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import numpy as np
import random
import tenacity
import requests
from urllib.parse import quote
from PIL import Image
from io import BytesIO
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
models_list=['flux', 'turbo', 'gptimage']
try:
models_list=requests.get('https://image.pollinations.ai/models',timeout=2).json()
except:
pass
@tenacity.retry(stop=tenacity.stop_after_attempt(3)|tenacity.stop_after_delay(120),
wait=tenacity.wait_exponential(multiplier=2, min=10, max=120)
)
def infer(
prompt,
seed,
randomize_seed,
width,
height,
# Enhancement
enhance=False, # Auto-enhance prompt (default: False)
image=None, # Input image URL for img2img
# Privacy
private=False, # Private generation (default: False)
nologo=False, # Remove watermark (default: False)
nofeed=False, # Don't add to public feed (default: False)
model="flux"
):
"""
calling the pollinations.ai image generator
Args:
prompt: The description of the image (required) - "a fluffy dog in a forest"
width: Image width in pixels
height: Image height in pixels
seed: A number to get the same image every time
Returns:
PIL.Image: The generated image.
"""
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Encode the prompt to handle spaces
url = f"https://image.pollinations.ai/prompt/{quote(prompt)}"
# Customize the image size and model
params = {"width": width, "height": height, "model": model,"seed":seed}
if image is not None and len(image)>1:
params["image"]=image
# Make the request
response = requests.get(url, params=params, timeout=60)
image_data = BytesIO(response.content)
image = Image.open(image_data)
return image, seed
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"An astronaut riding a green horse",
"A delicious ceviche cheesecake slice"
]
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(" # Text-to-Image using pollinations ai")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=3,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Replace with defaults that work for your model
)
with gr.Row():
enhance= gr.Checkbox(label="enhance prompt", value=False)
nologo=gr.Checkbox(label="nologo", value=False)
nofeed=gr.Checkbox(label="nofeed", value=False)
private=gr.Checkbox(label="private", value=False)
image=gr.Text(
label="image",
show_label=False,
max_lines=3,
placeholder="image url",
container=False,
)
model=gr.Dropdown(models_list,
label="model",
info="selected model",
interactive=True,
show_label=False,
container=False
)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
prompt,
seed,
randomize_seed,
width,
height,
enhance,
image,
private,
nologo, # Remove watermark (default: False)
nofeed, # Don't add to public feed (default: False)
],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch(mcp_server=True)
|