File size: 1,878 Bytes
78ee100 |
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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
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") |