Solve missing clip_sample implementation in FlaxDDIMScheduler. (#7017)

* I added a new doc string to the class. This is more flexible to understanding other developers what are doing and where it's using.

* Update src/diffusers/models/unet_2d_blocks.py

This changes suggest by maintener.
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update src/diffusers/models/unet_2d_blocks.py

Add suggested text
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

* Update unet_2d_blocks.py

I changed the Parameter to Args text.

* Update unet_2d_blocks.py

proper indentation set in this file.

* Update unet_2d_blocks.py

a little bit of change in the act_fun argument line.

* I run the black command to reformat style in the code

* Update unet_2d_blocks.py

similar doc-string add to have in the original diffusion repository.

* Fix bug for mention in this issue section #6901

* Update src/diffusers/schedulers/scheduling_ddim_flax.py
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Fix linter

* Restore empty line

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

src/diffusers/schedulers/scheduling_ddim_flax.py

@@ -85,7 +85,9 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         trained_betas (`jnp.ndarray`, optional):
             option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
         clip_sample (`bool`, default `True`):
-            option to clip predicted sample between -1 and 1 for numerical stability.
+            option to clip predicted sample between for numerical stability. The clip range is determined by `clip_sample_range`.
+        clip_sample_range (`float`, default `1.0`):
+            the maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
         set_alpha_to_one (`bool`, default `True`):
             each diffusion step uses the value of alphas product at that step and at the previous one. For the final
             step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
@@ -117,6 +119,8 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         beta_end: float = 0.02,
         beta_schedule: str = "linear",
         trained_betas: Optional[jnp.ndarray] = None,
+        clip_sample: bool = True,
+        clip_sample_range: float = 1.0,
         set_alpha_to_one: bool = True,
         steps_offset: int = 0,
         prediction_type: str = "epsilon",
@@ -267,6 +271,12 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
                 " `v_prediction`"
             )
 
+        # 4. Clip or threshold "predicted x_0"
+        if self.config.clip_sample:
+            pred_original_sample = pred_original_sample.clip(
+                -self.config.clip_sample_range, self.config.clip_sample_range
+            )
+
         # 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(state, timestep, prev_timestep)