i

entry.py

@@ -151,6 +151,6 @@ with torch.no_grad():
         model.first_stage_model.cpu()
 
     import cv2
-    samples = einops.rearrange(samples, 'b c h w -> b h w c')[0] * 255.0
+    samples = einops.rearrange(samples, 'b c h w -> b h w c')[0, :, :, ::-1] * 255.0
     samples = samples.cpu().numpy().clip(0, 255).astype(np.uint8)
     cv2.imwrite('img.png', samples)

sgm/modules/diffusionmodules/util.py

@@ -272,7 +272,9 @@ class SiLU(nn.Module):
 
 class GroupNorm32(nn.GroupNorm):
     def forward(self, x):
-        return super().forward(x)
+        self.weight.to(torch.float32)
+        self.bias.to(torch.float32)
+        return super().forward(x.float()).type(x.dtype)
 
 
 def conv_nd(dims, *args, **kwargs):