diff --git a/javascript/hints.js b/javascript/hints.js index dc75ce313..41201b2f5 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -84,8 +84,6 @@ var titles = { "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.", "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.", - "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).", - "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.", "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", diff --git a/modules/call_queue.py b/modules/call_queue.py index 3b94f8a4c..61aa240fb 100644 --- a/modules/call_queue.py +++ b/modules/call_queue.py @@ -85,9 +85,9 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): elapsed = time.perf_counter() - t elapsed_m = int(elapsed // 60) elapsed_s = elapsed % 60 - elapsed_text = f"{elapsed_s:.2f}s" + elapsed_text = f"{elapsed_s:.1f} sec." if elapsed_m > 0: - elapsed_text = f"{elapsed_m}m "+elapsed_text + elapsed_text = f"{elapsed_m} min. "+elapsed_text if run_memmon: mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()} @@ -95,14 +95,22 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False): reserved_peak = mem_stats['reserved_peak'] sys_peak = mem_stats['system_peak'] sys_total = mem_stats['total'] - sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2) + sys_pct = sys_peak/max(sys_total, 1) * 100 - vram_html = f"
Torch active/reserved: {active_peak}/{reserved_peak} MiB,
{text_a},
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