ate the use of this method,
>>> # we'll first cast to fp16 and back to fp32
>>> params = model.to_f16(params)
>>> # now cast back to fp32
>>> params = model.to_fp32(params)

FlaxUNet2DConditionOutput
	

class diffusers.models.unet_2d_condition_flax.FlaxUNet2DConditionOutput

<
source
>
(
sample: ndarray

)


Parameters 

sample (jnp.ndarray of shape (batch_size, num_channels, height, width)) —
Hidden states conditioned on encoder_hidden_states 