SYMBOL INDEX (5205 symbols across 233 files)

FILE: .ci/update_windows/update.py
  function pull (line 8) | def pull(repo, remote_name='origin', branch='master'):
  function latest_tag (line 96) | def latest_tag(repo):
  function files_equal (line 130) | def files_equal(file1, file2):
  function file_size (line 136) | def file_size(f):

FILE: alembic_db/env.py
  function run_migrations_offline (line 20) | def run_migrations_offline() -> None:
  function run_migrations_online (line 41) | def run_migrations_online() -> None:

FILE: alembic_db/versions/0001_assets.py
  function upgrade (line 17) | def upgrade() -> None:
  function downgrade (line 144) | def downgrade() -> None:

FILE: alembic_db/versions/0002_merge_to_asset_references.py
  function upgrade (line 21) | def upgrade() -> None:
  function downgrade (line 175) | def downgrade() -> None:

FILE: alembic_db/versions/0003_add_metadata_job_id.py
  function upgrade (line 21) | def upgrade() -> None:
  function downgrade (line 63) | def downgrade() -> None:

FILE: api_server/routes/internal/internal_routes.py
  class InternalRoutes (line 8) | class InternalRoutes:
    method __init__ (line 15) | def __init__(self, prompt_server):
    method setup_routes (line 21) | def setup_routes(self):
    method get_app (line 73) | def get_app(self):

FILE: api_server/services/terminal_service.py
  class TerminalService (line 6) | class TerminalService:
    method __init__ (line 7) | def __init__(self, server):
    method get_terminal_size (line 14) | def get_terminal_size(self):
    method update_size (line 25) | def update_size(self):
    method subscribe (line 42) | def subscribe(self, client_id):
    method unsubscribe (line 45) | def unsubscribe(self, client_id):
    method send_messages (line 48) | def send_messages(self, entries):

FILE: api_server/utils/file_operations.py
  class FileInfo (line 4) | class FileInfo(TypedDict):
  class DirectoryInfo (line 10) | class DirectoryInfo(TypedDict):
  function is_file_info (line 17) | def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:
  class FileSystemOperations (line 20) | class FileSystemOperations:
    method walk_directory (line 22) | def walk_directory(directory: str) -> List[FileSystemItem]:

FILE: app/app_settings.py
  class AppSettings (line 7) | class AppSettings():
    method __init__ (line 8) | def __init__(self, user_manager):
    method get_settings (line 11) | def get_settings(self, request):
    method save_settings (line 30) | def save_settings(self, request, settings):
    method add_routes (line 36) | def add_routes(self, routes):

FILE: app/assets/api/routes.py
  function _require_assets_feature_enabled (line 49) | def _require_assets_feature_enabled(handler):
  function get_query_dict (line 67) | def get_query_dict(request: web.Request) -> dict[str, Any]:
  function register_assets_routes (line 87) | def register_assets_routes(
  function disable_assets_routes (line 98) | def disable_assets_routes() -> None:
  function _build_error_response (line 104) | def _build_error_response(
  function _build_validation_error_response (line 113) | def _build_validation_error_response(code: str, ve: ValidationError) -> ...
  function _validate_sort_field (line 118) | def _validate_sort_field(requested: str | None) -> str:
  function _build_preview_url_from_view (line 127) | def _build_preview_url_from_view(tags: list[str], user_metadata: dict[st...
  function _build_asset_response (line 153) | def _build_asset_response(result: schemas.AssetDetailResult | schemas.Up...
  function head_asset_by_hash (line 184) | async def head_asset_by_hash(request: web.Request) -> web.Response:
  function list_assets_route (line 198) | async def list_assets_route(request: web.Request) -> web.Response:
  function get_asset_route (line 236) | async def get_asset_route(request: web.Request) -> web.Response:
  function download_asset_content (line 271) | async def download_asset_content(request: web.Request) -> web.Response:
  function create_asset_from_hash_route (line 337) | async def create_asset_from_hash_route(request: web.Request) -> web.Resp...
  function upload_asset (line 377) | async def upload_asset(request: web.Request) -> web.Response:
  function update_asset_route (line 480) | async def update_asset_route(request: web.Request) -> web.Response:
  function delete_asset_route (line 518) | async def delete_asset_route(request: web.Request) -> web.Response:
  function get_tags (line 550) | async def get_tags(request: web.Request) -> web.Response:
  function add_asset_tags (line 587) | async def add_asset_tags(request: web.Request) -> web.Response:
  function delete_asset_tags (line 635) | async def delete_asset_tags(request: web.Request) -> web.Response:
  function get_tags_refine (line 682) | async def get_tags_refine(request: web.Request) -> web.Response:
  function seed_assets (line 704) | async def seed_assets(request: web.Request) -> web.Response:
  function get_seed_status (line 754) | async def get_seed_status(request: web.Request) -> web.Response:
  function cancel_seed (line 776) | async def cancel_seed(request: web.Request) -> web.Response:
  function mark_missing_assets (line 786) | async def mark_missing_assets(request: web.Request) -> web.Response:

FILE: app/assets/api/schemas_in.py
  class UploadError (line 16) | class UploadError(Exception):
    method __init__ (line 19) | def __init__(self, status: int, code: str, message: str):
  class AssetValidationError (line 26) | class AssetValidationError(Exception):
    method __init__ (line 29) | def __init__(self, code: str, message: str):
  class ParsedUpload (line 36) | class ParsedUpload:
  class ListAssetsQuery (line 52) | class ListAssetsQuery(BaseModel):
    method _split_csv_tags (line 70) | def _split_csv_tags(cls, v):
    method _parse_metadata_json (line 86) | def _parse_metadata_json(cls, v):
  class UpdateAssetBody (line 100) | class UpdateAssetBody(BaseModel):
    method _validate_at_least_one_field (line 106) | def _validate_at_least_one_field(self):
  class CreateFromHashBody (line 117) | class CreateFromHashBody(BaseModel):
    method _require_blake3 (line 129) | def _require_blake3(cls, v):
    method _normalize_tags_field (line 134) | def _normalize_tags_field(cls, v):
  class TagsRefineQuery (line 151) | class TagsRefineQuery(BaseModel):
    method _split_csv_tags (line 160) | def _split_csv_tags(cls, v):
    method _parse_metadata_json (line 175) | def _parse_metadata_json(cls, v):
  class TagsListQuery (line 189) | class TagsListQuery(BaseModel):
    method normalize_prefix (line 200) | def normalize_prefix(cls, v: str | None) -> str | None:
  class TagsAdd (line 207) | class TagsAdd(BaseModel):
    method normalize_tags (line 213) | def normalize_tags(cls, v: list[str]) -> list[str]:
  class TagsRemove (line 230) | class TagsRemove(TagsAdd):
  class UploadAssetSpec (line 234) | class UploadAssetSpec(BaseModel):
    method _parse_hash (line 259) | def _parse_hash(cls, v):
    method _parse_tags (line 269) | def _parse_tags(cls, v):
    method _parse_metadata_json (line 315) | def _parse_metadata_json(cls, v):
    method _validate_order (line 332) | def _validate_order(self):

FILE: app/assets/api/schemas_out.py
  class Asset (line 7) | class Asset(BaseModel):
    method _serialize_datetime (line 31) | def _serialize_datetime(self, v: datetime | None, _info):
  class AssetCreated (line 35) | class AssetCreated(Asset):
  class AssetsList (line 39) | class AssetsList(BaseModel):
  class TagUsage (line 45) | class TagUsage(BaseModel):
  class TagsList (line 51) | class TagsList(BaseModel):
  class TagsAdd (line 57) | class TagsAdd(BaseModel):
  class TagsRemove (line 64) | class TagsRemove(BaseModel):
  class TagHistogram (line 71) | class TagHistogram(BaseModel):

FILE: app/assets/api/upload.py
  function normalize_and_validate_hash (line 13) | def normalize_and_validate_hash(s: str) -> str:
  function parse_multipart_upload (line 24) | async def parse_multipart_upload(
  function delete_temp_file_if_exists (line 172) | def delete_temp_file_if_exists(tmp_path: str | None) -> None:

FILE: app/assets/database/models.py
  class Asset (line 26) | class Asset(Base):
    method __repr__ (line 56) | def __repr__(self) -> str:
  class AssetReference (line 60) | class AssetReference(Base):
    method __repr__ (line 164) | def __repr__(self) -> str:
  class AssetReferenceMeta (line 169) | class AssetReferenceMeta(Base):
  class AssetReferenceTag (line 201) | class AssetReferenceTag(Base):
  class Tag (line 226) | class Tag(Base):
    method __repr__ (line 245) | def __repr__(self) -> str:

FILE: app/assets/database/queries/asset.py
  function asset_exists_by_hash (line 10) | def asset_exists_by_hash(
  function get_asset_by_hash (line 28) | def get_asset_by_hash(
  function upsert_asset (line 39) | def upsert_asset(
  function bulk_insert_assets (line 81) | def bulk_insert_assets(
  function get_existing_asset_ids (line 93) | def get_existing_asset_ids(
  function update_asset_hash_and_mime (line 109) | def update_asset_hash_and_mime(
  function reassign_asset_references (line 126) | def reassign_asset_references(

FILE: app/assets/database/queries/asset_reference.py
  function _check_is_scalar (line 37) | def _check_is_scalar(v):
  function _scalar_to_row (line 47) | def _scalar_to_row(key: str, ordinal: int, value) -> dict:
  function convert_metadata_to_rows (line 59) | def convert_metadata_to_rows(key: str, value) -> list[dict]:
  function get_reference_by_id (line 77) | def get_reference_by_id(
  function get_reference_with_owner_check (line 84) | def get_reference_with_owner_check(
  function get_reference_by_file_path (line 103) | def get_reference_by_file_path(
  function reference_exists_for_asset_id (line 117) | def reference_exists_for_asset_id(
  function reference_exists (line 131) | def reference_exists(
  function insert_reference (line 146) | def insert_reference(
  function get_or_create_reference (line 177) | def get_or_create_reference(
  function update_reference_timestamps (line 229) | def update_reference_timestamps(
  function list_references_page (line 241) | def list_references_page(
  function fetch_reference_asset_and_tags (line 321) | def fetch_reference_asset_and_tags(
  function fetch_reference_and_asset (line 358) | def fetch_reference_and_asset(
  function update_reference_access_time (line 380) | def update_reference_access_time(
  function update_reference_name (line 398) | def update_reference_name(
  function update_reference_updated_at (line 412) | def update_reference_updated_at(
  function rebuild_metadata_projection (line 426) | def rebuild_metadata_projection(session: Session, ref: AssetReference) -...
  function set_reference_metadata (line 462) | def set_reference_metadata(
  function set_reference_system_metadata (line 478) | def set_reference_system_metadata(
  function delete_reference_by_id (line 495) | def delete_reference_by_id(
  function soft_delete_reference_by_id (line 507) | def soft_delete_reference_by_id(
  function set_reference_preview (line 529) | def set_reference_preview(
  class CacheStateRow (line 550) | class CacheStateRow(NamedTuple):
  function list_references_by_asset_id (line 562) | def list_references_by_asset_id(
  function list_all_file_paths_by_asset_id (line 579) | def list_all_file_paths_by_asset_id(
  function upsert_reference (line 598) | def upsert_reference(
  function mark_references_missing_outside_prefixes (line 655) | def mark_references_missing_outside_prefixes(
  function restore_references_by_paths (line 679) | def restore_references_by_paths(session: Session, file_paths: list[str])...
  function get_unreferenced_unhashed_asset_ids (line 700) | def get_unreferenced_unhashed_asset_ids(session: Session) -> list[str]:
  function delete_assets_by_ids (line 722) | def delete_assets_by_ids(session: Session, asset_ids: list[str]) -> int:
  function get_references_for_prefixes (line 739) | def get_references_for_prefixes(
  function bulk_update_needs_verify (line 797) | def bulk_update_needs_verify(
  function bulk_update_is_missing (line 817) | def bulk_update_is_missing(
  function update_is_missing_by_asset_id (line 837) | def update_is_missing_by_asset_id(
  function delete_references_by_ids (line 853) | def delete_references_by_ids(session: Session, reference_ids: list[str])...
  function delete_orphaned_seed_asset (line 869) | def delete_orphaned_seed_asset(session: Session, asset_id: str) -> bool:
  class UnenrichedReferenceRow (line 886) | class UnenrichedReferenceRow(NamedTuple):
  function get_unenriched_references (line 895) | def get_unenriched_references(
  function bulk_update_enrichment_level (line 945) | def bulk_update_enrichment_level(
  function bulk_insert_references_ignore_conflicts (line 964) | def bulk_insert_references_ignore_conflicts(
  function get_references_by_paths_and_asset_ids (line 983) | def get_references_by_paths_and_asset_ids(
  function get_reference_ids_by_ids (line 1014) | def get_reference_ids_by_ids(

FILE: app/assets/database/queries/common.py
  function calculate_rows_per_statement (line 16) | def calculate_rows_per_statement(cols: int) -> int:
  function iter_chunks (line 21) | def iter_chunks(seq, n: int):
  function iter_row_chunks (line 27) | def iter_row_chunks(rows: list[dict], cols_per_row: int) -> Iterable[lis...
  function build_visible_owner_clause (line 34) | def build_visible_owner_clause(owner_id: str) -> sa.sql.ClauseElement:
  function build_prefix_like_conditions (line 45) | def build_prefix_like_conditions(
  function apply_tag_filters (line 59) | def apply_tag_filters(
  function apply_metadata_filter (line 87) | def apply_metadata_filter(

FILE: app/assets/database/queries/tags.py
  class AddTagsResult (line 27) | class AddTagsResult:
  class RemoveTagsResult (line 34) | class RemoveTagsResult:
  class SetTagsResult (line 41) | class SetTagsResult:
  function validate_tags_exist (line 47) | def validate_tags_exist(session: Session, tags: list[str]) -> None:
  function ensure_tags_exist (line 58) | def ensure_tags_exist(
  function get_reference_tags (line 73) | def get_reference_tags(session: Session, reference_id: str) -> list[str]:
  function set_reference_tags (line 86) | def set_reference_tags(
  function add_tags_to_reference (line 126) | def add_tags_to_reference(
  function remove_tags_from_reference (line 178) | def remove_tags_from_reference(
  function add_missing_tag_for_asset_id (line 210) | def add_missing_tag_for_asset_id(
  function remove_missing_tag_for_asset_id (line 247) | def remove_missing_tag_for_asset_id(
  function list_tags_with_usage (line 261) | def list_tags_with_usage(
  function list_tag_counts_for_filtered_assets (line 338) | def list_tag_counts_for_filtered_assets(
  function bulk_insert_tags_and_meta (line 385) | def bulk_insert_tags_and_meta(

FILE: app/assets/helpers.py
  function select_best_live_path (line 6) | def select_best_live_path(states: Sequence) -> str:
  function escape_sql_like_string (line 26) | def escape_sql_like_string(s: str, escape: str = "!") -> tuple[str, str]:
  function get_utc_now (line 36) | def get_utc_now() -> datetime:
  function normalize_tags (line 41) | def normalize_tags(tags: list[str] | None) -> list[str]:
  function validate_blake3_hash (line 50) | def validate_blake3_hash(s: str) -> str:

FILE: app/assets/scanner.py
  class _RefInfo (line 44) | class _RefInfo(TypedDict):
  class _AssetAccumulator (line 52) | class _AssetAccumulator(TypedDict):
  function get_prefixes_for_root (line 61) | def get_prefixes_for_root(root: RootType) -> list[str]:
  function get_all_known_prefixes (line 74) | def get_all_known_prefixes() -> list[str]:
  function collect_models_files (line 80) | def collect_models_files() -> list[str]:
  function sync_references_with_filesystem (line 102) | def sync_references_with_filesystem(
  function sync_root_safely (line 232) | def sync_root_safely(root: RootType) -> set[str]:
  function mark_missing_outside_prefixes_safely (line 252) | def mark_missing_outside_prefixes_safely(prefixes: list[str]) -> int:
  function collect_paths_for_roots (line 267) | def collect_paths_for_roots(roots: tuple[RootType, ...]) -> list[str]:
  function build_asset_specs (line 279) | def build_asset_specs(
  function insert_asset_specs (line 349) | def insert_asset_specs(specs: list[SeedAssetSpec], tag_pool: set[str]) -...
  function get_unenriched_assets_for_roots (line 367) | def get_unenriched_assets_for_roots(
  function enrich_asset (line 395) | def enrich_asset(
  function enrich_assets_batch (line 514) | def enrich_assets_batch(

FILE: app/assets/seeder.py
  class ScanInProgressError (line 28) | class ScanInProgressError(Exception):
  class State (line 32) | class State(Enum):
  class ScanPhase (line 41) | class ScanPhase(Enum):
  class Progress (line 50) | class Progress:
  class ScanStatus (line 60) | class ScanStatus:
  class _AssetSeeder (line 71) | class _AssetSeeder:
    method __init__ (line 79) | def __init__(self) -> None:
    method disable (line 96) | def disable(self) -> None:
    method is_disabled (line 101) | def is_disabled(self) -> bool:
    method start (line 105) | def start(
    method start_fast (line 151) | def start_fast(
    method start_enrich (line 175) | def start_enrich(
    method cancel (line 199) | def cancel(self) -> bool:
    method stop (line 214) | def stop(self) -> bool:
    method pause (line 222) | def pause(self) -> bool:
    method resume (line 238) | def resume(self) -> bool:
    method restart (line 255) | def restart(
    method wait (line 300) | def wait(self, timeout: float | None = None) -> bool:
    method get_status (line 316) | def get_status(self) -> ScanStatus:
    method shutdown (line 333) | def shutdown(self, timeout: float = 5.0) -> None:
    method mark_missing_outside_prefixes (line 344) | def mark_missing_outside_prefixes(self) -> int:
    method _is_cancelled (line 388) | def _is_cancelled(self) -> bool:
    method _is_paused_or_cancelled (line 392) | def _is_paused_or_cancelled(self) -> bool:
    method _check_pause_and_cancel (line 402) | def _check_pause_and_cancel(self) -> bool:
    method _emit_event (line 417) | def _emit_event(self, event_type: str, data: dict) -> None:
    method _update_progress (line 427) | def _update_progress(
    method _add_error (line 466) | def _add_error(self, message: str) -> None:
    method _log_scan_config (line 472) | def _log_scan_config(self, roots: tuple[RootType, ...]) -> None:
    method _run_scan (line 487) | def _run_scan(self) -> None:
    method _run_fast_phase (line 601) | def _run_fast_phase(self, roots: tuple[RootType, ...]) -> tuple[int, i...
    method _run_enrich_phase (line 709) | def _run_enrich_phase(self, roots: tuple[RootType, ...]) -> tuple[bool...

FILE: app/assets/services/asset_management.py
  function get_asset_detail (line 44) | def get_asset_detail(
  function update_asset_metadata (line 65) | def update_asset_metadata(
  function delete_asset_reference (line 147) | def delete_asset_reference(
  function set_asset_preview (line 203) | def set_asset_preview(
  function asset_exists (line 234) | def asset_exists(asset_hash: str) -> bool:
  function get_asset_by_hash (line 239) | def get_asset_by_hash(asset_hash: str) -> AssetData | None:
  function list_assets_page (line 245) | def list_assets_page(
  function resolve_hash_to_path (line 283) | def resolve_hash_to_path(
  function resolve_asset_for_download (line 324) | def resolve_asset_for_download(

FILE: app/assets/services/bulk_ingest.py
  class SeedAssetSpec (line 28) | class SeedAssetSpec(TypedDict):
  class AssetRow (line 42) | class AssetRow(TypedDict):
  class ReferenceRow (line 52) | class ReferenceRow(TypedDict):
  class TagRow (line 68) | class TagRow(TypedDict):
  class MetadataRow (line 77) | class MetadataRow(TypedDict):
  class BulkInsertResult (line 90) | class BulkInsertResult:
  function batch_insert_seed_assets (line 98) | def batch_insert_seed_assets(
  function cleanup_unreferenced_assets (line 270) | def cleanup_unreferenced_assets(session: Session) -> int:

FILE: app/assets/services/file_utils.py
  function get_mtime_ns (line 4) | def get_mtime_ns(stat_result: os.stat_result) -> int:
  function get_size_and_mtime_ns (line 11) | def get_size_and_mtime_ns(path: str, follow_symlinks: bool = True) -> tu...
  function verify_file_unchanged (line 17) | def verify_file_unchanged(
  function is_visible (line 39) | def is_visible(name: str) -> bool:
  function list_files_recursively (line 44) | def list_files_recursively(base_dir: str) -> list[str]:

FILE: app/assets/services/hashing.py
  class HashCheckpoint (line 19) | class HashCheckpoint:
  function _open_for_hashing (line 29) | def _open_for_hashing(fp: str | IO[bytes]) -> Iterator[tuple[IO[bytes], ...
  function compute_blake3_hash (line 51) | def compute_blake3_hash(

FILE: app/assets/services/ingest.py
  function _ingest_file_from_path (line 45) | def _ingest_file_from_path(
  function _register_existing_asset (line 133) | def _register_existing_asset(
  function _update_metadata_with_filename (line 213) | def _update_metadata_with_filename(
  function _sanitize_filename (line 237) | def _sanitize_filename(name: str | None, fallback: str) -> str:
  class HashMismatchError (line 242) | class HashMismatchError(Exception):
  class DependencyMissingError (line 246) | class DependencyMissingError(Exception):
    method __init__ (line 247) | def __init__(self, message: str):
  function upload_from_temp_path (line 252) | def upload_from_temp_path(
  function register_file_in_place (line 364) | def register_file_in_place(
  function create_from_hash (line 430) | def create_from_hash(

FILE: app/assets/services/metadata_extract.py
  class ExtractedMetadata (line 29) | class ExtractedMetadata:
    method to_user_metadata (line 58) | def to_user_metadata(self) -> dict[str, Any]:
    method to_meta_rows (line 104) | def to_meta_rows(self, reference_id: str) -> list[dict]:
  function _read_safetensors_header (line 177) | def _read_safetensors_header(
  function _extract_safetensors_metadata (line 208) | def _extract_safetensors_metadata(
  function extract_file_metadata (line 278) | def extract_file_metadata(

FILE: app/assets/services/path_utils.py
  function get_comfy_models_folders (line 12) | def get_comfy_models_folders() -> list[tuple[str, list[str]]]:
  function resolve_destination_from_tags (line 29) | def resolve_destination_from_tags(tags: list[str]) -> tuple[str, list[st...
  function validate_path_within_base (line 61) | def validate_path_within_base(candidate: str, base: str) -> None:
  function compute_relative_filename (line 68) | def compute_relative_filename(file_path: str) -> str | None:
  function get_asset_category_and_relative_path (line 94) | def get_asset_category_and_relative_path(
  function get_name_and_tags_from_asset_path (line 153) | def get_name_and_tags_from_asset_path(file_path: str) -> tuple[str, list...

FILE: app/assets/services/schemas.py
  class AssetData (line 11) | class AssetData:
  class ReferenceData (line 18) | class ReferenceData:
  class AssetDetailResult (line 34) | class AssetDetailResult:
  class RegisterAssetResult (line 41) | class RegisterAssetResult:
  class IngestResult (line 49) | class IngestResult:
  class TagUsage (line 57) | class TagUsage(NamedTuple):
  class AssetSummaryData (line 64) | class AssetSummaryData:
  class ListAssetsResult (line 71) | class ListAssetsResult:
  class DownloadResolutionResult (line 77) | class DownloadResolutionResult:
  class UploadResult (line 84) | class UploadResult:
  function extract_reference_data (line 91) | def extract_reference_data(ref: AssetReference) -> ReferenceData:
  function extract_asset_data (line 106) | def extract_asset_data(asset: Asset | None) -> AssetData | None:

FILE: app/assets/services/tagging.py
  function apply_tags (line 16) | def apply_tags(
  function remove_tags (line 38) | def remove_tags(
  function list_tags (line 56) | def list_tags(
  function list_tag_histogram (line 81) | def list_tag_histogram(

FILE: app/custom_node_manager.py
  function safe_load_json_file (line 22) | def safe_load_json_file(file_path: str) -> dict:
  class CustomNodeManager (line 34) | class CustomNodeManager:
    method build_translations (line 36) | def build_translations(self):
    method add_routes (line 94) | def add_routes(self, routes, webapp, loadedModules):

FILE: app/database/db.py
  function dependencies_available (line 38) | def dependencies_available():
  function can_create_session (line 45) | def can_create_session():
  function get_alembic_config (line 53) | def get_alembic_config():
  function get_db_path (line 65) | def get_db_path():
  function _acquire_file_lock (line 75) | def _acquire_file_lock(db_path):
  function _is_memory_db (line 94) | def _is_memory_db(db_url):
  function init_db (line 99) | def init_db():
  function _init_memory_db (line 109) | def _init_memory_db(db_url):
  function _init_file_db (line 134) | def _init_file_db(db_url):
  function create_session (line 190) | def create_session():

FILE: app/database/models.py
  class Base (line 14) | class Base(DeclarativeBase):
  function to_dict (line 17) | def to_dict(obj: Any, include_none: bool = False) -> dict[str, Any]:

FILE: app/frontend_management.py
  function frontend_install_warning_message (line 26) | def frontend_install_warning_message():
  function parse_version (line 33) | def parse_version(version: str) -> tuple[int, int, int]:
  function is_valid_version (line 36) | def is_valid_version(version: str) -> bool:
  function get_installed_frontend_version (line 41) | def get_installed_frontend_version():
  function get_required_frontend_version (line 47) | def get_required_frontend_version():
  function check_frontend_version (line 51) | def check_frontend_version():
  class Asset (line 80) | class Asset(TypedDict):
  class Release (line 84) | class Release(TypedDict):
  class FrontEndProvider (line 96) | class FrontEndProvider:
    method folder_name (line 101) | def folder_name(self) -> str:
    method release_url (line 105) | def release_url(self) -> str:
    method all_releases (line 109) | def all_releases(self) -> list[Release]:
    method latest_release (line 124) | def latest_release(self) -> Release:
    method latest_prerelease (line 131) | def latest_prerelease(self) -> Release:
    method get_release (line 141) | def get_release(self, version: str) -> Release:
  function download_release_asset_zip (line 153) | def download_release_asset_zip(release: Release, destination_path: str) ...
  class FrontendManager (line 183) | class FrontendManager:
    method get_required_frontend_version (line 187) | def get_required_frontend_version(cls) -> str:
    method get_installed_templates_version (line 192) | def get_installed_templates_version(cls) -> str:
    method get_required_templates_version (line 201) | def get_required_templates_version(cls) -> str:
    method default_frontend_path (line 205) | def default_frontend_path(cls) -> str:
    method template_asset_map (line 225) | def template_asset_map(cls) -> Optional[Dict[str, str]]:
    method legacy_templates_path (line 271) | def legacy_templates_path(cls) -> Optional[str]:
    method embedded_docs_path (line 294) | def embedded_docs_path(cls) -> str:
    method parse_version_string (line 307) | def parse_version_string(cls, value: str) -> tuple[str, str, str]:
    method init_frontend_unsafe (line 326) | def init_frontend_unsafe(
    method init_frontend (line 391) | def init_frontend(cls, version_string: str) -> str:
    method template_asset_handler (line 409) | def template_asset_handler(cls):

FILE: app/logger.py
  class LogInterceptor (line 13) | class LogInterceptor(io.TextIOWrapper):
    method __init__ (line 14) | def __init__(self, stream,  *args, **kwargs):
    method write (line 22) | def write(self, data):
    method flush (line 34) | def flush(self):
    method on_flush (line 40) | def on_flush(self, callback):
  function get_logs (line 44) | def get_logs():
  function on_flush (line 48) | def on_flush(callback):
  function setup_logger (line 54) | def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdou...
  function log_startup_warning (line 90) | def log_startup_warning(msg):
  function print_startup_warnings (line 95) | def print_startup_warnings():

FILE: app/model_manager.py
  class ModelFileManager (line 17) | class ModelFileManager:
    method __init__ (line 18) | def __init__(self) -> None:
    method get_cache (line 21) | def get_cache(self, key: str, default=None) -> tuple[list[dict], dict[...
    method set_cache (line 24) | def set_cache(self, key: str, value: tuple[list[dict], dict[str, float...
    method clear_cache (line 27) | def clear_cache(self):
    method add_routes (line 30) | def add_routes(self, routes):
    method get_model_file_list (line 79) | def get_model_file_list(self, folder_name: str):
    method cache_model_file_list_ (line 95) | def cache_model_file_list_(self, folder: str):
    method recursive_search_models_ (line 112) | def recursive_search_models_(self, directory: str, pathIndex: int) -> ...
    method get_model_previews (line 160) | def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
    method __exit__ (line 194) | def __exit__(self, exc_type, exc_value, traceback):

FILE: app/node_replace_manager.py
  class NodeStruct (line 12) | class NodeStruct(TypedDict):
  function copy_node_struct (line 17) | def copy_node_struct(node_struct: NodeStruct, empty_inputs: bool = False...
  class NodeReplaceManager (line 27) | class NodeReplaceManager:
    method __init__ (line 30) | def __init__(self):
    method register (line 33) | def register(self, node_replace: NodeReplace):
    method get_replacement (line 37) | def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
    method has_replacement (line 41) | def has_replacement(self, old_node_id: str) -> bool:
    method apply_replacements (line 45) | def apply_replacements(self, prompt: dict[str, NodeStruct]):
    method as_dict (line 97) | def as_dict(self):
    method add_routes (line 104) | def add_routes(self, routes):

FILE: app/subgraph_manager.py
  class Source (line 11) | class Source:
  class SubgraphEntry (line 15) | class SubgraphEntry(TypedDict):
  class CustomNodeSubgraphEntryInfo (line 35) | class CustomNodeSubgraphEntryInfo(TypedDict):
  class SubgraphManager (line 39) | class SubgraphManager:
    method __init__ (line 40) | def __init__(self):
    method _create_entry (line 44) | def _create_entry(self, file: str, source: str, node_pack: str) -> tup...
    method load_entry_data (line 55) | async def load_entry_data(self, entry: SubgraphEntry):
    method sanitize_entry (line 60) | async def sanitize_entry(self, entry: SubgraphEntry | None, remove_dat...
    method sanitize_entries (line 69) | async def sanitize_entries(self, entries: dict[str, SubgraphEntry], re...
    method get_custom_node_subgraphs (line 75) | async def get_custom_node_subgraphs(self, loadedModules, force_reload=...
    method get_blueprint_subgraphs (line 92) | async def get_blueprint_subgraphs(self, force_reload=False):
    method get_all_subgraphs (line 109) | async def get_all_subgraphs(self, loadedModules, force_reload=False):
    method get_subgraph (line 115) | async def get_subgraph(self, id: str, loadedModules):
    method add_routes (line 122) | def add_routes(self, routes, loadedModules):

FILE: app/user_manager.py
  class FileInfo (line 20) | class FileInfo(TypedDict):
  function get_file_info (line 27) | def get_file_info(path: str, relative_to: str) -> FileInfo:
  class UserManager (line 36) | class UserManager():
    method __init__ (line 37) | def __init__(self):
    method get_users_file (line 56) | def get_users_file(self):
    method get_request_user_id (line 59) | def get_request_user_id(self, request):
    method get_request_user_filepath (line 72) | def get_request_user_filepath(self, request, file, type="userdata", cr...
    method add_user (line 105) | def add_user(self, name):
    method add_routes (line 123) | def add_routes(self, routes):

FILE: blueprints/.glsl/update_blueprints.py
  function get_blueprint_files (line 29) | def get_blueprint_files():
  function sanitize_filename (line 34) | def sanitize_filename(name):
  function extract_shaders (line 39) | def extract_shaders():
  function patch_shaders (line 76) | def patch_shaders():
  function main (line 141) | def main():

FILE: comfy/audio_encoders/audio_encoders.py
  class AudioEncoderModel (line 10) | class AudioEncoderModel():
    method __init__ (line 11) | def __init__(self, config):
    method load_sd (line 32) | def load_sd(self, sd):
    method get_sd (line 35) | def get_sd(self):
    method encode_audio (line 38) | def encode_audio(self, audio, sample_rate):
  function load_audio_encoder_from_sd (line 49) | def load_audio_encoder_from_sd(sd, prefix=""):

FILE: comfy/audio_encoders/wav2vec2.py
  class LayerNormConv (line 6) | class LayerNormConv(nn.Module):
    method __init__ (line 7) | def __init__(self, in_channels, out_channels, kernel_size, stride, bia...
    method forward (line 12) | def forward(self, x):
  class LayerGroupNormConv (line 16) | class LayerGroupNormConv(nn.Module):
    method __init__ (line 17) | def __init__(self, in_channels, out_channels, kernel_size, stride, bia...
    method forward (line 22) | def forward(self, x):
  class ConvNoNorm (line 26) | class ConvNoNorm(nn.Module):
    method __init__ (line 27) | def __init__(self, in_channels, out_channels, kernel_size, stride, bia...
    method forward (line 31) | def forward(self, x):
  class ConvFeatureEncoder (line 36) | class ConvFeatureEncoder(nn.Module):
    method __init__ (line 37) | def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=No...
    method forward (line 60) | def forward(self, x):
  class FeatureProjection (line 69) | class FeatureProjection(nn.Module):
    method __init__ (line 70) | def __init__(self, conv_dim, embed_dim, dtype=None, device=None, opera...
    method forward (line 75) | def forward(self, x):
  class PositionalConvEmbedding (line 81) | class PositionalConvEmbedding(nn.Module):
    method __init__ (line 82) | def __init__(self, embed_dim=768, kernel_size=128, groups=16):
    method forward (line 94) | def forward(self, x):
  class TransformerEncoder (line 102) | class TransformerEncoder(nn.Module):
    method __init__ (line 103) | def __init__(
    method forward (line 129) | def forward(self, x, mask=None):
  class Attention (line 143) | class Attention(nn.Module):
    method __init__ (line 144) | def __init__(self, embed_dim, num_heads, bias=True, dtype=None, device...
    method forward (line 155) | def forward(self, x, mask=None):
  class FeedForward (line 165) | class FeedForward(nn.Module):
    method __init__ (line 166) | def __init__(self, embed_dim, mlp_ratio, dtype=None, device=None, oper...
    method forward (line 171) | def forward(self, x):
  class TransformerEncoderLayer (line 178) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 179) | def __init__(
    method forward (line 196) | def forward(self, x, mask=None):
  class Wav2Vec2Model (line 209) | class Wav2Vec2Model(nn.Module):
    method __init__ (line 212) | def __init__(
    method forward (line 241) | def forward(self, x, mask_time_indices=None, return_dict=False):

FILE: comfy/audio_encoders/whisper.py
  class WhisperFeatureExtractor (line 9) | class WhisperFeatureExtractor(nn.Module):
    method __init__ (line 10) | def __init__(self, n_mels=128, device=None):
    method __call__ (line 30) | def __call__(self, audio):
  class MultiHeadAttention (line 54) | class MultiHeadAttention(nn.Module):
    method __init__ (line 55) | def __init__(self, d_model: int, n_heads: int, dtype=None, device=None...
    method forward (line 68) | def forward(
  class EncoderLayer (line 87) | class EncoderLayer(nn.Module):
    method __init__ (line 88) | def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, ...
    method forward (line 98) | def forward(
  class AudioEncoder (line 118) | class AudioEncoder(nn.Module):
    method __init__ (line 119) | def __init__(
    method forward (line 144) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class WhisperLargeV3 (line 162) | class WhisperLargeV3(nn.Module):
    method __init__ (line 163) | def __init__(
    method forward (line 183) | def forward(self, audio):

FILE: comfy/cldm/cldm.py
  class OptimizedAttention (line 19) | class OptimizedAttention(nn.Module):
    method __init__ (line 20) | def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, ope...
    method forward (line 28) | def forward(self, x):
  class QuickGELU (line 34) | class QuickGELU(nn.Module):
    method forward (line 35) | def forward(self, x: torch.Tensor):
  class ResBlockUnionControlnet (line 38) | class ResBlockUnionControlnet(nn.Module):
    method __init__ (line 39) | def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
    method attention (line 48) | def attention(self, x: torch.Tensor):
    method forward (line 51) | def forward(self, x: torch.Tensor):
  class ControlledUnetModel (line 56) | class ControlledUnetModel(UNetModel):
  class ControlNet (line 60) | class ControlNet(nn.Module):
    method __init__ (line 61) | def __init__(
    method union_controlnet_merge (line 353) | def union_controlnet_merge(self, hint, control_type, emb, context):
    method make_zero_conv (line 380) | def make_zero_conv(self, channels, operations=None, dtype=None, device...
    method forward (line 383) | def forward(self, x, hint, timesteps, context, y=None, **kwargs):

FILE: comfy/cldm/dit_embedder.py
  class ControlNetEmbedder (line 11) | class ControlNetEmbedder(nn.Module):
    method __init__ (line 13) | def __init__(
    method forward (line 88) | def forward(

FILE: comfy/cldm/mmdit.py
  class ControlNet (line 5) | class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
    method __init__ (line 6) | def __init__(
    method forward (line 36) | def forward(

FILE: comfy/cli_args.py
  class EnumAction (line 7) | class EnumAction(argparse.Action):
    method __init__ (line 11) | def __init__(self, **kwargs):
    method __call__ (line 30) | def __call__(self, parser, namespace, values, option_string=None):
  class LatentPreviewMethod (line 96) | class LatentPreviewMethod(enum.Enum):
    method from_string (line 103) | def from_string(cls, value: str):
  class PerformanceFeature (line 161) | class PerformanceFeature(enum.Enum):
  function is_valid_directory (line 206) | def is_valid_directory(path: str) -> str:
  function enables_dynamic_vram (line 265) | def enables_dynamic_vram():

FILE: comfy/clip_model.py
  function clip_preprocess (line 6) | def clip_preprocess(image, size=224, mean=[0.48145466, 0.4578275, 0.4082...
  function siglip2_flex_calc_resolution (line 25) | def siglip2_flex_calc_resolution(oh, ow, patch_size, max_num_patches, ep...
  function siglip2_preprocess (line 42) | def siglip2_preprocess(image, size, patch_size, num_patches, mean=[0.5, ...
  class CLIPAttention (line 58) | class CLIPAttention(torch.nn.Module):
    method __init__ (line 59) | def __init__(self, embed_dim, heads, dtype, device, operations):
    method forward (line 69) | def forward(self, x, mask=None, optimized_attention=None):
  class CLIPMLP (line 82) | class CLIPMLP(torch.nn.Module):
    method __init__ (line 83) | def __init__(self, embed_dim, intermediate_size, activation, dtype, de...
    method forward (line 89) | def forward(self, x):
  class CLIPLayer (line 95) | class CLIPLayer(torch.nn.Module):
    method __init__ (line 96) | def __init__(self, embed_dim, heads, intermediate_size, intermediate_a...
    method forward (line 103) | def forward(self, x, mask=None, optimized_attention=None):
  class CLIPEncoder (line 109) | class CLIPEncoder(torch.nn.Module):
    method __init__ (line 110) | def __init__(self, num_layers, embed_dim, heads, intermediate_size, in...
    method forward (line 114) | def forward(self, x, mask=None, intermediate_output=None):
  class CLIPEmbeddings (line 138) | class CLIPEmbeddings(torch.nn.Module):
    method __init__ (line 139) | def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtyp...
    method forward (line 144) | def forward(self, input_tokens, dtype=torch.float32):
  class CLIPTextModel_ (line 148) | class CLIPTextModel_(torch.nn.Module):
    method __init__ (line 149) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 163) | def forward(self, input_tokens=None, attention_mask=None, embeds=None,...
  class CLIPTextModel (line 192) | class CLIPTextModel(torch.nn.Module):
    method __init__ (line 193) | def __init__(self, config_dict, dtype, device, operations):
    method get_input_embeddings (line 201) | def get_input_embeddings(self):
    method set_input_embeddings (line 204) | def set_input_embeddings(self, embeddings):
    method forward (line 207) | def forward(self, *args, **kwargs):
  function siglip2_pos_embed (line 212) | def siglip2_pos_embed(embed_weight, embeds, orig_shape):
  class Siglip2Embeddings (line 219) | class Siglip2Embeddings(torch.nn.Module):
    method __init__ (line 220) | def __init__(self, embed_dim, num_channels=3, patch_size=14, image_siz...
    method forward (line 226) | def forward(self, pixel_values):
  class CLIPVisionEmbeddings (line 234) | class CLIPVisionEmbeddings(torch.nn.Module):
    method __init__ (line 235) | def __init__(self, embed_dim, num_channels=3, patch_size=14, image_siz...
    method forward (line 259) | def forward(self, pixel_values):
  class CLIPVision (line 266) | class CLIPVision(torch.nn.Module):
    method __init__ (line 267) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 289) | def forward(self, pixel_values, attention_mask=None, intermediate_outp...
  class LlavaProjector (line 301) | class LlavaProjector(torch.nn.Module):
    method __init__ (line 302) | def __init__(self, in_dim, out_dim, dtype, device, operations):
    method forward (line 307) | def forward(self, x):
  class CLIPVisionModelProjection (line 310) | class CLIPVisionModelProjection(torch.nn.Module):
    method __init__ (line 311) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 324) | def forward(self, *args, **kwargs):

FILE: comfy/clip_vision.py
  class Output (line 13) | class Output:
    method __getitem__ (line 14) | def __getitem__(self, key):
    method __setitem__ (line 16) | def __setitem__(self, key, item):
  class ClipVisionModel (line 28) | class ClipVisionModel():
    method __init__ (line 29) | def __init__(self, json_config):
    method load_sd (line 52) | def load_sd(self, sd):
    method get_sd (line 55) | def get_sd(self):
    method encode_image (line 58) | def encode_image(self, image, crop=True):
  function convert_to_transformers (line 80) | def convert_to_transformers(sd, prefix):
  function load_clipvision_from_sd (line 106) | def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
  function load (line 151) | def load(ckpt_path):

FILE: comfy/comfy_types/__init__.py
  class UnetApplyFunction (line 6) | class UnetApplyFunction(Protocol):
    method __call__ (line 9) | def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torc...
  class UnetApplyConds (line 13) | class UnetApplyConds(TypedDict):
  class UnetParams (line 22) | class UnetParams(TypedDict):

FILE: comfy/comfy_types/examples/example_nodes.py
  class ExampleNode (line 5) | class ExampleNode(ComfyNodeABC):
    method INPUT_TYPES (line 16) | def INPUT_TYPES(s) -> InputTypeDict:
    method execute (line 27) | def execute(self, input_int: int):

FILE: comfy/comfy_types/node_typing.py
  class StrEnum (line 10) | class StrEnum(str, Enum):
    method __str__ (line 13) | def __str__(self) -> str:
  class IO (line 17) | class IO(StrEnum):
    method __ne__ (line 65) | def __ne__(self, value: object) -> bool:
  class RemoteInputOptions (line 75) | class RemoteInputOptions(TypedDict):
  class MultiSelectOptions (line 90) | class MultiSelectOptions(TypedDict):
  class InputTypeOptions (line 97) | class InputTypeOptions(TypedDict):
  class HiddenInputTypeDict (line 183) | class HiddenInputTypeDict(TypedDict):
  class InputTypeDict (line 198) | class InputTypeDict(TypedDict):
  class ComfyNodeABC (line 215) | class ComfyNodeABC(ABC):
    method INPUT_TYPES (line 248) | def INPUT_TYPES(s) -> InputTypeDict:
  class CheckLazyMixin (line 325) | class CheckLazyMixin:
    method check_lazy_status (line 328) | def check_lazy_status(self, **kwargs) -> list[str]:
  class FileLocator (line 346) | class FileLocator(TypedDict):

FILE: comfy/conds.py
  function is_equal (line 7) | def is_equal(x, y):
  class CONDRegular (line 26) | class CONDRegular:
    method __init__ (line 27) | def __init__(self, cond):
    method _copy_with (line 30) | def _copy_with(self, cond):
    method process_cond (line 33) | def process_cond(self, batch_size, **kwargs):
    method can_concat (line 36) | def can_concat(self, other):
    method concat (line 44) | def concat(self, others):
    method size (line 50) | def size(self):
  class CONDNoiseShape (line 54) | class CONDNoiseShape(CONDRegular):
    method process_cond (line 55) | def process_cond(self, batch_size, area, **kwargs):
  class CONDCrossAttn (line 65) | class CONDCrossAttn(CONDRegular):
    method can_concat (line 66) | def can_concat(self, other):
    method concat (line 82) | def concat(self, others):
  class CONDConstant (line 98) | class CONDConstant(CONDRegular):
    method __init__ (line 99) | def __init__(self, cond):
    method process_cond (line 102) | def process_cond(self, batch_size, **kwargs):
    method can_concat (line 105) | def can_concat(self, other):
    method concat (line 110) | def concat(self, others):
    method size (line 113) | def size(self):
  class CONDList (line 117) | class CONDList(CONDRegular):
    method __init__ (line 118) | def __init__(self, cond):
    method process_cond (line 121) | def process_cond(self, batch_size, **kwargs):
    method can_concat (line 128) | def can_concat(self, other):
    method concat (line 137) | def concat(self, others):
    method size (line 147) | def size(self):  # hackish implementation to make the mem estimation work

FILE: comfy/context_windows.py
  class ContextWindowABC (line 17) | class ContextWindowABC(ABC):
    method __init__ (line 18) | def __init__(self):
    method get_tensor (line 22) | def get_tensor(self, full: torch.Tensor) -> torch.Tensor:
    method add_window (line 29) | def add_window(self, full: torch.Tensor, to_add: torch.Tensor) -> torc...
  class ContextHandlerABC (line 35) | class ContextHandlerABC(ABC):
    method __init__ (line 36) | def __init__(self):
    method should_use_context (line 40) | def should_use_context(self, model: BaseModel, conds: list[list[dict]]...
    method get_resized_cond (line 44) | def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, wi...
    method execute (line 48) | def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: ...
  class IndexListContextWindow (line 53) | class IndexListContextWindow(ContextWindowABC):
    method __init__ (line 54) | def __init__(self, index_list: list[int], dim: int=0, total_frames: in...
    method get_tensor (line 61) | def get_tensor(self, full: torch.Tensor, device=None, dim=None, retain...
    method add_window (line 73) | def add_window(self, full: torch.Tensor, to_add: torch.Tensor, dim=Non...
    method get_region_index (line 80) | def get_region_index(self, num_regions: int) -> int:
  class IndexListCallbacks (line 85) | class IndexListCallbacks:
    method init_callbacks (line 92) | def init_callbacks(self):
  function slice_cond (line 96) | def slice_cond(cond_value, window: IndexListContextWindow, x_in: torch.T...
  class ContextSchedule (line 141) | class ContextSchedule:
  class ContextFuseMethod (line 146) | class ContextFuseMethod:
  class IndexListContextHandler (line 151) | class IndexListContextHandler(ContextHandlerABC):
    method __init__ (line 152) | def __init__(self, context_schedule: ContextSchedule, fuse_method: Con...
    method should_use_context (line 168) | def should_use_context(self, model: BaseModel, conds: list[list[dict]]...
    method prepare_control_objects (line 177) | def prepare_control_objects(self, control: ControlBase, device=None) -...
    method get_resized_cond (line 182) | def get_resized_cond(self, cond_in: list[dict], x_in: torch.Tensor, wi...
    method set_step (line 264) | def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
    method get_context_windows (line 271) | def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, mo...
    method execute (line 277) | def execute(self, calc_cond_batch: Callable, model: BaseModel, conds: ...
    method evaluate_context_windows (line 314) | def evaluate_context_windows(self, calc_cond_batch: Callable, model: B...
    method combine_context_window_results (line 339) | def combine_context_window_results(self, x_in: torch.Tensor, sub_conds...
  function _prepare_sampling_wrapper (line 369) | def _prepare_sampling_wrapper(executor, model, noise_shape: torch.Tensor...
  function create_prepare_sampling_wrapper (line 381) | def create_prepare_sampling_wrapper(model: ModelPatcher):
  function _sampler_sample_wrapper (line 389) | def _sampler_sample_wrapper(executor, guider, sigmas, extra_args, callba...
  function create_sampler_sample_wrapper (line 403) | def create_sampler_sample_wrapper(model: ModelPatcher):
  function match_weights_to_dim (line 411) | def match_weights_to_dim(weights: list[float], x_in: torch.Tensor, dim: ...
  function get_shape_for_dim (line 420) | def get_shape_for_dim(x_in: torch.Tensor, dim: int) -> list[int]:
  class ContextSchedules (line 430) | class ContextSchedules:
  function create_windows_uniform_looped (line 438) | def create_windows_uniform_looped(num_frames: int, handler: IndexListCon...
  function create_windows_uniform_standard (line 457) | def create_windows_uniform_standard(num_frames: int, handler: IndexListC...
  function create_windows_static_standard (line 505) | def create_windows_static_standard(num_frames: int, handler: IndexListCo...
  function create_windows_batched (line 524) | def create_windows_batched(num_frames: int, handler: IndexListContextHan...
  function create_windows_default (line 537) | def create_windows_default(num_frames: int, handler: IndexListContextHan...
  function get_matching_context_schedule (line 549) | def get_matching_context_schedule(context_schedule: str) -> ContextSched...
  function get_context_weights (line 556) | def get_context_weights(length: int, full_length: int, idxs: list[int], ...
  function create_weights_flat (line 560) | def create_weights_flat(length: int, **kwargs) -> list[float]:
  function create_weights_pyramid (line 564) | def create_weights_pyramid(length: int, **kwargs) -> list[float]:
  function create_weights_overlap_linear (line 575) | def create_weights_overlap_linear(length: int, full_length: int, idxs: l...
  class ContextFuseMethods (line 589) | class ContextFuseMethods:
  function get_matching_fuse_method (line 606) | def get_matching_fuse_method(fuse_method: str) -> ContextFuseMethod:
  function ordered_halving (line 613) | def ordered_halving(val):
  function get_missing_indexes (line 625) | def get_missing_indexes(windows: list[list[int]], num_frames: int) -> li...
  function does_window_roll_over (line 636) | def does_window_roll_over(window: list[int], num_frames: int) -> tuple[b...
  function shift_window_to_start (line 646) | def shift_window_to_start(window: list[int], num_frames: int):
  function shift_window_to_end (line 654) | def shift_window_to_end(window: list[int], num_frames: int):
  function apply_freenoise (line 665) | def apply_freenoise(noise: torch.Tensor, dim: int, context_length: int, ...

