---
license: apache-2.0
datasets:
- Oysiyl/google-android-toy
language:
- en
---

### Demo
You can try the demo [here](https://sdloraandroidtoy.streamlit.app/). 

For hosting the [frontend](https://github.com/dmitriy-kisil/sd_lora_android_toy_frontend) part [Streamlit Community Cloud](https://streamlit.io/cloud) and [Cerebrium](https://www.cerebrium.ai/) for the [backend](https://github.com/dmitriy-kisil/sd_lora_android_toy_backend) part were used.

### Model card

Finetuned from SD 1.5 using LoRA.

W&B [run](https://wandb.ai/logart1995/text2image-fine-tune/runs/2o98mhc7?workspace=user-logart1995).

### Inference

```py
from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe.load_lora_weights("Oysiyl/sd-lora-android-google-toy", weights="pytorch_lora_weights.safetensors")
pipe = pipe.to("cuda")

g = torch.Generator(device="cuda").manual_seed(42)

image = pipe("An android toy near Eiffel tower",
              num_inference_steps=50,
              num_images_per_prompt=1,
              guidance_scale=7.5,
              temperature=1.0,
              generator=g).images[0]  

image.save("android_toy.png")
```

### Output

![example](./android_toy.png)