metadata
license: apache-2.0
datasets:
- Oysiyl/google-android-toy
language:
- en
Demo
You can try the demo here.
For hosting the frontend part Streamlit Community Cloud and Cerebrium for the backend part were used.
Model card
Finetuned from SD 1.5 using LoRA.
W&B run.
Inference
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
