514 lines
21 KiB
Python
514 lines
21 KiB
Python
"""
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FEnodes — Tiling nodes for VFX production pipelines.
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Author: FugitiveExpert01
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"""
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import logging
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import torch
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import torch.nn.functional as F
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import numpy as np
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from PIL import Image, ImageDraw, ImageFont
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log = logging.getLogger(__name__)
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class TilingNodeBase:
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"""
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Base class providing Laplacian pyramid blending utilities.
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How multi-band blending works:
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Each tile is decomposed into a Laplacian pyramid (detail at each frequency
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band + a coarse residual). The blending mask is simultaneously downsampled
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into a Gaussian pyramid. Each frequency band is blended independently using
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its corresponding mask level — coarse bands blend over a wide transition
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zone, fine detail bands blend over a narrow one. The blended pyramid is
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then collapsed back into the final image.
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This eliminates the classic trade-off of simple alpha blending: wide feather
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radii smear fine detail, narrow ones leave visible seams. Multi-band blending
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avoids both artefacts simultaneously.
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Video optimisation:
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Mask pyramids depend only on tile layout, not on pixel content. They are
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pre-computed once from the first frame and reused across all subsequent
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frames, avoiding redundant work proportional to frame count.
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"""
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BLEND_MODES = ["laplacian", "linear"]
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# ------------------------------------------------------------------
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# Tensor normalisation
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# ------------------------------------------------------------------
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@staticmethod
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def ensure_4d_BCHW(t, device):
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"""
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Force any tensor into (1, C, H, W) float32 on the correct device.
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Handles every shape a video model might produce for a single frame.
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"""
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t = t.to(device=device, dtype=torch.float32)
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if t.dim() == 2:
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log.debug("ensure_4d_BCHW: 2D greyscale %s → (1, 1, H, W)", t.shape)
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t = t.unsqueeze(0).unsqueeze(0)
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elif t.dim() == 3:
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log.debug("ensure_4d_BCHW: 3D %s → treating as (H, W, C)", t.shape)
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t = t.unsqueeze(0).permute(0, 3, 1, 2)
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elif t.dim() == 4:
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if t.shape[-1] in (1, 3, 4):
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log.debug("ensure_4d_BCHW: 4D channels-last %s → (B, C, H, W)", t.shape)
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t = t.permute(0, 3, 1, 2)
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else:
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log.debug("ensure_4d_BCHW: 4D %s already (B, C, H, W)", t.shape)
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else:
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raise ValueError(f"[FEnodes] ensure_4d_BCHW: unexpected shape {t.shape}")
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if t.dim() != 4:
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raise RuntimeError(f"[FEnodes] ensure_4d_BCHW failed — result shape: {t.shape}")
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return t.contiguous()
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# ------------------------------------------------------------------
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# Pyramid construction
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# ------------------------------------------------------------------
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@staticmethod
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def _build_laplacian_pyramid(img, levels):
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"""
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Build a Laplacian pyramid from a (1, C, H, W) tensor.
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Returns a list of `levels` tensors:
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indices 0 … levels-2 : Laplacian detail planes, finest → coarsest
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index levels-1 : coarsest Gaussian residual
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Pure-PyTorch, GPU-resident operation.
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"""
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pyramid = []
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current = img
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for _ in range(levels - 1):
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h, w = current.shape[2], current.shape[3]
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down = F.interpolate(current, scale_factor=0.5,
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mode='bilinear', align_corners=False)
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up = F.interpolate(down, size=(h, w),
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mode='bilinear', align_corners=False)
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pyramid.append(current - up) # detail = original − low-pass
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current = down
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pyramid.append(current) # coarsest Gaussian residual
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return pyramid
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@staticmethod
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def _build_gaussian_pyramid(mask, levels):
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"""
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Build a Gaussian pyramid for a (1, 1, H, W) blending mask.
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Returns `levels` tensors from finest to coarsest. Downsampling the
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mask means low-frequency pyramid bands blend over a proportionally
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wider spatial transition than high-frequency bands — the core benefit
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of multi-band blending.
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"""
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pyramid = [mask]
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current = mask
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for _ in range(levels - 1):
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current = F.interpolate(current, scale_factor=0.5,
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mode='bilinear', align_corners=False)
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pyramid.append(current)
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return pyramid
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@staticmethod
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def _collapse_pyramid(pyramid):
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"""
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Reconstruct a (1, C, H, W) image from a blended Laplacian pyramid.
