do not replace entire unet for the resolution hack

modules/sd_hijack.py

@@ -11,7 +11,7 @@ import modules.textual_inversion.textual_inversion
 from modules import prompt_parser, devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -35,11 +35,12 @@ ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"]
 ldm.modules.attention.print = lambda *args: None
 ldm.modules.diffusionmodules.model.print = lambda *args: None
 
+
 def apply_optimizations():
     undo_optimizations()
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
-    ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_hijack_optimizations.patched_unet_forward
+    ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")

modules/sd_hijack_optimizations.py

@@ -313,31 +313,3 @@ def xformers_attnblock_forward(self, x):
         return x + out
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
-
-def patched_unet_forward(self, x, timesteps=None, context=None, y=None,**kwargs):
-    assert (y is not None) == (
-        self.num_classes is not None
-    ), "must specify y if and only if the model is class-conditional"
-    hs = []
-    t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
-    emb = self.time_embed(t_emb)
-
-    if self.num_classes is not None:
-        assert y.shape == (x.shape[0],)
-        emb = emb + self.label_emb(y)
-
-    h = x.type(self.dtype)
-    for module in self.input_blocks:
-        h = module(h, emb, context)
-        hs.append(h)
-    h = self.middle_block(h, emb, context)
-    for module in self.output_blocks:
-        if h.shape[-2:] != hs[-1].shape[-2:]:
-            h = F.interpolate(h, hs[-1].shape[-2:], mode="nearest")
-        h = torch.cat([h, hs.pop()], dim=1)
-        h = module(h, emb, context)
-    h = h.type(x.dtype)
-    if self.predict_codebook_ids:
-        return self.id_predictor(h)
-    else:
-        return self.out(h)

modules/sd_hijack_unet.py

@@ -0,0 +1,30 @@
+import torch
+
+
+class TorchHijackForUnet:
+    """
+    This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
+    this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
+    """
+
+    def __getattr__(self, item):
+        if item == 'cat':
+            return self.cat
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+    def cat(self, tensors, *args, **kwargs):
+        if len(tensors) == 2:
+            a, b = tensors
+            if a.shape[-2:] != b.shape[-2:]:
+                a = torch.nn.functional.interpolate(a, b.shape[-2:], mode="nearest")
+
+            tensors = (a, b)
+
+        return torch.cat(tensors, *args, **kwargs)
+
+
+th = TorchHijackForUnet()