Merge pull request #6510 from brkirch/unet16-upcast-precision

Add upcast options, full precision sampling from float16 UNet and upcasting attention for inference using SD 2.1 models without --no-half

1 files changed, 29 insertions, 0 deletions

diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 18daf8c1..88c94e54 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -1,4 +1,8 @@
 import torch

+from packaging import version

+

+from modules import devices

+from modules.sd_hijack_utils import CondFunc

 

 

 class TorchHijackForUnet:

@@ -28,3 +32,28 @@ class TorchHijackForUnet:
 

 

 th = TorchHijackForUnet()

+

+

+# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling

+def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):

+    for y in cond.keys():

+        cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]

+    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)

+    def forward(self, x):

+        if devices.unet_needs_upcast:

+            return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)

+        else:

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

+

+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, *args, **kwargs: orig_func(*args, **kwargs).to(devices.dtype_unet), unet_needs_upcast)

+if version.parse(torch.__version__) <= version.parse("1.13.1"):

+    CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)

+    CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)

+    CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)