diff --git a/src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py b/src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py
index 61c9da0c9496..2984641bf689 100644
--- a/src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py
+++ b/src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py
@@ -446,9 +446,13 @@ def check_inputs(
         strength,
         height,
         width,
-        callback_on_step_end_tensor_inputs,
+        negative_prompt=None,
+        negative_prompt_2=None,
         prompt_embeds=None,
+        negative_prompt_embeds=None,
         pooled_prompt_embeds=None,
+        negative_pooled_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
         max_sequence_length=None,
     ):
         if strength < 0 or strength > 1:
@@ -485,10 +489,33 @@ def check_inputs(
         elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
             raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
 
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
         if prompt_embeds is not None and pooled_prompt_embeds is None:
             raise ValueError(
                 "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
             )
+        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
 
         if max_sequence_length is not None and max_sequence_length > 512:
             raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
@@ -638,6 +665,9 @@ def __call__(
         self,
         prompt: str | list[str] = None,
         prompt_2: str | list[str] | None = None,
+        negative_prompt: str | list[str] = None,
+        negative_prompt_2: str | list[str] | None = None,
+        true_cfg_scale: float = 1.0,
         image: PipelineImageInput = None,
         control_image: PipelineImageInput = None,
         height: int | None = None,
@@ -655,6 +685,8 @@ def __call__(
         latents: torch.FloatTensor | None = None,
         prompt_embeds: torch.FloatTensor | None = None,
         pooled_prompt_embeds: torch.FloatTensor | None = None,
+        negative_prompt_embeds: torch.FloatTensor | None = None,
+        negative_pooled_prompt_embeds: torch.FloatTensor | None = None,
         output_type: str | None = "pil",
         return_dict: bool = True,
         joint_attention_kwargs: dict[str, Any] | None = None,
@@ -670,6 +702,16 @@ def __call__(
                 The prompt or prompts to guide the image generation.
             prompt_2 (`str` or `list[str]`, *optional*):
                 The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`.
+            negative_prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
+                not greater than `1`).
+            negative_prompt_2 (`str` or `list[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                True classifier-free guidance (guidance scale) is enabled when `true_cfg_scale` > 1 and
+                `negative_prompt` is provided.
             image (`PIL.Image.Image` or `list[PIL.Image.Image]` or `torch.FloatTensor`):
                 The image(s) to modify with the pipeline.
             control_image (`PIL.Image.Image` or `list[PIL.Image.Image]` or `torch.FloatTensor`):
@@ -711,6 +753,14 @@ def __call__(
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
             pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated pooled text embeddings.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -751,9 +801,13 @@ def __call__(
             strength,
             height,
             width,
-            callback_on_step_end_tensor_inputs,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
             prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
             pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
             max_sequence_length=max_sequence_length,
         )
 
@@ -774,6 +828,10 @@ def __call__(
         lora_scale = (
             self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
         )
+        has_neg_prompt = negative_prompt is not None or (
+            negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
+        )
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
         (
             prompt_embeds,
             pooled_prompt_embeds,
@@ -788,6 +846,21 @@ def __call__(
             max_sequence_length=max_sequence_length,
             lora_scale=lora_scale,
         )
+        if do_true_cfg:
+            (
+                negative_prompt_embeds,
+                negative_pooled_prompt_embeds,
+                _,
+            ) = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_2=negative_prompt_2,
+                prompt_embeds=negative_prompt_embeds,
+                pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                lora_scale=lora_scale,
+            )
 
         init_image = self.image_processor.preprocess(image, height=height, width=width)
         init_image = init_image.to(dtype=torch.float32)
@@ -974,6 +1047,23 @@ def __call__(
                     controlnet_blocks_repeat=controlnet_blocks_repeat,
                 )[0]
 
+                if do_true_cfg:
+                    neg_noise_pred = self.transformer(
+                        hidden_states=latents,
+                        timestep=timestep / 1000,
+                        guidance=guidance,
+                        pooled_projections=negative_pooled_prompt_embeds,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        controlnet_block_samples=controlnet_block_samples,
+                        controlnet_single_block_samples=controlnet_single_block_samples,
+                        txt_ids=text_ids,
+                        img_ids=latent_image_ids,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                        controlnet_blocks_repeat=controlnet_blocks_repeat,
+                    )[0]
+                    noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
+
                 latents_dtype = latents.dtype
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
 
diff --git a/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py b/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py
index eed671152bc9..73f8e1fe00cd 100644
--- a/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py
+++ b/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py
@@ -460,8 +460,12 @@ def check_inputs(
         height,
         width,
         output_type,
+        negative_prompt=None,
+        negative_prompt_2=None,
         prompt_embeds=None,
+        negative_prompt_embeds=None,
         pooled_prompt_embeds=None,
+        negative_pooled_prompt_embeds=None,
         callback_on_step_end_tensor_inputs=None,
         padding_mask_crop=None,
         max_sequence_length=None,
@@ -500,10 +504,33 @@ def check_inputs(
         elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
             raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
 
