SYMBOL INDEX (152 symbols across 18 files)

FILE: chatbot/ace_inference.py
  function process_edit_image (line 26) | def process_edit_image(images,
  class TextEmbedding (line 83) | class TextEmbedding(nn.Module):
    method __init__ (line 84) | def __init__(self, embedding_shape):
  class RefinerInference (line 88) | class RefinerInference(DiffusionInference):
    method init_from_cfg (line 89) | def init_from_cfg(self, cfg):
    method encode_first_stage (line 101) | def encode_first_stage(self, x, **kwargs):
    method upscale_resize (line 113) | def upscale_resize(self, image, interpolation=T.InterpolationMode.BILI...
    method decode_first_stage (line 121) | def decode_first_stage(self, z):
    method noise_sample (line 128) | def noise_sample(self, num_samples, h, w, seed, device = None, dtype =...
    method refine (line 140) | def refine(self,
  class ACEInference (line 221) | class ACEInference(DiffusionInference):
    method __init__ (line 222) | def __init__(self, logger=None):
    method init_from_cfg (line 231) | def init_from_cfg(self, cfg):
    method encode_first_stage (line 292) | def encode_first_stage(self, x, **kwargs):
    method decode_first_stage (line 304) | def decode_first_stage(self, z):
    method __call__ (line 319) | def __call__(self,
    method cond_stage_embeddings (line 534) | def cond_stage_embeddings(self, prompt, edit_image, cont, cont_mask):

FILE: chatbot/example.py
  function download_image (line 9) | def download_image(image, local_path=None):
  function blank_image (line 14) | def blank_image():
  function get_examples (line 19) | def get_examples(cache_dir):

FILE: chatbot/infer.py
  function process_edit_image (line 26) | def process_edit_image(images,
  class TextEmbedding (line 83) | class TextEmbedding(nn.Module):
    method __init__ (line 84) | def __init__(self, embedding_shape):
  class ACEInference (line 89) | class ACEInference(DiffusionInference):
    method __init__ (line 90) | def __init__(self, logger=None):
    method init_from_cfg (line 99) | def init_from_cfg(self, cfg):
    method encode_first_stage (line 146) | def encode_first_stage(self, x, **kwargs):
    method decode_first_stage (line 158) | def decode_first_stage(self, z):
    method __call__ (line 171) | def __call__(self,
    method cond_stage_embeddings (line 346) | def cond_stage_embeddings(self, prompt, edit_image, cont, cont_mask):

FILE: chatbot/run_gradio.py
  class ChatBotUI (line 47) | class ChatBotUI(object):
    method __init__ (line 48) | def __init__(self,
    method create_ui (line 177) | def create_ui(self):
    method set_callbacks (line 523) | def set_callbacks(self, *args, **kwargs):
    method get_history (line 1274) | def get_history(self, history):
    method generate_random_string (line 1289) | def generate_random_string(self, length=20):
    method add_edited_image_to_history (line 1295) | def add_edited_image_to_history(self, image, mask_type, history, images):
    method add_uploaded_image_to_history (line 1356) | def add_uploaded_image_to_history(self, img, history, images):
  function run_gr (line 1401) | def run_gr(cfg):

FILE: chatbot/utils.py
  function build_transform (line 12) | def build_transform(input_size):
  function find_closest_aspect_ratio (line 24) | def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height,
  function dynamic_preprocess (line 41) | def dynamic_preprocess(image,
  function load_image (line 83) | def load_image(image_file, input_size=448, max_num=12):

FILE: modules/data/dataset/dataset.py
  class ACEDemoDataset (line 25) | class ACEDemoDataset(BaseDataset):
    method __init__ (line 81) | def __init__(self, cfg, logger=None):
    method __len__ (line 146) | def __len__(self):
    method _get (line 152) | def _get(self, index: int):
    method load_image (line 196) | def load_image(self, prefix, img_path, cvt_type=None):
    method image_preprocess (line 206) | def image_preprocess(self,
    method get_config_template (line 232) | def get_config_template():
    method collate_fn (line 239) | def collate_fn(batch):

