) | def get_input(
    method log_images (line 2288) | def log_images(self, *args, **kwargs):
  class LatentUpscaleFinetuneDiffusion (line 2298) | class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
    method __init__ (line 2303) | def __init__(
    method instantiate_low_stage (line 2321) | def instantiate_low_stage(self, config):
    method get_input (line 2329) | def get_input(
    method log_images (line 2377) | def log_images(self, *args, **kwargs):

FILE: iopaint/model/anytext/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: iopaint/model/anytext/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: iopaint/model/anytext/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: iopaint/model/anytext/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: iopaint/model/anytext/ldm/modules/attention.py
  function exists (line 18) | def exists(val):
  function uniq (line 22) | def uniq(arr):
  function default (line 26) | def default(val, d):
  function max_neg_value (line 32) | def max_neg_value(t):
  function init_ (line 36) | def init_(tensor):
  class GEGLU (line 44) | class GEGLU(nn.Module):
    method __init__ (line 45) | def __init__(self, dim_in, dim_out):
    method forward (line 49) | def forward(self, x):
  class FeedForward (line 54) | class FeedForward(nn.Module):
    method __init__ (line 55) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
    method forward (line 69) | def forward(self, x):
  function zero_module (line 73) | def zero_module(module):
  function Normalize (line 82) | def Normalize(in_channels):
  class SpatialSelfAttention (line 88) | class SpatialSelfAttention(nn.Module):
    method __init__ (line 89) | def __init__(self, in_channels):
    method forward (line 107) | def forward(self, x):
  class CrossAttention (line 133) | class CrossAttention(nn.Module):
    method __init__ (line 134) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 150) | def forward(self, x, context=None, mask=None):
  class SDPACrossAttention (line 184) | class SDPACrossAttention(CrossAttention):
    method forward (line 185) | def forward(self, x, context=None, mask=None):
  class BasicTransformerBlock (line 227) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 228) | def __init__(
    method forward (line 267) | def forward(self, x, context=None):
    method _forward (line 272) | def _forward(self, x, context=None):
  class SpatialTransformer (line 284) | class SpatialTransformer(nn.Module):
    method __init__ (line 294) | def __init__(
    method forward (line 341) | def forward(self, x, context=None):

FILE: iopaint/model/anytext/ldm/modules/diffusionmodules/model.py
  function get_timestep_embedding (line 9) | def get_timestep_embedding(timesteps, embedding_dim):
  function nonlinearity (line 30) | def nonlinearity(x):
  function Normalize (line 35) | def Normalize(in_channels, num_groups=32):
  class Upsample (line 41) | class Upsample(nn.Module):
    method __init__ (line 42) | def __init__(self, in_channels, with_conv):
    method forward (line 50) | def forward(self, x):
  class Downsample (line 57) | class Downsample(nn.Module):
    method __init__ (line 58) | def __init__(self, in_channels, with_conv):
    method forward (line 67) | def forward(self, x):
  class ResnetBlock (line 77) | class ResnetBlock(nn.Module):
    method __init__ (line 78) | def __init__(
    method forward (line 114) | def forward(self, x, temb):
  class AttnBlock (line 137) | class AttnBlock(nn.Module):
    method __init__ (line 138) | def __init__(self, in_channels):
    method forward (line 156) | def forward(self, x):
  class AttnBlock2_0 (line 183) | class AttnBlock2_0(nn.Module):
    method __init__ (line 184) | def __init__(self, in_channels):
    method forward (line 202) | def forward(self, x):
  function make_attn (line 231) | def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
  class Model (line 246) | class Model(nn.Module):
    method __init__ (line 247) | def __init__(
    method forward (line 368) | def forward(self, x, t=None, context=None):
    method get_last_layer (line 417) | def get_last_layer(self):
  class Encoder (line 421) | class Encoder(nn.Module):
    method __init__ (line 422) | def __init__(
    method forward (line 510) | def forward(self, x):
  class Decoder (line 538) | class Decoder(nn.Module):
    method __init__ (line 539) | def __init__(
    method forward (line 634) | def forward(self, z):
  class SimpleDecoder (line 670) | class SimpleDecoder(nn.Module):
    method __init__ (line 671) | def __init__(self, in_channels, out_channels, *args, **kwargs):
    method forward (line 704) | def forward(self, x):
  class UpsampleDecoder (line 717) | class UpsampleDecoder(nn.Module):
    method __init__ (line 718) | def __init__(
    method forward (line 761) | def forward(self, x):
  class LatentRescaler (line 775) | class LatentRescaler(nn.Module):
    method __init__ (line 776) | def __init__(self, factor, in_channels, mid_channels, out_channels, de...
    method forward (line 813) | def forward(self, x):
  class MergedRescaleEncoder (line 831) | class MergedRescaleEncoder(nn.Module):
    method __init__ (line 832) | def __init__(
    method forward (line 869) | def forward(self, x):
  class MergedRescaleDecoder (line 875) | class MergedRescaleDecoder(nn.Module):
    method __init__ (line 876) | def __init__(
    method forward (line 912) | def forward(self, x):
  class Upsampler (line 918) | class Upsampler(nn.Module):
    method __init__ (line 919) | def __init__(self, in_size, out_size, in_channels, out_channels, ch_mu...
    method forward (line 944) | def forward(self, x):
  class Resize (line 950) | class Resize(nn.Module):
    method __init__ (line 951) | def __init__(self, in_channels=None, learned=False, mode="bilinear"):
    method forward (line 966) | def forward(self, x, scale_factor=1.0):

FILE: iopaint/model/anytext/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):
  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: iopaint/model/anytext/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: iopaint/model/anytext/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 222) | def conv_nd(dims, *args, **kwargs):
  function linear (line 235) | def linear(*args, **kwargs):
  function avg_pool_nd (line 242) | def avg_pool_nd(dims, *args, **kwargs):
  class HybridConditioner (line 255) | class HybridConditioner(nn.Module):
    method __init__ (line 257) | def __init__(self, c_concat_config, c_crossattn_config):
    method forward (line 262) | def forward(self, c_concat, c_crossattn):
  function noise_like (line 268) | def noise_like(shape, device, repeat=False):

FILE: iopaint/model/anytext/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: iopaint/model/anytext/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: iopaint/model/anytext/ldm/modules/encoders/modules.py
  function _expand_mask (line 17) | def _expand_mask(mask, dtype, tgt_len=None):
  function _build_causal_attention_mask (line 33) | def _build_causal_attention_mask(bsz, seq_len, dtype):
  class AbstractEncoder (line 43) | class AbstractEncoder(nn.Module):
    method __init__ (line 44) | def __init__(self):
    method encode (line 47) | def encode(self, *args, **kwargs):
  class IdentityEncoder (line 51) | class IdentityEncoder(AbstractEncoder):
    method encode (line 52) | def encode(self, x):
  class ClassEmbedder (line 56) | class ClassEmbedder(nn.Module):
    method __init__ (line 57) | def __init__(self, embed_dim, n_classes=1000, key="class", ucg_rate=0.1):
    method forward (line 64) | def forward(self, batch, key=None, disable_dropout=False):
    method get_unconditional_conditioning (line 76) | def get_unconditional_conditioning(self, bs, device="cuda"):
  function disabled_train (line 85) | def disabled_train(self, mode=True):
  class FrozenT5Embedder (line 91) | class FrozenT5Embedder(AbstractEncoder):
    method __init__ (line 94) | def __init__(
    method freeze (line 105) | def freeze(self):
    method forward (line 111) | def forward(self, text):
    method encode (line 127) | def encode(self, text):
  class FrozenCLIPEmbedder (line 131) | class FrozenCLIPEmbedder(AbstractEncoder):
    method __init__ (line 136) | def __init__(
    method freeze (line 159) | def freeze(self):
    method forward (line 165) | def forward(self, text):
    method encode (line 187) | def encode(self, text):
  class FrozenCLIPT5Encoder (line 191) | class FrozenCLIPT5Encoder(AbstractEncoder):
    method __init__ (line 192) | def __init__(
    method encode (line 210) | def encode(self, text):
    method forward (line 213) | def forward(self, text):
  class FrozenCLIPEmbedderT3 (line 219) | class FrozenCLIPEmbedderT3(AbstractEncoder):
    method __init__ (line 222) | def __init__(
    method freeze (line 391) | def freeze(self):
    method forward (line 396) | def forward(self, text, **kwargs):
    method encode (line 410) | def encode(self, text, **kwargs):

FILE: iopaint/model/anytext/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, **kwargs):
  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: iopaint/model/anytext/ocr_recog/RNN.py
  class Swish (line 5) | class Swish(nn.Module):
    method __int__ (line 6) | def __int__(self):
    method forward (line 9) | def forward(self,x):
  class Im2Im (line 12) | class Im2Im(nn.Module):
    method __init__ (line 13) | def __init__(self, in_channels, **kwargs):
    method forward (line 17) | def forward(self, x):
  class Im2Seq (line 20) | class Im2Seq(nn.Module):
    method __init__ (line 21) | def __init__(self, in_channels, **kwargs):
    method forward (line 25) | def forward(self, x):
  class EncoderWithRNN (line 32) | class EncoderWithRNN(nn.Module):
    method __init__ (line 33) | def __init__(self, in_channels,**kwargs):
    method forward (line 39) | def forward(self, x):
  class SequenceEncoder (line 44) | class SequenceEncoder(nn.Module):
    method __init__ (line 45) | def __init__(self, in_channels, encoder_type='rnn',  **kwargs):
    method forward (line 66) | def forward(self, x):
  class ConvBNLayer (line 77) | class ConvBNLayer(nn.Module):
    method __init__ (line 78) | def __init__(self,
    method forward (line 100) | def forward(self, inputs):
  class EncoderWithSVTR (line 107) | class EncoderWithSVTR(nn.Module):
    method __init__ (line 108) | def __init__(
    method _init_weights (line 159) | def _init_weights(self, m):
    method forward (line 180) | def forward(self, x):

FILE: iopaint/model/anytext/ocr_recog/RecCTCHead.py
  class CTCHead (line 4) | class CTCHead(nn.Module):
    method __init__ (line 5) | def __init__(self,
    method forward (line 34) | def forward(self, x, labels=None):

FILE: iopaint/model/anytext/ocr_recog/RecModel.py
  class RecModel (line 12) | class RecModel(nn.Module):
    method __init__ (line 13) | def __init__(self, config):
    method load_3rd_state_dict (line 30) | def load_3rd_state_dict(self, _3rd_name, _state):
    method forward (line 35) | def forward(self, x):
    method encode (line 41) | def encode(self, x):

FILE: iopaint/model/anytext/ocr_recog/RecMv1_enhance.py
  class ConvBNLayer (line 7) | class ConvBNLayer(nn.Module):
    method __init__ (line 8) | def __init__(self,
    method forward (line 34) | def forward(self, inputs):
  class DepthwiseSeparable (line 42) | class DepthwiseSeparable(nn.Module):
    method __init__ (line 43) | def __init__(self,
    method forward (line 71) | def forward(self, inputs):
  class MobileNetV1Enhance (line 79) | class MobileNetV1Enhance(nn.Module):
    method __init__ (line 80) | def __init__(self,
    method forward (line 196) | def forward(self, inputs):
  function hardsigmoid (line 202) | def hardsigmoid(x):
  class SEModule (line 205) | class SEModule(nn.Module):
    method __init__ (line 206) | def __init__(self, channel, reduction=4):
    method forward (line 224) | def forward(self, inputs):

FILE: iopaint/model/anytext/ocr_recog/RecSVTR.py
  function drop_path (line 8) | def drop_path(x, drop_prob=0., training=False):
  class Swish (line 23) | class Swish(nn.Module):
    method __int__ (line 24) | def __int__(self):
    method forward (line 27) | def forward(self,x):
  class ConvBNLayer (line 31) | class ConvBNLayer(nn.Module):
    method __init__ (line 32) | def __init__(self,
    method forward (line 54) | def forward(self, inputs):
  class DropPath (line 61) | class DropPath(nn.Module):
    method __init__ (line 65) | def __init__(self, drop_prob=None):
    method forward (line 69) | def forward(self, x):
  class Identity (line 73) | class Identity(nn.Module):
    method __init__ (line 74) | def __init__(self):
    method forward (line 77) | def forward(self, input):
  class Mlp (line 81) | class Mlp(nn.Module):
    method __init__ (line 82) | def __init__(self,
    method forward (line 99) | def forward(self, x):
  class ConvMixer (line 108) | class ConvMixer(nn.Module):
    method __init__ (line 109) | def __init__(
    method forward (line 127) | def forward(self, x):
  class Attention (line 136) | class Attention(nn.Module):
    method __init__ (line 137) | def __init__(self,
    method forward (line 177) | def forward(self, x):
  class Block (line 198) | class Block(nn.Module):
    method __init__ (line 199) | def __init__(self,
    method forward (line 251) | def forward(self, x):
  class PatchEmbed (line 261) | class PatchEmbed(nn.Module):
    method __init__ (line 265) | def __init__(self,
    method forward (line 322) | def forward(self, x):
  class SubSample (line 330) | class SubSample(nn.Module):
    method __init__ (line 331) | def __init__(self,
    method forward (line 361) | def forward(self, x):
  class SVTRNet (line 378) | class SVTRNet(nn.Module):
    method __init__ (line 379) | def __init__(
    method _init_weights (line 533) | def _init_weights(self, m):
    method forward_features (line 542) | def forward_features(self, x):
    method forward (line 564) | def forward(self, x):

FILE: iopaint/model/anytext/ocr_recog/common.py
  class Hswish (line 8) | class Hswish(nn.Module):
    method __init__ (line 9) | def __init__(self, inplace=True):
    method forward (line 13) | def forward(self, x):
  class Hsigmoid (line 18) | class Hsigmoid(nn.Module):
    method __init__ (line 19) | def __init__(self, inplace=True):
    method forward (line 23) | def forward(self, x):
  class GELU (line 28) | class GELU(nn.Module):
    method __init__ (line 29) | def __init__(self, inplace=True):
    method forward (line 33) | def forward(self, x):
  class Swish (line 37) | class Swish(nn.Module):
    method __init__ (line 38) | def __init__(self, inplace=True):
    method forward (line 42) | def forward(self, x):
  class Activation (line 50) | class Activation(nn.Module):
    method __init__ (line 51) | def __init__(self, act_type, inplace=True):
    method forward (line 73) | def forward(self, inputs):

FILE: iopaint/model/anytext/utils.py
  function save_images (line 8) | def save_images(img_list, folder):
  function check_channels (line 24) | def check_channels(image):
  function resize_image (line 33) | def resize_image(img, max_length=768):
  function insert_spaces (line 48) | def insert_spaces(string, nSpace):
  function draw_glyph (line 57) | def draw_glyph(font, text):
  function draw_glyph2 (line 78) | def draw_glyph2(

FILE: iopaint/model/base.py
  class InpaintModel (line 20) | class InpaintModel:
    method __init__ (line 27) | def __init__(self, device, **kwargs):
    method init_model (line 38) | def init_model(self, device, **kwargs): ...
    method is_downloaded (line 42) | def is_downloaded() -> bool:
    method forward (line 46) | def forward(self, image, mask, config: InpaintRequest):
    method download (line 55) | def download(): ...
    method _pad_forward (line 57) | def _pad_forward(self, image, mask, config: InpaintRequest):
    method forward_pre_process (line 80) | def forward_pre_process(self, image, mask, config):
    method forward_post_process (line 83) | def forward_post_process(self, result, image, mask, config):
    method __call__ (line 87) | def __call__(self, image, mask, config: InpaintRequest):
    method _crop_box (line 142) | def _crop_box(self, image, mask, box, config: InpaintRequest):
    method _calculate_cdf (line 194) | def _calculate_cdf(self, histogram):
    method _calculate_lookup (line 199) | def _calculate_lookup(self, source_cdf, reference_cdf):
    method _match_histograms (line 210) | def _match_histograms(self, source, reference, mask):
    method _apply_cropper (line 237) | def _apply_cropper(self, image, mask, config: InpaintRequest):
    method _run_box (line 257) | def _run_box(self, image, mask, box, config: InpaintRequest):
  class DiffusionInpaintModel (line 273) | class DiffusionInpaintModel(InpaintModel):
    method __init__ (line 274) | def __init__(self, device, **kwargs):
    method __call__ (line 280) | def __call__(self, image, mask, config: InpaintRequest):
    method _do_outpainting (line 299) | def _do_outpainting(self, image, config: InpaintRequest):
    method _scaled_pad_forward (line 363) | def _scaled_pad_forward(self, image, mask, config: InpaintRequest):
    method set_scheduler (line 382) | def set_scheduler(self, config: InpaintRequest):
    method forward_pre_process (line 391) | def forward_pre_process(self, image, mask, config):
    method forward_post_process (line 398) | def forward_post_process(self, result, image, mask, config):

