comfy/ldm/lumina/model.py

@@ -491,8 +491,7 @@ class NextDiT(nn.Module):
                 for layer_id in range(n_layers)
             ]
         )
-        # This norm final is in the lumina 2.0 code but isn't actually used for anything.
-        # self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.norm_final = operation_settings.get("operations").RMSNorm(dim, eps=norm_eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.final_layer = FinalLayer(dim, patch_size, self.out_channels, z_image_modulation=z_image_modulation, operation_settings=operation_settings)
 
         if self.pad_tokens_multiple is not None:

comfy/model_management.py

@@ -1019,8 +1019,8 @@ NUM_STREAMS = 0
 if args.async_offload is not None:
     NUM_STREAMS = args.async_offload
 else:
-    #  Enable by default on Nvidia and AMD
-    if is_nvidia() or is_amd():
+    #  Enable by default on Nvidia
+    if is_nvidia():
         NUM_STREAMS = 2
 
 if args.disable_async_offload:
@@ -1126,16 +1126,6 @@ if not args.disable_pinned_memory:
 
 PINNING_ALLOWED_TYPES = set(["Parameter", "QuantizedTensor"])
 
-def discard_cuda_async_error():
-    try:
-        a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
-        b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
-        _ = a + b
-        torch.cuda.synchronize()
-    except torch.AcceleratorError:
-        #Dump it! We already know about it from the synchronous return
-        pass
-
 def pin_memory(tensor):
     global TOTAL_PINNED_MEMORY
     if MAX_PINNED_MEMORY <= 0:
@@ -1168,9 +1158,6 @@ def pin_memory(tensor):
         PINNED_MEMORY[ptr] = size
         TOTAL_PINNED_MEMORY += size
         return True
-    else:
-        logging.warning("Pin error.")
-        discard_cuda_async_error()
 
     return False
 
@@ -1199,9 +1186,6 @@ def unpin_memory(tensor):
         if len(PINNED_MEMORY) == 0:
             TOTAL_PINNED_MEMORY = 0
         return True
-    else:
-        logging.warning("Unpin error.")
-        discard_cuda_async_error()
 
     return False
 

comfy_api_nodes/nodes_veo2.py

@@ -168,8 +168,6 @@ class VeoVideoGenerationNode(IO.ComfyNode):
         # Only add generateAudio for Veo 3 models
         if model.find("veo-2.0") == -1:
             parameters["generateAudio"] = generate_audio
-            # force "enhance_prompt" to True for Veo3 models
-            parameters["enhancePrompt"] = True
 
         initial_response = await sync_op(
             cls,
@@ -293,7 +291,7 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode):
                 IO.Boolean.Input(
                     "enhance_prompt",
                     default=True,
-                    tooltip="This parameter is deprecated and ignored.",
+                    tooltip="Whether to enhance the prompt with AI assistance",
                     optional=True,
                 ),
                 IO.Combo.Input(

comfy_api_nodes/util/client.py

@@ -430,9 +430,9 @@ def _display_text(
     if status:
         display_lines.append(f"Status: {status.capitalize() if isinstance(status, str) else status}")
     if price is not None:
-        p = f"{float(price) * 211:,.1f}".rstrip("0").rstrip(".")
+        p = f"{float(price):,.4f}".rstrip("0").rstrip(".")
         if p != "0":
-            display_lines.append(f"Price: {p} credits")
+            display_lines.append(f"Price: ${p}")
     if text is not None:
         display_lines.append(text)
     if display_lines:

comfy_extras/nodes_dataset.py

@@ -667,19 +667,16 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
 
     @classmethod
     def _process(cls, image, longer_edge):
-        resized_images = []
-        for image_i in image:
-            img = tensor_to_pil(image_i)
-            w, h = img.size
-            if w > h:
-                new_w = longer_edge
-                new_h = int(h * (longer_edge / w))
-            else:
-                new_h = longer_edge
-                new_w = int(w * (longer_edge / h))
-            img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
-            resized_images.append(pil_to_tensor(img))
-        return torch.cat(resized_images, dim=0)
+        img = tensor_to_pil(image)
+        w, h = img.size
+        if w > h:
+            new_w = longer_edge
+            new_h = int(h * (longer_edge / w))
+        else:
+            new_h = longer_edge
+            new_w = int(w * (longer_edge / h))
+        img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
+        return pil_to_tensor(img)
 
 
 class CenterCropImagesNode(ImageProcessingNode):

comfy_extras/nodes_latent.py

@@ -255,7 +255,6 @@ class LatentBatch(io.ComfyNode):
         return io.Schema(
             node_id="LatentBatch",
             category="latent/batch",
-            is_deprecated=True,
             inputs=[
                 io.Latent.Input("samples1"),
                 io.Latent.Input("samples2"),

comfy_extras/nodes_post_processing.py

@@ -498,66 +498,6 @@ def batch_latents(latents: list[dict[str, torch.Tensor]]) -> dict[str, torch.Ten
     samples_out["samples"] = torch.cat(tensors, dim=0)
     return samples_out
 
-class BatchImagesNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        autogrow_template = io.Autogrow.TemplatePrefix(io.Image.Input("image"), prefix="image", min=2, max=50)
-        return io.Schema(
-            node_id="BatchImagesNode",
-            display_name="Batch Images",
-            category="image",
-            inputs=[
-                io.Autogrow.Input("images", template=autogrow_template)
-            ],
-            outputs=[
-                io.Image.Output()
-            ]
-        )
-
-    @classmethod
-    def execute(cls, images: io.Autogrow.Type) -> io.NodeOutput:
-        return io.NodeOutput(batch_images(list(images.values())))
-
-class BatchMasksNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        autogrow_template = io.Autogrow.TemplatePrefix(io.Mask.Input("mask"), prefix="mask", min=2, max=50)
-        return io.Schema(
-            node_id="BatchMasksNode",
-            display_name="Batch Masks",
-            category="mask",
-            inputs=[
-                io.Autogrow.Input("masks", template=autogrow_template)
-            ],
-            outputs=[
-                io.Mask.Output()
-            ]
-        )
-
-    @classmethod
-    def execute(cls, masks: io.Autogrow.Type) -> io.NodeOutput:
-        return io.NodeOutput(batch_masks(list(masks.values())))
-
-class BatchLatentsNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        autogrow_template = io.Autogrow.TemplatePrefix(io.Latent.Input("latent"), prefix="latent", min=2, max=50)
-        return io.Schema(
-            node_id="BatchLatentsNode",
-            display_name="Batch Latents",
-            category="latent",
-            inputs=[
-                io.Autogrow.Input("latents", template=autogrow_template)
-            ],
-            outputs=[
-                io.Latent.Output()
-            ]
-        )
-
-    @classmethod
-    def execute(cls, latents: io.Autogrow.Type) -> io.NodeOutput:
-        return io.NodeOutput(batch_latents(list(latents.values())))
-
 class BatchImagesMasksLatentsNode(io.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -602,9 +542,6 @@ class PostProcessingExtension(ComfyExtension):
             Sharpen,
             ImageScaleToTotalPixels,
             ResizeImageMaskNode,
-            BatchImagesNode,
-            BatchMasksNode,
-            BatchLatentsNode,
             # BatchImagesMasksLatentsNode,
         ]
 

nodes.py

@@ -1863,7 +1863,6 @@ class ImageBatch:
     FUNCTION = "batch"
 
     CATEGORY = "image"
-    DEPRECATED = True
 
     def batch(self, image1, image2):
         if image1.shape[-1] != image2.shape[-1]: