diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_flux2.py b/src/diffusers/models/autoencoders/autoencoder_kl_flux2.py
index 24a8b024c524..37d69c8a1dc2 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_flux2.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_flux2.py
@@ -56,10 +56,6 @@ class AutoencoderKLFlux2(
         act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
         latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
         sample_size (`int`, *optional*, defaults to `32`): Sample input size.
-        force_upcast (`bool`, *optional*, default to `True`):
-            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
-            can be fine-tuned / trained to a lower range without losing too much precision in which case `force_upcast`
-            can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
         mid_block_add_attention (`bool`, *optional*, default to `True`):
             If enabled, the mid_block of the Encoder and Decoder will have attention blocks. If set to false, the
             mid_block will only have resnet blocks
@@ -97,7 +93,6 @@ def __init__(
         latent_channels: int = 32,
         norm_num_groups: int = 32,
         sample_size: int = 1024,  # YiYi notes: not sure
-        force_upcast: bool = True,
         use_quant_conv: bool = True,
         use_post_quant_conv: bool = True,
         mid_block_add_attention: bool = True,