Refactor previews into one command line argument.

Clean up a few things.

comfy/cli_args.py

@@ -45,11 +45,12 @@ parser.add_argument("--force-fp32", action="store_true", help="Force fp32 (If th
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 class LatentPreviewMethod(enum.Enum):
+    NoPreviews = "none"
     Auto = "auto"
     Latent2RGB = "latent2rgb"
     TAESD = "taesd"
-parser.add_argument("--disable-previews", action="store_true", help="Disable showing node previews.")
-parser.add_argument("--default-preview-method", type=str, default=LatentPreviewMethod.Auto, metavar="PREVIEW_METHOD", help="Default preview method for sampler nodes.")
+
+parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization instead of the sub-quadratic one. Ignored when xformers is used.")

comfy/taesd/taesd.py

@@ -50,9 +50,9 @@ class TAESD(nn.Module):
         self.encoder = Encoder()
         self.decoder = Decoder()
         if encoder_path is not None:
-            self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu"))
+            self.encoder.load_state_dict(torch.load(encoder_path, map_location="cpu", weights_only=True))
         if decoder_path is not None:
-            self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu"))
+            self.decoder.load_state_dict(torch.load(decoder_path, map_location="cpu", weights_only=True))
 
     @staticmethod
     def scale_latents(x):

comfy/utils.py

@@ -1,7 +1,6 @@
 import torch
 import math
 import struct
-import comfy.model_management
 
 def load_torch_file(ckpt, safe_load=False):
     if ckpt.lower().endswith(".safetensors"):
@@ -167,8 +166,6 @@ def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_am
         out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device="cpu")
         for y in range(0, s.shape[2], tile_y - overlap):
             for x in range(0, s.shape[3], tile_x - overlap):
-                comfy.model_management.throw_exception_if_processing_interrupted()
-
                 s_in = s[:,:,y:y+tile_y,x:x+tile_x]
 
                 ps = function(s_in).cpu()