FILE: comfy/controlnet.py
  function broadcast_image_to (line 46) | def broadcast_image_to(tensor, target_batch_size, batched_number):
  class StrengthType (line 63) | class StrengthType(Enum):
  class ControlBase (line 67) | class ControlBase:
    method __init__ (line 68) | def __init__(self):
    method set_cond_hint (line 89) | def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_rang...
    method pre_run (line 102) | def pre_run(self, model, percent_to_timestep_function):
    method set_previous_controlnet (line 107) | def set_previous_controlnet(self, controlnet):
    method cleanup (line 111) | def cleanup(self):
    method get_models (line 119) | def get_models(self):
    method get_extra_hooks (line 125) | def get_extra_hooks(self):
    method copy_to (line 133) | def copy_to(self, c):
    method inference_memory_requirements (line 150) | def inference_memory_requirements(self, dtype):
    method control_merge (line 155) | def control_merge(self, control, control_prev, output_dtype):
    method set_extra_arg (line 196) | def set_extra_arg(self, argument, value=None):
  class ControlNet (line 200) | class ControlNet(ControlBase):
    method __init__ (line 201) | def __init__(self, control_model=None, global_average_pooling=False, c...
    method get_control (line 218) | def get_control(self, x_noisy, t, cond, batched_number, transformer_op...
    method copy (line 280) | def copy(self):
    method get_models (line 287) | def get_models(self):
    method pre_run (line 292) | def pre_run(self, model, percent_to_timestep_function):
    method cleanup (line 296) | def cleanup(self):
  class QwenFunControlNet (line 301) | class QwenFunControlNet(ControlNet):
    method get_control (line 302) | def get_control(self, x_noisy, t, cond, batched_number, transformer_op...
    method pre_run (line 313) | def pre_run(self, model, percent_to_timestep_function):
    method copy (line 317) | def copy(self):
  class ControlLoraOps (line 324) | class ControlLoraOps:
    class Linear (line 325) | class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
      method __init__ (line 326) | def __init__(self, in_features: int, out_features: int, bias: bool =...
      method forward (line 336) | def forward(self, input):
    class Conv2d (line 345) | class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
      method __init__ (line 346) | def __init__(
      method forward (line 378) | def forward(self, input):
  class ControlLora (line 387) | class ControlLora(ControlNet):
    method __init__ (line 388) | def __init__(self, control_weights, global_average_pooling=False, mode...
    method pre_run (line 394) | def pre_run(self, model, percent_to_timestep_function):
    method copy (line 428) | def copy(self):
    method cleanup (line 433) | def cleanup(self):
    method get_models (line 438) | def get_models(self):
    method inference_memory_requirements (line 442) | def inference_memory_requirements(self, dtype):
  function controlnet_config (line 445) | def controlnet_config(sd, model_options={}):
  function controlnet_load_state_dict (line 465) | def controlnet_load_state_dict(control_model, sd):
  function load_controlnet_mmdit (line 476) | def load_controlnet_mmdit(sd, model_options={}):
  class ControlNetSD35 (line 497) | class ControlNetSD35(ControlNet):
    method pre_run (line 498) | def pre_run(self, model, percent_to_timestep_function):
    method copy (line 505) | def copy(self):
  function load_controlnet_sd35 (line 512) | def load_controlnet_sd35(sd, model_options={}):
  function load_controlnet_hunyuandit (line 573) | def load_controlnet_hunyuandit(controlnet_data, model_options={}):
  function load_controlnet_flux_xlabs_mistoline (line 584) | def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False, model_opti...
  function load_controlnet_flux_instantx (line 593) | def load_controlnet_flux_instantx(sd, model_options={}):
  function load_controlnet_qwen_instantx (line 617) | def load_controlnet_qwen_instantx(sd, model_options={}):
  function load_controlnet_qwen_fun (line 634) | def load_controlnet_qwen_fun(sd, model_options={}):
  function convert_mistoline (line 680) | def convert_mistoline(sd):
  function load_controlnet_state_dict (line 684) | def load_controlnet_state_dict(state_dict, model=None, model_options={}):
  function load_controlnet (line 839) | def load_controlnet(ckpt_path, model=None, model_options={}):
  class T2IAdapter (line 851) | class T2IAdapter(ControlBase):
    method __init__ (line 852) | def __init__(self, t2i_model, channels_in, compression_ratio, upscale_...
    method scale_image_to (line 863) | def scale_image_to(self, width, height):
    method get_control (line 869) | def get_control(self, x_noisy, t, cond, batched_number, transformer_op...
    method copy (line 904) | def copy(self):
  function load_t2i_adapter (line 909) | def load_t2i_adapter(t2i_data, model_options={}): #TODO: model_options

FILE: comfy/diffusers_convert.py
  function reshape_weight_for_sd (line 61) | def reshape_weight_for_sd(w, conv3d=False):
  function convert_vae_state_dict (line 69) | def convert_vae_state_dict(vae_state_dict):
  function cat_tensors (line 119) | def cat_tensors(tensors):
  function convert_text_enc_state_dict_v20 (line 135) | def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
  function convert_text_enc_state_dict (line 188) | def convert_text_enc_state_dict(text_enc_dict):

FILE: comfy/diffusers_load.py
  function first_file (line 5) | def first_file(path, filenames):
  function load_diffusers (line 12) | def load_diffusers(model_path, output_vae=True, output_clip=True, embedd...

FILE: comfy/extra_samplers/uni_pc.py
  class NoiseScheduleVP (line 10) | class NoiseScheduleVP:
    method __init__ (line 11) | def __init__(
    method marginal_log_mean_coeff (line 129) | def marginal_log_mean_coeff(self, t):
    method marginal_alpha (line 142) | def marginal_alpha(self, t):
    method marginal_std (line 148) | def marginal_std(self, t):
    method marginal_lambda (line 154) | def marginal_lambda(self, t):
    method inverse_lambda (line 162) | def inverse_lambda(self, lamb):
  function model_wrapper (line 181) | def model_wrapper(
  class UniPC (line 352) | class UniPC:
    method __init__ (line 353) | def __init__(
    method dynamic_thresholding_fn (line 373) | def dynamic_thresholding_fn(self, x0, t=None):
    method noise_prediction_fn (line 384) | def noise_prediction_fn(self, x, t):
    method data_prediction_fn (line 390) | def data_prediction_fn(self, x, t):
    method model_fn (line 405) | def model_fn(self, x, t):
    method get_time_steps (line 414) | def get_time_steps(self, skip_type, t_T, t_0, N, device):
    method get_orders_and_timesteps_for_singlestep_solver (line 431) | def get_orders_and_timesteps_for_singlestep_solver(self, steps, order,...
    method denoise_to_zero_fn (line 462) | def denoise_to_zero_fn(self, x, s):
    method multistep_uni_pc_update (line 468) | def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, ...
    method multistep_uni_pc_vary_update (line 477) | def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list...
    method multistep_uni_pc_bh_update (line 579) | def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, ...
    method sample (line 700) | def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip...
  function interpolate_fn (line 766) | def interpolate_fn(x, xp, yp):
  function expand_dims (line 808) | def expand_dims(v, dims):
  class SigmaConvert (line 821) | class SigmaConvert:
    method marginal_log_mean_coeff (line 823) | def marginal_log_mean_coeff(self, sigma):
    method marginal_alpha (line 826) | def marginal_alpha(self, t):
    method marginal_std (line 829) | def marginal_std(self, t):
    method marginal_lambda (line 832) | def marginal_lambda(self, t):
  function predict_eps_sigma (line 840) | def predict_eps_sigma(model, input, sigma_in, **kwargs):
  function sample_unipc (line 846) | def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, d...
  function sample_unipc_bh2 (line 872) | def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=Non...

FILE: comfy/float.py
  function calc_mantissa (line 3) | def calc_mantissa(abs_x, exponent, normal_mask, MANTISSA_BITS, EXPONENT_...
  function manual_stochastic_round_to_float8 (line 14) | def manual_stochastic_round_to_float8(x, dtype, generator=None):
  function stochastic_rounding (line 50) | def stochastic_rounding(value, dtype, seed=0):
  function stochastic_float_to_fp4_e2m1 (line 71) | def stochastic_float_to_fp4_e2m1(x, generator):
  function to_blocked (line 99) | def to_blocked(input_matrix, flatten: bool = True) -> torch.Tensor:
  function stochastic_round_quantize_nvfp4_block (line 140) | def stochastic_round_quantize_nvfp4_block(x, per_tensor_scale, generator):
  function stochastic_round_quantize_nvfp4 (line 157) | def stochastic_round_quantize_nvfp4(x, per_tensor_scale, pad_16x, seed=0):
  function stochastic_round_quantize_nvfp4_by_block (line 177) | def stochastic_round_quantize_nvfp4_by_block(x, per_tensor_scale, pad_16...
  function stochastic_round_quantize_mxfp8_by_block (line 214) | def stochastic_round_quantize_mxfp8_by_block(x, pad_32x, seed=0):

FILE: comfy/gligen.py
  class GatedCrossAttentionDense (line 9) | class GatedCrossAttentionDense(nn.Module):
    method __init__ (line 10) | def __init__(self, query_dim, context_dim, n_heads, d_head):
    method forward (line 32) | def forward(self, x, objs):
  class GatedSelfAttentionDense (line 42) | class GatedSelfAttentionDense(nn.Module):
    method __init__ (line 43) | def __init__(self, query_dim, context_dim, n_heads, d_head):
    method forward (line 69) | def forward(self, x, objs):
  class GatedSelfAttentionDense2 (line 82) | class GatedSelfAttentionDense2(nn.Module):
    method __init__ (line 83) | def __init__(self, query_dim, context_dim, n_heads, d_head):
    method forward (line 105) | def forward(self, x, objs):
  class FourierEmbedder (line 136) | class FourierEmbedder():
    method __init__ (line 137) | def __init__(self, num_freqs=64, temperature=100):
    method __call__ (line 144) | def __call__(self, x, cat_dim=-1):
  class PositionNet (line 153) | class PositionNet(nn.Module):
    method __init__ (line 154) | def __init__(self, in_dim, out_dim, fourier_freqs=8):
    method forward (line 175) | def forward(self, boxes, masks, positive_embeddings):
  class Gligen (line 198) | class Gligen(nn.Module):
    method __init__ (line 199) | def __init__(self, modules, position_net, key_dim):
    method _set_position (line 207) | def _set_position(self, boxes, masks, positive_embeddings):
    method set_position (line 215) | def set_position(self, latent_image_shape, position_params, device):
    method set_empty (line 246) | def set_empty(self, latent_image_shape, device):
  function load_gligen (line 259) | def load_gligen(sd):

FILE: comfy/hooks.py
  class EnumHookMode (line 29) | class EnumHookMode(enum.Enum):
  class EnumHookType (line 39) | class EnumHookType(enum.Enum):
  class EnumWeightTarget (line 49) | class EnumWeightTarget(enum.Enum):
  class EnumHookScope (line 53) | class EnumHookScope(enum.Enum):
  class _HookRef (line 64) | class _HookRef:
  function default_should_register (line 68) | def default_should_register(hook: Hook, model: ModelPatcher, model_optio...
  function create_target_dict (line 73) | def create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[...
  class Hook (line 82) | class Hook:
    method __init__ (line 83) | def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=No...
    method strength (line 99) | def strength(self):
    method initialize_timesteps (line 102) | def initialize_timesteps(self, model: BaseModel):
    method reset (line 106) | def reset(self):
    method clone (line 109) | def clone(self):
    method should_register (line 119) | def should_register(self, model: ModelPatcher, model_options: dict, ta...
    method add_hook_patches (line 122) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
    method __eq__ (line 125) | def __eq__(self, other: Hook):
    method __hash__ (line 128) | def __hash__(self):
  class WeightHook (line 131) | class WeightHook(Hook):
    method __init__ (line 137) | def __init__(self, strength_model=1.0, strength_clip=1.0):
    method strength_model (line 147) | def strength_model(self):
    method strength_clip (line 151) | def strength_clip(self):
    method add_hook_patches (line 154) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
    method clone (line 182) | def clone(self):
  class ObjectPatchHook (line 191) | class ObjectPatchHook(Hook):
    method __init__ (line 192) | def __init__(self, object_patches: dict[str]=None,
    method clone (line 198) | def clone(self):
    method add_hook_patches (line 203) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
  class AdditionalModelsHook (line 206) | class AdditionalModelsHook(Hook):
    method __init__ (line 212) | def __init__(self, models: list[ModelPatcher]=None, key: str=None):
    method clone (line 217) | def clone(self):
    method add_hook_patches (line 223) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
  class TransformerOptionsHook (line 229) | class TransformerOptionsHook(Hook):
    method __init__ (line 233) | def __init__(self, transformers_dict: dict[str, dict[str, dict[str, li...
    method clone (line 241) | def clone(self):
    method add_hook_patches (line 247) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
    method on_apply_hooks (line 263) | def on_apply_hooks(self, model: ModelPatcher, transformer_options: dic...
  class InjectionsHook (line 270) | class InjectionsHook(Hook):
    method __init__ (line 271) | def __init__(self, key: str=None, injections: list[PatcherInjection]=N...
    method clone (line 278) | def clone(self):
    method add_hook_patches (line 284) | def add_hook_patches(self, model: ModelPatcher, model_options: dict, t...
  class HookGroup (line 287) | class HookGroup:
    method __init__ (line 294) | def __init__(self):
    method __len__ (line 298) | def __len__(self):
    method add (line 301) | def add(self, hook: Hook):
    method remove (line 306) | def remove(self, hook: Hook):
    method get_type (line 311) | def get_type(self, hook_type: EnumHookType):
    method contains (line 314) | def contains(self, hook: Hook):
    method is_subset_of (line 317) | def is_subset_of(self, other: HookGroup):
    method new_with_common_hooks (line 322) | def new_with_common_hooks(self, other: HookGroup):
    method clone (line 329) | def clone(self):
    method clone_and_combine (line 335) | def clone_and_combine(self, other: HookGroup):
    method set_keyframes_on_hooks (line 342) | def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup):
    method get_hooks_for_clip_schedule (line 350) | def get_hooks_for_clip_schedule(self):
    method reset (line 399) | def reset(self):
    method combine_all_hooks (line 404) | def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) ->...
  class HookKeyframe (line 426) | class HookKeyframe:
    method __init__ (line 427) | def __init__(self, strength: float, start_percent=0.0, guarantee_steps...
    method get_effective_guarantee_steps (line 434) | def get_effective_guarantee_steps(self, max_sigma: torch.Tensor):
    method clone (line 440) | def clone(self):
  class HookKeyframeGroup (line 446) | class HookKeyframeGroup:
    method __init__ (line 447) | def __init__(self):
    method strength (line 457) | def strength(self):
    method reset (line 462) | def reset(self):
    method add (line 470) | def add(self, keyframe: HookKeyframe):
    method _set_first_as_current (line 476) | def _set_first_as_current(self):
    method has_guarantee_steps (line 482) | def has_guarantee_steps(self):
    method has_index (line 488) | def has_index(self, index: int):
    method is_empty (line 491) | def is_empty(self):
    method clone (line 494) | def clone(self):
    method initialize_timesteps (line 501) | def initialize_timesteps(self, model: BaseModel):
    method prepare_current_keyframe (line 505) | def prepare_current_keyframe(self, curr_t: float, transformer_options:...
  class InterpolationMethod (line 540) | class InterpolationMethod:
    method get_weights (line 549) | def get_weights(cls, num_from: float, num_to: float, length: int, meth...
  function get_sorted_list_via_attr (line 568) | def get_sorted_list_via_attr(objects: list, attr: str) -> list:
  function create_transformer_options_from_hooks (line 591) | def create_transformer_options_from_hooks(model: ModelPatcher, hooks: Ho...
  function create_hook_lora (line 602) | def create_hook_lora(lora: dict[str, torch.Tensor], strength_model: floa...
  function create_hook_model_as_lora (line 609) | def create_hook_model_as_lora(weights_model, weights_clip, strength_mode...
  function get_patch_weights_from_model (line 628) | def get_patch_weights_from_model(model: ModelPatcher, discard_model_samp...
  function load_hook_lora_for_models (line 640) | def load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dic...
  function _combine_hooks_from_values (line 672) | def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dic...
  function conditioning_set_values_with_hooks (line 692) | def conditioning_set_values_with_hooks(conditioning, values={}, append_h...
  function set_hooks_for_conditioning (line 708) | def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True...
  function set_timesteps_for_conditioning (line 713) | def set_timesteps_for_conditioning(cond, timestep_range: tuple[float,flo...
  function set_mask_for_conditioning (line 719) | def set_mask_for_conditioning(cond, mask: torch.Tensor, set_cond_area: s...
  function combine_conditioning (line 731) | def combine_conditioning(conds: list):
  function combine_with_new_conds (line 737) | def combine_with_new_conds(conds: list, new_conds: list):
  function set_conds_props (line 743) | def set_conds_props(conds: list, strength: float, set_cond_area: str,
  function set_conds_props_and_combine (line 758) | def set_conds_props_and_combine(conds: list, new_conds: list, strength: ...
  function set_default_conds_and_combine (line 773) | def set_default_conds_and_combine(conds: list, new_conds: list,

FILE: comfy/image_encoders/dino2.py
  class Dino2AttentionOutput (line 7) | class Dino2AttentionOutput(torch.nn.Module):
    method __init__ (line 8) | def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, devic...
    method forward (line 12) | def forward(self, x):
  class Dino2AttentionBlock (line 16) | class Dino2AttentionBlock(torch.nn.Module):
    method __init__ (line 17) | def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, op...
    method forward (line 22) | def forward(self, x, mask, optimized_attention):
  class LayerScale (line 26) | class LayerScale(torch.nn.Module):
    method __init__ (line 27) | def __init__(self, dim, dtype, device, operations):
    method forward (line 31) | def forward(self, x):
  class Dinov2MLP (line 34) | class Dinov2MLP(torch.nn.Module):
    method __init__ (line 35) | def __init__(self, hidden_size: int, dtype, device, operations):
    method forward (line 43) | def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
  class SwiGLUFFN (line 49) | class SwiGLUFFN(torch.nn.Module):
    method __init__ (line 50) | def __init__(self, dim, dtype, device, operations):
    method forward (line 59) | def forward(self, x):
  class Dino2Block (line 66) | class Dino2Block(torch.nn.Module):
    method __init__ (line 67) | def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, oper...
    method forward (line 79) | def forward(self, x, optimized_attention):
  class Dino2Encoder (line 85) | class Dino2Encoder(torch.nn.Module):
    method __init__ (line 86) | def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, ...
    method forward (line 91) | def forward(self, x, intermediate_output=None):
  class Dino2PatchEmbeddings (line 106) | class Dino2PatchEmbeddings(torch.nn.Module):
    method __init__ (line 107) | def __init__(self, dim, num_channels=3, patch_size=14, image_size=518,...
    method forward (line 119) | def forward(self, pixel_values):
  class Dino2Embeddings (line 123) | class Dino2Embeddings(torch.nn.Module):
    method __init__ (line 124) | def __init__(self, dim, dtype, device, operations):
    method forward (line 134) | def forward(self, pixel_values):
  class Dinov2Model (line 142) | class Dinov2Model(torch.nn.Module):
    method __init__ (line 143) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 155) | def forward(self, pixel_values, attention_mask=None, intermediate_outp...

FILE: comfy/k_diffusion/deis.py
  function edm2t (line 13) | def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):
  function cal_poly (line 22) | def cal_poly(prev_t, j, taus):
  function t2alpha_fn (line 33) | def t2alpha_fn(beta_0, beta_1, t):
  function cal_intergrand (line 38) | def cal_intergrand(beta_0, beta_1, taus):
  function get_deis_coeff_list (line 54) | def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):

FILE: comfy/k_diffusion/sa_solver.py
  function compute_exponential_coeffs (line 10) | def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_...
  function compute_simple_stochastic_adams_b_coeffs (line 53) | def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, c...
  function compute_stochastic_adams_b_coeffs (line 69) | def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lam...
  function get_tau_interval_func (line 103) | def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: flo...

FILE: comfy/k_diffusion/sampling.py
  function append_zero (line 19) | def append_zero(x):
  function get_sigmas_karras (line 23) | def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
  function get_sigmas_exponential (line 32) | def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
  function get_sigmas_polyexponential (line 38) | def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='...
  function get_sigmas_vp (line 45) | def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
  function get_sigmas_laplace (line 52) | def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device=...
  function to_d (line 63) | def to_d(x, sigma, denoised):
  function get_ancestral_step (line 68) | def get_ancestral_step(sigma_from, sigma_to, eta=1.):
  function default_noise_sampler (line 78) | def default_noise_sampler(x, seed=None):
  class BatchedBrownianTree (line 91) | class BatchedBrownianTree:
    method __init__ (line 94) | def __init__(self, x, t0, t1, seed=None, **kwargs):
    method sort (line 115) | def sort(a, b):
    method __call__ (line 118) | def __call__(self, t0, t1):
  class BrownianTreeNoiseSampler (line 127) | class BrownianTreeNoiseSampler:
    method __init__ (line 142) | def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambd...
    method __call__ (line 147) | def __call__(self, sigma, sigma_next):
  function sigma_to_half_log_snr (line 152) | def sigma_to_half_log_snr(sigma, model_sampling):
  function half_log_snr_to_sigma (line 160) | def half_log_snr_to_sigma(half_log_snr, model_sampling):
  function offset_first_sigma_for_snr (line 168) | def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e...
  function ei_h_phi_1 (line 179) | def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor:
  function ei_h_phi_2 (line 184) | def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor:
  function sample_euler (line 190) | def sample_euler(model, x, sigmas, extra_args=None, callback=None, disab...
  function sample_euler_ancestral (line 216) | def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=N...
  function sample_euler_ancestral_RF (line 240) | def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callbac...
  function sample_heun (line 268) | def sample_heun(model, x, sigmas, extra_args=None, callback=None, disabl...
  function sample_dpm_2 (line 303) | def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disab...
  function sample_dpm_2_ancestral (line 339) | def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=N...
  function sample_dpm_2_ancestral_RF (line 371) | def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callbac...
  function linear_multistep_coeff (line 404) | def linear_multistep_coeff(order, t, i, j):
  function sample_lms (line 418) | def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable...
  class PIDStepSizeController (line 441) | class PIDStepSizeController:
    method __init__ (line 443) | def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0...
    method limiter (line 452) | def limiter(self, x):
    method propose_step (line 455) | def propose_step(self, error):
  class DPMSolver (line 470) | class DPMSolver(nn.Module):
    method __init__ (line 473) | def __init__(self, model, extra_args=None, eps_callback=None, info_cal...
    method t (line 480) | def t(self, sigma):
    method sigma (line 483) | def sigma(self, t):
    method eps (line 486) | def eps(self, eps_cache, key, x, t, *args, **kwargs):
    method dpm_solver_1_step (line 495) | def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
    method dpm_solver_2_step (line 502) | def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
    method dpm_solver_3_step (line 512) | def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cach...
    method dpm_solver_fast (line 525) | def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., ...
    method dpm_solver_adaptive (line 564) | def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, a...
  function sample_dpm_fast (line 619) | def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, ...
  function sample_dpm_adaptive (line 631) | def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None,...
  function sample_dpmpp_2s_ancestral (line 646) | def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callbac...
  function sample_dpmpp_2s_ancestral_RF (line 684) | def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, call...
  function sample_dpmpp_sde (line 735) | def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, d...
  function sample_dpmpp_2m (line 792) | def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, di...
  function sample_dpmpp_2m_sde (line 818) | def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None...
  function sample_dpmpp_2m_sde_heun (line 872) | def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback...
  function sample_dpmpp_3m_sde (line 877) | def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None...
  function sample_dpmpp_3m_sde_gpu (line 939) | def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=...
  function sample_dpmpp_2m_sde_heun_gpu (line 949) | def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, call...
  function sample_dpmpp_2m_sde_gpu (line 959) | def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=...
  function sample_dpmpp_sde_gpu (line 969) | def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=Non...
  function DDPMSampler_step (line 978) | def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
  function generic_step_sampler (line 988) | def generic_step_sampler(model, x, sigmas, extra_args=None, callback=Non...
  function sample_ddpm (line 1005) | def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disabl...
  function sample_lcm (line 1009) | def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable...
  function sample_heunpp2 (line 1027) | def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_ipndm (line 1085) | def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disab...
  function sample_ipndm_v (line 1128) | def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_deis (line 1195) | def sample_deis(model, x, sigmas, extra_args=None, callback=None, disabl...
  function sample_euler_ancestral_cfg_pp (line 1244) | def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, cal...
  function sample_euler_cfg_pp (line 1288) | def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None...
  function sample_dpmpp_2s_ancestral_cfg_pp (line 1294) | def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, ...
  function sample_dpmpp_2m_cfg_pp (line 1337) | def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=N...
  function res_multistep (line 1370) | def res_multistep(model, x, sigmas, extra_args=None, callback=None, disa...
  function sample_res_multistep (line 1434) | def sample_res_multistep(model, x, sigmas, extra_args=None, callback=Non...
  function sample_res_multistep_cfg_pp (line 1438) | def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callb...
  function sample_res_multistep_ancestral (line 1442) | def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, ca...
  function sample_res_multistep_ancestral_cfg_pp (line 1446) | def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=N...
  function sample_gradient_estimation (line 1451) | def sample_gradient_estimation(model, x, sigmas, extra_args=None, callba...
  function sample_gradient_estimation_cfg_pp (line 1495) | def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None,...
  function sample_er_sde (line 1500) | def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disa...
  function sample_seeds_2 (line 1566) | def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_exp_heun_2_x0 (line 1628) | def sample_exp_heun_2_x0(model, x, sigmas, extra_args=None, callback=Non...
  function sample_exp_heun_2_x0_sde (line 1634) | def sample_exp_heun_2_x0_sde(model, x, sigmas, extra_args=None, callback...
  function sample_seeds_3 (line 1640) | def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, dis...
  function sample_sa_solver (line 1706) | def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, d...
  function sample_sa_solver_pece (line 1810) | def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=No...

FILE: comfy/k_diffusion/utils.py
  function hf_datasets_augs_helper (line 15) | def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'):
  function append_dims (line 21) | def append_dims(x, target_dims):
  function n_params (line 32) | def n_params(module):
  function download_file (line 37) | def download_file(path, url, digest=None):
  function train_mode (line 52) | def train_mode(model, mode=True):
  function eval_mode (line 63) | def eval_mode(model):
  function ema_update (line 70) | def ema_update(model, averaged_model, decay):
  class EMAWarmup (line 88) | class EMAWarmup:
    method __init__ (line 104) | def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1.,...
    method state_dict (line 113) | def state_dict(self):
    method load_state_dict (line 117) | def load_state_dict(self, state_dict):
    method get_value (line 125) | def get_value(self):
    method step (line 131) | def step(self):
  class InverseLR (line 136) | class InverseLR(optim.lr_scheduler._LRScheduler):
    method __init__ (line 153) | def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_l...
    method get_lr (line 163) | def get_lr(self):
    method _get_closed_form_lr (line 170) | def _get_closed_form_lr(self):
  class ExponentialLR (line 177) | class ExponentialLR(optim.lr_scheduler._LRScheduler):
    method __init__ (line 194) | def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0.,
    method get_lr (line 204) | def get_lr(self):
    method _get_closed_form_lr (line 211) | def _get_closed_form_lr(self):
  function rand_log_normal (line 218) | def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.f...
  function rand_log_logistic (line 223) | def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=f...
  function rand_log_uniform (line 233) | def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=to...
  function rand_v_diffusion (line 240) | def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float...
  function rand_split_log_normal (line 248) | def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dt...
  class FolderOfImages (line 258) | class FolderOfImages(data.Dataset):
    method __init__ (line 264) | def __init__(self, root, transform=None):
    method __repr__ (line 270) | def __repr__(self):
    method __len__ (line 273) | def __len__(self):
    method __getitem__ (line 276) | def __getitem__(self, key):
  class CSVLogger (line 284) | class CSVLogger:
    method __init__ (line 285) | def __init__(self, filename, columns):
    method write (line 294) | def write(self, *args):
  function tf32_mode (line 299) | def tf32_mode(cudnn=None, matmul=None):

FILE: comfy/latent_formats.py
  class LatentFormat (line 3) | class LatentFormat:
    method process_in (line 13) | def process_in(self, latent):
    method process_out (line 16) | def process_out(self, latent):
  class SD15 (line 19) | class SD15(LatentFormat):
    method __init__ (line 20) | def __init__(self, scale_factor=0.18215):
  class SDXL (line 31) | class SDXL(LatentFormat):
    method __init__ (line 34) | def __init__(self):
  class SDXL_Playground_2_5 (line 46) | class SDXL_Playground_2_5(LatentFormat):
    method __init__ (line 47) | def __init__(self):
    method process_in (line 61) | def process_in(self, latent):
    method process_out (line 66) | def process_out(self, latent):
  class SD_X4 (line 72) | class SD_X4(LatentFormat):
    method __init__ (line 73) | def __init__(self):
  class SC_Prior (line 82) | class SC_Prior(LatentFormat):
    method __init__ (line 85) | def __init__(self):
  class SC_B (line 106) | class SC_B(LatentFormat):
    method __init__ (line 108) | def __init__(self):
  class SD3 (line 117) | class SD3(LatentFormat):
    method __init__ (line 119) | def __init__(self):
    method process_in (line 143) | def process_in(self, latent):
    method process_out (line 146) | def process_out(self, latent):
  class StableAudio1 (line 149) | class StableAudio1(LatentFormat):
  class Flux (line 153) | class Flux(SD3):
    method __init__ (line 155) | def __init__(self):
    method process_in (line 179) | def process_in(self, latent):
    method process_out (line 182) | def process_out(self, latent):
  class Flux2 (line 185) | class Flux2(LatentFormat):
    method __init__ (line 189) | def __init__(self):
    method process_in (line 228) | def process_in(self, latent):
    method process_out (line 231) | def process_out(self, latent):
  class Mochi (line 234) | class Mochi(LatentFormat):
    method __init__ (line 238) | def __init__(self):
    method process_in (line 266) | def process_in(self, latent):
    method process_out (line 271) | def process_out(self, latent):
  class LTXV (line 276) | class LTXV(LatentFormat):
    method __init__ (line 281) | def __init__(self):
  class LTXAV (line 415) | class LTXAV(LTXV):
    method __init__ (line 416) | def __init__(self):
  class HunyuanVideo (line 420) | class HunyuanVideo(LatentFormat):
  class Cosmos1CV8x8x8 (line 446) | class Cosmos1CV8x8x8(LatentFormat):
  class Wan21 (line 471) | class Wan21(LatentFormat):
    method __init__ (line 496) | def __init__(self):
    method process_in (line 510) | def process_in(self, latent):
    method process_out (line 515) | def process_out(self, latent):
  class Wan22 (line 520) | class Wan22(Wan21):
    method __init__ (line 578) | def __init__(self):
  class HunyuanImage21 (line 598) | class HunyuanImage21(LatentFormat):
  class HunyuanImage21Refiner (line 672) | class HunyuanImage21Refiner(LatentFormat):
    method process_in (line 677) | def process_in(self, latent):
    method process_out (line 686) | def process_out(self, latent):
  class HunyuanVideo15 (line 696) | class HunyuanVideo15(LatentFormat):
  class Hunyuan3Dv2 (line 739) | class Hunyuan3Dv2(LatentFormat):
  class Hunyuan3Dv2_1 (line 744) | class Hunyuan3Dv2_1(LatentFormat):
  class Hunyuan3Dv2mini (line 749) | class Hunyuan3Dv2mini(LatentFormat):
  class ACEAudio (line 754) | class ACEAudio(LatentFormat):
  class ACEAudio15 (line 758) | class ACEAudio15(LatentFormat):
  class ChromaRadiance (line 762) | class ChromaRadiance(LatentFormat):
    method __init__ (line 766) | def __init__(self):
    method process_in (line 774) | def process_in(self, latent):
    method process_out (line 777) | def process_out(self, latent):
  class ZImagePixelSpace (line 781) | class ZImagePixelSpace(ChromaRadiance):

FILE: comfy/ldm/ace/ace_step15.py
  function get_silence_latent (line 10) | def get_silence_latent(length, device):
  function get_layer_class (line 71) | def get_layer_class(operations, layer_name):
  class RotaryEmbedding (line 76) | class RotaryEmbedding(nn.Module):
    method __init__ (line 77) | def __init__(self, dim, max_position_embeddings=32768, base=1000000.0,...
    method _set_cos_sin_cache (line 87) | def _set_cos_sin_cache(self, seq_len, device, dtype):
    method forward (line 95) | def forward(self, x, seq_len=None):
  function rotate_half (line 103) | def rotate_half(x):
  function apply_rotary_pos_emb (line 108) | def apply_rotary_pos_emb(q, k, cos, sin):
  class MLP (line 115) | class MLP(nn.Module):
    method __init__ (line 116) | def __init__(self, hidden_size, intermediate_size, dtype=None, device=...
    method forward (line 124) | def forward(self, x):
  class TimestepEmbedding (line 127) | class TimestepEmbedding(nn.Module):
    method __init__ (line 128) | def __init__(self, in_channels: int, time_embed_dim: int, scale: float...
    method forward (line 139) | def forward(self, t, dtype=None):
  class AceStepAttention (line 147) | class AceStepAttention(nn.Module):
    method __init__ (line 148) | def __init__(
    method forward (line 179) | def forward(
  class AceStepDiTLayer (line 252) | class AceStepDiTLayer(nn.Module):
    method __init__ (line 253) | def __init__(
    method forward (line 289) | def forward(
  class AceStepEncoderLayer (line 327) | class AceStepEncoderLayer(nn.Module):
    method __init__ (line 328) | def __init__(
    method forward (line 349) | def forward(self, hidden_states, position_embeddings, attention_mask=N...
  class AceStepLyricEncoder (line 365) | class AceStepLyricEncoder(nn.Module):
    method __init__ (line 366) | def __init__(
    method forward (line 401) | def forward(self, inputs_embeds, attention_mask=None):
  class AceStepTimbreEncoder (line 417) | class AceStepTimbreEncoder(nn.Module):
    method __init__ (line 418) | def __init__(
    method unpack_timbre_embeddings (line 454) | def unpack_timbre_embeddings(self, timbre_embs_packed, refer_audio_ord...
    method forward (line 485) | def forward(self, refer_audio_acoustic_hidden_states_packed, refer_aud...
  function pack_sequences (line 506) | def pack_sequences(hidden1, hidden2, mask1, mask2):
  class AceStepConditionEncoder (line 523) | class AceStepConditionEncoder(nn.Module):
    method __init__ (line 524) | def __init__(
    method forward (line 572) | def forward(
  class AceStepDiTModel (line 602) | class AceStepDiTModel(nn.Module):
    method __init__ (line 603) | def __init__(
    method forward (line 668) | def forward(
  class AttentionPooler (line 721) | class AttentionPooler(nn.Module):
    method __init__ (line 722) | def __init__(self, hidden_size, num_layers, head_dim, rms_norm_eps, dt...
    method forward (line 738) | def forward(self, x):
  class FSQ (line 753) | class FSQ(nn.Module):
    method __init__ (line 754) | def __init__(
    method bound (line 791) | def bound(self, z):
    method _indices_to_codes (line 801) | def _indices_to_codes(self, indices):
    method codes_to_indices (line 806) | def codes_to_indices(self, zhat):
    method forward (line 810) | def forward(self, z):
  class ResidualFSQ (line 821) | class ResidualFSQ(nn.Module):
    method __init__ (line 822) | def __init__(
    method get_output_from_indices (line 873) | def get_output_from_indices(self, indices, dtype=torch.float32):
    method forward (line 886) | def forward(self, x):
  class AceStepAudioTokenizer (line 913) | class AceStepAudioTokenizer(nn.Module):
    method __init__ (line 914) | def __init__(
    method forward (line 945) | def forward(self, hidden_states):
    method tokenize (line 951) | def tokenize(self, x):
  class AudioTokenDetokenizer (line 967) | class AudioTokenDetokenizer(nn.Module):
    method __init__ (line 968) | def __init__(
    method forward (line 995) | def forward(self, x):
  class AceStepConditionGenerationModel (line 1011) | class AceStepConditionGenerationModel(nn.Module):
    method __init__ (line 1012) | def __init__(
    method prepare_condition (line 1076) | def prepare_condition(
    method forward (line 1113) | def forward(self, x, timestep, context, lyric_embed=None, refer_audio=...

FILE: comfy/ldm/ace/attention.py
  class Attention (line 24) | class Attention(nn.Module):
    method __init__ (line 25) | def __init__(
    method forward (line 131) | def forward(
  class CustomLiteLAProcessor2_0 (line 149) | class CustomLiteLAProcessor2_0:
    method __init__ (line 152) | def __init__(self):
    method apply_rotary_emb (line 157) | def apply_rotary_emb(
    method __call__ (line 187) | def __call__(
  class CustomerAttnProcessor2_0 (line 327) | class CustomerAttnProcessor2_0:
    method apply_rotary_emb (line 332) | def apply_rotary_emb(
    method __call__ (line 362) | def __call__(
  function val2list (line 457) | def val2list(x: list or tuple or any, repeat_time=1) -> list:  # type: i...
  function val2tuple (line 464) | def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int...
  function t2i_modulate (line 476) | def t2i_modulate(x, shift, scale):
  function get_same_padding (line 480) | def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[...
  class ConvLayer (line 487) | class ConvLayer(nn.Module):
    method __init__ (line 488) | def __init__(
    method forward (line 537) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class GLUMBConv (line 546) | class GLUMBConv(nn.Module):
    method __init__ (line 547) | def __init__(
    method forward (line 606) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LinearTransformerBlock (line 621) | class LinearTransformerBlock(nn.Module):
    method __init__ (line 625) | def __init__(
    method forward (line 694) | def forward(

FILE: comfy/ldm/ace/lyric_encoder.py
  class ConvolutionModule (line 9) | class ConvolutionModule(nn.Module):
    method __init__ (line 12) | def __init__(self,
    method forward (line 79) | def forward(
  class PositionwiseFeedForward (line 136) | class PositionwiseFeedForward(torch.nn.Module):
    method __init__ (line 149) | def __init__(
    method forward (line 164) | def forward(self, xs: torch.Tensor) -> torch.Tensor:
  class Swish (line 174) | class Swish(torch.nn.Module):
    method forward (line 177) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class MultiHeadedAttention (line 181) | class MultiHeadedAttention(nn.Module):
    method __init__ (line 191) | def __init__(self,
    method forward_qkv (line 209) | def forward_qkv(
    method forward_attention (line 237) | def forward_attention(
    method forward (line 279) | def forward(
  class RelPositionMultiHeadedAttention (line 331) | class RelPositionMultiHeadedAttention(MultiHeadedAttention):
    method __init__ (line 340) | def __init__(self,
    method rel_shift (line 357) | def rel_shift(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 381) | def forward(
  function subsequent_mask (line 450) | def subsequent_mask(
  function subsequent_chunk_mask (line 486) | def subsequent_chunk_mask(
  function add_optional_chunk_mask (line 523) | def add_optional_chunk_mask(xs: torch.Tensor,
  class ConformerEncoderLayer (line 597) | class ConformerEncoderLayer(nn.Module):
    method __init__ (line 617) | def __init__(
    method forward (line 649) | def forward(
  class EspnetRelPositionalEncoding (line 729) | class EspnetRelPositionalEncoding(torch.nn.Module):
    method __init__ (line 743) | def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5...
    method extend_pe (line 752) | def extend_pe(self, x: torch.Tensor):
    method forward (line 784) | def forward(self, x: torch.Tensor, offset: Union[int, torch.Tensor] = ...
    method position_encoding (line 800) | def position_encoding(self,
  class LinearEmbed (line 826) | class LinearEmbed(torch.nn.Module):
    method __init__ (line 836) | def __init__(self, idim: int, odim: int, dropout_rate: float,
    method position_encoding (line 847) | def position_encoding(self, offset: Union[int, torch.Tensor],
    method forward (line 851) | def forward(
  function make_pad_mask (line 889) | def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
  class ConformerEncoder (line 918) | class ConformerEncoder(torch.nn.Module):
    method __init__ (line 921) | def __init__(
    method forward_layers (line 1011) | def forward_layers(self, xs: torch.Tensor, chunk_masks: torch.Tensor,
    method forward (line 1018) | def forward(

FILE: comfy/ldm/ace/model.py
  function cross_norm (line 29) | def cross_norm(hidden_states, controlnet_input):
  class Qwen2RotaryEmbedding (line 38) | class Qwen2RotaryEmbedding(nn.Module):
    method __init__ (line 39) | def __init__(self, dim, max_position_embeddings=2048, base=10000, dtyp...
    method _set_cos_sin_cache (line 53) | def _set_cos_sin_cache(self, seq_len, device, dtype):
    method forward (line 63) | def forward(self, x, seq_len=None):
  class T2IFinalLayer (line 74) | class T2IFinalLayer(nn.Module):
    method __init__ (line 79) | def __init__(self, hidden_size, patch_size=[16, 1], out_channels=256, ...
    method unpatchfy (line 87) | def unpatchfy(
    method forward (line 107) | def forward(self, x, t, output_length):
  class PatchEmbed (line 116) | class PatchEmbed(nn.Module):
    method __init__ (line 119) | def __init__(
    method forward (line 140) | def forward(self, latent):
  class ACEStepTransformer2DModel (line 147) | class ACEStepTransformer2DModel(nn.Module):
    method __init__ (line 150) | def __init__(
    method forward_lyric_encoder (line 258) | def forward_lyric_encoder(
    method encode (line 270) | def encode(
    method decode (line 307) | def decode(
    method forward (line 349) | def forward(self,
    method _forward (line 370) | def _forward(

FILE: comfy/ldm/ace/vae/autoencoder_dc.py
  class RMSNorm (line 12) | class RMSNorm(ops.RMSNorm):
    method __init__ (line 13) | def __init__(self, dim, eps=1e-5, elementwise_affine=True, bias=False):
    method forward (line 18) | def forward(self, x):
  function get_normalization (line 26) | def get_normalization(norm_type, num_features, num_groups=32, eps=1e-5):
  function get_activation (line 39) | def get_activation(activation_type):
  class ResBlock (line 52) | class ResBlock(nn.Module):
    method __init__ (line 53) | def __init__(
    method forward (line 68) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class SanaMultiscaleAttentionProjection (line 82) | class SanaMultiscaleAttentionProjection(nn.Module):
    method __init__ (line 83) | def __init__(
    method forward (line 102) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class SanaMultiscaleLinearAttention (line 107) | class SanaMultiscaleLinearAttention(nn.Module):
    method __init__ (line 108) | def __init__(
    method apply_linear_attention (line 148) | def apply_linear_attention(self, query, key, value):
    method apply_quadratic_attention (line 157) | def apply_quadratic_attention(self, query, key, value):
    method forward (line 164) | def forward(self, hidden_states):
  class EfficientViTBlock (line 219) | class EfficientViTBlock(nn.Module):
    method __init__ (line 220) | def __init__(
    method forward (line 246) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class GLUMBConv (line 252) | class GLUMBConv(nn.Module):
    method __init__ (line 253) | def __init__(
    method forward (line 276) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  function get_block (line 299) | def get_block(
  class DCDownBlock2d (line 323) | class DCDownBlock2d(nn.Module):
    method __init__ (line 324) | def __init__(self, in_channels: int, out_channels: int, downsample: bo...
    method forward (line 346) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class DCUpBlock2d (line 362) | class DCUpBlock2d(nn.Module):
    method __init__ (line 363) | def __init__(
    method forward (line 385) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class Encoder (line 403) | class Encoder(nn.Module):
    method __init__ (line 404) | def __init__(
    method forward (line 474) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class Decoder (line 489) | class Decoder(nn.Module):
    method __init__ (line 490) | def __init__(
    method forward (line 563) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class AutoencoderDC (line 581) | class AutoencoderDC(nn.Module):
    method __init__ (line 582) | def __init__(
    method encode (line 630) | def encode(self, x: torch.Tensor) -> torch.Tensor:
    method decode (line 635) | def decode(self, z: torch.Tensor) -> torch.Tensor:
    method forward (line 641) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: comfy/ldm/ace/vae/music_dcae_pipeline.py
  class MusicDCAE (line 14) | class MusicDCAE(torch.nn.Module):
    method __init__ (line 15) | def __init__(self, source_sample_rate=None, dcae_config={}, vocoder_co...
    method forward_mel (line 38) | def forward_mel(self, audios):
    method encode (line 47) | def encode(self, audios, audio_lengths=None, sr=None):
    method decode (line 74) | def decode(self, latents, audio_lengths=None, sr=None):
    method forward (line 95) | def forward(self, audios, audio_lengths=None, sr=None):

