# Copyright (c) 2023, Teriks
#
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import diffusers
import dgenerate.hfhub as _hfhub
import dgenerate.memoize as _d_memoize
import dgenerate.memory as _memory
import dgenerate.messages as _messages
import dgenerate.pipelinewrapper.constants as _constants
import dgenerate.pipelinewrapper.enums as _enums
import dgenerate.pipelinewrapper.util as _pipelinewrapper_util
import dgenerate.textprocessing as _textprocessing
import dgenerate.types as _types
from dgenerate.memoize import memoize as _memoize
from dgenerate.pipelinewrapper.uris import exceptions as _exceptions
from dgenerate.pipelinewrapper.uris import util as _util
_t2i_adapter_uri_parser = _textprocessing.ConceptUriParser(
'T2IAdapter', ['scale', 'revision', 'variant', 'subfolder', 'dtype']
)
_t2i_adapter_cache = _d_memoize.create_object_cache(
't2i_adapter', cache_type=_memory.SizedConstrainedObjectCache
)
[docs]
class T2IAdapterUri:
"""
Representation of ``--t2i-adapters`` URI.
"""
# pipelinewrapper.uris.util.get_uri_accepted_args_schema metadata
NAMES = ['T2I Adapter']
[docs]
@staticmethod
def help():
import dgenerate.arguments as _a
return _a.get_raw_help_text('--t2i-adapters')
OPTION_ARGS = {
'dtype': ['float16', 'bfloat16', 'float32']
}
FILE_ARGS = {
'model': {'mode': ['in', 'dir'], 'filetypes': [('Models', ['*.safetensors', '*.pt', '*.pth', '*.cpkt', '*.bin'])]}
}
# ===
@property
def model(self) -> str:
"""
Model path, huggingface slug
"""
return self._model
@property
def revision(self) -> _types.OptionalString:
"""
Model repo revision
"""
return self._revision
@property
def variant(self) -> _types.OptionalString:
"""
Model repo revision
"""
return self._variant
@property
def subfolder(self) -> _types.OptionalPath:
"""
Model repo subfolder
"""
return self._subfolder
@property
def dtype(self) -> _enums.DataType | None:
"""
Model dtype (precision)
"""
return self._dtype
@property
def scale(self) -> float:
"""
T2IAdapter scale
"""
return self._scale
[docs]
def __init__(self,
model: str,
revision: _types.OptionalString,
variant: _types.OptionalString,
subfolder: _types.OptionalPath,
dtype: _enums.DataType | str | None = None,
scale: float = 1.0):
"""
:param model: model path
:param revision: model revision (branch name)
:param variant: model variant, for example ``fp16``
:param subfolder: model subfolder
:param dtype: model data type (precision)
:param scale: t2i adapter scale
:raises InvalidT2IAdapterUriError: If ``dtype`` is passed an invalid data type string.
"""
self._model = model
self._revision = revision
self._variant = variant
self._subfolder = subfolder
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise _exceptions.InvalidT2IAdapterUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._scale = scale
[docs]
def load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False,
no_cache: bool = False) -> diffusers.T2IAdapter:
"""
Load a :py:class:`diffusers.T2IAdapter` from this URI.
:param dtype_fallback: Fallback datatype if ``dtype`` was not specified in the URI.
:param use_auth_token: Optional huggingface API auth token, used for downloading
restricted repos that your account has access to.
:param local_files_only: Avoid connecting to huggingface to download models and
only use cached models?
:param no_cache: If True, force the returned object not to be cached by the memoize decorator.
:raises ModelNotFoundError: If the model could not be found.
:return: :py:class:`diffusers.T2IAdapter`
"""
def cache_all(e):
raise _exceptions.T2IAdapterUriLoadError(
f'error loading t2i adapter "{self.model}": {e}') from e
with _hfhub.with_hf_errors_as_model_not_found(cache_all):
args = locals()
args.pop('self')
args.pop('cache_all')
return self._load(**args)
@staticmethod
def _enforce_cache_size(new_adapter_size):
_t2i_adapter_cache.enforce_cpu_mem_constraints(
_constants.ADAPTER_CACHE_MEMORY_CONSTRAINTS,
size_var='adapter_size',
new_object_size=new_adapter_size)
@_memoize(_t2i_adapter_cache,
exceptions={'local_files_only'},
hasher=lambda args: _d_memoize.args_cache_key(
args, {'self': lambda o: _d_memoize.property_hasher(
o, exclude={'scale'})}),
on_hit=lambda key, hit: _d_memoize.simple_cache_hit_debug("Torch T2IAdapter", key, hit),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Torch T2IAdapter", key, new))
def _load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False,
no_cache: bool = False) -> diffusers.T2IAdapter:
model_path = _hfhub.download_non_hf_slug_model(self.model)
single_file_load_path = _hfhub.is_single_file_model_load(model_path)
torch_dtype = _enums.get_torch_dtype(
dtype_fallback if self.dtype is None else self.dtype)
if single_file_load_path:
estimated_memory_usage = _pipelinewrapper_util.estimate_model_memory_use(
repo_id=model_path,
revision=self.revision,
use_auth_token=use_auth_token,
local_files_only=local_files_only
)
self._enforce_cache_size(estimated_memory_usage)
new_adapter = diffusers.T2IAdapter.from_single_file(
model_path,
revision=self.revision,
torch_dtype=torch_dtype,
token=use_auth_token,
local_files_only=local_files_only)
else:
estimated_memory_usage = _pipelinewrapper_util.estimate_model_memory_use(
repo_id=model_path,
revision=self.revision,
variant=self.variant,
subfolder=self.subfolder,
use_auth_token=use_auth_token,
local_files_only=local_files_only
)
self._enforce_cache_size(estimated_memory_usage)
new_adapter = diffusers.T2IAdapter.from_pretrained(
model_path,
revision=self.revision,
variant=self.variant,
subfolder=self.subfolder,
torch_dtype=torch_dtype,
token=use_auth_token,
local_files_only=local_files_only)
_messages.debug_log('Estimated Torch T2IAdapter Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_usage))
_util._patch_module_to_for_sized_cache(_t2i_adapter_cache, new_adapter)
# noinspection PyTypeChecker
return new_adapter, _d_memoize.CachedObjectMetadata(
size=estimated_memory_usage,
skip=no_cache
)
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'T2IAdapterUri':
"""
Parse a ``--t2i-adapters`` uri specification and
return an object representing its constituents
:param uri: string with ``--t2i-adapters`` uri syntax
:raise InvalidT2IAdapterUriError:
:return: :py:class:`.T2IAdapterUri`
"""
try:
r = _t2i_adapter_uri_parser.parse(uri)
dtype = r.args.get('dtype')
scale = r.args.get('scale', 1.0)
supported_dtypes = _enums.supported_data_type_strings()
if dtype is not None and dtype not in supported_dtypes:
raise _exceptions.InvalidT2IAdapterUriError(
f'Torch T2IAdapter "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
try:
scale = float(scale)
except ValueError:
raise _exceptions.InvalidT2IAdapterUriError(
f'Torch T2IAdapter "scale" must be a floating point number, received: {scale}')
return T2IAdapterUri(
model=r.concept,
revision=r.args.get('revision', None),
variant=r.args.get('variant', None),
subfolder=r.args.get('subfolder', None),
dtype=dtype,
scale=scale)
except _textprocessing.ConceptUriParseError as e:
raise _exceptions.InvalidT2IAdapterUriError(e)