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import os
import random
import gradio as gr
import numpy as np
from huggingface_hub import InferenceClient, login
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
client = InferenceClient(provider="fal-ai")
image = client.text_to_image(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
seed=seed,
model="black-forest-labs/FLUX.1-dev"
)
return image, seed
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Sidebar():
gr.Markdown("# Inference Provider")
gr.Markdown("This Space showcases the black-forest-labs/FLUX.1-dev model, served by the nebius API. Sign in with your Hugging Face account to use this API.")
button = gr.LoginButton("Sign in")
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 [schnell] with fal-ai through HF Inference Providers ⚡
learn more about HF Inference Providers [here](https://huggingface.co/docs/inference-providers/index)""")
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, format="png")
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,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=4,
)
gr.Examples(
examples = examples,
fn = infer,
inputs = [prompt],
outputs = [result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
outputs = [result, seed]
)
demo.launch(mcp_server=True) |