FILE: iopaint/model/brushnet/brushnet.py
  class BrushNetOutput (line 31) | class BrushNetOutput(BaseOutput):
  class BrushNetModel (line 55) | class BrushNetModel(ModelMixin, ConfigMixin):
    method __init__ (line 132) | def __init__(
    method from_unet (line 445) | def from_unet(
    method attn_processors (line 526) | def attn_processors(self) -> Dict[str, AttentionProcessor]:
    method set_attn_processor (line 550) | def set_attn_processor(self, processor: Union[AttentionProcessor, Dict...
    method set_default_attn_processor (line 585) | def set_default_attn_processor(self):
    method set_attention_slice (line 601) | def set_attention_slice(self, slice_size: Union[str, int, List[int]]) ...
    method _set_gradient_checkpointing (line 666) | def _set_gradient_checkpointing(self, module, value: bool = False) -> ...
    method forward (line 670) | def forward(
  function zero_module (line 923) | def zero_module(module):

FILE: iopaint/model/brushnet/brushnet_unet_forward.py
  function brushnet_unet_forward (line 13) | def brushnet_unet_forward(

FILE: iopaint/model/brushnet/brushnet_wrapper.py
  class BrushNetWrapper (line 27) | class BrushNetWrapper(DiffusionInpaintModel):
    method init_model (line 31) | def init_model(self, device: torch.device, **kwargs):
    method switch_brushnet_method (line 136) | def switch_brushnet_method(self, new_method: str):
    method forward (line 145) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/brushnet/brushnet_xl_wrapper.py
  class BrushNetXLWrapper (line 28) | class BrushNetXLWrapper(DiffusionInpaintModel):
    method init_model (line 34) | def init_model(self, device: torch.device, **kwargs):
    method switch_brushnet_method (line 140) | def switch_brushnet_method(self, new_method: str):
    method forward (line 149) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/brushnet/pipeline_brushnet.py
  function retrieve_timesteps (line 85) | def retrieve_timesteps(
  class StableDiffusionBrushNetPipeline (line 129) | class StableDiffusionBrushNetPipeline(
    method __init__ (line 177) | def __init__(
    method _encode_prompt (line 224) | def _encode_prompt(
    method encode_prompt (line 257) | def encode_prompt(
    method encode_image (line 439) | def encode_image(self, image, device, num_images_per_prompt, output_hi...
    method prepare_ip_adapter_image_embeds (line 464) | def prepare_ip_adapter_image_embeds(
    method run_safety_checker (line 516) | def run_safety_checker(self, image, device, dtype):
    method decode_latents (line 531) | def decode_latents(self, latents):
    method prepare_extra_step_kwargs (line 543) | def prepare_extra_step_kwargs(self, generator, eta):
    method check_inputs (line 560) | def check_inputs(
    method check_image (line 675) | def check_image(self, image, mask, prompt, prompt_embeds):
    method prepare_image (line 731) | def prepare_image(
    method prepare_latents (line 762) | def prepare_latents(self, batch_size, num_channels_latents, height, wi...
    method get_guidance_scale_embedding (line 780) | def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=tor...
    method guidance_scale (line 809) | def guidance_scale(self):
    method clip_skip (line 813) | def clip_skip(self):
    method do_classifier_free_guidance (line 820) | def do_classifier_free_guidance(self):
    method cross_attention_kwargs (line 824) | def cross_attention_kwargs(self):
    method num_timesteps (line 828) | def num_timesteps(self):
    method __call__ (line 833) | def __call__(

FILE: iopaint/model/brushnet/pipeline_brushnet_sd_xl.py
  class StableDiffusionXLBrushNetPipeline (line 103) | class StableDiffusionXLBrushNetPipeline(
    method __init__ (line 166) | def __init__(
    method encode_prompt (line 213) | def encode_prompt(
    method encode_image (line 448) | def encode_image(self, image, device, num_images_per_prompt, output_hi...
    method prepare_ip_adapter_image_embeds (line 473) | def prepare_ip_adapter_image_embeds(
    method prepare_extra_step_kwargs (line 525) | def prepare_extra_step_kwargs(self, generator, eta):
    method check_inputs (line 542) | def check_inputs(
    method check_image (line 733) | def check_image(self, image, mask, prompt, prompt_embeds):
    method prepare_image (line 791) | def prepare_image(
    method prepare_latents (line 822) | def prepare_latents(self, batch_size, num_channels_latents, height, wi...
    method _get_add_time_ids (line 840) | def _get_add_time_ids(
    method upcast_vae (line 859) | def upcast_vae(self):
    method get_guidance_scale_embedding (line 879) | def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=tor...
    method guidance_scale (line 908) | def guidance_scale(self):
    method clip_skip (line 912) | def clip_skip(self):
    method do_classifier_free_guidance (line 919) | def do_classifier_free_guidance(self):
    method cross_attention_kwargs (line 923) | def cross_attention_kwargs(self):
    method denoising_end (line 927) | def denoising_end(self):
    method num_timesteps (line 931) | def num_timesteps(self):
    method __call__ (line 936) | def __call__(

FILE: iopaint/model/brushnet/unet_2d_blocks.py
  class MidBlock2D (line 10) | class MidBlock2D(nn.Module):
    method __init__ (line 11) | def __init__(
    method forward (line 66) | def forward(
  function DownBlock2D_forward (line 98) | def DownBlock2D_forward(
  function CrossAttnDownBlock2D_forward (line 141) | def CrossAttnDownBlock2D_forward(
  function CrossAttnUpBlock2D_forward (line 217) | def CrossAttnUpBlock2D_forward(
  function UpBlock2D_forward (line 314) | def UpBlock2D_forward(

FILE: iopaint/model/controlnet.py
  class ControlNet (line 26) | class ControlNet(DiffusionInpaintModel):
    method lcm_lora_id (line 32) | def lcm_lora_id(self):
    method init_model (line 45) | def init_model(self, device: torch.device, **kwargs):
    method switch_controlnet_method (line 139) | def switch_controlnet_method(self, new_method: str):
    method _get_control_image (line 148) | def _get_control_image(self, image, mask):
    method forward (line 161) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/ddim_sampler.py
  class DDIMSampler (line 10) | class DDIMSampler(object):
    method __init__ (line 11) | def __init__(self, model, schedule="linear"):
    method register_buffer (line 17) | def register_buffer(self, name, attr):
    method make_schedule (line 20) | def make_schedule(
    method sample (line 82) | def sample(self, steps, conditioning, batch_size, shape):
    method ddim_sampling (line 99) | def ddim_sampling(
    method p_sample_ddim (line 144) | def p_sample_ddim(

FILE: iopaint/model/fcf.py
  function upfirdn2d (line 39) | def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, ...
  function _upfirdn2d_ref (line 46) | def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gai...
  class EncoderEpilogue (line 94) | class EncoderEpilogue(torch.nn.Module):
    method __init__ (line 95) | def __init__(
    method forward (line 139) | def forward(self, x, cmap, force_fp32=False):
  class EncoderBlock (line 162) | class EncoderBlock(torch.nn.Module):
    method __init__ (line 163) | def __init__(
    method forward (line 252) | def forward(self, x, img, force_fp32=False):
  class EncoderNetwork (line 292) | class EncoderNetwork(torch.nn.Module):
    method __init__ (line 293) | def __init__(
    method forward (line 369) | def forward(self, img, c, **block_kwargs):
  function fma (line 390) | def fma(a, b, c):  # => a * b + c
  class _FusedMultiplyAdd (line 394) | class _FusedMultiplyAdd(torch.autograd.Function):  # a * b + c
    method forward (line 396) | def forward(ctx, a, b, c):  # pylint: disable=arguments-differ
    method backward (line 403) | def backward(ctx, dout):  # pylint: disable=arguments-differ
  function _unbroadcast (line 422) | def _unbroadcast(x, shape):
  function modulated_conv2d (line 438) | def modulated_conv2d(
  class SynthesisLayer (line 516) | class SynthesisLayer(torch.nn.Module):
    method __init__ (line 517) | def __init__(
    method forward (line 560) | def forward(self, x, w, noise_mode="none", fused_modconv=True, gain=1):
  class ToRGBLayer (line 599) | class ToRGBLayer(torch.nn.Module):
    method __init__ (line 600) | def __init__(
    method forward (line 623) | def forward(self, x, w, fused_modconv=True):
  class SynthesisForeword (line 636) | class SynthesisForeword(torch.nn.Module):
    method __init__ (line 637) | def __init__(
    method forward (line 668) | def forward(self, x, ws, feats, img, force_fp32=False):
  class SELayer (line 703) | class SELayer(nn.Module):
    method __init__ (line 704) | def __init__(self, channel, reduction=16):
    method forward (line 714) | def forward(self, x):
  class FourierUnit (line 722) | class FourierUnit(nn.Module):
    method __init__ (line 723) | def __init__(
    method forward (line 764) | def forward(self, x):
  class SpectralTransform (line 840) | class SpectralTransform(nn.Module):
    method __init__ (line 841) | def __init__(
    method forward (line 873) | def forward(self, x):
  class FFC (line 896) | class FFC(nn.Module):
    method __init__ (line 897) | def __init__(
    method forward (line 982) | def forward(self, x, fname=None):
  class FFC_BN_ACT (line 1007) | class FFC_BN_ACT(nn.Module):
    method __init__ (line 1008) | def __init__(
    method forward (line 1053) | def forward(self, x, fname=None):
  class FFCResnetBlock (line 1063) | class FFCResnetBlock(nn.Module):
    method __init__ (line 1064) | def __init__(
    method forward (line 1103) | def forward(self, x, fname=None):
  class ConcatTupleLayer (line 1124) | class ConcatTupleLayer(nn.Module):
    method forward (line 1125) | def forward(self, x):
  class FFCBlock (line 1134) | class FFCBlock(torch.nn.Module):
    method __init__ (line 1135) | def __init__(
    method forward (line 1163) | def forward(self, gen_ft, mask, fname=None):
  class FFCSkipLayer (line 1179) | class FFCSkipLayer(torch.nn.Module):
    method __init__ (line 1180) | def __init__(
    method forward (line 1199) | def forward(self, gen_ft, mask, fname=None):
  class SynthesisBlock (line 1204) | class SynthesisBlock(torch.nn.Module):
    method __init__ (line 1205) | def __init__(
    method forward (line 1296) | def forward(
  class SynthesisNetwork (line 1384) | class SynthesisNetwork(torch.nn.Module):
    method __init__ (line 1385) | def __init__(
    method forward (line 1439) | def forward(self, x_global, mask, feats, ws, fname=None, **block_kwargs):
  class MappingNetwork (line 1472) | class MappingNetwork(torch.nn.Module):
    method __init__ (line 1473) | def __init__(
    method forward (line 1520) | def forward(
  class Generator (line 1562) | class Generator(torch.nn.Module):
    method __init__ (line 1563) | def __init__(
    method forward (line 1599) | def forward(
  class FcF (line 1624) | class FcF(InpaintModel):
    method init_model (line 1631) | def init_model(self, device, **kwargs):
    method download (line 1660) | def download():
    method is_downloaded (line 1664) | def is_downloaded() -> bool:
    method __call__ (line 1668) | def __call__(self, image, mask, config: InpaintRequest):
    method forward (line 1708) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/helper/controlnet_preprocess.py
  function make_canny_control_image (line 10) | def make_canny_control_image(image: np.ndarray) -> Image:
  function make_openpose_control_image (line 19) | def make_openpose_control_image(image: np.ndarray) -> Image:
  function resize_image (line 27) | def resize_image(input_image, resolution):
  function make_depth_control_image (line 44) | def make_depth_control_image(image: np.ndarray) -> Image:
  function make_inpaint_control_image (line 59) | def make_inpaint_control_image(image: np.ndarray, mask: np.ndarray) -> t...

FILE: iopaint/model/helper/cpu_text_encoder.py
  class CPUTextEncoderWrapper (line 7) | class CPUTextEncoderWrapper(PreTrainedModel):
    method __init__ (line 8) | def __init__(self, text_encoder, torch_dtype):
    method __call__ (line 19) | def __call__(self, x, **kwargs):
    method dtype (line 32) | def dtype(self):
    method device (line 36) | def device(self) -> torch.device:

FILE: iopaint/model/helper/g_diffuser_bot.py
  function expand_image (line 5) | def expand_image(cv2_img, top: int, right: int, bottom: int, left: int):

FILE: iopaint/model/instruct_pix2pix.py
  class InstructPix2Pix (line 12) | class InstructPix2Pix(DiffusionInpaintModel):
    method init_model (line 17) | def init_model(self, device: torch.device, **kwargs):
    method forward (line 44) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/kandinsky.py
  class Kandinsky (line 12) | class Kandinsky(DiffusionInpaintModel):
    method init_model (line 16) | def init_model(self, device: torch.device, **kwargs):
    method forward (line 32) | def forward(self, image, mask, config: InpaintRequest):
  class Kandinsky22 (line 64) | class Kandinsky22(Kandinsky):

FILE: iopaint/model/lama.py
  class LaMa (line 31) | class LaMa(InpaintModel):
    method download (line 37) | def download():
    method init_model (line 40) | def init_model(self, device, **kwargs):
    method is_downloaded (line 44) | def is_downloaded() -> bool:
    method forward (line 47) | def forward(self, image, mask, config: InpaintRequest):
  class AnimeLaMa (line 68) | class AnimeLaMa(LaMa):
    method download (line 72) | def download():
    method init_model (line 75) | def init_model(self, device, **kwargs):
    method is_downloaded (line 81) | def is_downloaded() -> bool:

FILE: iopaint/model/ldm.py
  class DDPM (line 51) | class DDPM(nn.Module):
    method __init__ (line 53) | def __init__(
    method register_schedule (line 84) | def register_schedule(
  class LatentDiffusion (line 177) | class LatentDiffusion(DDPM):
    method __init__ (line 178) | def __init__(
    method make_cond_schedule (line 200) | def make_cond_schedule(
    method register_schedule (line 213) | def register_schedule(
    method apply_model (line 230) | def apply_model(self, x_noisy, t, cond):
  class LDM (line 237) | class LDM(InpaintModel):
    method __init__ (line 242) | def __init__(self, device, fp16: bool = True, **kwargs):
    method init_model (line 247) | def init_model(self, device, **kwargs):
    method download (line 265) | def download():
    method is_downloaded (line 271) | def is_downloaded() -> bool:
    method forward (line 280) | def forward(self, image, mask, config: InpaintRequest):
    method _norm (line 335) | def _norm(self, tensor):

