Add stable diffusion integration

docs/_toc.yml

@@ -95,6 +95,8 @@ parts:
             title: OpenAI 
           - file: source/reference/ai/yolo
             title: YOLO 
+          - file: source/reference/ai/stablediffusion
+            title: Stable Diffusion
 
       - file: source/reference/ai/custom-ai-function
         title: Bring Your Own AI Function

docs/source/reference/ai/stablediffusion.rst

@@ -0,0 +1,27 @@
+.. _stablediffusion:
+
+Stable Diffusion Models
+======================================
+
+This section provides an overview of how you can generate images from prompts in EvaDB using a Stable Diffusion model.
+
+
+Introduction
+------------
+
+Stable Diffusion models leverage a controlled random walk process to generate intricate patterns and images from textual prompts,
+bridging the gap between text and visual representation. EvaDB uses the stable diffusion implementation from `Replicate <https://replicate.com>`_.
+
+Stable Diffusion UDF
+--------------------
+
+In order to create an image generation function in EvaDB, use the following SQL command:
+
+.. code-block:: sql
+
+    CREATE FUNCTION IF NOT EXISTS StableDiffusion
+    IMPL 'evadb/functions/stable_diffusion.py';
+
+EvaDB automatically uses the latest `stable diffusion release <https://replicate.com/stability-ai/stable-diffusion/versions>`_ available on Replicate.
+
+To see a demo of how the function can be used, please check the `demo notebook <https://colab.research.google.com/github/georgia-tech-db/eva/blob/master/tutorials/18-stable-diffusion.ipynb>`_ on stable diffusion.

evadb/functions/dalle.py

@@ -0,0 +1,88 @@
+# coding=utf-8
+# Copyright 2018-2023 EvaDB
+#
+# 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.
+
+import os
+from io import BytesIO
+
+import numpy as np
+import pandas as pd
+import requests
+from PIL import Image
+
+from evadb.catalog.catalog_type import NdArrayType
+from evadb.configuration.configuration_manager import ConfigurationManager
+from evadb.functions.abstract.abstract_function import AbstractFunction
+from evadb.functions.decorators.decorators import forward
+from evadb.functions.decorators.io_descriptors.data_types import PandasDataframe
+from evadb.utils.generic_utils import try_to_import_openai
+
+
+class DallEFunction(AbstractFunction):
+    @property
+    def name(self) -> str:
+        return "DallE"
+
+    def setup(self) -> None:
+        pass
+
+    @forward(
+        input_signatures=[
+            PandasDataframe(
+                columns=["prompt"],
+                column_types=[
+                    NdArrayType.STR,
+                ],
+                column_shapes=[(None,)],
+            )
+        ],
+        output_signatures=[
+            PandasDataframe(
+                columns=["response"],
+                column_types=[NdArrayType.FLOAT32],
+                column_shapes=[(None, None, 3)],
+            )
+        ],
+    )
+    def forward(self, text_df):
+        try_to_import_openai()
+        import openai
+
+        # Register API key, try configuration manager first
+        openai.api_key = ConfigurationManager().get_value("third_party", "OPENAI_KEY")
+        # If not found, try OS Environment Variable
+        if len(openai.api_key) == 0:
+            openai.api_key = os.environ.get("OPENAI_KEY", "")
+        assert (
+            len(openai.api_key) != 0
+        ), "Please set your OpenAI API key in evadb.yml file (third_party, open_api_key) or environment variable (OPENAI_KEY)"
+
+        def generate_image(text_df: PandasDataframe):
+            results = []
+            queries = text_df[text_df.columns[0]]
+            for query in queries:
+                response = openai.Image.create(prompt=query, n=1, size="1024x1024")
+
+                # Download the image from the link
+                image_response = requests.get(response["data"][0]["url"])
+                image = Image.open(BytesIO(image_response.content))
+
+                # Convert the image to an array format suitable for the DataFrame
+                frame = np.array(image)
+                results.append(frame)
+
+            return results
+
+        df = pd.DataFrame({"response": generate_image(text_df=text_df)})
+        return df

evadb/functions/function_bootstrap_queries.py

@@ -202,6 +202,18 @@
             MODEL 'yolov8n.pt';
         """
 
