Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+import torch
+
+model_id = "yutengz/ip2p-RoboPredict"
+pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+def infer(prompt, image):
+    image = image.convert("RGB").resize((256, 256))
+    result = pipe(prompt=prompt, image=image).images[0]
+    return result
+
+demo = gr.Interface(
+    fn=infer,
+    inputs=[
+        gr.Textbox(label="Instruction Prompt"),
+        gr.Image(type="pil", label="Source Image"),
+    ],
+    outputs=gr.Image(label="Predicted Image"),
+    title="InstructPix2Pix Fine-Tuned on Robotic Frames",
+)
+
+demo.launch()