[Flux.1] improve pos embed for ascend npu by computing on npu

[zhangtao0408]

What does this PR do?

Moving pos_embed computation from CPU back to NPU results in a 1.07x speedup in Flux.1's end-to-end latency.

Since CANN updated to 8.3.RC1, the bad performance of torch.repeat_interleave operator has been optimized. Results shown below:

Model	Device	Resolution	Steps	e2e latency
FLUX.1-DEV	npu	1024 x 1024	50	25.54
FLUX.1-DEV	cpu	1024 x 1024	50	27.41

Tested Hardware

Ascend 910B3

Repro Code

FLUX.1-dev

import time

import torch
import torch_npu
from torch_npu.contrib import transfer_to_npu

from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("npu")

prompt = "A cat holding a sign that says hello world"

# Warmup
_ = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=3.5,
    num_inference_steps=2,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
)

# Inference
start_time = time.time()

image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=3.5,
    num_inference_steps=50,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-dev.png")

end_time = time.time()
print(f"Time: {end_time - start_time:.2f}s")

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[zhangtao0408]

@sayakpaul Please review this pr, thanks.