From b1fdbeb9a71ca3b51e594ba457c1d5f001359c92 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 26 Mar 2026 19:18:16 -0700 Subject: [PATCH] Fix blur and sharpen nodes not working with fp16 intermediates. (#13181) --- comfy_extras/nodes_post_processing.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 06626f9dd..9037c3d20 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -67,11 +67,11 @@ class Blend(io.ComfyNode): def g(cls, x): return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) -def gaussian_kernel(kernel_size: int, sigma: float, device=None): +def gaussian_kernel(kernel_size: int, sigma: float, device=None, dtype=torch.float32): x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") d = torch.sqrt(x * x + y * y) g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) - return g / g.sum() + return (g / g.sum()).to(dtype) class Blur(io.ComfyNode): @classmethod @@ -99,7 +99,7 @@ class Blur(io.ComfyNode): batch_size, height, width, channels = image.shape kernel_size = blur_radius * 2 + 1 - kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) + kernel = gaussian_kernel(kernel_size, sigma, device=image.device, dtype=image.dtype).repeat(channels, 1, 1).unsqueeze(1) image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') @@ -200,7 +200,7 @@ class Sharpen(io.ComfyNode): image = image.to(comfy.model_management.get_torch_device()) kernel_size = sharpen_radius * 2 + 1 - kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) + kernel = gaussian_kernel(kernel_size, sigma, device=image.device, dtype=image.dtype) * -(alpha*10) kernel = kernel.to(dtype=image.dtype) center = kernel_size // 2 kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0