FILE: comfy/ldm/ace/vae/music_log_mel.py
  class LinearSpectrogram (line 13) | class LinearSpectrogram(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 32) | def forward(self, y: Tensor) -> Tensor:
  class LogMelSpectrogram (line 65) | class LogMelSpectrogram(nn.Module):
    method __init__ (line 66) | def __init__(
    method compress (line 99) | def compress(self, x: Tensor) -> Tensor:
    method decompress (line 102) | def decompress(self, x: Tensor) -> Tensor:
    method forward (line 105) | def forward(self, x: Tensor, return_linear: bool = False) -> Tensor:

FILE: comfy/ldm/ace/vae/music_vocoder.py
  function drop_path (line 20) | def drop_path(
  class DropPath (line 45) | class DropPath(nn.Module):
    method __init__ (line 48) | def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
    method forward (line 53) | def forward(self, x):
    method extra_repr (line 56) | def extra_repr(self):
  class LayerNorm (line 60) | class LayerNorm(nn.Module):
    method __init__ (line 67) | def __init__(self, normalized_shape, eps=1e-6, data_format="channels_l...
    method forward (line 77) | def forward(self, x):
  class ConvNeXtBlock (line 90) | class ConvNeXtBlock(nn.Module):
    method __init__ (line 105) | def __init__(
    method forward (line 137) | def forward(self, x, apply_residual: bool = True):
  class ParallelConvNeXtBlock (line 159) | class ParallelConvNeXtBlock(nn.Module):
    method __init__ (line 160) | def __init__(self, kernel_sizes: List[int], *args, **kwargs):
    method forward (line 169) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class ConvNeXtEncoder (line 176) | class ConvNeXtEncoder(nn.Module):
    method __init__ (line 177) | def __init__(
    method forward (line 237) | def forward(
  function get_padding (line 248) | def get_padding(kernel_size, dilation=1):
  class ResBlock1 (line 252) | class ResBlock1(torch.nn.Module):
    method __init__ (line 253) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
    method forward (line 326) | def forward(self, x):
    method remove_weight_norm (line 335) | def remove_weight_norm(self):
  class HiFiGANGenerator (line 342) | class HiFiGANGenerator(nn.Module):
    method __init__ (line 343) | def __init__(
    method forward (line 430) | def forward(self, x, template=None):
    method remove_weight_norm (line 456) | def remove_weight_norm(self):
  class ADaMoSHiFiGANV1 (line 465) | class ADaMoSHiFiGANV1(nn.Module):
    method __init__ (line 466) | def __init__(
    method decode (line 526) | def decode(self, mel):
    method encode (line 532) | def encode(self, x):
    method forward (line 535) | def forward(self, mel):

FILE: comfy/ldm/anima/model.py
  function rotate_half (line 7) | def rotate_half(x):
  function apply_rotary_pos_emb (line 13) | def apply_rotary_pos_emb(x, cos, sin, unsqueeze_dim=1):
  class RotaryEmbedding (line 20) | class RotaryEmbedding(nn.Module):
    method __init__ (line 21) | def __init__(self, head_dim):
    method forward (line 28) | def forward(self, x, position_ids):
  class Attention (line 42) | class Attention(nn.Module):
    method __init__ (line 43) | def __init__(self, query_dim, context_dim, n_heads, head_dim, device=N...
    method forward (line 62) | def forward(self, x, mask=None, context=None, position_embeddings=None...
    method init_weights (line 86) | def init_weights(self):
  class TransformerBlock (line 90) | class TransformerBlock(nn.Module):
    method __init__ (line 91) | def __init__(self, source_dim, model_dim, num_heads=16, mlp_ratio=4.0,...
    method forward (line 125) | def forward(self, x, context, target_attention_mask=None, source_atten...
    method init_weights (line 138) | def init_weights(self):
  class LLMAdapter (line 143) | class LLMAdapter(nn.Module):
    method __init__ (line 144) | def __init__(
    method forward (line 171) | def forward(self, source_hidden_states, target_input_ids, target_atten...
  class Anima (line 193) | class Anima(MiniTrainDIT):
    method __init__ (line 194) | def __init__(self, *args, **kwargs):
    method preprocess_text_embeds (line 198) | def preprocess_text_embeds(self, text_embeds, text_ids, t5xxl_weights=...
    method forward (line 210) | def forward(self, x, timesteps, context, **kwargs):

FILE: comfy/ldm/audio/autoencoder.py
  function vae_sample (line 10) | def vae_sample(mean, scale):
  class VAEBottleneck (line 20) | class VAEBottleneck(nn.Module):
    method __init__ (line 21) | def __init__(self):
    method encode (line 25) | def encode(self, x, return_info=False, **kwargs):
    method decode (line 39) | def decode(self, x):
  function snake_beta (line 43) | def snake_beta(x, alpha, beta):
  class SnakeBeta (line 47) | class SnakeBeta(nn.Module):
    method __init__ (line 49) | def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha...
    method forward (line 67) | def forward(self, x):
  function WNConv1d (line 77) | def WNConv1d(*args, **kwargs):
  function WNConvTranspose1d (line 80) | def WNConvTranspose1d(*args, **kwargs):
  function get_activation (line 83) | def get_activation(activation: Literal["elu", "snake", "none"], antialia...
  class ResidualUnit (line 99) | class ResidualUnit(nn.Module):
    method __init__ (line 100) | def __init__(self, in_channels, out_channels, dilation, use_snake=Fals...
    method forward (line 116) | def forward(self, x):
  class EncoderBlock (line 124) | class EncoderBlock(nn.Module):
    method __init__ (line 125) | def __init__(self, in_channels, out_channels, stride, use_snake=False,...
    method forward (line 140) | def forward(self, x):
  class DecoderBlock (line 143) | class DecoderBlock(nn.Module):
    method __init__ (line 144) | def __init__(self, in_channels, out_channels, stride, use_snake=False,...
    method forward (line 173) | def forward(self, x):
  class OobleckEncoder (line 176) | class OobleckEncoder(nn.Module):
    method __init__ (line 177) | def __init__(self,
    method forward (line 206) | def forward(self, x):
  class OobleckDecoder (line 210) | class OobleckDecoder(nn.Module):
    method __init__ (line 211) | def __init__(self,
    method forward (line 250) | def forward(self, x):
  class AudioOobleckVAE (line 254) | class AudioOobleckVAE(nn.Module):
    method __init__ (line 255) | def __init__(self,
    method encode (line 271) | def encode(self, x):
    method decode (line 274) | def decode(self, x):

FILE: comfy/ldm/audio/dit.py
  class FourierFeatures (line 14) | class FourierFeatures(nn.Module):
    method __init__ (line 15) | def __init__(self, in_features, out_features, std=1., dtype=None, devi...
    method forward (line 21) | def forward(self, input):
  class LayerNorm (line 26) | class LayerNorm(nn.Module):
    method __init__ (line 27) | def __init__(self, dim, bias=False, fix_scale=False, dtype=None, devic...
    method forward (line 40) | def forward(self, x):
  class GLU (line 46) | class GLU(nn.Module):
    method __init__ (line 47) | def __init__(
    method forward (line 63) | def forward(self, x):
  class AbsolutePositionalEmbedding (line 74) | class AbsolutePositionalEmbedding(nn.Module):
    method __init__ (line 75) | def __init__(self, dim, max_seq_len):
    method forward (line 81) | def forward(self, x, pos = None, seq_start_pos = None):
  class ScaledSinusoidalEmbedding (line 95) | class ScaledSinusoidalEmbedding(nn.Module):
    method __init__ (line 96) | def __init__(self, dim, theta = 10000):
    method forward (line 106) | def forward(self, x, pos = None, seq_start_pos = None):
  class RotaryEmbedding (line 119) | class RotaryEmbedding(nn.Module):
    method __init__ (line 120) | def __init__(
    method forward_from_seq_len (line 152) | def forward_from_seq_len(self, seq_len, device, dtype):
    method forward (line 158) | def forward(self, t):
  function rotate_half (line 178) | def rotate_half(x):
  function apply_rotary_pos_emb (line 183) | def apply_rotary_pos_emb(t, freqs, scale = 1):
  class FeedForward (line 203) | class FeedForward(nn.Module):
    method __init__ (line 204) | def __init__(
    method forward (line 253) | def forward(self, x):
  class Attention (line 256) | class Attention(nn.Module):
    method __init__ (line 257) | def __init__(
    method forward (line 294) | def forward(
  class ConformerModule (line 376) | class ConformerModule(nn.Module):
    method __init__ (line 377) | def __init__(
    method forward (line 395) | def forward(self, x):
  class TransformerBlock (line 412) | class TransformerBlock(nn.Module):
    method __init__ (line 413) | def __init__(
    method forward (line 485) | def forward(
  class ContinuousTransformer (line 534) | class ContinuousTransformer(nn.Module):
    method __init__ (line 535) | def __init__(
    method forward (line 601) | def forward(
  class AudioDiffusionTransformer (line 670) | class AudioDiffusionTransformer(nn.Module):
    method __init__ (line 671) | def __init__(self,
    method _forward (line 776) | def _forward(
    method forward (line 872) | def forward(

FILE: comfy/ldm/audio/embedders.py
  class LearnedPositionalEmbedding (line 11) | class LearnedPositionalEmbedding(nn.Module):
    method __init__ (line 14) | def __init__(self, dim: int):
    method forward (line 20) | def forward(self, x: Tensor) -> Tensor:
  function TimePositionalEmbedding (line 27) | def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
  class NumberEmbedder (line 34) | class NumberEmbedder(nn.Module):
    method __init__ (line 35) | def __init__(
    method forward (line 44) | def forward(self, x: Union[List[float], Tensor]) -> Tensor:
  class Conditioner (line 56) | class Conditioner(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 70) | def forward(self, x):
  class NumberConditioner (line 73) | class NumberConditioner(Conditioner):
    method __init__ (line 77) | def __init__(self,
    method forward (line 89) | def forward(self, floats, device=None):

FILE: comfy/ldm/aura/mmdit.py
  function modulate (line 15) | def modulate(x, shift, scale):
  function find_multiple (line 19) | def find_multiple(n: int, k: int) -> int:
  class MLP (line 25) | class MLP(nn.Module):
    method __init__ (line 26) | def __init__(self, dim, hidden_dim=None, dtype=None, device=None, oper...
    method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class MultiHeadLayerNorm (line 44) | class MultiHeadLayerNorm(nn.Module):
    method __init__ (line 45) | def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
    method forward (line 52) | def forward(self, hidden_states):
  class SingleAttention (line 63) | class SingleAttention(nn.Module):
    method __init__ (line 64) | def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=N...
    method forward (line 88) | def forward(self, c, transformer_options={}):
  class DoubleAttention (line 104) | class DoubleAttention(nn.Module):
    method __init__ (line 105) | def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=N...
    method forward (line 147) | def forward(self, c, x, transformer_options={}):
  class MMDiTBlock (line 180) | class MMDiTBlock(nn.Module):
    method __init__ (line 181) | def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, d...
    method forward (line 210) | def forward(self, c, x, global_cond, transformer_options={}, **kwargs):
  class DiTBlock (line 241) | class DiTBlock(nn.Module):
    method __init__ (line 243) | def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, devi...
    method forward (line 258) | def forward(self, cx, global_cond, transformer_options={}, **kwargs):
  class TimestepEmbedder (line 275) | class TimestepEmbedder(nn.Module):
    method __init__ (line 276) | def __init__(self, hidden_size, frequency_embedding_size=256, dtype=No...
    method timestep_embedding (line 286) | def timestep_embedding(t, dim, max_period=10000):
    method forward (line 300) | def forward(self, t, dtype):
  class MMDiT (line 306) | class MMDiT(nn.Module):
    method __init__ (line 307) | def __init__(
    method extend_pe (line 371) | def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
    method pe_selection_index_based_on_dim (line 386) | def pe_selection_index_based_on_dim(self, h, w):
    method unpatchify (line 399) | def unpatchify(self, x, h, w):
    method patchify (line 408) | def patchify(self, x):
    method apply_pos_embeds (line 422) | def apply_pos_embeds(self, x, h, w):
    method forward (line 439) | def forward(self, x, timestep, context, transformer_options={}, **kwar...
    method _forward (line 446) | def _forward(self, x, timestep, context, transformer_options={}, **kwa...

FILE: comfy/ldm/cascade/common.py
  class OptimizedAttention (line 24) | class OptimizedAttention(nn.Module):
    method __init__ (line 25) | def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, ope...
    method forward (line 35) | def forward(self, q, k, v, transformer_options={}):
  class Attention2D (line 44) | class Attention2D(nn.Module):
    method __init__ (line 45) | def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, ope...
    method forward (line 50) | def forward(self, x, kv, self_attn=False, transformer_options={}):
  function LayerNorm2d_op (line 61) | def LayerNorm2d_op(operations):
  class GlobalResponseNorm (line 70) | class GlobalResponseNorm(nn.Module):
    method __init__ (line 72) | def __init__(self, dim, dtype=None, device=None):
    method forward (line 77) | def forward(self, x):
  class ResBlock (line 83) | class ResBlock(nn.Module):
    method __init__ (line 84) | def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None...
    method forward (line 97) | def forward(self, x, x_skip=None):
  class AttnBlock (line 106) | class AttnBlock(nn.Module):
    method __init__ (line 107) | def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtyp...
    method forward (line 117) | def forward(self, x, kv, transformer_options={}):
  class FeedForwardBlock (line 123) | class FeedForwardBlock(nn.Module):
    method __init__ (line 124) | def __init__(self, c, dropout=0.0, dtype=None, device=None, operations...
    method forward (line 135) | def forward(self, x):
  class TimestepBlock (line 140) | class TimestepBlock(nn.Module):
    method __init__ (line 141) | def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=No...
    method forward (line 148) | def forward(self, x, t):

FILE: comfy/ldm/cascade/controlnet.py
  class CNetResBlock (line 24) | class CNetResBlock(nn.Module):
    method __init__ (line 25) | def __init__(self, c, dtype=None, device=None, operations=None):
    method forward (line 36) | def forward(self, x):
  class ControlNet (line 40) | class ControlNet(nn.Module):
    method __init__ (line 41) | def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_m...
    method forward (line 87) | def forward(self, x):

FILE: comfy/ldm/cascade/stage_a.py
  class vector_quantize (line 27) | class vector_quantize(Function):
    method forward (line 29) | def forward(ctx, x, codebook):
    method backward (line 44) | def backward(ctx, grad_output, grad_indices):
  class VectorQuantize (line 59) | class VectorQuantize(nn.Module):
    method __init__ (line 60) | def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False):
    method _laplace_smoothing (line 80) | def _laplace_smoothing(self, x, epsilon):
    method _updateEMA (line 84) | def _updateEMA(self, z_e_x, indices):
    method idx2vq (line 95) | def idx2vq(self, idx, dim=-1):
    method forward (line 101) | def forward(self, x, get_losses=True, dim=-1):
  class ResBlock (line 121) | class ResBlock(nn.Module):
    method __init__ (line 122) | def __init__(self, c, c_hidden):
    method _norm (line 141) | def _norm(self, x, norm):
    method forward (line 144) | def forward(self, x):
  class StageA (line 160) | class StageA(nn.Module):
    method __init__ (line 161) | def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_lat...
    method encode (line 205) | def encode(self, x, quantize=False):
    method decode (line 214) | def decode(self, x):
    method forward (line 219) | def forward(self, x, quantize=False):
  class Discriminator (line 225) | class Discriminator(nn.Module):
    method __init__ (line 226) | def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6):
    method forward (line 243) | def forward(self, x, cond=None):

FILE: comfy/ldm/cascade/stage_b.py
  class StageB (line 24) | class StageB(nn.Module):
    method __init__ (line 25) | def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280,...
    method gen_r_embedding (line 158) | def gen_r_embedding(self, r, max_positions=10000):
    method gen_c_embeddings (line 169) | def gen_c_embeddings(self, clip):
    method _down_encode (line 176) | def _down_encode(self, x, r_embed, clip, transformer_options={}):
    method _up_decode (line 202) | def _up_decode(self, level_outputs, r_embed, clip, transformer_options...
    method forward (line 231) | def forward(self, x, r, effnet, clip, pixels=None, transformer_options...
    method update_weights_ema (line 252) | def update_weights_ema(self, src_model, beta=0.999):

FILE: comfy/ldm/cascade/stage_c.py
  class UpDownBlock2d (line 25) | class UpDownBlock2d(nn.Module):
    method __init__ (line 26) | def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device...
    method forward (line 34) | def forward(self, x):
  class StageC (line 40) | class StageC(nn.Module):
    method __init__ (line 41) | def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=204...
    method gen_r_embedding (line 162) | def gen_r_embedding(self, r, max_positions=10000):
    method gen_c_embeddings (line 173) | def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img):
    method _down_encode (line 185) | def _down_encode(self, x, r_embed, clip, cnet=None, transformer_option...
    method _up_decode (line 216) | def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transfor...
    method forward (line 250) | def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control...
    method update_weights_ema (line 269) | def update_weights_ema(self, src_model, beta=0.999):

FILE: comfy/ldm/cascade/stage_c_coder.py
  class EfficientNetEncoder (line 27) | class EfficientNetEncoder(nn.Module):
    method __init__ (line 28) | def __init__(self, c_latent=16):
    method forward (line 38) | def forward(self, x):
  class Previewer (line 46) | class Previewer(nn.Module):
    method __init__ (line 47) | def __init__(self, c_in=16, c_hidden=512, c_out=3):
    method forward (line 85) | def forward(self, x):
  class StageC_coder (line 88) | class StageC_coder(nn.Module):
    method __init__ (line 89) | def __init__(self):
    method encode (line 94) | def encode(self, x):
    method decode (line 97) | def decode(self, x):

FILE: comfy/ldm/chroma/layers.py
  class ChromaModulationOut (line 14) | class ChromaModulationOut(ModulationOut):
    method from_offset (line 16) | def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> Modulat...
  class Approximator (line 26) | class Approximator(nn.Module):
    method __init__ (line 27) | def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layer...
    method device (line 35) | def device(self):
    method forward (line 39) | def forward(self, x: Tensor) -> Tensor:
  class LastLayer (line 50) | class LastLayer(nn.Module):
    method __init__ (line 51) | def __init__(self, hidden_size: int, patch_size: int, out_channels: in...
    method forward (line 56) | def forward(self, x: Tensor, vec: Tensor) -> Tensor:

FILE: comfy/ldm/chroma/model.py
  class ChromaParams (line 26) | class ChromaParams:
  class Chroma (line 48) | class Chroma(nn.Module):
    method __init__ (line 53) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method get_modulations (line 115) | def get_modulations(self, tensor: torch.Tensor, block_type: str, *, id...
    method forward_orig (line 143) | def forward_orig(
    method forward (line 270) | def forward(self, x, timestep, context, guidance, control=None, transf...
    method _forward (line 277) | def _forward(self, x, timestep, context, guidance, control=None, trans...

FILE: comfy/ldm/chroma_radiance/layers.py
  class NerfEmbedder (line 8) | class NerfEmbedder(nn.Module):
    method __init__ (line 17) | def __init__(
    method fetch_pos (line 48) | def fetch_pos(self, patch_size: int, device: torch.device, dtype: torc...
    method forward (line 102) | def forward(self, inputs: torch.Tensor) -> torch.Tensor:
  class NerfGLUBlock (line 135) | class NerfGLUBlock(nn.Module):
    method __init__ (line 139) | def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, ...
    method forward (line 150) | def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
  class NerfFinalLayer (line 176) | class NerfFinalLayer(nn.Module):
    method __init__ (line 177) | def __init__(self, hidden_size, out_channels, dtype=None, device=None,...
    method forward (line 182) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class NerfFinalLayerConv (line 188) | class NerfFinalLayerConv(nn.Module):
    method __init__ (line 189) | def __init__(self, hidden_size: int, out_channels: int, dtype=None, de...
    method forward (line 201) | def forward(self, x: torch.Tensor) -> torch.Tensor:

FILE: comfy/ldm/chroma_radiance/model.py
  class ChromaRadianceParams (line 28) | class ChromaRadianceParams(ChromaParams):
  class ChromaRadiance (line 42) | class ChromaRadiance(Chroma):
    method __init__ (line 47) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method _nerf_final_layer (line 166) | def _nerf_final_layer(self) -> nn.Module:
    method img_in (line 174) | def img_in(self, img: Tensor) -> Tensor:
    method forward_nerf (line 179) | def forward_nerf(
    method forward_tiled_nerf (line 223) | def forward_tiled_nerf(
    method radiance_get_override_params (line 260) | def radiance_get_override_params(self, overrides: dict) -> ChromaRadia...
    method _apply_x0_residual (line 282) | def _apply_x0_residual(self, predicted, noisy, timesteps):
    method _forward (line 288) | def _forward(

FILE: comfy/ldm/common_dit.py
  function pad_to_patch_size (line 5) | def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):

FILE: comfy/ldm/cosmos/blocks.py
  function get_normalization (line 29) | def get_normalization(name: str, channels: int, weight_args={}, operatio...
  class BaseAttentionOp (line 38) | class BaseAttentionOp(nn.Module):
    method __init__ (line 39) | def __init__(self):
  class Attention (line 43) | class Attention(nn.Module):
    method __init__ (line 74) | def __init__(
    method cal_qkv (line 128) | def cal_qkv(
    method forward (line 173) | def forward(
  class FeedForward (line 194) | class FeedForward(nn.Module):
    method __init__ (line 215) | def __init__(
    method forward (line 237) | def forward(self, x: torch.Tensor):
  class GPT2FeedForward (line 247) | class GPT2FeedForward(FeedForward):
    method __init__ (line 248) | def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias...
    method forward (line 260) | def forward(self, x: torch.Tensor):
  function modulate (line 270) | def modulate(x, shift, scale):
  class Timesteps (line 274) | class Timesteps(nn.Module):
    method __init__ (line 275) | def __init__(self, num_channels):
    method forward (line 279) | def forward(self, timesteps):
  class TimestepEmbedding (line 294) | class TimestepEmbedding(nn.Module):
    method __init__ (line 295) | def __init__(self, in_features: int, out_features: int, use_adaln_lora...
    method forward (line 308) | def forward(self, sample: torch.Tensor) -> torch.Tensor:
  class FourierFeatures (line 323) | class FourierFeatures(nn.Module):
    method __init__ (line 343) | def __init__(self, num_channels, bandwidth=1, normalize=False):
    method forward (line 349) | def forward(self, x, gain: float = 1.0):
  class PatchEmbed (line 366) | class PatchEmbed(nn.Module):
    method __init__ (line 381) | def __init__(
    method forward (line 408) | def forward(self, x):
  class FinalLayer (line 431) | class FinalLayer(nn.Module):
    method __init__ (line 436) | def __init__(
    method forward (line 466) | def forward(
  class VideoAttn (line 489) | class VideoAttn(nn.Module):
    method __init__ (line 516) | def __init__(
    method forward (line 544) | def forward(
  function adaln_norm_state (line 582) | def adaln_norm_state(norm_state, x, scale, shift):
  class DITBuildingBlock (line 587) | class DITBuildingBlock(nn.Module):
    method __init__ (line 609) | def __init__(
    method forward (line 663) | def forward(
  class GeneralDITTransformerBlock (line 725) | class GeneralDITTransformerBlock(nn.Module):
    method __init__ (line 753) | def __init__(
    method forward (line 785) | def forward(

FILE: comfy/ldm/cosmos/cosmos_tokenizer/layers3d.py
  class CausalConv3d (line 59) | class CausalConv3d(nn.Module):
    method __init__ (line 60) | def __init__(
    method _replication_pad (line 98) | def _replication_pad(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 104) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalUpsample3d (line 109) | class CausalUpsample3d(nn.Module):
    method __init__ (line 110) | def __init__(self, in_channels: int) -> None:
    method forward (line 116) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalDownsample3d (line 129) | class CausalDownsample3d(nn.Module):
    method __init__ (line 130) | def __init__(self, in_channels: int) -> None:
    method forward (line 141) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalHybridUpsample3d (line 149) | class CausalHybridUpsample3d(nn.Module):
    method __init__ (line 150) | def __init__(
    method forward (line 188) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalHybridDownsample3d (line 211) | class CausalHybridDownsample3d(nn.Module):
    method __init__ (line 212) | def __init__(
    method forward (line 251) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalResnetBlock3d (line 275) | class CausalResnetBlock3d(nn.Module):
    method __init__ (line 276) | def __init__(
    method forward (line 303) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalResnetBlockFactorized3d (line 318) | class CausalResnetBlockFactorized3d(nn.Module):
    method __init__ (line 319) | def __init__(
    method forward (line 372) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalAttnBlock (line 387) | class CausalAttnBlock(nn.Module):
    method __init__ (line 388) | def __init__(self, in_channels: int, num_groups: int) -> None:
    method forward (line 407) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalTemporalAttnBlock (line 427) | class CausalTemporalAttnBlock(nn.Module):
    method __init__ (line 428) | def __init__(self, in_channels: int, num_groups: int) -> None:
    method forward (line 445) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class EncoderBase (line 479) | class EncoderBase(nn.Module):
    method __init__ (line 480) | def __init__(
    method patcher3d (line 561) | def patcher3d(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 567) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DecoderBase (line 607) | class DecoderBase(nn.Module):
    method __init__ (line 608) | def __init__(
    method unpatcher3d (line 696) | def unpatcher3d(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 703) | def forward(self, z):
  class EncoderFactorized (line 734) | class EncoderFactorized(nn.Module):
    method __init__ (line 735) | def __init__(
    method forward (line 863) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DecoderFactorized (line 888) | class DecoderFactorized(nn.Module):
    method __init__ (line 889) | def __init__(
    method forward (line 1020) | def forward(self, z):

FILE: comfy/ldm/cosmos/cosmos_tokenizer/patching.py
  class Patcher (line 39) | class Patcher(torch.nn.Module):
    method __init__ (line 49) | def __init__(self, patch_size=1, patch_method="haar"):
    method forward (line 65) | def forward(self, x):
    method _dwt (line 73) | def _dwt(self, x, mode="reflect", rescale=False):
    method _haar (line 97) | def _haar(self, x):
    method _arrange (line 102) | def _arrange(self, x):
  class Patcher3D (line 112) | class Patcher3D(Patcher):
    method __init__ (line 115) | def __init__(self, patch_size=1, patch_method="haar"):
    method _dwt (line 123) | def _dwt(self, x, wavelet, mode="reflect", rescale=False):
    method _haar (line 161) | def _haar(self, x):
    method _arrange (line 168) | def _arrange(self, x):
  class UnPatcher (line 181) | class UnPatcher(torch.nn.Module):
    method __init__ (line 191) | def __init__(self, patch_size=1, patch_method="haar"):
    method forward (line 207) | def forward(self, x):
    method _idwt (line 215) | def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
    method _ihaar (line 252) | def _ihaar(self, x):
    method _iarrange (line 257) | def _iarrange(self, x):
  class UnPatcher3D (line 267) | class UnPatcher3D(UnPatcher):
    method __init__ (line 270) | def __init__(self, patch_size=1, patch_method="haar"):
    method _idwt (line 273) | def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
    method _ihaar (line 362) | def _ihaar(self, x):
    method _iarrange (line 368) | def _iarrange(self, x):

FILE: comfy/ldm/cosmos/cosmos_tokenizer/utils.py
  function time2batch (line 26) | def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
  function batch2time (line 31) | def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor:
  function space2batch (line 35) | def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
  function batch2space (line 40) | def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch....
  function cast_tuple (line 44) | def cast_tuple(t: Any, length: int = 1) -> Any:
  function replication_pad (line 48) | def replication_pad(x):
  function divisible_by (line 52) | def divisible_by(num: int, den: int) -> bool:
  function is_odd (line 56) | def is_odd(n: int) -> bool:
  function nonlinearity (line 60) | def nonlinearity(x):
  function Normalize (line 65) | def Normalize(in_channels, num_groups=32):
  class CausalNormalize (line 71) | class CausalNormalize(torch.nn.Module):
    method __init__ (line 72) | def __init__(self, in_channels, num_groups=1):
    method forward (line 82) | def forward(self, x):
  function exists (line 91) | def exists(v):
  function default (line 95) | def default(*args):
  function round_ste (line 102) | def round_ste(z: torch.Tensor) -> torch.Tensor:
  function log (line 108) | def log(t, eps=1e-5):
  function entropy (line 112) | def entropy(prob):

FILE: comfy/ldm/cosmos/model.py
  class DataType (line 43) | class DataType(Enum):
  class GeneralDIT (line 48) | class GeneralDIT(nn.Module):
    method __init__ (line 91) | def __init__(
    method build_pos_embed (line 213) | def build_pos_embed(self, device=None, dtype=None):
    method prepare_embedded_sequence (line 250) | def prepare_embedded_sequence(
    method decoder_head (line 310) | def decoder_head(
    method forward_before_blocks (line 342) | def forward_before_blocks(
    method forward (line 423) | def forward(
    method _forward (line 459) | def _forward(

FILE: comfy/ldm/cosmos/position_embedding.py
  function normalize (line 24) | def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: flo...
  class VideoPositionEmb (line 43) | class VideoPositionEmb(nn.Module):
    method forward (line 44) | def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor...
    method generate_embeddings (line 53) | def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torc...
  class VideoRopePosition3DEmb (line 57) | class VideoRopePosition3DEmb(VideoPositionEmb):
    method __init__ (line 58) | def __init__(
    method generate_embeddings (line 100) | def generate_embeddings(
  class LearnablePosEmbAxis (line 166) | class LearnablePosEmbAxis(VideoPositionEmb):
    method __init__ (line 167) | def __init__(
    method generate_embeddings (line 192) | def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torc...

FILE: comfy/ldm/cosmos/predict2.py
  function apply_rotary_pos_emb (line 18) | def apply_rotary_pos_emb(
  class GPT2FeedForward (line 29) | class GPT2FeedForward(nn.Module):
    method __init__ (line 30) | def __init__(self, d_model: int, d_ff: int, device=None, dtype=None, o...
    method forward (line 40) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function torch_attention_op (line 48) | def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor,...
  class Attention (line 78) | class Attention(nn.Module):
    method __init__ (line 110) | def __init__(
    method compute_qkv (line 154) | def compute_qkv(
    method compute_attention (line 184) | def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch...
    method forward (line 188) | def forward(
  class Timesteps (line 204) | class Timesteps(nn.Module):
    method __init__ (line 205) | def __init__(self, num_channels: int):
    method forward (line 209) | def forward(self, timesteps_B_T: torch.Tensor) -> torch.Tensor:
  class TimestepEmbedding (line 226) | class TimestepEmbedding(nn.Module):
    method __init__ (line 227) | def __init__(self, in_features: int, out_features: int, use_adaln_lora...
    method forward (line 242) | def forward(self, sample: torch.Tensor) -> Tuple[torch.Tensor, Optiona...
  class PatchEmbed (line 257) | class PatchEmbed(nn.Module):
    method __init__ (line 272) | def __init__(
    method forward (line 297) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class FinalLayer (line 322) | class FinalLayer(nn.Module):
    method __init__ (line 327) | def __init__(
    method forward (line 357) | def forward(
  class Block (line 388) | class Block(nn.Module):
    method __init__ (line 409) | def __init__(
    method forward (line 456) | def forward(
  class MiniTrainDIT (line 573) | class MiniTrainDIT(nn.Module):
    method __init__ (line 608) | def __init__(
    method build_pos_embed (line 718) | def build_pos_embed(self, device=None, dtype=None) -> None:
    method prepare_embedded_sequence (line 755) | def prepare_embedded_sequence(
    method unpatchify (line 808) | def unpatchify(self, x_B_T_H_W_M: torch.Tensor) -> torch.Tensor:
    method forward (line 818) | def forward(self,
    method _forward (line 832) | def _forward(

FILE: comfy/ldm/cosmos/vae.py
  class IdentityDistribution (line 30) | class IdentityDistribution(torch.nn.Module):
    method __init__ (line 31) | def __init__(self):
    method forward (line 34) | def forward(self, parameters):
  class GaussianDistribution (line 38) | class GaussianDistribution(torch.nn.Module):
    method __init__ (line 39) | def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0):
    method sample (line 44) | def sample(self, mean, logvar):
    method forward (line 48) | def forward(self, parameters):
  class ContinuousFormulation (line 54) | class ContinuousFormulation(Enum):
  class CausalContinuousVideoTokenizer (line 59) | class CausalContinuousVideoTokenizer(nn.Module):
    method __init__ (line 60) | def __init__(
    method encode (line 103) | def encode(self, x):
    method decode (line 117) | def decode(self, z):

FILE: comfy/ldm/flux/controlnet.py
  class MistolineCondDownsamplBlock (line 14) | class MistolineCondDownsamplBlock(nn.Module):
    method __init__ (line 15) | def __init__(self, dtype=None, device=None, operations=None):
    method forward (line 39) | def forward(self, x):
  class MistolineControlnetBlock (line 42) | class MistolineControlnetBlock(nn.Module):
    method __init__ (line 43) | def __init__(self, hidden_size, dtype=None, device=None, operations=No...
    method forward (line 48) | def forward(self, x):
  class ControlNetFlux (line 52) | class ControlNetFlux(Flux):
    method __init__ (line 53) | def __init__(self, latent_input=False, num_union_modes=0, mistoline=Fa...
    method forward_orig (line 109) | def forward_orig(
    method forward (line 182) | def forward(self, x, timesteps, context, y=None, guidance=None, hint=N...

FILE: comfy/ldm/flux/layers.py
  class EmbedND (line 12) | class EmbedND(nn.Module):
    method __init__ (line 13) | def __init__(self, dim: int, theta: int, axes_dim: list):
    method forward (line 19) | def forward(self, ids: Tensor) -> Tensor:
  function timestep_embedding (line 29) | def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: fl...
  class MLPEmbedder (line 50) | class MLPEmbedder(nn.Module):
    method __init__ (line 51) | def __init__(self, in_dim: int, hidden_dim: int, bias=True, dtype=None...
    method forward (line 57) | def forward(self, x: Tensor) -> Tensor:
  class YakMLP (line 60) | class YakMLP(nn.Module):
    method __init__ (line 61) | def __init__(self, hidden_size: int, intermediate_size: int, dtype=Non...
    method forward (line 70) | def forward(self, x: Tensor) -> Tensor:
  function build_mlp (line 74) | def build_mlp(hidden_size, mlp_hidden_dim, mlp_silu_act=False, yak_mlp=F...
  class QKNorm (line 91) | class QKNorm(torch.nn.Module):
    method __init__ (line 92) | def __init__(self, dim: int, dtype=None, device=None, operations=None):
    method forward (line 97) | def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
  class SelfAttention (line 103) | class SelfAttention(nn.Module):
    method __init__ (line 104) | def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = Fals...
  class ModulationOut (line 115) | class ModulationOut:
  class Modulation (line 121) | class Modulation(nn.Module):
    method __init__ (line 122) | def __init__(self, dim: int, double: bool, bias=True, dtype=None, devi...
    method forward (line 128) | def forward(self, vec: Tensor) -> tuple:
  function apply_mod (line 139) | def apply_mod(tensor, m_mult, m_add=None, modulation_dims=None):
  class SiLUActivation (line 153) | class SiLUActivation(nn.Module):
    method __init__ (line 154) | def __init__(self):
    method forward (line 158) | def forward(self, x: Tensor) -> Tensor:
  class DoubleStreamBlock (line 163) | class DoubleStreamBlock(nn.Module):
    method __init__ (line 164) | def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float,...
    method forward (line 192) | def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, a...
  class SingleStreamBlock (line 261) | class SingleStreamBlock(nn.Module):
    method __init__ (line 267) | def __init__(
    method forward (line 316) | def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, ...
  class LastLayer (line 358) | class LastLayer(nn.Module):
    method __init__ (line 359) | def __init__(self, hidden_size: int, patch_size: int, out_channels: in...
    method forward (line 365) | def forward(self, x: Tensor, vec: Tensor, modulation_dims=None) -> Ten...

FILE: comfy/ldm/flux/math.py
  function attention (line 10) | def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, tr...
  function rope (line 17) | def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
  function _apply_rope1 (line 32) | def _apply_rope1(x: Tensor, freqs_cis: Tensor):
  function _apply_rope (line 43) | def _apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
  function apply_rope (line 51) | def apply_rope(xq, xk, freqs_cis):
  function apply_rope1 (line 56) | def apply_rope1(x, freqs_cis):

FILE: comfy/ldm/flux/model.py
  class FluxParams (line 22) | class FluxParams:
  function invert_slices (line 47) | def invert_slices(slices, length):
  class Flux (line 63) | class Flux(nn.Module):
    method __init__ (line 68) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method forward_orig (line 147) | def forward_orig(
    method process_img (line 314) | def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_...
    method forward (line 344) | def forward(self, x, timestep, context, y=None, guidance=None, ref_lat...
    method _forward (line 351) | def _forward(self, x, timestep, context, y=None, guidance=None, ref_la...

FILE: comfy/ldm/flux/redux.py
  class ReduxImageEncoder (line 6) | class ReduxImageEncoder(torch.nn.Module):
    method __init__ (line 7) | def __init__(
    method forward (line 23) | def forward(self, sigclip_embeds) -> torch.Tensor:

FILE: comfy/ldm/genmo/joint_model/asymm_models_joint.py
  function modulated_rmsnorm (line 33) | def modulated_rmsnorm(x, scale, eps=1e-6):
  function residual_tanh_gated_rmsnorm (line 41) | def residual_tanh_gated_rmsnorm(x, x_res, gate, eps=1e-6):
  class AsymmetricAttention (line 53) | class AsymmetricAttention(nn.Module):
    method __init__ (line 54) | def __init__(
    method forward (line 105) | def forward(
  class AsymmetricJointBlock (line 160) | class AsymmetricJointBlock(nn.Module):
    method __init__ (line 161) | def __init__(
    method forward (line 223) | def forward(
    method ff_block_x (line 277) | def ff_block_x(self, x, scale_x, gate_x):
    method ff_block_y (line 283) | def ff_block_y(self, y, scale_y, gate_y):
  class FinalLayer (line 290) | class FinalLayer(nn.Module):
    method __init__ (line 295) | def __init__(
    method forward (line 313) | def forward(self, x, c):
  class AsymmDiTJoint (line 321) | class AsymmDiTJoint(nn.Module):
    method __init__ (line 328) | def __init__(
    method embed_x (line 443) | def embed_x(self, x: torch.Tensor) -> torch.Tensor:
    method prepare (line 453) | def prepare(
    method forward (line 487) | def forward(

FILE: comfy/ldm/genmo/joint_model/layers.py
  function _ntuple (line 17) | def _ntuple(n):
  class TimestepEmbedder (line 29) | class TimestepEmbedder(nn.Module):
    method __init__ (line 30) | def __init__(
    method timestep_embedding (line 51) | def timestep_embedding(t, dim, max_period=10000):
    method forward (line 63) | def forward(self, t, out_dtype):
  class FeedForward (line 71) | class FeedForward(nn.Module):
    method __init__ (line 72) | def __init__(
    method forward (line 94) | def forward(self, x):
  class PatchEmbed (line 100) | class PatchEmbed(nn.Module):
    method __init__ (line 101) | def __init__(
    method forward (line 133) | def forward(self, x):

FILE: comfy/ldm/genmo/joint_model/rope_mixed.py
  function centers (line 9) | def centers(start: float, stop, num, dtype=None, device=None):
  function create_position_matrix (line 27) | def create_position_matrix(
  function compute_mixed_rotation (line 68) | def compute_mixed_rotation(

FILE: comfy/ldm/genmo/joint_model/temporal_rope.py
  function apply_rotary_emb_qk_real (line 7) | def apply_rotary_emb_qk_real(

FILE: comfy/ldm/genmo/joint_model/utils.py
  function modulate (line 11) | def modulate(x, shift, scale):
  function pool_tokens (line 15) | def pool_tokens(x: torch.Tensor, mask: torch.Tensor, *, keepdim=False) -...
  class AttentionPool (line 36) | class AttentionPool(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 59) | def forward(self, x, mask):

FILE: comfy/ldm/genmo/vae/model.py
  function cast_tuple (line 22) | def cast_tuple(t, length=1):
  class GroupNormSpatial (line 26) | class GroupNormSpatial(ops.GroupNorm):
    method forward (line 31) | def forward(self, x: torch.Tensor, *, chunk_size: int = 8):
  class PConv3d (line 40) | class PConv3d(ops.Conv3d):
    method __init__ (line 41) | def __init__(
    method forward (line 68) | def forward(self, x: torch.Tensor):
  class Conv1x1 (line 85) | class Conv1x1(ops.Linear):
    method __init__ (line 88) | def __init__(self, in_features: int, out_features: int, *args, **kwargs):
    method forward (line 91) | def forward(self, x: torch.Tensor):
  class DepthToSpaceTime (line 106) | class DepthToSpaceTime(nn.Module):
    method __init__ (line 107) | def __init__(
    method extra_repr (line 117) | def extra_repr(self):
    method forward (line 120) | def forward(self, x: torch.Tensor):
  function norm_fn (line 149) | def norm_fn(
  class ResBlock (line 156) | class ResBlock(nn.Module):
    method __init__ (line 159) | def __init__(
    method forward (line 201) | def forward(self, x: torch.Tensor):
  class Attention (line 215) | class Attention(nn.Module):
    method __init__ (line 216) | def __init__(
    method forward (line 232) | def forward(
  class AttentionBlock (line 276) | class AttentionBlock(nn.Module):
    method __init__ (line 277) | def __init__(
    method forward (line 286) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CausalUpsampleBlock (line 290) | class CausalUpsampleBlock(nn.Module):
    method __init__ (line 291) | def __init__(
    method forward (line 321) | def forward(self, x):
  function block_fn (line 328) | def block_fn(channels, *, affine: bool = True, has_attention: bool = Fal...
  class DownsampleBlock (line 333) | class DownsampleBlock(nn.Module):
    method __init__ (line 334) | def __init__(
    method forward (line 378) | def forward(self, x):
  function add_fourier_features (line 382) | def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1):
  class FourierFeatures (line 409) | class FourierFeatures(nn.Module):
    method __init__ (line 410) | def __init__(self, start: int = 6, stop: int = 8, step: int = 1):
    method forward (line 416) | def forward(self, inputs):
  class Decoder (line 428) | class Decoder(nn.Module):
    method __init__ (line 429) | def __init__(
    method forward (line 509) | def forward(self, x):
  class LatentDistribution (line 532) | class LatentDistribution:
    method __init__ (line 533) | def __init__(self, mean: torch.Tensor, logvar: torch.Tensor):
    method sample (line 544) | def sample(self, temperature=1.0, generator: torch.Generator = None, n...
    method mode (line 560) | def mode(self):
  class Encoder (line 563) | class Encoder(nn.Module):
    method __init__ (line 564) | def __init__(
    method temporal_downsample (line 637) | def temporal_downsample(self):
    method spatial_downsample (line 641) | def spatial_downsample(self):
    method forward (line 644) | def forward(self, x) -> LatentDistribution:
  class VideoVAE (line 673) | class VideoVAE(nn.Module):
    method __init__ (line 674) | def __init__(self):
    method encode (line 707) | def encode(self, x):
    method decode (line 710) | def decode(self, x):

FILE: comfy/ldm/hidream/model.py
  class EmbedND (line 21) | class EmbedND(nn.Module):
    method __init__ (line 22) | def __init__(self, theta: int, axes_dim: List[int]):
    method forward (line 27) | def forward(self, ids: torch.Tensor) -> torch.Tensor:
  class PatchEmbed (line 36) | class PatchEmbed(nn.Module):
    method __init__ (line 37) | def __init__(
    method forward (line 49) | def forward(self, latent):
  class PooledEmbed (line 54) | class PooledEmbed(nn.Module):
    method __init__ (line 55) | def __init__(self, text_emb_dim, hidden_size, dtype=None, device=None,...
    method forward (line 59) | def forward(self, pooled_embed):
  class TimestepEmbed (line 63) | class TimestepEmbed(nn.Module):
    method __init__ (line 64) | def __init__(self, hidden_size, frequency_embedding_size=256, dtype=No...
    method forward (line 69) | def forward(self, timesteps, wdtype):
  function attention (line 75) | def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tenso...
  class HiDreamAttnProcessor_flashattn (line 79) | class HiDreamAttnProcessor_flashattn:
    method __call__ (line 82) | def __call__(
  class HiDreamAttention (line 148) | class HiDreamAttention(nn.Module):
    method __init__ (line 149) | def __init__(
    method forward (line 197) | def forward(
  class FeedForwardSwiGLU (line 215) | class FeedForwardSwiGLU(nn.Module):
    method __init__ (line 216) | def __init__(
    method forward (line 237) | def forward(self, x):
  class MoEGate (line 242) | class MoEGate(nn.Module):
    method __init__ (line 243) | def __init__(self, embed_dim, num_routed_experts=4, num_activated_expe...
    method reset_parameters (line 258) | def reset_parameters(self) -> None:
    method forward (line 263) | def forward(self, hidden_states):
  class MOEFeedForwardSwiGLU (line 287) | class MOEFeedForwardSwiGLU(nn.Module):
    method __init__ (line 288) | def __init__(
    method forward (line 307) | def forward(self, x):
    method moe_infer (line 328) | def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
  class TextProjection (line 349) | class TextProjection(nn.Module):
    method __init__ (line 350) | def __init__(self, in_features, hidden_size, dtype=None, device=None, ...
    method forward (line 354) | def forward(self, caption):
  class BlockType (line 359) | class BlockType:
  class HiDreamImageSingleTransformerBlock (line 364) | class HiDreamImageSingleTransformerBlock(nn.Module):
    method __init__ (line 365) | def __init__(
    method forward (line 405) | def forward(
  class HiDreamImageTransformerBlock (line 437) | class HiDreamImageTransformerBlock(nn.Module):
    method __init__ (line 438) | def __init__(
    method forward (line 483) | def forward(
  class HiDreamImageBlock (line 527) | class HiDreamImageBlock(nn.Module):
    method __init__ (line 528) | def __init__(
    method forward (line 552) | def forward(
  class HiDreamImageTransformer2DModel (line 571) | class HiDreamImageTransformer2DModel(nn.Module):
    method __init__ (line 572) | def __init__(
    method expand_timesteps (line 654) | def expand_timesteps(self, timesteps, batch_size, device):
    method unpatchify (line 668) | def unpatchify(self, x: torch.Tensor, img_sizes: List[Tuple[int, int]]...
    method patchify (line 679) | def patchify(self, x, max_seq, img_sizes=None):
    method forward (line 704) | def forward(self,
    method _forward (line 720) | def _forward(

FILE: comfy/ldm/hunyuan3d/model.py
  class Hunyuan3Dv2 (line 13) | class Hunyuan3Dv2(nn.Module):
    method __init__ (line 14) | def __init__(
    method forward (line 70) | def forward(self, x, timestep, context, guidance=None, transformer_opt...
    method _forward (line 77) | def _forward(self, x, timestep, context, guidance=None, transformer_op...

