Create README.md

README.md

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+---
+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](https://github.com/jl-wei/guing) 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](https://ieeexplore.ieee.org/abstract/document/10260853)
+
+Using with Diffusers
+```python
+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.