Support nested tensor denoise masks.

Author: comfyanonymous

comfy/samplers.py

@@ -984,9 +984,6 @@ def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None,
         self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
-        if denoise_mask is not None:
-            denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
-
         noise = noise.to(device)
         latent_image = latent_image.to(device)
         sigmas = sigmas.to(device)
@@ -1013,6 +1010,24 @@ def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callba
         else:
             latent_shapes = [latent_image.shape]
 
+        if denoise_mask is not None:
+            if denoise_mask.is_nested:
+                denoise_masks = denoise_mask.unbind()
+                denoise_masks = denoise_masks[:len(latent_shapes)]
+            else:
+                denoise_masks = [denoise_mask]
+
+            for i in range(len(denoise_masks), len(latent_shapes)):
+                denoise_masks.append(torch.ones(latent_shapes[i]))
+
+            for i in range(len(denoise_masks)):
+                denoise_masks[i] = comfy.sampler_helpers.prepare_mask(denoise_masks[i], latent_shapes[i], self.model_patcher.load_device)
+
+            if len(denoise_masks) > 1:
+                denoise_mask, _ = comfy.utils.pack_latents(denoise_masks)
+            else:
+                denoise_mask = denoise_masks[0]
+
         self.conds = {}
         for k in self.original_conds:
             self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))