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| author | fuchen.ljl <yjqqqqdx_01@163.com> | 2023-12-06 12:42:04 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-12-06 12:42:04 +0000 |
| commit | c2bdbb67b66de06f1163de3f10c290213cd6bdb0 (patch) | |
| tree | 0fcb3010a72ad253862f317ea18fdeb46b05a322 /modules/sd_samplers_timesteps_impl.py | |
| parent | 4d56383025f2cbd00dc6296161e31a896624ab75 (diff) | |
| parent | f92d61497a426a19818625c3ccdaae9beeb82b31 (diff) | |
| download | stable-diffusion-webui-gfx803-c2bdbb67b66de06f1163de3f10c290213cd6bdb0.tar.gz stable-diffusion-webui-gfx803-c2bdbb67b66de06f1163de3f10c290213cd6bdb0.tar.bz2 stable-diffusion-webui-gfx803-c2bdbb67b66de06f1163de3f10c290213cd6bdb0.zip | |
Merge branch 'dev' into kingljl-patch-memory-leak
Diffstat (limited to 'modules/sd_samplers_timesteps_impl.py')
| -rw-r--r-- | modules/sd_samplers_timesteps_impl.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/modules/sd_samplers_timesteps_impl.py b/modules/sd_samplers_timesteps_impl.py index a72daafd..930a64af 100644 --- a/modules/sd_samplers_timesteps_impl.py +++ b/modules/sd_samplers_timesteps_impl.py @@ -11,7 +11,7 @@ from modules.models.diffusion.uni_pc import uni_pc def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
@@ -43,7 +43,7 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta= def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
- alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
+ alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
extra_args = {} if extra_args is None else extra_args
|