diff --git a/docs/source/en/api/pipelines/flux2.md b/docs/source/en/api/pipelines/flux2.md
index 87f0fd92658e..393e0d03c341 100644
--- a/docs/source/en/api/pipelines/flux2.md
+++ b/docs/source/en/api/pipelines/flux2.md
@@ -26,6 +26,12 @@ Original model checkpoints for Flux can be found [here](https://huggingface.co/b
 >
 > [Caching](../../optimization/cache) may also speed up inference by storing and reusing intermediate outputs.
 
+## Caption upsampling
+
+Flux.2 can potentially generate better better outputs with better prompts. We can "upsample"
+an input prompt by setting the `caption_upsample_temperature` argument in the pipeline call arguments.
+The [official implementation](https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/text_encoder.py#L140) recommends this value to be 0.15.
+
 ## Flux2Pipeline
 
 [[autodoc]] Flux2Pipeline
diff --git a/src/diffusers/pipelines/flux2/image_processor.py b/src/diffusers/pipelines/flux2/image_processor.py
index 91c8f875dd1d..a5920548ca86 100644
--- a/src/diffusers/pipelines/flux2/image_processor.py
+++ b/src/diffusers/pipelines/flux2/image_processor.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import math
-from typing import Tuple
+from typing import List
 
 import PIL.Image
 
@@ -96,9 +96,9 @@ def check_image_input(
             )
 
         return image
-
+    
     @staticmethod
-    def _resize_to_target_area(image: PIL.Image.Image, target_area: int = 1024 * 1024) -> Tuple[int, int]:
+    def _resize_to_target_area(image: PIL.Image.Image, target_area: int = 1024 * 1024) -> PIL.Image.Image:
         image_width, image_height = image.size
 
         scale = math.sqrt(target_area / (image_width * image_height))
@@ -106,6 +106,14 @@ def _resize_to_target_area(image: PIL.Image.Image, target_area: int = 1024 * 102
         height = int(image_height * scale)
 
         return image.resize((width, height), PIL.Image.Resampling.LANCZOS)
+    
+    @staticmethod
+    def _resize_if_exceeds_area(image, target_area=1024 * 1024) -> PIL.Image.Image:
+        image_width, image_height = image.size
+        pixel_count = image_width * image_height
+        if pixel_count <= target_area:
+            return image
+        return Flux2ImageProcessor._resize_to_target_area(image, target_area)
 
     def _resize_and_crop(
         self,
@@ -136,3 +144,35 @@ def _resize_and_crop(
         bottom = top + height
 
         return image.crop((left, top, right, bottom))
+
+    # Taken from
+    # https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/sampling.py#L310C1-L339C19
+    @staticmethod
+    def concatenate_images(images: List[PIL.Image.Image]) -> PIL.Image.Image:
+        """
+        Concatenate a list of PIL images horizontally with center alignment and white background.
+        """
+
+        # If only one image, return a copy of it
+        if len(images) == 1:
+            return images[0].copy()
+
+        # Convert all images to RGB if not already
+        images = [img.convert("RGB") if img.mode != "RGB" else img for img in images]
+
+        # Calculate dimensions for horizontal concatenation
+        total_width = sum(img.width for img in images)
+        max_height = max(img.height for img in images)
+
+        # Create new image with white background
+        background_color = (255, 255, 255)
+        new_img = PIL.Image.new("RGB", (total_width, max_height), background_color)
+
+        # Paste images with center alignment
+        x_offset = 0
+        for img in images:
+            y_offset = (max_height - img.height) // 2
+            new_img.paste(img, (x_offset, y_offset))
+            x_offset += img.width
+
+        return new_img
diff --git a/src/diffusers/pipelines/flux2/pipeline_flux2.py b/src/diffusers/pipelines/flux2/pipeline_flux2.py
index 676bf6d98429..db63f8d1e966 100644
--- a/src/diffusers/pipelines/flux2/pipeline_flux2.py
+++ b/src/diffusers/pipelines/flux2/pipeline_flux2.py
@@ -28,6 +28,7 @@
 from ..pipeline_utils import DiffusionPipeline
 from .image_processor import Flux2ImageProcessor
 from .pipeline_output import Flux2PipelineOutput
+from .system_messages import SYSTEM_MESSAGE, SYSTEM_MESSAGE_UPSAMPLING_I2I, SYSTEM_MESSAGE_UPSAMPLING_T2I
 
 
 if is_torch_xla_available():
@@ -56,25 +57,107 @@
         ```
 """
 
