Z-Image Turbo Acceleration Capability Fix LoRA

Model Introduction

This model is a LoRA used to fix the acceleration capability of Z-Image Turbo LoRA.

LoRAs trained directly based on Z-Image Turbo will lose their acceleration capability. Images generated under acceleration configuration (steps=8, cfg=1) become blurry, while images generated under non-acceleration configuration (steps=30, cfg=2) remain normal.

Results

Training Data:


Generation Results:

|steps=8, cfg=1|steps=30, cfg=2|steps=8, cfg=1, with our model fix|

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Training with Z-Image Turbo

If you want to train LoRAs based on Z-Image Turbo while maintaining its acceleration capability, please refer to our detailed training strategies guide:

📖 Training Strategies of Z-Image Turbo

This guide covers four different training approaches:

• Scheme 1: Standard SFT Training + No Acceleration Configuration
• Scheme 2: Differential LoRA Training + Acceleration Configuration
• Scheme 3: Standard SFT + Trajectory Imitation Distillation + Acceleration Configuration
• Scheme 4: Standard SFT + Loading DistillPatch LoRA (Recommended) + Acceleration Configuration

We recommend Scheme 4 as it offers the best trade-off between training simplicity and inference speed.

Inference Code

from diffsynth.pipelines.z_image import ZImagePipeline, ModelConfig
import torch

pipe = ZImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="transformer/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
        ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
)
pipe.load_lora(pipe.dit, "path/to/your/lora.safetensors")
pipe.load_lora(pipe.dit, ModelConfig(model_id="DiffSynth-Studio/Z-Image-Turbo-DistillPatch", origin_file_pattern="model.safetensors"))

image = pipe(prompt="a dog", seed=42, rand_device="cuda")
image.save("image.jpg")