FILE: comfy/ldm/hunyuan3d/vae.py
  function fps (line 18) | def fps(src: torch.Tensor, batch: torch.Tensor, sampling_ratio: float, s...
  class PointCrossAttention (line 62) | class PointCrossAttention(nn.Module):
    method __init__ (line 63) | def __init__(self,
    method sample_points_and_latents (line 113) | def sample_points_and_latents(self, point_cloud: torch.Tensor, feature...
    method forward (line 199) | def forward(self, point_cloud: torch.Tensor, features: torch.Tensor):
    method subsample (line 219) | def subsample(self, pc, num_query, input_pc_size: int):
    method handle_features (line 247) | def handle_features(self, features, idx_pc, input_pc_size, batch_size:...
  function normalize_mesh (line 258) | def normalize_mesh(mesh, scale = 0.9999):
  function sample_pointcloud (line 270) | def sample_pointcloud(mesh, num = 200000):
  function detect_sharp_edges (line 277) | def detect_sharp_edges(mesh, threshold=0.985):
  function sharp_sample_pointcloud (line 297) | def sharp_sample_pointcloud(mesh, num = 16384):
  function load_surface_sharpedge (line 317) | def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096,...
  class SharpEdgeSurfaceLoader (line 377) | class SharpEdgeSurfaceLoader:
    method __init__ (line 380) | def __init__(self, num_uniform_points = 8192, num_sharp_points = 8192):
    method __call__ (line 386) | def __call__(self, mesh_input, device = "cuda"):
    method _load_mesh (line 391) | def _load_mesh(mesh_input):
  class DiagonalGaussianDistribution (line 407) | class DiagonalGaussianDistribution:
    method __init__ (line 408) | def __init__(self, params: torch.Tensor, feature_dim: int = -1):
    method sample (line 416) | def sample(self):
  class VanillaVolumeDecoder (line 427) | class VanillaVolumeDecoder():
    method __call__ (line 429) | def __call__(self, latents: torch.Tensor, geo_decoder: callable, octre...
  class FourierEmbedder (line 459) | class FourierEmbedder(nn.Module):
    method __init__ (line 496) | def __init__(self,
    method get_dims (line 529) | def get_dims(self, input_dim):
    method forward (line 535) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class CrossAttentionProcessor (line 555) | class CrossAttentionProcessor:
    method __call__ (line 556) | def __call__(self, attn, q, k, v):
  class DropPath (line 560) | class DropPath(nn.Module):
    method __init__ (line 564) | def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
    method forward (line 569) | def forward(self, x):
    method extra_repr (line 588) | def extra_repr(self):
  class MLP (line 592) | class MLP(nn.Module):
    method __init__ (line 593) | def __init__(
    method forward (line 607) | def forward(self, x):
  class QKVMultiheadCrossAttention (line 610) | class QKVMultiheadCrossAttention(nn.Module):
    method __init__ (line 611) | def __init__(
    method forward (line 625) | def forward(self, q, kv):
  class MultiheadCrossAttention (line 646) | class MultiheadCrossAttention(nn.Module):
    method __init__ (line 647) | def __init__(
    method forward (line 674) | def forward(self, x, data):
  class ResidualCrossAttentionBlock (line 687) | class ResidualCrossAttentionBlock(nn.Module):
    method __init__ (line 688) | def __init__(
    method forward (line 717) | def forward(self, x: torch.Tensor, data: torch.Tensor):
  class QKVMultiheadAttention (line 723) | class QKVMultiheadAttention(nn.Module):
    method __init__ (line 724) | def __init__(
    method forward (line 737) | def forward(self, qkv):
  class MultiheadAttention (line 751) | class MultiheadAttention(nn.Module):
    method __init__ (line 752) | def __init__(
    method forward (line 774) | def forward(self, x):
  class ResidualAttentionBlock (line 781) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 782) | def __init__(
    method forward (line 805) | def forward(self, x: torch.Tensor):
  class Transformer (line 811) | class Transformer(nn.Module):
    method __init__ (line 812) | def __init__(
    method forward (line 840) | def forward(self, x: torch.Tensor):
  class CrossAttentionDecoder (line 846) | class CrossAttentionDecoder(nn.Module):
    method __init__ (line 848) | def __init__(
    method forward (line 886) | def forward(self, queries=None, query_embeddings=None, latents=None):
  class ShapeVAE (line 899) | class ShapeVAE(nn.Module):
    method __init__ (line 900) | def __init__(
    method decode (line 966) | def decode(self, latents, **kwargs):
    method encode (line 978) | def encode(self, surface):

FILE: comfy/ldm/hunyuan3dv2_1/hunyuandit.py
  class GELU (line 8) | class GELU(nn.Module):
    method __init__ (line 10) | def __init__(self, dim_in: int, dim_out: int, operations, device, dtype):
    method gelu (line 14) | def gelu(self, gate: torch.Tensor) -> torch.Tensor:
    method forward (line 21) | def forward(self, hidden_states):
  class FeedForward (line 28) | class FeedForward(nn.Module):
    method __init__ (line 30) | def __init__(self, dim: int, dim_out = None, mult: int = 4,
    method forward (line 47) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class AddAuxLoss (line 52) | class AddAuxLoss(torch.autograd.Function):
    method forward (line 55) | def forward(ctx, x, loss):
    method backward (line 63) | def backward(ctx, grad_output):
  class MoEGate (line 73) | class MoEGate(nn.Module):
    method __init__ (line 75) | def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, a...
    method forward (line 86) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  class MoEBlock (line 114) | class MoEBlock(nn.Module):
    method __init__ (line 115) | def __init__(self, dim, num_experts: int = 6, moe_top_k: int = 2, drop...
    method forward (line 130) | def forward(self, hidden_states) -> torch.Tensor:
    method moe_infer (line 160) | def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
  class Timesteps (line 190) | class Timesteps(nn.Module):
    method __init__ (line 191) | def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0,
    method forward (line 214) | def forward(self, timesteps: torch.Tensor):
  class TimestepEmbedder (line 235) | class TimestepEmbedder(nn.Module):
    method __init__ (line 236) | def __init__(self, hidden_size, frequency_embedding_size = 256, cond_p...
    method forward (line 251) | def forward(self, timesteps, condition):
  class MLP (line 264) | class MLP(nn.Module):
    method __init__ (line 265) | def __init__(self, *, width: int, operations = None, device = None, dt...
    method forward (line 272) | def forward(self, x):
  class CrossAttention (line 275) | class CrossAttention(nn.Module):
    method __init__ (line 276) | def __init__(
    method forward (line 317) | def forward(self, x, y):
  class Attention (line 353) | class Attention(nn.Module):
    method __init__ (line 355) | def __init__(
    method forward (line 391) | def forward(self, x):
  class HunYuanDiTBlock (line 422) | class HunYuanDiTBlock(nn.Module):
    method __init__ (line 423) | def __init__(
    method forward (line 491) | def forward(self, hidden_states, conditioning=None, text_states=None, ...
  class FinalLayer (line 519) | class FinalLayer(nn.Module):
    method __init__ (line 521) | def __init__(self, final_hidden_size, out_channels, operations, use_fp...
    method forward (line 532) | def forward(self, x):
  class HunYuanDiTPlain (line 538) | class HunYuanDiTPlain(nn.Module):
    method __init__ (line 541) | def __init__(
    method forward (line 607) | def forward(self, x, t, context, transformer_options = {}, **kwargs):

FILE: comfy/ldm/hunyuan_video/model.py
  class HunyuanVideoParams (line 27) | class HunyuanVideoParams:
  class SelfAttentionRef (line 49) | class SelfAttentionRef(nn.Module):
    method __init__ (line 50) | def __init__(self, dim: int, qkv_bias: bool = False, dtype=None, devic...
  class TokenRefinerBlock (line 56) | class TokenRefinerBlock(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 85) | def forward(self, x, c, mask, transformer_options={}):
  class IndividualTokenRefiner (line 98) | class IndividualTokenRefiner(nn.Module):
    method __init__ (line 99) | def __init__(
    method forward (line 122) | def forward(self, x, c, mask, transformer_options={}):
  class TokenRefiner (line 134) | class TokenRefiner(nn.Module):
    method __init__ (line 135) | def __init__(
    method forward (line 152) | def forward(
  class ByT5Mapper (line 173) | class ByT5Mapper(nn.Module):
    method __init__ (line 174) | def __init__(self, in_dim, out_dim, hidden_dim, out_dim1, use_res=Fals...
    method forward (line 183) | def forward(self, x):
  class HunyuanVideo (line 196) | class HunyuanVideo(nn.Module):
    method __init__ (line 201) | def __init__(self, image_model=None, final_layer=True, dtype=None, dev...
    method forward_orig (line 289) | def forward_orig(
    method img_ids (line 459) | def img_ids(self, x):
    method img_ids_2d (line 471) | def img_ids_2d(self, x):
    method forward (line 481) | def forward(self, x, timestep, context, y=None, txt_byt5=None, clip_fe...
    method _forward (line 488) | def _forward(self, x, timestep, context, y=None, txt_byt5=None, clip_f...

FILE: comfy/ldm/hunyuan_video/upsampler.py
  class SRResidualCausalBlock3D (line 9) | class SRResidualCausalBlock3D(nn.Module):
    method __init__ (line 10) | def __init__(self, channels: int):
    method forward (line 20) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class SRModel3DV2 (line 23) | class SRModel3DV2(nn.Module):
    method __init__ (line 24) | def __init__(
    method forward (line 38) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Upsampler (line 49) | class Upsampler(nn.Module):
    method __init__ (line 50) | def __init__(
    method forward (line 81) | def forward(self, z):
  class HunyuanVideo15SRModel (line 104) | class HunyuanVideo15SRModel():
    method __init__ (line 105) | def __init__(self, model_type, config):
    method load_sd (line 114) | def load_sd(self, sd):
    method get_sd (line 117) | def get_sd(self):
    method resample_latent (line 120) | def resample_latent(self, latent):

FILE: comfy/ldm/hunyuan_video/vae.py
  class PixelShuffle2D (line 8) | class PixelShuffle2D(nn.Module):
    method __init__ (line 9) | def __init__(self, in_dim, out_dim, op=ops.Conv2d):
    method forward (line 14) | def forward(self, x):
  class PixelUnshuffle2D (line 22) | class PixelUnshuffle2D(nn.Module):
    method __init__ (line 23) | def __init__(self, in_dim, out_dim, op=ops.Conv2d):
    method forward (line 28) | def forward(self, x):
  class Encoder (line 36) | class Encoder(nn.Module):
    method __init__ (line 37) | def __init__(self, in_channels, z_channels, block_out_channels, num_re...
    method forward (line 71) | def forward(self, x):
  class Decoder (line 89) | class Decoder(nn.Module):
    method __init__ (line 90) | def __init__(self, z_channels, out_channels, block_out_channels, num_r...
    method forward (line 126) | def forward(self, z):

FILE: comfy/ldm/hunyuan_video/vae_refiner.py
  class RMS_norm (line 11) | class RMS_norm(nn.Module):
    method __init__ (line 12) | def __init__(self, dim):
    method forward (line 18) | def forward(self, x):
  class DnSmpl (line 21) | class DnSmpl(nn.Module):
    method __init__ (line 22) | def __init__(self, ic, oc, tds, refiner_vae, op):
    method forward (line 32) | def forward(self, x, conv_carry_in=None, conv_carry_out=None):
  class UpSmpl (line 81) | class UpSmpl(nn.Module):
    method __init__ (line 82) | def __init__(self, ic, oc, tus, refiner_vae, op):
    method forward (line 91) | def forward(self, x, conv_carry_in=None, conv_carry_out=None):
  class Encoder (line 136) | class Encoder(nn.Module):
    method __init__ (line 137) | def __init__(self, in_channels, z_channels, block_out_channels, num_re...
    method forward (line 184) | def forward(self, x):
  class Decoder (line 232) | class Decoder(nn.Module):
    method __init__ (line 233) | def __init__(self, z_channels, out_channels, block_out_channels, num_r...
    method forward (line 278) | def forward(self, z):

FILE: comfy/ldm/hydit/attn_layers.py
  function reshape_for_broadcast (line 7) | def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Ten...
  function rotate_half (line 49) | def rotate_half(x):
  function apply_rotary_emb (line 54) | def apply_rotary_emb(
  class CrossAttention (line 96) | class CrossAttention(nn.Module):
    method __init__ (line 100) | def __init__(self,
    method forward (line 134) | def forward(self, x, y, freqs_cis_img=None):
  class Attention (line 174) | class Attention(nn.Module):
    method __init__ (line 178) | def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_...
    method forward (line 198) | def forward(self, x, freqs_cis_img=None):

FILE: comfy/ldm/hydit/controlnet.py
  class HunYuanControlNet (line 17) | class HunYuanControlNet(nn.Module):
    method __init__ (line 47) | def __init__(
    method forward (line 203) | def forward(

FILE: comfy/ldm/hydit/models.py
  function calc_rope (line 14) | def calc_rope(x, patch_size, head_size):
  function modulate (line 26) | def modulate(x, shift, scale):
  class HunYuanDiTBlock (line 30) | class HunYuanDiTBlock(nn.Module):
    method __init__ (line 34) | def __init__(self,
    method _forward (line 89) | def _forward(self, x, c=None, text_states=None, freq_cis_img=None, ski...
    method forward (line 117) | def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip...
  class FinalLayer (line 123) | class FinalLayer(nn.Module):
    method __init__ (line 127) | def __init__(self, final_hidden_size, c_emb_size, patch_size, out_chan...
    method forward (line 136) | def forward(self, x, c):
  class HunYuanDiT (line 143) | class HunYuanDiT(nn.Module):
    method __init__ (line 173) | def __init__(self,
    method forward (line 274) | def forward(self,
    method unpatchify (line 404) | def unpatchify(self, x, h, w):

FILE: comfy/ldm/hydit/poolers.py
  class AttentionPool (line 6) | class AttentionPool(nn.Module):
    method __init__ (line 7) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
    method forward (line 17) | def forward(self, x):

FILE: comfy/ldm/hydit/posemb_layers.py
  function _to_tuple (line 6) | def _to_tuple(x):
  function get_fill_resize_and_crop (line 13) | def get_fill_resize_and_crop(src, tgt):
  function get_meshgrid (line 34) | def get_meshgrid(start, *args):
  function get_2d_sincos_pos_embed (line 64) | def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, ex...
  function get_2d_sincos_pos_embed_from_grid (line 83) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 94) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  function get_2d_rotary_pos_embed (line 120) | def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True):
  function get_2d_rotary_pos_embed_from_grid (line 145) | def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
  function get_1d_rotary_pos_embed (line 161) | def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta...
  function calc_sizes (line 195) | def calc_sizes(rope_img, patch_size, th, tw):
  function init_image_posemb (line 209) | def init_image_posemb(rope_img,

FILE: comfy/ldm/kandinsky5/model.py
  function attention (line 10) | def attention(q, k, v, heads, transformer_options={}):
  function apply_scale_shift_norm (line 20) | def apply_scale_shift_norm(norm, x, scale, shift):
  function apply_gate_sum (line 23) | def apply_gate_sum(x, out, gate):
  function get_shift_scale_gate (line 26) | def get_shift_scale_gate(params):
  function get_freqs (line 30) | def get_freqs(dim, max_period=10000.0):
  class TimeEmbeddings (line 34) | class TimeEmbeddings(nn.Module):
    method __init__ (line 35) | def __init__(self, model_dim, time_dim, max_period=10000.0, operation_...
    method forward (line 46) | def forward(self, timestep, dtype):
  class TextEmbeddings (line 53) | class TextEmbeddings(nn.Module):
    method __init__ (line 54) | def __init__(self, text_dim, model_dim, operation_settings=None):
    method forward (line 60) | def forward(self, text_embed):
  class VisualEmbeddings (line 65) | class VisualEmbeddings(nn.Module):
    method __init__ (line 66) | def __init__(self, visual_dim, model_dim, patch_size, operation_settin...
    method forward (line 72) | def forward(self, x):
  class Modulation (line 88) | class Modulation(nn.Module):
    method __init__ (line 89) | def __init__(self, time_dim, model_dim, num_params, operation_settings...
    method forward (line 94) | def forward(self, x):
  class SelfAttention (line 98) | class SelfAttention(nn.Module):
    method __init__ (line 99) | def __init__(self, num_channels, head_dim, operation_settings=None):
    method _compute_qk (line 115) | def _compute_qk(self, x, freqs, proj_fn, norm_fn):
    method _forward (line 119) | def _forward(self, x, freqs, transformer_options={}):
    method _forward_chunked (line 126) | def _forward_chunked(self, x, freqs, transformer_options={}):
    method forward (line 141) | def forward(self, x, freqs, transformer_options={}):
  class CrossAttention (line 148) | class CrossAttention(SelfAttention):
    method get_qkv (line 149) | def get_qkv(self, x, context):
    method forward (line 155) | def forward(self, x, context, transformer_options={}):
  class FeedForward (line 161) | class FeedForward(nn.Module):
    method __init__ (line 162) | def __init__(self, dim, ff_dim, operation_settings=None):
    method _forward (line 170) | def _forward(self, x):
    method _forward_chunked (line 173) | def _forward_chunked(self, x):
    method forward (line 180) | def forward(self, x):
  class OutLayer (line 187) | class OutLayer(nn.Module):
    method __init__ (line 188) | def __init__(self, model_dim, time_dim, visual_dim, patch_size, operat...
    method forward (line 196) | def forward(self, visual_embed, time_embed):
  class TransformerEncoderBlock (line 213) | class TransformerEncoderBlock(nn.Module):
    method __init__ (line 214) | def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_se...
    method forward (line 225) | def forward(self, x, time_embed, freqs, transformer_options={}):
  class TransformerDecoderBlock (line 239) | class TransformerDecoderBlock(nn.Module):
    method __init__ (line 240) | def __init__(self, model_dim, time_dim, ff_dim, head_dim, operation_se...
    method forward (line 254) | def forward(self, visual_embed, text_embed, time_embed, freqs, transfo...
  class Kandinsky5 (line 274) | class Kandinsky5(nn.Module):
    method __init__ (line 275) | def __init__(
    method rope_encode_1d (line 311) | def rope_encode_1d(self, seq_len, seq_start=0, steps=None, device=None...
    method rope_encode_3d (line 318) | def rope_encode_3d(self, t, h, w, t_start=0, steps_t=None, steps_h=Non...
    method forward_orig (line 362) | def forward_orig(self, x, timestep, context, y, freqs, freqs_text, tra...
    method _forward (line 389) | def _forward(self, x, timestep, context, y, time_dim_replace=None, tra...
    method forward (line 408) | def forward(self, x, timestep, context, y, time_dim_replace=None, tran...

FILE: comfy/ldm/lightricks/av_model.py
  class CompressedTimestep (line 20) | class CompressedTimestep:
    method __init__ (line 24) | def __init__(self, tensor: torch.Tensor, patches_per_frame: int):
    method expand (line 46) | def expand(self):
    method expand_for_computation (line 55) | def expand_for_computation(self, scale_shift_table: torch.Tensor, batc...
  class BasicAVTransformerBlock (line 83) | class BasicAVTransformerBlock(nn.Module):
    method __init__ (line 84) | def __init__(
    method get_ada_values (line 202) | def get_ada_values(
    method get_av_ca_ada_values (line 216) | def get_av_ca_ada_values(
    method _apply_text_cross_attention (line 237) | def _apply_text_cross_attention(
    method forward (line 256) | def forward(
  class LTXAVModel (line 378) | class LTXAVModel(LTXVModel):
    method __init__ (line 381) | def __init__(
    method _init_model_components (line 448) | def _init_model_components(self, device, dtype, **kwargs):
    method preprocess_text_embeds (line 563) | def preprocess_text_embeds(self, context, unprocessed=False):
    method _init_transformer_blocks (line 582) | def _init_transformer_blocks(self, device, dtype, **kwargs):
    method _init_output_components (line 605) | def _init_output_components(self, device, dtype):
    method separate_audio_and_video_latents (line 621) | def separate_audio_and_video_latents(self, x, audio_length):
    method recombine_audio_and_video_latents (line 640) | def recombine_audio_and_video_latents(self, vx, ax, target_shape=None):
    method _process_input (line 663) | def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
    method _prepare_timestep (line 689) | def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwar...
    method _prepare_context (line 789) | def _prepare_context(self, context, batch_size, x, attention_mask=None):
    method _prepare_positional_embeddings (line 811) | def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_d...
    method _process_transformer_blocks (line 850) | def _process_transformer_blocks(
    method _process_output (line 952) | def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
    method forward (line 984) | def forward(

FILE: comfy/ldm/lightricks/embeddings_connector.py
  class BasicTransformerBlock1D (line 16) | class BasicTransformerBlock1D(nn.Module):
    method __init__ (line 46) | def __init__(
    method forward (line 83) | def forward(self, hidden_states, attention_mask=None, pe=None) -> torc...
  class Embeddings1DConnector (line 112) | class Embeddings1DConnector(nn.Module):
    method __init__ (line 115) | def __init__(
    method get_fractional_positions (line 169) | def get_fractional_positions(self, indices_grid):
    method precompute_freqs (line 179) | def precompute_freqs(self, indices_grid, spacing):
    method generate_freq_grid (line 212) | def generate_freq_grid(self, spacing, dtype, device):
    method precompute_freqs_cis (line 239) | def precompute_freqs_cis(self, indices_grid, spacing="exp", out_dtype=...
    method forward (line 254) | def forward(

FILE: comfy/ldm/lightricks/latent_upsampler.py
  function _rational_for_scale (line 8) | def _rational_for_scale(scale: float) -> Tuple[int, int]:
  class PixelShuffleND (line 17) | class PixelShuffleND(nn.Module):
    method __init__ (line 18) | def __init__(self, dims, upscale_factors=(2, 2, 2)):
    method forward (line 24) | def forward(self, x):
  class BlurDownsample (line 48) | class BlurDownsample(nn.Module):
    method __init__ (line 54) | def __init__(self, dims: int, stride: int):
    method forward (line 67) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class SpatialRationalResampler (line 92) | class SpatialRationalResampler(nn.Module):
    method __init__ (line 100) | def __init__(self, mid_channels: int, scale: float):
    method forward (line 110) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class ResBlock (line 120) | class ResBlock(nn.Module):
    method __init__ (line 121) | def __init__(
    method forward (line 136) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class LatentUpsampler (line 147) | class LatentUpsampler(nn.Module):
    method __init__ (line 160) | def __init__(
    method forward (line 223) | def forward(self, latent: torch.Tensor) -> torch.Tensor:
    method from_config (line 269) | def from_config(cls, config):
    method config (line 281) | def config(self):

FILE: comfy/ldm/lightricks/model.py
  function _log_base (line 20) | def _log_base(x, base):
  class LTXRopeType (line 23) | class LTXRopeType(str, Enum):
    method from_dict (line 30) | def from_dict(cls, kwargs, default=None):
  class LTXFrequenciesPrecision (line 36) | class LTXFrequenciesPrecision(str, Enum):
    method from_dict (line 43) | def from_dict(cls, kwargs, default=None):
  function get_timestep_embedding (line 49) | def get_timestep_embedding(
  class TimestepEmbedding (line 101) | class TimestepEmbedding(nn.Module):
    method __init__ (line 102) | def __init__(
    method forward (line 139) | def forward(self, sample, condition=None):
  class Timesteps (line 154) | class Timesteps(nn.Module):
    method __init__ (line 155) | def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale...
    method forward (line 162) | def forward(self, timesteps):
  class PixArtAlphaCombinedTimestepSizeEmbeddings (line 173) | class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
    method __init__ (line 181) | def __init__(
    method forward (line 198) | def forward(self, timestep, resolution, aspect_ratio, batch_size, hidd...
  class AdaLayerNormSingle (line 204) | class AdaLayerNormSingle(nn.Module):
    method __init__ (line 215) | def __init__(
    method forward (line 232) | def forward(
  class PixArtAlphaTextProjection (line 245) | class PixArtAlphaTextProjection(nn.Module):
    method __init__ (line 252) | def __init__(
    method forward (line 271) | def forward(self, caption):
  class NormSingleLinearTextProjection (line 278) | class NormSingleLinearTextProjection(nn.Module):
    method __init__ (line 281) | def __init__(
    method forward (line 296) | def forward(self, caption):
  class GELU_approx (line 302) | class GELU_approx(nn.Module):
    method __init__ (line 303) | def __init__(self, dim_in, dim_out, dtype=None, device=None, operation...
    method forward (line 307) | def forward(self, x):
  class FeedForward (line 311) | class FeedForward(nn.Module):
    method __init__ (line 312) | def __init__(self, dim, dim_out, mult=4, glu=False, dropout=0.0, dtype...
    method forward (line 321) | def forward(self, x):
  function apply_rotary_emb (line 324) | def apply_rotary_emb(input_tensor, freqs_cis):
  function apply_interleaved_rotary_emb (line 333) | def apply_interleaved_rotary_emb(input_tensor, cos_freqs, sin_freqs):  #...
  function apply_split_rotary_emb (line 343) | def apply_split_rotary_emb(input_tensor, cos, sin):
  class CrossAttention (line 361) | class CrossAttention(nn.Module):
    method __init__ (line 362) | def __init__(
    method forward (line 400) | def forward(self, x, context=None, mask=None, pe=None, k_pe=None, tran...
  class BasicTransformerBlock (line 433) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 434) | def __init__(
    method forward (line 470) | def forward(self, x, context=None, attention_mask=None, timestep=None,...
  function compute_prompt_timestep (line 490) | def compute_prompt_timestep(adaln_module, timestep_scaled, batch_size, h...
  function apply_cross_attention_adaln (line 512) | def apply_cross_attention_adaln(
  function get_fractional_positions (line 531) | def get_fractional_positions(indices_grid, max_pos):
  function generate_freq_grid_np (line 542) | def generate_freq_grid_np(positional_embedding_theta, positional_embeddi...
  function generate_freq_grid_pytorch (line 559) | def generate_freq_grid_pytorch(positional_embedding_theta, positional_em...
  function generate_freqs (line 580) | def generate_freqs(indices, indices_grid, max_pos, use_middle_indices_gr...
  function interleaved_freqs_cis (line 599) | def interleaved_freqs_cis(freqs, pad_size):
  function split_freqs_cis (line 609) | def split_freqs_cis(freqs, pad_size, num_attention_heads):
  class LTXBaseModel (line 630) | class LTXBaseModel(torch.nn.Module, ABC):
    method __init__ (line 638) | def __init__(
    method _init_common_components (line 701) | def _init_common_components(self, device, dtype):
    method _init_model_components (line 744) | def _init_model_components(self, device, dtype, **kwargs):
    method _init_transformer_blocks (line 749) | def _init_transformer_blocks(self, device, dtype, **kwargs):
    method _init_output_components (line 754) | def _init_output_components(self, device, dtype):
    method _process_input (line 759) | def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
    method _build_guide_self_attention_mask (line 763) | def _build_guide_self_attention_mask(self, x, transformer_options, mer...
    method _process_transformer_blocks (line 772) | def _process_transformer_blocks(self, x, context, attention_mask, time...
    method _process_output (line 777) | def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):
    method _prepare_timestep (line 781) | def _prepare_timestep(self, timestep, batch_size, hidden_dtype, **kwar...
    method _prepare_context (line 805) | def _prepare_context(self, context, batch_size, x, attention_mask=None):
    method _precompute_freqs_cis (line 813) | def _precompute_freqs_cis(
    method _prepare_positional_embeddings (line 838) | def _prepare_positional_embeddings(self, pixel_coords, frame_rate, x_d...
    method _prepare_attention_mask (line 852) | def _prepare_attention_mask(self, attention_mask, x_dtype):
    method forward (line 860) | def forward(
    method _forward (line 887) | def _forward(
  class LTXVModel (line 944) | class LTXVModel(LTXBaseModel):
    method __init__ (line 947) | def __init__(
    method _init_model_components (line 989) | def _init_model_components(self, device, dtype, **kwargs):
    method _init_transformer_blocks (line 993) | def _init_transformer_blocks(self, device, dtype, **kwargs):
    method _init_output_components (line 1011) | def _init_output_components(self, device, dtype):
    method _process_input (line 1020) | def _process_input(self, x, keyframe_idxs, denoise_mask, **kwargs):
    method _build_guide_self_attention_mask (line 1074) | def _build_guide_self_attention_mask(self, x, transformer_options, mer...
    method _downsample_mask_to_latent (line 1174) | def _downsample_mask_to_latent(mask, f_lat, h_lat, w_lat):
    method _build_self_attention_mask (line 1238) | def _build_self_attention_mask(total_tokens, num_guide_tokens, tracked...
    method _process_transformer_blocks (line 1276) | def _process_transformer_blocks(self, x, context, attention_mask, time...
    method _process_output (line 1306) | def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs):

FILE: comfy/ldm/lightricks/symmetric_patchifier.py
  function latent_to_pixel_coords (line 9) | def latent_to_pixel_coords(
  class Patchifier (line 36) | class Patchifier(ABC):
    method __init__ (line 37) | def __init__(self, patch_size: int, start_end: bool=False):
    method patchify (line 43) | def patchify(
    method unpatchify (line 49) | def unpatchify(
    method patch_size (line 60) | def patch_size(self):
    method get_latent_coords (line 63) | def get_latent_coords(
  class SymmetricPatchifier (line 97) | class SymmetricPatchifier(Patchifier):
    method patchify (line 98) | def patchify(
    method unpatchify (line 113) | def unpatchify(
  class AudioPatchifier (line 135) | class AudioPatchifier(Patchifier):
    method __init__ (line 136) | def __init__(self, patch_size: int,
    method copy_with_shift (line 151) | def copy_with_shift(self, shift):
    method _get_audio_latent_time_in_sec (line 157) | def _get_audio_latent_time_in_sec(self, start_latent, end_latent: int,...
    method patchify (line 165) | def patchify(self, audio_latents: torch.Tensor) -> Tuple[torch.Tensor,...
    method unpatchify (line 185) | def unpatchify(self, audio_latents: torch.Tensor, channels: int, freq:...

FILE: comfy/ldm/lightricks/vae/audio_vae.py
  class AudioVAEComponentConfig (line 22) | class AudioVAEComponentConfig:
    method from_metadata (line 29) | def from_metadata(cls, metadata: dict) -> "AudioVAEComponentConfig":
  class ModelDeviceManager (line 47) | class ModelDeviceManager:
    method __init__ (line 50) | def __init__(self, module: torch.nn.Module):
    method ensure_model_loaded (line 55) | def ensure_model_loaded(self) -> None:
    method move_to_load_device (line 62) | def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor:
    method load_device (line 66) | def load_device(self):
  class AudioLatentNormalizer (line 70) | class AudioLatentNormalizer:
    method __init__ (line 73) | def __init__(self, patchfier: AudioPatchifier, statistics_processor: t...
    method normalize (line 77) | def normalize(self, latents: torch.Tensor) -> torch.Tensor:
    method denormalize (line 84) | def denormalize(self, latents: torch.Tensor) -> torch.Tensor:
  class AudioPreprocessor (line 92) | class AudioPreprocessor:
    method __init__ (line 95) | def __init__(self, target_sample_rate: int, mel_bins: int, mel_hop_len...
    method resample (line 101) | def resample(self, waveform: torch.Tensor, source_rate: int) -> torch....
    method waveform_to_mel (line 106) | def waveform_to_mel(
  class AudioVAE (line 132) | class AudioVAE(torch.nn.Module):
    method __init__ (line 135) | def __init__(self, state_dict: dict, metadata: dict):
    method encode (line 173) | def encode(self, audio: dict) -> torch.Tensor:
    method decode (line 203) | def decode(self, latents: torch.Tensor) -> torch.Tensor:
    method target_shape_from_latents (line 219) | def target_shape_from_latents(self, latents_shape):
    method num_of_latents_from_frames (line 231) | def num_of_latents_from_frames(self, frames_number: int, frame_rate: i...
    method run_vocoder (line 234) | def run_vocoder(self, mel_spec: torch.Tensor) -> torch.Tensor:
    method sample_rate (line 246) | def sample_rate(self) -> int:
    method mel_hop_length (line 250) | def mel_hop_length(self) -> int:
    method mel_bins (line 254) | def mel_bins(self) -> int:
    method latent_channels (line 258) | def latent_channels(self) -> int:
    method latent_frequency_bins (line 262) | def latent_frequency_bins(self) -> int:
    method latents_per_second (line 266) | def latents_per_second(self) -> float:
    method output_sample_rate (line 270) | def output_sample_rate(self) -> int:
    method memory_required (line 281) | def memory_required(self, input_shape):

FILE: comfy/ldm/lightricks/vae/causal_audio_autoencoder.py
  class StringConvertibleEnum (line 14) | class StringConvertibleEnum(Enum):
    method str_to_enum (line 23) | def str_to_enum(cls, value):
  class AttentionType (line 76) | class AttentionType(StringConvertibleEnum):
  class CausalityAxis (line 84) | class CausalityAxis(StringConvertibleEnum):
  function Normalize (line 93) | def Normalize(in_channels, *, num_groups=32, normtype="group"):
  class CausalConv2d (line 102) | class CausalConv2d(nn.Module):
    method __init__ (line 111) | def __init__(
    method forward (line 157) | def forward(self, x):
  function make_conv2d (line 163) | def make_conv2d(
  class Upsample (line 213) | class Upsample(nn.Module):
    method __init__ (line 214) | def __init__(self, in_channels, with_conv, causality_axis: CausalityAx...
    method forward (line 221) | def forward(self, x):
  class Downsample (line 254) | class Downsample(nn.Module):
    method __init__ (line 261) | def __init__(self, in_channels, with_conv, causality_axis: CausalityAx...
    method forward (line 274) | def forward(self, x):
  class ResnetBlock (line 298) | class ResnetBlock(nn.Module):
    method __init__ (line 299) | def __init__(
    method forward (line 338) | def forward(self, x, temb):
  class AttnBlock (line 361) | class AttnBlock(nn.Module):
    method __init__ (line 362) | def __init__(self, in_channels, norm_type="group"):
    method forward (line 372) | def forward(self, x):
  function make_attn (line 399) | def make_attn(in_channels, attn_type="vanilla", norm_type="group"):
  class Encoder (line 419) | class Encoder(nn.Module):
    method __init__ (line 420) | def __init__(
    method forward (line 532) | def forward(self, x):
  class Decoder (line 588) | class Decoder(nn.Module):
    method __init__ (line 589) | def __init__(
    method _adjust_output_shape (line 691) | def _adjust_output_shape(self, decoded_output, target_shape):
    method get_config (line 735) | def get_config(self):
    method forward (line 746) | def forward(self, latent_features, target_shape=None):
  class processor (line 807) | class processor(nn.Module):
    method __init__ (line 808) | def __init__(self):
    method un_normalize (line 813) | def un_normalize(self, x):
    method normalize (line 816) | def normalize(self, x):
  class CausalAudioAutoencoder (line 820) | class CausalAudioAutoencoder(nn.Module):
    method __init__ (line 821) | def __init__(self, config=None):
    method get_default_config (line 850) | def get_default_config(self):
    method get_config (line 886) | def get_config(self):
    method encode (line 896) | def encode(self, x):
    method decode (line 899) | def decode(self, x, target_shape=None):

FILE: comfy/ldm/lightricks/vae/causal_conv3d.py
  class CausalConv3d (line 9) | class CausalConv3d(nn.Module):
    method __init__ (line 10) | def __init__(
    method forward (line 52) | def forward(self, x, causal: bool = True):
    method weight (line 89) | def weight(self):

FILE: comfy/ldm/lightricks/vae/causal_video_autoencoder.py
  function in_meta_context (line 19) | def in_meta_context():
  function mark_conv3d_ended (line 22) | def mark_conv3d_ended(module):
  function split2 (line 29) | def split2(tensor, split_point, dim=2):
  function add_exchange_cache (line 32) | def add_exchange_cache(dest, cache_in, new_input, dim=2):
  class Encoder (line 43) | class Encoder(nn.Module):
    method __init__ (line 68) | def __init__(
    method _forward_chunk (line 236) | def _forward_chunk(self, sample: torch.FloatTensor) -> Optional[torch....
    method forward_orig (line 286) | def forward_orig(self, sample: torch.FloatTensor, device=None) -> torc...
    method forward (line 314) | def forward(self, *args, **kwargs):
  function get_max_chunk_size (line 332) | def get_max_chunk_size(device: torch.device) -> int:
  class Decoder (line 345) | class Decoder(nn.Module):
    method __init__ (line 370) | def __init__(
    method decode_output_shape (line 535) | def decode_output_shape(self, input_shape):
    method run_up (line 539) | def run_up(self, idx, sample_ref, ended, timestep_shift_scale, scaled_...
    method forward_orig (line 586) | def forward_orig(
    method forward (line 646) | def forward(self, *args, **kwargs):
  class UNetMidBlock3D (line 658) | class UNetMidBlock3D(nn.Module):
    method __init__ (line 682) | def __init__(
    method forward (line 725) | def forward(
  class SpaceToDepthDownsample (line 754) | class SpaceToDepthDownsample(nn.Module):
    method __init__ (line 755) | def __init__(self, dims, in_channels, out_channels, stride, spatial_pa...
    method forward (line 770) | def forward(self, x, causal: bool = True):
  class DepthToSpaceUpsample (line 825) | class DepthToSpaceUpsample(nn.Module):
    method __init__ (line 826) | def __init__(
    method forward (line 853) | def forward(self, x, causal: bool = True, timestep: Optional[torch.Ten...
  class LayerNorm (line 893) | class LayerNorm(nn.Module):
    method __init__ (line 894) | def __init__(self, dim, eps, elementwise_affine=True) -> None:
    method forward (line 898) | def forward(self, x):
  class ResnetBlock3D (line 905) | class ResnetBlock3D(nn.Module):
    method __init__ (line 918) | def __init__(
    method _feed_spatial_noise (line 1019) | def _feed_spatial_noise(
    method forward (line 1033) | def forward(
  function patchify (line 1100) | def patchify(x, patch_size_hw, patch_size_t=1):
  function unpatchify (line 1121) | def unpatchify(x, patch_size_hw, patch_size_t=1):
  class processor (line 1140) | class processor(nn.Module):
    method __init__ (line 1141) | def __init__(self):
    method un_normalize (line 1146) | def un_normalize(self, x):
    method normalize (line 1149) | def normalize(self, x):
  class VideoVAE (line 1152) | class VideoVAE(nn.Module):
    method __init__ (line 1155) | def __init__(self, version=0, config=None):
    method get_default_config (line 1197) | def get_default_config(self, version):
    method encode (line 1297) | def encode(self, x, device=None):
    method decode_output_shape (line 1302) | def decode_output_shape(self, input_shape):
    method decode (line 1305) | def decode(self, x, output_buffer=None):

FILE: comfy/ldm/lightricks/vae/conv_nd_factory.py
  function make_conv_nd (line 9) | def make_conv_nd(
  function make_linear_nd (line 75) | def make_linear_nd(

FILE: comfy/ldm/lightricks/vae/dual_conv3d.py
  class DualConv3d (line 10) | class DualConv3d(nn.Module):
    method __init__ (line 11) | def __init__(
    method reset_parameters (line 86) | def reset_parameters(self):
    method forward (line 97) | def forward(self, x, use_conv3d=False, skip_time_conv=False):
    method forward_with_3d (line 103) | def forward_with_3d(self, x, skip_time_conv):
    method forward_with_2d (line 133) | def forward_with_2d(self, x, skip_time_conv):
    method weight (line 185) | def weight(self):
  function test_dual_conv3d_consistency (line 189) | def test_dual_conv3d_consistency():

FILE: comfy/ldm/lightricks/vae/pixel_norm.py
  class PixelNorm (line 5) | class PixelNorm(nn.Module):
    method __init__ (line 6) | def __init__(self, dim=1, eps=1e-8):
    method forward (line 11) | def forward(self, x):

FILE: comfy/ldm/lightricks/vocoders/vocoder.py
  function get_padding (line 13) | def get_padding(kernel_size, dilation=1):
  function _sinc (line 23) | def _sinc(x: torch.Tensor):
  function kaiser_sinc_filter1d (line 31) | def kaiser_sinc_filter1d(cutoff, half_width, kernel_size):
  class LowPassFilter1d (line 56) | class LowPassFilter1d(nn.Module):
    method __init__ (line 57) | def __init__(
    method forward (line 81) | def forward(self, x):
  class UpSample1d (line 88) | class UpSample1d(nn.Module):
    method __init__ (line 89) | def __init__(self, ratio=2, kernel_size=None, persistent=True, window_...
    method forward (line 125) | def forward(self, x):
  class DownSample1d (line 135) | class DownSample1d(nn.Module):
    method __init__ (line 136) | def __init__(self, ratio=2, kernel_size=None):
    method forward (line 149) | def forward(self, x):
  class Activation1d (line 153) | class Activation1d(nn.Module):
    method __init__ (line 154) | def __init__(
    method forward (line 167) | def forward(self, x):
  class Snake (line 179) | class Snake(nn.Module):
    method __init__ (line 180) | def __init__(
    method forward (line 193) | def forward(self, x):
  class SnakeBeta (line 200) | class SnakeBeta(nn.Module):
    method __init__ (line 201) | def __init__(
    method forward (line 220) | def forward(self, x):
  class AMPBlock1 (line 234) | class AMPBlock1(torch.nn.Module):
    method __init__ (line 235) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), activa...
    method forward (line 303) | def forward(self, x):
  class ResBlock1 (line 318) | class ResBlock1(torch.nn.Module):
    method __init__ (line 319) | def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
    method forward (line 379) | def forward(self, x):
  class ResBlock2 (line 389) | class ResBlock2(torch.nn.Module):
    method __init__ (line 390) | def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
    method forward (line 413) | def forward(self, x):
  class Vocoder (line 421) | class Vocoder(torch.nn.Module):
    method __init__ (line 428) | def __init__(self, config=None):
    method get_default_config (line 504) | def get_default_config(self):
    method forward (line 522) | def forward(self, x):
  class _STFTFn (line 563) | class _STFTFn(nn.Module):
    method __init__ (line 572) | def __init__(self, filter_length: int, hop_length: int, win_length: int):
    method forward (line 580) | def forward(self, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
  class MelSTFT (line 608) | class MelSTFT(nn.Module):
    method __init__ (line 618) | def __init__(
    method mel_spectrogram (line 634) | def mel_spectrogram(
  class VocoderWithBWE (line 656) | class VocoderWithBWE(torch.nn.Module):
    method __init__ (line 663) | def __init__(self, config):
    method _compute_mel (line 692) | def _compute_mel(self, audio):
    method forward (line 699) | def forward(self, mel_spec):

FILE: comfy/ldm/lumina/controlnet.py
  class ZImageControlTransformerBlock (line 6) | class ZImageControlTransformerBlock(JointTransformerBlock):
    method __init__ (line 7) | def __init__(
    method forward (line 27) | def forward(self, c, x, **kwargs):
  class ZImage_Control (line 34) | class ZImage_Control(torch.nn.Module):
    method __init__ (line 35) | def __init__(
    method forward (line 127) | def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):
    method forward_noise_refiner_block (line 141) | def forward_noise_refiner_block(self, layer_id, control_context, x, x_...
    method forward_control_block (line 159) | def forward_control_block(self, layer_id, control_context, x, x_attn_m...

FILE: comfy/ldm/lumina/model.py
  function invert_slices (line 20) | def invert_slices(slices, length):
  function modulate (line 36) | def modulate(x, scale, timestep_zero_index=None):
  function apply_gate (line 51) | def apply_gate(gate, x, timestep_zero_index=None):
  function clamp_fp16 (line 69) | def clamp_fp16(x):
  class JointAttention (line 74) | class JointAttention(nn.Module):
    method __init__ (line 77) | def __init__(
    method forward (line 123) | def forward(
  class FeedForward (line 169) | class FeedForward(nn.Module):
    method __init__ (line 170) | def __init__(
    method _forward_silu_gating (line 219) | def _forward_silu_gating(self, x1, x3):
    method forward (line 222) | def forward(self, x):
  class JointTransformerBlock (line 226) | class JointTransformerBlock(nn.Module):
    method __init__ (line 227) | def __init__(
    method forward (line 299) | def forward(
  class FinalLayer (line 355) | class FinalLayer(nn.Module):
    method __init__ (line 360) | def __init__(self, hidden_size, patch_size, out_channels, z_image_modu...
    method forward (line 393) | def forward(self, x, c, timestep_zero_index=None):
  function pad_zimage (line 400) | def pad_zimage(feats, pad_token, pad_tokens_multiple):
  function pos_ids_x (line 405) | def pos_ids_x(start_t, H_tokens, W_tokens, batch_size, device, transform...
  class NextDiT (line 424) | class NextDiT(nn.Module):
    method __init__ (line 429) | def __init__(
    method unpatchify (line 615) | def unpatchify(
    method embed_cap (line 640) | def embed_cap(self, cap_feats=None, offset=0, bsz=1, device=None, dtyp...
    method embed_all (line 656) | def embed_all(self, x, cap_feats=None, siglip_feats=None, offset=0, om...
    method patchify_and_embed (line 703) | def patchify_and_embed(
    method forward (line 803) | def forward(self, x, timesteps, context, num_tokens, attention_mask=No...
    method _forward (line 811) | def _forward(self, x, timesteps, context, num_tokens, attention_mask=N...
  function _modulate_shift_scale (line 867) | def _modulate_shift_scale(x, shift, scale):
  class PixelResBlock (line 871) | class PixelResBlock(nn.Module):
    method __init__ (line 877) | def __init__(self, channels: int, dtype=None, device=None, operations=...
    method forward (line 890) | def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
  class DCTFinalLayer (line 897) | class DCTFinalLayer(nn.Module):
    method __init__ (line 900) | def __init__(self, model_channels: int, out_channels: int, dtype=None,...
    method forward (line 905) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class SimpleMLPAdaLN (line 909) | class SimpleMLPAdaLN(nn.Module):
    method __init__ (line 921) | def __init__(
    method forward (line 957) | def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
  class NextDiTPixelSpace (line 973) | class NextDiTPixelSpace(NextDiT):
    method __init__ (line 989) | def __init__(
    method _forward (line 1031) | def _forward(self, x, timesteps, context, num_tokens, attention_mask=N...
    method forward (line 1108) | def forward(self, x, timesteps, context, num_tokens, attention_mask=No...

