Refactor.

comfy/sd.py

@@ -515,6 +515,8 @@ class VAE:
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
         steps = samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
+        steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += samples.shape[0] * utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = utils.ProgressBar(steps)
 
         decode_fn = lambda a: (self.first_stage_model.decode(1. / self.scale_factor * a.to(self.device)) + 1.0)
@@ -566,7 +568,9 @@ class VAE:
         self.first_stage_model = self.first_stage_model.to(self.device)
         pixel_samples = pixel_samples.movedim(-1,1).to(self.device)
 
-        steps = utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
+        steps = pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
+        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
+        steps += pixel_samples.shape[0] * utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = utils.ProgressBar(steps)
 
         samples = utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x, tile_y, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)

comfy/utils.py

 import torch
+import math
 
 def load_torch_file(ckpt, safe_load=False):
     if ckpt.lower().endswith(".safetensors"):
@@ -63,10 +64,7 @@ def common_upscale(samples, width, height, upscale_method, crop):
         return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
 
 def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
-    it_1 = -(height // -(tile_y * 2 - overlap)) * -(width // -(tile_x // 2 - overlap))
-    it_2 = -(height // -(tile_y // 2 - overlap)) * -(width // -(tile_x * 2 - overlap))
-    it_3 = -(height // -(tile_y - overlap)) * -(width // -(tile_x - overlap))
-    return it_1 + it_2 + it_3
+    return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
 
 @torch.inference_mode()
 def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, pbar = None):

comfy_extras/nodes_upscale_model.py

@@ -40,7 +40,7 @@ class ImageUpscaleWithModel:
 
         tile = 128 + 64
         overlap = 8
-        steps = -(in_img.shape[2] // -(tile - overlap)) * -(in_img.shape[3] // -(tile - overlap))
+        steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap)
         pbar = comfy.utils.ProgressBar(steps)
         s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar)
         upscale_model.cpu()