prompt_embeds_scale in FluxPriorReduxPipeline seems to have no effect.

[Meatfucker]

Describe the bug

When using the FluxPriorReduxPipeline the prompt_embeds_scale and pooled_prompt_embeds_scale seem to have no effect on the final generation.

Reproduction

async def get_redux_embeds(image, prompt, strength):
    redux_repo = "black-forest-labs/FLUX.1-Redux-dev"
    text_encoder, tokenizer, text_encoder_2, tokenizer_2 = await get_text_encoders()
    redux_pipeline = FluxPriorReduxPipeline.from_pretrained(redux_repo,
                                                            text_encoder=text_encoder,
                                                            tokenizer=tokenizer,
                                                            text_encoder_2=text_encoder_2,
                                                            tokenizer_2=tokenizer_2,
                                                            torch_dtype=dtype).to("cuda")
    redux_embeds, redux_pooled_embeds = redux_pipeline(image=image,
                                                       prompt=prompt,
                                                       prompt_2=prompt,
                                                       prompt_embeds_scale=strength,
                                                       pooled_prompt_embeds_scale=strength,
                                                       return_dict=False)
    redux_pipeline.to("cpu")
    del redux_pipeline, text_encoder, tokenizer, text_encoder_2, tokenizer_2
    torch.cuda.empty_cache()
    gc.collect()

    return redux_embeds, redux_pooled_embeds

async def get_text_encoders():
    model_name = "black-forest-labs/FLUX.1-dev"
    revision = "refs/pr/3"
    text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
    text_encoder_2 = T5EncoderModel.from_pretrained(model_name, subfolder="text_encoder_2", torch_dtype=dtype,
                                                    revision=revision)
    tokenizer_2 = T5TokenizerFast.from_pretrained(model_name, subfolder="tokenizer_2", torch_dtype=dtype,
                                                  revision=revision)
    return text_encoder, tokenizer, text_encoder_2, tokenizer_2

Logs

System Info

• 🤗 Diffusers version: 0.33.1
• Platform: Linux-6.8.0-60-generic-x86_64-with-glibc2.39
• Running on Google Colab?: No
• Python version: 3.12.3
• PyTorch version (GPU?): 2.7.0+cu126 (True)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Huggingface_hub version: 0.31.2
• Transformers version: 4.51.3
• Accelerate version: 1.7.0
• PEFT version: 0.15.2
• Bitsandbytes version: 0.45.5
• Safetensors version: 0.5.3
• xFormers version: not installed
• Accelerator: NVIDIA GeForce RTX 3090, 24576 MiB
• Using GPU in script?:
• Using distributed or parallel set-up in script?:

Who can help?

@yiyixuxu @DN6

[DN6]

cc: @yiyixuxu