do not replace entire unet for the resolution hack

1 files changed, 0 insertions, 28 deletions

diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 8cd4c954..85909eb9 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/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)