readme.md

metadata

base_model: Tongyi-MAI/Z-Image-Turbo
tags:
  - lora
  - text-to-image
  - z-image-turbo
  - style
  - diffusion
license: other

a-cold-wall — LoRA

A LoRA adapter trained for the concept/style "a-cold-wall".

Trigger word

Use this token in your prompt:

• a-cold-wall

Base model

• Tongyi-MAI/Z-Image-Turbo

Files

• a-cold-wall.safetensors — the LoRA weights
• config.yaml, job_config.json — training configuration (for reproducibility)

How to use

A) ComfyUI / AUTOMATIC1111

1. Put a-cold-wall.safetensors into your LoRA folder.
2. Use it in your prompt, e.g.:
   • a-cold-wall, fashion outfits, editorial photo, high detail

(Adjust LoRA strength to taste, e.g. 0.6–1.0.)

B) Diffusers (generic example)

Depending on your setup, you may need to use the correct pipeline class for Z-Image-Turbo.

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "Tongyi-MAI/Z-Image-Turbo",
    torch_dtype=torch.bfloat16
).to("cuda")

pipe.load_lora_weights("thorjank/a-cold-wall-lora", weight_name="a-cold-wall.safetensors")

prompt = "a-cold-wall, fashion outfits, editorial photo, high detail"
image = pipe(prompt).images[0]
image.save("out.png")

Recommended prompting
    •	Start simple:
    •	a-cold-wall, fashion outfits
    •	Then add camera / lighting / composition as needed.

Training details (summary)
    •	Dataset: 35 images
    •	Steps: 3000
    •	Batch size: 1
    •	Learning rate: 1e-4
    •	Network: LoRA
    •	linear rank/alpha: 32/32
    •	conv rank/alpha: 16/16
    •	Trained modules: U-Net (text encoder not trained)
    •	Precision: bf16
    •	Noise scheduler: flowmatch
    •	Resolution buckets configured: 512 / 768 / 1024
    •	Default caption: fashion outfits

Notes / License

This repo contains only LoRA weights. Please ensure your use complies with the base model’s license and with the rights for any content you generate.