Diffusers

You are viewing v0.32.2 version. A newer version v0.38.0 is available.

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

SD3Transformer2D

This class is useful when only loading weights into a SD3Transformer2DModel. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check SD3LoraLoaderMixin class instead.

The SD3Transformer2DLoadersMixin class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.

To learn more about how to load LoRA weights, see the LoRA loading guide.

SD3Transformer2DLoadersMixin

class diffusers.loaders.SD3Transformer2DLoadersMixin

< source >

( )

Load IP-Adapters and LoRA layers into a [SD3Transformer2DModel].

_load_ip_adapter_weights

< source >

( state_dict: typing.Dict low_cpu_mem_usage: bool = True )

Parameters

state_dict (Dict) — State dict with keys “ip_adapter”, which contains parameters for attention processors, and “image_proj”, which contains parameters for image projection net.
low_cpu_mem_usage (bool, optional, defaults to True if torch version >= 1.9.0 else False) — Speed up model loading only loading the pretrained weights and not initializing the weights. This also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this argument to True will raise an error.

Sets IP-Adapter attention processors, image projection, and loads state_dict.

< > Update on GitHub

←UNet PEFT→