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pyramid: list of tensors, finest detail first, coarsest Gaussian last.
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"""
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result = pyramid[-1]
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for level in reversed(pyramid[:-1]):
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h, w = level.shape[2], level.shape[3]
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result = F.interpolate(result, size=(h, w),
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mode='bilinear', align_corners=False)
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result = result + level
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return result
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@staticmethod
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def _canvas_pyramid_sizes(orig_h, orig_w, levels):
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"""
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Compute canvas (H, W) at each pyramid level.
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Uses the same floor-division that F.interpolate(scale_factor=0.5)
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applies internally, so canvas and tile pyramids stay aligned.
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"""
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sizes = []
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h, w = orig_h, orig_w
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for _ in range(levels):
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sizes.append((h, w))
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h = h // 2
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w = w // 2
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return sizes
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# ------------------------------------------------------------------
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# Blending mask
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# ------------------------------------------------------------------
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@staticmethod
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def create_gaussian_mask(h, w, overlap_top, overlap_bottom,
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overlap_left, overlap_right, device):
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"""
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Linear feathering mask — ramps from 0 → 1 across each overlap edge.
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Used as the level-0 mask; pyramid downsampling widens the ramp at
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coarser levels automatically.
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"""
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mask = torch.ones((1, 1, h, w), device=device)
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if overlap_top > 0:
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mask[:, :, :overlap_top, :] *= torch.linspace(
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0, 1, overlap_top, device=device).view(1, 1, overlap_top, 1)
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if overlap_bottom > 0:
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mask[:, :, -overlap_bottom:, :] *= torch.linspace(
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1, 0, overlap_bottom, device=device).view(1, 1, overlap_bottom, 1)
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if overlap_left > 0:
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mask[:, :, :, :overlap_left] *= torch.linspace(
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0, 1, overlap_left, device=device).view(1, 1, 1, overlap_left)
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if overlap_right > 0:
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mask[:, :, :, -overlap_right:] *= torch.linspace(
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1, 0, overlap_right, device=device).view(1, 1, 1, overlap_right)
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return mask
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# ======================================================================
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# TileSplit
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# ======================================================================
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class TileSplit(TilingNodeBase):
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"""Splits an image or video batch into an overlapping grid of tiles."""
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"tiles_x": ("INT", {"default": 2, "min": 1, "max": 10}),
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"tiles_y": ("INT", {"default": 2, "min": 1, "max": 10}),
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"overlap_percent": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 0.5, "step": 0.01}),
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}
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}
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RETURN_TYPES = ("IMAGE", "IMAGE", "TILE_CALC")
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RETURN_NAMES = ("tiles", "debug_image", "tile_calc")
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OUTPUT_IS_LIST = (True, False, False)
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FUNCTION = "split"
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CATEGORY = "FEnodes"
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def split(self, image, tiles_x, tiles_y, overlap_percent):
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B, H, W, C = image.shape
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# overlap_percent means exactly "this fraction of the tile width/height
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# is shared with each neighbour."
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#
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# Derivation:
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# Each interior tile contributes (tile_w × (1 - overlap)) unique pixels.
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# The last tile contributes tile_w unique pixels.
