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Default to sampling entire image

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By default, when applying a mask to a condition, the entire image will
still be used for sampling. The new "set_area_to_bounds" option on the
node will allow the user to automatically limit conditioning to the
bounds of the mask.

I've also removed the dependency on torchvision for calculating bounding
boxes. I've taken the opportunity to fix some frustrating details in the
other version:
1. An all-0 mask will no longer cause an error
2. Indices are returned as integers instead of floats so they can be
   used to index into tensors.
This commit is contained in:
Jacob Segal
2023-04-29 00:16:58 -07:00
parent e214c917ae
commit af02393c2a
2 changed files with 35 additions and 11 deletions
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4
nodes.py
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@@ -90,6 +90,7 @@ class ConditioningSetMask:
def INPUT_TYPES(s):
return {"required": {"conditioning": ("CONDITIONING", ),
"mask": ("MASK", ),
"set_area_to_bounds": ([False, True],),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CONDITIONING",)
@@ -97,7 +98,7 @@ class ConditioningSetMask:
CATEGORY = "conditioning"
def append(self, conditioning, mask, strength, min_sigma=0.0, max_sigma=99.0):
def append(self, conditioning, mask, set_area_to_bounds, strength, min_sigma=0.0, max_sigma=99.0):
c = []
if len(mask.shape) < 3:
mask = mask.unsqueeze(0)
@@ -105,6 +106,7 @@ class ConditioningSetMask:
n = [t[0], t[1].copy()]
_, h, w = mask.shape
n[1]['mask'] = mask
n[1]['set_area_to_bounds'] = set_area_to_bounds
n[1]['strength'] = strength
n[1]['min_sigma'] = min_sigma
n[1]['max_sigma'] = max_sigma