FILE: comfy/ldm/mmaudio/vae/activations.py
  class Snake (line 9) | class Snake(nn.Module):
    method __init__ (line 25) | def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha...
    method forward (line 48) | def forward(self, x):
  class SnakeBeta (line 62) | class SnakeBeta(nn.Module):
    method __init__ (line 79) | def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha...
    method forward (line 107) | def forward(self, x):

FILE: comfy/ldm/mmaudio/vae/alias_free_torch.py
  function sinc (line 13) | def sinc(x: torch.Tensor):
  function kaiser_sinc_filter1d (line 26) | def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filt...
  class LowPassFilter1d (line 58) | class LowPassFilter1d(nn.Module):
    method __init__ (line 59) | def __init__(self,
    method forward (line 84) | def forward(self, x):
  class UpSample1d (line 96) | class UpSample1d(nn.Module):
    method __init__ (line 97) | def __init__(self, ratio=2, kernel_size=None):
    method forward (line 111) | def forward(self, x):
  class DownSample1d (line 122) | class DownSample1d(nn.Module):
    method __init__ (line 123) | def __init__(self, ratio=2, kernel_size=None):
    method forward (line 132) | def forward(self, x):
  class Activation1d (line 137) | class Activation1d(nn.Module):
    method __init__ (line 138) | def __init__(self,
    method forward (line 152) | def forward(self, x):

FILE: comfy/ldm/mmaudio/vae/autoencoder.py
  function dynamic_range_compression_torch (line 16) | def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
  function spectral_normalize_torch (line 20) | def spectral_normalize_torch(magnitudes, norm_fn):
  class MelConverter (line 24) | class MelConverter(nn.Module):
    method __init__ (line 26) | def __init__(
    method device (line 61) | def device(self):
    method forward (line 64) | def forward(self, waveform: torch.Tensor, center: bool = False) -> tor...
  class AudioAutoencoder (line 92) | class AudioAutoencoder(nn.Module):
    method __init__ (line 94) | def __init__(
    method encode_audio (line 136) | def encode_audio(self, x) -> DiagonalGaussianDistribution:
    method decode (line 144) | def decode(self, z):
    method encode (line 152) | def encode(self, audio):

FILE: comfy/ldm/mmaudio/vae/bigvgan.py
  function get_padding (line 15) | def get_padding(kernel_size, dilation=1):
  class AMPBlock1 (line 18) | class AMPBlock1(torch.nn.Module):
    method __init__ (line 20) | def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), act...
    method forward (line 85) | def forward(self, x):
  class AMPBlock2 (line 97) | class AMPBlock2(torch.nn.Module):
    method __init__ (line 99) | def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activa...
    method forward (line 137) | def forward(self, x):
  class BigVGANVocoder (line 146) | class BigVGANVocoder(torch.nn.Module):
    method __init__ (line 148) | def __init__(self, h):
    method forward (line 197) | def forward(self, x):

FILE: comfy/ldm/mmaudio/vae/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: comfy/ldm/mmaudio/vae/vae.py
  class VAE (line 68) | class VAE(nn.Module):
    method __init__ (line 70) | def __init__(
    method initialize_weights (line 115) | def initialize_weights(self):
    method encode (line 118) | def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalG...
    method decode (line 125) | def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.T...
    method normalize (line 131) | def normalize(self, x: torch.Tensor) -> torch.Tensor:
    method unnormalize (line 134) | def unnormalize(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 137) | def forward(
    method load_weights (line 154) | def load_weights(self, src_dict) -> None:
    method device (line 158) | def device(self) -> torch.device:
    method get_last_layer (line 161) | def get_last_layer(self):
    method remove_weight_norm (line 164) | def remove_weight_norm(self):
  class Encoder1D (line 168) | class Encoder1D(nn.Module):
    method __init__ (line 170) | def __init__(self,
    method forward (line 237) | def forward(self, x):
  class Decoder1D (line 262) | class Decoder1D(nn.Module):
    method __init__ (line 264) | def __init__(self,
    method forward (line 319) | def forward(self, z):
  function VAE_16k (line 344) | def VAE_16k(**kwargs) -> VAE:
  function VAE_44k (line 348) | def VAE_44k(**kwargs) -> VAE:
  function get_my_vae (line 352) | def get_my_vae(name: str, **kwargs) -> VAE:

FILE: comfy/ldm/mmaudio/vae/vae_modules.py
  function nonlinearity (line 9) | def nonlinearity(x):
  function mp_sum (line 13) | def mp_sum(a, b, t=0.5):
  function normalize (line 16) | def normalize(x, dim=None, eps=1e-4):
  class ResnetBlock1D (line 23) | class ResnetBlock1D(nn.Module):
    method __init__ (line 25) | def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kerne...
    method forward (line 41) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class AttnBlock1D (line 63) | class AttnBlock1D(nn.Module):
    method __init__ (line 65) | def __init__(self, in_channels, num_heads=1):
    method forward (line 74) | def forward(self, x):
  class Upsample1D (line 86) | class Upsample1D(nn.Module):
    method __init__ (line 88) | def __init__(self, in_channels, with_conv):
    method forward (line 94) | def forward(self, x):
  class Downsample1D (line 101) | class Downsample1D(nn.Module):
    method __init__ (line 103) | def __init__(self, in_channels, with_conv):
    method forward (line 111) | def forward(self, x):

FILE: comfy/ldm/models/autoencoder.py
  class DiagonalGaussianRegularizer (line 15) | class DiagonalGaussianRegularizer(torch.nn.Module):
    method __init__ (line 16) | def __init__(self, sample: bool = False):
    method get_trainable_parameters (line 20) | def get_trainable_parameters(self) -> Any:
    method forward (line 23) | def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
  class EmptyRegularizer (line 31) | class EmptyRegularizer(torch.nn.Module):
    method __init__ (line 32) | def __init__(self):
    method forward (line 35) | def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
  class AbstractAutoencoder (line 38) | class AbstractAutoencoder(torch.nn.Module):
    method __init__ (line 45) | def __init__(
    method get_input (line 63) | def get_input(self, batch) -> Any:
    method on_train_batch_end (line 66) | def on_train_batch_end(self, *args, **kwargs):
    method ema_scope (line 72) | def ema_scope(self, context=None):
    method encode (line 86) | def encode(self, *args, **kwargs) -> torch.Tensor:
    method decode (line 89) | def decode(self, *args, **kwargs) -> torch.Tensor:
    method instantiate_optimizer_from_config (line 92) | def instantiate_optimizer_from_config(self, params, lr, cfg):
    method configure_optimizers (line 98) | def configure_optimizers(self) -> Any:
  class AutoencodingEngine (line 102) | class AutoencodingEngine(AbstractAutoencoder):
    method __init__ (line 109) | def __init__(
    method get_last_layer (line 125) | def get_last_layer(self):
    method encode (line 128) | def encode(
    method decode (line 142) | def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
    method forward (line 146) | def forward(
  class AutoencodingEngineLegacy (line 154) | class AutoencodingEngineLegacy(AutoencodingEngine):
    method __init__ (line 155) | def __init__(self, embed_dim: int, **kwargs):
    method get_autoencoder_params (line 199) | def get_autoencoder_params(self) -> list:
    method encode (line 203) | def encode(
    method decode (line 239) | def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
  class AutoencoderKL (line 268) | class AutoencoderKL(AutoencodingEngineLegacy):
    method __init__ (line 269) | def __init__(self, **kwargs):

FILE: comfy/ldm/modules/attention.py
  function register_attention_function (line 50) | def register_attention_function(name: str, func: Callable):
  function get_attention_function (line 57) | def get_attention_function(name: str, default: Any=...) -> Union[Callabl...
  function get_attn_precision (line 73) | def get_attn_precision(attn_precision, current_dtype):
  function exists (line 81) | def exists(val):
  function default (line 85) | def default(val, d):
  class GEGLU (line 92) | class GEGLU(nn.Module):
    method __init__ (line 93) | def __init__(self, dim_in, dim_out, dtype=None, device=None, operation...
    method forward (line 97) | def forward(self, x):
  class FeedForward (line 102) | class FeedForward(nn.Module):
    method __init__ (line 103) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., d...
    method forward (line 118) | def forward(self, x):
  function Normalize (line 121) | def Normalize(in_channels, dtype=None, device=None):
  function wrap_attn (line 125) | def wrap_attn(func):
  function attention_basic (line 144) | def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip...
  function attention_sub_quad (line 213) | def attention_sub_quad(query, key, value, heads, mask=None, attn_precisi...
  function attention_split (line 284) | def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip...
  function attention_xformers (line 415) | def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, s...
  function attention_pytorch (line 484) | def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, sk...
  function attention_sage (line 527) | def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_...
  function attention3_sage (line 578) | def attention3_sage(q, k, v, heads, mask=None, attn_precision=None, skip...
  function flash_attn_wrapper (line 667) | def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
  function flash_attn_fake (line 673) | def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):
  function flash_attn_wrapper (line 679) | def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
  function attention_flash (line 684) | def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip...
  function optimized_attention_for_device (line 762) | def optimized_attention_for_device(device, mask=False, small_input=False):
  class CrossAttention (line 778) | class CrossAttention(nn.Module):
    method __init__ (line 779) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
    method forward (line 794) | def forward(self, x, context=None, value=None, mask=None, transformer_...
  class BasicTransformerBlock (line 811) | class BasicTransformerBlock(nn.Module):
    method __init__ (line 812) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
    method forward (line 852) | def forward(self, x, context=None, transformer_options={}):
  class SpatialTransformer (line 968) | class SpatialTransformer(nn.Module):
    method __init__ (line 977) | def __init__(self, in_channels, n_heads, d_head,
    method forward (line 1010) | def forward(self, x, context=None, transformer_options={}):
  class SpatialVideoTransformer (line 1034) | class SpatialVideoTransformer(SpatialTransformer):
    method __init__ (line 1035) | def __init__(
    method forward (line 1121) | def forward(

FILE: comfy/ldm/modules/diffusionmodules/mmdit.py
  function default (line 13) | def default(x, y):
  class Mlp (line 18) | class Mlp(nn.Module):
    method __init__ (line 21) | def __init__(
    method forward (line 48) | def forward(self, x):
  class PatchEmbed (line 57) | class PatchEmbed(nn.Module):
    method __init__ (line 62) | def __init__(
    method forward (line 100) | def forward(self, x):
  function modulate (line 109) | def modulate(x, shift, scale):
  function get_2d_sincos_pos_embed (line 120) | def get_2d_sincos_pos_embed(
  function get_2d_sincos_pos_embed_from_grid (line 151) | def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
  function get_1d_sincos_pos_embed_from_grid (line 162) | def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
  function get_1d_sincos_pos_embed_from_grid_torch (line 182) | def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None,...
  function get_2d_sincos_pos_embed_torch (line 193) | def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_m...
  class TimestepEmbedder (line 209) | class TimestepEmbedder(nn.Module):
    method __init__ (line 214) | def __init__(self, hidden_size, frequency_embedding_size=256, output_s...
    method forward (line 225) | def forward(self, t, dtype, **kwargs):
  class VectorEmbedder (line 231) | class VectorEmbedder(nn.Module):
    method __init__ (line 236) | def __init__(self, input_dim: int, hidden_size: int, dtype=None, devic...
    method forward (line 244) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  function split_qkv (line 254) | def split_qkv(qkv, head_dim):
  class SelfAttention (line 259) | class SelfAttention(nn.Module):
    method __init__ (line 262) | def __init__(
    method pre_attention (line 301) | def pre_attention(self, x: torch.Tensor) -> torch.Tensor:
    method post_attention (line 309) | def post_attention(self, x: torch.Tensor) -> torch.Tensor:
    method forward (line 315) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class RMSNorm (line 324) | class RMSNorm(torch.nn.Module):
    method __init__ (line 325) | def __init__(
    method forward (line 345) | def forward(self, x):
  class SwiGLUFeedForward (line 350) | class SwiGLUFeedForward(nn.Module):
    method __init__ (line 351) | def __init__(
    method forward (line 384) | def forward(self, x):
  class DismantledBlock (line 388) | class DismantledBlock(nn.Module):
    method __init__ (line 395) | def __init__(
    method pre_attention (line 488) | def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Ten...
    method post_attention (line 532) | def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate...
    method pre_attention_x (line 540) | def pre_attention_x(self, x: torch.Tensor, c: torch.Tensor) -> torch.T...
    method post_attention_x (line 565) | def post_attention_x(self, attn, attn2, x, gate_msa, shift_mlp, scale_...
    method forward (line 575) | def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
  function gate_cat (line 596) | def gate_cat(x, gate_msa, gate_msa2, attn1, attn2):
  function block_mixing (line 602) | def block_mixing(*args, use_checkpoint=True, **kwargs):
  function _block_mixing (line 611) | def _block_mixing(context, x, context_block, x_block, c, transformer_opt...
  class JointBlock (line 651) | class JointBlock(nn.Module):
    method __init__ (line 654) | def __init__(
    method forward (line 670) | def forward(self, *args, **kwargs):
  class FinalLayer (line 676) | class FinalLayer(nn.Module):
    method __init__ (line 681) | def __init__(
    method forward (line 702) | def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
  class SelfAttentionContext (line 708) | class SelfAttentionContext(nn.Module):
    method __init__ (line 709) | def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None,...
    method forward (line 721) | def forward(self, x):
  class ContextProcessorBlock (line 727) | class ContextProcessorBlock(nn.Module):
    method __init__ (line 728) | def __init__(self, context_size, dtype=None, device=None, operations=N...
    method forward (line 735) | def forward(self, x):
  class ContextProcessor (line 740) | class ContextProcessor(nn.Module):
    method __init__ (line 741) | def __init__(self, context_size, num_layers, dtype=None, device=None, ...
    method forward (line 746) | def forward(self, x):
  class MMDiT (line 751) | class MMDiT(nn.Module):
    method __init__ (line 756) | def __init__(
    method cropped_pos_embed (line 891) | def cropped_pos_embed(self, hw, device=None):
    method unpatchify (line 919) | def unpatchify(self, x, hw=None):
    method forward_core_with_concat (line 939) | def forward_core_with_concat(
    method forward (line 989) | def forward(
  class OpenAISignatureMMDITWrapper (line 1024) | class OpenAISignatureMMDITWrapper(MMDiT):
    method forward (line 1025) | def forward(

FILE: comfy/ldm/modules/diffusionmodules/model.py
  function torch_cat_if_needed (line 16) | def torch_cat_if_needed(xl, dim):
  function get_timestep_embedding (line 25) | def get_timestep_embedding(timesteps, embedding_dim):
  function nonlinearity (line 46) | def nonlinearity(x):
  function Normalize (line 51) | def Normalize(in_channels, num_groups=32):
  class CarriedConv3d (line 55) | class CarriedConv3d(nn.Module):
    method __init__ (line 56) | def __init__(self, n_channels, out_channels, kernel_size, stride=1, di...
    method forward (line 60) | def forward(self, x):
  function conv_carry_causal_3d (line 64) | def conv_carry_causal_3d(xl, op, conv_carry_in=None, conv_carry_out=None):
  class VideoConv3d (line 86) | class VideoConv3d(nn.Module):
    method __init__ (line 87) | def __init__(self, n_channels, out_channels, kernel_size, stride=1, di...
    method forward (line 99) | def forward(self, x):
  function interpolate_up (line 104) | def interpolate_up(x, scale_factor):
  class Upsample (line 107) | class Upsample(nn.Module):
    method __init__ (line 108) | def __init__(self, in_channels, with_conv, conv_op=ops.Conv2d, scale_f...
    method forward (line 120) | def forward(self, x, conv_carry_in=None, conv_carry_out=None):
  class Downsample (line 141) | class Downsample(nn.Module):
    method __init__ (line 142) | def __init__(self, in_channels, with_conv, stride=2, conv_op=ops.Conv2d):
    method forward (line 153) | def forward(self, x, conv_carry_in=None, conv_carry_out=None):
  class ResnetBlock (line 172) | class ResnetBlock(nn.Module):
    method __init__ (line 173) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
    method forward (line 212) | def forward(self, x, temb=None, conv_carry_in=None, conv_carry_out=None):
  function slice_attention (line 234) | def slice_attention(q, k, v):
  function normal_attention (line 271) | def normal_attention(q, k, v):
  function xformers_attention (line 287) | def xformers_attention(q, k, v):
  function pytorch_attention (line 304) | def pytorch_attention(q, k, v):
  function vae_attention (line 327) | def vae_attention():
  class AttnBlock (line 338) | class AttnBlock(nn.Module):
    method __init__ (line 339) | def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize):
    method forward (line 367) | def forward(self, x):
  function make_attn (line 381) | def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None, conv_o...
  class Model (line 385) | class Model(nn.Module):
    method __init__ (line 386) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 486) | def forward(self, x, t=None, context=None):
    method get_last_layer (line 534) | def get_last_layer(self):
  class Encoder (line 538) | class Encoder(nn.Module):
    method __init__ (line 539) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 631) | def forward(self, x):
  class Decoder (line 683) | class Decoder(nn.Module):
    method __init__ (line 684) | def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
    method forward (line 781) | def forward(self, z, **kwargs):

FILE: comfy/ldm/modules/diffusionmodules/openaimodel.py
  class TimestepBlock (line 23) | class TimestepBlock(nn.Module):
    method forward (line 29) | def forward(self, x, emb):
  function forward_timestep_embed (line 35) | def forward_timestep_embed(ts, x, emb, context=None, transformer_options...
  class TimestepEmbedSequential (line 64) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    method forward (line 70) | def forward(self, *args, **kwargs):
  class Upsample (line 73) | class Upsample(nn.Module):
    method __init__ (line 82) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 91) | def forward(self, x, output_shape=None):
  class Downsample (line 109) | class Downsample(nn.Module):
    method __init__ (line 118) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 133) | def forward(self, x):
  class ResBlock (line 138) | class ResBlock(TimestepBlock):
    method __init__ (line 154) | def __init__(
    method forward (line 234) | def forward(self, x, emb):
    method _forward (line 246) | def _forward(self, x, emb):
  class VideoResBlock (line 278) | class VideoResBlock(ResBlock):
    method __init__ (line 279) | def __init__(
    method forward (line 337) | def forward(
  class Timestep (line 359) | class Timestep(nn.Module):
    method __init__ (line 360) | def __init__(self, dim):
    method forward (line 364) | def forward(self, t):
  function apply_control (line 367) | def apply_control(h, control, name):
  class UNetModel (line 377) | class UNetModel(nn.Module):
    method __init__ (line 403) | def __init__(
    method forward (line 836) | def forward(self, x, timesteps=None, context=None, y=None, control=Non...
    method _forward (line 843) | def _forward(self, x, timesteps=None, context=None, y=None, control=No...

FILE: comfy/ldm/modules/diffusionmodules/upscaling.py
  class AbstractLowScaleModel (line 9) | class AbstractLowScaleModel(nn.Module):
    method __init__ (line 11) | def __init__(self, noise_schedule_config=None):
    method register_schedule (line 16) | def register_schedule(self, beta_schedule="linear", timesteps=1000,
    method q_sample (line 43) | def q_sample(self, x_start, t, noise=None, seed=None):
    method forward (line 52) | def forward(self, x):
    method decode (line 55) | def decode(self, x):
  class SimpleImageConcat (line 59) | class SimpleImageConcat(AbstractLowScaleModel):
    method __init__ (line 61) | def __init__(self):
    method forward (line 65) | def forward(self, x):
  class ImageConcatWithNoiseAugmentation (line 70) | class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
    method __init__ (line 71) | def __init__(self, noise_schedule_config, max_noise_level=1000, to_cud...
    method forward (line 75) | def forward(self, x, noise_level=None, seed=None):

FILE: comfy/ldm/modules/diffusionmodules/util.py
  class AlphaBlender (line 20) | class AlphaBlender(nn.Module):
    method __init__ (line 23) | def __init__(
    method get_alpha (line 49) | def get_alpha(self, image_only_indicator: torch.Tensor, device) -> tor...
    method forward (line 75) | def forward(
  function make_beta_schedule (line 89) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e...
  function make_ddim_timesteps (line 121) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_...
  function make_ddim_sampling_parameters (line 138) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos...
  function betas_for_alpha_bar (line 152) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
  function extract_into_tensor (line 171) | def extract_into_tensor(a, t, x_shape):
  function checkpoint (line 177) | def checkpoint(func, inputs, params, flag):
  class CheckpointFunction (line 194) | class CheckpointFunction(torch.autograd.Function):
    method forward (line 196) | def forward(ctx, run_function, length, *args):
    method backward (line 208) | def backward(ctx, *output_grads):
  function timestep_embedding (line 229) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal...
  function zero_module (line 252) | def zero_module(module):
  function scale_module (line 261) | def scale_module(module, scale):
  function mean_flat (line 270) | def mean_flat(tensor):
  function avg_pool_nd (line 277) | def avg_pool_nd(dims, *args, **kwargs):
  class HybridConditioner (line 290) | class HybridConditioner(nn.Module):
    method __init__ (line 292) | def __init__(self, c_concat_config, c_crossattn_config):
    method forward (line 297) | def forward(self, c_concat, c_crossattn):
  function noise_like (line 303) | def noise_like(shape, device, repeat=False):

FILE: comfy/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: comfy/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: comfy/ldm/modules/encoders/noise_aug_modules.py
  class CLIPEmbeddingNoiseAugmentation (line 5) | class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation):
    method __init__ (line 6) | def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kw...
    method scale (line 16) | def scale(self, x):
    method unscale (line 21) | def unscale(self, x):
    method forward (line 26) | def forward(self, x, noise_level=None, seed=None):

FILE: comfy/ldm/modules/sdpose.py
  class HeatmapHead (line 5) | class HeatmapHead(torch.nn.Module):
    method __init__ (line 6) | def __init__(
    method forward (line 66) | def forward(self, x): # Decode heatmaps to keypoints

FILE: comfy/ldm/modules/sub_quadratic_attention.py
  function dynamic_slice (line 29) | def dynamic_slice(
  class AttnChunk (line 37) | class AttnChunk(NamedTuple):
  class SummarizeChunk (line 42) | class SummarizeChunk(Protocol):
    method __call__ (line 44) | def __call__(
  class ComputeQueryChunkAttn (line 50) | class ComputeQueryChunkAttn(Protocol):
    method __call__ (line 52) | def __call__(
  function _summarize_chunk (line 58) | def _summarize_chunk(
  function _query_chunk_attention (line 96) | def _query_chunk_attention(
  function _get_attention_scores_no_kv_chunking (line 139) | def _get_attention_scores_no_kv_chunking(
  class ScannedChunk (line 184) | class ScannedChunk(NamedTuple):
  function efficient_dot_product_attention (line 188) | def efficient_dot_product_attention(

FILE: comfy/ldm/modules/temporal_ae.py
  function partialclass (line 18) | def partialclass(cls, *args, **kwargs):
  class VideoResBlock (line 25) | class VideoResBlock(ResnetBlock):
    method __init__ (line 26) | def __init__(
    method get_alpha (line 63) | def get_alpha(self, bs):
    method forward (line 71) | def forward(self, x, temb, skip_video=False, timesteps=None):
  class AE3DConv (line 92) | class AE3DConv(ops.Conv2d):
    method __init__ (line 93) | def __init__(self, in_channels, out_channels, video_kernel_size=3, *ar...
    method forward (line 107) | def forward(self, input, timesteps=None, skip_video=False):
  class AttnVideoBlock (line 118) | class AttnVideoBlock(AttnBlock):
    method __init__ (line 119) | def __init__(
    method forward (line 149) | def forward(self, x, timesteps=None, skip_time_block=False):
    method get_alpha (line 179) | def get_alpha(
  function make_time_attn (line 191) | def make_time_attn(
  class Conv2DWrapper (line 204) | class Conv2DWrapper(torch.nn.Conv2d):
    method forward (line 205) | def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor:
  class VideoDecoder (line 209) | class VideoDecoder(Decoder):
    method __init__ (line 212) | def __init__(
    method get_last_layer (line 238) | def get_last_layer(self, skip_time_mix=False, **kwargs):

FILE: comfy/ldm/omnigen/omnigen2.py
  function apply_rotary_emb (line 16) | def apply_rotary_emb(x, freqs_cis):
  function swiglu (line 25) | def swiglu(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
  class TimestepEmbedding (line 29) | class TimestepEmbedding(nn.Module):
    method __init__ (line 30) | def __init__(self, in_channels: int, time_embed_dim: int, dtype=None, ...
    method forward (line 36) | def forward(self, sample: torch.Tensor) -> torch.Tensor:
  class LuminaRMSNormZero (line 43) | class LuminaRMSNormZero(nn.Module):
    method __init__ (line 44) | def __init__(self, embedding_dim: int, norm_eps: float = 1e-5, dtype=N...
    method forward (line 50) | def forward(self, x: torch.Tensor, emb: torch.Tensor) -> Tuple[torch.T...
  class LuminaLayerNormContinuous (line 57) | class LuminaLayerNormContinuous(nn.Module):
    method __init__ (line 58) | def __init__(self, embedding_dim: int, conditioning_embedding_dim: int...
    method forward (line 65) | def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tenso...
  class LuminaFeedForward (line 73) | class LuminaFeedForward(nn.Module):
    method __init__ (line 74) | def __init__(self, dim: int, inner_dim: int, multiple_of: int = 256, d...
    method forward (line 81) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class Lumina2CombinedTimestepCaptionEmbedding (line 86) | class Lumina2CombinedTimestepCaptionEmbedding(nn.Module):
    method __init__ (line 87) | def __init__(self, hidden_size: int = 4096, text_feat_dim: int = 2048,...
    method forward (line 96) | def forward(self, timestep: torch.Tensor, text_hidden_states: torch.Te...
  class Attention (line 103) | class Attention(nn.Module):
    method __init__ (line 104) | def __init__(self, query_dim: int, dim_head: int, heads: int, kv_heads...
    method forward (line 123) | def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: ...
  class OmniGen2TransformerBlock (line 154) | class OmniGen2TransformerBlock(nn.Module):
    method __init__ (line 155) | def __init__(self, dim: int, num_attention_heads: int, num_kv_heads: i...
    method forward (line 185) | def forward(self, hidden_states: torch.Tensor, attention_mask: torch.T...
  class OmniGen2RotaryPosEmbed (line 201) | class OmniGen2RotaryPosEmbed(nn.Module):
    method __init__ (line 202) | def __init__(self, theta: int, axes_dim: Tuple[int, int, int], axes_le...
    method forward (line 210) | def forward(self, batch_size, encoder_seq_len, l_effective_cap_len, l_...
  class OmniGen2Transformer2DModel (line 273) | class OmniGen2Transformer2DModel(nn.Module):
    method __init__ (line 274) | def __init__(
    method flat_and_pad_to_seq (line 358) | def flat_and_pad_to_seq(self, hidden_states, ref_image_hidden_states):
    method img_patch_embed_and_refine (line 393) | def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_s...
    method forward (line 418) | def forward(self, x, timesteps, context, num_tokens, ref_latents=None,...

FILE: comfy/ldm/pixart/blocks.py
  function modulate (line 19) | def modulate(x, shift, scale):
  function t2i_modulate (line 22) | def t2i_modulate(x, shift, scale):
  class MultiHeadCrossAttention (line 25) | class MultiHeadCrossAttention(nn.Module):
    method __init__ (line 26) | def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dty...
    method forward (line 40) | def forward(self, x, cond, mask=None):
  class AttentionKVCompress (line 80) | class AttentionKVCompress(nn.Module):
    method __init__ (line 82) | def __init__(self, dim, num_heads=8, qkv_bias=True, sampling='conv', s...
    method downsample_2d (line 113) | def downsample_2d(self, tensor, H, W, scale_factor, sampling=None):
    method forward (line 139) | def forward(self, x, mask=None, HW=None, block_id=None):
  class FinalLayer (line 179) | class FinalLayer(nn.Module):
    method __init__ (line 183) | def __init__(self, hidden_size, patch_size, out_channels, dtype=None, ...
    method forward (line 192) | def forward(self, x, c):
  class T2IFinalLayer (line 198) | class T2IFinalLayer(nn.Module):
    method __init__ (line 202) | def __init__(self, hidden_size, patch_size, out_channels, dtype=None, ...
    method forward (line 209) | def forward(self, x, t):
  class MaskFinalLayer (line 216) | class MaskFinalLayer(nn.Module):
    method __init__ (line 220) | def __init__(self, final_hidden_size, c_emb_size, patch_size, out_chan...
    method forward (line 228) | def forward(self, x, t):
  class DecoderLayer (line 235) | class DecoderLayer(nn.Module):
    method __init__ (line 239) | def __init__(self, hidden_size, decoder_hidden_size, dtype=None, devic...
    method forward (line 247) | def forward(self, x, t):
  class SizeEmbedder (line 254) | class SizeEmbedder(TimestepEmbedder):
    method __init__ (line 258) | def __init__(self, hidden_size, frequency_embedding_size=256, dtype=No...
    method forward (line 268) | def forward(self, s, bs):
  class LabelEmbedder (line 283) | class LabelEmbedder(nn.Module):
    method __init__ (line 287) | def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None,...
    method token_drop (line 294) | def token_drop(self, labels, force_drop_ids=None):
    method forward (line 305) | def forward(self, labels, train, force_drop_ids=None):
  class CaptionEmbedder (line 313) | class CaptionEmbedder(nn.Module):
    method __init__ (line 317) | def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn...
    method token_drop (line 326) | def token_drop(self, caption, force_drop_ids=None):
    method forward (line 337) | def forward(self, caption, train, force_drop_ids=None):
  class CaptionEmbedderDoubleBr (line 347) | class CaptionEmbedderDoubleBr(nn.Module):
    method __init__ (line 351) | def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn...
    method token_drop (line 361) | def token_drop(self, global_caption, caption, force_drop_ids=None):
    method forward (line 373) | def forward(self, caption, train, force_drop_ids=None):

FILE: comfy/ldm/pixart/pixartms.py
  function get_2d_sincos_pos_embed_torch (line 18) | def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0,...
  class PixArtMSBlock (line 29) | class PixArtMSBlock(nn.Module):
    method __init__ (line 33) | def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0....
    method forward (line 54) | def forward(self, x, y, t, mask=None, HW=None, **kwargs):
  class PixArtMS (line 66) | class PixArtMS(nn.Module):
    method __init__ (line 70) | def __init__(
    method forward_orig (line 164) | def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=No...
    method forward (line 219) | def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwa...
    method unpatchify (line 242) | def unpatchify(self, x, h, w):

FILE: comfy/ldm/qwen_image/controlnet.py
  class QwenImageFunControlBlock (line 8) | class QwenImageFunControlBlock(QwenImageTransformerBlock):
    method __init__ (line 9) | def __init__(self, dim, num_attention_heads, attention_head_dim, has_b...
  class QwenImageFunControlNetModel (line 24) | class QwenImageFunControlNetModel(torch.nn.Module):
    method __init__ (line 25) | def __init__(
    method _process_hint_tokens (line 61) | def _process_hint_tokens(self, hint):
    method forward (line 110) | def forward(
  class QwenImageControlNetModel (line 197) | class QwenImageControlNetModel(QwenImageTransformer2DModel):
    method __init__ (line 198) | def __init__(
    method forward (line 215) | def forward(

FILE: comfy/ldm/qwen_image/model.py
  class GELU (line 15) | class GELU(nn.Module):
    method __init__ (line 16) | def __init__(self, dim_in: int, dim_out: int, approximate: str = "none...
    method forward (line 21) | def forward(self, hidden_states):
  class FeedForward (line 27) | class FeedForward(nn.Module):
    method __init__ (line 28) | def __init__(
    method forward (line 48) | def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> tor...
  function apply_rotary_emb (line 54) | def apply_rotary_emb(x, freqs_cis):
  class QwenTimestepProjEmbeddings (line 63) | class QwenTimestepProjEmbeddings(nn.Module):
    method __init__ (line 64) | def __init__(self, embedding_dim, pooled_projection_dim, use_additiona...
    method forward (line 79) | def forward(self, timestep, hidden_states, addition_t_cond=None):
  class Attention (line 91) | class Attention(nn.Module):
    method __init__ (line 92) | def __init__(
    method forward (line 139) | def forward(
  class QwenImageTransformerBlock (line 203) | class QwenImageTransformerBlock(nn.Module):
    method __init__ (line 204) | def __init__(
    method _apply_gate (line 247) | def _apply_gate(self, x, y, gate, timestep_zero_index=None):
    method _modulate (line 253) | def _modulate(self, x: torch.Tensor, mod_params: torch.Tensor, timeste...
    method forward (line 266) | def forward(
  class LastLayer (line 316) | class LastLayer(nn.Module):
    method __init__ (line 317) | def __init__(
    method forward (line 331) | def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tenso...
  class QwenImageTransformer2DModel (line 338) | class QwenImageTransformer2DModel(nn.Module):
    method __init__ (line 339) | def __init__(
    method process_img (line 400) | def process_img(self, x, index=0, h_offset=0, w_offset=0):
    method forward (line 426) | def forward(self, x, timestep, context, attention_mask=None, ref_laten...
    method _forward (line 433) | def _forward(

FILE: comfy/ldm/util.py
  function log_txt_as_img (line 12) | def log_txt_as_img(wh, xc, size=10):
  function ismap (line 36) | def ismap(x):
  function isimage (line 42) | def isimage(x):
  function exists (line 48) | def exists(x):
  function default (line 52) | def default(val, d):
  function mean_flat (line 58) | def mean_flat(tensor):
  function count_params (line 66) | def count_params(model, verbose=False):
  function instantiate_from_config (line 73) | def instantiate_from_config(config):
  function get_obj_from_str (line 83) | def get_obj_from_str(string, reload=False):
  class AdamWwithEMAandWings (line 91) | class AdamWwithEMAandWings(optim.Optimizer):
    method __init__ (line 93) | def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8,  #...
    method __setstate__ (line 114) | def __setstate__(self, state):
    method step (line 120) | def step(self, closure=None):

FILE: comfy/ldm/wan/model.py
  function sinusoidal_embedding_1d (line 17) | def sinusoidal_embedding_1d(dim, position):
  class WanSelfAttention (line 30) | class WanSelfAttention(nn.Module):
    method __init__ (line 32) | def __init__(self,
    method forward (line 59) | def forward(self, x, freqs, transformer_options={}):
  class WanT2VCrossAttention (line 99) | class WanT2VCrossAttention(WanSelfAttention):
    method forward (line 101) | def forward(self, x, context, transformer_options={}, **kwargs):
  class WanI2VCrossAttention (line 119) | class WanI2VCrossAttention(WanSelfAttention):
    method __init__ (line 121) | def __init__(self,
    method forward (line 134) | def forward(self, x, context, context_img_len, transformer_options={}):
  function repeat_e (line 165) | def repeat_e(e, x):
  class WanAttentionBlock (line 177) | class WanAttentionBlock(nn.Module):
    method __init__ (line 179) | def __init__(self,
    method forward (line 217) | def forward(
  class VaceWanAttentionBlock (line 263) | class VaceWanAttentionBlock(WanAttentionBlock):
    method __init__ (line 264) | def __init__(
    method forward (line 283) | def forward(self, c, x, **kwargs):
  class WanCamAdapter (line 291) | class WanCamAdapter(nn.Module):
    method __init__ (line 292) | def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_...
    method forward (line 307) | def forward(self, x):
  class WanCamResidualBlock (line 330) | class WanCamResidualBlock(nn.Module):
    method __init__ (line 331) | def __init__(self, dim, operation_settings={}):
    method forward (line 337) | def forward(self, x):
  class Head (line 345) | class Head(nn.Module):
    method __init__ (line 347) | def __init__(self, dim, out_dim, patch_size, eps=1e-6, operation_setti...
    method forward (line 362) | def forward(self, x, e):
  class MLPProj (line 378) | class MLPProj(torch.nn.Module):
    method __init__ (line 380) | def __init__(self, in_dim, out_dim, flf_pos_embed_token_number=None, o...
    method forward (line 393) | def forward(self, image_embeds):
  class WanModel (line 401) | class WanModel(torch.nn.Module):
    method __init__ (line 406) | def __init__(self,
    method forward_orig (line 523) | def forward_orig(
    method rope_encode (line 609) | def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, ...
    method forward (line 643) | def forward(self, x, timestep, context, clip_fea=None, time_dim_concat...
    method _forward (line 650) | def _forward(self, x, timestep, context, clip_fea=None, time_dim_conca...
    method unpatchify (line 666) | def unpatchify(self, x, grid_sizes):
  class VaceWanModel (line 691) | class VaceWanModel(WanModel):
    method __init__ (line 696) | def __init__(self,
    method forward_orig (line 740) | def forward_orig(
  class CameraWanModel (line 811) | class CameraWanModel(WanModel):
    method __init__ (line 816) | def __init__(self,
    method forward_orig (line 851) | def forward_orig(
  class CausalConv1d (line 908) | class CausalConv1d(nn.Module):
    method __init__ (line 910) | def __init__(self,
    method forward (line 933) | def forward(self, x):
  class MotionEncoder_tc (line 938) | class MotionEncoder_tc(nn.Module):
    method __init__ (line 940) | def __init__(self,
    method forward (line 986) | def forward(self, x):
  class CausalAudioEncoder (line 1032) | class CausalAudioEncoder(nn.Module):
    method __init__ (line 1034) | def __init__(self,
    method forward (line 1055) | def forward(self, features):
  class AdaLayerNorm (line 1066) | class AdaLayerNorm(nn.Module):
    method __init__ (line 1067) | def __init__(self, embedding_dim, output_dim=None, norm_elementwise_af...
    method forward (line 1076) | def forward(self, x, temb):
  class AudioInjector_WAN (line 1085) | class AudioInjector_WAN(nn.Module):
    method __init__ (line 1087) | def __init__(self,
    method forward (line 1138) | def forward(self, x, block_id, audio_emb, audio_emb_global, seq_len):
  class FramePackMotioner (line 1160) | class FramePackMotioner(nn.Module):
    method __init__ (line 1161) | def __init__(
    method forward (line 1183) | def forward(self, motion_latents, rope_embedder, add_last_motion=2):
  class WanModel_S2V (line 1221) | class WanModel_S2V(WanModel):
    method __init__ (line 1222) | def __init__(self,
    method forward_orig (line 1286) | def forward_orig(
  class WanT2VCrossAttentionGather (line 1376) | class WanT2VCrossAttentionGather(WanSelfAttention):
    method forward (line 1378) | def forward(self, x, context, transformer_options={}, **kwargs):
  class AudioCrossAttentionWrapper (line 1404) | class AudioCrossAttentionWrapper(nn.Module):
    method __init__ (line 1405) | def __init__(self, dim, kv_dim, num_heads, qk_norm=True, eps=1e-6, ope...
    method forward (line 1411) | def forward(self, x, audio, transformer_options={}):
  class WanAttentionBlockAudio (line 1416) | class WanAttentionBlockAudio(WanAttentionBlock):
    method __init__ (line 1418) | def __init__(self,
    method forward (line 1430) | def forward(
  class DummyAdapterLayer (line 1469) | class DummyAdapterLayer(nn.Module):
    method __init__ (line 1470) | def __init__(self, layer):
    method forward (line 1474) | def forward(self, *args, **kwargs):
  class AudioProjModel (line 1478) | class AudioProjModel(nn.Module):
    method __init__ (line 1479) | def __init__(
    method forward (line 1508) | def forward(self, audio_embeds):
  class HumoWanModel (line 1525) | class HumoWanModel(WanModel):
    method __init__ (line 1530) | def __init__(self,
    method forward_orig (line 1558) | def forward_orig(
  class SCAILWanModel (line 1625) | class SCAILWanModel(WanModel):
    method __init__ (line 1626) | def __init__(self, model_type="scail", patch_size=(1, 2, 2), in_dim=20...
    method forward_orig (line 1631) | def forward_orig(self, x, t, context, clip_fea=None, freqs=None, trans...
    method rope_encode (line 1695) | def rope_encode(self, t, h, w, t_start=0, steps_t=None, steps_h=None, ...
    method _forward (line 1719) | def _forward(self, x, timestep, context, clip_fea=None, time_dim_conca...

FILE: comfy/ldm/wan/model_animate.py
  class CausalConv1d (line 11) | class CausalConv1d(nn.Module):
    method __init__ (line 13) | def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilatio...
    method forward (line 22) | def forward(self, x):
  class FaceEncoder (line 27) | class FaceEncoder(nn.Module):
    method __init__ (line 28) | def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=...
    method forward (line 48) | def forward(self, x):
  function get_norm_layer (line 77) | def get_norm_layer(norm_layer, operations=None):
  class FaceAdapter (line 95) | class FaceAdapter(nn.Module):
    method __init__ (line 96) | def __init__(
    method forward (line 124) | def forward(
  class FaceBlock (line 137) | class FaceBlock(nn.Module):
    method __init__ (line 138) | def __init__(
    method forward (line 175) | def forward(
  function upfirdn2d_native (line 216) | def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, ...
  function upfirdn2d (line 233) | def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
  class FusedLeakyReLU (line 237) | class FusedLeakyReLU(torch.nn.Module):
    method __init__ (line 238) | def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=...
    method forward (line 244) | def forward(self, input):
  function fused_leaky_relu (line 247) | def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
  class Blur (line 250) | class Blur(torch.nn.Module):
    method __init__ (line 251) | def __init__(self, kernel, pad, dtype=None, device=None):
    method forward (line 259) | def forward(self, input):
  class ScaledLeakyReLU (line 263) | class ScaledLeakyReLU(torch.nn.Module):
    method __init__ (line 264) | def __init__(self, negative_slope=0.2):
    method forward (line 268) | def forward(self, input):
  class EqualConv2d (line 272) | class EqualConv2d(torch.nn.Module):
    method __init__ (line 273) | def __init__(self, in_channel, out_channel, kernel_size, stride=1, pad...
    method forward (line 281) | def forward(self, input):
  class EqualLinear (line 290) | class EqualLinear(torch.nn.Module):
    method __init__ (line 291) | def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, ...
    method forward (line 299) | def forward(self, input):
  class ConvLayer (line 311) | class ConvLayer(torch.nn.Sequential):
    method __init__ (line 312) | def __init__(self, in_channel, out_channel, kernel_size, downsample=Fa...
  class ResBlock (line 331) | class ResBlock(torch.nn.Module):
    method __init__ (line 332) | def __init__(self, in_channel, out_channel, dtype=None, device=None, o...
    method forward (line 338) | def forward(self, input):
  class EncoderApp (line 344) | class EncoderApp(torch.nn.Module):
    method __init__ (line 345) | def __init__(self, w_dim=512, dtype=None, device=None, operations=None):
    method forward (line 357) | def forward(self, x):
  class Encoder (line 363) | class Encoder(torch.nn.Module):
    method __init__ (line 364) | def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, op...
    method encode_motion (line 369) | def encode_motion(self, x):
  class Direction (line 372) | class Direction(torch.nn.Module):
    method __init__ (line 373) | def __init__(self, motion_dim, dtype=None, device=None, operations=None):
    method forward (line 378) | def forward(self, input):
  class Synthesis (line 385) | class Synthesis(torch.nn.Module):
    method __init__ (line 386) | def __init__(self, motion_dim, dtype=None, device=None, operations=None):
  class Generator (line 390) | class Generator(torch.nn.Module):
    method __init__ (line 391) | def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=No...
    method get_motion (line 396) | def get_motion(self, img):
  class AnimateWanModel (line 400) | class AnimateWanModel(WanModel):
    method __init__ (line 405) | def __init__(self,
    method after_patch_embedding (line 451) | def after_patch_embedding(self, x, pose_latents, face_pixel_values):
    method forward_orig (line 483) | def forward_orig(

FILE: comfy/ldm/wan/model_multitalk.py
  function calculate_x_ref_attn_map (line 7) | def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, split_nu...
  function get_attn_map_with_target (line 45) | def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=No...
  function normalize_and_scale (line 74) | def normalize_and_scale(column, source_range, target_range, epsilon=1e-8):
  function rotate_half (line 82) | def rotate_half(x):
  function get_audio_embeds (line 89) | def get_audio_embeds(encoded_audio, audio_start, audio_end):
  function project_audio_features (line 113) | def project_audio_features(audio_proj, encoded_audio, audio_start, audio...
  class RotaryPositionalEmbedding1D (line 136) | class RotaryPositionalEmbedding1D(torch.nn.Module):
    method __init__ (line 137) | def __init__(self,
    method precompute_freqs_cis_1d (line 144) | def precompute_freqs_cis_1d(self, pos_indices):
    method forward (line 151) | def forward(self, x, pos_indices):
  class SingleStreamAttention (line 163) | class SingleStreamAttention(torch.nn.Module):
    method __init__ (line 164) | def __init__(
    method forward (line 182) | def forward(self, x: torch.Tensor, encoder_hidden_states: torch.Tensor...
  class SingleStreamMultiAttention (line 221) | class SingleStreamMultiAttention(SingleStreamAttention):
    method __init__ (line 222) | def __init__(
    method forward (line 252) | def forward(
  class MultiTalkAudioProjModel (line 345) | class MultiTalkAudioProjModel(torch.nn.Module):
    method __init__ (line 346) | def __init__(
    method forward (line 375) | def forward(self, audio_embeds, audio_embeds_vf):
  class WanMultiTalkAttentionBlock (line 410) | class WanMultiTalkAttentionBlock(torch.nn.Module):
    method __init__ (line 411) | def __init__(self, in_dim=5120, out_dim=768, device=None, dtype=None, ...
  class MultiTalkGetAttnMapPatch (line 417) | class MultiTalkGetAttnMapPatch:
    method __init__ (line 418) | def __init__(self, ref_target_masks=None):
    method __call__ (line 421) | def __call__(self, kwargs):
  class MultiTalkCrossAttnPatch (line 431) | class MultiTalkCrossAttnPatch:
    method __init__ (line 432) | def __init__(self, model_patch, audio_scale=1.0, ref_target_masks=None):
    method __call__ (line 437) | def __call__(self, kwargs):
    method models (line 456) | def models(self):
  class MultiTalkApplyModelWrapper (line 459) | class MultiTalkApplyModelWrapper:
    method __init__ (line 460) | def __init__(self, init_latents):
    method __call__ (line 463) | def __call__(self, executor, x, *args, **kwargs):
  class InfiniteTalkOuterSampleWrapper (line 469) | class InfiniteTalkOuterSampleWrapper:
    method __init__ (line 470) | def __init__(self, motion_frames_latent, model_patch, is_extend=False):
    method __call__ (line 475) | def __call__(self, executor, *args, **kwargs):
    method to (line 496) | def to(self, device_or_dtype):

FILE: comfy/ldm/wan/vae.py
  class CausalConv3d (line 16) | class CausalConv3d(ops.Conv3d):
    method __init__ (line 21) | def __init__(self, *args, **kwargs):
    method forward (line 26) | def forward(self, x, cache_x=None, cache_list=None, cache_idx=None):
  class RMS_norm (line 50) | class RMS_norm(nn.Module):
    method __init__ (line 52) | def __init__(self, dim, channel_first=True, images=True, bias=False):
    method forward (line 62) | def forward(self, x):
  class Resample (line 67) | class Resample(nn.Module):
    method __init__ (line 69) | def __init__(self, dim, mode):
    method forward (line 102) | def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
  class ResidualBlock (line 154) | class ResidualBlock(nn.Module):
    method __init__ (line 156) | def __init__(self, in_dim, out_dim, dropout=0.0):
    method forward (line 170) | def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
  class AttentionBlock (line 184) | class AttentionBlock(nn.Module):
    method __init__ (line 189) | def __init__(self, dim):
    method forward (line 199) | def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
  class Encoder3d (line 215) | class Encoder3d(nn.Module):
    method __init__ (line 217) | def __init__(self,
    method forward (line 269) | def forward(self, x, feat_cache=None, feat_idx=[0], final=False):
  class Decoder3d (line 308) | class Decoder3d(nn.Module):
    method __init__ (line 310) | def __init__(self,
    method run_up (line 363) | def run_up(self, layer_idx, x_ref, feat_cache, feat_idx, out_chunks):
    method forward (line 400) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  function count_cache_layers (line 424) | def count_cache_layers(model):
  class WanVAE (line 432) | class WanVAE(nn.Module):
    method __init__ (line 434) | def __init__(self,
    method encode (line 461) | def encode(self, x):
    method decode (line 491) | def decode(self, z):

FILE: comfy/ldm/wan/vae2_2.py
  class Resample (line 16) | class Resample(nn.Module):
    method __init__ (line 18) | def __init__(self, dim, mode):
    method forward (line 57) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class ResidualBlock (line 117) | class ResidualBlock(nn.Module):
    method __init__ (line 119) | def __init__(self, in_dim, out_dim, dropout=0.0):
    method forward (line 138) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  function patchify (line 162) | def patchify(x, patch_size):
  function unpatchify (line 181) | def unpatchify(x, patch_size):
  class AvgDown3D (line 198) | class AvgDown3D(nn.Module):
    method __init__ (line 200) | def __init__(
    method forward (line 217) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class DupUp3D (line 252) | class DupUp3D(nn.Module):
    method __init__ (line 254) | def __init__(
    method forward (line 272) | def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
  class Down_ResidualBlock (line 297) | class Down_ResidualBlock(nn.Module):
    method __init__ (line 299) | def __init__(self,
    method forward (line 329) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class Up_ResidualBlock (line 337) | class Up_ResidualBlock(nn.Module):
    method __init__ (line 339) | def __init__(self,
    method forward (line 371) | def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
  class Encoder3d (line 382) | class Encoder3d(nn.Module):
    method __init__ (line 384) | def __init__(
    method forward (line 441) | def forward(self, x, feat_cache=None, feat_idx=[0]):
  class Decoder3d (line 498) | class Decoder3d(nn.Module):
    method __init__ (line 500) | def __init__(
    method forward (line 553) | def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
  function count_conv3d (line 607) | def count_conv3d(model):
  class WanVAE (line 615) | class WanVAE(nn.Module):
    method __init__ (line 617) | def __init__(
    method encode (line 659) | def encode(self, x):
    method decode (line 683) | def decode(self, z):
    method reparameterize (line 707) | def reparameterize(self, mu, log_var):
    method sample (line 712) | def sample(self, imgs, deterministic=False):

FILE: comfy/lora.py
  function load_lora (line 37) | def load_lora(lora, to_load, log_missing=True):
  function model_lora_keys_clip (line 97) | def model_lora_keys_clip(model, key_map={}):
  function model_lora_keys_unet (line 178) | def model_lora_keys_unet(model, key_map={}):
  function pad_tensor_to_shape (line 348) | def pad_tensor_to_shape(tensor: torch.Tensor, new_shape: list[int]) -> t...
  function calculate_shape (line 381) | def calculate_shape(patches, weight, key, original_weights=None):
  function calculate_weight (line 406) | def calculate_weight(patches, weight, key, intermediate_dtype=torch.floa...

