diff --git a/src/diffusers/models/embeddings_flax.py b/src/diffusers/models/embeddings_flax.py
index 63442ab9..50ccc238 100644
--- a/src/diffusers/models/embeddings_flax.py
+++ b/src/diffusers/models/embeddings_flax.py
@@ -19,7 +19,7 @@ import jax.numpy as jnp
 
 # This is like models.embeddings.get_timestep_embedding (PyTorch) but
 # less general (only handles the case we currently need).
-def get_sinusoidal_embeddings(timesteps, embedding_dim):
+def get_sinusoidal_embeddings(timesteps, embedding_dim, freq_shift: float = 1):
     """
     This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
 
@@ -29,7 +29,7 @@ def get_sinusoidal_embeddings(timesteps, embedding_dim):
     embeddings. :return: an [N x dim] tensor of positional embeddings.
     """
     half_dim = embedding_dim // 2
-    emb = math.log(10000) / (half_dim - 1)
+    emb = math.log(10000) / (half_dim - freq_shift)
     emb = jnp.exp(jnp.arange(half_dim) * -emb)
     emb = timesteps[:, None] * emb[None, :]
     emb = jnp.concatenate([jnp.cos(emb), jnp.sin(emb)], -1)
@@ -50,7 +50,8 @@ class FlaxTimestepEmbedding(nn.Module):
 
 class FlaxTimesteps(nn.Module):
     dim: int = 32
+    freq_shift: float = 1
 
     @nn.compact
     def __call__(self, timesteps):
-        return get_sinusoidal_embeddings(timesteps, self.dim)
+        return get_sinusoidal_embeddings(timesteps, self.dim, freq_shift=self.freq_shift)
diff --git a/src/diffusers/models/unet_2d_condition_flax.py b/src/diffusers/models/unet_2d_condition_flax.py
index d0fcd9f6..cab229f2 100644
--- a/src/diffusers/models/unet_2d_condition_flax.py
+++ b/src/diffusers/models/unet_2d_condition_flax.py
@@ -73,6 +73,7 @@ class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
     cross_attention_dim: int = 1280
     dropout: float = 0.0
     dtype: jnp.dtype = jnp.float32
+    freq_shift: int = 0
 
     def init_weights(self, rng: jax.random.PRNGKey) -> FrozenDict:
         # init input tensors
@@ -100,7 +101,7 @@ class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
         )
 
         # time
-        self.time_proj = FlaxTimesteps(block_out_channels[0])
+        self.time_proj = FlaxTimesteps(block_out_channels[0], freq_shift=self.config.freq_shift)
         self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
 
         # down
diff --git a/src/diffusers/pipeline_flax_utils.py b/src/diffusers/pipeline_flax_utils.py
index fca793c0..e65d95a3 100644
--- a/src/diffusers/pipeline_flax_utils.py
+++ b/src/diffusers/pipeline_flax_utils.py
@@ -354,7 +354,7 @@ class FlaxDiffusionPipeline(ConfigMixin):
             # TODO(Patrick, Suraj) - delete later
             if class_name == "DummyChecker":
                 library_name = "stable_diffusion"
-                class_name = "StableDiffusionSafetyChecker"
+                class_name = "FlaxStableDiffusionSafetyChecker"
 
             is_pipeline_module = hasattr(pipelines, library_name)
             loaded_sub_model = None
@@ -421,16 +421,14 @@ class FlaxDiffusionPipeline(ConfigMixin):
                     loaded_sub_model = cached_folder
 
                 if issubclass(class_obj, FlaxModelMixin):
-                    # TODO(Patrick, Suraj) - Fix this as soon as Safety checker is fixed here
+                    loaded_sub_model, loaded_params = load_method(loadable_folder, from_pt=from_pt, dtype=dtype)
+                    params[name] = loaded_params
+                elif is_transformers_available() and issubclass(class_obj, FlaxPreTrainedModel):
+                    # make sure we don't initialize the weights to save time
                     if name == "safety_checker":
                         loaded_sub_model = DummyChecker()
                         loaded_params = DummyChecker()
-                    else:
-                        loaded_sub_model, loaded_params = load_method(loadable_folder, from_pt=from_pt, dtype=dtype)
-                        params[name] = loaded_params
-                elif is_transformers_available() and issubclass(class_obj, FlaxPreTrainedModel):
-                    # make sure we don't initialize the weights to save time
-                    if from_pt:
+                    elif from_pt:
                         # TODO(Suraj): Fix this in Transformers. We should be able to use `_do_init=False` here
                         loaded_sub_model = load_method(loadable_folder, from_pt=from_pt)
                         loaded_params = loaded_sub_model.params
diff --git a/src/diffusers/pipeline_utils.py b/src/diffusers/pipeline_utils.py
index 847513bf..15334e24 100644
--- a/src/diffusers/pipeline_utils.py
+++ b/src/diffusers/pipeline_utils.py
@@ -341,6 +341,10 @@ class DiffusionPipeline(ConfigMixin):
 
         # 3. Load each module in the pipeline
         for name, (library_name, class_name) in init_dict.items():
+            # 3.1 - now that JAX/Flax is an official framework of the library, we might load from Flax names
+            if class_name.startswith("Flax"):
+                class_name = class_name[4:]
+
             is_pipeline_module = hasattr(pipelines, library_name)
             loaded_sub_model = None
 
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 6cca3767..7f068d71 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
@@ -178,7 +178,6 @@ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
                 jnp.array(latents_input),
                 jnp.array(timestep, dtype=jnp.int32),
                 encoder_hidden_states=context,
-                rngs={},
             ).sample
             # perform guidance
             noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
diff --git a/src/diffusers/schedulers/scheduling_ddim.py b/src/diffusers/schedulers/scheduling_ddim.py
index 32be871f..a5369b16 100644
--- a/src/diffusers/schedulers/scheduling_ddim.py
+++ b/src/diffusers/schedulers/scheduling_ddim.py
@@ -222,6 +222,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         # 2. compute alphas, betas
         alpha_prod_t = self.alphas_cumprod[timestep]
         alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
+
         beta_prod_t = 1 - alpha_prod_t
 
         # 3. compute predicted original sample from predicted noise also called
diff --git a/src/diffusers/schedulers/scheduling_ddim_flax.py b/src/diffusers/schedulers/scheduling_ddim_flax.py
index dd5a87df..30c873b4 100644
--- a/src/diffusers/schedulers/scheduling_ddim_flax.py
+++ b/src/diffusers/schedulers/scheduling_ddim_flax.py
@@ -216,6 +216,9 @@ class FlaxDDIMScheduler(SchedulerMixin, ConfigMixin):
         # - pred_sample_direction -> "direction pointing to x_t"
         # - pred_prev_sample -> "x_t-1"
 
+        # TODO(Patrick) - eta is always 0.0 for now, allow to be set in step function
+        eta = 0.0
+
         # 1. get previous step value (=t-1)
         prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps
 
@@ -224,6 +227,7 @@ class FlaxDDIMScheduler(SchedulerMixin, ConfigMixin):
         # 2. compute alphas, betas
         alpha_prod_t = alphas_cumprod[timestep]
         alpha_prod_t_prev = jnp.where(prev_timestep >= 0, alphas_cumprod[prev_timestep], self.final_alpha_cumprod)
+
         beta_prod_t = 1 - alpha_prod_t
 
         # 3. compute predicted original sample from predicted noise also called
@@ -233,7 +237,7 @@ class FlaxDDIMScheduler(SchedulerMixin, ConfigMixin):
         # 4. compute variance: "sigma_t(η)" -> see formula (16)
         # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
         variance = self._get_variance(timestep, prev_timestep, alphas_cumprod)
-        std_dev_t = variance ** (0.5)
+        std_dev_t = eta * variance ** (0.5)
 
         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output