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

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Add upcast options, full precision sampling from float16 UNet and upcasting attention for inference using SD 2.1 models without --no-half
This commit is contained in:
AUTOMATIC1111
2023-01-25 19:12:29 +03:00
committed by GitHub
parent 1982ef6890 e3b53fd295
commit 1574e96729
10 changed files with 189 additions and 71 deletions
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10
modules/sd_models.py
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@@ -258,16 +258,24 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo):
if not shared.cmd_opts.no_half:
vae = model.first_stage_model
depth_model = getattr(model, 'depth_model', None)
# with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
if shared.cmd_opts.no_half_vae:
model.first_stage_model = None
# with --upcast-sampling, don't convert the depth model weights to float16
if shared.cmd_opts.upcast_sampling and depth_model:
model.depth_model = None
model.half()
model.first_stage_model = vae
if depth_model:
model.depth_model = depth_model
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
devices.dtype_unet = 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)
@@ -382,6 +390,8 @@ def load_model(checkpoint_info=None):
if shared.cmd_opts.no_half:
sd_config.model.params.unet_config.params.use_fp16 = False
elif shared.cmd_opts.upcast_sampling:
sd_config.model.params.unet_config.params.use_fp16 = True
timer = Timer()