diff options
| author | brkirch <brkirch@users.noreply.github.com> | 2023-07-17 23:39:38 -0400 |
|---|---|---|
| committer | brkirch <brkirch@users.noreply.github.com> | 2023-07-18 00:39:50 -0400 |
| commit | f0e2098f1a533c88396536282c1d6cd7d847a51c (patch) | |
| tree | c6df7315687e0692837655c2b2b62bed64d7b529 | |
| parent | a99d5708e6d603e8f7cfd1b8c6595f8026219ba0 (diff) | |
Add support for `--upcast-sampling` with SD XL
| -rw-r--r-- | modules/sd_hijack_unet.py | 8 | ||||
| -rw-r--r-- | modules/sd_models.py | 2 |
2 files changed, 8 insertions, 2 deletions
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py index ca1daf45..2101f1a0 100644 --- a/modules/sd_hijack_unet.py +++ b/modules/sd_hijack_unet.py @@ -39,7 +39,10 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs): if isinstance(cond, dict):
for y in cond.keys():
- cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+ if isinstance(cond[y], list):
+ cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+ else:
+ cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
with devices.autocast():
return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
@@ -77,3 +80,6 @@ first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devi CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
+
+CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
+CondFunc('sgm.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)
diff --git a/modules/sd_models.py b/modules/sd_models.py index 4d9382dd..5813b550 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -326,7 +326,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer timer.record("apply half()")
- devices.dtype_unet = model.model.diffusion_model.dtype
+ devices.dtype_unet = torch.float16 if model.is_sdxl and not shared.cmd_opts.no_half else model.model.diffusion_model.dtype
devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
model.first_stage_model.to(devices.dtype_vae)
|