This is a specialized LoRA adapter for Tongyi-MAI/Z-Image-Turbo, finetuned using Flow-DPO (Direct Preference Optimization for Flow Matching) to significantly enhance photorealistic lighting, cinematic shadows, and overall image quality.

By utilizing Flow-DPO on perfectly spatially-aligned image pairs, this LoRA fixes the common "flat," "washed-out," or "plastic" artifacts often found in ultra-fast distilled models, delivering stunning, physically accurate lighting in just 8 inference steps.

🌟 Visual Showcase

| Prompt: woman, Asian ethnicity, white dress, looking away, long hair, outdoor setting, building facade, plants, serene expression, elegance, side profile, standing, daylight, soft focus, pastel colors, fashion, youthful, casual elegance, architectural elements, natural light, tassel detail on dress |

🚀 Usage

This model is a standard LoRA adapter and can be used directly with the diffusers library.

Installation

pip install diffusers transformers accelerate peft

Inference Script

Since Z-Image-Turbo is a distilled model, it requires exactly 8 inference steps and a fixed guidance scale.

python

import torch
from diffusers import DiffusionPipeline

# 1. Load the base Z-Image-Turbo model
base_model_id = "Tongyi-MAI/Z-Image-Turbo"
pipeline = DiffusionPipeline.from_pretrained(
    base_model_id, 
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
).to("cuda")

# 2. Load this Flow-DPO LoRA
lora_id = "F16/z-image-turbo-flow-dpo"
pipeline.load_lora_weights(lora_id, adapter_name="lighting_dpo")
# Optional: Adjust LoRA scale (0.6 - 1.0 usually works best)
pipeline.set_adapters(["lighting_dpo"], adapter_weights=[1.0])

# 3. Generate Image
prompt = "A professional realistic photograph of a woman standing by a window, golden hour lighting, cinematic shadows, highly detailed, 8k resolution."

image = pipeline(
    prompt=prompt,
    num_inference_steps=8,  # Turbo model must use 8 steps
    guidance_scale=1.0,     # Turbo model does not use CFG
    generator=torch.Generator("cuda").manual_seed(42)
).images[0]

image.save("dpo_lighting_output.jpg")

🧠 Training Details & Methodology

This model was trained using a custom implementation of Flow-DPO (Improving Video Generation with Human Feedback, arXiv:2501.13918).

1. The Dataset (Strict Spatial Alignment)

To prevent the model from hallucinating or altering image structures (Catastrophic Forgetting), the preference dataset was constructed using strict spatial alignment:

Win (Chosen): High-quality, professional photographs with perfect lighting and textures.
Lose (Rejected): The exact same images degraded programmatically (Gaussian blur, lowered contrast, extreme exposure shifts, gaussian noise, and heavy JPEG compression artifacts).
Alignment: No cropping or warping was applied, ensuring the Flow Matching trajectory learned to solely correct lighting and texture.

2. Discrete Timestep Distillation Preservation

Unlike standard diffusion models where $t$ is sampled continuously $t \in [0, 1]$, Z-Image-Turbo is a distilled model specifically optimized for 8 fixed timesteps. During the Flow-DPO training, we dynamically extracted the exact discrete $t$-distribution from the FlowMatchEulerDiscreteScheduler and restricted the random sampling to these exact 8 nodes. This ensures the LoRA retains the turbo model's extreme speed without causing output blurriness.

3. Hyperparameters

Base Model: Alibaba-Tongyi/Z-Image-Turbo (6B Single-Stream DiT)
Learning Rate: 1e-4
KL Penalty ($\beta$): 1.0
Effective Batch Size: 1
Mixed Precision: bfloat16

⚠️ Limitations

Not an Image-to-Image Restorer: This LoRA changes the prior distribution of the Text-to-Image generation. It is designed to generate better original images from text prompts, not to be used as an img2img filter to fix user-uploaded bad photos (unless combined with RF-Inversion techniques, which are highly unstable for 8-step models).
Color Saturation: Pushing the LoRA scale too high (e.g., > 1.5) might result in over-sharpened or overly saturated images due to the nature of DPO margin maximization. Keep the scale around 0.6 - 1.0 for the most photorealistic results.

📚 Citation

If you find this model or training methodology useful, please consider referencing:

bibtex

@article{liu2025improving,
  title={Improving video generation with human feedback},
  author={Liu, Jie and Liu, Gongye and Liang, Jiajun and Yuan, Ziyang and Liu, Xiaokun and Zheng, Mingwu and Wu, Xiele and Wang, Qiulin and Qin, Wenyu and Xia, Menghan and others},
  journal={arXiv preprint arXiv:2501.13918},
  year={2025}
}


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