FILE: iopaint/model/manga.py
  class Manga (line 32) | class Manga(InpaintModel):
    method init_model (line 37) | def init_model(self, device, **kwargs):
    method download (line 47) | def download():
    method is_downloaded (line 52) | def is_downloaded() -> bool:
    method forward (line 59) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/mat.py
  class ModulatedConv2d (line 34) | class ModulatedConv2d(nn.Module):
    method __init__ (line 35) | def __init__(
    method forward (line 69) | def forward(self, x, style):
  class StyleConv (line 96) | class StyleConv(torch.nn.Module):
    method __init__ (line 97) | def __init__(
    method forward (line 140) | def forward(self, x, style, noise_mode="random", gain=1):
  class ToRGB (line 165) | class ToRGB(torch.nn.Module):
    method __init__ (line 166) | def __init__(
    method forward (line 191) | def forward(self, x, style, skip=None):
  function get_style_code (line 203) | def get_style_code(a, b):
  class DecBlockFirst (line 207) | class DecBlockFirst(nn.Module):
    method __init__ (line 208) | def __init__(
    method forward (line 242) | def forward(self, x, ws, gs, E_features, noise_mode="random"):
  class DecBlockFirstV2 (line 253) | class DecBlockFirstV2(nn.Module):
    method __init__ (line 254) | def __init__(
    method forward (line 289) | def forward(self, x, ws, gs, E_features, noise_mode="random"):
    method __init__ (line 1280) | def __init__(
    method forward (line 1318) | def forward(self, x, ws, gs, E_features, noise_mode="random"):
  class DecBlock (line 301) | class DecBlock(nn.Module):
    method __init__ (line 302) | def __init__(
    method forward (line 345) | def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
    method __init__ (line 1331) | def __init__(
    method forward (line 1374) | def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
  class MappingNet (line 357) | class MappingNet(torch.nn.Module):
    method __init__ (line 358) | def __init__(
    method forward (line 407) | def forward(
  class DisFromRGB (line 444) | class DisFromRGB(nn.Module):
    method __init__ (line 445) | def __init__(
    method forward (line 456) | def forward(self, x):
  class DisBlock (line 460) | class DisBlock(nn.Module):
    method __init__ (line 461) | def __init__(
    method forward (line 486) | def forward(self, x):
  class Discriminator (line 495) | class Discriminator(torch.nn.Module):
    method __init__ (line 496) | def __init__(
    method forward (line 554) | def forward(self, images_in, masks_in, c):
    method __init__ (line 1770) | def __init__(
    method forward (line 1851) | def forward(self, images_in, masks_in, images_stg1, c):
  function nf (line 568) | def nf(stage, channel_base=32768, channel_decay=1.0, channel_max=512):
  class Mlp (line 573) | class Mlp(nn.Module):
    method __init__ (line 574) | def __init__(
    method forward (line 592) | def forward(self, x):
  function window_partition (line 598) | def window_partition(x, window_size):
  function window_reverse (line 614) | def window_reverse(windows, window_size: int, H: int, W: int):
  class Conv2dLayerPartial (line 633) | class Conv2dLayerPartial(nn.Module):
    method __init__ (line 634) | def __init__(
    method forward (line 671) | def forward(self, x, mask=None):
  class WindowAttention (line 694) | class WindowAttention(nn.Module):
    method __init__ (line 707) | def __init__(
    method forward (line 732) | def forward(self, x, mask_windows=None, mask=None):
  class SwinTransformerBlock (line 781) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 799) | def __init__(
    method calculate_mask (line 863) | def calculate_mask(self, x_size):
    method forward (line 894) | def forward(self, x, x_size, mask=None):
  class PatchMerging (line 975) | class PatchMerging(nn.Module):
    method __init__ (line 976) | def __init__(self, in_channels, out_channels, down=2):
    method forward (line 987) | def forward(self, x, x_size, mask=None):
  class PatchUpsampling (line 1001) | class PatchUpsampling(nn.Module):
    method __init__ (line 1002) | def __init__(self, in_channels, out_channels, up=2):
    method forward (line 1013) | def forward(self, x, x_size, mask=None):
  class BasicLayer (line 1026) | class BasicLayer(nn.Module):
    method __init__ (line 1045) | def __init__(
    method forward (line 1104) | def forward(self, x, x_size, mask=None):
  class ToToken (line 1122) | class ToToken(nn.Module):
    method __init__ (line 1123) | def __init__(self, in_channels=3, dim=128, kernel_size=5, stride=1):
    method forward (line 1133) | def forward(self, x, mask):
  class EncFromRGB (line 1139) | class EncFromRGB(nn.Module):
    method __init__ (line 1140) | def __init__(
    method forward (line 1157) | def forward(self, x):
  class ConvBlockDown (line 1164) | class ConvBlockDown(nn.Module):
    method __init__ (line 1165) | def __init__(
    method forward (line 1184) | def forward(self, x):
  function token2feature (line 1191) | def token2feature(x, x_size):
  function feature2token (line 1198) | def feature2token(x):
  class Encoder (line 1204) | class Encoder(nn.Module):
    method __init__ (line 1205) | def __init__(
    method forward (line 1227) | def forward(self, x):
  class ToStyle (line 1237) | class ToStyle(nn.Module):
    method __init__ (line 1238) | def __init__(self, in_channels, out_channels, activation, drop_rate):
    method forward (line 1270) | def forward(self, x):
  class DecBlockFirstV2 (line 1279) | class DecBlockFirstV2(nn.Module):
    method __init__ (line 254) | def __init__(
    method forward (line 289) | def forward(self, x, ws, gs, E_features, noise_mode="random"):
    method __init__ (line 1280) | def __init__(
    method forward (line 1318) | def forward(self, x, ws, gs, E_features, noise_mode="random"):
  class DecBlock (line 1330) | class DecBlock(nn.Module):
    method __init__ (line 302) | def __init__(
    method forward (line 345) | def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
    method __init__ (line 1331) | def __init__(
    method forward (line 1374) | def forward(self, x, img, ws, gs, E_features, noise_mode="random"):
  class Decoder (line 1386) | class Decoder(nn.Module):
    method __init__ (line 1387) | def __init__(
    method forward (line 1411) | def forward(self, x, ws, gs, E_features, noise_mode="random"):
  class DecStyleBlock (line 1420) | class DecStyleBlock(nn.Module):
    method __init__ (line 1421) | def __init__(
    method forward (line 1464) | def forward(self, x, img, style, skip, noise_mode="random"):
  class FirstStage (line 1473) | class FirstStage(nn.Module):
    method __init__ (line 1474) | def __init__(
    method forward (line 1577) | def forward(self, images_in, masks_in, ws, noise_mode="random"):
  class SynthesisNet (line 1632) | class SynthesisNet(nn.Module):
    method __init__ (line 1633) | def __init__(
    method forward (line 1681) | def forward(self, images_in, masks_in, ws, noise_mode="random", return...
  class Generator (line 1714) | class Generator(nn.Module):
    method __init__ (line 1715) | def __init__(
    method forward (line 1746) | def forward(
  class Discriminator (line 1769) | class Discriminator(torch.nn.Module):
    method __init__ (line 496) | def __init__(
    method forward (line 554) | def forward(self, images_in, masks_in, c):
    method __init__ (line 1770) | def __init__(
    method forward (line 1851) | def forward(self, images_in, masks_in, images_stg1, c):
  class MAT (line 1878) | class MAT(InpaintModel):
    method init_model (line 1885) | def init_model(self, device, **kwargs):
    method download (line 1908) | def download():
    method is_downloaded (line 1912) | def is_downloaded() -> bool:
    method forward (line 1915) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/mi_gan.py
  class MIGAN (line 24) | class MIGAN(InpaintModel):
    method init_model (line 31) | def init_model(self, device, **kwargs):
    method download (line 35) | def download():
    method is_downloaded (line 39) | def is_downloaded() -> bool:
    method __call__ (line 43) | def __call__(self, image, mask, config: InpaintRequest):
    method forward (line 83) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/opencv2.py
  class OpenCV2 (line 8) | class OpenCV2(InpaintModel):
    method is_downloaded (line 14) | def is_downloaded() -> bool:
    method forward (line 17) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/original_sd_configs/__init__.py
  function get_config_files (line 7) | def get_config_files() -> Dict[str, Path]:

FILE: iopaint/model/paint_by_example.py
  class PaintByExample (line 13) | class PaintByExample(DiffusionInpaintModel):
    method init_model (line 18) | def init_model(self, device: torch.device, **kwargs):
    method forward (line 44) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/plms_sampler.py
  class PLMSSampler (line 8) | class PLMSSampler(object):
    method __init__ (line 9) | def __init__(self, model, schedule="linear", **kwargs):
    method register_buffer (line 15) | def register_buffer(self, name, attr):
    method make_schedule (line 18) | def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddi...
    method sample (line 52) | def sample(self,
    method plms_sampling (line 109) | def plms_sampling(self, cond, shape,
    method p_sample_plms (line 162) | def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_origin...

FILE: iopaint/model/power_paint/pipeline_powerpaint.py
  function prepare_mask_and_masked_image (line 53) | def prepare_mask_and_masked_image(
  class StableDiffusionInpaintPipeline (line 188) | class StableDiffusionInpaintPipeline(
    method __init__ (line 223) | def __init__(
    method enable_model_cpu_offload (line 342) | def enable_model_cpu_offload(self, gpu_id=0):
    method _encode_prompt (line 377) | def _encode_prompt(
    method run_safety_checker (line 608) | def run_safety_checker(self, image, device, dtype):
    method prepare_extra_step_kwargs (line 627) | def prepare_extra_step_kwargs(self, generator, eta):
    method check_inputs (line 648) | def check_inputs(
    method prepare_latents (line 708) | def prepare_latents(
    method _encode_vae_image (line 774) | def _encode_vae_image(self, image: torch.Tensor, generator: torch.Gene...
    method prepare_mask_latents (line 792) | def prepare_mask_latents(
    method get_timesteps (line 847) | def get_timesteps(self, num_inference_steps, strength, device):
    method __call__ (line 857) | def __call__(

FILE: iopaint/model/power_paint/power_paint.py
  class PowerPaint (line 20) | class PowerPaint(DiffusionInpaintModel):
    method init_model (line 26) | def init_model(self, device: torch.device, **kwargs):
    method forward (line 66) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/power_paint/power_paint_v2.py
  class PowerPaintV2 (line 31) | class PowerPaintV2(DiffusionInpaintModel):
    method init_model (line 37) | def init_model(self, device: torch.device, **kwargs):
    method forward (line 144) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/power_paint/powerpaint_tokenizer.py
  function add_task_to_prompt (line 9) | def add_task_to_prompt(prompt, negative_prompt, task: PowerPaintTask):
  function task_to_prompt (line 39) | def task_to_prompt(task: PowerPaintTask):
  class PowerPaintTokenizer (line 51) | class PowerPaintTokenizer:
    method __init__ (line 52) | def __init__(self, tokenizer: CLIPTokenizer):
    method __getattr__ (line 64) | def __getattr__(self, name: str) -> Any:
    method try_adding_tokens (line 80) | def try_adding_tokens(self, tokens: Union[str, List[str]], *args, **kw...
    method get_token_info (line 93) | def get_token_info(self, token: str) -> dict:
    method add_placeholder_token (line 108) | def add_placeholder_token(
    method replace_placeholder_tokens_in_text (line 139) | def replace_placeholder_tokens_in_text(
    method replace_text_with_placeholder_tokens (line 178) | def replace_text_with_placeholder_tokens(
    method __call__ (line 202) | def __call__(
    method encode (line 226) | def encode(self, text: Union[str, List[str]], *args, **kwargs):
    method decode (line 236) | def decode(

FILE: iopaint/model/power_paint/v2/BrushNet_CA.py
  class BrushNetOutput (line 37) | class BrushNetOutput(BaseOutput):
  class BrushNetModel (line 61) | class BrushNetModel(ModelMixin, ConfigMixin):
    method __init__ (line 138) | def __init__(
    method from_unet (line 496) | def from_unet(
    method attn_processors (line 621) | def attn_processors(self) -> Dict[str, AttentionProcessor]:
    method set_attn_processor (line 651) | def set_attn_processor(
    method set_default_attn_processor (line 688) | def set_default_attn_processor(self):
    method set_attention_slice (line 710) | def set_attention_slice(self, slice_size: Union[str, int, List[int]]) ...
    method _set_gradient_checkpointing (line 781) | def _set_gradient_checkpointing(self, module, value: bool = False) -> ...
    method forward (line 785) | def forward(
  function zero_module (line 1091) | def zero_module(module):

FILE: iopaint/model/power_paint/v2/pipeline_PowerPaint_Brushnet_CA.py
  function retrieve_timesteps (line 103) | def retrieve_timesteps(
  class StableDiffusionPowerPaintBrushNetPipeline (line 149) | class StableDiffusionPowerPaintBrushNetPipeline(
    method __init__ (line 197) | def __init__(
    method _encode_prompt (line 248) | def _encode_prompt(
    method encode_prompt (line 479) | def encode_prompt(
    method encode_image (line 691) | def encode_image(
    method prepare_ip_adapter_image_embeds (line 724) | def prepare_ip_adapter_image_embeds(
    method run_safety_checker (line 794) | def run_safety_checker(self, image, device, dtype):
    method decode_latents (line 813) | def decode_latents(self, latents):
    method prepare_extra_step_kwargs (line 825) | def prepare_extra_step_kwargs(self, generator, eta):
    method check_inputs (line 846) | def check_inputs(
    method check_image (line 974) | def check_image(self, image, mask, prompt, prompt_embeds):
    method prepare_image (line 1038) | def prepare_image(
    method prepare_latents (line 1071) | def prepare_latents(
    method get_guidance_scale_embedding (line 1104) | def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=tor...
    method guidance_scale (line 1133) | def guidance_scale(self):
    method clip_skip (line 1137) | def clip_skip(self):
    method do_classifier_free_guidance (line 1144) | def do_classifier_free_guidance(self):
    method cross_attention_kwargs (line 1148) | def cross_attention_kwargs(self):
    method num_timesteps (line 1152) | def num_timesteps(self):
    method __call__ (line 1157) | def __call__(

FILE: iopaint/model/power_paint/v2/unet_2d_blocks.py
  function CrossAttnDownBlock2D_forward (line 23) | def CrossAttnDownBlock2D_forward(
  function DownBlock2D_forward (line 107) | def DownBlock2D_forward(
  function CrossAttnUpBlock2D_forward (line 158) | def CrossAttnUpBlock2D_forward(
  function UpBlock2D_forward (line 261) | def UpBlock2D_forward(

FILE: iopaint/model/power_paint/v2/unet_2d_condition.py
  function UNet2DConditionModel_forward (line 30) | def UNet2DConditionModel_forward(

FILE: iopaint/model/sd.py
  class SD (line 18) | class SD(DiffusionInpaintModel):
    method init_model (line 23) | def init_model(self, device: torch.device, **kwargs):
    method forward (line 82) | def forward(self, image, mask, config: InpaintRequest):
  class SD15 (line 112) | class SD15(SD):
  class Anything4 (line 117) | class Anything4(SD):
  class RealisticVision14 (line 122) | class RealisticVision14(SD):
  class SD2 (line 127) | class SD2(SD):

FILE: iopaint/model/sdxl.py
  class SDXL (line 22) | class SDXL(DiffusionInpaintModel):
    method init_model (line 29) | def init_model(self, device: torch.device, **kwargs):
    method forward (line 83) | def forward(self, image, mask, config: InpaintRequest):

FILE: iopaint/model/utils.py
  function make_beta_schedule (line 32) | def make_beta_schedule(
  function make_ddim_sampling_parameters (line 67) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos...
  function make_ddim_timesteps (line 87) | def make_ddim_timesteps(
  function noise_like (line 110) | def noise_like(shape, device, repeat=False):
  function timestep_embedding (line 118) | def timestep_embedding(device, timesteps, dim, max_period=10000, repeat_...
  function normalize_2nd_moment (line 145) | def normalize_2nd_moment(x, dim=1):
  class EasyDict (line 151) | class EasyDict(dict):
    method __getattr__ (line 154) | def __getattr__(self, name: str) -> Any:
    method __setattr__ (line 160) | def __setattr__(self, name: str, value: Any) -> None:
    method __delattr__ (line 163) | def __delattr__(self, name: str) -> None:
  function _bias_act_ref (line 167) | def _bias_act_ref(x, b=None, dim=1, act="linear", alpha=None, gain=None,...
  function bias_act (line 198) | def bias_act(
  function _get_filter_size (line 237) | def _get_filter_size(f):
  function _get_weight_shape (line 251) | def _get_weight_shape(w):
  function _parse_scaling (line 256) | def _parse_scaling(scaling):
  function _parse_padding (line 266) | def _parse_padding(padding):
  function setup_filter (line 278) | def setup_filter(
  function _ntuple (line 332) | def _ntuple(n):
  function upfirdn2d (line 419) | def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, ...
  function _upfirdn2d_ref (line 466) | def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gai...
  function downsample2d (line 519) | def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, imp...
  function upsample2d (line 561) | def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl="c...
  class MinibatchStdLayer (line 609) | class MinibatchStdLayer(torch.nn.Module):
    method __init__ (line 610) | def __init__(self, group_size, num_channels=1):
    method forward (line 615) | def forward(self, x):
  class FullyConnectedLayer (line 638) | class FullyConnectedLayer(torch.nn.Module):
    method __init__ (line 639) | def __init__(
    method forward (line 662) | def forward(self, x):
  function _conv2d_wrapper (line 678) | def _conv2d_wrapper(
  function conv2d_resample (line 717) | def conv2d_resample(
  class Conv2dLayer (line 854) | class Conv2dLayer(torch.nn.Module):
    method __init__ (line 855) | def __init__(
    method forward (line 901) | def forward(self, x, gain=1):
  function torch_gc (line 920) | def torch_gc():
  function set_seed (line 927) | def set_seed(seed: int):
  function get_scheduler (line 934) | def get_scheduler(sd_sampler, scheduler_config):
  function is_local_files_only (line 974) | def is_local_files_only(**kwargs) -> bool:
  function handle_from_pretrained_exceptions (line 980) | def handle_from_pretrained_exceptions(func, **kwargs):
  function get_torch_dtype (line 1009) | def get_torch_dtype(device, no_half: bool):
  function enable_low_mem (line 1021) | def enable_low_mem(pipe, enable: bool):