FILE: comfy/lora_convert.py
  function convert_lora_bfl_control (line 5) | def convert_lora_bfl_control(sd): #BFL loras for Flux
  function convert_lora_wan_fun (line 15) | def convert_lora_wan_fun(sd): #Wan Fun loras
  function convert_uso_lora (line 18) | def convert_uso_lora(sd):
  function convert_lora (line 36) | def convert_lora(sd):

FILE: comfy/memory_management.py
  class TensorFileSlice (line 11) | class TensorFileSlice(NamedTuple):
  function read_tensor_file_slice_into (line 18) | def read_tensor_file_slice_into(tensor, destination):
  class TensorGeometry (line 69) | class TensorGeometry(NamedTuple):
    method element_size (line 73) | def element_size(self):
    method numel (line 77) | def numel(self):
  function tensors_to_geometries (line 80) | def tensors_to_geometries(tensors, dtype=None):
  function vram_aligned_size (line 94) | def vram_aligned_size(tensor):
  function interpret_gathered_like (line 109) | def interpret_gathered_like(tensors, gathered):

FILE: comfy/model_base.py
  class ModelType (line 68) | class ModelType(Enum):
  function model_sampling (line 82) | def model_sampling(model_config, model_type):
  function convert_tensor (line 121) | def convert_tensor(extra, dtype, device):
  class BaseModel (line 130) | class BaseModel(torch.nn.Module):
    method __init__ (line 131) | def __init__(self, model_config, model_type=ModelType.EPS, device=None...
    method apply_model (line 169) | def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=N...
    method _apply_model (line 176) | def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=...
    method process_timestep (line 215) | def process_timestep(self, timestep, **kwargs):
    method get_dtype (line 218) | def get_dtype(self):
    method get_dtype_inference (line 221) | def get_dtype_inference(self):
    method encode_adm (line 228) | def encode_adm(self, **kwargs):
    method concat_cond (line 231) | def concat_cond(self, **kwargs):
    method resize_cond_for_context_window (line 288) | def resize_cond_for_context_window(self, cond_key, cond_value, window,...
    method extra_conds (line 294) | def extra_conds(self, **kwargs):
    method load_model_weights (line 318) | def load_model_weights(self, sd, unet_prefix="", assign=False):
    method process_latent_in (line 335) | def process_latent_in(self, latent):
    method process_latent_out (line 338) | def process_latent_out(self, latent):
    method state_dict_for_saving (line 341) | def state_dict_for_saving(self, unet_state_dict, clip_state_dict=None,...
    method set_inpaint (line 358) | def set_inpaint(self):
    method scale_latent_inpaint (line 370) | def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
    method memory_required (line 373) | def memory_required(self, input_shape, cond_shapes={}):
    method extra_conds_shapes (line 397) | def extra_conds_shapes(self, **kwargs):
  function unclip_adm (line 401) | def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augme...
  class SD21UNCLIP (line 425) | class SD21UNCLIP(BaseModel):
    method __init__ (line 426) | def __init__(self, model_config, noise_aug_config, model_type=ModelTyp...
    method encode_adm (line 430) | def encode_adm(self, **kwargs):
  function sdxl_pooled (line 438) | def sdxl_pooled(args, noise_augmentor):
  class SDXLRefiner (line 444) | class SDXLRefiner(BaseModel):
    method __init__ (line 445) | def __init__(self, model_config, model_type=ModelType.EPS, device=None):
    method encode_adm (line 450) | def encode_adm(self, **kwargs):
  class SDXL (line 471) | class SDXL(BaseModel):
    method __init__ (line 472) | def __init__(self, model_config, model_type=ModelType.EPS, device=None):
    method encode_adm (line 477) | def encode_adm(self, **kwargs):
  class SVD_img2vid (line 497) | class SVD_img2vid(BaseModel):
    method __init__ (line 498) | def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM...
    method encode_adm (line 502) | def encode_adm(self, **kwargs):
    method extra_conds (line 515) | def extra_conds(self, **kwargs):
  class SV3D_u (line 544) | class SV3D_u(SVD_img2vid):
    method encode_adm (line 545) | def encode_adm(self, **kwargs):
  class SV3D_p (line 554) | class SV3D_p(SVD_img2vid):
    method __init__ (line 555) | def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM...
    method encode_adm (line 559) | def encode_adm(self, **kwargs):
  class Stable_Zero123 (line 574) | class Stable_Zero123(BaseModel):
    method __init__ (line 575) | def __init__(self, model_config, model_type=ModelType.EPS, device=None...
    method extra_conds (line 581) | def extra_conds(self, **kwargs):
  class SD_X4Upscaler (line 604) | class SD_X4Upscaler(BaseModel):
    method __init__ (line 605) | def __init__(self, model_config, model_type=ModelType.V_PREDICTION, de...
    method extra_conds (line 609) | def extra_conds(self, **kwargs):
  class IP2P (line 640) | class IP2P:
    method concat_cond (line 641) | def concat_cond(self, **kwargs):
  class SD15_instructpix2pix (line 658) | class SD15_instructpix2pix(IP2P, BaseModel):
    method __init__ (line 659) | def __init__(self, model_config, model_type=ModelType.EPS, device=None):
  class SDXL_instructpix2pix (line 664) | class SDXL_instructpix2pix(IP2P, SDXL):
    method __init__ (line 665) | def __init__(self, model_config, model_type=ModelType.EPS, device=None):
  class Lotus (line 672) | class Lotus(BaseModel):
    method extra_conds (line 673) | def extra_conds(self, **kwargs):
    method __init__ (line 683) | def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG, devi...
  class StableCascade_C (line 686) | class StableCascade_C(BaseModel):
    method __init__ (line 687) | def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, ...
    method extra_conds (line 690) | def extra_conds(self, **kwargs):
  class StableCascade_B (line 714) | class StableCascade_B(BaseModel):
    method __init__ (line 715) | def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, ...
    method extra_conds (line 718) | def extra_conds(self, **kwargs):
  class SD3 (line 734) | class SD3(BaseModel):
    method __init__ (line 735) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method encode_adm (line 738) | def encode_adm(self, **kwargs):
    method extra_conds (line 741) | def extra_conds(self, **kwargs):
  class AuraFlow (line 749) | class AuraFlow(BaseModel):
    method __init__ (line 750) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 753) | def extra_conds(self, **kwargs):
  class StableAudio1 (line 761) | class StableAudio1(BaseModel):
    method __init__ (line 762) | def __init__(self, model_config, seconds_start_embedder_weights, secon...
    method extra_conds (line 769) | def extra_conds(self, **kwargs):
    method state_dict_for_saving (line 790) | def state_dict_for_saving(self, unet_state_dict, clip_state_dict=None,...
  class HunyuanDiT (line 800) | class HunyuanDiT(BaseModel):
    method __init__ (line 801) | def __init__(self, model_config, model_type=ModelType.V_PREDICTION, de...
    method extra_conds (line 804) | def extra_conds(self, **kwargs):
  class PixArt (line 830) | class PixArt(BaseModel):
    method __init__ (line 831) | def __init__(self, model_config, model_type=ModelType.EPS, device=None):
    method extra_conds (line 834) | def extra_conds(self, **kwargs):
  class Flux (line 849) | class Flux(BaseModel):
    method __init__ (line 850) | def __init__(self, model_config, model_type=ModelType.FLUX, device=Non...
    method concat_cond (line 854) | def concat_cond(self, **kwargs):
    method encode_adm (line 891) | def encode_adm(self, **kwargs):
    method extra_conds (line 894) | def extra_conds(self, **kwargs):
    method extra_conds_shapes (line 927) | def extra_conds_shapes(self, **kwargs):
  class LongCatImage (line 934) | class LongCatImage(Flux):
    method _apply_model (line 935) | def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=...
    method encode_adm (line 945) | def encode_adm(self, **kwargs):
    method extra_conds (line 948) | def extra_conds(self, **kwargs):
  class Flux2 (line 953) | class Flux2(Flux):
    method extra_conds (line 954) | def extra_conds(self, **kwargs):
  class GenmoMochi (line 964) | class GenmoMochi(BaseModel):
    method __init__ (line 965) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 968) | def extra_conds(self, **kwargs):
  class LTXV (line 979) | class LTXV(BaseModel):
    method __init__ (line 980) | def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
    method extra_conds (line 983) | def extra_conds(self, **kwargs):
    method process_timestep (line 1008) | def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
    method scale_latent_inpaint (line 1013) | def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
  class LTXAV (line 1016) | class LTXAV(BaseModel):
    method __init__ (line 1017) | def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
    method extra_conds (line 1020) | def extra_conds(self, **kwargs):
    method process_timestep (line 1064) | def process_timestep(self, timestep, x, denoise_mask=None, audio_denoi...
    method scale_latent_inpaint (line 1075) | def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
  class HunyuanVideo (line 1078) | class HunyuanVideo(BaseModel):
    method __init__ (line 1079) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method encode_adm (line 1082) | def encode_adm(self, **kwargs):
    method extra_conds (line 1085) | def extra_conds(self, **kwargs):
    method scale_latent_inpaint (line 1108) | def scale_latent_inpaint(self, latent_image, **kwargs):
  class HunyuanVideoI2V (line 1111) | class HunyuanVideoI2V(HunyuanVideo):
    method __init__ (line 1112) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method scale_latent_inpaint (line 1116) | def scale_latent_inpaint(self, latent_image, **kwargs):
  class HunyuanVideoSkyreelsI2V (line 1119) | class HunyuanVideoSkyreelsI2V(HunyuanVideo):
    method __init__ (line 1120) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method scale_latent_inpaint (line 1124) | def scale_latent_inpaint(self, latent_image, **kwargs):
  class CosmosVideo (line 1127) | class CosmosVideo(BaseModel):
    method __init__ (line 1128) | def __init__(self, model_config, model_type=ModelType.EDM, image_to_vi...
    method extra_conds (line 1134) | def extra_conds(self, **kwargs):
    method scale_latent_inpaint (line 1146) | def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
  class CosmosPredict2 (line 1154) | class CosmosPredict2(BaseModel):
    method __init__ (line 1155) | def __init__(self, model_config, model_type=ModelType.FLOW_COSMOS, ima...
    method extra_conds (line 1161) | def extra_conds(self, **kwargs):
    method process_timestep (line 1174) | def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
    method scale_latent_inpaint (line 1184) | def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
  class Anima (line 1193) | class Anima(BaseModel):
    method __init__ (line 1194) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1197) | def extra_conds(self, **kwargs):
  class Lumina2 (line 1218) | class Lumina2(BaseModel):
    method __init__ (line 1219) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1223) | def extra_conds(self, **kwargs):
    method extra_conds_shapes (line 1265) | def extra_conds_shapes(self, **kwargs):
  class ZImagePixelSpace (line 1272) | class ZImagePixelSpace(Lumina2):
    method __init__ (line 1273) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
  class WAN21 (line 1277) | class WAN21(BaseModel):
    method __init__ (line 1278) | def __init__(self, model_config, model_type=ModelType.FLOW, image_to_v...
    method concat_cond (line 1282) | def concat_cond(self, **kwargs):
    method extra_conds (line 1329) | def extra_conds(self, **kwargs):
  class WAN21_Vace (line 1350) | class WAN21_Vace(WAN21):
    method __init__ (line 1351) | def __init__(self, model_config, model_type=ModelType.FLOW, image_to_v...
    method extra_conds (line 1355) | def extra_conds(self, **kwargs):
    method resize_cond_for_context_window (line 1384) | def resize_cond_for_context_window(self, cond_key, cond_value, window,...
  class WAN21_Camera (line 1390) | class WAN21_Camera(WAN21):
    method __init__ (line 1391) | def __init__(self, model_config, model_type=ModelType.FLOW, image_to_v...
    method extra_conds (line 1395) | def extra_conds(self, **kwargs):
  class WAN21_HuMo (line 1402) | class WAN21_HuMo(WAN21):
    method __init__ (line 1403) | def __init__(self, model_config, model_type=ModelType.FLOW, image_to_v...
    method extra_conds (line 1407) | def extra_conds(self, **kwargs):
    method resize_cond_for_context_window (line 1442) | def resize_cond_for_context_window(self, cond_key, cond_value, window,...
  class WAN22_Animate (line 1448) | class WAN22_Animate(WAN21):
    method __init__ (line 1449) | def __init__(self, model_config, model_type=ModelType.FLOW, image_to_v...
    method extra_conds (line 1453) | def extra_conds(self, **kwargs):
    method resize_cond_for_context_window (line 1465) | def resize_cond_for_context_window(self, cond_key, cond_value, window,...
  class WAN22_S2V (line 1473) | class WAN22_S2V(WAN21):
    method __init__ (line 1474) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1479) | def extra_conds(self, **kwargs):
    method extra_conds_shapes (line 1498) | def extra_conds_shapes(self, **kwargs):
    method resize_cond_for_context_window (line 1509) | def resize_cond_for_context_window(self, cond_key, cond_value, window,...
  class WAN22 (line 1515) | class WAN22(WAN21):
    method __init__ (line 1516) | def __init__(self, model_config, model_type=ModelType.FLOW, image_to_v...
    method extra_conds (line 1520) | def extra_conds(self, **kwargs):
    method process_timestep (line 1527) | def process_timestep(self, timestep, x, denoise_mask=None, **kwargs):
    method scale_latent_inpaint (line 1533) | def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
  class WAN21_FlowRVS (line 1536) | class WAN21_FlowRVS(WAN21):
    method __init__ (line 1537) | def __init__(self, model_config, model_type=ModelType.IMG_TO_IMG_FLOW,...
  class WAN21_SCAIL (line 1542) | class WAN21_SCAIL(WAN21):
    method __init__ (line 1543) | def __init__(self, model_config, model_type=ModelType.FLOW, image_to_v...
    method extra_conds (line 1549) | def extra_conds(self, **kwargs):
    method extra_conds_shapes (line 1568) | def extra_conds_shapes(self, **kwargs):
  class Hunyuan3Dv2 (line 1580) | class Hunyuan3Dv2(BaseModel):
    method __init__ (line 1581) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1584) | def extra_conds(self, **kwargs):
  class Hunyuan3Dv2_1 (line 1595) | class Hunyuan3Dv2_1(BaseModel):
    method __init__ (line 1596) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1599) | def extra_conds(self, **kwargs):
  class HiDream (line 1610) | class HiDream(BaseModel):
    method __init__ (line 1611) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method encode_adm (line 1614) | def encode_adm(self, **kwargs):
    method extra_conds (line 1617) | def extra_conds(self, **kwargs):
  class Chroma (line 1630) | class Chroma(Flux):
    method __init__ (line 1631) | def __init__(self, model_config, model_type=ModelType.FLUX, device=Non...
    method extra_conds (line 1634) | def extra_conds(self, **kwargs):
  class ChromaRadiance (line 1642) | class ChromaRadiance(Chroma):
    method __init__ (line 1643) | def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
  class ACEStep (line 1646) | class ACEStep(BaseModel):
    method __init__ (line 1647) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1650) | def extra_conds(self, **kwargs):
  class ACEStep15 (line 1665) | class ACEStep15(BaseModel):
    method __init__ (line 1666) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1669) | def extra_conds(self, **kwargs):
  class Omnigen2 (line 1708) | class Omnigen2(BaseModel):
    method __init__ (line 1709) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1713) | def extra_conds(self, **kwargs):
    method extra_conds_shapes (line 1731) | def extra_conds_shapes(self, **kwargs):
  class QwenImage (line 1738) | class QwenImage(BaseModel):
    method __init__ (line 1739) | def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
    method extra_conds (line 1743) | def extra_conds(self, **kwargs):
    method extra_conds_shapes (line 1763) | def extra_conds_shapes(self, **kwargs):
  class HunyuanImage21 (line 1770) | class HunyuanImage21(BaseModel):
    method __init__ (line 1771) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method extra_conds (line 1774) | def extra_conds(self, **kwargs):
  class HunyuanImage21Refiner (line 1794) | class HunyuanImage21Refiner(HunyuanImage21):
    method concat_cond (line 1795) | def concat_cond(self, **kwargs):
    method extra_conds (line 1817) | def extra_conds(self, **kwargs):
  class HunyuanVideo15 (line 1822) | class HunyuanVideo15(HunyuanVideo):
    method __init__ (line 1823) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method concat_cond (line 1826) | def concat_cond(self, **kwargs):
    method extra_conds (line 1858) | def extra_conds(self, **kwargs):
  class HunyuanVideo15_SR_Distilled (line 1882) | class HunyuanVideo15_SR_Distilled(HunyuanVideo15):
    method __init__ (line 1883) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method concat_cond (line 1886) | def concat_cond(self, **kwargs):
    method extra_conds (line 1908) | def extra_conds(self, **kwargs):
  class Kandinsky5 (line 1913) | class Kandinsky5(BaseModel):
    method __init__ (line 1914) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method encode_adm (line 1917) | def encode_adm(self, **kwargs):
    method concat_cond (line 1920) | def concat_cond(self, **kwargs):
    method extra_conds (line 1937) | def extra_conds(self, **kwargs):
  class Kandinsky5Image (line 1952) | class Kandinsky5Image(Kandinsky5):
    method __init__ (line 1953) | def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
    method concat_cond (line 1956) | def concat_cond(self, **kwargs):

FILE: comfy/model_detection.py
  function count_blocks (line 10) | def count_blocks(state_dict_keys, prefix_string):
  function any_suffix_in (line 23) | def any_suffix_in(keys, prefix, main, suffix_list=[]):
  function calculate_transformer_depth (line 29) | def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
  function detect_unet_config (line 44) | def detect_unet_config(state_dict, key_prefix, metadata=None):
  function model_config_from_unet_config (line 831) | def model_config_from_unet_config(unet_config, state_dict=None):
  function model_config_from_unet (line 839) | def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_m...
  function unet_prefix_from_state_dict (line 855) | def unet_prefix_from_state_dict(state_dict):
  function convert_config (line 874) | def convert_config(unet_config):
  function unet_config_from_diffusers_unet (line 912) | def unet_config_from_diffusers_unet(state_dict, dtype=None):
  function model_config_from_diffusers_unet (line 1065) | def model_config_from_diffusers_unet(state_dict):
  function convert_diffusers_mmdit (line 1071) | def convert_diffusers_mmdit(state_dict, output_prefix=""):

FILE: comfy/model_management.py
  class VRAMState (line 35) | class VRAMState(Enum):
  class CPUState (line 43) | class CPUState(Enum):
  function get_supported_float8_types (line 60) | def get_supported_float8_types():
  function is_intel_xpu (line 154) | def is_intel_xpu():
  function is_ascend_npu (line 162) | def is_ascend_npu():
  function is_mlu (line 168) | def is_mlu():
  function is_ixuca (line 174) | def is_ixuca():
  function is_wsl (line 180) | def is_wsl():
  function get_torch_device (line 188) | def get_torch_device():
  function get_total_memory (line 208) | def get_total_memory(dev=None, torch_total_too=False):
  function mac_version (line 250) | def mac_version():
  function is_oom (line 278) | def is_oom(e):
  function raise_non_oom (line 286) | def raise_non_oom(e):
  function is_nvidia (line 315) | def is_nvidia():
  function is_amd (line 322) | def is_amd():
  function amd_min_version (line 329) | def amd_min_version(device=None, min_rdna_version=0):
  function aotriton_supported (line 386) | def aotriton_supported(gpu_arch):
  function get_torch_device_name (line 471) | def get_torch_device_name(device):
  function module_size (line 500) | def module_size(module):
  function module_mmap_residency (line 508) | def module_mmap_residency(module, free=False):
  class LoadedModel (line 530) | class LoadedModel:
    method __init__ (line 531) | def __init__(self, model):
    method _set_model (line 539) | def _set_model(self, model):
    method _switch_parent (line 546) | def _switch_parent(self):
    method model (line 552) | def model(self):
    method model_memory (line 555) | def model_memory(self):
    method model_mmap_residency (line 558) | def model_mmap_residency(self, free=False):
    method model_loaded_memory (line 561) | def model_loaded_memory(self):
    method model_offloaded_memory (line 564) | def model_offloaded_memory(self):
    method model_memory_required (line 567) | def model_memory_required(self, device):
    method model_load (line 573) | def model_load(self, lowvram_model_memory=0, force_patch_weights=False):
    method should_reload_model (line 594) | def should_reload_model(self, force_patch_weights=False):
    method model_unload (line 599) | def model_unload(self, memory_to_free=None, unpatch_weights=True):
    method model_use_more_vram (line 611) | def model_use_more_vram(self, extra_memory, force_patch_weights=False):
    method __eq__ (line 614) | def __eq__(self, other):
    method __del__ (line 617) | def __del__(self):
    method is_dead (line 621) | def is_dead(self):
  function use_more_memory (line 625) | def use_more_memory(extra_memory, loaded_models, device):
  function offloaded_memory (line 632) | def offloaded_memory(loaded_models, device):
  function get_free_ram (line 647) | def get_free_ram():
  function get_free_ram (line 650) | def get_free_ram():
  function extra_reserved_memory (line 657) | def extra_reserved_memory():
  function minimum_inference_memory (line 660) | def minimum_inference_memory():
  function free_memory (line 663) | def free_memory(memory_required, device, keep_loaded=[], for_dynamic=Fal...
  function load_models_gpu (line 717) | def load_models_gpu(models, memory_required=0, force_patch_weights=False...
  function load_model_gpu (line 823) | def load_model_gpu(model):
  function loaded_models (line 826) | def loaded_models(only_currently_used=False):
  function cleanup_models_gc (line 837) | def cleanup_models_gc():
  function archive_model_dtypes (line 859) | def archive_model_dtypes(model):
  function cleanup_models (line 867) | def cleanup_models():
  function dtype_size (line 877) | def dtype_size(dtype):
  function unet_offload_device (line 890) | def unet_offload_device():
  function unet_inital_load_device (line 896) | def unet_inital_load_device(parameters, dtype):
  function maximum_vram_for_weights (line 917) | def maximum_vram_for_weights(device=None):
  function unet_dtype (line 920) | def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.floa...
  function unet_manual_cast (line 973) | def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[t...
  function text_encoder_offload_device (line 997) | def text_encoder_offload_device():
  function text_encoder_device (line 1003) | def text_encoder_device():
  function text_encoder_initial_device (line 1014) | def text_encoder_initial_device(load_device, offload_device, model_size=0):
  function text_encoder_dtype (line 1031) | def text_encoder_dtype(device=None):
  function intermediate_device (line 1049) | def intermediate_device():
  function intermediate_dtype (line 1055) | def intermediate_dtype():
  function vae_device (line 1061) | def vae_device():
  function vae_offload_device (line 1066) | def vae_offload_device():
  function vae_dtype (line 1072) | def vae_dtype(device=None, allowed_dtypes=[]):
  function get_autocast_device (line 1089) | def get_autocast_device(dev):
  function supports_dtype (line 1094) | def supports_dtype(device, dtype): #TODO
  function supports_cast (line 1105) | def supports_cast(device, dtype): #TODO
  function pick_weight_dtype (line 1122) | def pick_weight_dtype(dtype, fallback_dtype, device=None):
  function device_supports_non_blocking (line 1133) | def device_supports_non_blocking(device):
  function force_channels_last (line 1146) | def force_channels_last():
  function current_stream (line 1169) | def current_stream(device):
  function get_cast_buffer (line 1184) | def get_cast_buffer(offload_stream, device, size, ref):
  function reset_cast_buffers (line 1216) | def reset_cast_buffers():
  function get_offload_stream (line 1225) | def get_offload_stream(device):
  function sync_stream (line 1262) | def sync_stream(device, stream):
  function cast_to_gathered (line 1268) | def cast_to_gathered(tensors, r, non_blocking=False, stream=None):
  function cast_to (line 1289) | def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=Fa...
  function cast_to_device (line 1317) | def cast_to_device(tensor, device, dtype, copy=False):
  function discard_cuda_async_error (line 1335) | def discard_cuda_async_error():
  function pin_memory (line 1345) | def pin_memory(tensor):
  function unpin_memory (line 1383) | def unpin_memory(tensor):
  function sage_attention_enabled (line 1414) | def sage_attention_enabled():
  function flash_attention_enabled (line 1417) | def flash_attention_enabled():
  function xformers_enabled (line 1420) | def xformers_enabled():
  function xformers_enabled_vae (line 1438) | def xformers_enabled_vae():
  function pytorch_attention_enabled (line 1445) | def pytorch_attention_enabled():
  function pytorch_attention_enabled_vae (line 1449) | def pytorch_attention_enabled_vae():
  function pytorch_attention_flash_attention (line 1454) | def pytorch_attention_flash_attention():
  function force_upcast_attention_dtype (line 1472) | def force_upcast_attention_dtype():
  function get_free_memory (line 1484) | def get_free_memory(dev=None, torch_free_too=False):
  function cpu_mode (line 1530) | def cpu_mode():
  function mps_mode (line 1534) | def mps_mode():
  function is_device_type (line 1538) | def is_device_type(device, type):
  function is_device_cpu (line 1544) | def is_device_cpu(device):
  function is_device_mps (line 1547) | def is_device_mps(device):
  function is_device_xpu (line 1550) | def is_device_xpu(device):
  function is_device_cuda (line 1553) | def is_device_cuda(device):
  function is_directml_enabled (line 1556) | def is_directml_enabled():
  function should_use_fp16 (line 1563) | def should_use_fp16(device=None, model_params=0, prioritize_performance=...
  function should_use_bf16 (line 1633) | def should_use_bf16(device=None, model_params=0, prioritize_performance=...
  function supports_fp8_compute (line 1689) | def supports_fp8_compute(device=None):
  function supports_nvfp4_compute (line 1713) | def supports_nvfp4_compute(device=None):
  function supports_mxfp8_compute (line 1723) | def supports_mxfp8_compute(device=None):
  function extended_fp16_support (line 1736) | def extended_fp16_support():
  function lora_compute_dtype (line 1744) | def lora_compute_dtype(device):
  function synchronize (line 1757) | def synchronize():
  function soft_empty_cache (line 1765) | def soft_empty_cache(force=False):
  function unload_all_models (line 1782) | def unload_all_models():
  function debug_memory_summary (line 1785) | def debug_memory_summary():
  class InterruptProcessingException (line 1790) | class InterruptProcessingException(Exception):
  function interrupt_current_processing (line 1796) | def interrupt_current_processing(value=True):
  function processing_interrupted (line 1802) | def processing_interrupted():
  function throw_exception_if_processing_interrupted (line 1808) | def throw_exception_if_processing_interrupted():

FILE: comfy/model_patcher.py
  function set_model_options_patch_replace (line 42) | def set_model_options_patch_replace(model_options, patch, name, block_na...
  function set_model_options_post_cfg_function (line 63) | def set_model_options_post_cfg_function(model_options, post_cfg_function...
  function set_model_options_pre_cfg_function (line 69) | def set_model_options_pre_cfg_function(model_options, pre_cfg_function, ...
  function create_model_options_clone (line 75) | def create_model_options_clone(orig_model_options: dict):
  function create_hook_patches_clone (line 78) | def create_hook_patches_clone(orig_hook_patches):
  function wipe_lowvram_weight (line 86) | def wipe_lowvram_weight(m):
  function move_weight_functions (line 97) | def move_weight_functions(m, device):
  function string_to_seed (line 113) | def string_to_seed(data):
  class LowVramPatch (line 117) | class LowVramPatch:
    method __init__ (line 118) | def __init__(self, key, patches, convert_func=None, set_func=None):
    method __call__ (line 124) | def __call__(self, weight):
  function low_vram_patch_estimate_vram (line 129) | def low_vram_patch_estimate_vram(model, key):
  function get_key_weight (line 139) | def get_key_weight(model, key):
  function key_param_name_to_key (line 163) | def key_param_name_to_key(key, param):
  class AutoPatcherEjector (line 168) | class AutoPatcherEjector:
    method __init__ (line 169) | def __init__(self, model: 'ModelPatcher', skip_and_inject_on_exit_only...
    method __enter__ (line 175) | def __enter__(self):
    method __exit__ (line 184) | def __exit__(self, *args):
  class MemoryCounter (line 192) | class MemoryCounter:
    method __init__ (line 193) | def __init__(self, initial: int, minimum=0):
    method use (line 198) | def use(self, weight: torch.Tensor):
    method is_useable (line 205) | def is_useable(self, used: int):
    method decrement (line 208) | def decrement(self, used: int):
  class LazyCastingParam (line 213) | class LazyCastingParam(torch.nn.Parameter):
    method __new__ (line 214) | def __new__(cls, model, key, tensor):
    method __init__ (line 217) | def __init__(self, model, key, tensor):
    method device (line 222) | def device(self):
    method to (line 228) | def to(self, *args, **kwargs):
  class ModelPatcher (line 232) | class ModelPatcher:
    method __init__ (line 233) | def __init__(self, model, load_device, offload_device, size=0, weight_...
    method is_dynamic (line 291) | def is_dynamic(self):
    method model_size (line 294) | def model_size(self):
    method model_mmap_residency (line 300) | def model_mmap_residency(self, free=False):
    method get_ram_usage (line 303) | def get_ram_usage(self):
    method loaded_size (line 306) | def loaded_size(self):
    method lowvram_patch_counter (line 309) | def lowvram_patch_counter(self):
    method get_free_memory (line 312) | def get_free_memory(self, device):
    method get_clone_model_override (line 321) | def get_clone_model_override(self):
    method clone (line 324) | def clone(self, disable_dynamic=False, model_override=None):
    method is_clone (line 395) | def is_clone(self, other):
    method clone_has_same_weights (line 400) | def clone_has_same_weights(self, clone: 'ModelPatcher'):
    method memory_required (line 432) | def memory_required(self, input_shape):
    method disable_model_cfg1_optimization (line 435) | def disable_model_cfg1_optimization(self):
    method set_model_sampler_cfg_function (line 438) | def set_model_sampler_cfg_function(self, sampler_cfg_function, disable...
    method set_model_sampler_post_cfg_function (line 446) | def set_model_sampler_post_cfg_function(self, post_cfg_function, disab...
    method set_model_sampler_pre_cfg_function (line 449) | def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable...
    method set_model_sampler_calc_cond_batch_function (line 452) | def set_model_sampler_calc_cond_batch_function(self, sampler_calc_cond...
    method set_model_unet_function_wrapper (line 455) | def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetW...
    method set_model_denoise_mask_function (line 458) | def set_model_denoise_mask_function(self, denoise_mask_function):
    method set_model_patch (line 461) | def set_model_patch(self, patch, name):
    method set_model_patch_replace (line 467) | def set_model_patch_replace(self, patch, name, block_name, number, tra...
    method set_model_attn1_patch (line 470) | def set_model_attn1_patch(self, patch):
    method set_model_attn2_patch (line 473) | def set_model_attn2_patch(self, patch):
    method set_model_attn1_replace (line 476) | def set_model_attn1_replace(self, patch, block_name, number, transform...
    method set_model_attn2_replace (line 479) | def set_model_attn2_replace(self, patch, block_name, number, transform...
    method set_model_attn1_output_patch (line 482) | def set_model_attn1_output_patch(self, patch):
    method set_model_attn2_output_patch (line 485) | def set_model_attn2_output_patch(self, patch):
    method set_model_input_block_patch (line 488) | def set_model_input_block_patch(self, patch):
    method set_model_input_block_patch_after_skip (line 491) | def set_model_input_block_patch_after_skip(self, patch):
    method set_model_output_block_patch (line 494) | def set_model_output_block_patch(self, patch):
    method set_model_emb_patch (line 497) | def set_model_emb_patch(self, patch):
    method set_model_forward_timestep_embed_patch (line 500) | def set_model_forward_timestep_embed_patch(self, patch):
    method set_model_double_block_patch (line 503) | def set_model_double_block_patch(self, patch):
    method set_model_post_input_patch (line 506) | def set_model_post_input_patch(self, patch):
    method set_model_noise_refiner_patch (line 509) | def set_model_noise_refiner_patch(self, patch):
    method set_model_rope_options (line 512) | def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, s...
    method add_object_patch (line 525) | def add_object_patch(self, name, obj):
    method set_model_compute_dtype (line 528) | def set_model_compute_dtype(self, dtype):
    method add_weight_wrapper (line 534) | def add_weight_wrapper(self, name, function):
    method get_model_object (line 538) | def get_model_object(self, name: str) -> torch.nn.Module:
    method model_patches_to (line 560) | def model_patches_to(self, device):
    method model_patches_models (line 581) | def model_patches_models(self):
    method model_patches_call_function (line 605) | def model_patches_call_function(self, function_name="cleanup", argumen...
    method model_dtype (line 626) | def model_dtype(self):
    method add_patches (line 630) | def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
    method get_key_patches (line 654) | def get_key_patches(self, filter_prefix=None):
    method model_state_dict (line 677) | def model_state_dict(self, filter_prefix=None):
    method patch_weight_to_device (line 687) | def patch_weight_to_device(self, key, device_to=None, inplace_update=F...
    method pin_weight_to_device (line 717) | def pin_weight_to_device(self, key):
    method unpin_weight (line 722) | def unpin_weight(self, key):
    method unpin_all_weights (line 728) | def unpin_all_weights(self):
    method _load_list (line 732) | def _load_list(self, for_dynamic=False, default_device=None):
    method load (line 769) | def load(self, device_to=None, lowvram_model_memory=0, force_patch_wei...
    method patch_model (line 898) | def patch_model(self, device_to=None, lowvram_model_memory=0, load_wei...
    method unpatch_model (line 915) | def unpatch_model(self, device_to=None, unpatch_weights=True):
    method partially_unload (line 956) | def partially_unload(self, device_to, memory_to_free=0, force_patch_we...
    method partially_load (line 1041) | def partially_load(self, device_to, extra_memory=0, force_patch_weight...
    method pinned_memory_size (line 1069) | def pinned_memory_size(self):
    method partially_unload_ram (line 1073) | def partially_unload_ram(self, ram_to_unload):
    method detach (line 1076) | def detach(self, unpatch_all=True):
    method current_loaded_device (line 1085) | def current_loaded_device(self):
    method calculate_weight (line 1088) | def calculate_weight(self, patches, weight, key, intermediate_dtype=to...
    method cleanup (line 1092) | def cleanup(self):
    method add_callback (line 1100) | def add_callback(self, call_type: str, callback: Callable):
    method add_callback_with_key (line 1103) | def add_callback_with_key(self, call_type: str, key: str, callback: Ca...
    method remove_callbacks_with_key (line 1107) | def remove_callbacks_with_key(self, call_type: str, key: str):
    method get_callbacks (line 1112) | def get_callbacks(self, call_type: str, key: str):
    method get_all_callbacks (line 1115) | def get_all_callbacks(self, call_type: str):
    method add_wrapper (line 1121) | def add_wrapper(self, wrapper_type: str, wrapper: Callable):
    method add_wrapper_with_key (line 1124) | def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: C...
    method remove_wrappers_with_key (line 1128) | def remove_wrappers_with_key(self, wrapper_type: str, key: str):
    method get_wrappers (line 1133) | def get_wrappers(self, wrapper_type: str, key: str):
    method get_all_wrappers (line 1136) | def get_all_wrappers(self, wrapper_type: str):
    method set_attachments (line 1142) | def set_attachments(self, key: str, attachment):
    method remove_attachments (line 1145) | def remove_attachments(self, key: str):
    method get_attachment (line 1149) | def get_attachment(self, key: str):
    method set_injections (line 1152) | def set_injections(self, key: str, injections: list[PatcherInjection]):
    method remove_injections (line 1155) | def remove_injections(self, key: str):
    method get_injections (line 1159) | def get_injections(self, key: str):
    method set_additional_models (line 1162) | def set_additional_models(self, key: str, models: list['ModelPatcher']):
    method remove_additional_models (line 1165) | def remove_additional_models(self, key: str):
    method get_additional_models_with_key (line 1169) | def get_additional_models_with_key(self, key: str):
    method get_additional_models (line 1172) | def get_additional_models(self):
    method get_nested_additional_models (line 1178) | def get_nested_additional_models(self):
    method use_ejected (line 1197) | def use_ejected(self, skip_and_inject_on_exit_only=False):
    method inject_model (line 1200) | def inject_model(self):
    method eject_model (line 1211) | def eject_model(self):
    method pre_run (line 1221) | def pre_run(self):
    method prepare_state (line 1227) | def prepare_state(self, timestep):
    method restore_hook_patches (line 1231) | def restore_hook_patches(self):
    method set_hook_mode (line 1236) | def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode):
    method prepare_hook_patches_current_keyframe (line 1239) | def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_...
    method register_all_hook_patches (line 1260) | def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, targ...
    method add_hook_patches (line 1281) | def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, stre...
    method get_combined_hook_patches (line 1308) | def get_combined_hook_patches(self, hooks: comfy.hooks.HookGroup):
    method apply_hooks (line 1327) | def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_option...
    method patch_hooks (line 1336) | def patch_hooks(self, hooks: comfy.hooks.HookGroup):
    method patch_cached_hook_weights (line 1372) | def patch_cached_hook_weights(self, cached_weights: dict, key: str, me...
    method clear_cached_hook_weights (line 1383) | def clear_cached_hook_weights(self):
    method patch_hook_weight_to_device (line 1387) | def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, co...
    method unpatch_hooks (line 1426) | def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:
    method clean_hooks (line 1444) | def clean_hooks(self):
    method state_dict_for_saving (line 1448) | def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=N...
    method __del__ (line 1465) | def __del__(self):
  class ModelPatcherDynamic (line 1469) | class ModelPatcherDynamic(ModelPatcher):
    method __new__ (line 1471) | def __new__(cls, model=None, load_device=None, offload_device=None, si...
    method __init__ (line 1477) | def __init__(self, model, load_device, offload_device, size=0, weight_...
    method is_dynamic (line 1484) | def is_dynamic(self):
    method _vbar_get (line 1487) | def _vbar_get(self, create=False):
    method loaded_size (line 1499) | def loaded_size(self):
    method pin_weight_to_device (line 1505) | def pin_weight_to_device(self, key):
    method unpin_weight (line 1508) | def unpin_weight(self, key):
    method unpin_all_weights (line 1511) | def unpin_all_weights(self):
    method memory_required (line 1514) | def memory_required(self, input_shape):
    method load (line 1521) | def load(self, device_to=None, lowvram_model_memory=0, force_patch_wei...
    method partially_unload (line 1645) | def partially_unload(self, device_to, memory_to_free=0, force_patch_we...
    method pinned_memory_size (line 1663) | def pinned_memory_size(self):
    method partially_unload_ram (line 1673) | def partially_unload_ram(self, ram_to_unload):
    method patch_model (line 1681) | def patch_model(self, device_to=None, lowvram_model_memory=0, load_wei...
    method unpatch_model (line 1689) | def unpatch_model(self, device_to=None, unpatch_weights=True):
    method partially_load (line 1698) | def partially_load(self, device_to, extra_memory=0, force_patch_weight...
    method patch_cached_hook_weights (line 1717) | def patch_cached_hook_weights(self, cached_weights: dict, key: str, me...
    method patch_hook_weight_to_device (line 1720) | def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, co...
    method unpatch_hooks (line 1726) | def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:
    method get_non_dynamic_delegate (line 1729) | def get_non_dynamic_delegate(self):

FILE: comfy/model_sampling.py
  function rescale_zero_terminal_snr_sigmas (line 5) | def rescale_zero_terminal_snr_sigmas(sigmas):
  function reshape_sigma (line 24) | def reshape_sigma(sigma, noise_dim):
  class EPS (line 30) | class EPS:
    method calculate_input (line 31) | def calculate_input(self, sigma, noise):
    method calculate_denoised (line 35) | def calculate_denoised(self, sigma, model_output, model_input):
    method noise_scaling (line 39) | def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
    method inverse_noise_scaling (line 49) | def inverse_noise_scaling(self, sigma, latent):
  class V_PREDICTION (line 52) | class V_PREDICTION(EPS):
    method calculate_denoised (line 53) | def calculate_denoised(self, sigma, model_output, model_input):
  class EDM (line 57) | class EDM(V_PREDICTION):
    method calculate_denoised (line 58) | def calculate_denoised(self, sigma, model_output, model_input):
  class CONST (line 62) | class CONST:
    method calculate_input (line 63) | def calculate_input(self, sigma, noise):
    method calculate_denoised (line 66) | def calculate_denoised(self, sigma, model_output, model_input):
    method noise_scaling (line 70) | def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
    method inverse_noise_scaling (line 74) | def inverse_noise_scaling(self, sigma, latent):
  class X0 (line 78) | class X0(EPS):
    method calculate_denoised (line 79) | def calculate_denoised(self, sigma, model_output, model_input):
  class IMG_TO_IMG (line 82) | class IMG_TO_IMG(X0):
    method calculate_input (line 83) | def calculate_input(self, sigma, noise):
  class IMG_TO_IMG_FLOW (line 86) | class IMG_TO_IMG_FLOW(CONST):
    method calculate_denoised (line 87) | def calculate_denoised(self, sigma, model_output, model_input):
    method noise_scaling (line 90) | def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
    method inverse_noise_scaling (line 93) | def inverse_noise_scaling(self, sigma, latent):
  class COSMOS_RFLOW (line 96) | class COSMOS_RFLOW:
    method calculate_input (line 97) | def calculate_input(self, sigma, noise):
    method calculate_denoised (line 102) | def calculate_denoised(self, sigma, model_output, model_input):
    method noise_scaling (line 107) | def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
    method inverse_noise_scaling (line 113) | def inverse_noise_scaling(self, sigma, latent):
  class ModelSamplingDiscrete (line 116) | class ModelSamplingDiscrete(torch.nn.Module):
    method __init__ (line 117) | def __init__(self, model_config=None, zsnr=None):
    method _register_schedule (line 136) | def _register_schedule(self, given_betas=None, beta_schedule="linear",...
    method set_sigmas (line 161) | def set_sigmas(self, sigmas):
    method sigma_min (line 166) | def sigma_min(self):
    method sigma_max (line 170) | def sigma_max(self):
    method timestep (line 173) | def timestep(self, sigma):
    method sigma (line 178) | def sigma(self, timestep):
    method percent_to_sigma (line 186) | def percent_to_sigma(self, percent):
  class ModelSamplingDiscreteEDM (line 194) | class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
    method timestep (line 195) | def timestep(self, sigma):
    method sigma (line 198) | def sigma(self, timestep):
  class ModelSamplingContinuousEDM (line 201) | class ModelSamplingContinuousEDM(torch.nn.Module):
    method __init__ (line 202) | def __init__(self, model_config=None):
    method set_parameters (line 214) | def set_parameters(self, sigma_min, sigma_max, sigma_data):
    method sigma_min (line 222) | def sigma_min(self):
    method sigma_max (line 226) | def sigma_max(self):
    method timestep (line 229) | def timestep(self, sigma):
    method sigma (line 232) | def sigma(self, timestep):
    method percent_to_sigma (line 235) | def percent_to_sigma(self, percent):
  class ModelSamplingContinuousV (line 246) | class ModelSamplingContinuousV(ModelSamplingContinuousEDM):
    method timestep (line 247) | def timestep(self, sigma):
    method sigma (line 250) | def sigma(self, timestep):
  function time_snr_shift (line 254) | def time_snr_shift(alpha, t):
  class ModelSamplingDiscreteFlow (line 259) | class ModelSamplingDiscreteFlow(torch.nn.Module):
    method __init__ (line 260) | def __init__(self, model_config=None):
    method set_parameters (line 269) | def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):
    method sigma_min (line 276) | def sigma_min(self):
    method sigma_max (line 280) | def sigma_max(self):
    method timestep (line 283) | def timestep(self, sigma):
    method sigma (line 286) | def sigma(self, timestep):
    method percent_to_sigma (line 289) | def percent_to_sigma(self, percent):
  class StableCascadeSampling (line 296) | class StableCascadeSampling(ModelSamplingDiscrete):
    method __init__ (line 297) | def __init__(self, model_config=None):
    method set_parameters (line 307) | def set_parameters(self, shift=1.0, cosine_s=8e-3):
    method sigma (line 321) | def sigma(self, timestep):
    method timestep (line 333) | def timestep(self, sigma):
    method percent_to_sigma (line 340) | def percent_to_sigma(self, percent):
  function flux_time_shift (line 350) | def flux_time_shift(mu: float, sigma: float, t):
  class ModelSamplingFlux (line 353) | class ModelSamplingFlux(torch.nn.Module):
    method __init__ (line 354) | def __init__(self, model_config=None):
    method set_parameters (line 363) | def set_parameters(self, shift=1.15, timesteps=10000):
    method sigma_min (line 369) | def sigma_min(self):
    method sigma_max (line 373) | def sigma_max(self):
    method timestep (line 376) | def timestep(self, sigma):
    method sigma (line 379) | def sigma(self, timestep):
    method percent_to_sigma (line 382) | def percent_to_sigma(self, percent):
  class ModelSamplingCosmosRFlow (line 390) | class ModelSamplingCosmosRFlow(ModelSamplingContinuousEDM):
    method timestep (line 391) | def timestep(self, sigma):
    method sigma (line 394) | def sigma(self, timestep):

FILE: comfy/nested_tensor.py
  class NestedTensor (line 3) | class NestedTensor:
    method __init__ (line 4) | def __init__(self, tensors):
    method _copy (line 8) | def _copy(self):
    method apply_operation (line 11) | def apply_operation(self, other, operation):
    method __add__ (line 21) | def __add__(self, b):
    method __sub__ (line 24) | def __sub__(self, b):
    method __mul__ (line 27) | def __mul__(self, b):
    method __truediv__ (line 33) | def __truediv__(self, b):
    method __getitem__ (line 36) | def __getitem__(self, *args, **kwargs):
    method unbind (line 39) | def unbind(self):
    method to (line 42) | def to(self, *args, **kwargs):
    method new_ones (line 48) | def new_ones(self, *args, **kwargs):
    method float (line 51) | def float(self):
    method chunk (line 54) | def chunk(self, *args, **kwargs):
    method size (line 57) | def size(self):
    method shape (line 61) | def shape(self):
    method ndim (line 65) | def ndim(self):
    method device (line 72) | def device(self):
    method dtype (line 76) | def dtype(self):
    method layout (line 80) | def layout(self):
  function cat_nested (line 84) | def cat_nested(tensors, *args, **kwargs):

FILE: comfy/ops.py
  function run_every_op (line 32) | def run_every_op():
  function scaled_dot_product_attention (line 38) | def scaled_dot_product_attention(q, k, v, *args, **kwargs):
  function scaled_dot_product_attention (line 55) | def scaled_dot_product_attention(q, k, v, *args, **kwargs):
  function cast_to_input (line 78) | def cast_to_input(weight, input, non_blocking=False, copy=True):
  function cast_bias_weight_with_vbar (line 82) | def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blockin...
  function cast_bias_weight (line 210) | def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=...
  function uncast_bias_weight (line 282) | def uncast_bias_weight(s, weight, bias, offload_stream):
  class CastWeightBiasOp (line 303) | class CastWeightBiasOp:
  class disable_weight_init (line 308) | class disable_weight_init:
    method _lazy_load_from_state_dict (line 310) | def _lazy_load_from_state_dict(module, state_dict, prefix, local_metad...
    class Linear (line 336) | class Linear(torch.nn.Linear, CastWeightBiasOp):
      method __init__ (line 338) | def __init__(self, in_features, out_features, bias=True, device=None...
      method _load_from_state_dict (line 360) | def _load_from_state_dict(self, state_dict, prefix, local_metadata,
      method reset_parameters (line 380) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 383) | def forward_comfy_cast_weights(self, input):
      method forward (line 389) | def forward(self, *args, **kwargs):
    class Conv1d (line 396) | class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
      method reset_parameters (line 397) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 400) | def forward_comfy_cast_weights(self, input):
      method forward (line 406) | def forward(self, *args, **kwargs):
    class Conv2d (line 413) | class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
      method reset_parameters (line 414) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 417) | def forward_comfy_cast_weights(self, input):
      method forward (line 423) | def forward(self, *args, **kwargs):
    class Conv3d (line 430) | class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
      method reset_parameters (line 431) | def reset_parameters(self):
      method _conv_forward (line 434) | def _conv_forward(self, input, weight, bias, autopad=None, *args, **...
      method forward_comfy_cast_weights (line 445) | def forward_comfy_cast_weights(self, input, autopad=None):
      method forward (line 451) | def forward(self, *args, **kwargs):
    class GroupNorm (line 458) | class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
      method reset_parameters (line 459) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 462) | def forward_comfy_cast_weights(self, input):
      method forward (line 468) | def forward(self, *args, **kwargs):
    class LayerNorm (line 475) | class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
      method reset_parameters (line 476) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 479) | def forward_comfy_cast_weights(self, input):
      method forward (line 490) | def forward(self, *args, **kwargs):
    class RMSNorm (line 497) | class RMSNorm(torch.nn.RMSNorm, CastWeightBiasOp):
      method reset_parameters (line 498) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 502) | def forward_comfy_cast_weights(self, input):
      method forward (line 513) | def forward(self, *args, **kwargs):
    class ConvTranspose2d (line 520) | class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
      method reset_parameters (line 521) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 524) | def forward_comfy_cast_weights(self, input, output_size=None):
      method forward (line 537) | def forward(self, *args, **kwargs):
    class ConvTranspose1d (line 544) | class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
      method reset_parameters (line 545) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 548) | def forward_comfy_cast_weights(self, input, output_size=None):
      method forward (line 561) | def forward(self, *args, **kwargs):
    class Embedding (line 568) | class Embedding(torch.nn.Embedding, CastWeightBiasOp):
      method __init__ (line 569) | def __init__(self, num_embeddings, embedding_dim, padding_idx=None, ...
      method _load_from_state_dict (line 598) | def _load_from_state_dict(self, state_dict, prefix, local_metadata,
      method reset_parameters (line 616) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 620) | def forward_comfy_cast_weights(self, input, out_dtype=None):
      method forward (line 630) | def forward(self, *args, **kwargs):
    method conv_nd (line 640) | def conv_nd(s, dims, *args, **kwargs):
  class manual_cast (line 649) | class manual_cast(disable_weight_init):
    class Linear (line 650) | class Linear(disable_weight_init.Linear):
    class Conv1d (line 653) | class Conv1d(disable_weight_init.Conv1d):
    class Conv2d (line 656) | class Conv2d(disable_weight_init.Conv2d):
    class Conv3d (line 659) | class Conv3d(disable_weight_init.Conv3d):
    class GroupNorm (line 662) | class GroupNorm(disable_weight_init.GroupNorm):
    class LayerNorm (line 665) | class LayerNorm(disable_weight_init.LayerNorm):
    class ConvTranspose2d (line 668) | class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
    class ConvTranspose1d (line 671) | class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
    class RMSNorm (line 674) | class RMSNorm(disable_weight_init.RMSNorm):
    class Embedding (line 677) | class Embedding(disable_weight_init.Embedding):
  function fp8_linear (line 681) | def fp8_linear(self, input):
  class fp8_ops (line 721) | class fp8_ops(manual_cast):
    class Linear (line 722) | class Linear(manual_cast.Linear):
      method reset_parameters (line 723) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 728) | def forward_comfy_cast_weights(self, input):
  class cublas_ops (line 750) | class cublas_ops(manual_cast):
    class Linear (line 751) | class Linear(CublasLinear, manual_cast.Linear):
      method reset_parameters (line 752) | def reset_parameters(self):
      method forward_comfy_cast_weights (line 755) | def forward_comfy_cast_weights(self, input):
      method forward (line 761) | def forward(self, *args, **kwargs):
  class QuantLinearFunc (line 779) | class QuantLinearFunc(torch.autograd.Function):
    method forward (line 785) | def forward(ctx, input_float, weight, bias, layout_type, input_scale, ...
    method backward (line 814) | def backward(ctx, grad_output):
  function mixed_precision_ops (line 844) | def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, f...
  function pick_operations (line 1127) | def pick_operations(weight_dtype, compute_dtype, load_device=None, disab...

