commit
2fec94710b
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@ -572,7 +572,7 @@ class LatentDiffusionV1(DDPMV1):
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:param h: height
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:param h: height
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:param w: width
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:param w: width
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:return: normalized distance to image border,
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:return: normalized distance to image border,
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wtith min distance = 0 at border and max dist = 0.5 at image center
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with min distance = 0 at border and max dist = 0.5 at image center
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"""
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"""
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lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
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lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
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arr = self.meshgrid(h, w) / lower_right_corner
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arr = self.meshgrid(h, w) / lower_right_corner
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@ -372,7 +372,7 @@ class Api:
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return {}
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return {}
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possible_fields = infotext_utils.paste_fields[tabname]["fields"]
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possible_fields = infotext_utils.paste_fields[tabname]["fields"]
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set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have differenrt names for this
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set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have different names for this
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params = infotext_utils.parse_generation_parameters(request.infotext)
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params = infotext_utils.parse_generation_parameters(request.infotext)
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def get_field_value(field, params):
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def get_field_value(field, params):
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@ -268,7 +268,7 @@ def test_for_nans(x, where):
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@lru_cache
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@lru_cache
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def first_time_calculation():
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def first_time_calculation():
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"""
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"""
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just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
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just do any calculation with pytorch layers - the first time this is done it allocates about 700MB of memory and
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spends about 2.7 seconds doing that, at least with NVidia.
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spends about 2.7 seconds doing that, at least with NVidia.
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"""
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"""
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@ -323,7 +323,7 @@ def model_wrapper(
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def model_fn(x, t_continuous, condition, unconditional_condition):
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def model_fn(x, t_continuous, condition, unconditional_condition):
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"""
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"""
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The noise predicition model function that is used for DPM-Solver.
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The noise prediction model function that is used for DPM-Solver.
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"""
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"""
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if t_continuous.reshape((-1,)).shape[0] == 1:
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if t_continuous.reshape((-1,)).shape[0] == 1:
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t_continuous = t_continuous.expand((x.shape[0]))
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t_continuous = t_continuous.expand((x.shape[0]))
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@ -47,7 +47,7 @@ restricted_opts: set[str] = None
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sd_model: sd_models_types.WebuiSdModel = None
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sd_model: sd_models_types.WebuiSdModel = None
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settings_components: dict = None
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settings_components: dict = None
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"""assigned from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
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"""assigned from ui.py, a mapping on setting names to gradio components responsible for those settings"""
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tab_names = []
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tab_names = []
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@ -156,7 +156,7 @@ class MassFileLister:
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def topological_sort(dependencies):
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def topological_sort(dependencies):
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"""Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
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"""Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
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Ignores errors relating to missing dependeencies or circular dependencies
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Ignores errors relating to missing dependencies or circular dependencies
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"""
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"""
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visited = {}
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visited = {}
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