File size: 1,511 Bytes
7b5a285 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ---
base_model: Tongyi-MAI/Z-Image-Turbo
tags:
- lora
- text-to-image
- diffusion
- z-image-turbo
- character
license: other
---
# hardbody — LoRA
LoRA adapter trained on **Tongyi-MAI/Z-Image-Turbo**.
> Note: `trigger_word` is **not set** in the training config. In practice, use the concept name **`hardbody`** in your prompt, and/or rely on the dataset’s default caption described below.
## Base model
- **Tongyi-MAI/Z-Image-Turbo**
## Trigger / keyword
- Suggested keyword: **`hardbody`**
- Default caption used during training: **`curvy female body`**
## Files
- `*.safetensors` — LoRA weights
- `config.yaml`, `job_config.json` — training configuration
- (optional) `log.txt` — training log
## How to use
### A) ComfyUI / AUTOMATIC1111
1. Put the `.safetensors` file into your LoRA folder.
2. Prompt examples (safe / non-explicit):
- `hardbody, athletic figure, studio photo, soft lighting, high detail`
- `hardbody, fashion shoot, street style, natural light, high detail`
(Adjust LoRA strength to taste, e.g. 0.6–1.0.)
### B) Diffusers (generic example)
```python
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/<REPO_NAME>", weight_name="<YOUR_LORA_FILENAME>.safetensors")
prompt = "hardbody, athletic figure, studio photo, soft lighting, high detail"
image = pipe(prompt).images[0]
image.save("out.png") |