+stablediffusion_function_query = """CREATE FUNCTION IF NOT EXISTS StableDiffusion
+        IMPL '{}/functions/stable_diffusion.py';
+        """.format(
+    EvaDB_INSTALLATION_DIR
+)
+
+dalle_function_query = """CREATE FUNCTION IF NOT EXISTS DallE
+        IMPL '{}/functions/dalle.py';
+        """.format(
+    EvaDB_INSTALLATION_DIR
+)
+
 
 def init_builtin_functions(db: EvaDBDatabase, mode: str = "debug") -> None:
     """Load the built-in functions into the system during system bootstrapping.
@@ -247,6 +259,8 @@ def init_builtin_functions(db: EvaDBDatabase, mode: str = "debug") -> None:
         # Mvit_function_query,
         Sift_function_query,
         Yolo_function_query,
+        stablediffusion_function_query,
+        dalle_function_query,
     ]
 
     # if mode is 'debug', add debug functions

evadb/functions/stable_diffusion.py

@@ -0,0 +1,95 @@
+# coding=utf-8
+# Copyright 2018-2023 EvaDB
+#
+# 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.
+
+import os
+from io import BytesIO
+
+import numpy as np
+import pandas as pd
+import requests
+from PIL import Image
+
+from evadb.catalog.catalog_type import NdArrayType
+from evadb.functions.abstract.abstract_function import AbstractFunction
+from evadb.functions.decorators.decorators import forward
+from evadb.functions.decorators.io_descriptors.data_types import PandasDataframe
+from evadb.utils.generic_utils import try_to_import_replicate
+
+
+class StableDiffusion(AbstractFunction):
+    @property
+    def name(self) -> str:
+        return "StableDiffusion"
+
+    def setup(
+        self,
+    ) -> None:
+        pass
+
+    @forward(
+        input_signatures=[
+            PandasDataframe(
+                columns=["prompt"],
+                column_types=[
+                    NdArrayType.STR,
+                ],
+                column_shapes=[(None,)],
+            )
+        ],
+        output_signatures=[
+            PandasDataframe(
+                columns=["response"],
+                column_types=[
+                    # FileFormatType.IMAGE,
+                    NdArrayType.FLOAT32
+                ],
+                column_shapes=[(None, None, 3)],
+            )
+        ],
+    )
+    def forward(self, text_df):
+        try_to_import_replicate()
+        import replicate
+
+        if os.environ.get("REPLICATE_API_TOKEN") is None:
+            replicate_api_key = (
+                "r8_Q75IAgbaHFvYVfLSMGmjQPcW5uZZoXz0jGalu"  # token for testing
+            )
+            os.environ["REPLICATE_API_TOKEN"] = replicate_api_key
+
+        model_id = (
+            replicate.models.get("stability-ai/stable-diffusion").versions.list()[0].id
+        )
+
+        def generate_image(text_df: PandasDataframe):
+            results = []
+            queries = text_df[text_df.columns[0]]
+            for query in queries:
+                output = replicate.run(
+                    "stability-ai/stable-diffusion:" + model_id, input={"prompt": query}
+                )
+
+                # Download the image from the link
+                response = requests.get(output[0])
+                image = Image.open(BytesIO(response.content))
+
+                # Convert the image to an array format suitable for the DataFrame
+                frame = np.array(image)
+                results.append(frame)
+
+            return results
+
+        df = pd.DataFrame({"response": generate_image(text_df=text_df)})
+        return df

evadb/utils/generic_utils.py

@@ -629,3 +629,21 @@ def string_comparison_case_insensitive(string_1, string_2) -> bool:
         return False
 
     return string_1.lower() == string_2.lower()
+
+
+def try_to_import_replicate():
+    try:
+        import replicate  # noqa: F401
+    except ImportError:
+        raise ValueError(
+            """Could not import replicate python package.
+                Please install it with `pip install replicate`."""
+        )
+
+
+def is_replicate_available():
+    try:
+        try_to_import_replicate()
+        return True
+    except ValueError:
+        return False

