Refactor previews into one command line argument.

Clean up a few things.

README.md

@@ -184,7 +184,9 @@ You can set this command line setting to disable the upcasting to fp32 in some c
 
 ## How to show high-quality previews?
 
-The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_encoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_encoder.pth) and [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) models and place them in the `models/taesd` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
+Use ```--preview-method auto``` to enable previews.
+
+The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_encoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_encoder.pth) and [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
 
 ## Support and dev channel
 

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

 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()

folder_paths.py

@@ -18,7 +18,7 @@ folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision"
 folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions)
 folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions)
 folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"])
-folder_names_and_paths["taesd"] = ([os.path.join(models_dir, "taesd")], supported_pt_extensions)
+folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions)
 
 folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions)
 folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions)

latent_preview.py

+import torch
+from PIL import Image, ImageOps
+from io import BytesIO
+import struct
+import numpy as np
+
+from comfy.cli_args import args, LatentPreviewMethod
+from comfy.taesd.taesd import TAESD
+import folder_paths
+
+MAX_PREVIEW_RESOLUTION = 512
+
+class LatentPreviewer:
+    def decode_latent_to_preview(self, x0):
+        pass
+
+    def decode_latent_to_preview_image(self, preview_format, x0):
+        preview_image = self.decode_latent_to_preview(x0)
+        preview_image = ImageOps.contain(preview_image, (MAX_PREVIEW_RESOLUTION, MAX_PREVIEW_RESOLUTION), Image.ANTIALIAS)
+
+        preview_type = 1
+        if preview_format == "JPEG":
+            preview_type = 1
+        elif preview_format == "PNG":
+            preview_type = 2
+
+        bytesIO = BytesIO()
+        header = struct.pack(">I", preview_type)
+        bytesIO.write(header)
+        preview_image.save(bytesIO, format=preview_format, quality=95)
+        preview_bytes = bytesIO.getvalue()
+        return preview_bytes
+
+class TAESDPreviewerImpl(LatentPreviewer):
+    def __init__(self, taesd):
+        self.taesd = taesd
+
+    def decode_latent_to_preview(self, x0):
+        x_sample = self.taesd.decoder(x0)[0].detach()
+        # x_sample = self.taesd.unscale_latents(x_sample).div(4).add(0.5)  # returns value in [-2, 2]
+        x_sample = x_sample.sub(0.5).mul(2)
+
+        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+        x_sample = x_sample.astype(np.uint8)
+
+        preview_image = Image.fromarray(x_sample)
+        return preview_image
+
+
+class Latent2RGBPreviewer(LatentPreviewer):
+    def __init__(self):
+        self.latent_rgb_factors = torch.tensor([
+                    #   R        G        B
+                    [0.298, 0.207, 0.208],  # L1
+                    [0.187, 0.286, 0.173],  # L2
+                    [-0.158, 0.189, 0.264],  # L3
+                    [-0.184, -0.271, -0.473],  # L4
+                ], device="cpu")
+
+    def decode_latent_to_preview(self, x0):
+        latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
+
+        latents_ubyte = (((latent_image + 1) / 2)
+                            .clamp(0, 1)  # change scale from -1..1 to 0..1
+                            .mul(0xFF)  # to 0..255
+                            .byte()).cpu()
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+
+def get_previewer(device):
+    previewer = None
+    method = args.preview_method
+    if method != LatentPreviewMethod.NoPreviews:
+        # TODO previewer methods
+        taesd_decoder_path = folder_paths.get_full_path("vae_approx", "taesd_decoder.pth")
+
+        if method == LatentPreviewMethod.Auto:
+            method = LatentPreviewMethod.Latent2RGB
+            if taesd_decoder_path:
+                method = LatentPreviewMethod.TAESD
+
+        if method == LatentPreviewMethod.TAESD:
+            if taesd_decoder_path:
+                taesd = TAESD(None, taesd_decoder_path).to(device)
+                previewer = TAESDPreviewerImpl(taesd)
+            else:
+                print("Warning: TAESD previews enabled, but could not find models/vae_approx/taesd_decoder.pth")
+
+        if previewer is None:
+            previewer = Latent2RGBPreviewer()
+    return previewer
+
+

nodes.py

@@ -7,15 +7,12 @@ import hashlib
 import traceback
 import math
 import time
-import struct
-from io import BytesIO
 
