Merge branch 'Main' into feature/preview-latent

comfy/k_diffusion/sampling.py

@@ -620,6 +620,7 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
 
     old_denoised = None
     h_last = None
+    h = None
 
     for i in trange(len(sigmas) - 1, disable=disable):
         denoised = model(x, sigmas[i] * s_in, **extra_args)
@@ -628,7 +629,6 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
         if sigmas[i + 1] == 0:
             # Denoising step
             x = denoised
-            h = None
         else:
             # DPM-Solver++(2M) SDE
             t, s = -sigmas[i].log(), -sigmas[i + 1].log()

nodes.py

@@ -8,7 +8,7 @@ import traceback
 import math
 import time
 
-from PIL import Image, ImageDraw
+from PIL import Image, ImageDraw, ImageOps
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
 import safetensors.torch
@@ -1187,6 +1187,7 @@ class LoadImage:
     def load_image(self, image):
         image_path = folder_paths.get_annotated_filepath(image)
         i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
         image = i.convert("RGB")
         image = np.array(image).astype(np.float32) / 255.0
         image = torch.from_numpy(image)[None,]
@@ -1230,6 +1231,7 @@ class LoadImageMask:
     def load_image(self, image, channel):
         image_path = folder_paths.get_annotated_filepath(image)
         i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
         if i.getbands() != ("R", "G", "B", "A"):
             i = i.convert("RGBA")
         mask = None