FILE: modules/model/backbone/ace.py
  class DiTACE (line 29) | class DiTACE(BaseModel):
    method __init__ (line 87) | def __init__(self, cfg, logger):
    method load_pretrained_model (line 152) | def load_pretrained_model(self, pretrained_model):
    method forward (line 204) | def forward(self,
    method initialize_weights (line 336) | def initialize_weights(self):
    method dtype (line 365) | def dtype(self):
    method get_config_template (line 369) | def get_config_template():

FILE: modules/model/backbone/layers.py
  function drop_path (line 30) | def drop_path(x, drop_prob: float = 0., training: bool = False):
  function modulate (line 50) | def modulate(x, shift, scale, unsqueeze=False):
  class PatchEmbed (line 57) | class PatchEmbed(nn.Module):
    method __init__ (line 60) | def __init__(
    method forward (line 78) | def forward(self, x):
  class DropPath (line 86) | class DropPath(nn.Module):
    method __init__ (line 89) | def __init__(self, drop_prob=None):
    method forward (line 93) | def forward(self, x):
  class RMSNorm (line 97) | class RMSNorm(nn.Module):
    method __init__ (line 98) | def __init__(self, dim, eps=1e-6):
    method forward (line 104) | def forward(self, x):
    method _norm (line 107) | def _norm(self, x):
  class Mlp (line 111) | class Mlp(nn.Module):
    method __init__ (line 114) | def __init__(self,
    method forward (line 128) | def forward(self, x):
  class TimestepEmbedder (line 137) | class TimestepEmbedder(nn.Module):
    method __init__ (line 141) | def __init__(self, hidden_size, frequency_embedding_size=256):
    method timestep_embedding (line 151) | def timestep_embedding(t, dim, max_period=10000):
    method forward (line 173) | def forward(self, t):
  class DiTACEBlock (line 179) | class DiTACEBlock(nn.Module):
    method __init__ (line 180) | def __init__(self,
    method forward (line 226) | def forward(self, x, y, t, **kwargs):
  class MultiHeadAttention (line 244) | class MultiHeadAttention(nn.Module):
    method __init__ (line 245) | def __init__(self,
    method flash_attn (line 291) | def flash_attn(self, x, context=None, **kwargs):
    method forward (line 359) | def forward(self, x, context=None, **kwargs):
  class T2IFinalLayer (line 364) | class T2IFinalLayer(nn.Module):
    method __init__ (line 368) | def __init__(self, hidden_size, patch_size, out_channels):
    method forward (line 380) | def forward(self, x, t):

FILE: modules/model/backbone/pos_embed.py
  function frame_pad (line 9) | def frame_pad(x, seq_len, shapes):
  function frame_unpad (line 26) | def frame_unpad(x, shapes):
  function rope_apply_multires (line 38) | def rope_apply_multires(x, x_lens, x_shapes, freqs, pad=True):
  function rope_params (line 75) | def rope_params(max_seq_len, dim, theta=10000):

FILE: modules/model/diffusion/diffusions.py
  class ACEDiffusion (line 18) | class ACEDiffusion(object):
    method __init__ (line 29) | def __init__(self, cfg, logger=None):
    method init_params (line 35) | def init_params(self):
    method sample (line 53) | def sample(self,
    method loss (line 133) | def loss(self,
    method get_sampler (line 159) | def get_sampler(self, sampler):
    method __repr__ (line 185) | def __repr__(self) -> str:
    method get_config_template (line 189) | def get_config_template():

FILE: modules/model/diffusion/samplers.py
  function _i (line 9) | def _i(tensor, t, x):
  class DDIMSampler (line 20) | class DDIMSampler(BaseDiffusionSampler):
    method init_params (line 21) | def init_params(self):
    method preprare_sampler (line 27) | def preprare_sampler(self,
    method step (line 60) | def step(self, sampler_output):

