metadata

tags:
  - text-to-image
  - stable-diffusion
  - lora
  - diffusers
language:
  - en
library_name: diffusers
pipeline_tag: text-to-image
base_model: stabilityai/stable-diffusion-2-base
instance_prompt: 'Mobile app:'

UI-Diffuser-V2

UI-Diffuser-V2 is fine tuned from "stabilityai/stable-diffusion-2-base" with the GPSCap dataset for mobile UI generation.

This iteration, UI-Diffuser-V2, represents the second version of the UI-Diffuser model.

The first version, UI-Diffuser-V1, was introduced in our paper titled Boosting GUI Prototyping with Diffusion Models

Using with Diffusers

import torch
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

model_id = "stabilityai/stable-diffusion-2-base"
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)

lora_path = "Jl-wei/ui-diffuser-v2"
pipe.load_lora_weights(lora_path)
pipe.to("cuda")

prompt = "Mobile app: health monitoring report"
images = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, height=512, width=288, num_images_per_prompt=10).images

columns = 5
fig = plt.figure(figsize=(20,10))
for i, image in enumerate(images):
    plt.subplot(int(len(images) / columns), columns, i + 1)
    plt.imshow(image)
for ax in fig.axes:
    ax.axis("off")

Please note that the model can only be used for academic purpose.