+UPSAMPLING_MAX_IMAGE_SIZE = 768**2
 
-def format_text_input(prompts: List[str], system_message: str = None):
+# Adapted from
+# https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/text_encoder.py#L68
+def format_input(
+    prompts: List[str],
+    system_message: str = SYSTEM_MESSAGE,
+    images: Optional[Union[List[PIL.Image.Image], List[List[PIL.Image.Image]]]] = None,
+):
+    """
+    Format a batch of text prompts into the conversation format expected by apply_chat_template. Optionally, add images
+    to the input.
+
+    Args:
+        prompts: List of text prompts
+        system_message: System message to use (default: CREATIVE_SYSTEM_MESSAGE)
+        images (optional): List of images to add to the input.
+
+    Returns:
+        List of conversations, where each conversation is a list of message dicts
+    """
     # Remove [IMG] tokens from prompts to avoid Pixtral validation issues
     # when truncation is enabled. The processor counts [IMG] tokens and fails
     # if the count changes after truncation.
     cleaned_txt = [prompt.replace("[IMG]", "") for prompt in prompts]
 
-    return [
+    if images is None or len(images) == 0:
+        return [
+            [
+                {
+                    "role": "system",
+                    "content": [{"type": "text", "text": system_message}],
+                },
+                {"role": "user", "content": [{"type": "text", "text": prompt}]},
+            ]
+            for prompt in cleaned_txt
+        ]
+    else:
+        assert len(images) == len(prompts), "Number of images must match number of prompts"
+        messages = [
+            [
+                {
+                    "role": "system",
+                    "content": [{"type": "text", "text": system_message}],
+                },
+            ]
+            for _ in cleaned_txt
+        ]
+
+        for i, (el, images) in enumerate(zip(messages, images)):
+            # optionally add the images per batch element.
+            if images is not None:
+                el.append(
+                    {
+                        "role": "user",
+                        "content": [{"type": "image", "image": image_obj} for image_obj in images],
+                    }
+                )
+            # add the text.
+            el.append(
+                {
+                    "role": "user",
+                    "content": [{"type": "text", "text": cleaned_txt[i]}],
+                }
+            )
+
+        return messages
+
+
+# Adapted from
+# https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/text_encoder.py#L49C5-L66C19
+def _validate_and_process_images(
+    images: List[List[PIL.Image.Image]] | List[PIL.Image.Image],
+    image_processor: Flux2ImageProcessor,
+    upsampling_max_image_size: int,
+) -> List[List[PIL.Image.Image]]:
+    # Simple validation: ensure it's a list of PIL images or list of lists of PIL images
+    if not images:
+        return []
+
+    # Check if it's a list of lists or a list of images
+    if isinstance(images[0], PIL.Image.Image):
+        # It's a list of images, convert to list of lists
+        images = [[im] for im in images]
+
+    # potentially concatenate multiple images to reduce the size
+    images = [[image_processor.concatenate_images(img_i)] if len(img_i) > 1 else img_i for img_i in images]
+
+    # cap the pixels
+    images = [
         [
-            {
-                "role": "system",
-                "content": [{"type": "text", "text": system_message}],
-            },
-            {"role": "user", "content": [{"type": "text", "text": prompt}]},
+            image_processor._resize_if_exceeds_area(img_i, upsampling_max_image_size)
+            for img_i in img_i
         ]
-        for prompt in cleaned_txt
+        for img_i in images
     ]
+    return images
 
