Create app.py

app.py

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+import torch
+import gradio as gr
+from PIL import Image
+import PIL.Image
+from diffusers import StableDiffusionPipeline
+from diffusers.utils import load_image
+from accelerate import Accelerator
+from optimum.onnxruntime import ORTStableDiffusionPipeline
+accelerator = Accelerator(cpu=True)
+pipe = accelerator.prepare(ORTStableDiffusionPipeline.from_pretrained("JoPmt/AbsoluteReality-onnx"))
+pipe.to("cpu")
+def plex(prompt,neg_prompt,stips,scaly):
+    apol=[]
+    image = pipe(prompt=prompt, negative_prompt=neg_prompt, num_inference_steps=stips, guidance_scale=scaly)
+    for a, imze in enumerate(image["images"]):
+        apol.append(imze)
+    return apol
+
+iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="negative_prompt", value="low quality, bad quality"), gr.Slider(label="num inference steps",minimum=1,step=1,maximum=30,value=25), gr.Slider(label="guidance_scale",minimum=1,step=1,maximum=10,value=7)],outputs=gr.Gallery(label="Generated Output Image", columns=1),description="Running on cpu, very slow! by JoPmt.")
+iface.queue(max_size=1,api_open=False)
+iface.launch(max_threads=1)