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import gradio as gr
import requests
import random
import os
from io import BytesIO
from PIL import Image
import numpy as np
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1344
class APIClient:
def __init__(self, api_key=os.getenv("API_KEY"), base_url="inference.prodia.com"):
self.headers = {
"Content-Type": "application/json",
"Accept": "image/jpeg",
"Authorization": f"Bearer {api_key}"
}
self.base_url = f"https://{base_url}"
def _post(self, url, json=None):
r = requests.post(url, headers=self.headers, json=json)
r.raise_for_status()
return Image.open(BytesIO(r.content)).convert("RGBA")
def job(self, config):
body = {"type": "v2.job.sd3.txt2img", "config": config}
return self._post(f"{self.base_url}/v2/job", json=body)
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
image = generative_api.job(
{
"prompt": prompt,
"negative_prompt": negative_prompt,
"width": width,
"height": height,
"guidance_scale": guidance_scale,
"steps": num_inference_steps,
# "refiner": True
"seed": seed
}
)
return image, seed
generative_api = APIClient()
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: 580px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Demo [Stable Diffusion 3 - Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
Learn more about [Stable Diffusion 3](https://stability.ai/news/stable-diffusion-3). Powered by [Prodia API](https://prodia.com).
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
)
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=64,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=64,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=5.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
gr.Examples(
examples=examples,
inputs=[prompt]
)
gr.on(
triggers=[run_button.click, prompt.submit, negative_prompt.submit],
fn=infer,
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed]
)
demo.launch() |