diff --git a/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py b/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
index 5c3d6ce611cc..86a8c5fbb732 100644
--- a/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
+++ b/src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video.py
@@ -466,12 +466,15 @@ def __call__(
         self,
         prompt: Union[str, List[str]] = None,
         prompt_2: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        negative_prompt_2: Union[str, List[str]] = None,
         height: int = 720,
         width: int = 1280,
         num_frames: int = 129,
         num_inference_steps: int = 50,
         sigmas: List[float] = None,
         guidance_scale: float = 6.0,
+        true_cfg_scale: float = 1.0,
         num_videos_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
@@ -590,6 +593,7 @@ def __call__(
             batch_size = prompt_embeds.shape[0]
 
         # 3. Encode input prompt
+        do_true_cfg = true_cfg_scale > 1.0 and negative_prompt is not None
         prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = self.encode_prompt(
             prompt=prompt,
             prompt_2=prompt_2,
@@ -601,12 +605,29 @@ def __call__(
             device=device,
             max_sequence_length=max_sequence_length,
         )
+        if do_true_cfg:
+            negative_prompt_embeds, negative_pooled_prompt_embeds, negative_prompt_attention_mask = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_2=negative_prompt_2,
+                prompt_template=prompt_template,
+                num_videos_per_prompt=num_videos_per_prompt,
+                prompt_embeds=None,
+                pooled_prompt_embeds=None,
+                prompt_attention_mask=None,
+                device=device,
+                max_sequence_length=max_sequence_length,
+            )
 
         transformer_dtype = self.transformer.dtype
         prompt_embeds = prompt_embeds.to(transformer_dtype)
         prompt_attention_mask = prompt_attention_mask.to(transformer_dtype)
         if pooled_prompt_embeds is not None:
             pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype)
+        if do_true_cfg:
+            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
+            negative_prompt_attention_mask = negative_prompt_attention_mask.to(transformer_dtype)
+            if negative_pooled_prompt_embeds is not None:
+                negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(transformer_dtype)
 
         # 4. Prepare timesteps
         sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
@@ -658,6 +679,18 @@ def __call__(
                     attention_kwargs=attention_kwargs,
                     return_dict=False,
                 )[0]
+                if do_true_cfg:
+                    neg_noise_pred = self.transformer(
+                        hidden_states=latent_model_input,
+                        timestep=timestep,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        encoder_attention_mask=negative_prompt_attention_mask,
+                        pooled_projections=negative_pooled_prompt_embeds,
+                        guidance=guidance,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0]
+                    noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]