# Copyright (c) 2023, Teriks
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import enum
import diffusers
import huggingface_hub
import dgenerate.memoize as _d_memoize
import dgenerate.memory as _memory
import dgenerate.messages as _messages
import dgenerate.pipelinewrapper.cache as _cache
import dgenerate.pipelinewrapper.enums as _enums
import dgenerate.pipelinewrapper.hfutil as _hfutil
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
_controlnet_uri_parser = _textprocessing.ConceptUriParser(
'ControlNet', ['scale', 'start', 'end', 'mode', 'revision', 'variant', 'subfolder', 'dtype'])
class FluxControlNetUriModes(enum.IntEnum):
"""
Represents control net modes associated with the Flux Union controlnet.
"""
CANNY = 0
TILE = 1
DEPTH = 2
BLUR = 3
POSE = 4
GRAY = 5
LQ = 6
[docs]
class ControlNetUri:
"""
Representation of ``--control-nets`` uri when ``--model-type`` torch*
"""
@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:
"""
ControlNet guidance scale
"""
return self._scale
@property
def start(self) -> float:
"""
ControlNet guidance start point, fraction of inference / timesteps.
"""
return self._start
@property
def end(self) -> float:
"""
ControlNet guidance end point, fraction of inference / timesteps.
"""
return self._end
@property
def mode(self) -> int | None:
"""
Flux Union ControlNet mode.
"""
return self._mode
[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,
start: float = 0.0,
end: float = 1.0,
mode: int | str | FluxControlNetUriModes | None = None):
"""
: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: control net scale
:param start: control net guidance start value
:param end: control net guidance end value
:param mode: Flux Union control net mode.
:raises InvalidControlNetUriError: If ``dtype`` is passed an invalid data type string.
"""
self._model = model
self._revision = revision
self._variant = variant
self._subfolder = subfolder
if isinstance(mode, str):
self._mode = self._flux_mode_int_from_str(mode)
else:
self._mode = int(mode) if mode is not None else None
try:
self._dtype = _enums.get_data_type_enum(dtype) if dtype else None
except ValueError:
raise _exceptions.InvalidControlNetUriError(
f'invalid dtype string, must be one of: {_textprocessing.oxford_comma(_enums.supported_data_type_strings(), "or")}')
self._scale = scale
self._start = start
self._end = end
[docs]
def load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False,
sequential_cpu_offload_member: bool = False,
model_cpu_offload_member: bool = False,
model_class:
type[diffusers.ControlNetModel] |
type[diffusers.SD3ControlNetModel] |
type[diffusers.FluxControlNetModel] = diffusers.ControlNetModel) -> \
diffusers.ControlNetModel | diffusers.SD3ControlNetModel:
"""
Load a :py:class:`diffusers.ControlNetModel` 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 sequential_cpu_offload_member: This model will be attached to
a pipeline which will have sequential cpu offload enabled?
:param model_cpu_offload_member: This model will be attached to a pipeline
which will have model cpu offload enabled?
:param model_class: What class of control net model should be loaded?
:py:class:`diffusers.ControlNetModel` or :py:class:`diffusers.SD3ControlNetModel`.
The first class is for Stable Diffusion 1 & 2, and the second class is used for
Stable Diffusion 3.
:raises ModelNotFoundError: If the model could not be found.
:return: :py:class:`diffusers.ControlNetModel`, :py:class:`diffusers.SD3ControlNetModel`, or :py:class:`diffusers.FluxControlNetModel`
"""
try:
return self._load(dtype_fallback,
use_auth_token,
local_files_only,
sequential_cpu_offload_member,
model_cpu_offload_member,
model_class)
except (huggingface_hub.utils.HFValidationError,
huggingface_hub.utils.HfHubHTTPError) as e:
raise _hfutil.ModelNotFoundError(e)
except Exception as e:
raise _exceptions.ControlNetUriLoadError(
f'error loading controlnet "{self.model}": {e}')
@_memoize(_cache._CONTROLNET_CACHE,
exceptions={'local_files_only'},
hasher=lambda args: _d_memoize.args_cache_key(
args, {'self': lambda o: _d_memoize.struct_hasher(
o, exclude={'scale', 'start', 'end'})}),
on_hit=lambda key, hit: _d_memoize.simple_cache_hit_debug("Torch ControlNet", key, hit),
on_create=lambda key, new: _d_memoize.simple_cache_miss_debug("Torch ControlNet", key, new))
def _load(self,
dtype_fallback: _enums.DataType = _enums.DataType.AUTO,
use_auth_token: _types.OptionalString = None,
local_files_only: bool = False,
sequential_cpu_offload_member: bool = False,
model_cpu_offload_member: bool = False,
model_class:
type[diffusers.ControlNetModel] |
type[diffusers.SD3ControlNetModel] |
type[diffusers.FluxControlNetModel] = diffusers.ControlNetModel) \
-> diffusers.ControlNetModel | diffusers.SD3ControlNetModel:
if sequential_cpu_offload_member and model_cpu_offload_member:
# these are used for cache differentiation only
raise ValueError('sequential_cpu_offload_member and model_cpu_offload_member cannot both be True.')
