Create app.py

app.py

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+from diffusers import AutoPipelineForText2Image
+import torch
+import gradio as gr
+from PIL import Image
+import os, random
+from diffusers.utils import load_image
+from accelerate import Accelerator
+
+accelerator = Accelerator()
+
+def plex(prompt,neg_prompt):
+    pipe = accelerator.prepare(AutoPipelineForText2Image.from_pretrained("openskyml/overall-v1", torch_dtype=torch.float32, use_safetensors=True, safety_checker=False))
+    pipe = accelerator.prepare(pipe.to("cpu"))
+    image = pipe(prompt=prompt, negative_prompt=neg_prompt,num_inference_steps=10).images[0]
+    return image
+
+iface = gr.Interface(fn=plex,inputs=[gr.Dropdown(gr.Textbox(label="Prompt"), gr.Textbox(label="negative_prompt", value="low quality, bad quality")],outputs=gr.Image(label="Generated Output Image"), title="Txt2Img_Overall_v1_SD",description="Running on cpu, very slow!")
+iface.queue(max_size=1,api_open=False)
+iface.launch(max_threads=1)