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| author | hako-mikan <122196982+hako-mikan@users.noreply.github.com> | 2024-02-09 14:17:40 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2024-02-09 14:17:40 +0000 |
| commit | 0bc7867ccd4ac24f5f270cb767c4642d0a0c001c (patch) | |
| tree | 2ad13a0cf77bc189a8c9097bd507f9674f993da6 /modules/sd_samplers_timesteps_impl.py | |
| parent | 816096e642187a18b11e2729c42c0b5f677f047d (diff) | |
| parent | cf2772fab0af5573da775e7437e6acdca424f26e (diff) | |
| download | stable-diffusion-webui-gfx803-0bc7867ccd4ac24f5f270cb767c4642d0a0c001c.tar.gz stable-diffusion-webui-gfx803-0bc7867ccd4ac24f5f270cb767c4642d0a0c001c.tar.bz2 stable-diffusion-webui-gfx803-0bc7867ccd4ac24f5f270cb767c4642d0a0c001c.zip | |
Merge branch 'AUTOMATIC1111:master' into master
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
|