diff --git a/src/diffusers/configuration_utils.py b/src/diffusers/configuration_utils.py
index 2ab85ecee16d..1c5c3d7afd58 100644
--- a/src/diffusers/configuration_utils.py
+++ b/src/diffusers/configuration_utils.py
@@ -154,9 +154,12 @@ def from_config(cls, pretrained_model_name_or_path: Union[str, os.PathLike], ret
 
         """
         config_dict = cls.get_config_dict(pretrained_model_name_or_path=pretrained_model_name_or_path, **kwargs)
-
         init_dict, unused_kwargs = cls.extract_init_dict(config_dict, **kwargs)
 
+        # Allow dtype to be specified on initialization
+        if "dtype" in unused_kwargs:
+            init_dict["dtype"] = unused_kwargs.pop("dtype")
+
         model = cls(**init_dict)
 
         if return_unused_kwargs:
diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
index 7f068d7183ba..675b61266285 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
@@ -30,7 +30,7 @@ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
             Tokenizer of class
             [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
         unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`FlaxSchedulerMixin`]):
+        scheduler ([`SchedulerMixin`]):
             A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
             [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], or [`FlaxPNDMScheduler`].
         safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
@@ -157,7 +157,7 @@ def __call__(
             self.unet.sample_size,
         )
         if latents is None:
-            latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=self.dtype)
+            latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
         else:
             if latents.shape != latents_shape:
                 raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")