FILE: modules/model/diffusion/schedules.py
  class ScheduleOutput (line 10) | class ScheduleOutput(object):
    method add_custom_field (line 18) | def add_custom_field(self, key: str, value) -> None:
  class LinearScheduler (line 23) | class LinearScheduler(BaseNoiseScheduler):
    method init_params (line 26) | def init_params(self):
    method betas_to_sigmas (line 31) | def betas_to_sigmas(self, betas):
    method get_schedule (line 34) | def get_schedule(self):
    method add_noise (line 46) | def add_noise(self, x_0, noise=None, t=None, **kwargs):

FILE: modules/model/embedder/embedder.py
  class ACETextEmbedder (line 25) | class ACETextEmbedder(BaseEmbedder):
    method __init__ (line 70) | def __init__(self, cfg, logger=None):
    method freeze (line 112) | def freeze(self):
    method forward (line 118) | def forward(self, tokens, return_mask=False, use_mask=True):
    method _clean (line 134) | def _clean(self, text):
    method encode (line 145) | def encode(self, text, return_mask=False, use_mask=True):
    method encode_list (line 167) | def encode_list(self, text_list, return_mask=True):
    method get_config_template (line 180) | def get_config_template():

FILE: modules/model/network/ldm_ace.py
  class TextEmbedding (line 23) | class TextEmbedding(nn.Module):
    method __init__ (line 24) | def __init__(self, embedding_shape):
  class LdmACE (line 30) | class LdmACE(LatentDiffusion):
    method __init__ (line 34) | def __init__(self, cfg, logger=None):
    method encode_first_stage (line 53) | def encode_first_stage(self, x, **kwargs):
    method decode_first_stage (line 61) | def decode_first_stage(self, z):
    method cond_stage_embeddings (line 67) | def cond_stage_embeddings(self, prompt, edit_image, cont, cont_mask):
    method limit_batch_data (line 85) | def limit_batch_data(self, batch_data_list, log_num):
    method forward_train (line 96) | def forward_train(self,
    method forward_test (line 186) | def forward_test(self,
    method get_config_template (line 348) | def get_config_template():

FILE: modules/model/utils/basic_utils.py
  function exists (line 11) | def exists(x):
  function default (line 15) | def default(val, d):
  function disabled_train (line 21) | def disabled_train(self, mode=True):
  function transfer_size (line 27) | def transfer_size(para_num):
  function count_params (line 44) | def count_params(model):
  function expand_dims_like (line 49) | def expand_dims_like(x, y):
  function unpack_tensor_into_imagelist (line 55) | def unpack_tensor_into_imagelist(image_tensor, shapes):
  function find_example (line 64) | def find_example(tensor_list, image_list):
  function pack_imagelist_into_tensor_v2 (line 75) | def pack_imagelist_into_tensor_v2(image_list):
  function to_device (line 95) | def to_device(inputs, strict=True):
  function check_list_of_list (line 103) | def check_list_of_list(ll):

FILE: modules/solver/ace_solver.py
  class ACESolverV1 (line 16) | class ACESolverV1(LatentDiffusionSolver):
    method __init__ (line 17) | def __init__(self, cfg, logger=None):
    method save_results (line 21) | def save_results(self, results):
    method run_eval (line 65) | def run_eval(self):
    method run_test (line 95) | def run_test(self):
    method probe_data (line 126) | def probe_data(self):

FILE: tools/run_inference.py
  function run_one_case (line 31) | def run_one_case(pipe, input_image, input_mask, edit_k,
  function run (line 63) | def run():

FILE: tools/run_train.py
  function run_task (line 21) | def run_task(cfg):
  function update_config (line 29) | def update_config(cfg):
  function run (line 45) | def run():