diff --git a/lit_nlp/examples/dalle_mini/README.md b/lit_nlp/examples/dalle_mini/README.md
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+Dalle_Mini Demo for the Learning Interpretability Tool
+=======================================================
+
+This demo showcases how LIT can be used in text-to-image generation mode. It is
+based on the mini-dalle Mini model
+(https://www.piwheels.org/project/dalle-mini/).
+
+You will need a standalone virtual environment for the Python libraries, which
+you can set up using the following commands from the root of the LIT repo.
+
+```sh
+# Create the virtual environment. You may want to use python3 or python3.10
+# depends on how many Python versions you have installed and their aliases.
+python -m venv .dalle-mini
+source .dalle-mini/bin/activate
+# This requirements.txt file will also install the core LIT library deps.
+pip install -r ./lit_nlp/examples/dalle_mini/requirements.txt
+# The LIT web app still needs to be built in the usual way.
+(cd ./lit_nlp && yarn && yarn build)
+```
+
+Once your virtual environment is setup, you can launch the demo with the
+following command.
+
+```sh
+python -m lit_nlp.examples.dalle_mini.demo
+```
\ No newline at end of file
diff --git a/lit_nlp/examples/dalle_mini/data.py b/lit_nlp/examples/dalle_mini/data.py
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+++ b/lit_nlp/examples/dalle_mini/data.py
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+"""Data loaders for dalle-mini model."""
+
+from lit_nlp.api import dataset as lit_dataset
+from lit_nlp.api import types as lit_types
+
+
+class DallePrompts(lit_dataset.Dataset):
+
+  def __init__(self, prompts: list[str]):
+    self.examples = []
+    for prompt in prompts:
+      self.examples.append({"prompt": prompt})
+
+  def spec(self) -> lit_types.Spec:
+    return {"prompt": lit_types.TextSegment()}
+
+  def __iter__(self):
+    return iter(self.examples)
diff --git a/lit_nlp/examples/dalle_mini/demo.py b/lit_nlp/examples/dalle_mini/demo.py
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+++ b/lit_nlp/examples/dalle_mini/demo.py
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+r"""Example for dalle-mini demo model.
+
+To run locally with a small number of examples:
+  python -m lit_nlp.examples.dalle_mini.demo
+
+
+Then navigate to localhost:5432 to access the demo UI.
+"""
+
+from collections.abc import Sequence
+import sys
+from typing import Optional
+
+from absl import app
+from absl import flags
+from lit_nlp import app as lit_app
+from lit_nlp import dev_server
+from lit_nlp import server_flags
+from lit_nlp.api import layout
+from lit_nlp.examples.dalle_mini import data as dalle_data
+from lit_nlp.examples.dalle_mini import model as dalle_model
+
+
+# NOTE: additional flags defined in server_flags.py
+_FLAGS = flags.FLAGS
+_FLAGS.set_default("development_demo", True)
+_FLAGS.set_default("default_layout", "DALLE_LAYOUT")
+
+_FLAGS.DEFINE_integer("grid_size", 4, "The grid size to use for the model.")
+
+_MODELS = (["dalle-mini"],)
+
+_CANNED_PROMPTS = ["I have a dream", "I have a shiba dog named cola"]
+
+# Custom frontend layout; see api/layout.py
+_modules = layout.LitModuleName
+_DALLE_LAYOUT = layout.LitCanonicalLayout(
+    upper={
+        "Main": [
+            _modules.DataTableModule,
+            _modules.DatapointEditorModule,
+        ]
+    },
+    lower={
+        "Predictions": [
+            _modules.GeneratedImageModule,
+            _modules.GeneratedTextModule,
+        ],
+    },
+    description="Custom layout for Text to Image models.",
+)
+
+
+CUSTOM_LAYOUTS = layout.DEFAULT_LAYOUTS | {"DALLE_LAYOUT": _DALLE_LAYOUT}
+
+
+def get_wsgi_app() -> Optional[dev_server.LitServerType]:
+  _FLAGS.set_default("server_type", "external")
+  _FLAGS.set_default("demo_mode", True)
+  # Parse flags without calling app.run(main), to avoid conflict with
+  # gunicorn command line flags.
+  unused = _FLAGS(sys.argv, known_only=True)
+  return main(unused)
+
+
+def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
+  if len(argv) > 1:
+    raise app.UsageError("Too many command-line arguments.")
+
+  # Load models, according to the --models flag.
