minor changes for tiled sampler

comfy/ldm/modules/tomesd.py

@@ -36,7 +36,7 @@ def bipartite_soft_matching_random2d(metric: torch.Tensor,
     """
     B, N, _ = metric.shape
 
-    if r <= 0:
+    if r <= 0 or w == 1 or h == 1:
         return do_nothing, do_nothing
 
     gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather

comfy/sd.py

@@ -581,10 +581,7 @@ class VAE:
         samples = samples.cpu()
         return samples
 
-def resize_image_to(tensor, target_latent_tensor, batched_number):
-    tensor = utils.common_upscale(tensor, target_latent_tensor.shape[3] * 8, target_latent_tensor.shape[2] * 8, 'nearest-exact', "center")
-    target_batch_size = target_latent_tensor.shape[0]
-
+def broadcast_image_to(tensor, target_batch_size, batched_number):
     current_batch_size = tensor.shape[0]
     print(current_batch_size, target_batch_size)
     if current_batch_size == 1:
@@ -623,7 +620,9 @@ class ControlNet:
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
-            self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).to(self.control_model.dtype).to(self.device)
+            self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(self.control_model.dtype).to(self.device)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
         if self.control_model.dtype == torch.float16:
             precision_scope = torch.autocast
@@ -794,10 +793,14 @@ class T2IAdapter:
         if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
             if self.cond_hint is not None:
                 del self.cond_hint
+            self.control_input = None
             self.cond_hint = None
-            self.cond_hint = resize_image_to(self.cond_hint_original, x_noisy, batched_number).float().to(self.device)
+            self.cond_hint = utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").float().to(self.device)
             if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
                 self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
+        if x_noisy.shape[0] != self.cond_hint.shape[0]:
+            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
+        if self.control_input is None:
             self.t2i_model.to(self.device)
             self.control_input = self.t2i_model(self.cond_hint)
             self.t2i_model.cpu()