From ad21dc703835d3a853cd885ba185e96e0b4441b9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 14 Aug 2023 21:08:45 -0400
Subject: [PATCH] Make Blur node use the image device for processing.

---
 README.md                             |   3 ++-
 comfy.lnk                             | Bin 0 -> 882 bytes
 comfy_extras/nodes_post_processing.py |   6 +++---
 3 files changed, 5 insertions(+), 4 deletions(-)
 create mode 100644 comfy.lnk

diff --git a/README.md b/README.md
index b055325ed..c69350a9f 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,5 @@
-ComfyUI
+
+pComfyUI
 =======
 A powerful and modular stable diffusion GUI and backend.
 -----------
diff --git a/comfy.lnk b/comfy.lnk
new file mode 100644
index 0000000000000000000000000000000000000000..65b3f8206192366a0936882725ca3e6ade762781
GIT binary patch
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diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 3be141dfe..a138b292e 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -59,8 +59,8 @@ class Blend:
     def g(self, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
-def gaussian_kernel(kernel_size: int, sigma: float):
-    x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size), torch.linspace(-1, 1, kernel_size), indexing="ij")
+def gaussian_kernel(kernel_size: int, sigma: float, device=None):
+    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()
@@ -101,7 +101,7 @@ class Blur:
         batch_size, height, width, channels = image.shape
 
         kernel_size = blur_radius * 2 + 1
-        kernel = gaussian_kernel(kernel_size, sigma).repeat(channels, 1, 1).unsqueeze(1)
+        kernel = gaussian_kernel(kernel_size, sigma, device=image.device).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')