if model_class is not diffusers.FluxControlNetModel:
if self.mode is not None:
raise ValueError(
f'The "mode" argument of ControlNet "{self.model}" is invalid to use '
'with non Flux models.'
)
model_path = _hfutil.download_non_hf_model(self.model)
single_file_load_path = _hfutil.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 = _hfutil.estimate_model_memory_use(
repo_id=model_path,
revision=self.revision,
use_auth_token=use_auth_token,
local_files_only=local_files_only
)
_cache.enforce_controlnet_cache_constraints(
new_controlnet_size=estimated_memory_usage)
new_net = model_class.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 = _hfutil.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
)
_cache.enforce_controlnet_cache_constraints(
new_controlnet_size=estimated_memory_usage)
new_net = model_class.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 ControlNet Memory Use:',
_memory.bytes_best_human_unit(estimated_memory_usage))
_cache.controlnet_create_update_cache_info(
controlnet=new_net,
estimated_size=estimated_memory_usage)
return new_net
[docs]
@staticmethod
def parse(uri: _types.Uri) -> 'ControlNetUri':
"""
Parse a ``--model-type`` torch* ``--control-nets`` uri specification and return an object representing its constituents
:param uri: string with ``--control-nets`` uri syntax
:raise InvalidControlNetUriError:
:return: :py:class:`.TorchControlNetUri`
"""
try:
r = _controlnet_uri_parser.parse(uri)
dtype = r.args.get('dtype')
scale = r.args.get('scale', 1.0)
start = r.args.get('start', 0.0)
end = r.args.get('end', 1.0)
mode = r.args.get('mode', None)
supported_dtypes = _enums.supported_data_type_strings()
if dtype is not None and dtype not in supported_dtypes:
raise _exceptions.InvalidControlNetUriError(
f'Torch ControlNet "dtype" must be {", ".join(supported_dtypes)}, '
f'or left undefined, received: {dtype}')
try:
scale = float(scale)
except ValueError:
raise _exceptions.InvalidControlNetUriError(
f'Torch ControlNet "scale" must be a floating point number, received: {scale}')
try:
start = float(start)
except ValueError:
raise _exceptions.InvalidControlNetUriError(
f'Torch ControlNet "start" must be a floating point number, received: {start}')
try:
end = float(end)
except ValueError:
raise _exceptions.InvalidControlNetUriError(
f'Torch ControlNet "end" must be a floating point number, received: {end}')
if start > end:
raise _exceptions.InvalidControlNetUriError(
f'Torch ControlNet "start" must be less than or equal to "end".')
if mode is not None:
mode = ControlNetUri._flux_mode_int_from_str(mode)
return ControlNetUri(
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,
start=start,
end=end,
mode=mode)
except _textprocessing.ConceptUriParseError as e:
raise _exceptions.InvalidControlNetUriError(e)
@staticmethod
def _flux_mode_int_from_str(mode):
modes = _textprocessing.oxford_comma(
[n.name.lower() for n in FluxControlNetUriModes], "or")
try:
try:
mode = int(mode)
except ValueError:
mode = FluxControlNetUriModes[mode.upper()].value
except KeyError as e:
raise _exceptions.InvalidControlNetUriError(
f'Torch Flux Union ControlNet "mode" must be an integer, '
f'or one of: {modes}. received: {mode}')
if mode >= len(FluxControlNetUriModes) or mode < 0:
raise _exceptions.InvalidControlNetUriError(
f'Torch Flux Union ControlNet "mode" must be less than '
f'{len(FluxControlNetUriModes)} and greater than zero, '
f'mode number {mode} does not exist.')
return mode