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import os
import gradio as gr
import requests
import base64
from io import BytesIO
from PIL import Image # Corrected import
import random
# Get API key from environment variable
api_key = os.environ.get("NVCF_API_KEY")
if not api_key:
raise ValueError("Please set the NVCF_API_KEY environment variable.")
# API details
invoke_url = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/89848fb8-549f-41bb-88cb-95d6597044a4"
fetch_url_format = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/"
headers = {
"Authorization": f"Bearer {api_key}",
"Accept": "application/json",
}
# Function to generate image using the API
def generate_image(prompt, negative_prompt, sampler, seed, guidance_scale, inference_steps):
if seed is None or seed == 0:
seed = random.randint(11111111, 999999999999999)
payload = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"sampler": sampler,
"seed": seed,
"guidance_scale": guidance_scale,
"inference_steps": inference_steps
}
print(payload)
session = requests.Session()
response = session.post(invoke_url, headers=headers, json=payload)
while response.status_code == 202:
request_id = response.headers.get("NVCF-REQID")
fetch_url = fetch_url_format + request_id
response = session.get(fetch_url, headers=headers)
response.raise_for_status()
response_body = response.json()
# Print the API response for debugging
print("API Response:", response_body)
# Decode the base64-encoded image data
b64_image_data = response_body.get("b64_json")
if b64_image_data is None:
return "Error: API response does not contain 'b64_json' key."
image_data = base64.b64decode(b64_image_data)
# Convert the binary data to a PIL Image
image = Image.open(BytesIO(image_data))
return image
# Create Gradio interface
iface = gr.Interface(
fn=generate_image,
inputs=[
gr.Textbox(label="Prompt", placeholder="Describe the image you want to generate"),
gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image"),
gr.Dropdown(label="Sampler", choices=["DPM", "EulerA", "LMS", "DDIM"], value="DPM"),
gr.Number(label="Seed", value=0),
gr.Slider(label="Guidance Scale", minimum=1, maximum=9, value=5),
gr.Slider(label="Inference Steps", minimum=5, maximum=100, value=35)
],
outputs=gr.Image(label="Generated Image"),
description = """
<div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;">
<strong>Generate Stunning Images with Stable Diffusion XL</strong>
</div>
<p>
This Gradio app harnesses the power of Stable Diffusion XL image generation capabilities to bring your creative visions to life. Using NVIDIA NGC.
Simply provide a text prompt describing the image you desire, and let the AI do its magic!
</p>
<p>
<strong>How to Use:</strong>
</p>
<ol>
<li>Enter a detailed <strong>prompt</strong> describing the image you want to generate.</li>
<li>Optionally, add a <strong>negative prompt</strong> to specify elements you want to avoid.</li>
<li>Choose a <strong>sampler</strong> (algorithm) for image generation.</li>
<li>Set a <strong>seed</strong> for reproducibility (or leave it blank for random results).</li>
<li>Adjust the <strong>guidance scale</strong> to influence how closely the image follows the prompt.</li>
<li>Set the <strong>inference steps</strong> to control the level of detail and processing time.</li>
<li>Click <strong>Generate</strong> and marvel at your creation!
</ol>
<p>
<strong>This service is powered by NVIDIA NGC and is completely free to use.</strong>
</p>
<p>
<strong>Created by:</strong> @artificialguybr (<a href="https://twitter.com/artificialguybr">Twitter</a>)
</p>
<p>
<strong>Explore more:</strong> <a href="https://artificialguy.com">artificialguy.com</a>
</p>
"""
)
# Launch the Gradio app
iface.launch()
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