FILE: iopaint/model/zits.py
  function resize (line 47) | def resize(img, height, width, center_crop=False):
  function to_tensor (line 66) | def to_tensor(img, scale=True, norm=False):
  function load_masked_position_encoding (line 83) | def load_masked_position_encoding(mask):
  function load_image (line 146) | def load_image(img, mask, device, sigma256=3.0):
  function to_device (line 212) | def to_device(data, device):
  class ZITS (line 224) | class ZITS(InpaintModel):
    method __init__ (line 231) | def __init__(self, device, **kwargs):
    method init_model (line 241) | def init_model(self, device, **kwargs):
    method download (line 256) | def download():
    method is_downloaded (line 265) | def is_downloaded() -> bool:
    method wireframe_edge_and_line (line 274) | def wireframe_edge_and_line(self, items, enable: bool):
    method forward (line 346) | def forward(self, image, mask, config: InpaintRequest):
    method wireframe_forward (line 377) | def wireframe_forward(self, images, h, w, masks, mask_th=0.925):
    method sample_edge_line_logits (line 430) | def sample_edge_line_logits(

FILE: iopaint/model_manager.py
  class ModelManager (line 17) | class ModelManager:
    method __init__ (line 18) | def __init__(self, name: str, device: torch.device, **kwargs):
    method current_model (line 43) | def current_model(self) -> ModelInfo:
    method init_model (line 46) | def init_model(self, name: str, device, **kwargs):
    method __call__ (line 93) | def __call__(self, image, mask, config: InpaintRequest):
    method scan_models (line 113) | def scan_models(self) -> List[ModelInfo]:
    method switch (line 118) | def switch(self, new_name: str):
    method switch_brushnet_method (line 149) | def switch_brushnet_method(self, config):
    method switch_controlnet_method (line 193) | def switch_controlnet_method(self, config):
    method enable_disable_powerpaint_v2 (line 231) | def enable_disable_powerpaint_v2(self, config: InpaintRequest):
    method enable_disable_lcm_lora (line 250) | def enable_disable_lcm_lora(self, config: InpaintRequest):

FILE: iopaint/plugins/__init__.py
  function build_plugins (line 14) | def build_plugins(

FILE: iopaint/plugins/anime_seg.py
  class REBNCONV (line 13) | class REBNCONV(nn.Module):
    method __init__ (line 14) | def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
    method forward (line 23) | def forward(self, x):
  function _upsample_like (line 31) | def _upsample_like(src, tar):
  class RSU7 (line 38) | class RSU7(nn.Module):
    method __init__ (line 39) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
    method forward (line 74) | def forward(self, x):
  class RSU6 (line 120) | class RSU6(nn.Module):
    method __init__ (line 121) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 148) | def forward(self, x):
  class RSU5 (line 187) | class RSU5(nn.Module):
    method __init__ (line 188) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 211) | def forward(self, x):
  class RSU4 (line 244) | class RSU4(nn.Module):
    method __init__ (line 245) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 264) | def forward(self, x):
  class RSU4F (line 291) | class RSU4F(nn.Module):
    method __init__ (line 292) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 307) | def forward(self, x):
  class ISNetDIS (line 325) | class ISNetDIS(nn.Module):
    method __init__ (line 326) | def __init__(self, in_ch=3, out_ch=1):
    method forward (line 358) | def forward(self, x):
  class AnimeSeg (line 416) | class AnimeSeg(BasePlugin):
    method __init__ (line 422) | def __init__(self):
    method gen_image (line 431) | def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method gen_mask (line 440) | def gen_mask(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method forward (line 444) | def forward(self, rgb_np_img):

FILE: iopaint/plugins/base_plugin.py
  class BasePlugin (line 7) | class BasePlugin:
    method __init__ (line 12) | def __init__(self):
    method gen_image (line 18) | def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method gen_mask (line 22) | def gen_mask(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method check_dep (line 26) | def check_dep(self):
    method switch_model (line 29) | def switch_model(self, new_model_name: str):

FILE: iopaint/plugins/basicsr/arch_util.py
  function default_init_weights (line 10) | def default_init_weights(
  function make_layer (line 45) | def make_layer(
  function pixel_unshuffle (line 64) | def pixel_unshuffle(x: torch.Tensor, scale: int) -> torch.Tensor:

FILE: iopaint/plugins/basicsr/img_util.py
  function img2tensor (line 9) | def img2tensor(imgs, bgr2rgb=True, float32=True):
  function tensor2img (line 38) | def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)):
  function tensor2img_fast (line 97) | def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)):
  function imfrombytes (line 114) | def imfrombytes(content, flag='color', float32=False):
  function imwrite (line 135) | def imwrite(img, file_path, params=None, auto_mkdir=True):
  function crop_border (line 156) | def crop_border(imgs, crop_border):

FILE: iopaint/plugins/basicsr/rrdbnet_arch.py
  class ResidualDenseBlock (line 8) | class ResidualDenseBlock(nn.Module):
    method __init__ (line 18) | def __init__(self, num_feat: int = 64, num_grow_ch: int = 32) -> None:
    method forward (line 33) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class RRDB (line 43) | class RRDB(nn.Module):
    method __init__ (line 53) | def __init__(self, num_feat: int, num_grow_ch: int = 32) -> None:
    method forward (line 59) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class RRDBNet (line 67) | class RRDBNet(nn.Module):
    method __init__ (line 87) | def __init__(
    method forward (line 115) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: iopaint/plugins/briarmbg.py
  class REBNCONV (line 11) | class REBNCONV(nn.Module):
    method __init__ (line 12) | def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
    method forward (line 21) | def forward(self, x):
  function _upsample_like (line 29) | def _upsample_like(src, tar):
  class RSU7 (line 36) | class RSU7(nn.Module):
    method __init__ (line 37) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
    method forward (line 72) | def forward(self, x):
  class RSU6 (line 118) | class RSU6(nn.Module):
    method __init__ (line 119) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 146) | def forward(self, x):
  class RSU5 (line 185) | class RSU5(nn.Module):
    method __init__ (line 186) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 209) | def forward(self, x):
  class RSU4 (line 242) | class RSU4(nn.Module):
    method __init__ (line 243) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 262) | def forward(self, x):
  class RSU4F (line 289) | class RSU4F(nn.Module):
    method __init__ (line 290) | def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
    method forward (line 305) | def forward(self, x):
  class myrebnconv (line 323) | class myrebnconv(nn.Module):
    method __init__ (line 324) | def __init__(
    method forward (line 348) | def forward(self, x):
  class BriaRMBG (line 352) | class BriaRMBG(nn.Module):
    method __init__ (line 353) | def __init__(self, in_ch=3, out_ch=1):
    method forward (line 392) | def forward(self, x):
  function resize_image (line 466) | def resize_image(image):
  function create_briarmbg_session (line 473) | def create_briarmbg_session():
  function briarmbg_process (line 483) | def briarmbg_process(device, bgr_np_image, session, only_mask=False):

FILE: iopaint/plugins/briarmbg2.py
  class Config (line 29) | class Config:
    method __init__ (line 30) | def __init__(self) -> None:
    method print_task (line 234) | def print_task(self) -> None:
  class Mlp (line 239) | class Mlp(nn.Module):
    method __init__ (line 240) | def __init__(
    method _init_weights (line 259) | def _init_weights(self, m):
    method forward (line 274) | def forward(self, x, H, W):
    method __init__ (line 871) | def __init__(
    method forward (line 887) | def forward(self, x):
  class Attention (line 284) | class Attention(nn.Module):
    method __init__ (line 285) | def __init__(
    method _init_weights (line 319) | def _init_weights(self, m):
    method forward (line 334) | def forward(self, x, H, W):
  class Block (line 384) | class Block(nn.Module):
    method __init__ (line 385) | def __init__(
    method _init_weights (line 423) | def _init_weights(self, m):
    method forward (line 438) | def forward(self, x, H, W):
  class OverlapPatchEmbed (line 445) | class OverlapPatchEmbed(nn.Module):
    method __init__ (line 448) | def __init__(
    method _init_weights (line 470) | def _init_weights(self, m):
    method forward (line 485) | def forward(self, x):
  class PyramidVisionTransformerImpr (line 494) | class PyramidVisionTransformerImpr(nn.Module):
    method __init__ (line 495) | def __init__(
    method _init_weights (line 636) | def _init_weights(self, m):
    method init_weights (line 651) | def init_weights(self, pretrained=None):
    method reset_drop_path (line 656) | def reset_drop_path(self, drop_path_rate):
    method freeze_patch_emb (line 674) | def freeze_patch_emb(self):
    method no_weight_decay (line 678) | def no_weight_decay(self):
    method get_classifier (line 687) | def get_classifier(self):
    method reset_classifier (line 690) | def reset_classifier(self, num_classes, global_pool=""):
    method forward_features (line 696) | def forward_features(self, x):
    method forward (line 736) | def forward(self, x):
  class DWConv (line 743) | class DWConv(nn.Module):
    method __init__ (line 744) | def __init__(self, dim=768):
    method forward (line 748) | def forward(self, x, H, W):
  class pvt_v2_b0 (line 757) | class pvt_v2_b0(PyramidVisionTransformerImpr):
    method __init__ (line 758) | def __init__(self, **kwargs):
  class pvt_v2_b1 (line 773) | class pvt_v2_b1(PyramidVisionTransformerImpr):
    method __init__ (line 774) | def __init__(self, **kwargs):
  class pvt_v2_b2 (line 790) | class pvt_v2_b2(PyramidVisionTransformerImpr):
    method __init__ (line 791) | def __init__(self, in_channels=3, **kwargs):
  class pvt_v2_b3 (line 808) | class pvt_v2_b3(PyramidVisionTransformerImpr):
    method __init__ (line 809) | def __init__(self, **kwargs):
  class pvt_v2_b4 (line 825) | class pvt_v2_b4(PyramidVisionTransformerImpr):
    method __init__ (line 826) | def __init__(self, **kwargs):
  class pvt_v2_b5 (line 842) | class pvt_v2_b5(PyramidVisionTransformerImpr):
    method __init__ (line 843) | def __init__(self, **kwargs):
  class Mlp (line 868) | class Mlp(nn.Module):
    method __init__ (line 240) | def __init__(
    method _init_weights (line 259) | def _init_weights(self, m):
    method forward (line 274) | def forward(self, x, H, W):
    method __init__ (line 871) | def __init__(
    method forward (line 887) | def forward(self, x):
  function window_partition (line 896) | def window_partition(x, window_size):
  function window_reverse (line 913) | def window_reverse(windows, window_size, H, W):
  class WindowAttention (line 932) | class WindowAttention(nn.Module):
    method __init__ (line 946) | def __init__(
    method forward (line 996) | def forward(self, x, mask=None):
  class SwinTransformerBlock (line 1063) | class SwinTransformerBlock(nn.Module):
    method __init__ (line 1081) | def __init__(
    method forward (line 1130) | def forward(self, x, mask_matrix):
  class PatchMerging (line 1200) | class PatchMerging(nn.Module):
    method __init__ (line 1208) | def __init__(self, dim, norm_layer=nn.LayerNorm):
    method forward (line 1214) | def forward(self, x, H, W):
  class BasicLayer (line 1244) | class BasicLayer(nn.Module):
    method __init__ (line 1263) | def __init__(
    method forward (line 1313) | def forward(self, x, H, W):
  class PatchEmbed (line 1364) | class PatchEmbed(nn.Module):
    method __init__ (line 1374) | def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_lay...
    method forward (line 1390) | def forward(self, x):
  class SwinTransformer (line 1409) | class SwinTransformer(nn.Module):
    method __init__ (line 1438) | def __init__(
    method _freeze_stages (line 1530) | def _freeze_stages(self):
    method forward (line 1547) | def forward(self, x):
    method train (line 1579) | def train(self, mode=True):
  function swin_v1_t (line 1585) | def swin_v1_t():
  function swin_v1_s (line 1592) | def swin_v1_s():
  function swin_v1_b (line 1599) | def swin_v1_b():
  function swin_v1_l (line 1606) | def swin_v1_l():
  class DeformableConv2d (line 1620) | class DeformableConv2d(nn.Module):
    method __init__ (line 1621) | def __init__(
    method forward (line 1667) | def forward(self, x):
  function build_act_layer (line 1691) | def build_act_layer(act_layer):
  function build_norm_layer (line 1702) | def build_norm_layer(
  class to_channels_first (line 1723) | class to_channels_first(nn.Module):
    method __init__ (line 1724) | def __init__(self):
    method forward (line 1727) | def forward(self, x):
  class to_channels_last (line 1731) | class to_channels_last(nn.Module):
    method __init__ (line 1732) | def __init__(self):
    method forward (line 1735) | def forward(self, x):
  function build_backbone (line 1765) | def build_backbone(bb_name, pretrained=True, params_settings=""):
  function load_weights (line 1819) | def load_weights(model, model_name):
  class BasicDecBlk (line 1863) | class BasicDecBlk(nn.Module):
    method __init__ (line 1864) | def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
    method forward (line 1881) | def forward(self, x):
  class ResBlk (line 1892) | class ResBlk(nn.Module):
    method __init__ (line 1893) | def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
    method forward (line 1917) | def forward(self, x):
  class BasicLatBlk (line 1943) | class BasicLatBlk(nn.Module):
    method __init__ (line 1944) | def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
    method forward (line 1949) | def forward(self, x):
  class _ASPPModule (line 1966) | class _ASPPModule(nn.Module):
    method __init__ (line 1967) | def __init__(self, in_channels, planes, kernel_size, padding, dilation):
    method forward (line 1981) | def forward(self, x):
  class ASPP (line 1988) | class ASPP(nn.Module):
    method __init__ (line 1989) | def __init__(self, in_channels=64, out_channels=None, output_stride=16):
    method forward (line 2042) | def forward(self, x):
  class _ASPPModuleDeformable (line 2059) | class _ASPPModuleDeformable(nn.Module):
    method __init__ (line 2060) | def __init__(self, in_channels, planes, kernel_size, padding):
    method forward (line 2073) | def forward(self, x):
  class ASPPDeformable (line 2080) | class ASPPDeformable(nn.Module):
    method __init__ (line 2081) | def __init__(self, in_channels, out_channels=None, parallel_block_size...
    method forward (line 2123) | def forward(self, x):
  class RefinerPVTInChannels4 (line 2140) | class RefinerPVTInChannels4(nn.Module):
    method __init__ (line 2141) | def __init__(self, in_channels=3 + 1):
    method forward (line 2166) | def forward(self, x):
  class Refiner (line 2188) | class Refiner(nn.Module):
    method __init__ (line 2189) | def __init__(self, in_channels=3 + 1):
    method forward (line 2220) | def forward(self, x):
  class Decoder (line 2243) | class Decoder(nn.Module):
    method __init__ (line 2244) | def __init__(self, channels):
    method forward (line 2265) | def forward(self, features):
    method __init__ (line 2608) | def __init__(self, channels):
    method get_patches_batch (line 2726) | def get_patches_batch(self, x, p):
    method forward (line 2743) | def forward(self, features):
  class RefUNet (line 2292) | class RefUNet(nn.Module):
    method __init__ (line 2294) | def __init__(self, in_channels=3 + 1):
    method forward (line 2350) | def forward(self, x):
  class StemLayer (line 2383) | class StemLayer(nn.Module):
    method __init__ (line 2392) | def __init__(
    method forward (line 2415) | def forward(self, x):
  class BiRefNetConfig (line 2427) | class BiRefNetConfig(PretrainedConfig):
    method __init__ (line 2430) | def __init__(self, bb_pretrained=False, **kwargs):
  class BiRefNet (line 2435) | class BiRefNet(PreTrainedModel):
    method __init__ (line 2438) | def __init__(self, bb_pretrained=True, config=BiRefNetConfig()):
    method forward_enc (line 2494) | def forward_enc(self, x):
    method forward_ori (line 2589) | def forward_ori(self, x):
    method forward (line 2601) | def forward(self, x):
  class Decoder (line 2607) | class Decoder(nn.Module):
    method __init__ (line 2244) | def __init__(self, channels):
    method forward (line 2265) | def forward(self, features):
    method __init__ (line 2608) | def __init__(self, channels):
    method get_patches_batch (line 2726) | def get_patches_batch(self, x, p):
    method forward (line 2743) | def forward(self, features):
  class SimpleConvs (line 2914) | class SimpleConvs(nn.Module):
    method __init__ (line 2915) | def __init__(self, in_channels: int, out_channels: int, inter_channels...
    method forward (line 2920) | def forward(self, x):
  function create_briarmbg2_session (line 2924) | def create_briarmbg2_session():
  function briarmbg2_process (line 2929) | def briarmbg2_process(device, bgr_np_image, session, only_mask=False):

FILE: iopaint/plugins/facexlib/detection/__init__.py
  function init_detection_model (line 8) | def init_detection_model(model_name, half=False, device='cuda', model_ro...

FILE: iopaint/plugins/facexlib/detection/align_trans.py
  class FaceWarpException (line 13) | class FaceWarpException(Exception):
    method __str__ (line 15) | def __str__(self):
  function get_reference_facial_points (line 19) | def get_reference_facial_points(output_size=None, inner_padding_factor=0...
  function get_affine_transform_matrix (line 112) | def get_affine_transform_matrix(src_pts, dst_pts):
  function warp_and_crop_face (line 145) | def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_siz...

FILE: iopaint/plugins/facexlib/detection/matlab_cp2tform.py
  class MatlabCp2tormException (line 7) | class MatlabCp2tormException(Exception):
    method __str__ (line 9) | def __str__(self):
  function tformfwd (line 13) | def tformfwd(trans, uv):
  function tforminv (line 37) | def tforminv(trans, uv):
  function findNonreflectiveSimilarity (line 60) | def findNonreflectiveSimilarity(uv, xy, options=None):
  function findSimilarity (line 94) | def findSimilarity(uv, xy, options=None):
  function get_similarity_transform (line 130) | def get_similarity_transform(src_pts, dst_pts, reflective=True):
  function cvt_tform_mat_for_cv2 (line 170) | def cvt_tform_mat_for_cv2(trans):
  function get_similarity_transform_for_cv2 (line 198) | def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):

FILE: iopaint/plugins/facexlib/detection/retinaface.py
  function generate_config (line 28) | def generate_config(network_name):
  class RetinaFace (line 75) | class RetinaFace(nn.Module):
    method __init__ (line 76) | def __init__(self, network_name="resnet50", half=False, phase="test", ...
    method forward (line 130) | def forward(self, inputs):
    method __detect_faces (line 163) | def __detect_faces(self, inputs):
    method transform (line 196) | def transform(self, image, use_origin_size):
    method detect_faces (line 225) | def detect_faces(
    method __align_multi (line 270) | def __align_multi(self, image, boxes, landmarks, limit=None):
    method align_multi (line 289) | def align_multi(self, img, conf_threshold=0.8, limit=None):
    method batched_transform (line 296) | def batched_transform(self, frames, use_origin_size):
    method batched_detect_faces (line 348) | def batched_detect_faces(

FILE: iopaint/plugins/facexlib/detection/retinaface_net.py
  function conv_bn (line 6) | def conv_bn(inp, oup, stride=1, leaky=0):
  function conv_bn_no_relu (line 12) | def conv_bn_no_relu(inp, oup, stride):
  function conv_bn1X1 (line 19) | def conv_bn1X1(inp, oup, stride, leaky=0):
  function conv_dw (line 25) | def conv_dw(inp, oup, stride, leaky=0.1):
  class SSH (line 36) | class SSH(nn.Module):
    method __init__ (line 38) | def __init__(self, in_channel, out_channel):
    method forward (line 52) | def forward(self, input):
  class FPN (line 66) | class FPN(nn.Module):
    method __init__ (line 68) | def __init__(self, in_channels_list, out_channels):
    method forward (line 80) | def forward(self, input):
  class MobileNetV1 (line 100) | class MobileNetV1(nn.Module):
    method __init__ (line 102) | def __init__(self):
    method forward (line 127) | def forward(self, x):
  class ClassHead (line 138) | class ClassHead(nn.Module):
    method __init__ (line 140) | def __init__(self, inchannels=512, num_anchors=3):
    method forward (line 145) | def forward(self, x):
  class BboxHead (line 152) | class BboxHead(nn.Module):
    method __init__ (line 154) | def __init__(self, inchannels=512, num_anchors=3):
    method forward (line 158) | def forward(self, x):
  class LandmarkHead (line 165) | class LandmarkHead(nn.Module):
    method __init__ (line 167) | def __init__(self, inchannels=512, num_anchors=3):
    method forward (line 171) | def forward(self, x):
  function make_class_head (line 178) | def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
  function make_bbox_head (line 185) | def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
  function make_landmark_head (line 192) | def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):

FILE: iopaint/plugins/facexlib/detection/retinaface_utils.py
  class PriorBox (line 8) | class PriorBox(object):
    method __init__ (line 10) | def __init__(self, cfg, image_size=None, phase='train'):
    method forward (line 19) | def forward(self):
  function py_cpu_nms (line 39) | def py_cpu_nms(dets, thresh):
  function point_form (line 50) | def point_form(boxes):
  function center_size (line 65) | def center_size(boxes):
  function intersect (line 79) | def intersect(box_a, box_b):
  function jaccard (line 98) | def jaccard(box_a, box_b):
  function matrix_iou (line 117) | def matrix_iou(a, b):
  function matrix_iof (line 130) | def matrix_iof(a, b):
  function match (line 142) | def match(threshold, truths, priors, variances, labels, landms, loc_t, c...
  function encode (line 200) | def encode(matched, priors, variances):
  function encode_landm (line 224) | def encode_landm(matched, priors, variances):
  function decode (line 254) | def decode(loc, priors, variances):
  function decode_landm (line 274) | def decode_landm(pre, priors, variances):
  function batched_decode (line 297) | def batched_decode(b_loc, priors, variances):
  function batched_decode_landm (line 320) | def batched_decode_landm(pre, priors, variances):
  function log_sum_exp (line 343) | def log_sum_exp(x):
  function nms (line 357) | def nms(boxes, scores, overlap=0.5, top_k=200):

FILE: iopaint/plugins/facexlib/parsing/__init__.py
  function init_parsing_model (line 8) | def init_parsing_model(model_name='bisenet', half=False, device='cuda', ...

FILE: iopaint/plugins/facexlib/parsing/bisenet.py
  class ConvBNReLU (line 8) | class ConvBNReLU(nn.Module):
    method __init__ (line 10) | def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):
    method forward (line 15) | def forward(self, x):
  class BiSeNetOutput (line 21) | class BiSeNetOutput(nn.Module):
    method __init__ (line 23) | def __init__(self, in_chan, mid_chan, num_class):
    method forward (line 28) | def forward(self, x):
  class AttentionRefinementModule (line 34) | class AttentionRefinementModule(nn.Module):
    method __init__ (line 36) | def __init__(self, in_chan, out_chan):
    method forward (line 43) | def forward(self, x):
  class ContextPath (line 53) | class ContextPath(nn.Module):
    method __init__ (line 55) | def __init__(self):
    method forward (line 64) | def forward(self, x):
  class FeatureFusionModule (line 87) | class FeatureFusionModule(nn.Module):
    method __init__ (line 89) | def __init__(self, in_chan, out_chan):
    method forward (line 97) | def forward(self, fsp, fcp):
  class BiSeNet (line 110) | class BiSeNet(nn.Module):
    method __init__ (line 112) | def __init__(self, num_class):
    method forward (line 120) | def forward(self, x, return_feat=False):

FILE: iopaint/plugins/facexlib/parsing/parsenet.py
  class NormLayer (line 8) | class NormLayer(nn.Module):
    method __init__ (line 16) | def __init__(self, channels, normalize_shape=None, norm_type='bn'):
    method forward (line 35) | def forward(self, x, ref=None):
  class ReluLayer (line 42) | class ReluLayer(nn.Module):
    method __init__ (line 54) | def __init__(self, channels, relu_type='relu'):
    method forward (line 70) | def forward(self, x):
  class ConvLayer (line 74) | class ConvLayer(nn.Module):
    method __init__ (line 76) | def __init__(self,
    method forward (line 103) | def forward(self, x):
  class ResidualBlock (line 113) | class ResidualBlock(nn.Module):
    method __init__ (line 118) | def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', sca...
    method forward (line 132) | def forward(self, x):
  class ParseNet (line 140) | class ParseNet(nn.Module):
    method __init__ (line 142) | def __init__(self,
    method forward (line 188) | def forward(self, x):

FILE: iopaint/plugins/facexlib/parsing/resnet.py
  function conv3x3 (line 5) | def conv3x3(in_planes, out_planes, stride=1):
  class BasicBlock (line 10) | class BasicBlock(nn.Module):
    method __init__ (line 12) | def __init__(self, in_chan, out_chan, stride=1):
    method forward (line 26) | def forward(self, x):
  function create_layer_basic (line 41) | def create_layer_basic(in_chan, out_chan, bnum, stride=1):
  class ResNet18 (line 48) | class ResNet18(nn.Module):
    method __init__ (line 50) | def __init__(self):
    method forward (line 60) | def forward(self, x):

FILE: iopaint/plugins/facexlib/utils/face_restoration_helper.py
  function get_largest_face (line 12) | def get_largest_face(det_faces, h, w):
  function get_center_face (line 33) | def get_center_face(det_faces, h=0, w=0, center=None):
  class FaceRestoreHelper (line 49) | class FaceRestoreHelper(object):
    method __init__ (line 52) | def __init__(
    method set_upscale_factor (line 124) | def set_upscale_factor(self, upscale_factor):
    method read_image (line 127) | def read_image(self, img):
    method get_face_landmarks_5 (line 142) | def get_face_landmarks_5(
    method align_warp_face (line 278) | def align_warp_face(self, save_cropped_path=None, border_mode="constan...
    method get_inverse_affine (line 317) | def get_inverse_affine(self, save_inverse_affine_path=None):
    method add_restored_face (line 329) | def add_restored_face(self, face):
    method paste_faces_to_input_image (line 332) | def paste_faces_to_input_image(self, save_path=None, upsample_img=None):
    method clean_all (line 466) | def clean_all(self):

FILE: iopaint/plugins/facexlib/utils/face_utils.py
  function compute_increased_bbox (line 6) | def compute_increased_bbox(bbox, increase_area, preserve_aspect=True):
  function get_valid_bboxes (line 23) | def get_valid_bboxes(bboxes, h, w):
  function align_crop_face_landmarks (line 31) | def align_crop_face_landmarks(img,
  function paste_face_back (line 190) | def paste_face_back(img, face, inverse_affine):

FILE: iopaint/plugins/facexlib/utils/misc.py
  function imwrite (line 11) | def imwrite(img, file_path, params=None, auto_mkdir=True):
  function img2tensor (line 30) | def img2tensor(imgs, bgr2rgb=True, float32=True):
  function load_file_from_url (line 59) | def load_file_from_url(url, model_dir=None, progress=True, file_name=Non...
  function scandir (line 81) | def scandir(dir_path, suffix=None, recursive=False, full_path=False):

FILE: iopaint/plugins/gfpgan/archs/gfpganv1_clean_arch.py
  class StyleGAN2GeneratorCSFT (line 10) | class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
    method __init__ (line 24) | def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_mu...
    method forward (line 33) | def forward(self,
  class ResBlock (line 119) | class ResBlock(nn.Module):
    method __init__ (line 128) | def __init__(self, in_channels, out_channels, mode='down'):
    method forward (line 139) | def forward(self, x):
  class GFPGANv1Clean (line 151) | class GFPGANv1Clean(nn.Module):
    method __init__ (line 172) | def __init__(
    method forward (line 275) | def forward(self, x, return_latents=False, return_rgb=True, randomize_...

FILE: iopaint/plugins/gfpgan/archs/restoreformer_arch.py
  class VectorQuantizer (line 9) | class VectorQuantizer(nn.Module):
    method __init__ (line 21) | def __init__(self, n_e, e_dim, beta):
    method forward (line 30) | def forward(self, z):
    method get_codebook_entry (line 94) | def get_codebook_entry(self, indices, shape):
  function nonlinearity (line 113) | def nonlinearity(x):
  function Normalize (line 118) | def Normalize(in_channels):
  class Upsample (line 124) | class Upsample(nn.Module):
    method __init__ (line 125) | def __init__(self, in_channels, with_conv):
    method forward (line 133) | def forward(self, x):
  class Downsample (line 140) | class Downsample(nn.Module):
    method __init__ (line 141) | def __init__(self, in_channels, with_conv):
    method forward (line 150) | def forward(self, x):
  class ResnetBlock (line 160) | class ResnetBlock(nn.Module):
    method __init__ (line 161) | def __init__(
    method forward (line 197) | def forward(self, x, temb):
  class MultiHeadAttnBlock (line 220) | class MultiHeadAttnBlock(nn.Module):
    method __init__ (line 221) | def __init__(self, in_channels, head_size=1):
    method forward (line 247) | def forward(self, x, y=None):
  class MultiHeadEncoder (line 290) | class MultiHeadEncoder(nn.Module):
    method __init__ (line 291) | def __init__(
    method forward (line 377) | def forward(self, x):
  class MultiHeadDecoder (line 415) | class MultiHeadDecoder(nn.Module):
    method __init__ (line 416) | def __init__(
    method forward (line 507) | def forward(self, z):
  class MultiHeadDecoderTransformer (line 542) | class MultiHeadDecoderTransformer(nn.Module):
    method __init__ (line 543) | def __init__(
    method forward (line 634) | def forward(self, z, hs):
  class RestoreFormer (line 672) | class RestoreFormer(nn.Module):
    method __init__ (line 673) | def __init__(
    method encode (line 743) | def encode(self, x):
    method decode (line 749) | def decode(self, quant, hs):
    method forward (line 755) | def forward(self, input, **kwargs):

FILE: iopaint/plugins/gfpgan/archs/stylegan2_clean_arch.py
  class NormStyleCode (line 10) | class NormStyleCode(nn.Module):
    method forward (line 11) | def forward(self, x):
  class ModulatedConv2d (line 23) | class ModulatedConv2d(nn.Module):
    method __init__ (line 38) | def __init__(
    method forward (line 74) | def forward(self, x, style):
    method __repr__ (line 112) | def __repr__(self):
  class StyleConv (line 119) | class StyleConv(nn.Module):
    method __init__ (line 131) | def __init__(
    method forward (line 153) | def forward(self, x, style, noise=None):
  class ToRGB (line 168) | class ToRGB(nn.Module):
    method __init__ (line 177) | def __init__(self, in_channels, num_style_feat, upsample=True):
    method forward (line 190) | def forward(self, x, style, skip=None):
  class ConstantInput (line 212) | class ConstantInput(nn.Module):
    method __init__ (line 220) | def __init__(self, num_channel, size):
    method forward (line 224) | def forward(self, batch):
  class StyleGAN2GeneratorClean (line 229) | class StyleGAN2GeneratorClean(nn.Module):
    method __init__ (line 240) | def __init__(
    method make_noise (line 330) | def make_noise(self):
    method get_latent (line 341) | def get_latent(self, x):
    method mean_latent (line 344) | def mean_latent(self, num_latent):
    method forward (line 351) | def forward(

