pulid.rst

PuLID

Nunchaku integrates `PuLID <paper_pulid_>`_, a tuning-free identity customization method for text-to-image generation. This feature allows you to generate images that maintain specific identity characteristics from reference photos.

.. literalinclude:: ../../../examples/flux.1-dev-pulid.py
   :language: python
   :caption: PuLID Example (`examples/flux.1-dev-pulid.py <https://github.com/nunchaku-tech/nunchaku/blob/main/examples/flux.1-dev-pulid.py>`__)
   :linenos:

The PuLID integration follows these key steps:

Model Initialization (lines 12-20): Load a Nunchaku FLUX.1-dev model using :meth:`~nunchaku.models.transformers.transformer_flux.NunchakuFluxTransformer2dModel.from_pretrained` and initialize the FLUX PuLID pipeline with :class:`~nunchaku.pipeline.pipeline_flux_pulid.PuLIDFluxPipeline`.

Forward Method Override (line 22): Replace the transformer's forward method with PuLID's specialized implementation using MethodType(pulid_forward, pipeline.transformer). This modification enables identity-aware generation capabilities. See :meth:`~nunchaku.models.pulid.pulid_forward.pulid_forward` for more details.

Reference Image Processing (line 24): Load and prepare the reference identity image that will guide the generation process. This image defines the identity characteristics to be preserved in the output.

Identity-Controlled Generation (lines 26-32): Execute the pipeline with identity-specific parameters:

• id_image: The reference identity image
• id_weight: Identity influence strength (range: 0.0-1.0, where 1.0 provides maximum identity preservation)
• Standard generation parameters (prompt, inference steps, guidance scale)

The generated image will incorporate the identity features from the reference photo while adhering to the provided text prompt.