From c8c9926eeb0b25dba86f3d9e574e8527c090fc37 Mon Sep 17 00:00:00 2001 From: pythongosssss <125205205+pythongosssss@users.noreply.github.com> Date: Mon, 24 Apr 2023 11:55:44 +0100 Subject: [PATCH] Add progress to vae decode tiled --- comfy/sd.py | 12 +++++++++--- comfy/utils.py | 4 +++- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/comfy/sd.py b/comfy/sd.py index 92dbb931..2aadefad 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1,6 +1,7 @@ import torch import contextlib import copy +from tqdm.auto import tqdm import sd1_clip import sd2_clip @@ -437,11 +438,16 @@ class VAE: self.device = device def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): + it_1 = -(samples.shape[2] // -(tile_y * 2 - overlap)) * -(samples.shape[3] // -(tile_x // 2 - overlap)) + it_2 = -(samples.shape[2] // -(tile_y // 2 - overlap)) * -(samples.shape[3] // -(tile_x * 2 - overlap)) + it_3 = -(samples.shape[2] // -(tile_y - overlap)) * -(samples.shape[3] // -(tile_x - overlap)) + pbar = tqdm(total=samples.shape[0] * (it_1 + it_2 + it_3)) + decode_fn = lambda a: (self.first_stage_model.decode(1. / self.scale_factor * a.to(self.device)) + 1.0) output = torch.clamp(( - (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8) + - utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8) + - utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8)) + (utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = 8, pbar = pbar) + + utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = 8, pbar = pbar) + + utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = 8, pbar = pbar)) / 3.0) / 2.0, min=0.0, max=1.0) return output diff --git a/comfy/utils.py b/comfy/utils.py index 68f93403..c7c6a08c 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -63,7 +63,7 @@ def common_upscale(samples, width, height, upscale_method, crop): return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) @torch.inference_mode() -def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3): +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None): output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device="cpu") for b in range(samples.shape[0]): s = samples[b:b+1] @@ -83,6 +83,8 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask + if pbar is not None: + pbar.update(1) output[b:b+1] = out/out_div return output