FILE: iopaint/plugins/gfpgan_plugin.py
  class GFPGANPlugin (line 10) | class GFPGANPlugin(BasePlugin):
    method __init__ (line 14) | def __init__(self, device, upscaler=None):
    method gen_image (line 37) | def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:

FILE: iopaint/plugins/gfpganer.py
  class MyGFPGANer (line 13) | class MyGFPGANer:
    method __init__ (line 29) | def __init__(
    method enhance (line 91) | def enhance(

FILE: iopaint/plugins/interactive_seg.py
  class InteractiveSeg (line 81) | class InteractiveSeg(BasePlugin):
    method __init__ (line 85) | def __init__(self, model_name, device):
    method _init_session (line 91) | def _init_session(self, model_name: str):
    method switch_model (line 112) | def switch_model(self, new_model_name):
    method gen_mask (line 122) | def gen_mask(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method forward (line 127) | def forward(self, rgb_np_img, clicks: List[List], img_md5: str):

FILE: iopaint/plugins/realesrgan.py
  class RealESRGANer (line 15) | class RealESRGANer:
    method __init__ (line 30) | def __init__(
    method dni (line 86) | def dni(self, net_a, net_b, dni_weight, key="params", loc="cpu"):
    method pre_process (line 97) | def pre_process(self, img):
    method process (line 123) | def process(self):
    method tile_process (line 127) | def tile_process(self):
    method post_process (line 202) | def post_process(self):
    method enhance (line 224) | def enhance(self, img, outscale=None, alpha_upsampler="realesrgan"):
  class SRVGGNetCompact (line 305) | class SRVGGNetCompact(nn.Module):
    method __init__ (line 320) | def __init__(
    method forward (line 366) | def forward(self, x):
  class RealESRGANUpscaler (line 378) | class RealESRGANUpscaler(BasePlugin):
    method __init__ (line 382) | def __init__(self, name, device, no_half=False):
    method _init_model (line 389) | def _init_model(self, name):
    method switch_model (line 451) | def switch_model(self, new_model_name: str):
    method gen_image (line 457) | def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method forward (line 465) | def forward(self, bgr_np_img, scale: float):

FILE: iopaint/plugins/remove_bg.py
  function _rmbg_remove (line 12) | def _rmbg_remove(device, *args, **kwargs):
  class RemoveBG (line 18) | class RemoveBG(BasePlugin):
    method __init__ (line 23) | def __init__(self, model_name, device):
    method _init_session (line 42) | def _init_session(self, model_name: str):
    method switch_model (line 67) | def switch_model(self, new_model_name):
    method gen_image (line 78) | def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method gen_mask (line 86) | def gen_mask(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:
    method check_dep (line 95) | def check_dep(self):
    method device_warning (line 104) | def device_warning(self):

FILE: iopaint/plugins/restoreformer.py
  class RestoreFormerPlugin (line 10) | class RestoreFormerPlugin(BasePlugin):
    method __init__ (line 14) | def __init__(self, device, upscaler=None):
    method gen_image (line 32) | def gen_image(self, rgb_np_img, req: RunPluginRequest) -> np.ndarray:

FILE: iopaint/plugins/segment_anything/build_sam.py
  function build_sam_vit_h (line 25) | def build_sam_vit_h(checkpoint=None):
  function build_sam_vit_l (line 35) | def build_sam_vit_l(checkpoint=None):
  function build_sam_vit_b (line 45) | def build_sam_vit_b(checkpoint=None):
  function build_sam_vit_t (line 55) | def build_sam_vit_t(checkpoint=None):
  function build_sam_vit_h_hq (line 107) | def build_sam_vit_h_hq(checkpoint=None):
  function build_sam_vit_l_hq (line 117) | def build_sam_vit_l_hq(checkpoint=None):
  function build_sam_vit_b_hq (line 127) | def build_sam_vit_b_hq(checkpoint=None):
  function _build_sam (line 149) | def _build_sam(
  function _build_sam_hq (line 204) | def _build_sam_hq(

FILE: iopaint/plugins/segment_anything/modeling/common.py
  class MLPBlock (line 13) | class MLPBlock(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 25) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LayerNorm2d (line 31) | class LayerNorm2d(nn.Module):
    method __init__ (line 32) | def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
    method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: iopaint/plugins/segment_anything/modeling/image_encoder.py
  class ImageEncoderViT (line 17) | class ImageEncoderViT(nn.Module):
    method __init__ (line 18) | def __init__(
    method forward (line 106) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Block (line 119) | class Block(nn.Module):
    method __init__ (line 122) | def __init__(
    method forward (line 166) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Attention (line 185) | class Attention(nn.Module):
    method __init__ (line 188) | def __init__(
    method forward (line 224) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function window_partition (line 243) | def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.T...
  function window_unpartition (line 267) | def window_unpartition(
  function get_rel_pos (line 292) | def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torc...
  function add_decomposed_rel_pos (line 325) | def add_decomposed_rel_pos(
  class PatchEmbed (line 364) | class PatchEmbed(nn.Module):
    method __init__ (line 369) | def __init__(
    method forward (line 391) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: iopaint/plugins/segment_anything/modeling/image_encoder_hq.py
  class ImageEncoderViTHQ (line 17) | class ImageEncoderViTHQ(nn.Module):
    method __init__ (line 18) | def __init__(
    method forward (line 108) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Block (line 124) | class Block(nn.Module):
    method __init__ (line 127) | def __init__(
    method forward (line 173) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Attention (line 192) | class Attention(nn.Module):
    method __init__ (line 195) | def __init__(
    method forward (line 231) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function window_partition (line 259) | def window_partition(
  function window_unpartition (line 287) | def window_unpartition(
  function get_rel_pos (line 317) | def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torc...
  function add_decomposed_rel_pos (line 350) | def add_decomposed_rel_pos(
  class PatchEmbed (line 391) | class PatchEmbed(nn.Module):
    method __init__ (line 396) | def __init__(
    method forward (line 418) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: iopaint/plugins/segment_anything/modeling/mask_decoder.py
  class MaskDecoder (line 16) | class MaskDecoder(nn.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 75) | def forward(
    method predict_masks (line 116) | def predict_masks(
  class MaskDecoderHQ (line 162) | class MaskDecoderHQ(nn.Module):
    method __init__ (line 163) | def __init__(
    method forward (line 256) | def forward(
    method predict_masks (line 321) | def predict_masks(
  class MLP (line 388) | class MLP(nn.Module):
    method __init__ (line 389) | def __init__(
    method forward (line 405) | def forward(self, x):

FILE: iopaint/plugins/segment_anything/modeling/prompt_encoder.py
  class PromptEncoder (line 16) | class PromptEncoder(nn.Module):
    method __init__ (line 17) | def __init__(
    method get_dense_pe (line 62) | def get_dense_pe(self) -> torch.Tensor:
    method _embed_points (line 73) | def _embed_points(
    method _embed_boxes (line 93) | def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
    method _embed_masks (line 102) | def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
    method _get_batch_size (line 107) | def _get_batch_size(
    method _get_device (line 125) | def _get_device(self) -> torch.device:
    method forward (line 128) | def forward(
  class PositionEmbeddingRandom (line 171) | class PositionEmbeddingRandom(nn.Module):
    method __init__ (line 176) | def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = N...
    method _pe_encoding (line 185) | def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
    method forward (line 194) | def forward(self, size: Tuple[int, int]) -> torch.Tensor:
    method forward_with_coords (line 207) | def forward_with_coords(

FILE: iopaint/plugins/segment_anything/modeling/sam.py
  class Sam (line 18) | class Sam(nn.Module):
    method __init__ (line 22) | def __init__(
    method device (line 50) | def device(self) -> Any:
    method forward (line 54) | def forward(
    method postprocess_masks (line 133) | def postprocess_masks(
    method preprocess (line 164) | def preprocess(self, x: torch.Tensor) -> torch.Tensor:

FILE: iopaint/plugins/segment_anything/modeling/sam_hq.py
  class SamHQ (line 18) | class SamHQ(nn.Module):
    method __init__ (line 22) | def __init__(
    method device (line 50) | def device(self) -> Any:
    method forward (line 53) | def forward(
    method postprocess_masks (line 136) | def postprocess_masks(
    method preprocess (line 167) | def preprocess(self, x: torch.Tensor) -> torch.Tensor:

FILE: iopaint/plugins/segment_anything/modeling/tiny_vit_sam.py
  function _ntuple (line 21) | def _ntuple(n):
  function _trunc_normal_ (line 33) | def _trunc_normal_(tensor, mean, std, a, b):
  function trunc_normal_ (line 70) | def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
  function drop_path (line 97) | def drop_path(
  class TimmDropPath (line 121) | class TimmDropPath(nn.Module):
    method __init__ (line 124) | def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
    method forward (line 129) | def forward(self, x):
    method extra_repr (line 132) | def extra_repr(self):
  class Conv2d_BN (line 136) | class Conv2d_BN(torch.nn.Sequential):
    method __init__ (line 137) | def __init__(
    method fuse (line 150) | def fuse(self):
  class DropPath (line 169) | class DropPath(TimmDropPath):
    method __init__ (line 170) | def __init__(self, drop_prob=None):
    method __repr__ (line 174) | def __repr__(self):
  class PatchEmbed (line 180) | class PatchEmbed(nn.Module):
    method __init__ (line 181) | def __init__(self, in_chans, embed_dim, resolution, activation):
    method forward (line 195) | def forward(self, x):
  class MBConv (line 199) | class MBConv(nn.Module):
    method __init__ (line 200) | def __init__(self, in_chans, out_chans, expand_ratio, activation, drop...
    method forward (line 224) | def forward(self, x):
  class PatchMerging (line 243) | class PatchMerging(nn.Module):
    method __init__ (line 244) | def __init__(self, input_resolution, dim, out_dim, activation):
    method forward (line 258) | def forward(self, x):
  class ConvLayer (line 275) | class ConvLayer(nn.Module):
    method __init__ (line 276) | def __init__(
    method forward (line 316) | def forward(self, x):
  class Mlp (line 327) | class Mlp(nn.Module):
    method __init__ (line 328) | def __init__(
    method forward (line 345) | def forward(self, x):
  class Attention (line 356) | class Attention(torch.nn.Module):
    method __init__ (line 357) | def __init__(
    method train (line 399) | def train(self, mode=True):
    method forward (line 410) | def forward(self, x):  # x (B,N,C)
  class TinyViTBlock (line 437) | class TinyViTBlock(nn.Module):
    method __init__ (line 453) | def __init__(
    method forward (line 497) | def forward(self, x):
    method extra_repr (line 544) | def extra_repr(self) -> str:
  class BasicLayer (line 551) | class BasicLayer(nn.Module):
    method __init__ (line 570) | def __init__(
    method forward (line 620) | def forward(self, x):
    method extra_repr (line 630) | def extra_repr(self) -> str:
  class LayerNorm2d (line 634) | class LayerNorm2d(nn.Module):
    method __init__ (line 635) | def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
    method forward (line 641) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class TinyViT (line 649) | class TinyViT(nn.Module):
    method __init__ (line 650) | def __init__(
    method set_layer_lr_decay (line 756) | def set_layer_lr_decay(self, layer_lr_decay):
    method _init_weights (line 789) | def _init_weights(self, m):
    method no_weight_decay_keywords (line 799) | def no_weight_decay_keywords(self):
    method forward_features (line 802) | def forward_features(self, x):
    method forward (line 818) | def forward(self, x):

FILE: iopaint/plugins/segment_anything/modeling/transformer.py
  class TwoWayTransformer (line 16) | class TwoWayTransformer(nn.Module):
    method __init__ (line 17) | def __init__(
    method forward (line 62) | def forward(
  class TwoWayAttentionBlock (line 109) | class TwoWayAttentionBlock(nn.Module):
    method __init__ (line 110) | def __init__(
    method forward (line 151) | def forward(
  class Attention (line 185) | class Attention(nn.Module):
    method __init__ (line 191) | def __init__(
    method _separate_heads (line 208) | def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
    method _recombine_heads (line 213) | def _recombine_heads(self, x: Tensor) -> Tensor:
    method forward (line 218) | def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:

FILE: iopaint/plugins/segment_anything/predictor.py
  class SamPredictor (line 15) | class SamPredictor:
    method __init__ (line 16) | def __init__(
    method set_image (line 34) | def set_image(
    method set_torch_image (line 65) | def set_torch_image(
    method predict (line 94) | def predict(
    method predict_torch (line 179) | def predict_torch(
    method get_image_embedding (line 259) | def get_image_embedding(self) -> torch.Tensor:
    method device (line 275) | def device(self) -> torch.device:
    method reset_image (line 278) | def reset_image(self) -> None:

FILE: iopaint/plugins/segment_anything/predictor_hq.py
  class SamHQPredictor (line 17) | class SamHQPredictor:
    method __init__ (line 18) | def __init__(
    method set_image (line 34) | def set_image(
    method set_torch_image (line 67) | def set_torch_image(
    method predict (line 96) | def predict(
    method predict_torch (line 183) | def predict_torch(
    method get_image_embedding (line 266) | def get_image_embedding(self) -> torch.Tensor:
    method device (line 282) | def device(self) -> torch.device:
    method reset_image (line 285) | def reset_image(self) -> None:

FILE: iopaint/plugins/segment_anything/utils/transforms.py
  class ResizeLongestSide (line 16) | class ResizeLongestSide:
    method __init__ (line 23) | def __init__(self, target_length: int) -> None:
    method apply_image (line 26) | def apply_image(self, image: np.ndarray) -> np.ndarray:
    method apply_coords (line 35) | def apply_coords(
    method apply_boxes (line 51) | def apply_boxes(
    method apply_image_torch (line 61) | def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
    method apply_coords_torch (line 75) | def apply_coords_torch(
    method apply_boxes_torch (line 91) | def apply_boxes_torch(
    method get_preprocess_shape (line 102) | def get_preprocess_shape(

FILE: iopaint/plugins/segment_anything2/build_sam.py
  function build_memory_attention (line 72) | def build_memory_attention():
  function build_memory_encoder (line 106) | def build_memory_encoder():
  function build_image_encoder_tiny (line 130) | def build_image_encoder_tiny():
  function build_image_encoder_small (line 156) | def build_image_encoder_small():
  function build_image_encoder_base (line 182) | def build_image_encoder_base():
  function build_image_encoder_large (line 208) | def build_image_encoder_large():
  function build_sam2_tiny (line 234) | def build_sam2_tiny():
  function build_sam2_small (line 243) | def build_sam2_small():
  function build_sam2_base (line 252) | def build_sam2_base():
  function build_sam2_large (line 261) | def build_sam2_large():
  function build_sam2_1_tiny (line 270) | def build_sam2_1_tiny():
  function build_sam2_1_small (line 279) | def build_sam2_1_small():
  function build_sam2_1_base (line 288) | def build_sam2_1_base():
  function build_sam2_1_large (line 297) | def build_sam2_1_large():
  function build_sam2 (line 318) | def build_sam2(
  function _load_checkpoint (line 332) | def _load_checkpoint(model, ckpt_path):

FILE: iopaint/plugins/segment_anything2/modeling/backbones/hieradet.py
  function do_pool (line 24) | def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) ->...
  class MultiScaleAttention (line 38) | class MultiScaleAttention(nn.Module):
    method __init__ (line 39) | def __init__(
    method forward (line 55) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class MultiScaleBlock (line 83) | class MultiScaleBlock(nn.Module):
    method __init__ (line 84) | def __init__(
    method forward (line 133) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Hiera (line 168) | class Hiera(nn.Module):
    method __init__ (line 173) | def __init__(
    method _get_pos_embed (line 271) | def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor:
    method forward (line 281) | def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
    method get_layer_id (line 299) | def get_layer_id(self, layer_name):
    method get_num_layers (line 314) | def get_num_layers(self) -> int:

