diff --git a/src/sentencefmricomparison/constants.py b/src/sentencefmricomparison/constants.py
index 6aa88b0..17456f4 100644
--- a/src/sentencefmricomparison/constants.py
+++ b/src/sentencefmricomparison/constants.py
@@ -31,6 +31,9 @@
 PEREIRA_PERMUTED_SENTENCES_PATH = os.path.join(
     PEREIRA_INPUT_DIR, "pereira_permuted_passages.csv"
 )
+# Dirs for text to image output
+TEXT_TO_IMAGE_OUTPUT_DIR = os.path.join(OUTPUT_DIR, "text_to_image")
+
 # Dirs for brain network specific NIfTI files
 NETWORK_NIFTI_DIR = os.path.join(INPUT_DIR, "networks")
 # Use dotenv to properly load the device-dependent mlflow tracking URI
@@ -70,6 +73,7 @@
 os.makedirs(RAW_DIR, exist_ok=True)
 os.makedirs(INPUT_DIR, exist_ok=True)
 os.makedirs(OUTPUT_DIR, exist_ok=True)
+os.makedirs(TEXT_TO_IMAGE_OUTPUT_DIR, exist_ok=True)
 os.makedirs(PEREIRA_RAW_DIR, exist_ok=True)
 os.makedirs(PEREIRA_INPUT_DIR, exist_ok=True)
 os.makedirs(PEREIRA_OUTPUT_DIR, exist_ok=True)
diff --git a/src/sentencefmricomparison/models/text_to_image.py b/src/sentencefmricomparison/models/text_to_image.py
new file mode 100644
index 0000000..308cc81
--- /dev/null
+++ b/src/sentencefmricomparison/models/text_to_image.py
@@ -0,0 +1,95 @@
+"""Generate images from text using a text-to-image model."""
+import os
+
+# Imports
+import click
+import torch
+from datasets import load_dataset
+from diffusers import StableDiffusionXLPipeline
+
+from sentencefmricomparison.constants import (
+    LARGE_DATASET_STORAGE_PATH,
+    LARGE_MODELS_STORAGE_PATH,
+    TEXT_TO_IMAGE_OUTPUT_DIR,
+)
+
+
+@click.command()
+@click.option(
+    "--stimuli_ds_name",
+    default="helena-balabin/pereira_fMRI_passages",
+    help="Name of the stimuli dataset to use",
+    type=str,
+)
+@click.option(
+    "--model_name",
+    default="segmind/SSD-1B",
+    help="Name of the text-to-image model to use",
+    type=str,
+)
+@click.option("--sentence_key", default="paragraphs", type=str)
+@click.option("--neg_prompt", default="ugly, blurry, poor quality", type=str)
+@click.option("--large_model_dir", default=LARGE_MODELS_STORAGE_PATH, type=str)
+@click.option("--large_dataset_dir", default=LARGE_DATASET_STORAGE_PATH, type=str)
+@click.option("--output_dir", default=TEXT_TO_IMAGE_OUTPUT_DIR, type=str)
+def text_to_image(
+    stimuli_ds_name: str,
+    model_name: str,
+    sentence_key: str = "paragraphs",
+    neg_prompt: str = "ugly, blurry, poor quality",
+    large_model_dir: str = LARGE_MODELS_STORAGE_PATH,
+    large_dataset_dir: str = LARGE_DATASET_STORAGE_PATH,
+    output_dir: str = TEXT_TO_IMAGE_OUTPUT_DIR,
+) -> None:
+    """Generate images from the text stimuli in the dataset using a text-to-image model.
+
+    :param stimuli_ds_name: Name of the stimuli dataset to use
+    :type stimuli_ds_name: str
+    :param model_name: Name of the text-to-image model to use
+    :type model_name: str
+    :param sentence_key: Name of the feature in the dataset that contains the sentences, defaults to "sentences"
+    :type sentence_key: str
+    :param neg_prompt: Negative prompt to use for the text-to-image model, defaults to "ugly, blurry, poor quality"
+    :type neg_prompt: str
+    :param large_model_dir: Directory for saving large models, defaults to LARGE_MODELS_STORAGE_PATH
+    :type large_model_dir: str
+    :param large_dataset_dir: Directory for saving large datasets, defaults to LARGE_DATASET_STORAGE_PATH
+    :type large_dataset_dir: str
+    :param output_dir: Output directory for saving the generated images, defaults to TEXT_TO_IMAGE_OUTPUT_DIR
+    :type output_dir: str
+    """
+    # 1. Load the stimuli dataset
+    stimuli_ds = load_dataset(
+        stimuli_ds_name,
+        cache_dir=large_dataset_dir,
+    )["train"]
+    # Get one test example from the stimuli dataset
+    examples = stimuli_ds[sentence_key][0]
+
+    # 2. Load the text-to-image model
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+        variant="fp16",
+        cache_dir=large_model_dir,
+        device_map="auto",
+    )
+    pipe.to("cuda")
+
+    # 3. Generate the image from the text
+    for i, example in enumerate(examples):
+        image = pipe(prompt=example, negative_prompt=neg_prompt).images[0]
+
+        # 4. Save the generated image
+        image.save(os.path.join(output_dir, f"stimulus_{i}.png"))
+
+
+@click.group()
+def cli() -> None:
+    """Generate images from the text stimuli in the dataset using a text-to-image model."""
+
+
+if __name__ == "__main__":
+    cli.add_command(text_to_image)
+    cli()