Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,364 Bytes
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import gradio as gr
import spaces
from panna import SD3
model = SD3("stabilityai/stable-diffusion-3-medium-diffusers")
title = ("# [Stable Diffusion 3 Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium)\n"
"The demo is part of [panna](https://github.com/abacws-abacus/panna) project.")
examples = [
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"A female model, high quality, fashion, Paris, Vogue, Maison Margiela, 8k",
]
css = """
#col-container {
margin: 0 auto;
max-width: 580px;
}
"""
@spaces.GPU
def infer(prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps):
return model.text2image(
prompt=[prompt],
negative_prompt=[negative_prompt],
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
seed=seed
)[0]
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(title)
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=1_000_000, step=1, value=0)
with gr.Row():
width = gr.Slider(label="Width", minimum=256, maximum=1344, step=64, value=1024)
height = gr.Slider(label="Height", minimum=256, maximum=1344, step=64, value=1024)
with gr.Row():
guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=7.5)
num_inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=50)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit, negative_prompt.submit],
fn=infer,
inputs=[prompt, negative_prompt, seed, width, height, guidance_scale, num_inference_steps],
outputs=[result]
)
demo.launch(server_name="0.0.0.0")
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