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author | AUTOMATIC1111 <16777216c@gmail.com> | 2022-10-02 12:35:55 +0000 |
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committer | GitHub <noreply@github.com> | 2022-10-02 12:35:55 +0000 |
commit | 46588c582da2fa104e76aa07d72974881bd1db69 (patch) | |
tree | f1b5433b66184953c75875c181d2491b29cab5dc /modules | |
parent | 3f417566b0bda8eab05d247567aebf001c1d1725 (diff) | |
parent | b8a2b0453b62e4e99d0e5c049313402bc79056b5 (diff) | |
download | stable-diffusion-webui-gfx803-46588c582da2fa104e76aa07d72974881bd1db69.tar.gz stable-diffusion-webui-gfx803-46588c582da2fa104e76aa07d72974881bd1db69.tar.bz2 stable-diffusion-webui-gfx803-46588c582da2fa104e76aa07d72974881bd1db69.zip |
Merge pull request #1459 from asimard1/master
Add progress bar for SwinIR upscale in cmd
Diffstat (limited to 'modules')
-rw-r--r-- | modules/swinir_model.py | 27 |
1 files changed, 15 insertions, 12 deletions
diff --git a/modules/swinir_model.py b/modules/swinir_model.py index 41fda5a7..9bd454c6 100644 --- a/modules/swinir_model.py +++ b/modules/swinir_model.py @@ -5,6 +5,7 @@ import numpy as np import torch from PIL import Image from basicsr.utils.download_util import load_file_from_url +from tqdm import tqdm from modules import modelloader from modules.paths import models_path @@ -122,18 +123,20 @@ def inference(img, model, tile, tile_overlap, window_size, scale): E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img) W = torch.zeros_like(E, dtype=torch.half, device=device) - for h_idx in h_idx_list: - for w_idx in w_idx_list: - in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] - out_patch = model(in_patch) - out_patch_mask = torch.ones_like(out_patch) - - E[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch) - W[ - ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf - ].add_(out_patch_mask) + with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar: + for h_idx in h_idx_list: + for w_idx in w_idx_list: + in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] + out_patch = model(in_patch) + out_patch_mask = torch.ones_like(out_patch) + + E[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch) + W[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch_mask) + pbar.update(1) output = E.div_(W) return output |