LIT Dalle-Mini demo.

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-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
+```

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):
+    self.examples = []
+
+  def spec(self) -> lit_types.Spec:
+    return {"prompt": lit_types.TextSegment()}

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 os
+import sys
+from typing import Optional
+
+from absl import app
+from absl import flags
+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")
+
+
+_MODELS = flags.DEFINE_list("models", ["dalle-mini"], "Models to load")
+
+
+_MAX_EXAMPLES = flags.DEFINE_integer(
+    "max_examples",
+    5,
+    "Maximum number of examples to load from each evaluation set. Set to None "
+    "to load the full set.",
+)
+
+
+# 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 = {}
+  for model_name_or_path in _MODELS.value:
+    # Ignore path prefix, if using /path/to/<model_name> to load from a
+    # specific directory rather than the default shortcut.
+    model_name = os.path.basename(model_name_or_path)
+    models[model_name] = dalle_model.DalleMiniModel(
+        model_name=model_name_or_path
+    )
+
+  datasets = {"Prompts": dalle_data.DallePrompts()}
+
+  lit_demo = dev_server.Server(
+      models,
+      datasets,
+      layouts=CUSTOM_LAYOUTS,
+      **server_flags.get_flags(),
+  )
+  return lit_demo.serve()
+
+
+if __name__ == "__main__":
+  app.run(main)

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,
+      model_name: str = "dalle-mini",
+      device: str = "cuda",  # Use "cuda" for GPU or "cpu" for CPU
+      predictions: int = 1,
+  ):
+    super().__init__()
+    self.model_name = model_name
+    self.device = device
+    self.n_predictions = predictions
+
+    # Load Dalle-Mini model
+    self.model = MinDalle(
+        models_root="./pretrained",
+        dtype=torch.float32,
+        device="cuda",
+        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=4,
+          is_seamless=False,
+          temperature=0.5,
+          top_k=256,
+          supercondition_factor=32,
+          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 {"prompt": lit_types.TextSegment()}
+
+  def output_spec(self):
+    return {
+        "image": lit_types.ImageBytesList(),
+        "prompt": lit_types.TextSegment(),
+    }

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==0.1.5