script/test/test.sh

@@ -94,7 +94,7 @@ long_integration_test() {
 }
 
 notebook_test() {
-  PYTHONPATH=./ python -m pytest --durations=5 --nbmake --overwrite "./tutorials" --capture=sys --tb=short -v --log-level=WARNING --nbmake-timeout=3000 --ignore="tutorials/08-chatgpt.ipynb" --ignore="tutorials/14-food-review-tone-analysis-and-response.ipynb" --ignore="tutorials/15-AI-powered-join.ipynb" --ignore="tutorials/16-homesale-forecasting.ipynb" --ignore="tutorials/17-home-rental-prediction.ipynb"
+  PYTHONPATH=./ python -m pytest --durations=5 --nbmake --overwrite "./tutorials" --capture=sys --tb=short -v --log-level=WARNING --nbmake-timeout=3000 --ignore="tutorials/08-chatgpt.ipynb" --ignore="tutorials/14-food-review-tone-analysis-and-response.ipynb" --ignore="tutorials/15-AI-powered-join.ipynb" --ignore="tutorials/16-homesale-forecasting.ipynb" --ignore="tutorials/17-home-rental-prediction.ipynb" --ignore="tutorials/18-stable-diffusion.ipynb"
   code=$?
   print_error_code $code "NOTEBOOK TEST"
 }

setup.py

@@ -125,6 +125,10 @@ def read(path, encoding="utf-8"):
     "neuralforecast" # MODEL TRAIN AND FINE TUNING
 ]
 
+imagegen_libs = [
+    "replicate"
+]
+
 ### NEEDED FOR DEVELOPER TESTING ONLY
 
 dev_libs = [
@@ -167,7 +171,7 @@ def read(path, encoding="utf-8"):
     "sklearn": sklearn_libs,
     "forecasting": forecasting_libs,
     # everything except ray, qdrant, ludwig and postgres. The first three fail on pyhton 3.11.
-    "dev": dev_libs + vision_libs + document_libs + function_libs + notebook_libs + forecasting_libs + sklearn_libs,
+    "dev": dev_libs + vision_libs + document_libs + function_libs + notebook_libs + forecasting_libs + sklearn_libs + imagegen_libs,
 }
 
 setup(

test/integration_tests/long/functions/test_stablediffusion.py

@@ -0,0 +1,68 @@
+# coding=utf-8
+# Copyright 2018-2023 EvaDB
+#
+# 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.
+
+import unittest
+from test.markers import stable_diffusion_skip_marker
+from test.util import get_evadb_for_testing
+
+import numpy as np
+import pytest
+
+from evadb.server.command_handler import execute_query_fetch_all
+
+
+class StableDiffusionTest(unittest.TestCase):
+    def setUp(self) -> None:
+        self.evadb = get_evadb_for_testing()
+        self.evadb.catalog().reset()
+        create_table_query = """CREATE TABLE IF NOT EXISTS ImageGen (
+             prompt TEXT);
+                """
+        execute_query_fetch_all(self.evadb, create_table_query)
+
+        test_prompts = ["pink cat riding a rocket to the moon"]
+
+        for prompt in test_prompts:
+            insert_query = f"""INSERT INTO ImageGen (prompt) VALUES ('{prompt}')"""
+            execute_query_fetch_all(self.evadb, insert_query)
+
+    def tearDown(self) -> None:
+        execute_query_fetch_all(self.evadb, "DROP TABLE IF EXISTS ImageGen;")
+
+    @stable_diffusion_skip_marker
+    @pytest.mark.xfail(
+        reason="API call might be flaky due to rate limits or other issues."
+    )
+    def test_stable_diffusion_image_generation(self):
+        function_name = "StableDiffusion"
+
+        execute_query_fetch_all(self.evadb, f"DROP FUNCTION IF EXISTS {function_name};")
+
+        create_function_query = f"""CREATE FUNCTION IF NOT EXISTS {function_name}
+            IMPL 'evadb/functions/stable_diffusion.py';
+        """
+        execute_query_fetch_all(self.evadb, create_function_query)
+
+        gpt_query = f"SELECT {function_name}(prompt) FROM ImageGen;"
+        output_batch = execute_query_fetch_all(self.evadb, gpt_query)
+
+        self.assertEqual(output_batch.columns, ["stablediffusion.response"])
+
+        # Check if the returned data is an np.array representing an image
+        img_data = output_batch.frames["stablediffusion.response"][0]
+        self.assertIsInstance(img_data, np.ndarray)
+        self.assertEqual(
+            img_data.shape[2], 3
+        )  # Check if the image has 3 channels (RGB)

test/markers.py

@@ -25,6 +25,7 @@
     is_ludwig_available,
     is_pinecone_available,
     is_qdrant_available,
+    is_replicate_available,
     is_sklearn_available,
 )
 
@@ -96,3 +97,7 @@
     is_forecast_available() is False,
     reason="Run only if forecasting packages available",
 )
+
+stable_diffusion_skip_marker = pytest.mark.skipif(
+    is_replicate_available() is False, reason="requires replicate"
+)