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
         if prompt_embeds is not None and pooled_prompt_embeds is None:
             raise ValueError(
                 "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
             )
+        if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+            )
 
         if padding_mask_crop is not None:
             if not isinstance(image, PIL.Image.Image):
@@ -742,6 +769,9 @@ def __call__(
         self,
         prompt: str | list[str] = None,
         prompt_2: str | list[str] | None = None,
+        negative_prompt: str | list[str] = None,
+        negative_prompt_2: str | list[str] | None = None,
+        true_cfg_scale: float = 1.0,
         image: PipelineImageInput = None,
         mask_image: PipelineImageInput = None,
         masked_image_latents: PipelineImageInput = None,
@@ -762,6 +792,8 @@ def __call__(
         latents: torch.FloatTensor | None = None,
         prompt_embeds: torch.FloatTensor | None = None,
         pooled_prompt_embeds: torch.FloatTensor | None = None,
+        negative_prompt_embeds: torch.FloatTensor | None = None,
+        negative_pooled_prompt_embeds: torch.FloatTensor | None = None,
         output_type: str | None = "pil",
         return_dict: bool = True,
         joint_attention_kwargs: dict[str, Any] | None = None,
@@ -777,6 +809,16 @@ def __call__(
                 The prompt or prompts to guide the image generation.
             prompt_2 (`str` or `list[str]`, *optional*):
                 The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`.
+            negative_prompt (`str` or `list[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
+                not greater than `1`).
+            negative_prompt_2 (`str` or `list[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
+            true_cfg_scale (`float`, *optional*, defaults to 1.0):
+                True classifier-free guidance (guidance scale) is enabled when `true_cfg_scale` > 1 and
+                `negative_prompt` is provided.
             image (`PIL.Image.Image` or `list[PIL.Image.Image]` or `torch.FloatTensor`):
                 The image(s) to inpaint.
             mask_image (`PIL.Image.Image` or `list[PIL.Image.Image]` or `torch.FloatTensor`):
@@ -825,6 +867,14 @@ def __call__(
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
             pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated pooled text embeddings.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between `PIL.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
@@ -872,8 +922,12 @@ def __call__(
             height,
             width,
             output_type=output_type,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
             prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
             pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
             callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
             padding_mask_crop=padding_mask_crop,
             max_sequence_length=max_sequence_length,
@@ -898,6 +952,10 @@ def __call__(
         lora_scale = (
             self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
         )
+        has_neg_prompt = negative_prompt is not None or (
+            negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
+        )
+        do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
         prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
             prompt=prompt,
             prompt_2=prompt_2,
@@ -908,6 +966,17 @@ def __call__(
             max_sequence_length=max_sequence_length,
             lora_scale=lora_scale,
         )
+        if do_true_cfg:
+            negative_prompt_embeds, negative_pooled_prompt_embeds, _ = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_2=negative_prompt_2,
+                prompt_embeds=negative_prompt_embeds,
+                pooled_prompt_embeds=negative_pooled_prompt_embeds,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                lora_scale=lora_scale,
+            )
 
         # 4. Preprocess mask and image
         if padding_mask_crop is not None:
@@ -1152,6 +1221,23 @@ def __call__(
                     controlnet_blocks_repeat=controlnet_blocks_repeat,
                 )[0]
 
+                if do_true_cfg:
+                    neg_noise_pred = self.transformer(
+                        hidden_states=latents,
+                        timestep=timestep / 1000,
+                        guidance=guidance,
+                        pooled_projections=negative_pooled_prompt_embeds,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        controlnet_block_samples=controlnet_block_samples,
+                        controlnet_single_block_samples=controlnet_single_block_samples,
+                        txt_ids=text_ids,
+                        img_ids=latent_image_ids,
+                        joint_attention_kwargs=self.joint_attention_kwargs,
+                        return_dict=False,
+                        controlnet_blocks_repeat=controlnet_blocks_repeat,
+                    )[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_dtype = latents.dtype
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
diff --git a/tests/pipelines/controlnet_flux/test_controlnet_flux_img2img.py b/tests/pipelines/controlnet_flux/test_controlnet_flux_img2img.py
index 165e7bda2dc9..5f159172fd3e 100644
--- a/tests/pipelines/controlnet_flux/test_controlnet_flux_img2img.py
+++ b/tests/pipelines/controlnet_flux/test_controlnet_flux_img2img.py
@@ -158,6 +158,66 @@ def test_flux_controlnet_different_prompts(self):
 
         assert max_diff > 1e-6, "Outputs should be different for different prompts."
 
