From 1cac48dfcb1c5a17d15c3112a00e5b508dc90ad2 Mon Sep 17 00:00:00 2001 From: pfpb Date: Thu, 1 Jan 2026 14:50:17 -0500 Subject: [PATCH] Update tiled_diffusion.py Correct gaussian weights denominator for y_probs --- tiled_diffusion.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tiled_diffusion.py b/tiled_diffusion.py index 7552af4..9079981 100644 --- a/tiled_diffusion.py +++ b/tiled_diffusion.py @@ -454,9 +454,9 @@ def gaussian_weights(tile_w:int, tile_h:int) -> Tensor: This generates gaussian weights to smooth the noise of each tile. This is critical for this method to work. ''' - f = lambda x, midpoint, var=0.01: exp(-(x-midpoint)*(x-midpoint) / (tile_w*tile_w) / (2*var)) / sqrt(2*pi*var) - x_probs = [f(x, (tile_w - 1) / 2) for x in range(tile_w)] # -1 because index goes from 0 to latent_width - 1 - y_probs = [f(y, tile_h / 2) for y in range(tile_h)] + f = lambda x, midpoint, tile_dim, var=0.01: exp(-(x-midpoint)*(x-midpoint) / (tile_dim*tile_dim) / (2*var)) / sqrt(2*pi*var) + x_probs = [f(x, (tile_w - 1) / 2,tile_w) for x in range(tile_w)] # -1 because index goes from 0 to latent_width - 1 + y_probs = [f(y, tile_h / 2,tile_h) for y in range(tile_h)] w = np.outer(y_probs, x_probs) return torch.from_numpy(w).to(devices.device, dtype=torch.float32)