Update README.md

README.md

@@ -1,3 +1,54 @@
----
-license: mit
----
+---
+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")