185 lines
7.8 KiB
Python
185 lines
7.8 KiB
Python
# Created By MSXYZ and Claude Opus 4.6
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# GPU başında ciddi işler döndürüyoruz. 🚀
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# TAA (Temporal Anti-Aliasing) + DLAA (Deep Learning Anti-Aliasing) Adaptation
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# v0.1.1 - Fixed: Batch Size validation and Range Error
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import threading
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import logging
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logger = logging.getLogger("VideoTAADLAA")
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if not logger.handlers:
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_handler = logging.StreamHandler()
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_handler.setFormatter(logging.Formatter("[%(name)s] %(message)s"))
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logger.addHandler(_handler)
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logger.setLevel(logging.INFO)
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class _DLAANet(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(3, 16, 3, padding=1, bias=False),
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nn.ReLU(),
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nn.Conv2d(16, 16, 3, padding=1, bias=False),
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nn.ReLU(),
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nn.Conv2d(16, 3, 3, padding=1, bias=False),
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)
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self.register_buffer("sobel_x", torch.tensor([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=torch.float32).view(1, 1, 3, 3))
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self.register_buffer("sobel_y", torch.tensor([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=torch.float32).view(1, 1, 3, 3))
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self.register_buffer("jitter_offsets", torch.tensor([
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[0.25, 0.25], [-0.25, -0.25], [-0.25, 0.25], [0.25, -0.25]
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], dtype=torch.float32))
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self._init_weights()
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def _init_weights(self):
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sharpen = torch.tensor([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=torch.float32)
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convs = [m for m in self.conv if isinstance(m, nn.Conv2d)]
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nn.init.dirac_(convs[0].weight)
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nn.init.dirac_(convs[1].weight)
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nn.init.zeros_(convs[2].weight)
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with torch.no_grad():
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for i in range(3):
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for j in range(3):
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convs[2].weight[i, j] = sharpen * 0.1
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def forward(self, x):
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return torch.clamp(x + self.conv(x) * 0.15, 0.0, 1.0)
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class _TAAState:
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def __init__(self):
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self.history: torch.Tensor | None = None
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self.frame_count: int = 0
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def reset(self):
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self.history = None
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self.frame_count = 0
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def update(self, frame: torch.Tensor, alpha: float, motion_threshold: float) -> torch.Tensor:
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if self.history is None or self.history.shape != frame.shape:
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self.history = frame.clone()
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self.frame_count += 1
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return frame
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diff = torch.abs(frame - self.history).mean(dim=1, keepdim=True)
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motion_mask = torch.clamp(diff * motion_threshold * 10.0, 0.0, 1.0)
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dynamic_alpha = alpha * (1.0 - motion_mask)
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out = dynamic_alpha * self.history + (1.0 - dynamic_alpha) * frame
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self.history = out.detach()
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self.frame_count += 1
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return out
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class VideoTAADLAA:
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def __init__(self):
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self._lock = threading.Lock()
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self._net_cache: dict[str, _DLAANet] = {}
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self._taa = _TAAState()
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"taa_strength": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.05}),
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"taa_alpha": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 0.99, "step": 0.01}),
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"motion_sensitivity": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}),
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"jitter_scale": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 2.0, "step": 0.1}),
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"dlaa_strength": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 1.0, "step": 0.05}),
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"edge_threshold": ("FLOAT", {"default": 0.12, "min": 0.0, "max": 1.0, "step": 0.01}),
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"blur_radius": ("INT", {"default": 1, "min": 0, "max": 5, "step": 1}), # Min 0 yapıldı
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"reset_history": ("BOOLEAN", {"default": False}),
