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Add VTO lighting controls.

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This commit is contained in:
Ryan Dick 2024-07-25 18:00:43 -04:00
parent 6fc3c959e0
commit 5dd0aeb1ab
1 changed files with 26 additions and 2 deletions
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28
invokeai/app/invocations/vto_workflow.py
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@ -10,14 +10,27 @@ from invokeai.backend.vto_workflow.overlay_pattern import multiply_images
from invokeai.backend.vto_workflow.seamless_mapping import map_seamless_tiles
@invocation("vto", title="Virtual Try-On", tags=["vto"], category="vto", version="1.0.0")
@invocation("vto", title="Virtual Try-On", tags=["vto"], category="vto", version="1.1.0")
class VTOInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Virtual try-on."""
original_image: ImageField = InputField(description="The input image")
clothing_mask: ImageField = InputField(description="Clothing mask.")
pattern_image: ImageField = InputField(description="Pattern image.")
pattern_vertical_repeats: int = InputField(description="Number of vertical repeats for the pattern.", default=1)
pattern_vertical_repeats: float = InputField(
description="Number of vertical repeats for the pattern.", gt=0.01, default=1.0
)
shading_max: float = InputField(
description="The lightness of the light spots on the clothing. Default is 1.0. Typically in the range [0.7, 1.2]. Must be > shading_min",
default=1.0,
ge=0.0,
)
shading_min: float = InputField(
description="The lightness of the dark spots on the clothing. Default id 0.5. Typically in the range [0.2, 0.7]",
default=0.5,
ge=0.0,
)
def invoke(self, context: InvocationContext) -> ImageOutput:
# TODO(ryand): Avoid all the unnecessary flip-flopping between PIL and numpy.
@ -27,6 +40,17 @@ class VTOInvocation(BaseInvocation, WithMetadata, WithBoard):
shadows = extract_channel(np.array(original_image), ImageChannel.LAB_L)
# Clip the shadows to the 0.05 and 0.95 percentiles to eliminate outliers.
shadows = np.clip(shadows, np.percentile(shadows, 5), np.percentile(shadows, 95))
# Normalize the shadows to the range [shading_min, shading_max].
assert self.shading_min < self.shading_max
shadows = shadows.astype(np.float32)
shadows = (shadows - shadows.min()) / (shadows.max() - shadows.min())
shadows = self.shading_min + (self.shading_max - self.shading_min) * shadows
shadows = np.clip(shadows, 0.0, 1.0)
shadows = (shadows * 255).astype(np.uint8)
expanded_pattern = map_seamless_tiles(
seamless_tile=pattern_image,
target_hw=(original_image.height, original_image.width),