What does this PR do?

Expand LoRA support for FLUX.2 series single stream blocks and update docs

1. Architectural Evolution

Compared to the original FLUX framework, the FLUX.2 architecture has undergone significant changes. Firstly, the number of single-stream layers is far greater than that of double-stream layers. Furthermore, In the Single transformer Blocks, the q,k,v projections are fused with the MLP into a single unified linear layer: attn.to_qkv_mlp_proj.

Therefore, using Flux's Lora configuration to train Flux2 is insufficient.

2. Implementation Updates

To address these changes, I have updated the LoRA configuration in the following training scripts and added additional notes to the readme:

• examples/dreambooth/README_flux2.md
• examples/dreambooth/train_dreambooth_lora_flux2.py
• examples/dreambooth/train_dreambooth_lora_flux2_klein.py

The target_modules logic has been modified to ensure that the Lora adapter can correctly train the main attention layers of both double-stream and single-stream layers.Flux2 has 48 single-stream layers, while Klein has 24.

# Updated LoRA targeting logic for FLUX.2 series
target_modules = [
    "to_k", "to_q", "to_v", "to_out.0"
] + [
    "to_qkv_mlp_proj", 
    *[f"single_transformer_blocks.{i}.attn.to_out" for i in range(nums)]
]

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