Set multimodal status during Model Loading (#7199)

modules/exllamav3.py

@@ -177,9 +177,6 @@ class Exllamav3Model:
         Process all possible image inputs and return modified prompt + embeddings.
         Returns: (processed_prompt, image_embeddings)
         """
-        if not self.is_multimodal():
-            return prompt, []
-
         # Collect images from various sources using shared utilities
         pil_images = []
 
@@ -234,8 +231,11 @@ class Exllamav3Model:
         """
         Generate text with streaming using native ExLlamaV3 API
         """
-        # Process images and modify prompt (ExLlamaV3-specific)
-        prompt, image_embeddings = self._process_images_for_generation(prompt, state)
+        image_embeddings = []
+
+        if shared.is_multimodal:
+            # Process images and modify prompt (ExLlamaV3-specific)
+            prompt, image_embeddings = self._process_images_for_generation(prompt, state)
 
         # Greedy decoding is a special case
         if state['temperature'] == 0:

modules/llama_cpp_server.py

@@ -8,6 +8,7 @@ import sys
 import threading
 import time
 from pathlib import Path
+from typing import Any, List
 
 import llama_cpp_binaries
 import requests
@@ -129,10 +130,10 @@ class LlamaServer:
 
         return payload
 
-    def generate_with_streaming(self, prompt, state):
-        url = f"http://127.0.0.1:{self.port}/completion"
-        payload = self.prepare_payload(state)
-
+    def _process_images_for_generation(self, state: dict) -> List[Any]:
+        """
+        Process all possible image inputs and return PIL images
+        """
         pil_images = []
         # Source 1: Web UI (from chatbot_wrapper)
         if 'image_attachments' in state and state['image_attachments']:
@@ -144,6 +145,21 @@ class LlamaServer:
         elif 'raw_images' in state and state['raw_images']:
             pil_images.extend(state.get('raw_images', []))
 
+        return pil_images
+
+    def is_multimodal(self) -> bool:
+        """Check if this model supports multimodal input."""
+        return shared.args.mmproj not in [None, 'None']
+
+    def generate_with_streaming(self, prompt, state):
+        url = f"http://127.0.0.1:{self.port}/completion"
+        payload = self.prepare_payload(state)
+
+        pil_images = []
+
+        if shared.is_multimodal:
+            pil_images = self._process_images_for_generation(state)
+
         if pil_images:
             # Multimodal case
             IMAGE_TOKEN_COST_ESTIMATE = 600  # A safe, conservative estimate per image

modules/models.py

@@ -55,6 +55,10 @@ def load_model(model_name, loader=None):
     if loader.lower().startswith('exllama') or loader.lower().startswith('tensorrt') or loader == 'llama.cpp':
         shared.settings['truncation_length'] = shared.args.ctx_size
 
+    shared.is_multimodal = False    
+    if loader.lower() in ('exllamav3', 'llama.cpp'):
+        shared.is_multimodal = model.is_multimodal()
+
     logger.info(f"Loaded \"{model_name}\" in {(time.time()-t0):.2f} seconds.")
     logger.info(f"LOADER: \"{loader}\"")
     logger.info(f"TRUNCATION LENGTH: {shared.settings['truncation_length']}")

modules/shared.py

@@ -16,6 +16,7 @@ model = None
 tokenizer = None
 model_name = 'None'
 is_seq2seq = False
+is_multimodal = False
 model_dirty_from_training = False
 lora_names = []