diff --git a/src/diffusers/pipelines/flux/pipeline_flux.py b/src/diffusers/pipelines/flux/pipeline_flux.py
index 5e2df4c910..6dbcd5c6db 100644
--- a/src/diffusers/pipelines/flux/pipeline_flux.py
+++ b/src/diffusers/pipelines/flux/pipeline_flux.py
@@ -191,7 +191,6 @@ class FluxPipeline(
         transformer: FluxTransformer2DModel,
         image_encoder: CLIPVisionModelWithProjection = None,
         feature_extractor: CLIPImageProcessor = None,
-        variant: str = "flux",
     ):
         super().__init__()
 
@@ -214,17 +213,6 @@ class FluxPipeline(
             self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
         )
         self.default_sample_size = 128
-        if variant not in {"flux", "chroma"}:
-            raise ValueError("`variant` must be `'flux' or `'chroma'`.")
-
-        self.variant = variant
-
-    def _get_chroma_attn_mask(self, length: torch.Tensor, max_sequence_length: int) -> torch.Tensor:
-        attention_mask = torch.zeros((length.shape[0], max_sequence_length), dtype=torch.bool, device=length.device)
-        for i, n_tokens in enumerate(length):
-            n_tokens = torch.max(n_tokens + 1, max_sequence_length)
-            attention_mask[i, :n_tokens] = True
-        return attention_mask
 
     def _get_t5_prompt_embeds(
         self,
@@ -248,7 +236,7 @@ class FluxPipeline(
             padding="max_length",
             max_length=max_sequence_length,
             truncation=True,
-            return_length=True,
+            return_length=False,
             return_overflowing_tokens=False,
             return_tensors="pt",
         )
@@ -262,10 +250,7 @@ class FluxPipeline(
                 f" {max_sequence_length} tokens: {removed_text}"
             )
 
-        text_inputs.attention_mask[:, : text_inputs.length + 1] = 1.0
-        prompt_embeds = self.text_encoder_2(
-            text_input_ids.to(device), output_hidden_states=False, attention_mask=text_inputs.attention_mask.to(device)
-        )[0]
+        prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0]
 
         dtype = self.text_encoder_2.dtype
         prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
@@ -702,11 +687,11 @@ class FluxPipeline(
                 their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
                 will be used.
             guidance_scale (`float`, *optional*, defaults to 3.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
+                Guidance scale as defined in [Classifier-Free Diffusion
+                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
+                `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
+                the text `prompt`, usually at the expense of lower image quality.
             num_images_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
@@ -715,7 +700,7 @@ class FluxPipeline(
             latents (`torch.FloatTensor`, *optional*):
                 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`.
+                tensor will be generated by sampling using the supplied random `generator`.
             prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                 provided, text embeddings will be generated from `prompt` input argument.