Revert "Fix code style with make style."

This reverts commit 10a174a12c.

src/diffusers/pipelines/unidiffuser/modeling_uvit.py

@@ -1,5 +1,5 @@
 import math
-from typing import Optional, Union
+from typing import Optional, Union, Tuple, List
 
 import torch
 from torch import nn
@@ -21,7 +21,7 @@ def _no_grad_trunc_normal_(tensor, mean, std, a, b):
     # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
     def norm_cdf(x):
         # Computes standard normal cumulative distribution function
-        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
 
     if (mean < a - 2 * std) or (mean > b + 2 * std):
         logger.warning(
@@ -45,7 +45,7 @@ def _no_grad_trunc_normal_(tensor, mean, std, a, b):
         tensor.erfinv_()
 
         # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.0))
+        tensor.mul_(std * math.sqrt(2.))
         tensor.add_(mean)
 
         # Clamp to ensure it's in the proper range
@@ -53,13 +53,14 @@ def _no_grad_trunc_normal_(tensor, mean, std, a, b):
         return tensor
 
 
-def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
     # type: (torch.Tensor, float, float, float, float) -> torch.Tensor
     r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean},
-    \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for
-    generating the random values works best when :math:`a \leq \text{mean} \leq b`.
-
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
     Args:
         tensor: an n-dimensional `torch.Tensor`
         mean: the mean of the normal distribution
@@ -67,7 +68,8 @@ def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
         a: the minimum cutoff value
         b: the maximum cutoff value
     Examples:
-        >>> w = torch.empty(3, 5) >>> nn.init.trunc_normal_(w)
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
     """
     return _no_grad_trunc_normal_(tensor, mean, std, a, b)
 
@@ -100,7 +102,7 @@ class PatchEmbed(nn.Module):
             self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
         else:
             self.norm = None
-
+        
         self.use_pos_embed = use_pos_embed
         if self.use_pos_embed:
             pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5))
@@ -913,7 +915,7 @@ class UniDiffuserModel(ModelMixin, ConfigMixin):
         self.num_tokens = 1 + 1 + num_text_tokens + 1 + self.num_patches
         self.pos_embed = nn.Parameter(torch.zeros(1, self.num_tokens, self.inner_dim))
         self.pos_embed_drop = nn.Dropout(p=dropout)
-        trunc_normal_(self.pos_embed, std=0.02)
+        trunc_normal_(self.pos_embed, std=.02)
 
         # 2. Define transformer blocks
         self.transformer = UTransformer2DModel(
@@ -947,10 +949,10 @@ class UniDiffuserModel(ModelMixin, ConfigMixin):
         self.vae_img_out = nn.Linear(self.inner_dim, patch_dim)
         self.clip_img_out = nn.Linear(self.inner_dim, clip_img_dim)
         self.text_out = nn.Linear(self.inner_dim, text_dim)
-
+    
     @torch.jit.ignore
     def no_weight_decay(self):
-        return {"pos_embed"}
+        return {'pos_embed'}
 
     def forward(
         self,

src/diffusers/pipelines/unidiffuser/pipeline_unidiffuser.py

@@ -161,7 +161,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
 
         # TODO: handle safety checking?
         self.safety_checker = None
-
+    
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_sequential_cpu_offload
     def enable_sequential_cpu_offload(self, gpu_id=0):
         r"""
@@ -754,7 +754,9 @@ class UniDiffuserPipeline(DiffusionPipeline):
             img_clip_T = randn_tensor(img_clip.shape, generator=generator, device=device, dtype=img_clip.dtype)
             text_T = randn_tensor(prompt_embeds.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
 
-            _, _, text_out_uncond = self.unet(img_vae_T, img_clip_T, text_latents, t_img=max_timestep, t_text=t)
+            _, _, text_out_uncond = self.unet(
+                img_vae_T, img_clip_T, text_latents, t_img=max_timestep, t_text=t
+            )
 
             img_vae_out_uncond, img_clip_out_uncond, _ = self.unet(
                 img_vae_latents, img_clip_latents, text_T, t_img=t, t_text=max_timestep
@@ -988,9 +990,7 @@ class UniDiffuserPipeline(DiffusionPipeline):
         )
 
         full_latents_available = latents is not None
-        individual_latents_available = (
-            prompt_latents is not None or vae_latents is not None or clip_latents is not None
-        )
+        individual_latents_available = prompt_latents is not None or vae_latents is not None or clip_latents is not None
         if full_latents_available and individual_latents_available:
             logger.warning(
                 "You have supplied both `latents` and at least one of `prompt_latents`, `vae_latents`, and"