Merge branch 'master' into weighted-learning

1 files changed, 11 insertions, 0 deletions

diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 45cf2b18..843ab66c 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -44,6 +44,7 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
     with devices.autocast():

         return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()

 

+

 class GELUHijack(torch.nn.GELU, torch.nn.Module):

     def __init__(self, *args, **kwargs):

         torch.nn.GELU.__init__(self, *args, **kwargs)

@@ -53,6 +54,16 @@ class GELUHijack(torch.nn.GELU, torch.nn.Module):
         else:

             return torch.nn.GELU.forward(self, x)

 

+

+ddpm_edit_hijack = None

+def hijack_ddpm_edit():

+    global ddpm_edit_hijack

+    if not ddpm_edit_hijack:

+        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)

+        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)

+        ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)

+

+

 unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast

 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)

 CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)