diff --git a/server/text_generation_server/models/idefics_causal_lm.py b/server/text_generation_server/models/idefics_causal_lm.py
index 8fd4d2f8..bdf1969c 100644
--- a/server/text_generation_server/models/idefics_causal_lm.py
+++ b/server/text_generation_server/models/idefics_causal_lm.py
@@ -119,7 +119,6 @@ class IdeficsCausalLMBatch(Batch):
         padding_right_offset = 0
         max_decode_tokens = 0
         for i, r in enumerate(pb.requests):
-            # from loguru import logger; logger.info(f"from_pb in idefics_causal_lm.py {i=} {r=}")
             requests_idx_mapping[r.id] = i
             inputs.append(r.inputs)
             next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
@@ -136,7 +135,6 @@ class IdeficsCausalLMBatch(Batch):
         prompts = []
         for inp in inputs:
             # Each input is encoded into a list, where each element of this input list is either a string or a URL
-            # from loguru import logger; logger.info(f"from_pb in idefics_causal_lm.py {inp=}")
             if isinstance(inp, str):
                 prompts.append([inp])
             elif isinstance(inp, list):
@@ -169,9 +167,6 @@ class IdeficsCausalLMBatch(Batch):
             max_length=max_truncation,
             add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token
         ).to(device)
-        # from loguru import logger; logger.info(f"from_pb in idefics_causal_lm.py - {tokenized_inputs['input_ids']=}")
-        # from loguru import logger; logger.info({k: v.size() for k,v in processed_inputs.items()})
-        # {'input_ids': torch.Size([4, 5]), 'attention_mask': torch.Size([4, 5]), 'pixel_values': torch.Size([4, num_images, 3, 224, 224]), 'image_attention_mask': torch.Size([4, 5, num_images])}
         for _ in pb.requests:
             input_len = tokenized_inputs["input_ids"].shape[1]
             prefix_offsets.append(input_len - 5) # To decode without potential fallbacks errors
@@ -193,8 +188,6 @@ class IdeficsCausalLMBatch(Batch):
         image_attention_mask = input_ids.new_zeros(
             (pb.size, max_input_length + padding_right_offset, tokenized_inputs["pixel_values"].size(1))
         )
-        # image_attention_mask = tokenized_inputs["image_attention_mask"]
-        # from loguru import logger; logger.info(f"from_pb in idefics_causal_lm.py - image_attention_mask {image_attention_mask.size()}")
 
 
         position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
@@ -227,7 +220,6 @@ class IdeficsCausalLMBatch(Batch):
 
     @tracer.start_as_current_span("filter")
     def filter(self, request_ids: List[int]) -> Optional["IdeficsCausalLMBatch"]:
-        # from loguru import logger; logger.info(f"filter in idefics_causal_lm.py")
         # It deletes requests from the batch. For instance when client lost connection
         if len(request_ids) == 0:
             raise ValueError("Batch must have at least one request")
@@ -339,7 +331,6 @@ class IdeficsCausalLMBatch(Batch):
     @classmethod
     @tracer.start_as_current_span("concatenate")
     def concatenate(cls, batches: List["IdeficsCausalLMBatch"]) -> "IdeficsCausalLMBatch":
-        # from loguru import logger; logger.info(f"concatenate in idefics_causal_lm.py")
         # It adds new requests to the batch
         # Used for padding
         total_batch_size = 0
@@ -436,8 +427,6 @@ class IdeficsCausalLMBatch(Batch):
                 :,
                 batch_left_offset : -batch.padding_right_offset,
             ]
-            # from loguru import logger; logger.info(f"concatenate in idefics_causal_lm.py - image_attention_mask {image_attention_mask.size()}")
-            # from loguru import logger; logger.info(f"concatenate in idefics_causal_lm.py - batch.image_attention_mask {batch.image_attention_mask.size()}")
             image_attention_mask[
                 start_index:end_index,
                 left_offset:-padding_right_offset,
@@ -678,7 +667,6 @@ class IdeficsCausalLM(Model):
     def generate_token(
         self, batch: IdeficsCausalLMBatch
     ) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch]]:
-        # from loguru import logger; logger.info("generate_token in idefics_causal_lm.py  - enter")
         # slice the attention mask to the correct shape
         attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
         if batch.input_ids.size(1) == 1:
@@ -690,14 +678,6 @@ class IdeficsCausalLM(Model):
             image_attention_mask = batch.image_attention_mask[:, -batch.padding_right_offset].unsqueeze(1) #TODO: verify that index. i have a doubt whether there is +1 hanging around
         else:
             image_attention_mask = batch.image_attention_mask[:, : -batch.padding_right_offset]
-            # image_hidden_states = batch.image_hidden_states[:, :-batch.padding_right_offset]
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {batch.padding_right_offset=}")
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {batch.attention_mask.size()=}")
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {attention_mask.size()=}")
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {batch.image_attention_mask=}")
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {batch.image_attention_mask.size()=}")
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {image_attention_mask.size()=}")
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {image_attention_mask=}")
 
         logits, past, image_hidden_states = self.forward(
             input_ids=batch.input_ids,
@@ -708,6 +688,8 @@ class IdeficsCausalLM(Model):
             image_attention_mask=image_attention_mask,
             past_key_values=batch.past_key_values,
         )
+        # Hardcoded remove image tokens
+        logits[:, 32000:32001] = torch.finfo(logits.dtype).min
 
         # Results
         generations: List[Generation] = []
@@ -815,7 +797,6 @@ class IdeficsCausalLM(Model):
 
             # Update values
             batch.input_ids[i, 0] = next_token_id
-            # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - batch.input_ids 1 {batch.input_ids.size()}")
             batch.all_input_ids[i] = all_input_ids
             batch.input_lengths[i] = new_input_length
             batch.prefix_offsets[i] = prefix_offset
@@ -828,7 +809,6 @@ class IdeficsCausalLM(Model):
 
         # Slice unused values from prefill
         batch.input_ids = batch.input_ids[:, :1]
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - batch.input_ids 2 {batch.input_ids.size()}")
 
         # Update attention_mask as we added a new token to input_ids
         batch.attention_mask[:, -batch.padding_right_offset] = 1
@@ -843,5 +823,4 @@ class IdeficsCausalLM(Model):
         batch.past_key_values = past
         batch.image_hidden_states = image_hidden_states
 
-        # from loguru import logger; logger.info(f"generate_token in idefics_causal_lm.py  - {stopped=}")
         return generations, batch