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# Total unique pixels = tile_w × ((1 - overlap) × (n-1) + 1) = W
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# → tile_w = W / ((1 - overlap) × (n-1) + 1)
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#
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# At overlap=0.50, tiles_x=2: tile_w = W / 1.5 (exactly half overlaps)
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# At overlap=0.00, tiles_x=2: tile_w = W / 2 (no overlap)
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tile_w_f = W / ((1.0 - overlap_percent) * (tiles_x - 1) + 1)
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tile_h_f = H / ((1.0 - overlap_percent) * (tiles_y - 1) + 1)
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# Round up to nearest multiple of 8 (model compatibility), clamp to canvas
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tile_w = min(((int(tile_w_f) + 7) // 8) * 8, W)
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tile_h = min(((int(tile_h_f) + 7) // 8) * 8, H)
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# Space tile origins evenly so they cover the full canvas
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start_x = np.linspace(0, W - tile_w, tiles_x, dtype=int) if tiles_x > 1 else [0]
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start_y = np.linspace(0, H - tile_h, tiles_y, dtype=int) if tiles_y > 1 else [0]
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# Actual overlap in pixels after rounding (for the log)
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actual_ov_x = tile_w - int((W - tile_w) / max(tiles_x - 1, 1)) if tiles_x > 1 else 0
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actual_ov_y = tile_h - int((H - tile_h) / max(tiles_y - 1, 1)) if tiles_y > 1 else 0
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log.info(
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"TileSplit: %dx%d px, %d frames — %dx%d grid, "
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"tile %dx%d px, overlap ~%d×%d px (%.0f%% × %.0f%%)",
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W, H, B, tiles_x, tiles_y, tile_w, tile_h,
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actual_ov_x, actual_ov_y,
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(actual_ov_x / tile_w) * 100, (actual_ov_y / tile_h) * 100,
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)
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img_tensor = image.permute(0, 3, 1, 2).to(device)
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num_tiles = tiles_x * tiles_y
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tile_sequences = [[] for _ in range(num_tiles)]
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tile_layouts = []
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# Debug overlay
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debug_pil = Image.fromarray(
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(image[0].cpu().numpy() * 255).astype(np.uint8)).convert("RGBA")
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overlay = Image.new("RGBA", debug_pil.size, (0, 0, 0, 120))
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debug_pil = Image.alpha_composite(debug_pil, overlay)
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draw = ImageDraw.Draw(debug_pil)
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try:
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font = ImageFont.truetype("LiberationSans-Bold.ttf", int(tile_h * 0.15))
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except Exception:
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log.debug("TileSplit: LiberationSans-Bold.ttf not found, using default font")
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font = ImageFont.load_default()
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for b in range(B):
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tile_count = 0
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for y_idx, y in enumerate(start_y):
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for x_idx, x in enumerate(start_x):
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tile_frame = img_tensor[b:b+1, :, y:y+tile_h, x:x+tile_w]
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tile_sequences[tile_count].append(
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tile_frame.permute(0, 2, 3, 1).cpu())
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if b == 0:
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ov_l = int(tile_w - (start_x[x_idx] - start_x[x_idx-1])) if x_idx > 0 else 0
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ov_r = int(tile_w - (start_x[x_idx+1] - start_x[x_idx])) if x_idx < tiles_x - 1 else 0
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ov_t = int(tile_h - (start_y[y_idx] - start_y[y_idx-1])) if y_idx > 0 else 0
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ov_b = int(tile_h - (start_y[y_idx+1] - start_y[y_idx])) if y_idx < tiles_y - 1 else 0
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tile_layouts.append({
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"x": int(x), "y": int(y),
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"ov_l": ov_l, "ov_r": ov_r,
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"ov_t": ov_t, "ov_b": ov_b,
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})
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log.debug("TileSplit: tile %d at (%d,%d) overlaps L=%d R=%d T=%d B=%d",
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tile_count, x, y, ov_l, ov_r, ov_t, ov_b)
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color = ((x_idx * 40) % 255, (y_idx * 60) % 255, 255, 100)
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draw.rectangle([x, y, x + tile_w, y + tile_h],
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outline="white", fill=color, width=2)
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draw.text((x + 10, y + 10), f"Tile {tile_count}",
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fill="white", font=font)
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tile_count += 1
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final_tile_batches = [torch.cat(seq, dim=0) for seq in tile_sequences]
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debug_out = torch.from_numpy(
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np.array(debug_pil.convert("RGB")).astype(np.float32) / 255.0
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).unsqueeze(0)
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tile_calc = {
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"orig_w": W, "orig_h": H,
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"tile_w": tile_w, "tile_h": tile_h,
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"batch_size": B,
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"num_tiles": num_tiles,
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"layouts": tile_layouts,
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}
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log.info("TileSplit: done — %d tile sequences produced", num_tiles)
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return (final_tile_batches, debug_out, tile_calc)
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# ======================================================================
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# TileMerge
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# ======================================================================
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class TileMerge(TilingNodeBase):
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"""
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Merges processed tiles back into a full image or video sequence.
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Two blending modes are available via the blend_mode dropdown:
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laplacian — Multi-band blending using a Laplacian pyramid.
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Each frequency band is blended independently so coarse bands
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(colour, tone) transition smoothly over a wide zone while fine
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detail bands transition sharply. Eliminates the classic trade-off
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where wide feathering blurs detail and narrow feathering leaves
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visible seams. Best quality; recommended for final output.
