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| author | ParityError <36368048+ParityError@users.noreply.github.com> | 2023-03-17 07:36:17 +0000 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-03-17 07:36:17 +0000 |
| commit | 34c0f499c540f69a6de11caa940483e8f581074d (patch) | |
| tree | a3d77fc1a626b372305b8f7220617f1ed97afc21 /modules/models/diffusion/uni_pc/uni_pc.py | |
| parent | 5c051c0618bec1417827910b601ba915d0ca6c4e (diff) | |
| parent | a9fed7c364061ae6efb37f797b6b522cb3cf7aa2 (diff) | |
| download | stable-diffusion-webui-gfx803-34c0f499c540f69a6de11caa940483e8f581074d.tar.gz stable-diffusion-webui-gfx803-34c0f499c540f69a6de11caa940483e8f581074d.tar.bz2 stable-diffusion-webui-gfx803-34c0f499c540f69a6de11caa940483e8f581074d.zip | |
Merge branch 'AUTOMATIC1111:master' into master
Diffstat (limited to 'modules/models/diffusion/uni_pc/uni_pc.py')
| -rw-r--r-- | modules/models/diffusion/uni_pc/uni_pc.py | 5 |
1 files changed, 3 insertions, 2 deletions
diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py index e9a093a2..eb5f4e76 100644 --- a/modules/models/diffusion/uni_pc/uni_pc.py +++ b/modules/models/diffusion/uni_pc/uni_pc.py @@ -1,6 +1,7 @@ import torch import torch.nn.functional as F import math +from tqdm.auto import trange class NoiseScheduleVP: @@ -750,7 +751,7 @@ class UniPC: if method == 'multistep': assert steps >= order, "UniPC order must be < sampling steps" timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) - print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}") + #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}") assert timesteps.shape[0] - 1 == steps with torch.no_grad(): vec_t = timesteps[0].expand((x.shape[0])) @@ -766,7 +767,7 @@ class UniPC: self.after_update(x, model_x) model_prev_list.append(model_x) t_prev_list.append(vec_t) - for step in range(order, steps + 1): + for step in trange(order, steps + 1): vec_t = timesteps[step].expand(x.shape[0]) if lower_order_final: step_order = min(order, steps + 1 - step) |