FILE: comfy/options.py
  function enable_args_parsing (line 4) | def enable_args_parsing(enable=True):

FILE: comfy/patcher_extension.py
  class CallbacksMP (line 4) | class CallbacksMP:
    method init_callbacks (line 19) | def init_callbacks(cls) -> dict[str, dict[str, list[Callable]]]:
  function add_callback (line 22) | def add_callback(call_type: str, callback: Callable, transformer_options...
  function add_callback_with_key (line 25) | def add_callback_with_key(call_type: str, key: str, callback: Callable, ...
  function get_callbacks_with_key (line 32) | def get_callbacks_with_key(call_type: str, key: str, transformer_options...
  function get_all_callbacks (line 40) | def get_all_callbacks(call_type: str, transformer_options: dict, is_mode...
  class WrappersMP (line 49) | class WrappersMP:
    method init_wrappers (line 61) | def init_wrappers(cls) -> dict[str, dict[str, list[Callable]]]:
  function add_wrapper (line 64) | def add_wrapper(wrapper_type: str, wrapper: Callable, transformer_option...
  function add_wrapper_with_key (line 67) | def add_wrapper_with_key(wrapper_type: str, key: str, wrapper: Callable,...
  function get_wrappers_with_key (line 74) | def get_wrappers_with_key(wrapper_type: str, key: str, transformer_optio...
  function get_all_wrappers (line 82) | def get_all_wrappers(wrapper_type: str, transformer_options: dict, is_mo...
  class WrapperExecutor (line 91) | class WrapperExecutor:
    method __init__ (line 93) | def __init__(self, original: Callable, class_obj: object, wrappers: li...
    method __call__ (line 102) | def __call__(self, *args, **kwargs):
    method execute (line 107) | def execute(self, *args, **kwargs):
    method _create_next_executor (line 115) | def _create_next_executor(self) -> 'WrapperExecutor':
    method new_executor (line 124) | def new_executor(cls, original: Callable, wrappers: list[Callable], id...
    method new_class_executor (line 128) | def new_class_executor(cls, original: Callable, class_obj: object, wra...
  class PatcherInjection (line 131) | class PatcherInjection:
    method __init__ (line 132) | def __init__(self, inject: Callable, eject: Callable):
  function copy_nested_dicts (line 136) | def copy_nested_dicts(input_dict: dict):
  function merge_nested_dicts (line 145) | def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True):

FILE: comfy/pinned_memory.py
  function get_pin (line 8) | def get_pin(module):
  function pin_memory (line 11) | def pin_memory(module):
  function unpin_memory (line 32) | def unpin_memory(module):

FILE: comfy/pixel_space_convert.py
  class PixelspaceConversionVAE (line 6) | class PixelspaceConversionVAE(torch.nn.Module):
    method __init__ (line 7) | def __init__(self):
    method encode (line 11) | def encode(self, pixels: torch.Tensor, *_args, **_kwargs) -> torch.Ten...
    method decode (line 14) | def decode(self, samples: torch.Tensor, *_args, **_kwargs) -> torch.Te...

FILE: comfy/quant_ops.py
  class QuantizedTensor (line 31) | class QuantizedTensor:
  class _CKFp8Layout (line 34) | class _CKFp8Layout:
  class _CKNvfp4Layout (line 37) | class _CKNvfp4Layout:
  function register_layout_class (line 40) | def register_layout_class(name, cls):
  function get_layout_class (line 43) | def get_layout_class(name):
  class _CKMxfp8Layout (line 55) | class _CKMxfp8Layout:
  class _TensorCoreFP8LayoutBase (line 64) | class _TensorCoreFP8LayoutBase(_CKFp8Layout):
    method quantize (line 68) | def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_o...
  class TensorCoreMXFP8Layout (line 99) | class TensorCoreMXFP8Layout(_CKMxfp8Layout):
    method quantize (line 101) | def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_o...
  class TensorCoreNVFP4Layout (line 124) | class TensorCoreNVFP4Layout(_CKNvfp4Layout):
    method quantize (line 126) | def quantize(cls, tensor, scale=None, stochastic_rounding=0, inplace_o...
  class TensorCoreFP8E4M3Layout (line 157) | class TensorCoreFP8E4M3Layout(_TensorCoreFP8LayoutBase):
  class TensorCoreFP8E5M2Layout (line 161) | class TensorCoreFP8E5M2Layout(_TensorCoreFP8LayoutBase):

FILE: comfy/rmsnorm.py
  function rms_norm (line 6) | def rms_norm(x, weight=None, eps=1e-6):

FILE: comfy/sample.py
  function prepare_noise_inner (line 9) | def prepare_noise_inner(latent_image, generator, noise_inds=None):
  function prepare_noise (line 22) | def prepare_noise(latent_image, seed, noise_inds=None):
  function fix_empty_latent_channels (line 40) | def fix_empty_latent_channels(model, latent_image, downscale_ratio_spaci...
  function prepare_sampling (line 56) | def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
  function cleanup_additional_models (line 60) | def cleanup_additional_models(models):
  function sample (line 63) | def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, ...
  function sample_custom (line 70) | def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative...

FILE: comfy/sampler_helpers.py
  function prepare_mask (line 16) | def prepare_mask(noise_mask, shape, device):
  function get_models_from_cond (line 19) | def get_models_from_cond(cond, model_type):
  function get_hooks_from_cond (line 29) | def get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup):
  function convert_cond (line 57) | def convert_cond(cond):
  function cond_has_hooks (line 69) | def cond_has_hooks(cond):
  function get_additional_models (line 81) | def get_additional_models(conds, dtype):
  function get_additional_models_from_model_options (line 105) | def get_additional_models_from_model_options(model_options: dict[str]=No...
  function cleanup_additional_models (line 115) | def cleanup_additional_models(models):
  function estimate_memory (line 121) | def estimate_memory(model, noise_shape, conds):
  function prepare_sampling (line 137) | def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_opti...
  function _prepare_sampling (line 144) | def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_opt...
  function cleanup_models (line 161) | def cleanup_models(conds, models):
  function prepare_model_patcher (line 170) | def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict):

FILE: comfy/samplers.py
  function add_area_dims (line 24) | def add_area_dims(area, num_dims):
  function get_area_and_mult (line 29) | def get_area_and_mult(conds, x_in, timestep_in):
  function cond_equal_size (line 114) | def cond_equal_size(c1, c2):
  function can_concat_cond (line 124) | def can_concat_cond(c1, c2):
  function cond_cat (line 144) | def cond_cat(c_list):
  function finalize_default_conds (line 159) | def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy...
  function calc_cond_batch (line 202) | def calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: tor...
  function _calc_cond_batch_outer (line 208) | def _calc_cond_batch_outer(model: BaseModel, conds: list[list[dict]], x_...
  function _calc_cond_batch (line 215) | def _calc_cond_batch(model: BaseModel, conds: list[list[dict]], x_in: to...
  function calc_cond_uncond_batch (line 348) | def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_op...
  function cfg_function (line 352) | def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep,...
  function sampling_function (line 369) | def sampling_function(model, x, timestep, uncond, cond, cond_scale, mode...
  class KSamplerX0Inpaint (line 390) | class KSamplerX0Inpaint:
    method __init__ (line 391) | def __init__(self, model, sigmas):
    method __call__ (line 394) | def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
  function simple_scheduler (line 405) | def simple_scheduler(model_sampling, steps):
  function ddim_scheduler (line 414) | def ddim_scheduler(model_sampling, steps):
  function normal_scheduler (line 431) | def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
  function beta_scheduler (line 456) | def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
  function linear_quadratic_schedule (line 471) | def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.0...
  function kl_optimal_scheduler (line 492) | def kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> ...
  function get_mask_aabb (line 498) | def get_mask_aabb(masks):
  function resolve_areas_and_cond_masks_multidim (line 521) | def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
  function resolve_areas_and_cond_masks (line 571) | def resolve_areas_and_cond_masks(conditions, h, w, device):
  function create_cond_with_same_area_if_none (line 575) | def create_cond_with_same_area_if_none(conds, c):
  function calculate_start_end_timesteps (line 619) | def calculate_start_end_timesteps(model, conds):
  function pre_run_control (line 644) | def pre_run_control(model, conds):
  function apply_empty_x_to_equal_area (line 653) | def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
  function encode_model_conds (line 688) | def encode_model_conds(model_function, conds, noise, device, prompt_type...
  class Sampler (line 713) | class Sampler:
    method sample (line 714) | def sample(self):
    method max_denoise (line 717) | def max_denoise(self, model_wrap, sigmas):
  class KSAMPLER (line 728) | class KSAMPLER(Sampler):
    method __init__ (line 729) | def __init__(self, sampler_function, extra_options={}, inpaint_options...
    method sample (line 734) | def sample(self, model_wrap, sigmas, extra_args, callback, noise, late...
  function ksampler (line 756) | def ksampler(sampler_name, extra_options={}, inpaint_options={}):
  function process_conds (line 784) | def process_conds(model, noise, conds, device, latent_image=None, denois...
  function preprocess_conds_hooks (line 822) | def preprocess_conds_hooks(conds: dict[str, list[dict[str]]]):
  function filter_registered_hooks_on_conds (line 849) | def filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], ...
  function get_total_hook_groups_in_conds (line 877) | def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]):
  function cast_to_load_options (line 885) | def cast_to_load_options(model_options: dict[str], device=None, dtype=No...
  class CFGGuider (line 934) | class CFGGuider:
    method __init__ (line 935) | def __init__(self, model_patcher: ModelPatcher):
    method set_conds (line 941) | def set_conds(self, positive, negative):
    method set_cfg (line 944) | def set_cfg(self, cfg):
    method inner_set_conds (line 947) | def inner_set_conds(self, conds):
    method __call__ (line 953) | def __call__(self, *args, **kwargs):
    method outer_predict_noise (line 956) | def outer_predict_noise(self, x, timestep, model_options={}, seed=None):
    method predict_noise (line 963) | def predict_noise(self, x, timestep, model_options={}, seed=None):
    method inner_sample (line 966) | def inner_sample(self, noise, latent_image, device, sampler, sigmas, d...
    method outer_sample (line 984) | def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_m...
    method sample (line 1004) | def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=No...
  function sample (line 1065) | def sample(model, noise, positive, negative, cfg, device, sampler, sigma...
  class SchedulerHandler (line 1074) | class SchedulerHandler(NamedTuple):
  function calculate_sigmas (line 1094) | def calculate_sigmas(model_sampling: object, scheduler_name: str, steps:...
  function sampler_object (line 1104) | def sampler_object(name):
  class KSampler (line 1115) | class KSampler:
    method __init__ (line 1120) | def __init__(self, model, steps, device, sampler=None, scheduler=None,...
    method calculate_sigmas (line 1133) | def calculate_sigmas(self, steps):
    method set_steps (line 1147) | def set_steps(self, steps, denoise=None):
    method sample (line 1159) | def sample(self, noise, positive, negative, cfg, latent_image=None, st...

FILE: comfy/sd.py
  function load_lora_for_models (line 76) | def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
  function load_bypass_lora_for_models (line 107) | def load_bypass_lora_for_models(model, clip, lora, strength_model, stren...
  class CLIP (line 206) | class CLIP:
    method __init__ (line 207) | def __init__(self, target=None, embedding_directory=None, no_init=Fals...
    method clone (line 271) | def clone(self, disable_dynamic=False):
    method get_ram_usage (line 282) | def get_ram_usage(self):
    method add_patches (line 285) | def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
    method set_tokenizer_option (line 288) | def set_tokenizer_option(self, option_name, value):
    method clip_layer (line 291) | def clip_layer(self, layer_idx):
    method tokenize (line 294) | def tokenize(self, text, return_word_ids=False, **kwargs):
    method add_hooks_to_dict (line 302) | def add_hooks_to_dict(self, pooled_dict: dict[str]):
    method encode_from_tokens_scheduled (line 307) | def encode_from_tokens_scheduled(self, tokens, unprojected=False, add_...
    method encode_from_tokens (line 366) | def encode_from_tokens(self, tokens, return_pooled=False, return_dict=...
    method encode (line 391) | def encode(self, text):
    method load_sd (line 395) | def load_sd(self, sd, full_model=False):
    method get_sd (line 411) | def get_sd(self):
    method load_model (line 418) | def load_model(self, tokens={}):
    method get_key_patches (line 425) | def get_key_patches(self):
    method generate (line 428) | def generate(self, tokens, do_sample=True, max_length=256, temperature...
    method decode (line 436) | def decode(self, token_ids, skip_special_tokens=True):
  class VAE (line 439) | class VAE:
    method __init__ (line 440) | def __init__(self, sd=None, device=None, config=None, dtype=None, meta...
    method model_size (line 836) | def model_size(self):
    method get_ram_usage (line 842) | def get_ram_usage(self):
    method throw_exception_if_invalid (line 845) | def throw_exception_if_invalid(self):
    method vae_encode_crop_pixels (line 849) | def vae_encode_crop_pixels(self, pixels):
    method vae_output_dtype (line 874) | def vae_output_dtype(self):
    method decode_tiled_ (line 877) | def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
    method decode_tiled_1d (line 891) | def decode_tiled_1d(self, samples, tile_x=256, overlap=32):
    method decode_tiled_3d (line 901) | def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, o...
    method encode_tiled_ (line 905) | def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap...
    method encode_tiled_1d (line 918) | def encode_tiled_1d(self, samples, tile_x=256 * 2048, overlap=64 * 2048):
    method encode_tiled_3d (line 937) | def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512...
    method decode (line 941) | def decode(self, samples_in, vae_options={}):
    method decode_tiled (line 995) | def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None...
    method encode (line 1024) | def encode(self, pixel_samples):
    method encode_tiled (line 1075) | def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overla...
    method get_sd (line 1120) | def get_sd(self):
    method spacial_compression_decode (line 1123) | def spacial_compression_decode(self):
    method spacial_compression_encode (line 1129) | def spacial_compression_encode(self):
    method temporal_compression_decode (line 1135) | def temporal_compression_decode(self):
  class StyleModel (line 1142) | class StyleModel:
    method __init__ (line 1143) | def __init__(self, model, device="cpu"):
    method get_cond (line 1146) | def get_cond(self, input):
  function load_style_model (line 1150) | def load_style_model(ckpt_path):
  class CLIPType (line 1162) | class CLIPType(Enum):
  function load_clip_model_patcher (line 1192) | def load_clip_model_patcher(ckpt_paths, embedding_directory=None, clip_t...
  function load_clip (line 1196) | def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.S...
  class TEModel (line 1208) | class TEModel(Enum):
  function detect_te_model (line 1233) | def detect_te_model(sd):
  function t5xxl_detect (line 1291) | def t5xxl_detect(clip_data):
  function llama_detect (line 1301) | def llama_detect(clip_data):
  function load_text_encoder_state_dicts (line 1310) | def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=No...
  function load_gligen (line 1533) | def load_gligen(ckpt_path):
  function model_detection_error_hint (line 1540) | def model_detection_error_hint(path, state_dict):
  function load_checkpoint (line 1546) | def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, o...
  function load_checkpoint_guess_config (line 1570) | def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip...
  function load_checkpoint_guess_config_model_only (line 1581) | def load_checkpoint_guess_config_model_only(ckpt_path, embedding_directo...
  function load_checkpoint_guess_config_clip_only (line 1589) | def load_checkpoint_guess_config_clip_only(ckpt_path, embedding_director...
  function load_state_dict_guess_config (line 1597) | def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, ...
  function load_diffusion_model_state_dict (line 1698) | def load_diffusion_model_state_dict(sd, model_options={}, metadata=None,...
  function load_diffusion_model (line 1792) | def load_diffusion_model(unet_path, model_options={}, disable_dynamic=Fa...
  function load_unet (line 1801) | def load_unet(unet_path, dtype=None):
  function load_unet_state_dict (line 1805) | def load_unet_state_dict(sd, dtype=None):
  function save_checkpoint (line 1809) | def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision...

FILE: comfy/sd1_clip.py
  function gen_empty_tokens (line 15) | def gen_empty_tokens(special_tokens, length):
  class ClipTokenWeightEncoder (line 27) | class ClipTokenWeightEncoder:
    method encode_token_weights (line 28) | def encode_token_weights(self, token_weight_pairs):
  class SDClipModel (line 81) | class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
    method __init__ (line 88) | def __init__(self, device="cpu", max_length=77,
    method freeze (line 146) | def freeze(self):
    method set_clip_options (line 152) | def set_clip_options(self, options):
    method reset_clip_options (line 166) | def reset_clip_options(self):
    method process_tokens (line 172) | def process_tokens(self, tokens, device):
    method forward (line 260) | def forward(self, tokens):
    method encode (line 305) | def encode(self, tokens):
    method load_sd (line 308) | def load_sd(self, sd):
    method generate (line 311) | def generate(self, tokens, do_sample, max_length, temperature, top_k, ...
  function parse_parentheses (line 320) | def parse_parentheses(string):
  function token_weights (line 348) | def token_weights(string, current_weight):
  function escape_important (line 368) | def escape_important(text):
  function unescape_important (line 373) | def unescape_important(text):
  function safe_load_embed_zip (line 378) | def safe_load_embed_zip(embed_path):
  function expand_directory_list (line 397) | def expand_directory_list(directories):
  function bundled_embed (line 405) | def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format
  function load_embed (line 415) | def load_embed(embedding_name, embedding_directory, embedding_size, embe...
  class SDTokenizer (line 486) | class SDTokenizer:
    method __init__ (line 487) | def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=Tr...
    method _try_get_embedding (line 543) | def _try_get_embedding(self, embedding_name:str):
    method pad_tokens (line 559) | def pad_tokens(self, tokens, amount):
    method tokenize_with_weights (line 566) | def tokenize_with_weights(self, text:str, return_word_ids=False, token...
    method untokenize (line 671) | def untokenize(self, token_weight_pair):
    method state_dict (line 674) | def state_dict(self):
    method decode (line 677) | def decode(self, token_ids, skip_special_tokens=True):
  class SD1Tokenizer (line 680) | class SD1Tokenizer:
    method __init__ (line 681) | def __init__(self, embedding_directory=None, tokenizer_data={}, clip_n...
    method tokenize_with_weights (line 692) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 697) | def untokenize(self, token_weight_pair):
    method state_dict (line 700) | def state_dict(self):
    method decode (line 703) | def decode(self, token_ids, skip_special_tokens=True):
  class SD1CheckpointClipModel (line 706) | class SD1CheckpointClipModel(SDClipModel):
    method __init__ (line 707) | def __init__(self, device="cpu", dtype=None, model_options={}):
  class SD1ClipModel (line 710) | class SD1ClipModel(torch.nn.Module):
    method __init__ (line 711) | def __init__(self, device="cpu", dtype=None, model_options={}, clip_na...
    method set_clip_options (line 729) | def set_clip_options(self, options):
    method reset_clip_options (line 732) | def reset_clip_options(self):
    method encode_token_weights (line 735) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 740) | def load_sd(self, sd):
    method generate (line 743) | def generate(self, tokens, do_sample=True, max_length=256, temperature...

FILE: comfy/sdxl_clip.py
  class SDXLClipG (line 5) | class SDXLClipG(sd1_clip.SDClipModel):
    method __init__ (line 6) | def __init__(self, device="cpu", max_length=77, freeze=True, layer="pe...
    method load_sd (line 16) | def load_sd(self, sd):
  class SDXLClipGTokenizer (line 19) | class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 20) | def __init__(self, tokenizer_path=None, embedding_directory=None, toke...
  class SDXLTokenizer (line 24) | class SDXLTokenizer:
    method __init__ (line 25) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 29) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 35) | def untokenize(self, token_weight_pair):
    method state_dict (line 38) | def state_dict(self):
  class SDXLClipModel (line 41) | class SDXLClipModel(torch.nn.Module):
    method __init__ (line 42) | def __init__(self, device="cpu", dtype=None, model_options={}):
    method set_clip_options (line 48) | def set_clip_options(self, options):
    method reset_clip_options (line 52) | def reset_clip_options(self):
    method encode_token_weights (line 56) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 64) | def load_sd(self, sd):
  class SDXLRefinerClipModel (line 70) | class SDXLRefinerClipModel(sd1_clip.SD1ClipModel):
    method __init__ (line 71) | def __init__(self, device="cpu", dtype=None, model_options={}):
  class StableCascadeClipGTokenizer (line 75) | class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 76) | def __init__(self, tokenizer_path=None, embedding_directory=None, toke...
  class StableCascadeTokenizer (line 79) | class StableCascadeTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 80) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class StableCascadeClipG (line 83) | class StableCascadeClipG(sd1_clip.SDClipModel):
    method __init__ (line 84) | def __init__(self, device="cpu", max_length=77, freeze=True, layer="hi...
    method load_sd (line 90) | def load_sd(self, sd):
  class StableCascadeClipModel (line 93) | class StableCascadeClipModel(sd1_clip.SD1ClipModel):
    method __init__ (line 94) | def __init__(self, device="cpu", dtype=None, model_options={}):

FILE: comfy/supported_models.py
  class SD15 (line 36) | class SD15(supported_models_base.BASE):
    method process_clip_state_dict (line 53) | def process_clip_state_dict(self, state_dict):
    method process_clip_state_dict_for_saving (line 70) | def process_clip_state_dict_for_saving(self, state_dict):
    method clip_target (line 79) | def clip_target(self, state_dict={}):
  class SD20 (line 82) | class SD20(supported_models_base.BASE):
    method model_type (line 100) | def model_type(self, state_dict, prefix=""):
    method process_clip_state_dict (line 108) | def process_clip_state_dict(self, state_dict):
    method process_clip_state_dict_for_saving (line 116) | def process_clip_state_dict_for_saving(self, state_dict):
    method clip_target (line 123) | def clip_target(self, state_dict={}):
  class SD21UnclipL (line 126) | class SD21UnclipL(SD20):
  class SD21UnclipH (line 139) | class SD21UnclipH(SD20):
  class SDXLRefiner (line 151) | class SDXLRefiner(supported_models_base.BASE):
    method get_model (line 164) | def get_model(self, state_dict, prefix="", device=None):
    method process_clip_state_dict (line 167) | def process_clip_state_dict(self, state_dict):
    method process_clip_state_dict_for_saving (line 177) | def process_clip_state_dict_for_saving(self, state_dict):
    method clip_target (line 186) | def clip_target(self, state_dict={}):
  class SDXL (line 189) | class SDXL(supported_models_base.BASE):
    method model_type (line 203) | def model_type(self, state_dict, prefix=""):
    method get_model (line 222) | def get_model(self, state_dict, prefix="", device=None):
    method process_clip_state_dict (line 228) | def process_clip_state_dict(self, state_dict):
    method process_clip_state_dict_for_saving (line 240) | def process_clip_state_dict_for_saving(self, state_dict):
    method clip_target (line 258) | def clip_target(self, state_dict={}):
  class SSD1B (line 261) | class SSD1B(SDXL):
  class Segmind_Vega (line 271) | class Segmind_Vega(SDXL):
  class KOALA_700M (line 281) | class KOALA_700M(SDXL):
  class KOALA_1B (line 291) | class KOALA_1B(SDXL):
  class SVD_img2vid (line 301) | class SVD_img2vid(supported_models_base.BASE):
    method get_model (line 325) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 329) | def clip_target(self, state_dict={}):
  class SV3D_u (line 332) | class SV3D_u(SVD_img2vid):
    method get_model (line 346) | def get_model(self, state_dict, prefix="", device=None):
  class SV3D_p (line 350) | class SV3D_p(SV3D_u):
    method get_model (line 363) | def get_model(self, state_dict, prefix="", device=None):
  class Stable_Zero123 (line 367) | class Stable_Zero123(supported_models_base.BASE):
    method get_model (line 391) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 395) | def clip_target(self, state_dict={}):
  class SD_X4Upscaler (line 398) | class SD_X4Upscaler(SD20):
    method get_model (line 422) | def get_model(self, state_dict, prefix="", device=None):
  class Stable_Cascade_C (line 426) | class Stable_Cascade_C(supported_models_base.BASE):
    method process_unet_state_dict (line 444) | def process_unet_state_dict(self, state_dict):
    method process_clip_state_dict (line 459) | def process_clip_state_dict(self, state_dict):
    method get_model (line 465) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 469) | def clip_target(self, state_dict={}):
  class Stable_Cascade_B (line 472) | class Stable_Cascade_B(Stable_Cascade_C):
    method get_model (line 488) | def get_model(self, state_dict, prefix="", device=None):
  class SD15_instructpix2pix (line 492) | class SD15_instructpix2pix(SD15):
    method get_model (line 502) | def get_model(self, state_dict, prefix="", device=None):
  class SDXL_instructpix2pix (line 505) | class SDXL_instructpix2pix(SDXL):
    method get_model (line 516) | def get_model(self, state_dict, prefix="", device=None):
  class LotusD (line 519) | class LotusD(SD20):
    method get_model (line 533) | def get_model(self, state_dict, prefix="", device=None):
  class SD3 (line 536) | class SD3(supported_models_base.BASE):
    method get_model (line 553) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 557) | def clip_target(self, state_dict={}):
  class StableAudio (line 572) | class StableAudio(supported_models_base.BASE):
    method get_model (line 585) | def get_model(self, state_dict, prefix="", device=None):
    method process_unet_state_dict (line 590) | def process_unet_state_dict(self, state_dict):
    method process_unet_state_dict_for_saving (line 596) | def process_unet_state_dict_for_saving(self, state_dict):
    method clip_target (line 600) | def clip_target(self, state_dict={}):
  class AuraFlow (line 603) | class AuraFlow(supported_models_base.BASE):
    method get_model (line 619) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 623) | def clip_target(self, state_dict={}):
  class PixArtAlpha (line 626) | class PixArtAlpha(supported_models_base.BASE):
    method get_model (line 646) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 650) | def clip_target(self, state_dict={}):
  class PixArtSigma (line 653) | class PixArtSigma(PixArtAlpha):
  class HunyuanDiT (line 659) | class HunyuanDiT(supported_models_base.BASE):
    method get_model (line 680) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 684) | def clip_target(self, state_dict={}):
  class HunyuanDiT1 (line 687) | class HunyuanDiT1(HunyuanDiT):
  class Flux (line 699) | class Flux(supported_models_base.BASE):
    method process_unet_state_dict (line 715) | def process_unet_state_dict(self, state_dict):
    method get_model (line 727) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 731) | def clip_target(self, state_dict={}):
  class FluxInpaint (line 736) | class FluxInpaint(Flux):
  class FluxSchnell (line 745) | class FluxSchnell(Flux):
    method get_model (line 756) | def get_model(self, state_dict, prefix="", device=None):
  class Flux2 (line 760) | class Flux2(Flux):
    method __init__ (line 777) | def __init__(self, unet_config):
    method get_model (line 781) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 785) | def clip_target(self, state_dict={}):
  class GenmoMochi (line 805) | class GenmoMochi(supported_models_base.BASE):
    method get_model (line 825) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 829) | def clip_target(self, state_dict={}):
  class LTXV (line 834) | class LTXV(supported_models_base.BASE):
    method __init__ (line 853) | def __init__(self, unet_config):
    method get_model (line 857) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 861) | def clip_target(self, state_dict={}):
  class LTXAV (line 866) | class LTXAV(LTXV):
    method __init__ (line 873) | def __init__(self, unet_config):
    method get_model (line 877) | def get_model(self, state_dict, prefix="", device=None):
  class HunyuanVideo (line 881) | class HunyuanVideo(supported_models_base.BASE):
    method get_model (line 900) | def get_model(self, state_dict, prefix="", device=None):
    method process_unet_state_dict (line 904) | def process_unet_state_dict(self, state_dict):
    method process_unet_state_dict_for_saving (line 922) | def process_unet_state_dict_for_saving(self, state_dict):
    method clip_target (line 926) | def clip_target(self, state_dict={}):
  class HunyuanVideoI2V (line 931) | class HunyuanVideoI2V(HunyuanVideo):
    method get_model (line 937) | def get_model(self, state_dict, prefix="", device=None):
  class HunyuanVideoSkyreelsI2V (line 941) | class HunyuanVideoSkyreelsI2V(HunyuanVideo):
    method get_model (line 947) | def get_model(self, state_dict, prefix="", device=None):
  class CosmosT2V (line 951) | class CosmosT2V(supported_models_base.BASE):
    method get_model (line 973) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 977) | def clip_target(self, state_dict={}):
  class CosmosI2V (line 982) | class CosmosI2V(CosmosT2V):
    method get_model (line 988) | def get_model(self, state_dict, prefix="", device=None):
  class CosmosT2IPredict2 (line 992) | class CosmosT2IPredict2(supported_models_base.BASE):
    method __init__ (line 1011) | def __init__(self, unet_config):
    method get_model (line 1015) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1019) | def clip_target(self, state_dict={}):
  class Anima (line 1024) | class Anima(supported_models_base.BASE):
    method get_model (line 1041) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1045) | def clip_target(self, state_dict={}):
    method set_inference_dtype (line 1050) | def set_inference_dtype(self, dtype, manual_cast_dtype, **kwargs):
  class CosmosI2VPredict2 (line 1056) | class CosmosI2VPredict2(CosmosT2IPredict2):
    method get_model (line 1062) | def get_model(self, state_dict, prefix="", device=None):
  class Lumina2 (line 1066) | class Lumina2(supported_models_base.BASE):
    method get_model (line 1086) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1090) | def clip_target(self, state_dict={}):
  class ZImage (line 1095) | class ZImage(Lumina2):
    method __init__ (line 1110) | def __init__(self, unet_config):
    method clip_target (line 1116) | def clip_target(self, state_dict={}):
  class ZImagePixelSpace (line 1121) | class ZImagePixelSpace(ZImage):
    method get_model (line 1132) | def get_model(self, state_dict, prefix="", device=None):
  class WAN21_T2V (line 1135) | class WAN21_T2V(supported_models_base.BASE):
    method __init__ (line 1155) | def __init__(self, unet_config):
    method get_model (line 1159) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1163) | def clip_target(self, state_dict={}):
  class WAN21_I2V (line 1168) | class WAN21_I2V(WAN21_T2V):
    method get_model (line 1175) | def get_model(self, state_dict, prefix="", device=None):
  class WAN21_FunControl2V (line 1179) | class WAN21_FunControl2V(WAN21_T2V):
    method get_model (line 1186) | def get_model(self, state_dict, prefix="", device=None):
  class WAN21_Camera (line 1190) | class WAN21_Camera(WAN21_T2V):
    method get_model (line 1197) | def get_model(self, state_dict, prefix="", device=None):
  class WAN22_Camera (line 1201) | class WAN22_Camera(WAN21_T2V):
    method get_model (line 1208) | def get_model(self, state_dict, prefix="", device=None):
  class WAN21_Vace (line 1212) | class WAN21_Vace(WAN21_T2V):
    method __init__ (line 1218) | def __init__(self, unet_config):
    method get_model (line 1222) | def get_model(self, state_dict, prefix="", device=None):
  class WAN21_HuMo (line 1226) | class WAN21_HuMo(WAN21_T2V):
    method get_model (line 1232) | def get_model(self, state_dict, prefix="", device=None):
  class WAN22_S2V (line 1236) | class WAN22_S2V(WAN21_T2V):
    method __init__ (line 1242) | def __init__(self, unet_config):
    method get_model (line 1245) | def get_model(self, state_dict, prefix="", device=None):
  class WAN22_Animate (line 1249) | class WAN22_Animate(WAN21_T2V):
    method __init__ (line 1255) | def __init__(self, unet_config):
    method get_model (line 1258) | def get_model(self, state_dict, prefix="", device=None):
  class WAN22_T2V (line 1262) | class WAN22_T2V(WAN21_T2V):
    method get_model (line 1271) | def get_model(self, state_dict, prefix="", device=None):
  class WAN21_FlowRVS (line 1275) | class WAN21_FlowRVS(WAN21_T2V):
    method get_model (line 1281) | def get_model(self, state_dict, prefix="", device=None):
  class WAN21_SCAIL (line 1285) | class WAN21_SCAIL(WAN21_T2V):
    method get_model (line 1291) | def get_model(self, state_dict, prefix="", device=None):
  class Hunyuan3Dv2 (line 1295) | class Hunyuan3Dv2(supported_models_base.BASE):
    method process_unet_state_dict (line 1314) | def process_unet_state_dict(self, state_dict):
    method process_unet_state_dict_for_saving (line 1323) | def process_unet_state_dict_for_saving(self, state_dict):
    method get_model (line 1327) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1331) | def clip_target(self, state_dict={}):
  class Hunyuan3Dv2_1 (line 1334) | class Hunyuan3Dv2_1(Hunyuan3Dv2):
    method get_model (line 1341) | def get_model(self, state_dict, prefix="", device=None):
  class Hunyuan3Dv2mini (line 1345) | class Hunyuan3Dv2mini(Hunyuan3Dv2):
  class HiDream (line 1353) | class HiDream(supported_models_base.BASE):
    method get_model (line 1375) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1379) | def clip_target(self, state_dict={}):
  class Chroma (line 1382) | class Chroma(supported_models_base.BASE):
    method process_unet_state_dict (line 1400) | def process_unet_state_dict(self, state_dict):
    method get_model (line 1409) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1413) | def clip_target(self, state_dict={}):
  class ChromaRadiance (line 1418) | class ChromaRadiance(Chroma):
    method get_model (line 1428) | def get_model(self, state_dict, prefix="", device=None):
  class ACEStep (line 1431) | class ACEStep(supported_models_base.BASE):
    method get_model (line 1452) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1456) | def clip_target(self, state_dict={}):
  class Omnigen2 (line 1459) | class Omnigen2(supported_models_base.BASE):
    method __init__ (line 1479) | def __init__(self, unet_config):
    method get_model (line 1484) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1488) | def clip_target(self, state_dict={}):
  class QwenImage (line 1493) | class QwenImage(supported_models_base.BASE):
    method get_model (line 1513) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1517) | def clip_target(self, state_dict={}):
  class HunyuanImage21 (line 1522) | class HunyuanImage21(HunyuanVideo):
    method get_model (line 1538) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1542) | def clip_target(self, state_dict={}):
  class HunyuanImage21Refiner (line 1547) | class HunyuanImage21Refiner(HunyuanVideo):
    method get_model (line 1560) | def get_model(self, state_dict, prefix="", device=None):
  class HunyuanVideo15 (line 1564) | class HunyuanVideo15(HunyuanVideo):
    method get_model (line 1578) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1582) | def clip_target(self, state_dict={}):
  class HunyuanVideo15_SR_Distilled (line 1588) | class HunyuanVideo15_SR_Distilled(HunyuanVideo):
    method get_model (line 1603) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1607) | def clip_target(self, state_dict={}):
  class Kandinsky5 (line 1613) | class Kandinsky5(supported_models_base.BASE):
    method get_model (line 1632) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1636) | def clip_target(self, state_dict={}):
  class Kandinsky5Image (line 1642) | class Kandinsky5Image(Kandinsky5):
    method get_model (line 1656) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1660) | def clip_target(self, state_dict={}):
  class ACEStep15 (line 1666) | class ACEStep15(supported_models_base.BASE):
    method get_model (line 1688) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1692) | def clip_target(self, state_dict={}):
  class LongCatImage (line 1706) | class LongCatImage(supported_models_base.BASE):
    method get_model (line 1728) | def get_model(self, state_dict, prefix="", device=None):
    method clip_target (line 1732) | def clip_target(self, state_dict={}):

FILE: comfy/supported_models_base.py
  class ClipTarget (line 25) | class ClipTarget:
    method __init__ (line 26) | def __init__(self, tokenizer, clip):
  class BASE (line 31) | class BASE:
    method matches (line 57) | def matches(s, unet_config, state_dict=None):
    method model_type (line 67) | def model_type(self, state_dict, prefix=""):
    method inpaint_model (line 70) | def inpaint_model(self):
    method __init__ (line 73) | def __init__(self, unet_config):
    method get_model (line 81) | def get_model(self, state_dict, prefix="", device=None):
    method process_clip_state_dict (line 90) | def process_clip_state_dict(self, state_dict):
    method process_unet_state_dict (line 94) | def process_unet_state_dict(self, state_dict):
    method process_vae_state_dict (line 97) | def process_vae_state_dict(self, state_dict):
    method process_clip_state_dict_for_saving (line 100) | def process_clip_state_dict_for_saving(self, state_dict):
    method process_clip_vision_state_dict_for_saving (line 104) | def process_clip_vision_state_dict_for_saving(self, state_dict):
    method process_unet_state_dict_for_saving (line 110) | def process_unet_state_dict_for_saving(self, state_dict):
    method process_vae_state_dict_for_saving (line 114) | def process_vae_state_dict_for_saving(self, state_dict):
    method set_inference_dtype (line 118) | def set_inference_dtype(self, dtype, manual_cast_dtype):
    method __getattr__ (line 122) | def __getattr__(self, name):

FILE: comfy/t2i_adapter/adapter.py
  function conv_nd (line 7) | def conv_nd(dims, *args, **kwargs):
  function avg_pool_nd (line 20) | def avg_pool_nd(dims, *args, **kwargs):
  class Downsample (line 33) | class Downsample(nn.Module):
    method __init__ (line 42) | def __init__(self, channels, use_conv, dims=2, out_channels=None, padd...
    method forward (line 57) | def forward(self, x):
  class ResnetBlock (line 67) | class ResnetBlock(nn.Module):
    method __init__ (line 68) | def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
    method forward (line 88) | def forward(self, x):
  class Adapter (line 103) | class Adapter(nn.Module):
    method __init__ (line 104) | def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64,...
    method forward (line 134) | def forward(self, x):
  class LayerNorm (line 165) | class LayerNorm(nn.LayerNorm):
    method forward (line 168) | def forward(self, x: torch.Tensor):
  class QuickGELU (line 174) | class QuickGELU(nn.Module):
    method forward (line 176) | def forward(self, x: torch.Tensor):
  class ResidualAttentionBlock (line 180) | class ResidualAttentionBlock(nn.Module):
    method __init__ (line 182) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ...
    method attention (line 193) | def attention(self, x: torch.Tensor):
    method forward (line 197) | def forward(self, x: torch.Tensor):
  class StyleAdapter (line 203) | class StyleAdapter(nn.Module):
    method __init__ (line 205) | def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3,...
    method forward (line 216) | def forward(self, x):
  class ResnetBlock_light (line 232) | class ResnetBlock_light(nn.Module):
    method __init__ (line 233) | def __init__(self, in_c):
    method forward (line 239) | def forward(self, x):
  class extractor (line 247) | class extractor(nn.Module):
    method __init__ (line 248) | def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
    method forward (line 260) | def forward(self, x):
  class Adapter_light (line 270) | class Adapter_light(nn.Module):
    method __init__ (line 271) | def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
    method forward (line 288) | def forward(self, x):

FILE: comfy/taesd/taehv.py
  function conv (line 15) | def conv(n_in, n_out, **kwargs):
  class Clamp (line 18) | class Clamp(nn.Module):
    method forward (line 19) | def forward(self, x):
  class MemBlock (line 22) | class MemBlock(nn.Module):
    method __init__ (line 23) | def __init__(self, n_in, n_out, act_func):
    method forward (line 28) | def forward(self, x, past):
  class TPool (line 31) | class TPool(nn.Module):
    method __init__ (line 32) | def __init__(self, n_f, stride):
    method forward (line 36) | def forward(self, x):
  class TGrow (line 40) | class TGrow(nn.Module):
    method __init__ (line 41) | def __init__(self, n_f, stride):
    method forward (line 45) | def forward(self, x):
  function apply_model_with_memblocks (line 50) | def apply_model_with_memblocks(model, x, parallel, show_progress_bar):
  class TAEHV (line 114) | class TAEHV(nn.Module):
    method __init__ (line 115) | def __init__(self, latent_channels, parallel=False, encoder_time_downs...
    method show_progress_bar (line 159) | def show_progress_bar(self):
    method show_progress_bar (line 163) | def show_progress_bar(self, value):
    method encode (line 166) | def encode(self, x, **kwargs):
    method decode (line 181) | def decode(self, x, **kwargs):

FILE: comfy/taesd/taesd.py
  function conv (line 12) | def conv(n_in, n_out, **kwargs):
  class Clamp (line 15) | class Clamp(nn.Module):
    method forward (line 16) | def forward(self, x):
  class Block (line 19) | class Block(nn.Module):
    method __init__ (line 20) | def __init__(self, n_in, n_out):
    method forward (line 25) | def forward(self, x):
  function Encoder (line 28) | def Encoder(latent_channels=4):
  function Decoder (line 38) | def Decoder(latent_channels=4):
  class TAESD (line 47) | class TAESD(nn.Module):
    method __init__ (line 51) | def __init__(self, encoder_path=None, decoder_path=None, latent_channe...
    method scale_latents (line 64) | def scale_latents(x):
    method unscale_latents (line 69) | def unscale_latents(x):
    method decode (line 73) | def decode(self, x):
    method encode (line 78) | def encode(self, x):

FILE: comfy/text_encoders/ace.py
  class VoiceBpeTokenizer (line 24) | class VoiceBpeTokenizer:
    method __init__ (line 25) | def __init__(self, vocab_file=DEFAULT_VOCAB_FILE):
    method preprocess_text (line 30) | def preprocess_text(self, txt, lang):
    method encode (line 34) | def encode(self, txt, lang='en'):
    method get_lang (line 43) | def get_lang(self, line):
    method __call__ (line 50) | def __call__(self, string):
    method from_pretrained (line 87) | def from_pretrained(path, **kwargs):
    method get_vocab (line 90) | def get_vocab(self):
  class UMT5BaseModel (line 94) | class UMT5BaseModel(sd1_clip.SDClipModel):
    method __init__ (line 95) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class UMT5BaseTokenizer (line 99) | class UMT5BaseTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 100) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method state_dict (line 104) | def state_dict(self):
  class LyricsTokenizer (line 107) | class LyricsTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 108) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class AceT5Tokenizer (line 112) | class AceT5Tokenizer:
    method __init__ (line 113) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 117) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 123) | def untokenize(self, token_weight_pair):
    method state_dict (line 126) | def state_dict(self):
  class AceT5Model (line 129) | class AceT5Model(torch.nn.Module):
    method __init__ (line 130) | def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
    method set_clip_options (line 137) | def set_clip_options(self, options):
    method reset_clip_options (line 140) | def reset_clip_options(self):
    method encode_token_weights (line 143) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 152) | def load_sd(self, sd):

FILE: comfy/text_encoders/ace15.py
  function sample_manual_loop_no_classes (line 10) | def sample_manual_loop_no_classes(
  function generate_audio_codes (line 116) | def generate_audio_codes(model, positive, negative, min_tokens=1, max_to...
  class ACE15Tokenizer (line 141) | class ACE15Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 142) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method _metas_to_cot (line 145) | def _metas_to_cot(self, *, return_yaml: bool = False, **kwargs) -> str:
    method _metas_to_cap (line 165) | def _metas_to_cap(self, **kwargs) -> str:
    method tokenize_with_weights (line 180) | def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
  class Qwen3_06BModel (line 249) | class Qwen3_06BModel(sd1_clip.SDClipModel):
    method __init__ (line 250) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class Qwen3_2B_ACE15 (line 253) | class Qwen3_2B_ACE15(sd1_clip.SDClipModel):
    method __init__ (line 254) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class Qwen3_4B_ACE15 (line 262) | class Qwen3_4B_ACE15(sd1_clip.SDClipModel):
    method __init__ (line 263) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class ACE15TEModel (line 271) | class ACE15TEModel(torch.nn.Module):
    method __init__ (line 272) | def __init__(self, device="cpu", dtype=None, dtype_llama=None, lm_mode...
    method encode_token_weights (line 292) | def encode_token_weights(self, token_weight_pairs):
    method set_clip_options (line 310) | def set_clip_options(self, options):
    method reset_clip_options (line 316) | def reset_clip_options(self):
    method load_sd (line 322) | def load_sd(self, sd):
    method memory_estimation_function (line 330) | def memory_estimation_function(self, token_weight_pairs, device=None):
  function te (line 341) | def te(dtype_llama=None, llama_quantization_metadata=None, lm_model="qwe...

