Fix styling issues (huggingface#5699)

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* Empty-Commit

* fix keyword argument call.

---------

Co-authored-by: Sayak Paul <[email protected]>

Author: patrickvonplaten

src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py

@@ -206,17 +206,15 @@ def _encode_prior_prompt(
             prior_text_encoder_output = self.prior_text_encoder(text_input_ids.to(device))
 
             prompt_embeds = prior_text_encoder_output.text_embeds
-            prior_text_encoder_hidden_states = prior_text_encoder_output.last_hidden_state
+            text_enc_hid_states = prior_text_encoder_output.last_hidden_state
 
         else:
             batch_size = text_model_output[0].shape[0]
-            prompt_embeds, prior_text_encoder_hidden_states = text_model_output[0], text_model_output[1]
+            prompt_embeds, text_enc_hid_states = text_model_output[0], text_model_output[1]
             text_mask = text_attention_mask
 
         prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        prior_text_encoder_hidden_states = prior_text_encoder_hidden_states.repeat_interleave(
-            num_images_per_prompt, dim=0
-        )
+        text_enc_hid_states = text_enc_hid_states.repeat_interleave(num_images_per_prompt, dim=0)
         text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
 
         if do_classifier_free_guidance:
@@ -235,21 +233,17 @@ def _encode_prior_prompt(
             )
 
             negative_prompt_embeds = negative_prompt_embeds_prior_text_encoder_output.text_embeds
-            uncond_prior_text_encoder_hidden_states = (
-                negative_prompt_embeds_prior_text_encoder_output.last_hidden_state
-            )
+            uncond_text_enc_hid_states = negative_prompt_embeds_prior_text_encoder_output.last_hidden_state
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
 
             seq_len = negative_prompt_embeds.shape[1]
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
 
-            seq_len = uncond_prior_text_encoder_hidden_states.shape[1]
-            uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.repeat(
-                1, num_images_per_prompt, 1
-            )
-            uncond_prior_text_encoder_hidden_states = uncond_prior_text_encoder_hidden_states.view(
+            seq_len = uncond_text_enc_hid_states.shape[1]
+            uncond_text_enc_hid_states = uncond_text_enc_hid_states.repeat(1, num_images_per_prompt, 1)
+            uncond_text_enc_hid_states = uncond_text_enc_hid_states.view(
                 batch_size * num_images_per_prompt, seq_len, -1
             )
             uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
@@ -260,13 +254,11 @@ def _encode_prior_prompt(
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            prior_text_encoder_hidden_states = torch.cat(
-                [uncond_prior_text_encoder_hidden_states, prior_text_encoder_hidden_states]
-            )
+            text_enc_hid_states = torch.cat([uncond_text_enc_hid_states, text_enc_hid_states])
 
             text_mask = torch.cat([uncond_text_mask, text_mask])
 
-        return prompt_embeds, prior_text_encoder_hidden_states, text_mask
+        return prompt_embeds, text_enc_hid_states, text_mask
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
     def _encode_prompt(

src/diffusers/pipelines/unclip/pipeline_unclip.py

@@ -156,15 +156,15 @@ def _encode_prompt(
             text_encoder_output = self.text_encoder(text_input_ids.to(device))
 
             prompt_embeds = text_encoder_output.text_embeds
-            text_encoder_hidden_states = text_encoder_output.last_hidden_state
+            text_enc_hid_states = text_encoder_output.last_hidden_state
 
         else:
             batch_size = text_model_output[0].shape[0]
-            prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
+            prompt_embeds, text_enc_hid_states = text_model_output[0], text_model_output[1]
             text_mask = text_attention_mask
 
         prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+        text_enc_hid_states = text_enc_hid_states.repeat_interleave(num_images_per_prompt, dim=0)
         text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
 
         if do_classifier_free_guidance:
@@ -181,17 +181,17 @@ def _encode_prompt(
             negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
 
             negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
-            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
+            uncond_text_enc_hid_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
 
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
 
             seq_len = negative_prompt_embeds.shape[1]
             negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
             negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
 
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
+            seq_len = uncond_text_enc_hid_states.shape[1]
+            uncond_text_enc_hid_states = uncond_text_enc_hid_states.repeat(1, num_images_per_prompt, 1)
+            uncond_text_enc_hid_states = uncond_text_enc_hid_states.view(
                 batch_size * num_images_per_prompt, seq_len, -1
             )
             uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
@@ -202,11 +202,11 @@ def _encode_prompt(
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
+            text_enc_hid_states = torch.cat([uncond_text_enc_hid_states, text_enc_hid_states])
 
             text_mask = torch.cat([uncond_text_mask, text_mask])
 
-        return prompt_embeds, text_encoder_hidden_states, text_mask
+        return prompt_embeds, text_enc_hid_states, text_mask
 
     @torch.no_grad()
     def __call__(
@@ -293,7 +293,7 @@ def __call__(
 
         do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
 
-        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
+        prompt_embeds, text_enc_hid_states, text_mask = self._encode_prompt(
             prompt, device, num_images_per_prompt, do_classifier_free_guidance, text_model_output, text_attention_mask
         )
 
@@ -321,7 +321,7 @@ def __call__(
                 latent_model_input,
                 timestep=t,
                 proj_embedding=prompt_embeds,
-                encoder_hidden_states=text_encoder_hidden_states,
+                encoder_hidden_states=text_enc_hid_states,
                 attention_mask=text_mask,
             ).predicted_image_embedding
 
@@ -352,10 +352,10 @@ def __call__(
 
         # decoder
 
-        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
+        text_enc_hid_states, additive_clip_time_embeddings = self.text_proj(
             image_embeddings=image_embeddings,
             prompt_embeds=prompt_embeds,
-            text_encoder_hidden_states=text_encoder_hidden_states,
+            text_encoder_hidden_states=text_enc_hid_states,
             do_classifier_free_guidance=do_classifier_free_guidance,
         )
 
@@ -377,7 +377,7 @@ def __call__(
 
         decoder_latents = self.prepare_latents(
             (batch_size, num_channels_latents, height, width),
-            text_encoder_hidden_states.dtype,
+            text_enc_hid_states.dtype,
             device,
             generator,
             decoder_latents,
@@ -391,7 +391,7 @@ def __call__(
             noise_pred = self.decoder(
                 sample=latent_model_input,
                 timestep=t,
-                encoder_hidden_states=text_encoder_hidden_states,
+                encoder_hidden_states=text_enc_hid_states,
                 class_labels=additive_clip_time_embeddings,
                 attention_mask=decoder_text_mask,
             ).sample

src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py

@@ -1494,7 +1494,6 @@ def forward(self, input_tensor, temb):
         return output_tensor
 
 
-# Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
 class DownBlockFlat(nn.Module):
     def __init__(
         self,
@@ -1583,7 +1582,6 @@ def custom_forward(*inputs):
         return hidden_states, output_states
 
 
-# Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim
 class CrossAttnDownBlockFlat(nn.Module):
     def __init__(
         self,