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import gradio as gr | |
import numpy as np | |
import os | |
import random | |
import requests | |
from PIL import Image | |
from io import BytesIO | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 2048 | |
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": "inference.flux.dev.txt2img.v1", "config": config} | |
return self._post(f"{self.base_url}/v2/job", json=body) | |
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)): | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
image = generative_api.job({ | |
"prompt": prompt, | |
"width": width, | |
"height": height, | |
"seed": seed, | |
"num_inference_steps": num_inference_steps, | |
"guidance_scale": guidance_scale | |
}) | |
return image, seed | |
generative_api = APIClient() | |
with open("header.html", "r") as file: | |
header = file.read() | |
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; | |
} | |
.image-container img { | |
max-width: 512px; | |
max-height: 512px; | |
margin: 0 auto; | |
border-radius: 0px; | |
} | |
.is-custom-button { | |
background-color: #3a4454; | |
border-color: #4b5563; | |
} | |
@media (prefers-color-scheme: light) { | |
.is-custom-button { | |
background-color: #f3f4f6; | |
border-color: #e5e7eb; | |
} | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown(f"""# Fast FLUX.1 [dev] | |
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) | |
[[non-commercial license](https://huggingface.co./black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co./black-forest-labs/FLUX.1-dev)] | |
""") | |
gr.HTML(header) | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt" | |
) | |
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, | |
interactive=False | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
interactive=False | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance Scale", | |
minimum=1, | |
maximum=15, | |
step=0.1, | |
value=3.5, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=28, | |
) | |
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, guidance_scale, num_inference_steps], | |
outputs = [result, seed] | |
) | |
demo.queue(default_concurrency_limit=4, max_size=5, api_open=False).launch(max_threads=256, show_api=False) |