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fix(grading): color grading type coercion, CLAHE, sharpness and tone scaling

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- add __post_init__ to GradingParams to coerce Gradio int returns to float
- apply CLAHE to L channel in Lab space instead of per-RGB channel
- fix sharpness scaling (kornia factor 1.0=no change, map 0-based slider)
- increase shadows/midtones/highlights gamma strength 2x for visible effect
- reduce CLAHE clip slider range to 0-5 with finer 0.25 step
This commit is contained in:
CalamitousFelicitousness
2026-03-19 04:00:12 +00:00
parent cb943919eb
commit ee2fc75619
2 changed files with 19 additions and 6 deletions
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modules/processing_grading.py
+18 -5
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@@ -69,6 +69,11 @@ class GradingParams:
lut_file: str = ""
lut_strength: float = 1.0
def __post_init__(self):
for f in fields(self):
if f.type is float:
setattr(self, f.name, float(getattr(self, f.name)))
_defaults = GradingParams()
@@ -112,18 +117,22 @@ def _apply_shadows_midtones_highlights(img: torch.Tensor, shadows: float, midton
kornia = _ensure_kornia()
lab = kornia.color.rgb_to_lab(img)
L = lab[:, 0:1, :, :] / 100.0 # normalize to [0, 1]
strength = 2.0 # scale slider values for more visible effect
if shadows != 0:
s = shadows * strength
shadow_mask = (1.0 - L).clamp(0, 1) ** 2
gamma = 1.0 / (1.0 + shadows) if shadows > 0 else 1.0 - shadows
gamma = 1.0 / (1.0 + s) if s > 0 else 1.0 - s
L = L + shadow_mask * (L.clamp(min=1e-6) ** gamma - L)
if highlights != 0:
h = highlights * strength
highlight_mask = L.clamp(0, 1) ** 2
gamma = 1.0 / (1.0 + highlights) if highlights > 0 else 1.0 - highlights
gamma = 1.0 / (1.0 + h) if h > 0 else 1.0 - h
L = L + highlight_mask * (L.clamp(min=1e-6) ** gamma - L)
if midtones != 0:
m = midtones * strength
mid_mask = 1.0 - 2.0 * (L - 0.5).abs()
mid_mask = mid_mask.clamp(0, 1) ** 2
gamma = 1.0 / (1.0 + midtones) if midtones > 0 else 1.0 - midtones
gamma = 1.0 / (1.0 + m) if m > 0 else 1.0 - m
L = L + mid_mask * (L.clamp(min=1e-6) ** gamma - L)
lab[:, 0:1, :, :] = L.clamp(0, 1) * 100.0
return kornia.color.lab_to_rgb(lab).clamp(0, 1)
@@ -207,7 +216,7 @@ def grade_image(image: Image.Image, params: GradingParams) -> Image.Image:
if params.gamma != 1.0:
tensor = kornia.enhance.adjust_gamma(tensor, params.gamma)
if params.sharpness != 0:
tensor = kornia.enhance.sharpness(tensor, params.sharpness)
tensor = kornia.enhance.sharpness(tensor, 1.0 + params.sharpness * 4.0)
if params.color_temp != 6500:
tensor = _apply_color_temp(tensor, params.color_temp)
@@ -215,7 +224,11 @@ def grade_image(image: Image.Image, params: GradingParams) -> Image.Image:
if params.shadows != 0 or params.midtones != 0 or params.highlights != 0:
tensor = _apply_shadows_midtones_highlights(tensor, params.shadows, params.midtones, params.highlights)
if params.clahe_clip > 0:
tensor = kornia.enhance.equalize_clahe(tensor, clip_limit=params.clahe_clip, grid_size=(params.clahe_grid, params.clahe_grid))
lab = kornia.color.rgb_to_lab(tensor)
L = lab[:, 0:1, :, :] / 100.0
L = kornia.enhance.equalize_clahe(L, clip_limit=params.clahe_clip, grid_size=(params.clahe_grid, params.clahe_grid))
lab[:, 0:1, :, :] = L * 100.0
tensor = kornia.color.lab_to_rgb(lab).clamp(0, 1)
# split toning
if params.shadows_tint != "#000000" or params.highlights_tint != "#ffffff":