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chore: bump version to 0.1.2 and update feature header

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MSXYZ-GenAI
2026-04-26 13:50:38 +03:00
parent e43d7c43b1
commit 3fa26aeb52
1 changed files with 82 additions and 26 deletions
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VideoTAADLAA.py
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@@ -1,7 +1,7 @@
# Author : MSXYZ
# Version : 0.1.1
# Version : 0.1.2
# Description : Video TAA + DLAA Pipeline
# Features : Temporal accumulation, DLAA refinement, VRAM-aware tiling
# Features : Temporal accumulation, DLAA refinement, VRAM-aware tiling, preset system (Auto/Balanced/Sharp/Cinematic), adaptive jitter
# Note : Developed with AI-assisted tooling
@@ -19,7 +19,6 @@ except:
logger = logging.getLogger("VideoTAADLAA")
class DLAANet(nn.Module):
""" Inference (~2.1M parameters) """
def __init__(self):
@@ -180,7 +179,13 @@ class TAAState:
motion_soft = motion_soft * motion_soft * (3.0 - 2.0 * motion_soft)
dynamic_alpha = alpha * (1.0 - motion_soft) * edge_guard
confidence = torch.exp(-diff * 10.0)
confidence = confidence * (1.0 - disocclusion)
confidence = confidence.clamp(0.15, 1.0)
dynamic_alpha = dynamic_alpha * confidence
reject_strength = ((diff - sensitivity * 1.5) / (sensitivity + 1e-6)).clamp(0.0, 1.0)
reject_strength = torch.maximum(reject_strength, disocclusion)
reject_strength = reject_strength * reject_strength * (3.0 - 2.0 * reject_strength)
@@ -190,16 +195,8 @@ class TAAState:
out = torch.lerp(frame, history_clipped, dynamic_alpha)
self.history = out.detach()
return out
def _unsharp_mask(x: torch.Tensor, strength: float) -> torch.Tensor:
if strength < 1e-4:
return x
blurred = F.avg_pool2d(F.pad(x, [2, 2, 2, 2], mode="reflect"), 5, stride=1)
return x + (x - blurred) * strength
class VideoTAADLAA:
def __init__(self):
self.net_cache = {}
@@ -239,11 +236,7 @@ class VideoTAADLAA:
return {
"required": {
"images" : ("IMAGE",),
"taa_strength" : ("FLOAT", {"default": 0.45, "min": 0, "max": 1, "step": 0.05}),
"dlaa_strength" : ("FLOAT", {"default": 0.65, "min": 0, "max": 1, "step": 0.05}),
"motion_sensitivity": ("FLOAT", {"default": 0.08, "min": 0, "max": 0.3, "step": 0.01}),
"tone_strength" : ("FLOAT", {"default": 0.12, "min": 0.0, "max": 0.3, "step": 0.01}),
"edge_sharp_strength": ("FLOAT", {"default": 0.14, "min": 0.05, "max": 0.2, "step": 0.01}),
"preset": (["Auto", "Balanced", "Sharp", "Cinematic"],),
}
}
@@ -365,11 +358,46 @@ class VideoTAADLAA:
return x*(1.0 - mask) + blurred*mask
def execute(self, images, taa_strength, motion_sensitivity, dlaa_strength, tone_strength, edge_sharp_strength):
def execute(self, images, preset):
device = mm.get_torch_device() if mm else \
torch.device("cuda" if torch.cuda.is_available() else "cpu")
preset_jitter_scale = self.jitter_scale
if preset == "Balanced":
taa_strength = 0.45
dlaa_strength = 0.65
tone_strength = 0.12
edge_sharp_strength = 0.14
motion_sensitivity = 0.08
preset_jitter_scale = 0.20
elif preset == "Sharp":
taa_strength = 0.35
dlaa_strength = 0.95
tone_strength = 0.12
edge_sharp_strength = 0.18
motion_sensitivity = 0.07
preset_jitter_scale = 0.15
elif preset == "Cinematic":
taa_strength = 0.65
dlaa_strength = 0.65
tone_strength = 0.16
edge_sharp_strength = 0.08
motion_sensitivity = 0.10
preset_jitter_scale = 0.25
else:
# safety fallback
taa_strength = 0.45
dlaa_strength = 0.65
tone_strength = 0.12
edge_sharp_strength = 0.14
motion_sensitivity = 0.08
preset_jitter_scale = 0.20
B, H, W, C = images.shape
is_single_image = (B == 1)
@@ -409,17 +437,47 @@ class VideoTAADLAA:
for i in range(B):
img = images[i:i+1].to(device).permute(0, 3, 1, 2).float()
rgb = img[:, :3]
if preset == "Auto":
if self.taa.history is not None and self.taa.history.shape == rgb.shape:
scene_motion = torch.abs(rgb - self.taa.history).mean().item()
else:
scene_motion = 0.02
if scene_motion < 0.015:
taa_strength = 0.35
dlaa_strength = 0.70
tone_strength = 0.10
edge_sharp_strength = 0.18
motion_sensitivity = 0.07
preset_jitter_scale = 0.15
elif scene_motion < 0.045:
taa_strength = 0.45
dlaa_strength = 0.65
tone_strength = 0.12
edge_sharp_strength = 0.14
motion_sensitivity = 0.08
preset_jitter_scale = 0.20
else:
taa_strength = 0.60
dlaa_strength = 0.60
tone_strength = 0.16
edge_sharp_strength = 0.08
motion_sensitivity = 0.10
preset_jitter_scale = 0.25
# Adaptive subpixel jitter
fid = self.taa.frame_id
self.taa.frame_id = (fid + 1) % net.jitter_offsets.shape[0]
adaptive_jitter_scale = self.jitter_scale
adaptive_jitter_scale = preset_jitter_scale
if self.taa.history is not None and self.taa.history.shape == rgb.shape:
motion_estimate = torch.abs(rgb - self.taa.history).mean()
jitter_damping = (1.0 - motion_estimate * 8.0).clamp(0.45, 1.0)
adaptive_jitter_scale = self.jitter_scale * jitter_damping
adaptive_jitter_scale = preset_jitter_scale * jitter_damping
rgb = self._jitter(rgb, fid, adaptive_jitter_scale, net)
@@ -511,11 +569,9 @@ class VideoTAADLAA:
detail_gain = detail_gain * (1.0 + edge_detail_weight * self.detail_edge_boost)
detail_gain = detail_gain * (1.0 - highlight_mask * self.detail_highlight_suppression)
edge = edge_for_detail
dlaa_out = dlaa_out + fine_detail_rgb * detail_gain
edge_mask = torch.sigmoid((edge - self.edge_sharp_threshold) * self.edge_sharp_slope)
edge_mask = torch.sigmoid((edge_for_detail - self.edge_sharp_threshold) * self.edge_sharp_slope)
edge_detail = fine_detail_rgb * edge_mask * (1.0 - highlight_mask)
dlaa_out = dlaa_out + edge_detail * edge_sharp_strength