soaska/my-sd
soaska/my-sd
1
0
Fork 0
Code Issues Pull Requests Actions Packages Activity

backend

Browse Source
This commit is contained in:
lllyasviel 2024-02-05 00:45:34 -08:00
parent affb40340e
commit 40afb9dfb0
2 changed files with 9 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
ldm_patched/modules/model_management.py
View File

@ -510,7 +510,7 @@ def unet_dtype(device=None, model_params=0):
return torch.float8_e4m3fn
if args.unet_in_fp8_e5m2:
return torch.float8_e5m2
if should_use_fp16(device=device, model_params=model_params):
if should_use_fp16(device=device, model_params=model_params, manual_cast=True):
return torch.float16
return torch.float32
@ -710,7 +710,7 @@ def is_device_mps(device):
return True
return False
def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
global directml_enabled
if device is not None:
@ -736,10 +736,13 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
if is_intel_xpu():
return True
if torch.cuda.is_bf16_supported():
if torch.version.hip:
return True
props = torch.cuda.get_device_properties("cuda")
if props.major >= 8:
return True
if props.major < 6:
return False
@ -752,7 +755,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True):
if x in props.name.lower():
fp16_works = True
if fp16_works:
if fp16_works or manual_cast:
free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
if (not prioritize_performance) or model_params * 4 > free_model_memory:
return True

2
ldm_patched/modules/utils.py
View File

@ -413,6 +413,8 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
for y in range(0, s.shape[2], tile_y - overlap):
for x in range(0, s.shape[3], tile_x - overlap):
x = max(0, min(s.shape[-1] - overlap, x))
y = max(0, min(s.shape[-2] - overlap, y))
s_in = s[:,:,y:y+tile_y,x:x+tile_x]
ps = function(s_in).to(output_device)