Image generation with `distilabel`¶

Create synthetic images using distilabel.

This example shows how distilabel can be used to generate image data, either using InferenceEndpointsImageGeneration or OpenAIImageGeneration, thanks to the ImageGeneration task.

Inference Endpoints - black-forest-labs/FLUX.1-schnellOpenAI - dall-e-3

from distilabel.pipeline import Pipeline
from distilabel.steps import KeepColumns
from distilabel.models.image_generation import InferenceEndpointsImageGeneration
from distilabel.steps.tasks import ImageGeneration

from datasets import load_dataset

ds = load_dataset("dvilasuero/finepersonas-v0.1-tiny", split="train").select(range(3))

with Pipeline(name="image_generation_pipeline") as pipeline:
    ilm = InferenceEndpointsImageGeneration(
        model_id="black-forest-labs/FLUX.1-schnell"
    )

    img_generation = ImageGeneration(
        name="flux_schnell",
        llm=ilm,
        input_mappings={"prompt": "persona"}
    )

    keep_columns = KeepColumns(columns=["persona", "model_name", "image"])

    img_generation >> keep_columns

Sample image for the prompt:

A local art historian and museum professional interested in 19th-century American art and the local cultural heritage of Cincinnati.

from distilabel.pipeline import Pipeline
from distilabel.steps import KeepColumns
from distilabel.models.image_generation import OpenAIImageGeneration
from distilabel.steps.tasks import ImageGeneration

from datasets import load_dataset

ds = load_dataset("dvilasuero/finepersonas-v0.1-tiny", split="train").select(range(3))

with Pipeline(name="image_generation_pipeline") as pipeline:
    ilm = OpenAIImageGeneration(
        model="dall-e-3",
        generation_kwargs={
            "size": "1024x1024",
            "quality": "standard",
            "style": "natural"
        }
    )

    img_generation = ImageGeneration(
        name="dalle-3"
        llm=ilm,
        input_mappings={"prompt": "persona"}
    )

    keep_columns = KeepColumns(columns=["persona", "model_name", "image"])

    img_generation >> keep_columns

Sample image for the prompt:

A local art historian and museum professional interested in 19th-century American art and the local cultural heritage of Cincinnati.

Save the Distiset as an Image Dataset

Note the call to Distiset.transform_columns_to_image, to have the images uploaded directly as an Image dataset:

if __name__ == "__main__":
    distiset = pipeline.run(use_cache=False, dataset=ds)
    # Save the images as `PIL.Image.Image`
    distiset = distiset.transform_columns_to_image("image")
    distiset.push_to_hub("plaguss/test-finepersonas-v0.1-tiny-flux-schnell")

The full pipeline can be run at the following example. Keep in mind, you need to install pillow first: pip install distilabel[vision].

Run

python examples/image_generation.py

image_generation.py

# Copyright 2023-present, Argilla, Inc.
#
# 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.

from datasets import load_dataset

from distilabel.models.image_generation import InferenceEndpointsImageGeneration
from distilabel.pipeline import Pipeline
from distilabel.steps import KeepColumns
from distilabel.steps.tasks import ImageGeneration

ds = load_dataset("dvilasuero/finepersonas-v0.1-tiny", split="train").select(range(3))

with Pipeline(name="image_generation_pipeline") as pipeline:
    igm = InferenceEndpointsImageGeneration(model_id="black-forest-labs/FLUX.1-schnell")

    img_generation = ImageGeneration(
        name="flux_schnell",
        image_generation_model=igm,
        input_mappings={"prompt": "persona"},
    )

    keep_columns = KeepColumns(columns=["persona", "model_name", "image"])

    img_generation >> keep_columns


if __name__ == "__main__":
    distiset = pipeline.run(use_cache=False, dataset=ds)
    # Save the images as `PIL.Image.Image`
    distiset = distiset.transform_columns_to_image("image")
    distiset.push_to_hub("plaguss/test-finepersonas-v0.1-tiny-flux-schnell")

Image generation with distilabel¶

Image generation with `distilabel`¶