Update pipeline.py

pipeline.py

@@ -472,7 +472,7 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
         eta: float = 0.0,
         generator: Optional[torch.Generator] = None,
         latents: Optional[torch.FloatTensor] = None,
-        max_embeddings_multiples: Optional[int] = 1,
+        max_embeddings_multiples: Optional[int] = 3,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
@@ -513,7 +513,7 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
                 Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                 generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                 tensor will ge generated by sampling using the supplied random `generator`.
-            max_embeddings_multiples (`int`, *optional*, defaults to `1`):
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
                 The max multiple length of prompt embeddings compared to the max output length of text encoder.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
@@ -684,7 +684,7 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
             num_images_per_prompt: Optional[int] = 1,
             eta: Optional[float] = 0.0,
             generator: Optional[torch.Generator] = None,
-            max_embeddings_multiples: Optional[int] = 1,
+            max_embeddings_multiples: Optional[int] = 3,
             output_type: Optional[str] = "pil",
             return_dict: bool = True,
             callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
@@ -726,7 +726,7 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
             generator (`torch.Generator`, *optional*):
                 A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
-            max_embeddings_multiples (`int`, *optional*, defaults to `1`):
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
                 The max multiple length of prompt embeddings compared to the max output length of text encoder.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
@@ -914,7 +914,7 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
             num_images_per_prompt: Optional[int] = 1,
             eta: Optional[float] = 0.0,
             generator: Optional[torch.Generator] = None,
-            max_embeddings_multiples: Optional[int] = 1,
+            max_embeddings_multiples: Optional[int] = 3,
             output_type: Optional[str] = "pil",
             return_dict: bool = True,
             callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
@@ -960,7 +960,7 @@ class StableDiffusionLongPromptPipeline(DiffusionPipeline):
             generator (`torch.Generator`, *optional*):
                 A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
-            max_embeddings_multiples (`int`, *optional*, defaults to `1`):
+            max_embeddings_multiples (`int`, *optional*, defaults to `3`):
                 The max multiple length of prompt embeddings compared to the max output length of text encoder.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between