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import gradio as gr
from gradio_client import Client, handle_file
import numpy as np
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
flux_1_schell_spaces = ["https://black-forest-labs-flux-1-schnell.hf.space", "ChristianHappy/FLUX.1-schnell", "innoai/FLUX.1-schnell", "tuan2308/FLUX.1-schnell", "FiditeNemini/FLUX.1-schnell"]
# flux_1_schnell_space = "https://black-forest-labs-flux-1-schnell.hf.space"
client = None
job = None
def infer(selected_space, prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
global job
global client
if client is None:
try:
client = Client(selected_space)
print(f"Loaded custom model from {selected_space}")
except ValueError as e:
client = None
print(f"Failed to load custom model from {selected_space}: {e}")
raise gr.Error("Failed to load client after trying all spaces.")
try:
job = client.submit(
prompt=prompt,
seed=seed,
randomize_seed=randomize_seed,
width=width,
height=height,
num_inference_steps=num_inference_steps,
api_name="/infer"
)
result = job.result()
except ValueError as e:
client = None
raise gr.Error(e)
return result
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:
selected_space_index = gr.State(0);
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 [schnell]
[black-forest-labs/FLUX.1-schnell](https://huggingface.co/spaces/black-forest-labs/FLUX.1-schnell)
12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
[[blog](https://blackforestlabs.ai/2024/07/31/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
""")
space = gr.Radio(flux_1_schell_spaces, label="HF Space", value=flux_1_schell_spaces[0])
with gr.Row():
prompt = gr.Textbox(
label="Prompt",
show_label=False,
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):
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 = [selected_space_index, prompt],
# outputs = [selected_space_index, space, result, seed],
# cache_examples="lazy"
# )
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [space, prompt, seed, randomize_seed, width, height, num_inference_steps],
outputs = [result, seed]
)
demo.launch() |