Should be working now.

comfy/supported_models.py

@@ -1784,6 +1784,10 @@ class LongCatImage(supported_models_base.BASE):
             elif k.startswith("time_embed.timestep_embedder.linear_2."):
                 out_sd["time_in.out_layer." + k.split(".")[-1]] = v
             elif k.startswith("norm_out.linear."):
+                # HF AdaLayerNormContinuous stores [scale | shift] but ComfyUI
+                # LastLayer expects [shift | scale], so swap the two halves.
+                half = v.shape[0] // 2
+                v = torch.cat([v[half:], v[:half]], dim=0)
                 out_sd["final_layer.adaLN_modulation.1." + k.split(".")[-1]] = v
             elif k == "proj_out.weight" or k == "proj_out.bias":
                 out_sd["final_layer.linear." + k.split(".")[-1]] = v

comfy/text_encoders/longcat_image.py

@@ -3,7 +3,9 @@ import numbers
 import torch
 from comfy import sd1_clip
 from comfy.text_encoders.qwen_image import Qwen25_7BVLITokenizer, Qwen25_7BVLIModel
+import logging
 
+logger = logging.getLogger(__name__)
 
 QUOTE_PAIRS = [("'", "'"), ('"', '"'), ("\u2018", "\u2019"), ("\u201c", "\u201d")]
 QUOTE_PATTERN = "|".join(
@@ -33,6 +35,10 @@ def split_quotation(prompt):
 
 
 class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.max_length = 512
+
     def tokenize_with_weights(self, text, return_word_ids=False, **kwargs):
         parts = split_quotation(text)
         all_tokens = []
@@ -45,11 +51,13 @@ class LongCatImageBaseTokenizer(Qwen25_7BVLITokenizer):
                 ids = self.tokenizer(part_text, add_special_tokens=False)["input_ids"]
                 all_tokens.extend(ids)
 
-        max_len = self.max_length if self.max_length < 99999999 else 512
-        if len(all_tokens) > max_len:
-            all_tokens = all_tokens[:max_len]
+        if len(all_tokens) > self.max_length:
+            all_tokens = all_tokens[:self.max_length]
+            logger.warning(f"Truncated prompt to {self.max_length} tokens")
 
         output = [(t, 1.0) for t in all_tokens]
+        # Pad to max length
+        self.pad_tokens(output, self.max_length - len(output))
         return [output]
 
 
@@ -113,7 +121,7 @@ class LongCatImageTEModel(sd1_clip.SD1ClipModel):
         for i in range(len(tok_pairs) - 1, -1, -1):
             elem = tok_pairs[i][0]
             if not torch.is_tensor(elem) and isinstance(elem, numbers.Integral):
-                if elem == 151644:
+                if elem == 151645:
                     suffix_start = i
                     break