diff --git a/torchchat/generate.py b/torchchat/generate.py
index d69989161..87902e180 100644
--- a/torchchat/generate.py
+++ b/torchchat/generate.py
@@ -353,27 +353,34 @@ def prefill(
         width = x.size(1)
         assert input_pos.size(0) == width
 
-        if batch is not None:
+        if self.model.config.model_type == ModelType.Flamingo:
+            assert batch is not None, "Flamingo requires batch"
+
             # TODO: Verify sequential prefill works with multimodal models
-            tokens = batch["tokens"]
+            is_multimodal = True
             if 'encoder_input' in batch:
                 encoder_input = batch['encoder_input']
+                encoder_mask = batch["encoder_mask"]
+                is_multimodal = True
             else:
                 encoder_input = None
+                encoder_mask = None
+                is_multimodal = False
 
-            seq_len = tokens.size(1)
+            seq_len = x.size(1)
             mask = batch["causal_mask"][None, :seq_len]
-            encoder_mask = batch["encoder_mask"]
             input_pos = input_pos.view(1, -1)
-            logits = model(tokens=tokens, mask=mask, encoder_input=encoder_input, input_pos=input_pos, encoder_mask=encoder_mask)[:, -1]
+            logits = model(tokens=x, mask=mask, encoder_input=encoder_input, input_pos=input_pos, encoder_mask=encoder_mask)[:, -1]
+
+            if is_multimodal:
+                batch["encoder_mask"] = batch["encoder_mask"][:, -1:]
+
             return tune_sample(logits, temperature=0, top_k=500)
         elif sequential_prefill:
             for i in range(width):
                 x_sliced, ip_sliced = x[:, i].view(-1, 1), input_pos[i].view(-1)
                 # logging.debug(f"<sliced> x: {x_sliced}, input_pos: {ip_sliced}")
-                logits = model(x_sliced, ip_sliced)  # (x[:, i], input_pos[i])
-        elif self.model.config.model_type == ModelType.Flamingo:
-            assert False, "Flamingo requires batch"
+                logits = model(x_sliced, ip_sliced)  # (x[:, i], input_pos[i])da
         else:
             # input_pos: [B, S]
             logits = model(x, input_pos)
@@ -397,7 +404,7 @@ def decode_one_token(
         if model.config.model_type == ModelType.Flamingo:
             assert batch is not None, "Flamingo requires batch"
             mask = batch["causal_mask"][None, input_pos.item(), None, :]
-            encoder_mask = batch["encoder_mask"][:, -1:]
+            encoder_mask = batch["encoder_mask"] if "encoder_mask" in batch else None
             logits = model(x, encoder_mask=encoder_mask, mask=mask, input_pos=input_pos)[:, -1:]
         else:
             logits = model(x, input_pos)
@@ -733,18 +740,22 @@ def chat(
         if generator_args.chat_mode:
             print("Starting Interactive Chat")
 
-        if generator_args.image_prompts is not None:
-            print("Image prompts", generator_args.image_prompts)
+        if self.model.config.model_type == ModelType.Flamingo:
+
+            is_multimodal = generator_args.image_prompts is not None
+            content = [{"type": "text", "content": generator_args.prompt}]
+
+            if is_multimodal:
+                print("Image prompts", generator_args.image_prompts)
+
+                # Support for just the first image prompt for now
+                images = [Image.open(generator_args.image_prompts[0])]
+                content = [{"type": "image", "content": images[0]}] + content
 
-            # Support for just the first image prompt for now
-            images = [Image.open(generator_args.image_prompts[0])]
             messages = [
                 Message(
                     role="user",
-                    content=[
-                        {"type": "image", "content": images[0]},
-                        {"type": "text", "content": generator_args.prompt},
-                    ],
+                    content=content,
                     eot=True,
                 ),
                 Message(role="assistant", content=""),
@@ -752,12 +763,26 @@ def chat(
 
             transform = llama3_2_vision_transform(str(self.tokenizer_args.tokenizer_path))
 
-            with torch.device(device=self.builder_args.device), set_default_dtype(self.dtype):
+            device = torch.device(device=self.builder_args.device)
+
+            with device, set_default_dtype(self.dtype):
                 data = transform({"messages": messages}, inference=True)
-                batch = padded_collate_tiled_images_and_mask([data], pad_direction="left", pad_max_images=1)
-                # set_default_dtype can not handle the dtype of the image tensor inside the batch; need to manually cast it
-                batch["encoder_input"]["images"] = batch["encoder_input"]["images"].to(self.dtype)
-                seq_len = len(data["tokens"])
+
+                if is_multimodal:
+                    batch = padded_collate_tiled_images_and_mask(
+                        [data], pad_direction="left", pad_max_images=1
+                    )
+                    encoded = batch.pop("tokens").to(device).view(-1)
+                    seq_len = encoded.size(0)
+                    batch["encoder_mask"] = batch["encoder_mask"][:, :seq_len]
+                    batch["encoder_input"]["images"] = batch["encoder_input"]["images"].to(self.dtype)
+                else:
+                    encoded = torch.tensor(
+                        data["tokens"], device=device
+                    ).view(-1)
+                    seq_len = encoded.size(0)
+                    batch = {}
+
                 total_response_length = seq_len + generator_args.max_new_tokens
                 batch["causal_mask"] = torch.tril(
                                             torch.ones(
@@ -765,9 +790,6 @@ def chat(
                                                 dtype=torch.bool,
                                             )
                                         )
-                batch["encoder_mask"] = batch["encoder_mask"][:, :seq_len]
-                encoded = batch["tokens"].view(-1)
-
         else:
             encoded = self.encode_tokens(
                 generator_args.prompt, bos=True, device=self.builder_args.device