[docs] add docstrings in pipline_stable_diffusion.py

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -65,9 +65,22 @@
 
 
 def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """
-    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
-    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    r"""
+
+    Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure.
+    Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). 
+    See Section 3.4.
+
+    Args:
+        noise_cfg (`torch.Tensor`):
+            The predicted noise tensor for the guided diffusion process.
+        noise_pred_text (`torch.Tensor`):
+            The predicted noise tensor for the text-guided diffusion process.
+        guidance_rescale (`float`, *optional*, defaults to 0.0):
+            A rescale factor applied to the noise predictions.
+
+    Returns:
+        noise_cfg (`torch.Tensor`): The rescaled noise prediction tensor.
     """
     std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
     std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
@@ -86,7 +99,7 @@ def retrieve_timesteps(
     sigmas: Optional[List[float]] = None,
     **kwargs,
 ):
-    """
+    r"""
     Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
     custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
 
@@ -145,7 +158,7 @@ class StableDiffusionPipeline(
     IPAdapterMixin,
     FromSingleFileMixin,
 ):
-    r"""
+    """
     Pipeline for text-to-image generation using Stable Diffusion.
 
     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
@@ -695,7 +708,7 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype
     def get_guidance_scale_embedding(
         self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
     ) -> torch.Tensor:
-        """
+        r"""
         See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
 
         Args: