Make stable diffusion pipelines compatible with compel

[JingyaHuang]

What does this PR do?

• [x] Make NeuronStableDiffusionXLPipeline compel compatible
• [x] Make NeuronStableDiffusionPipeline compel compatible
• [x] Tests
• [x] Fix the neuron config wrongly reconstructed for sdxl's text encoder 2

Fixes #357

@neo @Suprhimp

Before submitting

• [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• [ ] Did you make sure to update the documentation with your changes?
• [ ] Did you write any new necessary tests?

[HuggingFaceDocBuilderDev]

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[Suprhimp]

I tested with my own model even with lora, and it works pretty well.

I compiled with output_hidden_states params true

And I tested with this example code 😃

from optimum.neuron import NeuronStableDiffusionXLPipeline
from compel import Compel, ReturnedEmbeddingsType

pipe = NeuronStableDiffusionXLPipeline.from_pretrained("sdxl_turbo_neuron/", data_parallel_mode="all")
prompt = "Self-portrait oil painting++++++++++, a beautiful cyborg with golden hair, 8k"
negative_prompt = "worst quality"

compel = Compel(
        tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
        text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
        returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
        requires_pooled=[False, True],
    )
prompt_embeds, pooled = compel(prompt)
neg_prompt_embeds, neg_pooled = compel(negative_prompt)

images = pipe(
        prompt_embeds=prompt_embeds,
        negative_prompt_embeds=neg_prompt_embeds,
        pooled_prompt_embeds=pooled,
        negative_pooled_prompt_embeds=neg_pooled, 
        guidance_scale=0.0, num_inference_steps=1).images

Thanks for the update!

[JingyaHuang]

Thanks @neo and @Suprhimp for the help, the PR is merged. I will include it in our next release!