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.bfloat16, use_safetensors=True, variant="fp16"))
+pipe.unet.to(memory_format=torch.channels_last)
+pipe.to("cpu")
+
+def plex(prompt,neg_prompt,stips):
+    apol=[]
+    nm = random.randint(1, 4836928)
+    while nm % 32 != 0:
+        nm = random.randint(1, 4836928)
+    generator = torch.Generator(device="cpu").manual_seed(nm)
+    image = pipe(prompt=[prompt]*2, negative_prompt=[neg_prompt]*2, num_inference_steps=stips, output_type="pil",generator=generator)
+    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=4)], outputs=gr.Gallery(label="out", columns=2))
+iface.queue(max_size=1,api_open=False)
+iface.launch(max_threads=1)