use comfy progress bar

comfy/sd.py

@@ -516,7 +516,7 @@ 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)
-        pbar = tqdm(total=steps)
+        pbar = utils.ProgressBar(steps)
 
         decode_fn = lambda a: (self.first_stage_model.decode(1. / self.scale_factor * a.to(self.device)) + 1.0)
         output = torch.clamp((
@@ -568,8 +568,8 @@ class VAE:
         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)
-        pbar = tqdm(total=steps)
-        
+        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)
         samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)
         samples += utils.tiled_scale(pixel_samples, lambda a: self.first_stage_model.encode(2. * a - 1.).sample() * self.scale_factor, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/8), out_channels=4, pbar=pbar)

comfy_extras/nodes_upscale_model.py

@@ -41,8 +41,8 @@ class ImageUpscaleWithModel:
 
         tile = 128 + 64
         overlap = 8
-        its = -(in_img.shape[2] // -(tile - overlap)) * -(in_img.shape[3] // -(tile - overlap))
-        pbar = tqdm(total=its)
+        steps = -(in_img.shape[2] // -(tile - overlap)) * -(in_img.shape[3] // -(tile - 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()
         s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)