Patch UNet Forward to support resolutions that are not multiples of 64

Also modifed the UI to no longer step in 64

modules/sd_hijack_optimizations.py

@@ -5,6 +5,7 @@ import importlib
 
 import torch
 from torch import einsum
+import torch.nn.functional as F
 
 from ldm.util import default
 from einops import rearrange
@@ -12,6 +13,8 @@ from einops import rearrange
 from modules import shared
 from modules.hypernetworks import hypernetwork
 
+from ldm.modules.diffusionmodules.util import timestep_embedding
+
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
@@ -310,3 +313,31 @@ 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)