diff --git a/helpers/caching/text_embeds.py b/helpers/caching/text_embeds.py
index 84994320..f35e9350 100644
--- a/helpers/caching/text_embeds.py
+++ b/helpers/caching/text_embeds.py
@@ -599,14 +599,18 @@ def compute_t5_prompt(self, prompt: str):
 
         return result, attn_mask
 
-    def compute_gemma_prompt(self, prompt: str):
+    def compute_gemma_prompt(self, prompt: str, is_negative_prompt: bool):
         prompt_embeds, prompt_attention_mask, _, _ = self.pipeline.encode_prompt(
             prompt=prompt,
             do_classifier_free_guidance=False,
             device=self.accelerator.device,
             clean_caption=False,
             max_sequence_length=300,
-            complex_human_instruction=StateTracker.get_args().sana_complex_human_instruction,
+            complex_human_instruction=(
+                StateTracker.get_args().sana_complex_human_instruction
+                if not is_negative_prompt
+                else None
+            ),
         )
 
         return prompt_embeds, prompt_attention_mask
@@ -617,6 +621,7 @@ def compute_embeddings_for_prompts(
         return_concat: bool = True,
         is_validation: bool = False,
         load_from_cache: bool = True,
+        is_negative_prompt: bool = False,
     ):
         logger.debug("Initialising text embed calculator...")
         if not self.batch_write_thread.is_alive():
@@ -695,6 +700,7 @@ def compute_embeddings_for_prompts(
                 raw_prompts,
                 return_concat=return_concat,
                 load_from_cache=load_from_cache,
+                is_negative_prompt=is_negative_prompt,
             )
         else:
             raise ValueError(
@@ -1074,6 +1080,7 @@ def compute_embeddings_for_sana_prompts(
         prompts: list = None,
         return_concat: bool = True,
         load_from_cache: bool = True,
+        is_negative_prompt: bool = False,
     ):
         logger.debug(
             f"compute_embeddings_for_sana_prompts arguments: prompts={prompts}, return_concat={return_concat}, load_from_cache={load_from_cache}"
@@ -1172,7 +1179,7 @@ def compute_embeddings_for_sana_prompts(
                             time.sleep(5)
                     # TODO: Batch this
                     prompt_embeds, attention_mask = self.compute_gemma_prompt(
-                        prompt=prompt,
+                        prompt=prompt, is_negative_prompt=is_negative_prompt
                     )
                     if "deepfloyd" not in StateTracker.get_args().model_type:
                         # we have to store the attn mask with the embed for pixart.
diff --git a/helpers/configuration/cmd_args.py b/helpers/configuration/cmd_args.py
index b3ed0631..89ff22f0 100644
--- a/helpers/configuration/cmd_args.py
+++ b/helpers/configuration/cmd_args.py
@@ -2561,6 +2561,7 @@ def parse_cmdline_args(input_args=None, exit_on_error: bool = False):
     if (
         args.sana_complex_human_instruction is not None
         and type(args.sana_complex_human_instruction) is str
+        and args.sana_complex_human_instruction not in ["", "None"]
     ):
         try:
             import json
@@ -2569,8 +2570,12 @@ def parse_cmdline_args(input_args=None, exit_on_error: bool = False):
                 args.sana_complex_human_instruction
             )
         except Exception as e:
-            logger.error(f"Could not load complex human instruction: {e}")
+            logger.error(
+                f"Could not load complex human instruction ({args.sana_complex_human_instruction}): {e}"
+            )
             raise
+    elif args.sana_complex_human_instruction == "None":
+        args.sana_complex_human_instruction = None
 
     if args.enable_xformers_memory_efficient_attention:
         if args.attention_mechanism != "xformers":
diff --git a/helpers/training/validation.py b/helpers/training/validation.py
index 8055e452..de8cf7cf 100644
--- a/helpers/training/validation.py
+++ b/helpers/training/validation.py
@@ -258,11 +258,10 @@ def prepare_validation_prompt_list(args, embed_cache):
             or model_type == "sana"
         ):
             # we use the legacy encoder but we return no pooled embeds.
-            validation_negative_prompt_embeds = (
-                embed_cache.compute_embeddings_for_prompts(
-                    [StateTracker.get_args().validation_negative_prompt],
-                    load_from_cache=False,
-                )
+            validation_negative_prompt_embeds = embed_cache.compute_embeddings_for_prompts(
+                [StateTracker.get_args().validation_negative_prompt],
+                load_from_cache=False,
+                is_negative_prompt=True,  # sana needs this to disable Complex Human Instruction on negative embed generation
             )
 
             return (
@@ -1388,6 +1387,11 @@ def validate_prompt(
                 if StateTracker.get_model_family() == "flux":
                     if "negative_prompt" in pipeline_kwargs:
                         del pipeline_kwargs["negative_prompt"]
+                if self.args.model_family == "sana":
+                    pipeline_kwargs["complex_human_instruction"] = (
+                        self.args.sana_complex_human_instruction
+                    )
+
                 if (
                     StateTracker.get_model_family() == "pixart_sigma"
                     or StateTracker.get_model_family() == "smoldit"