Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: mit
----
+---
+library_name: peft
+base_model: Tongyi-MAI/Z-Image-Turbo
+tags:
+- lora
+- diffusion
+- image-generation
+- japan
+- photography
+- realistic
+license: other
+datasets:
+- ThePioneer/japanese-photos
+language:
+- en
+- fr
+pipeline_tag: text-to-image
+---
+
+# Japan Realistic LoRA for Z-Image-Turbo
+
+A LoRA adapter trained on realistic Japanese photography to enhance Z-Image-Turbo's ability to generate authentic Japanese scenes, urban landscapes, and cultural elements.
+
+## Model Description
+
+This is a LoRA (Low-Rank Adaptation) adapter trained on the [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) diffusion model. It specializes in generating realistic photographs of Japanese locations, transportation, architecture, and everyday scenes with authentic lighting and composition.
+
+## Training Details
+
+- **Base Model**: Tongyi-MAI/Z-Image-Turbo
+- **Training Steps**: 2,000
+- **LoRA Rank (r)**: 32
+- **LoRA Alpha**: 32
+- **Learning Rate**: 0.0001
+- **Optimizer**: AdamW 8-bit
+- **Batch Size**: 1 (with gradient accumulation of 4)
+- **Training Resolution**: 512x512
+- **Precision**: bfloat16
+- **Noise Scheduler**: FlowMatch
+- **Trained Using**: [Ostris AI-Toolkit](https://github.com/ostris/ai-toolkit)
+
+## Usage
+
+### Using with Diffusers
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+# Load base model
+pipe = DiffusionPipeline.from_pretrained(
+    "Tongyi-MAI/Z-Image-Turbo",
+    torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+# Load LoRA adapter
+pipe.load_lora_weights("your-username/japan_realistic")
+
+# Generate image
+prompt = "Photo of a Shinkansen bullet train stopped at a Japanese station platform, overhead roof structure, yellow tactile paving, natural daylight, ultra realistic."
+image = pipe(
+    prompt=prompt,
+    num_inference_steps=8,
+    guidance_scale=1.0,
+    width=1024,
+    height=1024
+).images
+
+image.save("output.png")