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"batch_size": ("INT", {"default": 0, "min": 0, "max": 64, "step": 1}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "CustomPostProcess"
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def _get_net(self, device: torch.device) -> _DLAANet:
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key = str(device)
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if key not in self._net_cache:
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self._net_cache[key] = _DLAANet().to(device).eval()
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return self._net_cache[key]
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def _jitter(self, x: torch.Tensor, idx: int, scale: float, net: _DLAANet) -> torch.Tensor:
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if scale <= 1e-4: return x
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off = net.jitter_offsets[idx % 4]
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B, C, H, W = x.shape
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theta = torch.eye(2, 3, device=x.device).unsqueeze(0).repeat(B, 1, 1)
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theta[:, 0, 2] = off[0] * scale / W
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theta[:, 1, 2] = off[1] * scale / H
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grid = F.affine_grid(theta, x.shape, align_corners=False)
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return F.grid_sample(x, grid, mode="bicubic", padding_mode="reflection", align_corners=False)
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def _edge_aa(self, x: torch.Tensor, thr: float, blur: int, net: _DLAANet) -> torch.Tensor:
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if blur <= 0: return x
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gray = 0.299 * x[:, 0:1] + 0.587 * x[:, 1:2] + 0.114 * x[:, 2:3]
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sx = F.conv2d(gray, net.sobel_x, padding=1)
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sy = F.conv2d(gray, net.sobel_y, padding=1)
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mask = (torch.sqrt(sx * sx + sy * sy + 1e-6) > thr).float()
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k = blur * 2 + 1
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blurred = F.avg_pool2d(F.pad(x, [blur] * 4, mode="reflect"), k, stride=1)
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return x * (1.0 - mask) + blurred * mask
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def execute(self, images, taa_strength, taa_alpha, motion_sensitivity, jitter_scale,
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dlaa_strength, edge_threshold, blur_radius, reset_history, batch_size):
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with self._lock:
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if reset_history:
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self._taa.reset()
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device = images.device
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net = self._get_net(device)
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B, H, W, C = images.shape
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# --- Kritik Hata Düzeltme: Batch Size Hesabı ---
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working_batch_size = batch_size
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if working_batch_size <= 0:
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cost = H * W * C * 4 * 14
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free, _ = torch.cuda.mem_get_info() if torch.cuda.is_available() else (0,0)
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working_batch_size = max(1, min(16, int((free * 0.6) // cost))) if free > 0 else 1
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# Range hatasını önlemek için batch_size'ın en az 1 olduğundan emin oluyoruz
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working_batch_size = max(1, working_batch_size)
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# ----------------------------------------------
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images = images.to(device)
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has_alpha = (C == 4)
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outputs = []
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with torch.no_grad():
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for i in range(0, B, working_batch_size):
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chunk = images[i:i + working_batch_size]
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img = chunk.permute(0, 3, 1, 2).float()
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img_rgb = img[:, :3]
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img_alpha = img[:, 3:4] if has_alpha else None
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results_rgb = []
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for f_idx in range(img_rgb.shape[0]):
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f = img_rgb[f_idx:f_idx + 1]
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f = self._jitter(f, self._taa.frame_count, jitter_scale, net)
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f = self._edge_aa(f, edge_threshold, blur_radius, net)
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taa_out = self._taa.update(f, taa_alpha, motion_sensitivity)
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f = torch.lerp(f, taa_out, taa_strength)
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if dlaa_strength > 0.0:
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f = torch.lerp(f, net(f), dlaa_strength)
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results_rgb.append(f.clamp(0.0, 1.0))
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batch_rgb = torch.cat(results_rgb, dim=0)
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batch_out = torch.cat([batch_rgb, img_alpha], dim=1) if has_alpha else batch_rgb
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outputs.append(batch_out.permute(0, 2, 3, 1).cpu())
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logger.info(f"İşlendi: {min(i + working_batch_size, B)} / {B}")
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return (torch.cat(outputs, dim=0),)
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NODE_CLASS_MAPPINGS = {"VideoTAADLAA": VideoTAADLAA}
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NODE_DISPLAY_NAME_MAPPINGS = {"VideoTAADLAA": "🎮 Video TAA + DLAA Anti-Aliasing"} |