Update pipeline.py

pipeline.py

@@ -67,39 +67,9 @@ def prepare_timesteps(
 
 # FLUX pipeline function
 class FluxWithCFGPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin):
-
-    r"""
-    The Flux pipeline for text-to-image generation.
-
-    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
-
-    Args:
-        transformer ([`FluxTransformer2DModel`]):
-            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
-        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
-            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        text_encoder_2 ([`T5EncoderModel`]):
-            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
-            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer_2 (`T5TokenizerFast`):
-            Second Tokenizer of class
-            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
-    """
-
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
-    _optional_components = []
-    _callback_tensor_inputs = ["latents", "prompt_embeds"]    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
     _optional_components = []
     _callback_tensor_inputs = ["latents", "prompt_embeds"]
-
+    
     def __init__(
         self,
         scheduler: FlowMatchEulerDiscreteScheduler,