RFInversionFluxPipeline, small fix for enable_model_cpu_offload & enable_sequential_cpu_offload compatibility

[Teriks]

Use self._execution_device instead of self.device when selecting a device for the input image tensor in RFInversionFluxPipeline.encode_image.

This allows for compatibility with enable_model_cpu_offload & enable_sequential_cpu_offload

Since this is in a copied method, might want to look elsewhere for this.

What does this PR do?

Allows for turning on VRAM optimizations without encountering meta tensor copy error / device mismatch etc.

import torch
import diffusers
from PIL import Image

pipe = RFInversionFluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev",
                                               torch_dtype=torch.bfloat16)
# potato hardware mode
# pipe.enable_model_cpu_offload()

# extra potato hardware mode
pipe.enable_sequential_cpu_offload()

image = Image.open("./example/cat.png").resize((1024, 1024))

inverted_latents, image_latents, latent_image_ids = pipe.invert(image=image,
                                                                num_inversion_steps=28,
                                                                gamma=0.5)

image = pipe("portrait of a tiger",
     inverted_latents=inverted_latents,
     image_latents=image_latents,
     latent_image_ids=latent_image_ids,
     start_timestep=0,
     stop_timestep=.38,
     num_inference_steps=28,
     eta=0.9,
     ).images[0]

image.save('tiger.png')

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Who can review?

@linoytsaban

[linoytsaban]

good catch thanks @Teriks!

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