 
+# Taken from
+# https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/sampling.py#L251
 def compute_empirical_mu(image_seq_len: int, num_steps: int) -> float:
     a1, b1 = 8.73809524e-05, 1.89833333
     a2, b2 = 0.00016927, 0.45666666
@@ -214,9 +297,10 @@ def __init__(
         self.tokenizer_max_length = 512
         self.default_sample_size = 128
 
-        # fmt: off
-        self.system_message = "You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object attribution and actions without speculation."
-        # fmt: on
+        self.system_message = SYSTEM_MESSAGE
+        self.system_message_upsampling_t2i = SYSTEM_MESSAGE_UPSAMPLING_T2I
+        self.system_message_upsampling_i2i = SYSTEM_MESSAGE_UPSAMPLING_I2I
+        self.upsampling_max_image_size = UPSAMPLING_MAX_IMAGE_SIZE
 
     @staticmethod
     def _get_mistral_3_small_prompt_embeds(
@@ -226,9 +310,7 @@ def _get_mistral_3_small_prompt_embeds(
         dtype: Optional[torch.dtype] = None,
         device: Optional[torch.device] = None,
         max_sequence_length: int = 512,
-        # fmt: off
-        system_message: str = "You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object attribution and actions without speculation.",
-        # fmt: on
+        system_message: str = SYSTEM_MESSAGE,
         hidden_states_layers: List[int] = (10, 20, 30),
     ):
         dtype = text_encoder.dtype if dtype is None else dtype
@@ -237,7 +319,7 @@ def _get_mistral_3_small_prompt_embeds(
         prompt = [prompt] if isinstance(prompt, str) else prompt
 
         # Format input messages
-        messages_batch = format_text_input(prompts=prompt, system_message=system_message)
+        messages_batch = format_input(prompts=prompt, system_message=system_message)
 
         # Process all messages at once
         inputs = tokenizer.apply_chat_template(
@@ -426,6 +508,68 @@ def _unpack_latents_with_ids(x: torch.Tensor, x_ids: torch.Tensor) -> list[torch
 
         return torch.stack(x_list, dim=0)
 
+    def upsample_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        images: Union[List[PIL.Image.Image], List[List[PIL.Image.Image]]] = None,
+        temperature: float = 0.15,
+        device: torch.device = None,
+    ) -> List[str]:
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        device = self.text_encoder.device if device is None else device
+
+        # Set system message based on whether images are provided
+        if images is None or len(images) == 0 or images[0] is None:
+            system_message = SYSTEM_MESSAGE_UPSAMPLING_T2I
+        else:
+            system_message = SYSTEM_MESSAGE_UPSAMPLING_I2I
+
+        # Validate and process the input images
+        if images:
+            images = _validate_and_process_images(images, self.image_processor, self.upsampling_max_image_size)
+        
+        # Format input messages
+        messages_batch = format_input(prompts=prompt, system_message=system_message, images=images)
+
+        # Process all messages at once
+        # with image processing a too short max length can throw an error in here.
+        inputs = self.tokenizer.apply_chat_template(
+            messages_batch,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding="max_length",
+            truncation=True,
+            max_length=2048,
+        )
+
+        # Move to device
+        inputs["input_ids"] = inputs["input_ids"].to(device)
+        inputs["attention_mask"] = inputs["attention_mask"].to(device)
+
+        if "pixel_values" in inputs:
+            inputs["pixel_values"] = inputs["pixel_values"].to(device, self.text_encoder.dtype)
+
+        # Generate text using the model's generate method
+        generated_ids = self.text_encoder.generate(
+            **inputs,
+            max_new_tokens=512,
+            do_sample=True,
+            temperature=temperature,
+            use_cache=True,
+        )
+
+        # Decode only the newly generated tokens (skip input tokens)
+        # Extract only the generated portion
+        input_length = inputs["input_ids"].shape[1]
+        generated_tokens = generated_ids[:, input_length:]
+
+        upsampled_prompt = self.tokenizer.tokenizer.batch_decode(
+            generated_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=True
+        )
+        return upsampled_prompt
+
     def encode_prompt(
         self,
         prompt: Union[str, List[str]],
@@ -620,6 +764,7 @@ def __call__(
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 512,
         text_encoder_out_layers: Tuple[int] = (10, 20, 30),
+        caption_upsample_temperature: float = None,
     ):
         r"""
         Function invoked when calling the pipeline for generation.
@@ -635,11 +780,11 @@ def __call__(
                 The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                 instead.
             guidance_scale (`float`, *optional*, defaults to 1.0):
-                Guidance scale as defined in [Classifier-Free Diffusion
-                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
-                of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
-                `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
-                the text `prompt`, usually at the expense of lower image quality.
+                Embedded guiddance scale is enabled by setting `guidance_scale` > 1. Higher `guidance_scale` encourages
+                a model to generate images more aligned with `prompt` at the expense of lower image quality.
+
+                Guidance-distilled models approximates true classifer-free guidance for `guidance_scale` > 1. Refer to
+                the [paper](https://huggingface.co/papers/2210.03142) to learn more.
             height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The height in pixels of the generated image. This is set to 1024 by default for the best results.
             width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
@@ -684,6 +829,9 @@ def __call__(
             max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
             text_encoder_out_layers (`Tuple[int]`):
                 Layer indices to use in the `text_encoder` to derive the final prompt embeddings.
+            caption_upsample_temperature (`float`):
+                When specified, we will try to perform caption upsampling for potentially improved outputs. We
+                recommend setting it to 0.15 if caption upsampling is to be performed.
 