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
 import safetensors.torch
 
-
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
 
 
@@ -24,8 +21,6 @@ import comfy.samplers
 import comfy.sample
 import comfy.sd
 import comfy.utils
-from comfy.cli_args import args, LatentPreviewMethod
-from comfy.taesd.taesd import TAESD
 
 import comfy.clip_vision
 
@@ -33,33 +28,7 @@ import comfy.model_management
 import importlib
 
 import folder_paths
-
-
-class LatentPreviewer:
-    def decode_latent_to_preview(self, device, x0):
-        pass
-
-
-class Latent2RGBPreviewer(LatentPreviewer):
-    def __init__(self):
-        self.latent_rgb_factors = torch.tensor([
-                    #   R        G        B
-                    [0.298, 0.207, 0.208],  # L1
-                    [0.187, 0.286, 0.173],  # L2
-                    [-0.158, 0.189, 0.264],  # L3
-                    [-0.184, -0.271, -0.473],  # L4
-                ], device="cpu")
-
-    def decode_latent_to_preview(self, device, x0):
-        latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
-
-        latents_ubyte = (((latent_image + 1) / 2)
-                            .clamp(0, 1)  # change scale from -1..1 to 0..1
-                            .mul(0xFF)  # to 0..255
-                            .byte()).cpu()
-
-        return Image.fromarray(latents_ubyte.numpy())
-
+import latent_preview
 
 def before_node_execution():
     comfy.model_management.throw_exception_if_processing_interrupted()
@@ -68,7 +37,6 @@ def interrupt_processing(value=True):
     comfy.model_management.interrupt_current_processing(value)
 
 MAX_RESOLUTION=8192
-MAX_PREVIEW_RESOLUTION = 512
 
 class CLIPTextEncode:
     @classmethod
@@ -279,22 +247,6 @@ class VAEEncodeForInpaint:
 
         return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, )
 
-class TAESDPreviewerImpl(LatentPreviewer):
-    def __init__(self, taesd):
-        self.taesd = taesd
-
-    def decode_latent_to_preview(self, device, x0):
-        x_sample = self.taesd.decoder(x0.to(device))[0].detach()
-        # x_sample = self.taesd.unscale_latents(x_sample).div(4).add(0.5)  # returns value in [-2, 2]
-        x_sample = x_sample.sub(0.5).mul(2)
-
-        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-
-        preview_image = Image.fromarray(x_sample)
-        return preview_image
-
 class SaveLatent:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
@@ -978,25 +930,6 @@ class SetLatentNoiseMask:
         return (s,)
 
 
-def decode_latent_to_preview_image(previewer, device, preview_format, x0):
-    preview_image = previewer.decode_latent_to_preview(device, x0)
-    preview_image = ImageOps.contain(preview_image, (MAX_PREVIEW_RESOLUTION, MAX_PREVIEW_RESOLUTION), Image.ANTIALIAS)
-
-    preview_type = 1
-    if preview_format == "JPEG":
-        preview_type = 1
-    elif preview_format == "PNG":
-        preview_type = 2
-
-    bytesIO = BytesIO()
-    header = struct.pack(">I", preview_type)
-    bytesIO.write(header)
-    preview_image.save(bytesIO, format=preview_format)
-    preview_bytes = bytesIO.getvalue()
-
-    return preview_bytes
-
-
 def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
     device = comfy.model_management.get_torch_device()
     latent_image = latent["samples"]
@@ -1015,34 +948,13 @@ def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive,
     if preview_format not in ["JPEG", "PNG"]:
         preview_format = "JPEG"
 
-    previewer = None
-    if not args.disable_previews:
-        # TODO previewer methods
-        taesd_encoder_path = folder_paths.get_full_path("taesd", "taesd_encoder.pth")
-        taesd_decoder_path = folder_paths.get_full_path("taesd", "taesd_decoder.pth")
-
-        method = args.default_preview_method
-
-        if method == LatentPreviewMethod.Auto:
-            method = LatentPreviewMethod.Latent2RGB
-            if taesd_encoder_path and taesd_encoder_path:
-                method = LatentPreviewMethod.TAESD
-
-        if method == LatentPreviewMethod.TAESD:
-            if taesd_encoder_path and taesd_encoder_path:
-                taesd = TAESD(taesd_encoder_path, taesd_decoder_path).to(device)
-                previewer = TAESDPreviewerImpl(taesd)
-            else:
-                print("Warning: TAESD previews enabled, but could not find models/taesd/taesd_encoder.pth and models/taesd/taesd_decoder.pth")
-
-        if previewer is None:
-            previewer = Latent2RGBPreviewer()
+    previewer = latent_preview.get_previewer(device)
 
     pbar = comfy.utils.ProgressBar(steps)
     def callback(step, x0, x, total_steps):
         preview_bytes = None
         if previewer:
-            preview_bytes = decode_latent_to_preview_image(previewer, device, preview_format, x0)
+            preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0)
         pbar.update_absolute(step + 1, total_steps, preview_bytes)
 
     samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,