Add use_real parameter to Z-Image for platform compatibility

[st7109]

What does this PR do?

Fixes # (issue)

Add optional real-number RoPE implementation to Z-Image transformer and controlnet. When use_real=True,
the rotary position embeddings use (cos, sin) tuples instead of complex numbers, enabling the model to run on platforms that don't support complex arithmetic (e.g., Cambricon MLU, NPU, etc).

Changes:

• Add apply_rotary_emb() with use_real parameter supporting both complex and real computation
• Propagate use_real through ZSingleStreamAttnProcessor, ZImageTransformerBlock, RopeEmbedder, ZImageTransformer2DModel, and controlnet variants
• Update _prepare_sequence and _build_unified_sequence to handle (cos, sin) tuples
• Default use_real=False maintains backward compatibility

Tested on Cambricon MLU and nvidia A100: successfully generates 1024x1024 images with numerical equivalence (max diff < 1e-6) compared to complex mode.

Test code:

import torch
from diffusers import ZImagePipeline

model_id = "/data/sd/sd_models/hf_models/Tongyi-MAI/Z-Image-Turbo/"
# 1. Load the pipeline
# Use bfloat16 for optimal performance on supported GPUs
pipe = ZImagePipeline.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=False,
)
pipe.to("mlu")

# [Optional] Attention Backend
# Diffusers uses SDPA by default. Switch to Flash Attention for better efficiency if supported:
# pipe.transformer.set_attention_backend("flash")    # Enable Flash-Attention-2
# pipe.transformer.set_attention_backend("_flash_3") # Enable Flash-Attention-3

# [Optional] Model Compilation
# Compiling the DiT model accelerates inference, but the first run will take longer to compile.
# pipe.transformer.compile()

# [Optional] CPU Offloading
# Enable CPU offloading for memory-constrained devices.
# pipe.enable_model_cpu_offload()

prompt = "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights."

# 2. Generate Image
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    num_inference_steps=9,  # This actually results in 8 DiT forwards
    guidance_scale=0.0,     # Guidance should be 0 for the Turbo models
    generator=torch.Generator("cpu").manual_seed(42),
).images[0]

image.save("example.png")

The test case is from https://huggingface.co/Tongyi-MAI/Z-Image-Turbo, if tests with Cambricon MLU platform, should set use_real=True, then generate the below:

python zimage_demo.py 
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 34.44it/s]
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:04<00:00,  1.48s/it]
Loading pipeline components...: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:05<00:00,  1.02s/it]

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:03<00:00,  2.27it/s]

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@sayakpaul

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[st7109]

@sayakpaul @yiyixuxu hello, Please help review this commit, any suggests, let me know. thanks.