update

Author: DN6

src/diffusers/models/transformers/transformer_mochi.py

@@ -18,7 +18,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from transformers.tokenization_utils_base import import_protobuf_decode_error
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...utils import is_torch_version, logging

src/diffusers/pipelines/mochi/pipeline_mochi.py

@@ -594,25 +594,24 @@ def __call__(
             batch_size = prompt_embeds.shape[0]
 
         device = self._execution_device
-        with torch.autocast("cuda", torch.float32):
-            # 3. Prepare text embeddings
-            (
-                prompt_embeds,
-                prompt_attention_mask,
-                negative_prompt_embeds,
-                negative_prompt_attention_mask,
-            ) = self.encode_prompt(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-                num_videos_per_prompt=num_videos_per_prompt,
-                prompt_embeds=prompt_embeds,
-                negative_prompt_embeds=negative_prompt_embeds,
-                prompt_attention_mask=prompt_attention_mask,
-                negative_prompt_attention_mask=negative_prompt_attention_mask,
-                max_sequence_length=max_sequence_length,
-                device=device,
-            )
+        # 3. Prepare text embeddings
+        (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_prompt_attention_mask,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_attention_mask=negative_prompt_attention_mask,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
         # 4. Prepare latent variables
         num_channels_latents = self.transformer.config.in_channels
         latents = self.prepare_latents(