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linear — Simple Gaussian-feathered weighted average.
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Fast and straightforward. Overlap regions are cross-faded with a
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linear ramp. Good for quick previews or when tile content is very
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similar and seams are not an issue.
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"""
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PYRAMID_LEVELS = 4 # depth of Laplacian decomposition; 4 is standard
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"tiles": ("IMAGE",),
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"tile_calc": ("TILE_CALC",),
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"blend_mode": (TilingNodeBase.BLEND_MODES, {"default": "laplacian"}),
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}
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}
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INPUT_IS_LIST = True
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "merge"
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CATEGORY = "FEnodes"
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def merge(self, tiles, tile_calc, blend_mode):
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tc = tile_calc[0]
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# blend_mode arrives as a list because INPUT_IS_LIST = True
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blend_mode = blend_mode[0] if isinstance(blend_mode, list) else blend_mode
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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orig_h = tc['orig_h']
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orig_w = tc['orig_w']
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# Unwrap any extra list nesting ComfyUI may add
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tile_list = tiles
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while (isinstance(tile_list, list)
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and len(tile_list) == 1
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and isinstance(tile_list[0], list)):
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tile_list = tile_list[0]
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num_frames = tile_list[0].shape[0]
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log.info(
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"TileMerge: %d tiles × %d frames — %s blend → %dx%d canvas",
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len(tile_list), num_frames, blend_mode, orig_w, orig_h,
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)
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for idx, t in enumerate(tile_list):
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log.debug("TileMerge: tile[%d] shape=%s dtype=%s", idx, t.shape, t.dtype)
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if blend_mode == "laplacian":
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result = self._merge_laplacian(tile_list, tc, device, orig_h, orig_w)
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else:
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result = self._merge_linear(tile_list, tc, device, orig_h, orig_w)
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log.info("TileMerge: done — output shape=%s", result.shape)
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return (result,)
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# ------------------------------------------------------------------
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# Laplacian pyramid blending
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# ------------------------------------------------------------------
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def _merge_laplacian(self, tile_list, tc, device, orig_h, orig_w):
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"""
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Multi-band blend: each Laplacian pyramid level is accumulated
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separately and the final image is collapsed from the blended pyramid.
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Mask pyramids are pre-computed once and reused across all frames
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(video optimisation — masks depend on layout, not pixel content).
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"""
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levels = self.PYRAMID_LEVELS
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canvas_sizes = self._canvas_pyramid_sizes(orig_h, orig_w, levels)
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log.debug("TileMerge [laplacian]: canvas pyramid sizes %s", canvas_sizes)
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# Pre-compute mask pyramids
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log.info("TileMerge [laplacian]: pre-computing mask pyramids for %d tiles",
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len(tc['layouts']))
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mask_pyramids = []
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cached_tile_sizes = []
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for i, layout in enumerate(tc['layouts']):
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tf_first = self.ensure_4d_BCHW(tile_list[i][0], device)
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th, tw = tf_first.shape[2], tf_first.shape[3]
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cached_tile_sizes.append((th, tw))
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mask = self.create_gaussian_mask(
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th, tw,
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layout['ov_t'], layout['ov_b'],
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layout['ov_l'], layout['ov_r'],
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device,
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)
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mask_pyramids.append(self._build_gaussian_pyramid(mask, levels))
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log.debug("TileMerge [laplacian]: mask pyramid %d base %dx%d", i, tw, th)
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output_frames = []
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for b in range(tc['batch_size'] if 'batch_size' in tc else tile_list[0].shape[0]):
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accum = [torch.zeros((1, 3, h, w), device=device, dtype=torch.float32)
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for h, w in canvas_sizes]
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weight = [torch.zeros((1, 1, h, w), device=device, dtype=torch.float32)
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for h, w in canvas_sizes]
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for i, layout in enumerate(tc['layouts']):
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raw_frame = tile_list[i][b]
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tile_frame = self.ensure_4d_BCHW(raw_frame, device)
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th, tw = tile_frame.shape[2], tile_frame.shape[3]
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log.debug("TileMerge [laplacian]: frame %d tile %d shape=%s", b, i, tile_frame.shape)
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# Rebuild mask pyramid if tile size changed (e.g. model resized it)
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if (th, tw) != cached_tile_sizes[i]:
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log.warning(
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"TileMerge: tile %d changed size at frame %d "
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"(expected %s, got %s) — rebuilding mask pyramid",
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i, b, cached_tile_sizes[i], (th, tw),
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)
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mask = self.create_gaussian_mask(
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th, tw,
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layout['ov_t'], layout['ov_b'],
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layout['ov_l'], layout['ov_r'],
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device,
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)
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tile_mask_pyr = self._build_gaussian_pyramid(mask, levels)
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else:
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tile_mask_pyr = mask_pyramids[i]
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tile_pyr = self._build_laplacian_pyramid(tile_frame, levels)
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for l in range(levels):
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# Scale tile origin to this pyramid level via right-shift
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# (floor division by 2^l, matching F.interpolate behaviour)
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x_l = layout['x'] >> l
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y_l = layout['y'] >> l
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canvas_h, canvas_w = canvas_sizes[l]
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h_sl = min(tile_pyr[l].shape[2], canvas_h - y_l)
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w_sl = min(tile_pyr[l].shape[3], canvas_w - x_l)
|
||
|
||
if h_sl <= 0 or w_sl <= 0:
|
||
log.debug("TileMerge: tile %d level %d out of canvas — skipping", i, l)
|
||
continue
|
||
|
||
m = tile_mask_pyr[l][:, :, :h_sl, :w_sl]
|
||
accum [l][:, :, y_l:y_l+h_sl, x_l:x_l+w_sl] += tile_pyr[l][:, :, :h_sl, :w_sl] * m
|
||
weight[l][:, :, y_l:y_l+h_sl, x_l:x_l+w_sl] += m
|
||
|
||
blended_pyr = [accum[l] / (weight[l] + 1e-8) for l in range(levels)]
|
||
frame_out = self._collapse_pyramid(blended_pyr)
|
||
output_frames.append(frame_out.permute(0, 2, 3, 1).cpu())
|
||
|
||
return torch.cat(output_frames, dim=0).clamp(0, 1)
|
||
|
||
# ------------------------------------------------------------------
|
||
# Linear (Gaussian feather) blending
|
||
# ------------------------------------------------------------------
|
||
|
||
def _merge_linear(self, tile_list, tc, device, orig_h, orig_w):
|
||
"""
|
||
Simple weighted-average blend using a linear feathering mask.
|
||
Fast; good for previews or low-overlap grids.
|
||
"""
|
||
output_frames = []
|
||
|
||
for b in range(tile_list[0].shape[0]):
|
||
accum = torch.zeros((1, 3, orig_h, orig_w), device=device, dtype=torch.float32)
|
||
weight = torch.zeros((1, 1, orig_h, orig_w), device=device, dtype=torch.float32)
|
||
|
||
for i, layout in enumerate(tc['layouts']):
|
||
tile_frame = self.ensure_4d_BCHW(tile_list[i][b], device)
|
||
th, tw = tile_frame.shape[2], tile_frame.shape[3]
|
||
log.debug("TileMerge [linear]: frame %d tile %d shape=%s", b, i, tile_frame.shape)
|
||
|
||
mask = self.create_gaussian_mask(
|
||
th, tw,
|
||
layout['ov_t'], layout['ov_b'],
|
||
layout['ov_l'], layout['ov_r'],
|
||
device,
|
||
)
|
||
|
||
x, y = layout['x'], layout['y']
|
||
h_sl = min(th, orig_h - y)
|
||
w_sl = min(tw, orig_w - x)
|
||
|
||
accum [:, :, y:y+h_sl, x:x+w_sl] += tile_frame[:, :, :h_sl, :w_sl] * mask[:, :, :h_sl, :w_sl]
|
||
weight[:, :, y:y+h_sl, x:x+w_sl] += mask[:, :, :h_sl, :w_sl]
|
||
|
||
output_frames.append((accum / (weight + 1e-8)).permute(0, 2, 3, 1).cpu())
|
||
|
||
return torch.cat(output_frames, dim=0).clamp(0, 1)
|
||
|
||
|
||
# ======================================================================
|
||
# ComfyUI registration
|
||
# ======================================================================
|
||
|
||
NODE_CLASS_MAPPINGS = {
|
||
"TileSplit": TileSplit,
|
||
"TileMerge": TileMerge,
|
||
}
|
||
|
||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||
"TileSplit": "TileSplit",
|
||
"TileMerge": "TileMerge",
|
||
}
|