Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+import os
+
+# Constants
+#SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", "0") == "1"
+
+# Initialize the model
+pipe = DiffusionPipeline.from_pretrained(
+    "playgroundai/playground-v2.5-1024px-aesthetic",
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+
+# Safety Checker (if necessary)
+#if SAFETY_CHECKER:
+    # Implement or import the safety checker code here
+
+def generate_image(prompt, num_inference_steps=50, guidance_scale=7):
+    # Generate image
+    results = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)
+
+    # Safety check (if necessary)
+    if SAFETY_CHECKER:
+        # Implement the safety check logic here
+        pass
+
+    return results.images[0]
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## Playground-V2.5 Demo")
+    with gr.Row():
+        prompt = gr.Textbox(label='Enter your image prompt')
+        num_inference_steps = gr.Slider(minimum=1, maximum=75, step=1, label='Number of Inference Steps', value=50)
+        guidance_scale = gr.Slider(minimum=1, maximum=20, step=0.1, label='Guidance Scale', value=7)
+        submit = gr.Button('Generate Image')
+    img = gr.Image(label='Generated Image')
+
+    submit.click(
+        fn=generate_image,
+        inputs=[prompt, num_inference_steps, guidance_scale],
+        outputs=img,
+    )
+
+
+demo.queue().launch()