Init

app.py

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+# from transformers import pipeline
+import gradio as gr
+from diffusers import DiffusionPipeline
+
+# get_completion = pipeline("image-to-text",model="nlpconnect/vit-gpt2-image-captioning")
+
+pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+
+# def summarize(input):
+#     output = get_completion(input)
+#     return output[0]['generated_text']
+
+# def captioner(image):
+#     result = get_completion(image)
+#     return result[0]['generated_text']
+
+def generate(prompt):
+    return pipeline(prompt).images[0]    
+
+gr.close_all()
+demo = gr.Interface(fn=generate,
+                    inputs=[gr.Textbox(label="Your prompt")],
+                    outputs=[gr.Image(label="Result")],
+                    title="Image Generation with Stable Diffusion",
+                    description="Generate any image with Stable Diffusion",
+                    allow_flagging="never",
+                    examples=["the spirit of a tamagotchi wandering in the city of Vienna","a mecha robot in a favela"])
+
+demo.launch()