+    def test_flux_controlnet_img2img_true_cfg(self):
+        pipe = self.get_pipeline().to(torch_device)
+
+        inputs = self.get_dummy_inputs()
+        inputs.pop("generator")
+        no_true_cfg_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        inputs["negative_prompt"] = "bad quality"
+        inputs["true_cfg_scale"] = 2.0
+        true_cfg_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        assert not torch.allclose(no_true_cfg_out, true_cfg_out), (
+            "Outputs should be different when true_cfg_scale is set."
+        )
+
+    def test_flux_controlnet_img2img_true_cfg_requires_negative_prompt(self):
+        # `true_cfg_scale` alone must stay a no-op: true CFG only kicks in once a negative prompt
+        # (or precomputed negative embeddings) is supplied.
+        pipe = self.get_pipeline().to(torch_device)
+
+        inputs = self.get_dummy_inputs()
+        inputs.pop("generator")
+        baseline_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        inputs["true_cfg_scale"] = 2.0
+        no_negative_prompt_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        assert torch.allclose(baseline_out, no_negative_prompt_out), (
+            "`true_cfg_scale` should be ignored when no negative prompt is provided."
+        )
+
+    def test_flux_controlnet_img2img_true_cfg_with_negative_embeds(self):
+        pipe = self.get_pipeline().to(torch_device)
+
+        inputs = self.get_dummy_inputs()
+        inputs.pop("generator")
+        prompt = inputs.pop("prompt")
+
+        prompt_embeds, pooled_prompt_embeds, _ = pipe.encode_prompt(
+            prompt=prompt, prompt_2=None, device=torch_device, num_images_per_prompt=1, max_sequence_length=48
+        )
+        negative_prompt_embeds, negative_pooled_prompt_embeds, _ = pipe.encode_prompt(
+            prompt="bad quality", prompt_2=None, device=torch_device, num_images_per_prompt=1, max_sequence_length=48
+        )
+        inputs.update(
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        )
+
+        inputs["true_cfg_scale"] = 1.0
+        cfg_off = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+        inputs["true_cfg_scale"] = 2.0
+        cfg_on = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        assert not torch.allclose(cfg_off, cfg_on), (
+            "Precomputed negative embeds should enable true CFG when negative_prompt is None."
+        )
+
     def test_fused_qkv_projections(self):
         # Run on CPU to keep the seeded generator deterministic across the three forward passes.
         pipe = self.get_pipeline()
diff --git a/tests/pipelines/controlnet_flux/test_controlnet_flux_inpaint.py b/tests/pipelines/controlnet_flux/test_controlnet_flux_inpaint.py
index eeacc636e89d..fd56fda8c88d 100644
--- a/tests/pipelines/controlnet_flux/test_controlnet_flux_inpaint.py
+++ b/tests/pipelines/controlnet_flux/test_controlnet_flux_inpaint.py
@@ -167,6 +167,66 @@ def test_flux_controlnet_inpaint_with_controlnet_conditioning_scale(self):
             "Changing `controlnet_conditioning_scale` should change the output."
         )
 
+    def test_flux_controlnet_inpaint_true_cfg(self):
+        pipe = self.get_pipeline().to(torch_device)
+
+        inputs = self.get_dummy_inputs()
+        inputs.pop("generator")
+        no_true_cfg_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        inputs["negative_prompt"] = "bad quality"
+        inputs["true_cfg_scale"] = 2.0
+        true_cfg_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        assert not torch.allclose(no_true_cfg_out, true_cfg_out), (
+            "Outputs should be different when true_cfg_scale is set."
+        )
+
+    def test_flux_controlnet_inpaint_true_cfg_requires_negative_prompt(self):
+        # `true_cfg_scale` alone must stay a no-op: true CFG only kicks in once a negative prompt
+        # (or precomputed negative embeddings) is supplied.
+        pipe = self.get_pipeline().to(torch_device)
+
+        inputs = self.get_dummy_inputs()
+        inputs.pop("generator")
+        baseline_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        inputs["true_cfg_scale"] = 2.0
+        no_negative_prompt_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        assert torch.allclose(baseline_out, no_negative_prompt_out), (
+            "`true_cfg_scale` should be ignored when no negative prompt is provided."
+        )
+
+    def test_flux_controlnet_inpaint_true_cfg_with_negative_embeds(self):
+        pipe = self.get_pipeline().to(torch_device)
+
+        inputs = self.get_dummy_inputs()
+        inputs.pop("generator")
+        prompt = inputs.pop("prompt")
+
+        prompt_embeds, pooled_prompt_embeds, _ = pipe.encode_prompt(
+            prompt=prompt, prompt_2=None, device=torch_device, num_images_per_prompt=1, max_sequence_length=48
+        )
+        negative_prompt_embeds, negative_pooled_prompt_embeds, _ = pipe.encode_prompt(
+            prompt="bad quality", prompt_2=None, device=torch_device, num_images_per_prompt=1, max_sequence_length=48
+        )
+        inputs.update(
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        )
+
+        inputs["true_cfg_scale"] = 1.0
+        cfg_off = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+        inputs["true_cfg_scale"] = 2.0
+        cfg_on = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
+
+        assert not torch.allclose(cfg_off, cfg_on), (
+            "Precomputed negative embeds should enable true CFG when negative_prompt is None."
+        )
+
     def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=3e-3):
         super().test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)