[Hunyuan] allow Hunyuan DiT to run under 6GB for GPU VRAM

docs/source/en/api/pipelines/hunyuandit.md

@@ -29,6 +29,10 @@ HunyuanDiT has the following components:
 * It combines two text encoders, a bilingual CLIP and a multilingual T5 encoder
 
 
+## Memory optimization
+
+By loading the T5 text encoder in 8 bits, you can run the pipeline in just under 6 GBs of GPU VRAM. Refer to [this script](https://gist.github.com/sayakpaul/3154605f6af05b98a41081aaba5ca43e) for details. 
+
 ## HunyuanDiTPipeline
 
 [[autodoc]] HunyuanDiTPipeline

src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py

@@ -228,16 +228,22 @@ def __init__(
                 " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
             )
 
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
+        )
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
         self.register_to_config(requires_safety_checker=requires_safety_checker)
-        self.default_sample_size = self.transformer.config.sample_size
+        self.default_sample_size = (
+            self.transformer.config.sample_size
+            if hasattr(self, "transformer") and self.transformer is not None
+            else 128
+        )
 
     def encode_prompt(
         self,
         prompt: str,
-        device: torch.device,
-        dtype: torch.dtype,
+        device: torch.device = None,
+        dtype: torch.dtype = None,
         num_images_per_prompt: int = 1,
         do_classifier_free_guidance: bool = True,
         negative_prompt: Optional[str] = None,
@@ -281,6 +287,17 @@ def encode_prompt(
             text_encoder_index (`int`, *optional*):
                 Index of the text encoder to use. `0` for clip and `1` for T5.
         """
+        if dtype is None:
+            if self.text_encoder_2 is not None:
+                dtype = self.text_encoder_2.dtype
+            elif self.transformer is not None:
+                dtype = self.transformer.dtype
+            else:
+                dtype = None
+
+        if device is None:
+            device = self._execution_device
+
         tokenizers = [self.tokenizer, self.tokenizer_2]
         text_encoders = [self.text_encoder, self.text_encoder_2]