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import replicate
import gradio as gr
from PIL import Image
import requests
from io import BytesIO
api = replicate.Client(api_token="r8_9BTRdfQjCrVHkVMyQ6xAYJS52S6mLzx4YP6VA")
# Setting up Replicate AI API for stable diffusion
def generate_image(input_text, width=768, height=768, guidance_scale=7.5, num_inference_steps=50):
guidance_scale = float(guidance_scale)
output = api.run(
"stability-ai/stable-diffusion:ac732df83cea7fff18b8472768c88ad041fa750ff7682a21affe81863cbe77e4",
input={
"width": width,
"height": height,
"prompt": input_text,
"scheduler": "K_EULER",
"num_outputs": 1,
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps
}
)
image_url = output[0]
response = requests.get(image_url)
image = Image.open(BytesIO(response.content))
return image
# Setting up a gradio interface
iface = gr.Interface(
fn=generate_image,
inputs=[gr.Textbox(label="Prompt"),
gr.Slider(label="Width", minimum = 64, maximum = 768, step = 64),
gr.Slider(label="Height", minimum = 64, maximum = 768, step = 64),
gr.Slider(label="Guidance Scale", minimum = 0, maximum = 20, step = 0.5),
gr.Radio([10,20,30,40,50], label="Inference Steps", info="Choose the number of Inference Steps")],
outputs="image")
iface.launch(debug=True)
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