FILE: comfy/text_encoders/ace_text_cleaners.py
  function japanese_to_romaji (line 7) | def japanese_to_romaji(japanese_text):
  function number_to_text (line 132) | def number_to_text(num, ordinal=False):
  function _int_to_text (line 178) | def _int_to_text(num):
  function _digit_to_text (line 205) | def _digit_to_text(digit):
  function expand_abbreviations_multilingual (line 242) | def expand_abbreviations_multilingual(text, lang="en"):
  function expand_symbols_multilingual (line 264) | def expand_symbols_multilingual(text, lang="en"):
  function _remove_commas (line 286) | def _remove_commas(m):
  function _remove_dots (line 293) | def _remove_dots(m):
  function _expand_decimal_point (line 300) | def _expand_decimal_point(m, lang="en"):
  function _expand_currency (line 305) | def _expand_currency(m, lang="en", currency="USD"):
  function _expand_ordinal (line 334) | def _expand_ordinal(m, lang="en"):
  function _expand_number (line 338) | def _expand_number(m, lang="en"):
  function expand_numbers_multilingual (line 342) | def expand_numbers_multilingual(text, lang="en"):
  function lowercase (line 360) | def lowercase(text):
  function collapse_whitespace (line 364) | def collapse_whitespace(text):
  function multilingual_cleaners (line 368) | def multilingual_cleaners(text, lang):
  function basic_cleaners (line 391) | def basic_cleaners(text):

FILE: comfy/text_encoders/anima.py
  class Qwen3Tokenizer (line 8) | class Qwen3Tokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 9) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class T5XXLTokenizer (line 13) | class T5XXLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 14) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class AnimaTokenizer (line 18) | class AnimaTokenizer:
    method __init__ (line 19) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 23) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 30) | def untokenize(self, token_weight_pair):
    method state_dict (line 33) | def state_dict(self):
    method decode (line 36) | def decode(self, token_ids, **kwargs):
  class Qwen3_06BModel (line 39) | class Qwen3_06BModel(sd1_clip.SDClipModel):
    method __init__ (line 40) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class AnimaTEModel (line 44) | class AnimaTEModel(sd1_clip.SD1ClipModel):
    method __init__ (line 45) | def __init__(self, device="cpu", dtype=None, model_options={}):
    method encode_token_weights (line 48) | def encode_token_weights(self, token_weight_pairs):
  function te (line 54) | def te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/aura_t5.py
  class PT5XlModel (line 6) | class PT5XlModel(sd1_clip.SDClipModel):
    method __init__ (line 7) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class PT5XlTokenizer (line 11) | class PT5XlTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 12) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class AuraT5Tokenizer (line 16) | class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 17) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class AuraT5Model (line 20) | class AuraT5Model(sd1_clip.SD1ClipModel):
    method __init__ (line 21) | def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):

FILE: comfy/text_encoders/bert.py
  class BertAttention (line 5) | class BertAttention(torch.nn.Module):
    method __init__ (line 6) | def __init__(self, embed_dim, heads, dtype, device, operations):
    method forward (line 15) | def forward(self, x, mask=None, optimized_attention=None):
  class BertOutput (line 23) | class BertOutput(torch.nn.Module):
    method __init__ (line 24) | def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, devic...
    method forward (line 30) | def forward(self, x, y):
  class BertAttentionBlock (line 36) | class BertAttentionBlock(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, op...
    method forward (line 42) | def forward(self, x, mask, optimized_attention):
  class BertIntermediate (line 46) | class BertIntermediate(torch.nn.Module):
    method __init__ (line 47) | def __init__(self, embed_dim, intermediate_dim, dtype, device, operati...
    method forward (line 51) | def forward(self, x):
  class BertBlock (line 56) | class BertBlock(torch.nn.Module):
    method __init__ (line 57) | def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps,...
    method forward (line 63) | def forward(self, x, mask, optimized_attention):
  class BertEncoder (line 68) | class BertEncoder(torch.nn.Module):
    method __init__ (line 69) | def __init__(self, num_layers, embed_dim, intermediate_dim, heads, lay...
    method forward (line 73) | def forward(self, x, mask=None, intermediate_output=None):
  class BertEmbeddings (line 87) | class BertEmbeddings(torch.nn.Module):
    method __init__ (line 88) | def __init__(self, vocab_size, max_position_embeddings, type_vocab_siz...
    method forward (line 96) | def forward(self, input_tokens, embeds=None, token_type_ids=None, dtyp...
  class BertModel_ (line 110) | class BertModel_(torch.nn.Module):
    method __init__ (line 111) | def __init__(self, config_dict, dtype, device, operations):
    method forward (line 119) | def forward(self, input_tokens, attention_mask=None, embeds=None, num_...
  class BertModel (line 130) | class BertModel(torch.nn.Module):
    method __init__ (line 131) | def __init__(self, config_dict, dtype, device, operations):
    method get_input_embeddings (line 136) | def get_input_embeddings(self):
    method set_input_embeddings (line 139) | def set_input_embeddings(self, embeddings):
    method forward (line 142) | def forward(self, *args, **kwargs):

FILE: comfy/text_encoders/cosmos.py
  class T5XXLModel (line 7) | class T5XXLModel(sd1_clip.SDClipModel):
    method __init__ (line 8) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class CosmosT5XXL (line 17) | class CosmosT5XXL(sd1_clip.SD1ClipModel):
    method __init__ (line 18) | def __init__(self, device="cpu", dtype=None, model_options={}):
  class T5XXLTokenizer (line 22) | class T5XXLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 23) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class CosmosT5Tokenizer (line 28) | class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 29) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  function te (line 33) | def te(dtype_t5=None, t5_quantization_metadata=None):

FILE: comfy/text_encoders/flux.py
  class T5XXLTokenizer (line 12) | class T5XXLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 13) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class FluxTokenizer (line 18) | class FluxTokenizer:
    method __init__ (line 19) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 23) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 29) | def untokenize(self, token_weight_pair):
    method state_dict (line 32) | def state_dict(self):
  class FluxClipModel (line 36) | class FluxClipModel(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_opti...
    method set_clip_options (line 44) | def set_clip_options(self, options):
    method reset_clip_options (line 48) | def reset_clip_options(self):
    method encode_token_weights (line 52) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 60) | def load_sd(self, sd):
  function flux_clip (line 66) | def flux_clip(dtype_t5=None, t5_quantization_metadata=None):
  function load_mistral_tokenizer (line 75) | def load_mistral_tokenizer(data):
  class MistralTokenizerClass (line 113) | class MistralTokenizerClass:
    method from_pretrained (line 115) | def from_pretrained(path, **kwargs):
  class Mistral3Tokenizer (line 118) | class Mistral3Tokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 119) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method state_dict (line 123) | def state_dict(self):
  class Flux2Tokenizer (line 126) | class Flux2Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 127) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 131) | def tokenize_with_weights(self, text, return_word_ids=False, llama_tem...
  class Mistral3_24BModel (line 140) | class Mistral3_24BModel(sd1_clip.SDClipModel):
    method __init__ (line 141) | def __init__(self, device="cpu", layer=[10, 20, 30], layer_idx=None, d...
  class Flux2TEModel (line 150) | class Flux2TEModel(sd1_clip.SD1ClipModel):
    method __init__ (line 151) | def __init__(self, device="cpu", dtype=None, model_options={}, name="m...
    method encode_token_weights (line 154) | def encode_token_weights(self, token_weight_pairs):
  function flux2_te (line 162) | def flux2_te(dtype_llama=None, llama_quantization_metadata=None, pruned=...
  class Qwen3Tokenizer (line 176) | class Qwen3Tokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 177) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class Qwen3Tokenizer8B (line 181) | class Qwen3Tokenizer8B(sd1_clip.SDTokenizer):
    method __init__ (line 182) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class KleinTokenizer (line 186) | class KleinTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 187) | def __init__(self, embedding_directory=None, tokenizer_data={}, name="...
    method tokenize_with_weights (line 196) | def tokenize_with_weights(self, text, return_word_ids=False, llama_tem...
  class KleinTokenizer8B (line 205) | class KleinTokenizer8B(KleinTokenizer):
    method __init__ (line 206) | def __init__(self, embedding_directory=None, tokenizer_data={}, name="...
  class Qwen3_4BModel (line 209) | class Qwen3_4BModel(sd1_clip.SDClipModel):
    method __init__ (line 210) | def __init__(self, device="cpu", layer=[9, 18, 27], layer_idx=None, dt...
  class Qwen3_8BModel (line 213) | class Qwen3_8BModel(sd1_clip.SDClipModel):
    method __init__ (line 214) | def __init__(self, device="cpu", layer=[9, 18, 27], layer_idx=None, dt...
  function klein_te (line 217) | def klein_te(dtype_llama=None, llama_quantization_metadata=None, model_t...

FILE: comfy/text_encoders/genmo.py
  class T5XXLModel (line 7) | class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
    method __init__ (line 8) | def __init__(self, **kwargs):
  class MochiT5XXL (line 13) | class MochiT5XXL(sd1_clip.SD1ClipModel):
    method __init__ (line 14) | def __init__(self, device="cpu", dtype=None, model_options={}):
  class T5XXLTokenizer (line 18) | class T5XXLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 19) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class MochiT5Tokenizer (line 24) | class MochiT5Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 25) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  function mochi_te (line 29) | def mochi_te(dtype_t5=None, t5_quantization_metadata=None):

FILE: comfy/text_encoders/hidream.py
  class HiDreamTokenizer (line 10) | class HiDreamTokenizer:
    method __init__ (line 11) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 17) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 26) | def untokenize(self, token_weight_pair):
    method state_dict (line 29) | def state_dict(self):
  class HiDreamTEModel (line 33) | class HiDreamTEModel(torch.nn.Module):
    method __init__ (line 34) | def __init__(self, clip_l=True, clip_g=True, t5=True, llama=True, dtyp...
    method set_clip_options (line 67) | def set_clip_options(self, options):
    method reset_clip_options (line 77) | def reset_clip_options(self):
    method encode_token_weights (line 87) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 134) | def load_sd(self, sd):
  function hidream_clip (line 145) | def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5...

FILE: comfy/text_encoders/hunyuan_image.py
  class ByT5SmallTokenizer (line 8) | class ByT5SmallTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 9) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class HunyuanImageTokenizer (line 13) | class HunyuanImageTokenizer(QwenImageTokenizer):
    method __init__ (line 14) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 20) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
  class Qwen25_7BVLIModel (line 41) | class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
    method __init__ (line 42) | def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=N...
  class ByT5SmallModel (line 50) | class ByT5SmallModel(sd1_clip.SDClipModel):
    method __init__ (line 51) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class HunyuanImageTEModel (line 56) | class HunyuanImageTEModel(QwenImageTEModel):
    method __init__ (line 57) | def __init__(self, byt5=True, device="cpu", dtype=None, model_options=...
    method encode_token_weights (line 65) | def encode_token_weights(self, token_weight_pairs):
    method set_clip_options (line 78) | def set_clip_options(self, options):
    method reset_clip_options (line 83) | def reset_clip_options(self):
    method load_sd (line 88) | def load_sd(self, sd):
  function te (line 94) | def te(byt5=True, dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/hunyuan_video.py
  function llama_detect (line 11) | def llama_detect(state_dict, prefix=""):
  class LLAMA3Tokenizer (line 26) | class LLAMA3Tokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 27) | def __init__(self, embedding_directory=None, tokenizer_data={}, min_le...
  class LLAMAModel (line 31) | class LLAMAModel(sd1_clip.SDClipModel):
    method __init__ (line 32) | def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=N...
  class HunyuanVideoTokenizer (line 47) | class HunyuanVideoTokenizer:
    method __init__ (line 48) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 53) | def tokenize_with_weights(self, text, return_word_ids=False, llama_tem...
    method untokenize (line 72) | def untokenize(self, token_weight_pair):
    method state_dict (line 75) | def state_dict(self):
  class HunyuanVideo15Tokenizer (line 79) | class HunyuanVideo15Tokenizer(HunyuanImageTokenizer):
    method __init__ (line 80) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 84) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
  class HunyuanVideoClipModel (line 87) | class HunyuanVideoClipModel(torch.nn.Module):
    method __init__ (line 88) | def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_o...
    method set_clip_options (line 95) | def set_clip_options(self, options):
    method reset_clip_options (line 99) | def reset_clip_options(self):
    method encode_token_weights (line 103) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 156) | def load_sd(self, sd):
  function hunyuan_video_clip (line 163) | def hunyuan_video_clip(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/hydit.py
  class HyditBertModel (line 9) | class HyditBertModel(sd1_clip.SDClipModel):
    method __init__ (line 10) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class HyditBertTokenizer (line 15) | class HyditBertTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 16) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class MT5XLModel (line 21) | class MT5XLModel(sd1_clip.SDClipModel):
    method __init__ (line 22) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class MT5XLTokenizer (line 27) | class MT5XLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 28) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method state_dict (line 33) | def state_dict(self):
  class HyditTokenizer (line 36) | class HyditTokenizer:
    method __init__ (line 37) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 42) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 48) | def untokenize(self, token_weight_pair):
    method state_dict (line 51) | def state_dict(self):
  class HyditModel (line 54) | class HyditModel(torch.nn.Module):
    method __init__ (line 55) | def __init__(self, device="cpu", dtype=None, model_options={}):
    method encode_token_weights (line 64) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 69) | def load_sd(self, sd):
    method set_clip_options (line 75) | def set_clip_options(self, options):
    method reset_clip_options (line 79) | def reset_clip_options(self):

FILE: comfy/text_encoders/jina_clip_2.py
  class JinaClip2Tokenizer (line 16) | class JinaClip2Tokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 17) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method state_dict (line 22) | def state_dict(self):
  class JinaClip2TokenizerWrapper (line 25) | class JinaClip2TokenizerWrapper(sd1_clip.SD1Tokenizer):
    method __init__ (line 26) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class XLMRobertaConfig (line 31) | class XLMRobertaConfig:
  class XLMRobertaEmbeddings (line 47) | class XLMRobertaEmbeddings(nn.Module):
    method __init__ (line 48) | def __init__(self, config, device=None, dtype=None, ops=None):
    method forward (line 54) | def forward(self, input_ids=None, embeddings=None):
  class RotaryEmbedding (line 64) | class RotaryEmbedding(nn.Module):
    method __init__ (line 65) | def __init__(self, dim, base, device=None):
    method _update_cos_sin_cache (line 73) | def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
    method forward (line 82) | def forward(self, q, k):
  class MHA (line 98) | class MHA(nn.Module):
    method __init__ (line 99) | def __init__(self, config, device=None, dtype=None, ops=None):
    method forward (line 109) | def forward(self, x, mask=None, optimized_attention=None):
  class MLP (line 125) | class MLP(nn.Module):
    method __init__ (line 126) | def __init__(self, config, device=None, dtype=None, ops=None):
    method forward (line 132) | def forward(self, x):
  class Block (line 138) | class Block(nn.Module):
    method __init__ (line 139) | def __init__(self, config, device=None, dtype=None, ops=None):
    method forward (line 148) | def forward(self, hidden_states, mask=None, optimized_attention=None):
  class XLMRobertaEncoder (line 155) | class XLMRobertaEncoder(nn.Module):
    method __init__ (line 156) | def __init__(self, config, device=None, dtype=None, ops=None):
    method forward (line 160) | def forward(self, hidden_states, attention_mask=None):
  class XLMRobertaModel_ (line 166) | class XLMRobertaModel_(nn.Module):
    method __init__ (line 167) | def __init__(self, config, device=None, dtype=None, ops=None):
    method forward (line 174) | def forward(self, input_ids, attention_mask=None, embeds=None, num_tok...
  class XLMRobertaModel (line 197) | class XLMRobertaModel(nn.Module):
    method __init__ (line 198) | def __init__(self, config_dict, dtype, device, operations):
    method get_input_embeddings (line 204) | def get_input_embeddings(self):
    method set_input_embeddings (line 207) | def set_input_embeddings(self, embeddings):
    method forward (line 210) | def forward(self, *args, **kwargs):
  class JinaClip2TextModel (line 213) | class JinaClip2TextModel(sd1_clip.SDClipModel):
    method __init__ (line 214) | def __init__(self, device="cpu", dtype=None, model_options={}):
  class JinaClip2TextModelWrapper (line 217) | class JinaClip2TextModelWrapper(sd1_clip.SD1ClipModel):
    method __init__ (line 218) | def __init__(self, device="cpu", dtype=None, model_options={}):

FILE: comfy/text_encoders/kandinsky5.py
  class Kandinsky5Tokenizer (line 6) | class Kandinsky5Tokenizer(QwenImageTokenizer):
    method __init__ (line 7) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 12) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
  class Kandinsky5TokenizerImage (line 19) | class Kandinsky5TokenizerImage(Kandinsky5Tokenizer):
    method __init__ (line 20) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class Qwen25_7BVLIModel (line 25) | class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
    method __init__ (line 26) | def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=N...
  class Kandinsky5TEModel (line 34) | class Kandinsky5TEModel(QwenImageTEModel):
    method __init__ (line 35) | def __init__(self, device="cpu", dtype=None, model_options={}):
    method encode_token_weights (line 39) | def encode_token_weights(self, token_weight_pairs):
    method set_clip_options (line 45) | def set_clip_options(self, options):
    method reset_clip_options (line 49) | def reset_clip_options(self):
    method load_sd (line 53) | def load_sd(self, sd):
  function te (line 59) | def te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/llama.py
  class Llama2Config (line 18) | class Llama2Config:
  class Mistral3Small24BConfig (line 41) | class Mistral3Small24BConfig:
  class Qwen25_3BConfig (line 64) | class Qwen25_3BConfig:
  class Qwen3_06BConfig (line 87) | class Qwen3_06BConfig:
  class Qwen3_06B_ACE15_Config (line 111) | class Qwen3_06B_ACE15_Config:
  class Qwen3_2B_ACE15_lm_Config (line 135) | class Qwen3_2B_ACE15_lm_Config:
  class Qwen3_4B_ACE15_lm_Config (line 159) | class Qwen3_4B_ACE15_lm_Config:
  class Qwen3_4BConfig (line 183) | class Qwen3_4BConfig:
  class Qwen3_8BConfig (line 207) | class Qwen3_8BConfig:
  class Ovis25_2BConfig (line 231) | class Ovis25_2BConfig:
  class Qwen25_7BVLI_Config (line 254) | class Qwen25_7BVLI_Config:
  class Gemma2_2B_Config (line 277) | class Gemma2_2B_Config:
  class Gemma3_4B_Config (line 302) | class Gemma3_4B_Config:
  class Gemma3_4B_Vision_Config (line 329) | class Gemma3_4B_Vision_Config(Gemma3_4B_Config):
  class Gemma3_12B_Config (line 334) | class Gemma3_12B_Config:
  class RMSNorm (line 360) | class RMSNorm(nn.Module):
    method __init__ (line 361) | def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None...
    method forward (line 367) | def forward(self, x: torch.Tensor):
  function precompute_freqs_cis (line 376) | def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None,...
  function apply_rope (line 412) | def apply_rope(xq, xk, freqs_cis):
  class Attention (line 431) | class Attention(nn.Module):
    method __init__ (line 432) | def __init__(self, config: Llama2Config, device=None, dtype=None, ops:...
    method forward (line 455) | def forward(
  class MLP (line 511) | class MLP(nn.Module):
    method __init__ (line 512) | def __init__(self, config: Llama2Config, device=None, dtype=None, ops:...
    method forward (line 523) | def forward(self, x):
  class TransformerBlock (line 526) | class TransformerBlock(nn.Module):
    method __init__ (line 527) | def __init__(self, config: Llama2Config, index, device=None, dtype=Non...
    method forward (line 534) | def forward(
  class TransformerBlockGemma2 (line 562) | class TransformerBlockGemma2(nn.Module):
    method __init__ (line 563) | def __init__(self, config: Llama2Config, index, device=None, dtype=Non...
    method forward (line 579) | def forward(
  class Llama2_ (line 626) | class Llama2_(nn.Module):
    method __init__ (line 627) | def __init__(self, config, device=None, dtype=None, ops=None):
    method forward (line 658) | def forward(self, x, attention_mask=None, embeds=None, num_tokens=None...
  class Gemma3MultiModalProjector (line 752) | class Gemma3MultiModalProjector(torch.nn.Module):
    method __init__ (line 753) | def __init__(self, config, dtype, device, operations):
    method forward (line 767) | def forward(self, vision_outputs: torch.Tensor):
  class BaseLlama (line 786) | class BaseLlama:
    method get_input_embeddings (line 787) | def get_input_embeddings(self):
    method set_input_embeddings (line 790) | def set_input_embeddings(self, embeddings):
    method forward (line 793) | def forward(self, input_ids, *args, **kwargs):
  class BaseGenerate (line 796) | class BaseGenerate:
    method logits (line 797) | def logits(self, x):
    method generate (line 815) | def generate(self, embeds=None, do_sample=True, max_length=256, temper...
    method sample_token (line 859) | def sample_token(self, logits, temperature, top_k, top_p, min_p, repet...
  class BaseQwen3 (line 897) | class BaseQwen3:
    method logits (line 898) | def logits(self, x):
  class Llama2 (line 913) | class Llama2(BaseLlama, torch.nn.Module):
    method __init__ (line 914) | def __init__(self, config_dict, dtype, device, operations):
  class Mistral3Small24B (line 922) | class Mistral3Small24B(BaseLlama, torch.nn.Module):
    method __init__ (line 923) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen25_3B (line 931) | class Qwen25_3B(BaseLlama, torch.nn.Module):
    method __init__ (line 932) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen3_06B (line 940) | class Qwen3_06B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
    method __init__ (line 941) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen3_06B_ACE15 (line 949) | class Qwen3_06B_ACE15(BaseLlama, BaseQwen3, torch.nn.Module):
    method __init__ (line 950) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen3_2B_ACE15_lm (line 958) | class Qwen3_2B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):
    method __init__ (line 959) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen3_4B (line 967) | class Qwen3_4B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
    method __init__ (line 968) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen3_4B_ACE15_lm (line 976) | class Qwen3_4B_ACE15_lm(BaseLlama, BaseQwen3, torch.nn.Module):
    method __init__ (line 977) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen3_8B (line 985) | class Qwen3_8B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module):
    method __init__ (line 986) | def __init__(self, config_dict, dtype, device, operations):
  class Ovis25_2B (line 994) | class Ovis25_2B(BaseLlama, torch.nn.Module):
    method __init__ (line 995) | def __init__(self, config_dict, dtype, device, operations):
  class Qwen25_7BVLI (line 1003) | class Qwen25_7BVLI(BaseLlama, BaseGenerate, torch.nn.Module):
    method __init__ (line 1004) | def __init__(self, config_dict, dtype, device, operations):
    method preprocess_embed (line 1016) | def preprocess_embed(self, embed, device):
    method forward (line 1022) | def forward(self, x, attention_mask=None, embeds=None, num_tokens=None...
  class Gemma2_2B (line 1049) | class Gemma2_2B(BaseLlama, BaseGenerate, torch.nn.Module):
    method __init__ (line 1050) | def __init__(self, config_dict, dtype, device, operations):
  class Gemma3_4B (line 1058) | class Gemma3_4B(BaseLlama, BaseGenerate, torch.nn.Module):
    method __init__ (line 1059) | def __init__(self, config_dict, dtype, device, operations):
  class Gemma3_4B_Vision (line 1067) | class Gemma3_4B_Vision(BaseLlama, BaseGenerate, torch.nn.Module):
    method __init__ (line 1068) | def __init__(self, config_dict, dtype, device, operations):
    method preprocess_embed (line 1079) | def preprocess_embed(self, embed, device):
  class Gemma3_12B (line 1085) | class Gemma3_12B(BaseLlama, BaseGenerate, torch.nn.Module):
    method __init__ (line 1086) | def __init__(self, config_dict, dtype, device, operations):
    method preprocess_embed (line 1097) | def preprocess_embed(self, embed, device):

FILE: comfy/text_encoders/long_clipl.py
  function model_options_long_clip (line 3) | def model_options_long_clip(sd, tokenizer_data, model_options):

FILE: comfy/text_encoders/longcat_image.py
  function split_quotation (line 20) | def split_quotation(prompt):
  class LongCatImageBaseTokenizer (line 40) | class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer):
    method __init__ (line 41) | def __init__(self, *args, **kwargs):
    method tokenize_with_weights (line 45) | def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
  class LongCatImageTokenizer (line 67) | class LongCatImageTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 68) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 78) | def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
  class LongCatImageTEModel (line 114) | class LongCatImageTEModel(sd1_clip.SD1ClipModel):
    method __init__ (line 115) | def __init__(self, device="cpu", dtype=None, model_options={}):
    method encode_token_weights (line 124) | def encode_token_weights(self, token_weight_pairs, template_end=-1):
  function te (line 174) | def te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/lt.py
  class T5XXLTokenizer (line 11) | class T5XXLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 12) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class LTXVT5Tokenizer (line 17) | class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 18) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  function ltxv_te (line 22) | def ltxv_te(*args, **kwargs):
  class Gemma3_Tokenizer (line 26) | class Gemma3_Tokenizer():
    method state_dict (line 27) | def state_dict(self):
    method tokenize_with_weights (line 30) | def tokenize_with_weights(self, text, return_word_ids=False, image=Non...
  class Gemma3_12BTokenizer (line 72) | class Gemma3_12BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
    method __init__ (line 73) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class LTXAVGemmaTokenizer (line 79) | class LTXAVGemmaTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 80) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class Gemma3_12BModel (line 84) | class Gemma3_12BModel(sd1_clip.SDClipModel):
    method __init__ (line 85) | def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=No...
    method generate (line 94) | def generate(self, tokens, do_sample, max_length, temperature, top_k, ...
  class DualLinearProjection (line 100) | class DualLinearProjection(torch.nn.Module):
    method __init__ (line 101) | def __init__(self, in_dim, out_dim_video, out_dim_audio, dtype=None, d...
    method forward (line 106) | def forward(self, x):
  class LTXAVTEModel (line 115) | class LTXAVTEModel(torch.nn.Module):
    method __init__ (line 116) | def __init__(self, dtype_llama=None, device="cpu", dtype=None, text_pr...
    method enable_compat_mode (line 134) | def enable_compat_mode(self):  # TODO: remove
    method set_clip_options (line 156) | def set_clip_options(self, options):
    method reset_clip_options (line 160) | def reset_clip_options(self):
    method encode_token_weights (line 164) | def encode_token_weights(self, token_weight_pairs):
    method generate (line 192) | def generate(self, tokens, do_sample, max_length, temperature, top_k, ...
    method load_sd (line 195) | def load_sd(self, sd):
    method memory_estimation_function (line 225) | def memory_estimation_function(self, token_weight_pairs, device=None):
  function ltxav_te (line 237) | def ltxav_te(dtype_llama=None, llama_quantization_metadata=None, text_pr...
  function sd_detect (line 249) | def sd_detect(state_dict_list, prefix=""):
  function gemma3_te (line 258) | def gemma3_te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/lumina2.py
  class Gemma2BTokenizer (line 7) | class Gemma2BTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 8) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method state_dict (line 13) | def state_dict(self):
  class Gemma3_4BTokenizer (line 16) | class Gemma3_4BTokenizer(Gemma3_Tokenizer, sd1_clip.SDTokenizer):
    method __init__ (line 17) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class LuminaTokenizer (line 22) | class LuminaTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 23) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class NTokenizer (line 26) | class NTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 27) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class Gemma2_2BModel (line 30) | class Gemma2_2BModel(sd1_clip.SDClipModel):
    method __init__ (line 31) | def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=N...
  class Gemma3_4BModel (line 34) | class Gemma3_4BModel(sd1_clip.SDClipModel):
    method __init__ (line 35) | def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=N...
  class Gemma3_4B_Vision_Model (line 43) | class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel):
    method __init__ (line 44) | def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=N...
    method process_tokens (line 52) | def process_tokens(self, tokens, device):
  class LuminaModel (line 57) | class LuminaModel(sd1_clip.SD1ClipModel):
    method __init__ (line 58) | def __init__(self, device="cpu", dtype=None, model_options={}, name="g...
  function te (line 62) | def te(dtype_llama=None, llama_quantization_metadata=None, model_type="g...

FILE: comfy/text_encoders/newbie.py
  class NewBieTokenizer (line 7) | class NewBieTokenizer:
    method __init__ (line 8) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 12) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 18) | def untokenize(self, token_weight_pair):
    method state_dict (line 21) | def state_dict(self):
  class NewBieTEModel (line 24) | class NewBieTEModel(torch.nn.Module):
    method __init__ (line 25) | def __init__(self, dtype_gemma=None, device="cpu", dtype=None, model_o...
    method set_clip_options (line 32) | def set_clip_options(self, options):
    method reset_clip_options (line 36) | def reset_clip_options(self):
    method encode_token_weights (line 40) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 49) | def load_sd(self, sd):
  function te (line 55) | def te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/omnigen2.py
  class Qwen25_3BTokenizer (line 7) | class Qwen25_3BTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 8) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class Omnigen2Tokenizer (line 13) | class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 14) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 18) | def tokenize_with_weights(self, text, return_word_ids=False, llama_tem...
  class Qwen25_3BModel (line 25) | class Qwen25_3BModel(sd1_clip.SDClipModel):
    method __init__ (line 26) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class Omnigen2Model (line 30) | class Omnigen2Model(sd1_clip.SD1ClipModel):
    method __init__ (line 31) | def __init__(self, device="cpu", dtype=None, model_options={}):
  function te (line 35) | def te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/ovis.py
  class Qwen3Tokenizer (line 8) | class Qwen3Tokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 9) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class OvisTokenizer (line 14) | class OvisTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 15) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 19) | def tokenize_with_weights(self, text, return_word_ids=False, llama_tem...
  class Ovis25_2BModel (line 28) | class Ovis25_2BModel(sd1_clip.SDClipModel):
    method __init__ (line 29) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class OvisTEModel (line 33) | class OvisTEModel(sd1_clip.SD1ClipModel):
    method __init__ (line 34) | def __init__(self, device="cpu", dtype=None, model_options={}):
    method encode_token_weights (line 37) | def encode_token_weights(self, token_weight_pairs, template_end=-1):
  function te (line 58) | def te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/pixart_t5.py
  class T5XXLModel (line 10) | class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
    method __init__ (line 11) | def __init__(self, **kwargs):
    method gen_empty_tokens (line 14) | def gen_empty_tokens(self, special_tokens, *args, **kwargs):
  class PixArtT5XXL (line 20) | class PixArtT5XXL(sd1_clip.SD1ClipModel):
    method __init__ (line 21) | def __init__(self, device="cpu", dtype=None, model_options={}):
  class T5XXLTokenizer (line 24) | class T5XXLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 25) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class PixArtTokenizer (line 29) | class PixArtTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 30) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  function pixart_te (line 33) | def pixart_te(dtype_t5=None, t5_quantization_metadata=None):

FILE: comfy/text_encoders/qwen_image.py
  class Qwen25_7BVLITokenizer (line 8) | class Qwen25_7BVLITokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 9) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class QwenImageTokenizer (line 14) | class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 15) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 20) | def tokenize_with_weights(self, text, return_word_ids=False, llama_tem...
  class Qwen25_7BVLIModel (line 52) | class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
    method __init__ (line 53) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class QwenImageTEModel (line 57) | class QwenImageTEModel(sd1_clip.SD1ClipModel):
    method __init__ (line 58) | def __init__(self, device="cpu", dtype=None, model_options={}):
    method encode_token_weights (line 61) | def encode_token_weights(self, token_weight_pairs, template_end=-1):
  function te (line 88) | def te(dtype_llama=None, llama_quantization_metadata=None):

FILE: comfy/text_encoders/qwen_vl.py
  function process_qwen2vl_images (line 9) | def process_qwen2vl_images(
  class VisionPatchEmbed (line 91) | class VisionPatchEmbed(nn.Module):
    method __init__ (line 92) | def __init__(
    method forward (line 119) | def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
  function rotate_half (line 127) | def rotate_half(x):
  function apply_rotary_pos_emb_vision (line 133) | def apply_rotary_pos_emb_vision(q, k, cos, sin):
  class VisionRotaryEmbedding (line 140) | class VisionRotaryEmbedding(nn.Module):
    method __init__ (line 141) | def __init__(self, dim: int, theta: float = 10000.0):
    method forward (line 146) | def forward(self, seqlen: int, device) -> torch.Tensor:
  class PatchMerger (line 153) | class PatchMerger(nn.Module):
    method __init__ (line 154) | def __init__(self, dim: int, context_dim: int, spatial_merge_size: int...
    method forward (line 164) | def forward(self, x: torch.Tensor) -> torch.Tensor:
  class VisionAttention (line 170) | class VisionAttention(nn.Module):
    method __init__ (line 171) | def __init__(self, hidden_size: int, num_heads: int, device=None, dtyp...
    method forward (line 181) | def forward(
  class VisionMLP (line 223) | class VisionMLP(nn.Module):
    method __init__ (line 224) | def __init__(self, hidden_size: int, intermediate_size: int, device=No...
    method forward (line 231) | def forward(self, hidden_state):
  class VisionBlock (line 235) | class VisionBlock(nn.Module):
    method __init__ (line 236) | def __init__(self, hidden_size: int, intermediate_size: int, num_heads...
    method forward (line 243) | def forward(
  class Qwen2VLVisionTransformer (line 263) | class Qwen2VLVisionTransformer(nn.Module):
    method __init__ (line 264) | def __init__(
    method get_window_index (line 313) | def get_window_index(self, grid_thw):
    method get_position_embeddings (line 357) | def get_position_embeddings(self, grid_thw, device):
    method forward (line 386) | def forward(

FILE: comfy/text_encoders/sa_t5.py
  class T5BaseModel (line 6) | class T5BaseModel(sd1_clip.SDClipModel):
    method __init__ (line 7) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class T5BaseTokenizer (line 11) | class T5BaseTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 12) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class SAT5Tokenizer (line 16) | class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 17) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class SAT5Model (line 20) | class SAT5Model(sd1_clip.SD1ClipModel):
    method __init__ (line 21) | def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):

FILE: comfy/text_encoders/sd2_clip.py
  class SD2ClipHModel (line 4) | class SD2ClipHModel(sd1_clip.SDClipModel):
    method __init__ (line 5) | def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freez...
  class SD2ClipHTokenizer (line 13) | class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 14) | def __init__(self, tokenizer_path=None, embedding_directory=None, toke...
  class SD2Tokenizer (line 17) | class SD2Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 18) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class SD2ClipModel (line 21) | class SD2ClipModel(sd1_clip.SD1ClipModel):
    method __init__ (line 22) | def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):

FILE: comfy/text_encoders/sd3_clip.py
  class T5XXLModel (line 11) | class T5XXLModel(sd1_clip.SDClipModel):
    method __init__ (line 12) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  function t5_xxl_detect (line 23) | def t5_xxl_detect(state_dict, prefix=""):
  class T5XXLTokenizer (line 35) | class T5XXLTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 36) | def __init__(self, embedding_directory=None, tokenizer_data={}, min_le...
  class SD3Tokenizer (line 41) | class SD3Tokenizer:
    method __init__ (line 42) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method tokenize_with_weights (line 47) | def tokenize_with_weights(self, text:str, return_word_ids=False, **kwa...
    method untokenize (line 54) | def untokenize(self, token_weight_pair):
    method state_dict (line 57) | def state_dict(self):
  class SD3ClipModel (line 60) | class SD3ClipModel(torch.nn.Module):
    method __init__ (line 61) | def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t...
    method set_clip_options (line 86) | def set_clip_options(self, options):
    method reset_clip_options (line 94) | def reset_clip_options(self):
    method encode_token_weights (line 102) | def encode_token_weights(self, token_weight_pairs):
    method load_sd (line 152) | def load_sd(self, sd):
  function sd3_clip (line 160) | def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_quanti...

FILE: comfy/text_encoders/spiece_tokenizer.py
  class SPieceTokenizer (line 4) | class SPieceTokenizer:
    method from_pretrained (line 6) | def from_pretrained(path, **kwargs):
    method __init__ (line 9) | def __init__(self, tokenizer_path, add_bos=False, add_eos=True, specia...
    method get_vocab (line 24) | def get_vocab(self):
    method __call__ (line 30) | def __call__(self, string):
    method decode (line 50) | def decode(self, token_ids, skip_special_tokens=False):
    method serialize_model (line 58) | def serialize_model(self):

FILE: comfy/text_encoders/t5.py
  class T5LayerNorm (line 6) | class T5LayerNorm(torch.nn.Module):
    method __init__ (line 7) | def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, ope...
    method forward (line 12) | def forward(self, x):
  class T5DenseActDense (line 22) | class T5DenseActDense(torch.nn.Module):
    method __init__ (line 23) | def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, op...
    method forward (line 30) | def forward(self, x):
  class T5DenseGatedActDense (line 36) | class T5DenseGatedActDense(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, op...
    method forward (line 45) | def forward(self, x):
  class T5LayerFF (line 53) | class T5LayerFF(torch.nn.Module):
    method __init__ (line 54) | def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype,...
    method forward (line 64) | def forward(self, x):
  class T5Attention (line 71) | class T5Attention(torch.nn.Module):
    method __init__ (line 72) | def __init__(self, model_dim, inner_dim, num_heads, relative_attention...
    method _relative_position_bucket (line 89) | def _relative_position_bucket(relative_position, bidirectional=True, n...
    method compute_bias (line 136) | def compute_bias(self, query_length, key_length, device, dtype):
    method forward (line 151) | def forward(self, x, mask=None, past_bias=None, optimized_attention=No...
  class T5LayerSelfAttention (line 167) | class T5LayerSelfAttention(torch.nn.Module):
    method __init__ (line 168) | def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_a...
    method forward (line 174) | def forward(self, x, mask=None, past_bias=None, optimized_attention=No...
  class T5Block (line 180) | class T5Block(torch.nn.Module):
    method __init__ (line 181) | def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_...
    method forward (line 187) | def forward(self, x, mask=None, past_bias=None, optimized_attention=No...
  class T5Stack (line 192) | class T5Stack(torch.nn.Module):
    method __init__ (line 193) | def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activa...
    method forward (line 202) | def forward(self, x, attention_mask=None, intermediate_output=None, fi...
  class T5 (line 225) | class T5(torch.nn.Module):
    method __init__ (line 226) | def __init__(self, config_dict, dtype, device, operations):
    method get_input_embeddings (line 236) | def get_input_embeddings(self):
    method set_input_embeddings (line 239) | def set_input_embeddings(self, embeddings):
    method forward (line 242) | def forward(self, input_ids, attention_mask, embeds=None, num_tokens=N...

FILE: comfy/text_encoders/wan.py
  class UMT5XXlModel (line 6) | class UMT5XXlModel(sd1_clip.SDClipModel):
    method __init__ (line 7) | def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=N...
  class UMT5XXlTokenizer (line 11) | class UMT5XXlTokenizer(sd1_clip.SDTokenizer):
    method __init__ (line 12) | def __init__(self, embedding_directory=None, tokenizer_data={}):
    method state_dict (line 16) | def state_dict(self):
  class WanT5Tokenizer (line 20) | class WanT5Tokenizer(sd1_clip.SD1Tokenizer):
    method __init__ (line 21) | def __init__(self, embedding_directory=None, tokenizer_data={}):
  class WanT5Model (line 24) | class WanT5Model(sd1_clip.SD1ClipModel):
    method __init__ (line 25) | def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
  function te (line 28) | def te(dtype_t5=None, t5_quantization_metadata=None):

FILE: comfy/utils.py
  class ModelCheckpoint (line 45) | class ModelCheckpoint:
  function scalar (line 49) | def scalar(*args, **kwargs):
  function encode (line 56) | def encode(*args, **kwargs):  # no longer necessary on newer torch
  function load_safetensors (line 85) | def load_safetensors(ckpt):
  function load_torch_file (line 122) | def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=...
  function save_torch_file (line 169) | def save_torch_file(sd, ckpt, metadata=None):
  function calculate_parameters (line 175) | def calculate_parameters(sd, prefix=""):
  function weight_dtype (line 183) | def weight_dtype(sd, prefix=""):
  function state_dict_key_replace (line 195) | def state_dict_key_replace(state_dict, keys_to_replace):
  function state_dict_prefix_replace (line 201) | def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=Fa...
  function transformers_convert (line 214) | def transformers_convert(sd, prefix_from, prefix_to, number):
  function clip_text_transformers_convert (line 255) | def clip_text_transformers_convert(sd, prefix_from, prefix_to):
  function unet_to_diffusers (line 336) | def unet_to_diffusers(unet_config):
  function swap_scale_shift (line 402) | def swap_scale_shift(weight):
  function mmdit_to_diffusers (line 454) | def mmdit_to_diffusers(mmdit_config, output_prefix=""):
  function pixart_to_diffusers (line 537) | def pixart_to_diffusers(mmdit_config, output_prefix=""):
  function auraflow_to_diffusers (line 570) | def auraflow_to_diffusers(mmdit_config, output_prefix=""):
  function flux_to_diffusers (line 639) | def flux_to_diffusers(mmdit_config, output_prefix=""):
  function z_image_to_diffusers (line 755) | def z_image_to_diffusers(mmdit_config, output_prefix=""):
  function repeat_to_batch_size (line 821) | def repeat_to_batch_size(tensor, batch_size, dim=0):
  function resize_to_batch_size (line 828) | def resize_to_batch_size(tensor, batch_size):
  function resize_list_to_batch_size (line 848) | def resize_list_to_batch_size(l, batch_size):
  function convert_sd_to (line 868) | def convert_sd_to(state_dict, dtype):
  function safetensors_header (line 874) | def safetensors_header(safetensors_path, max_size=100*1024*1024):
  function resolve_attr (line 884) | def resolve_attr(obj, attr):
  function set_attr (line 890) | def set_attr(obj, attr, value):
  function set_attr_param (line 899) | def set_attr_param(obj, attr, value):
  function set_attr_buffer (line 906) | def set_attr_buffer(obj, attr, value):
  function copy_to_param (line 913) | def copy_to_param(obj, attr, value):
  function get_attr (line 921) | def get_attr(obj, attr: str):
  function bislerp (line 945) | def bislerp(samples, width, height):
  function lanczos (line 1021) | def lanczos(samples, width, height):
  function common_upscale (line 1030) | def common_upscale(samples, width, height, upscale_method, crop):
  function get_tiled_scale_steps (line 1064) | def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
  function tiled_scale_multidim (line 1070) | def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, up...
  function tiled_scale (line 1180) | def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, up...
  function model_trange (line 1183) | def model_trange(*args, **kwargs):
  function set_progress_bar_enabled (line 1210) | def set_progress_bar_enabled(enabled):
  function set_progress_bar_global_hook (line 1215) | def set_progress_bar_global_hook(function):
  class ProgressBar (line 1223) | class ProgressBar:
    method __init__ (line 1224) | def __init__(self, total, node_id=None):
    method update_absolute (line 1233) | def update_absolute(self, value, total=None, preview=None):
    method update (line 1264) | def update(self, value):
  function reshape_mask (line 1267) | def reshape_mask(input_mask, output_shape):
  function upscale_dit_mask (line 1288) | def upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out):
  function pack_latents (line 1329) | def pack_latents(latents):
  function unpack_latents (line 1339) | def unpack_latents(combined_latent, latent_shapes):
  function detect_layer_quantization (line 1351) | def detect_layer_quantization(state_dict, prefix):
  function convert_old_quants (line 1358) | def convert_old_quants(state_dict, model_prefix="", metadata={}):
  function string_to_seed (line 1418) | def string_to_seed(data):
  function deepcopy_list_dict (line 1431) | def deepcopy_list_dict(obj, memo=None):
  function normalize_image_embeddings (line 1449) | def normalize_image_embeddings(embeds, embeds_info, scale_factor):

FILE: comfy/weight_adapter/glora.py
  class GLoRAAdapter (line 10) | class GLoRAAdapter(WeightAdapterBase):
    method __init__ (line 13) | def __init__(self, loaded_keys, weights):
    method load (line 18) | def load(
    method calculate_weight (line 49) | def calculate_weight(
    method _compute_paths (line 143) | def _compute_paths(self, x: torch.Tensor):
    method bypass_forward (line 246) | def bypass_forward(
    method h (line 273) | def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:

FILE: comfy/weight_adapter/lokr.py
  class LokrDiff (line 15) | class LokrDiff(WeightAdapterTrainBase):
    method __init__ (line 16) | def __init__(self, weights):
    method w1 (line 60) | def w1(self):
    method w2 (line 67) | def w2(self):
    method __call__ (line 82) | def __call__(self, w):
    method h (line 91) | def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:
    method passive_memory_usage (line 166) | def passive_memory_usage(self):
  class LoKrAdapter (line 170) | class LoKrAdapter(WeightAdapterBase):
    method __init__ (line 173) | def __init__(self, loaded_keys, weights):
    method create_train (line 178) | def create_train(cls, weight, rank=1, alpha=1.0):
    method to_train (line 197) | def to_train(self):
    method load (line 201) | def load(
    method calculate_weight (line 275) | def calculate_weight(
    method h (line 366) | def h(self, x: torch.Tensor, base_out: torch.Tensor) -> torch.Tensor:

FILE: comfy/windows.py
  class PERFORMANCE_INFORMATION (line 11) | class PERFORMANCE_INFORMATION(ctypes.Structure):
  function get_free_ram (line 29) | def get_free_ram():