All resolutions now work with t2i adapter for SDXL.

comfy/sd.py

@@ -2,6 +2,7 @@ import torch
 import contextlib
 import copy
 import inspect
+import math
 
 from comfy import model_management
 from .ldm.util import instantiate_from_config
@@ -1099,6 +1100,12 @@ class T2IAdapter(ControlBase):
         self.channels_in = channels_in
         self.control_input = None
 
+    def scale_image_to(self, width, height):
+        unshuffle_amount = self.t2i_model.unshuffle_amount
+        width = math.ceil(width / unshuffle_amount) * unshuffle_amount
+        height = math.ceil(height / unshuffle_amount) * unshuffle_amount
+        return width, height
+
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
         if self.previous_controlnet is not None:
@@ -1116,7 +1123,8 @@ class T2IAdapter(ControlBase):
                 del self.cond_hint
             self.control_input = None
             self.cond_hint = None
-            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)
+            width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
+            self.cond_hint = utils.common_upscale(self.cond_hint_original, width, height, '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]:

comfy/t2i_adapter/adapter.py

@@ -103,17 +103,17 @@ class ResnetBlock(nn.Module):
 class Adapter(nn.Module):
     def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True):
         super(Adapter, self).__init__()
-        unshuffle = 8
+        self.unshuffle_amount = 8
         resblock_no_downsample = []
         resblock_downsample = [3, 2, 1]
         self.xl = xl
         if self.xl:
-            unshuffle = 16
+            self.unshuffle_amount = 16
             resblock_no_downsample = [1]
             resblock_downsample = [2]
 
-        self.input_channels = cin // (unshuffle * unshuffle)
+        self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
-        self.unshuffle = nn.PixelUnshuffle(unshuffle)
+        self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
         self.channels = channels
         self.nums_rb = nums_rb
         self.body = []
@@ -264,9 +264,9 @@ class extractor(nn.Module):
 class Adapter_light(nn.Module):
     def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
         super(Adapter_light, self).__init__()
-        unshuffle = 8
+        self.unshuffle_amount = 8
-        self.unshuffle = nn.PixelUnshuffle(unshuffle)
+        self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
-        self.input_channels = cin // (unshuffle * unshuffle)
+        self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
         self.channels = channels
         self.nums_rb = nums_rb
         self.body = []