Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+from torch import autocast
+def generate_image(prompt, model_id, guidance_scale):
+  """Generate image"""
+  img_path = "image.png"
+  with autocast(device):
+    img = pipes[model_id](prompt, guidance_scale=guidance_scale)["images"][0]
+  img.save(img_path)
+  return img_path
+
+# the model id
+model_ids = ["CompVis/stable-diffusion-v1-4"]
+# check for device
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print(f"The code is runing on {device}")
+# load the p model pipeline
+pipes = {
+    model_id:StableDiffusionPipeline.from_pretrained(model_id, variant="fp16", torch_dtype=torch.float32).to(device)
+    for model_id in model_ids
+}
+
+example_prompt = "a photo of an astronaut riding a horse on mars"
+# gradio interface
+interface = gr.Interface(
+    fn=generate_image,
+    inputs= [gr.Textbox(label="Prompt", default=example_prompt),
+             gr.Dropdown(model_ids,label="Select the model ID"),
+             gr.Slider(minimum=0.1,maximum=10, default=7.5, label="Guidance Scale")],
+    outputs="image",
+    live=False,
+    title="Text to Image",
+    description="Enter the prompt in text input, click generate to generete the image")
+# lauch the gradio
+interface.launch(share=True, debug=True) # set share to False if you dont want to make public