Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from torch import autocast
+from diffusers import StableDiffusionImg2ImgPipeline
+from torch import autocast
+from tqdm.auto import tqdm
+import requests
+from io import BytesIO
+from PIL import Image
+from typing import List, Optional, Union
+import inspect
+import warnings
+
+# Load the Stable Diffusion model
+modelid = "CompVis/stable-diffusion-v1-4"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(modelid, revision="fp16", torch_dtype=torch.float16)
+pipe.to(device)
+
+def generate_image(prompt):
+    response = requests.get(url)
+    init_img = Image.open(BytesIO(response.content)).convert("RGB")
+    init_img = init_img.resize((768, 512))
+
+    generator = torch.Generator(device=device).manual_seed(1024)
+    with autocast("cuda"):
+        image = pipe(prompt=prompt, init_image=init_img, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
+
+    return image
+
+# Define the input and output components
+input_text = gr.inputs.Textbox(lines=10, label="Enter a prompt")
+output_image = gr.outputs.Image(label="Generated Image")
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=input_text,
+    outputs=output_image,
+    title="Stable Bud",
+    description="Generate images using Stable Diffusion",
+    layout="vertical",
+)
+
+if __name__ == "__main__":
+    iface.launch()