Update app.py

app.py

@@ -1,11 +1,13 @@
 import torch
 import gradio as gr
 from PIL import Image
+import numpy as np
 
 import janus  # noqa: F401
 from janus.models import VLChatProcessor
 from transformers import AutoModelForCausalLM
 from peft import PeftModel
+from transformers.modeling_utils import PreTrainedModel
 
 BASE_MODEL = "deepseek-ai/Janus-Pro-1B"
 ADAPTER_REPO = "Branis333/Janus_grade_final"
@@ -15,6 +17,19 @@ dtype = torch.float16 if device == "cuda" else torch.float32
 
 processor = VLChatProcessor.from_pretrained(BASE_MODEL)
 
+
+_original_adjust_tied = PreTrainedModel._adjust_tied_keys_with_tied_pointers
+
+
+def _safe_adjust_tied_keys_with_tied_pointers(self, tied_weights_keys_by_pointers):
+    if not hasattr(self, "all_tied_weights_keys"):
+        self.all_tied_weights_keys = set(getattr(self, "_tied_weights_keys", []) or [])
+    return _original_adjust_tied(self, tied_weights_keys_by_pointers)
+
+
+PreTrainedModel._adjust_tied_keys_with_tied_pointers = _safe_adjust_tied_keys_with_tied_pointers
+
+
 def _load_base_model():
     return AutoModelForCausalLM.from_pretrained(
         BASE_MODEL,
@@ -34,9 +49,19 @@ except RuntimeError as err:
 
     original_linspace = torch.linspace
 
+    class _ScalarFloat:
+        def __init__(self, value):
+            self._value = float(value)
+
+        def item(self):
+            return self._value
+
     def _safe_linspace(*args, **kwargs):
-        kwargs["device"] = "cpu"
-        return original_linspace(*args, **kwargs)
+        start = kwargs.get("start", args[0] if len(args) > 0 else 0.0)
+        end = kwargs.get("end", args[1] if len(args) > 1 else 1.0)
+        steps = kwargs.get("steps", args[2] if len(args) > 2 else 100)
+        values = np.linspace(float(start), float(end), int(steps), dtype=np.float32)
+        return [_ScalarFloat(v) for v in values]
 
     torch.linspace = _safe_linspace
     try: