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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

example

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Finetuned from

Dataset used to train Oysiyl/sd-lora-android-google-toy