         Examples:
 
@@ -718,6 +866,10 @@ def __call__(
         device = self._execution_device
 
         # 3. prepare text embeddings
+        if caption_upsample_temperature:
+            prompt = self.upsample_prompt(
+                prompt, images=image, temperature=caption_upsample_temperature, device=device
+            )
         prompt_embeds, text_ids = self.encode_prompt(
             prompt=prompt,
             prompt_embeds=prompt_embeds,
diff --git a/src/diffusers/pipelines/flux2/system_messages.py b/src/diffusers/pipelines/flux2/system_messages.py
new file mode 100644
index 000000000000..bbcb207ff62f
--- /dev/null
+++ b/src/diffusers/pipelines/flux2/system_messages.py
@@ -0,0 +1,29 @@
+"""
+These system prompts come from:
+https://github.com/black-forest-labs/flux2/blob/5a5d316b1b42f6b59a8c9194b77c8256be848432/src/flux2/system_messages.py#L54
+"""
+
+SYSTEM_MESSAGE = """You are an AI that reasons about image descriptions. You give structured responses focusing on object relationships, object
+attribution and actions without speculation."""
+
+SYSTEM_MESSAGE_UPSAMPLING_T2I = """You are an expert prompt engineer for FLUX.2 by Black Forest Labs. Rewrite user prompts to be more descriptive while strictly preserving their core subject and intent.
+
+Guidelines:
+1. Structure: Keep structured inputs structured (enhance within fields). Convert natural language to detailed paragraphs.
+2. Details: Add concrete visual specifics - form, scale, textures, materials, lighting (quality, direction, color), shadows, spatial relationships, and environmental context.
+3. Text in Images: Put ALL text in quotation marks, matching the prompt's language. Always provide explicit quoted text for objects that would contain text in reality (signs, labels, screens, etc.) - without it, the model generates gibberish.
+
+Output only the revised prompt and nothing else."""
+
+SYSTEM_MESSAGE_UPSAMPLING_I2I = """You are FLUX.2 by Black Forest Labs, an image-editing expert. You convert editing requests into one concise instruction (50-80 words, ~30 for brief requests).
+
+Rules:
+- Single instruction only, no commentary
+- Use clear, analytical language (avoid "whimsical," "cascading," etc.)
+- Specify what changes AND what stays the same (face, lighting, composition)
+- Reference actual image elements
+- Turn negatives into positives ("don't change X" → "keep X")
+- Make abstractions concrete ("futuristic" → "glowing cyan neon, metallic panels")
+- Keep content PG-13
+
+Output only the final instruction in plain text and nothing else."""