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import gradio as gr
from huggingface_hub import InferenceClient
import random,os
import numpy as np
MAX_SEED = np.iinfo(np.int32).max
model_list = ["Qwen/Qwen-Image", "black-forest-labs/FLUX.1-dev"]
client = InferenceClient(
provider="auto",
api_key = os.getenv("HF_API_KEY")
)
def infer(
prompt,
model_name,
seed,
randomize_seed,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Hugging Face InferenceClient doesn't use seed directly, but we keep it for display
image = client.text_to_image(prompt, model=model_name)
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 Gradio Template (Hugging Face InferenceClient)")
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, variant="primary")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
model_name = gr.Dropdown(
label="Model",
choices=model_list,
value=model_list[0],
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
gr.Examples(examples=examples, inputs=[prompt])
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, model_name, seed, randomize_seed],
outputs=[result, seed],
)
if __name__ == "__main__":
demo.launch()
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