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readme.md

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+---
+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.
+
+```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/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.
+