+  models = {}
+
+  model_loaders: lit_app.ModelLoadersMap = {}
+  model_loaders["dalle-mini"] = (
+      dalle_model.DalleMiniModel,
+      dalle_model.DalleMiniModel.init_spec(),
+  )
+
+  datasets = {"examples": dalle_data.DallePrompts(_CANNED_PROMPTS)}
+  dataset_loaders: lit_app.DatasetLoadersMap = {}
+  dataset_loaders["text_to_image"] = (
+      dalle_data.DallePrompts,
+      dalle_data.DallePrompts.init_spec(),
+  )
+
+  lit_demo = dev_server.Server(
+      models=models,
+      model_loaders=model_loaders,
+      datasets=datasets,
+      dataset_loaders=dataset_loaders,
+      layouts=CUSTOM_LAYOUTS,
+      **server_flags.get_flags(),
+  )
+  return lit_demo.serve()
+
+
+if __name__ == "__main__":
+  app.run(main)
diff --git a/lit_nlp/examples/dalle_mini/model.py b/lit_nlp/examples/dalle_mini/model.py
new file mode 100644
index 00000000..487072d6
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+++ b/lit_nlp/examples/dalle_mini/model.py
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+"""LIT wrappers for MiniDalleModel."""
+
+from collections.abc import Iterable
+
+from lit_nlp.api import model as lit_model
+from lit_nlp.api import types as lit_types
+from lit_nlp.lib import image_utils
+from min_dalle import MinDalle
+import numpy as np
+from PIL import Image
+import torch
+
+
+class DalleMiniModel(lit_model.Model):
+  """LIT model wrapper for Dalle-Mini Text-to-Image model.
+
+  This wrapper simplifies the pipeline using Dalle-Mini for text-to-image
+  generation.
+
+
+  The basic flow within this model wrapper's predict() function is:
+
+
+  1. Dalle-Mini processes the text prompt.
+  2. Images are directly generated by Dalle-Mini.
+  """
+
+  def __init__(
+      self,
+      device: str = "cuda",  # Use "cuda" for GPU or "cpu" for CPU
+      grid_size: int = 4,  # each batch will generate grid_size**2 images
+      temperature: float = 0.5,
+      top_k: int = 256,
+      supercondition_factor: int = 32,
+  ):
+    super().__init__()
+    self.grid_size = grid_size
+    self.temperature = temperature
+    self.top_k = top_k
+    self.supercondition_factor = supercondition_factor
+
+    # Load Dalle-Mini model
+    self.model = MinDalle(
+        models_root="./pretrained",
+        dtype=torch.float32,
+        device=device,
+        is_mega=True,
+        is_reusable=True,
+    )
+
+  def max_minibatch_size(self) -> int:
+    return 8
+
+  def predict(
+      self, inputs: Iterable[lit_types.JsonDict], **unused_kw
+  ) -> Iterable[lit_types.JsonDict]:
+    """Generate images based on the input prompts."""
+
+    def tensor_to_pil_image(tensor):
+      img_np = tensor.detach().cpu().numpy()
+      img_np = np.squeeze(img_np)
+      if img_np.ndim == 2:
+        img_np = np.stack([img_np] * 3, axis=-1)
+      elif img_np.ndim != 3 or img_np.shape[2] != 3:
+        raise ValueError(
+            f"Unexpected image shape: {img_np.shape}. Expected (H, W, 3)."
+        )
+
+      img_np = (img_np - img_np.min()) / (img_np.max() - img_np.min()) * 255
+      img_np = img_np.clip(0, 255).astype(np.uint8)
+      return Image.fromarray(img_np)
+
+    prompts = [ex["prompt"] for ex in inputs]
+    images = []
+    for prompt in prompts:
+      # Generate images using the model
+      generated_images = self.model.generate_images(
+          text=prompt,
+          seed=-1,
+          grid_size=self.grid_size,
+          is_seamless=False,
+          temperature=self.temperature,
+          top_k=self.top_k,
+          supercondition_factor=self.supercondition_factor,
+          is_verbose=False,
+      )
+      pil_images = []
+      for img_tensor in generated_images:
+        pil_images.append(tensor_to_pil_image(img_tensor))
+      images.append({
+          "image": [
+              image_utils.convert_pil_to_image_str(img) for img in pil_images
+          ],
+          "prompt": prompt,
+      })
+
+    return images
+
+  def input_spec(self):
+    return {
+        "grid_size": lit_types.Scalar(),
+        "temperature": lit_types.Scalar(),
+        "top_k": lit_types.Scalar(),
+        "supercondition_factor": lit_types.Scalar(),
+    }
+
+  def output_spec(self):
+    return {
+        "image": lit_types.ImageBytesList(),
+        "prompt": lit_types.TextSegment(),
+    }
diff --git a/lit_nlp/examples/dalle_mini/requirements.txt b/lit_nlp/examples/dalle_mini/requirements.txt
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+++ b/lit_nlp/examples/dalle_mini/requirements.txt
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+# Copyright 2025 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+-r ../../../requirements.txt
+
+# Dalle-Mini dependencies
+min_dalle==0.4.11