Merge branch 'comfyanonymous:master' into chroma-support

app/app_settings.py

@@ -9,8 +9,14 @@ class AppSettings():
         self.user_manager = user_manager
 
     def get_settings(self, request):
-        file = self.user_manager.get_request_user_filepath(
-            request, "comfy.settings.json")
+        try:
+            file = self.user_manager.get_request_user_filepath(
+                request,
+                "comfy.settings.json"
+            )
+        except KeyError as e:
+            logging.error("User settings not found.")
+            raise web.HTTPUnauthorized() from e
         if os.path.isfile(file):
             try:
                 with open(file) as f:

comfy/cli_args.py

@@ -79,6 +79,7 @@ fpte_group.add_argument("--fp8_e4m3fn-text-enc", action="store_true", help="Stor
 fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store text encoder weights in fp8 (e5m2 variant).")
 fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
+fpte_group.add_argument("--bf16-text-enc", action="store_true", help="Store text encoder weights in bf16.")
 
 parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 

comfy/model_management.py

@@ -823,6 +823,8 @@ def text_encoder_dtype(device=None):
         return torch.float8_e5m2
     elif args.fp16_text_enc:
         return torch.float16
+    elif args.bf16_text_enc:
+        return torch.bfloat16
     elif args.fp32_text_enc:
         return torch.float32
 
@@ -1235,6 +1237,8 @@ def soft_empty_cache(force=False):
         torch.xpu.empty_cache()
     elif is_ascend_npu():
         torch.npu.empty_cache()
+    elif is_mlu():
+        torch.mlu.empty_cache()
     elif torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()

comfy_extras/nodes_hunyuan3d.py

@@ -244,7 +244,7 @@ def save_glb(vertices, faces, filepath, metadata=None):
 
     Parameters:
     vertices: torch.Tensor of shape (N, 3) - The vertex coordinates
-    faces: torch.Tensor of shape (M, 4) or (M, 3) - The face indices (quad or triangle faces)
+    faces: torch.Tensor of shape (M, 3) - The face indices (triangle faces)
     filepath: str - Output filepath (should end with .glb)
     """
 

comfy_extras/nodes_mask.py

@@ -2,6 +2,7 @@ import numpy as np
 import scipy.ndimage
 import torch
 import comfy.utils
+import node_helpers
 
 from nodes import MAX_RESOLUTION
 
@@ -87,11 +88,7 @@ class ImageCompositeMasked:
     CATEGORY = "image"
 
     def composite(self, destination, source, x, y, resize_source, mask = None):
-        if destination.shape[-1] < source.shape[-1]:
-            source = source[...,:destination.shape[-1]]
-        elif destination.shape[-1] > source.shape[-1]:
-            destination = torch.nn.functional.pad(destination, (0, 1))
-            destination[..., -1] = source[..., -1]
+        destination, source = node_helpers.image_alpha_fix(destination, source)
         destination = destination.clone().movedim(-1, 1)
         output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
         return (output,)

comfy_extras/nodes_post_processing.py

@@ -6,7 +6,7 @@ import math
 
 import comfy.utils
 import comfy.model_management
-
+import node_helpers
 
 class Blend:
     def __init__(self):
@@ -34,6 +34,7 @@ class Blend:
     CATEGORY = "image/postprocessing"
 
     def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
+        image1, image2 = node_helpers.image_alpha_fix(image1, image2)
         image2 = image2.to(image1.device)
         if image1.shape != image2.shape:
             image2 = image2.permute(0, 3, 1, 2)

node_helpers.py

@@ -44,3 +44,11 @@ def string_to_torch_dtype(string):
         return torch.float16
     if string == "bf16":
         return torch.bfloat16
+
+def image_alpha_fix(destination, source):
+    if destination.shape[-1] < source.shape[-1]:
+        source = source[...,:destination.shape[-1]]
+    elif destination.shape[-1] > source.shape[-1]:
+        destination = torch.nn.functional.pad(destination, (0, 1))
+        destination[..., -1] = source[..., -1]
+    return destination, source