FILE: iopaint/plugins/segment_anything2/modeling/backbones/image_encoder.py
  class ImageEncoder (line 14) | class ImageEncoder(nn.Module):
    method __init__ (line 15) | def __init__(
    method forward (line 29) | def forward(self, sample: torch.Tensor):
  class FpnNeck (line 45) | class FpnNeck(nn.Module):
    method __init__ (line 52) | def __init__(
    method forward (line 102) | def forward(self, xs: List[torch.Tensor]):

FILE: iopaint/plugins/segment_anything2/modeling/backbones/utils.py
  function window_partition (line 16) | def window_partition(x, window_size):
  function window_unpartition (line 41) | def window_unpartition(windows, window_size, pad_hw, hw):
  class PatchEmbed (line 65) | class PatchEmbed(nn.Module):
    method __init__ (line 70) | def __init__(
    method forward (line 91) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: iopaint/plugins/segment_anything2/modeling/memory_attention.py
  class MemoryAttentionLayer (line 17) | class MemoryAttentionLayer(nn.Module):
    method __init__ (line 19) | def __init__(
    method _forward_sa (line 58) | def _forward_sa(self, tgt, query_pos):
    method _forward_ca (line 66) | def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0):
    method forward (line 83) | def forward(
  class MemoryAttention (line 102) | class MemoryAttention(nn.Module):
    method __init__ (line 103) | def __init__(
    method forward (line 119) | def forward(

FILE: iopaint/plugins/segment_anything2/modeling/memory_encoder.py
  class MaskDownSampler (line 17) | class MaskDownSampler(nn.Module):
    method __init__ (line 26) | def __init__(
    method forward (line 57) | def forward(self, x):
  class CXBlock (line 62) | class CXBlock(nn.Module):
    method __init__ (line 74) | def __init__(
    method forward (line 104) | def forward(self, x):
  class Fuser (line 120) | class Fuser(nn.Module):
    method __init__ (line 121) | def __init__(self, layer, num_layers, dim=None, input_projection=False):
    method forward (line 130) | def forward(self, x):
  class MemoryEncoder (line 138) | class MemoryEncoder(nn.Module):
    method __init__ (line 139) | def __init__(
    method forward (line 158) | def forward(

FILE: iopaint/plugins/segment_anything2/modeling/position_encoding.py
  class PositionEmbeddingSine (line 16) | class PositionEmbeddingSine(nn.Module):
    method __init__ (line 22) | def __init__(
    method _encode_xy (line 42) | def _encode_xy(self, x, y):
    method encode_boxes (line 62) | def encode_boxes(self, x, y, w, h):
    method encode_points (line 70) | def encode_points(self, x, y, labels):
    method forward (line 79) | def forward(self, x: torch.Tensor):
  class PositionEmbeddingRandom (line 115) | class PositionEmbeddingRandom(nn.Module):
    method __init__ (line 120) | def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = N...
    method _pe_encoding (line 129) | def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
    method forward (line 138) | def forward(self, size: Tuple[int, int]) -> torch.Tensor:
    method forward_with_coords (line 151) | def forward_with_coords(
  function init_t_xy (line 167) | def init_t_xy(end_x: int, end_y: int):
  function compute_axial_cis (line 174) | def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 1...
  function reshape_for_broadcast (line 186) | def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
  function apply_rotary_enc (line 194) | def apply_rotary_enc(

FILE: iopaint/plugins/segment_anything2/modeling/sam/mask_decoder.py
  class MaskDecoder (line 15) | class MaskDecoder(nn.Module):
    method __init__ (line 16) | def __init__(
    method forward (line 110) | def forward(
    method predict_masks (line 168) | def predict_masks(
    method _get_stability_scores (line 247) | def _get_stability_scores(self, mask_logits):
    method _dynamic_multimask_via_stability (line 259) | def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_sc...

FILE: iopaint/plugins/segment_anything2/modeling/sam/prompt_encoder.py
  class PromptEncoder (line 17) | class PromptEncoder(nn.Module):
    method __init__ (line 18) | def __init__(
    method get_dense_pe (line 68) | def get_dense_pe(self) -> torch.Tensor:
    method _embed_points (line 79) | def _embed_points(
    method _embed_boxes (line 103) | def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
    method _embed_masks (line 114) | def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
    method _get_batch_size (line 119) | def _get_batch_size(
    method _get_device (line 137) | def _get_device(self) -> torch.device:
    method forward (line 140) | def forward(

FILE: iopaint/plugins/segment_anything2/modeling/sam/transformer.py
  class TwoWayTransformer (line 25) | class TwoWayTransformer(nn.Module):
    method __init__ (line 26) | def __init__(
    method forward (line 71) | def forward(
  class TwoWayAttentionBlock (line 118) | class TwoWayAttentionBlock(nn.Module):
    method __init__ (line 119) | def __init__(
    method forward (line 162) | def forward(
  class Attention (line 196) | class Attention(nn.Module):
    method __init__ (line 202) | def __init__(
    method _separate_heads (line 226) | def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor:
    method _recombine_heads (line 231) | def _recombine_heads(self, x: Tensor) -> Tensor:
    method forward (line 236) | def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor:
  class RoPEAttention (line 263) | class RoPEAttention(Attention):
    method __init__ (line 266) | def __init__(
    method forward (line 285) | def forward(

FILE: iopaint/plugins/segment_anything2/modeling/sam2_base.py
  class SAM2Base (line 22) | class SAM2Base(torch.nn.Module):
    method __init__ (line 23) | def __init__(
    method device (line 199) | def device(self):
    method forward (line 202) | def forward(self, *args, **kwargs):
    method _build_sam_heads (line 208) | def _build_sam_heads(self):
    method _forward_sam_heads (line 258) | def _forward_sam_heads(
    method _use_mask_as_output (line 416) | def _use_mask_as_output(self, backbone_features, high_res_features, ma...
    method forward_image (line 468) | def forward_image(self, img_batch: torch.Tensor):
    method _prepare_backbone_features (line 482) | def _prepare_backbone_features(self, backbone_out):
    method _prepare_memory_conditioned_features (line 498) | def _prepare_memory_conditioned_features(
    method _encode_new_memory (line 677) | def _encode_new_memory(
    method _track_step (line 729) | def _track_step(
    method _encode_memory_in_output (line 790) | def _encode_memory_in_output(
    method track_step (line 815) | def track_step(
    method _use_multimask (line 882) | def _use_multimask(self, is_init_cond_frame, point_inputs):
    method _apply_non_overlapping_constraints (line 892) | def _apply_non_overlapping_constraints(self, pred_masks):

FILE: iopaint/plugins/segment_anything2/modeling/sam2_utils.py
  function select_closest_cond_frames (line 19) | def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_f...
  function get_1d_sine_pe (line 64) | def get_1d_sine_pe(pos_inds, dim, temperature=10000):
  function get_activation_fn (line 77) | def get_activation_fn(activation):
  function get_clones (line 88) | def get_clones(module, N):
  class DropPath (line 92) | class DropPath(nn.Module):
    method __init__ (line 94) | def __init__(self, drop_prob=0.0, scale_by_keep=True):
    method forward (line 99) | def forward(self, x):
  class MLP (line 112) | class MLP(nn.Module):
    method __init__ (line 113) | def __init__(
    method forward (line 131) | def forward(self, x):
  class LayerNorm2d (line 141) | class LayerNorm2d(nn.Module):
    method __init__ (line 142) | def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
    method forward (line 148) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function sample_box_points (line 156) | def sample_box_points(
  function sample_random_points_from_errors (line 202) | def sample_random_points_from_errors(gt_masks, pred_masks, num_pt=1):
  function sample_one_point_from_error_center (line 252) | def sample_one_point_from_error_center(gt_masks, pred_masks, padding=True):
  function get_next_point (line 317) | def get_next_point(gt_masks, pred_masks, method):

FILE: iopaint/plugins/segment_anything2/sam2_image_predictor.py
  class SAM2ImagePredictor (line 20) | class SAM2ImagePredictor:
    method __init__ (line 21) | def __init__(
    method set_image (line 66) | def set_image(
    method set_image_batch (line 112) | def set_image_batch(
    method predict_batch (line 155) | def predict_batch(
    method predict (line 217) | def predict(
    method _prep_prompts (line 285) | def _prep_prompts(
    method _predict (line 316) | def _predict(
    method get_image_embedding (line 419) | def get_image_embedding(self) -> torch.Tensor:
    method device (line 435) | def device(self) -> torch.device:
    method reset_predictor (line 438) | def reset_predictor(self) -> None:

FILE: iopaint/plugins/segment_anything2/utils/misc.py
  function get_sdpa_settings (line 14) | def get_sdpa_settings():
  function mask_to_box (line 44) | def mask_to_box(masks: torch.Tensor):
  function _load_img_as_tensor (line 70) | def _load_img_as_tensor(img_path, image_size):
  function concat_points (line 82) | def concat_points(old_point_inputs, new_points, new_labels):

FILE: iopaint/plugins/segment_anything2/utils/transforms.py
  class SAM2Transforms (line 12) | class SAM2Transforms(nn.Module):
    method __init__ (line 13) | def __init__(
    method __call__ (line 34) | def __call__(self, x):
    method forward_batch (line 38) | def forward_batch(self, img_list):
    method transform_coords (line 43) | def transform_coords(
    method transform_boxes (line 63) | def transform_boxes(
    method postprocess_masks (line 73) | def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Ten...

FILE: iopaint/runtime.py
  function dump_environment_info (line 44) | def dump_environment_info() -> Dict[str, str]:
  function check_device (line 57) | def check_device(device: Device) -> Device:
  function setup_model_dir (line 79) | def setup_model_dir(model_dir: Path):

FILE: iopaint/schema.py
  class ModelType (line 22) | class ModelType(str, Enum):
  class ModelInfo (line 31) | class ModelInfo(BaseModel):
    method need_prompt (line 39) | def need_prompt(self) -> bool:
    method controlnets (line 54) | def controlnets(self) -> List[str]:
    method brushnets (line 71) | def brushnets(self) -> List[str]:
    method support_strength (line 80) | def support_strength(self) -> bool:
    method support_outpainting (line 90) | def support_outpainting(self) -> bool:
    method support_lcm_lora (line 100) | def support_lcm_lora(self) -> bool:
    method support_controlnet (line 110) | def support_controlnet(self) -> bool:
    method support_brushnet (line 120) | def support_brushnet(self) -> bool:
    method support_powerpaint_v2 (line 128) | def support_powerpaint_v2(self) -> bool:
  class Choices (line 138) | class Choices(str, Enum):
    method values (line 140) | def values(cls):
  class RealESRGANModel (line 144) | class RealESRGANModel(Choices):
  class RemoveBGModel (line 150) | class RemoveBGModel(Choices):
  class Device (line 169) | class Device(Choices):
  class InteractiveSegModel (line 175) | class InteractiveSegModel(Choices):
  class PluginInfo (line 195) | class PluginInfo(BaseModel):
  class CV2Flag (line 201) | class CV2Flag(str, Enum):
  class HDStrategy (line 206) | class HDStrategy(str, Enum):
  class LDMSampler (line 217) | class LDMSampler(str, Enum):
  class SDSampler (line 222) | class SDSampler(str, Enum):
  class PowerPaintTask (line 245) | class PowerPaintTask(Choices):
  class ApiConfig (line 253) | class ApiConfig(BaseModel):
  class InpaintRequest (line 285) | class InpaintRequest(BaseModel):
    method validate_field (line 422) | def validate_field(cls, values: "InpaintRequest"):
  class RunPluginRequest (line 451) | class RunPluginRequest(BaseModel):
  class MediasResponse (line 463) | class MediasResponse(BaseModel):
  class GenInfoResponse (line 471) | class GenInfoResponse(BaseModel):
  class ServerConfigResponse (line 476) | class ServerConfigResponse(BaseModel):
  class SwitchModelRequest (line 494) | class SwitchModelRequest(BaseModel):
  class SwitchPluginModelRequest (line 498) | class SwitchPluginModelRequest(BaseModel):
  class AdjustMaskRequest (line 506) | class AdjustMaskRequest(BaseModel):

FILE: iopaint/tests/test_adjust_mask.py
  function test_adjust_mask (line 8) | def test_adjust_mask():

FILE: iopaint/tests/test_anytext.py
  function test_anytext (line 20) | def test_anytext(device):

FILE: iopaint/tests/test_brushnet.py
  function test_runway_brushnet (line 22) | def test_runway_brushnet(device, sampler):
  function test_runway_powerpaint_v2 (line 51) | def test_runway_powerpaint_v2(device, sampler):

FILE: iopaint/tests/test_controlnet.py
  function convert_controlnet_method_name (line 19) | def convert_controlnet_method_name(name):
  function test_runway_sd_1_5 (line 25) | def test_runway_sd_1_5(device, controlnet_method):
  function test_controlnet_switch (line 56) | def test_controlnet_switch(device):
  function test_local_file_path (line 88) | def test_local_file_path(device, local_file):

FILE: iopaint/tests/test_instruct_pix2pix.py
  function test_instruct_pix2pix (line 16) | def test_instruct_pix2pix(device, disable_nsfw, cpu_offload):

FILE: iopaint/tests/test_load_img.py
  function test_load_png_image (line 8) | def test_load_png_image():
  function test_load_jpg_image (line 15) | def test_load_jpg_image():

FILE: iopaint/tests/test_low_mem.py
  function test_runway_sd_1_5_low_mem (line 17) | def test_runway_sd_1_5_low_mem(device):
  function test_runway_sd_lcm_lora_low_mem (line 49) | def test_runway_sd_lcm_lora_low_mem(device, sampler):
  function test_runway_norm_sd_model (line 82) | def test_runway_norm_sd_model(device, strategy, sampler):

FILE: iopaint/tests/test_match_histograms.py
  function test_sd_match_histograms (line 11) | def test_sd_match_histograms(device, sampler):

FILE: iopaint/tests/test_model.py
  function test_lama (line 13) | def test_lama(device, strategy):
  function test_ldm (line 36) | def test_ldm(device, strategy, ldm_sampler):
  function test_zits (line 58) | def test_zits(device, strategy, zits_wireframe):
  function test_mat (line 80) | def test_mat(device, strategy, no_half):
  function test_fcf (line 94) | def test_fcf(device, strategy):
  function test_cv2 (line 108) | def test_cv2(strategy, cv2_flag, cv2_radius):
  function test_manga (line 127) | def test_manga(device, strategy):
  function test_mi_gan (line 145) | def test_mi_gan(device, strategy):

FILE: iopaint/tests/test_model_md5.py
  function test_load_model (line 1) | def test_load_model():

FILE: iopaint/tests/test_model_switch.py
  function test_model_switch (line 12) | def test_model_switch():
  function test_controlnet_switch_onoff (line 26) | def test_controlnet_switch_onoff(caplog):
  function test_switch_controlnet_method (line 48) | def test_switch_controlnet_method(caplog):

FILE: iopaint/tests/test_outpainting.py
  function test_outpainting (line 29) | def test_outpainting(name, device, rect):
  function test_kandinsky_outpainting (line 67) | def test_kandinsky_outpainting(name, device, rect):
  function test_powerpaint_outpainting (line 108) | def test_powerpaint_outpainting(name, device, rect):

FILE: iopaint/tests/test_paint_by_example.py
  function assert_equal (line 19) | def assert_equal(
  function test_paint_by_example (line 46) | def test_paint_by_example(device):

FILE: iopaint/tests/test_plugins.py
  function _save (line 35) | def _save(img, name):
  function test_remove_bg (line 42) | def test_remove_bg(model_name, device):
  function test_anime_seg (line 63) | def test_anime_seg():
  function test_upscale (line 78) | def test_upscale(device):
  function test_gfpgan (line 95) | def test_gfpgan(device):
  function test_restoreformer (line 105) | def test_restoreformer(device):
  function test_segment_anything (line 117) | def test_segment_anything(name, device):

FILE: iopaint/tests/test_save_exif.py
  function print_exif (line 13) | def print_exif(exif):
  function extra_info (line 18) | def extra_info(img_p: Path):
  function assert_keys (line 27) | def assert_keys(keys: List[str], infos, res_infos):
  function run_test (line 34) | def run_test(file_path, keys):
  function test_png_icc_profile_png (line 44) | def test_png_icc_profile_png():
  function test_png_icc_profile_jpeg (line 48) | def test_png_icc_profile_jpeg():
  function test_jpeg (line 52) | def test_jpeg():
  function test_png_parameter (line 57) | def test_png_parameter():

