Create app.py

app.py

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+import torch, os, gc, random
+import gradio as gr
+from PIL import Image
+from diffusers.utils import load_image
+from accelerate import Accelerator
+from diffusers import StableDiffusionXLPipeline
+accelerator = Accelerator(cpu=True)
+
+pipe = accelerator.prepare(StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float32, use_safetensors=True, variant="fp32"))
+pipe = accelerator.prepare(pipe.to("cpu"))
+
+def plex(prompt,neg_prompt,stips):
+    apol=[]
+    
+    image = pipe(prompt=prompt, negative_prompt=neg_prompt, num_inference_steps=stips, output_type="pil")
+    for i, imge in enumerate(image["images"]):
+        apol.append(imge)
+    return apol
+
+iface = gr.Interface(fn=plex, inputs=[gr.Textbox(label="prompt"),gr.Textbox(label="negative prompt",value="ugly, blurry, poor quality"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=2)], outputs=gr.Gallery(label="out", columns=2))
+iface.queue(max_size=1)
+iface.launch(max_threads=1)