FILE: iopaint/tests/test_save_quality.py
  function test_jpeg_quality (line 9) | def test_jpeg_quality():
  function test_png_parameters (line 30) | def test_png_parameters():
  function test_format_conversion (line 51) | def test_format_conversion():

FILE: iopaint/tests/test_sd_model.py
  function test_runway_sd_1_5_all_samplers (line 22) | def test_runway_sd_1_5_all_samplers(device):
  function test_runway_sd_lcm_lora (line 65) | def test_runway_sd_lcm_lora(device, sampler):
  function test_runway_sd_sd_strength (line 96) | def test_runway_sd_sd_strength(device, strategy, sampler):
  function test_runway_sd_cpu_textencoder (line 124) | def test_runway_sd_cpu_textencoder(device, strategy, sampler):
  function test_runway_norm_sd_model (line 151) | def test_runway_norm_sd_model(device, strategy, sampler):
  function test_runway_sd_1_5_cpu_offload (line 176) | def test_runway_sd_1_5_cpu_offload(device, strategy, sampler):
  function test_local_file_path (line 212) | def test_local_file_path(device, sampler, name):

FILE: iopaint/tests/test_sdxl.py
  function test_sdxl (line 18) | def test_sdxl(device, strategy, sampler):
  function test_sdxl_cpu_text_encoder (line 50) | def test_sdxl_cpu_text_encoder(device, strategy, sampler):
  function test_sdxl_outpainting (line 86) | def test_sdxl_outpainting(device, rect):

FILE: iopaint/tests/utils.py
  function check_device (line 14) | def check_device(device: str) -> int:
  function assert_equal (line 23) | def assert_equal(
  function get_data (line 52) | def get_data(
  function get_config (line 66) | def get_config(**kwargs):

FILE: iopaint/web_config.py
  class WebConfig (line 67) | class WebConfig(ApiConfig):
  function load_config (line 71) | def load_config(p: Path) -> WebConfig:
  function save_config (line 83) | def save_config(
  function change_current_model (line 138) | def change_current_model(new_model):
  function main (line 142) | def main(config_file: Path):

FILE: scripts/convert_vae_pt_to_diffusers.py
  function custom_convert_ldm_vae_checkpoint (line 18) | def custom_convert_ldm_vae_checkpoint(checkpoint, config):
  function vae_pt_to_vae_diffuser (line 187) | def vae_pt_to_vae_diffuser(

FILE: scripts/tool.py
  class CheckpointMergerPipeline (line 24) | class CheckpointMergerPipeline(DiffusionPipeline):
    method __init__ (line 45) | def __init__(self):
    method _compare_model_configs (line 49) | def _compare_model_configs(self, dict0, dict1):
    method _remove_meta_keys (line 60) | def _remove_meta_keys(self, config_dict: Dict):
    method merge (line 70) | def merge(
    method weighted_sum (line 314) | def weighted_sum(theta0, theta1, theta2, alpha):
    method sigmoid (line 319) | def sigmoid(theta0, theta1, theta2, alpha):
    method inv_sigmoid (line 325) | def inv_sigmoid(theta0, theta1, theta2, alpha):
    method add_difference (line 332) | def add_difference(theta0, theta1, theta2, alpha):

FILE: setup.py
  function load_requirements (line 18) | def load_requirements():

FILE: web_app/src/App.tsx
  constant SUPPORTED_FILE_TYPE (line 13) | const SUPPORTED_FILE_TYPE = [
  function Home (line 20) | function Home() {

FILE: web_app/src/components/Coffee.tsx
  function Coffee (line 7) | function Coffee() {

FILE: web_app/src/components/Cropper.tsx
  constant DOC_MOVE_OPTS (line 6) | const DOC_MOVE_OPTS = { capture: true, passive: false }
  constant DRAG_HANDLE_BORDER (line 8) | const DRAG_HANDLE_BORDER = 2
  type EVData (line 10) | interface EVData {
  type Props (line 20) | interface Props {

FILE: web_app/src/components/DiffusionProgress.tsx
  constant API_ENDPOINT (line 6) | const API_ENDPOINT = import.meta.env.DEV

FILE: web_app/src/components/Editor.tsx
  constant TOOLBAR_HEIGHT (line 39) | const TOOLBAR_HEIGHT = 200
  constant COMPARE_SLIDER_DURATION_MS (line 40) | const COMPARE_SLIDER_DURATION_MS = 300
  type EditorProps (line 42) | interface EditorProps {
  function Editor (line 46) | function Editor(props: EditorProps) {

FILE: web_app/src/components/Extender.tsx
  constant DOC_MOVE_OPTS (line 7) | const DOC_MOVE_OPTS = { capture: true, passive: false }
  constant DRAG_HANDLE_BORDER (line 9) | const DRAG_HANDLE_BORDER = 2
  type EVData (line 11) | interface EVData {
  type Props (line 21) | interface Props {

FILE: web_app/src/components/FileManager.tsx
  type Photo (line 40) | interface Photo {
  constant SORT_BY_NAME (line 47) | const SORT_BY_NAME = "Name"
  constant SORT_BY_CREATED_TIME (line 48) | const SORT_BY_CREATED_TIME = "Created time"
  constant SORT_BY_MODIFIED_TIME (line 49) | const SORT_BY_MODIFIED_TIME = "Modified time"
  constant IMAGE_TAB (line 51) | const IMAGE_TAB = "input"
  constant OUTPUT_TAB (line 52) | const OUTPUT_TAB = "output"
  constant MASK_TAB (line 53) | const MASK_TAB = "mask"
  type Props (line 61) | interface Props {
  function FileManager (line 66) | function FileManager(props: Props) {

FILE: web_app/src/components/FileSelect.tsx
  type FileSelectProps (line 4) | type FileSelectProps = {
  function FileSelect (line 8) | function FileSelect(props: FileSelectProps) {

FILE: web_app/src/components/InteractiveSeg.tsx
  type InteractiveSegReplaceModal (line 5) | interface InteractiveSegReplaceModal {
  type ItemProps (line 79) | interface ItemProps {

FILE: web_app/src/components/Plugins.tsx
  type PluginName (line 21) | enum PluginName {

FILE: web_app/src/components/Settings.tsx
  constant TAB_GENERAL (line 64) | const TAB_GENERAL = "General"
  constant TAB_MODEL (line 65) | const TAB_MODEL = "Model"
  constant TAB_PLUGINS (line 66) | const TAB_PLUGINS = "Plugins"
  constant TAB_NAMES (line 69) | const TAB_NAMES = [TAB_MODEL, TAB_GENERAL, TAB_PLUGINS]
  function SettingsDialog (line 71) | function SettingsDialog() {

FILE: web_app/src/components/Shortcuts.tsx
  type ShortcutProps (line 13) | interface ShortcutProps {
  function ShortCut (line 18) | function ShortCut(props: ShortcutProps) {
  function Shortcuts (line 44) | function Shortcuts() {

FILE: web_app/src/components/ui/button.tsx
  type ButtonProps (line 39) | interface ButtonProps
  type IconButtonProps (line 63) | interface IconButtonProps extends ButtonProps {
  type UploadButtonProps (line 91) | interface UploadButtonProps extends IconButtonProps {

FILE: web_app/src/components/ui/form.tsx
  type FormFieldContextValue (line 18) | type FormFieldContextValue<
  type FormItemContextValue (line 65) | type FormItemContextValue = {

FILE: web_app/src/components/ui/input.tsx
  type InputProps (line 6) | interface InputProps
  type NumberInputProps (line 40) | interface NumberInputProps extends InputProps {

FILE: web_app/src/components/ui/label.tsx
  type LabelProps (line 11) | interface LabelProps {

FILE: web_app/src/components/ui/sheet.tsx
  type SheetContentProps (line 49) | interface SheetContentProps

FILE: web_app/src/components/ui/textarea.tsx
  type TextareaProps (line 6) | interface TextareaProps

FILE: web_app/src/components/ui/toast.tsx
  type ToastProps (line 119) | type ToastProps = React.ComponentPropsWithoutRef<typeof Toast>
  type ToastActionElement (line 121) | type ToastActionElement = React.ReactElement<typeof ToastAction>

FILE: web_app/src/components/ui/toaster.tsx
  function Toaster (line 11) | function Toaster() {

FILE: web_app/src/components/ui/use-toast.ts
  constant TOAST_LIMIT (line 9) | const TOAST_LIMIT = 1
  constant TOAST_REMOVE_DELAY (line 10) | const TOAST_REMOVE_DELAY = 1000000
  type ToasterToast (line 12) | type ToasterToast = ToastProps & {
  function genId (line 28) | function genId() {
  type ActionType (line 33) | type ActionType = typeof actionTypes
  type Action (line 35) | type Action =
  type State (line 53) | interface State {
  function dispatch (line 134) | function dispatch(action: Action) {
  type Toast (line 141) | type Toast = Omit<ToasterToast, "id">
  function toast (line 143) | function toast({ ...props }: Toast) {
  function useToast (line 172) | function useToast() {

FILE: web_app/src/hooks/useAsyncMemo.tsx
  function useAsyncMemo (line 12) | function useAsyncMemo<T>(

FILE: web_app/src/hooks/useImage.tsx
  function useImage (line 3) | function useImage(file: File | null): [HTMLImageElement, boolean] {

FILE: web_app/src/hooks/useInputImage.tsx
  function useInputImage (line 4) | function useInputImage() {

FILE: web_app/src/lib/api.ts
  constant API_ENDPOINT (line 13) | const API_ENDPOINT = import.meta.env.DEV
  function inpaint (line 28) | async function inpaint(
  function getServerConfig (line 105) | async function getServerConfig(): Promise<ServerConfig> {
  function switchModel (line 110) | async function switchModel(name: string): Promise<ModelInfo> {
  function switchPluginModel (line 115) | async function switchPluginModel(
  function currentModel (line 122) | async function currentModel(): Promise<ModelInfo> {
  function runPlugin (line 127) | async function runPlugin(
  function getMediaFile (line 155) | async function getMediaFile(tab: string, filename: string) {
  function getMediaBlob (line 174) | async function getMediaBlob(tab: string, filename: string) {
  function getMedias (line 190) | async function getMedias(tab: string): Promise<Filename[]> {
  function downloadToOutput (line 195) | async function downloadToOutput(
  function getGenInfo (line 217) | async function getGenInfo(file: File): Promise<GenInfo> {
  function getSamplers (line 224) | async function getSamplers(): Promise<string[]> {
  function postAdjustMask (line 229) | async function postAdjustMask(

FILE: web_app/src/lib/const.ts
  constant ACCENT_COLOR (line 1) | const ACCENT_COLOR = "#ffcc00bb"
  constant DEFAULT_BRUSH_SIZE (line 2) | const DEFAULT_BRUSH_SIZE = 40
  constant MIN_BRUSH_SIZE (line 3) | const MIN_BRUSH_SIZE = 1
  constant MAX_BRUSH_SIZE (line 4) | const MAX_BRUSH_SIZE = 200
  constant MODEL_TYPE_INPAINT (line 5) | const MODEL_TYPE_INPAINT = "inpaint"
  constant MODEL_TYPE_DIFFUSERS_SD (line 6) | const MODEL_TYPE_DIFFUSERS_SD = "diffusers_sd"
  constant MODEL_TYPE_DIFFUSERS_SDXL (line 7) | const MODEL_TYPE_DIFFUSERS_SDXL = "diffusers_sdxl"
  constant MODEL_TYPE_DIFFUSERS_SD_INPAINT (line 8) | const MODEL_TYPE_DIFFUSERS_SD_INPAINT = "diffusers_sd_inpaint"
  constant MODEL_TYPE_DIFFUSERS_SDXL_INPAINT (line 9) | const MODEL_TYPE_DIFFUSERS_SDXL_INPAINT = "diffusers_sdxl_inpaint"
  constant MODEL_TYPE_OTHER (line 10) | const MODEL_TYPE_OTHER = "diffusers_other"
  constant BRUSH_COLOR (line 11) | const BRUSH_COLOR = "#ffcc00bb"
  constant LDM (line 13) | const LDM = "ldm"
  constant CV2 (line 14) | const CV2 = "cv2"
  constant PAINT_BY_EXAMPLE (line 16) | const PAINT_BY_EXAMPLE = "Fantasy-Studio/Paint-by-Example"
  constant INSTRUCT_PIX2PIX (line 17) | const INSTRUCT_PIX2PIX = "timbrooks/instruct-pix2pix"
  constant KANDINSKY_2_2 (line 18) | const KANDINSKY_2_2 = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
  constant POWERPAINT (line 19) | const POWERPAINT = "Sanster/PowerPaint-V1-stable-diffusion-inpainting"
  constant ANYTEXT (line 20) | const ANYTEXT = "Sanster/AnyText"
  constant DEFAULT_NEGATIVE_PROMPT (line 22) | const DEFAULT_NEGATIVE_PROMPT =
  constant SHORTCUT_KEY_CHANGE_BRUSH_SIZE (line 25) | const SHORTCUT_KEY_CHANGE_BRUSH_SIZE = "Alt"

FILE: web_app/src/lib/states.ts
  type FileManagerState (line 41) | type FileManagerState = {
  type CropperState (line 50) | type CropperState = {
  type Settings (line 57) | type Settings = {
  type InteractiveSegState (line 116) | type InteractiveSegState = {
  type EditorState (line 122) | type EditorState = {
  type AppState (line 141) | type AppState = {
  type AppAction (line 166) | type AppAction = {
  method updateEnableControlnet (line 836) | updateEnableControlnet(newValue) {
  method updateLCMLora (line 846) | updateLCMLora(newValue) {

FILE: web_app/src/lib/types.ts
  type Filename (line 1) | interface Filename {
  type PluginInfo (line 9) | interface PluginInfo {
  type ServerConfig (line 15) | interface ServerConfig {
  type GenInfo (line 33) | interface GenInfo {
  type ModelInfo (line 38) | interface ModelInfo {
  type PluginName (line 60) | enum PluginName {
  type PluginParams (line 69) | interface PluginParams {
  type SortBy (line 73) | enum SortBy {
  type SortOrder (line 79) | enum SortOrder {
  type LDMSampler (line 84) | enum LDMSampler {
  type CV2Flag (line 89) | enum CV2Flag {
  type Rect (line 94) | interface Rect {
  type Point (line 101) | interface Point {
  type Line (line 106) | interface Line {
  type LineGroup (line 111) | type LineGroup = Array<Line>
  type Size (line 113) | interface Size {
  type ExtenderDirection (line 118) | enum ExtenderDirection {
  type PowerPaintTask (line 124) | enum PowerPaintTask {
  type AdjustMaskOperate (line 132) | type AdjustMaskOperate = "expand" | "shrink" | "reverse"

FILE: web_app/src/lib/utils.ts
  function cn (line 7) | function cn(...inputs: ClassValue[]) {
  function keepGUIAlive (line 11) | function keepGUIAlive() {
  function dataURItoBlob (line 33) | function dataURItoBlob(dataURI: string) {
  function loadImage (line 43) | function loadImage(image: HTMLImageElement, src: string) {
  function blobToImage (line 56) | async function blobToImage(blob: Blob) {
  function canvasToImage (line 63) | function canvasToImage(
  function fileToImage (line 81) | function fileToImage(file: File): Promise<HTMLImageElement> {
  function srcToFile (line 101) | function srcToFile(src: string, fileName: string, mimeType: string) {
  function askWritePermission (line 111) | async function askWritePermission() {
  function canvasToBlob (line 125) | function canvasToBlob(canvas: HTMLCanvasElement, mime: string): Promise<...
  function isRightClick (line 142) | function isRightClick(ev: SyntheticEvent) {
  function isMidClick (line 147) | function isMidClick(ev: SyntheticEvent) {
  function copyCanvasImage (line 152) | async function copyCanvasImage(canvas: HTMLCanvasElement) {
  function downloadImage (line 161) | function downloadImage(uri: string, name: string) {
  function mouseXY (line 182) | function mouseXY(ev: SyntheticEvent) {
  function drawLines (line 197) | function drawLines(