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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.schnell.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() | |
| 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.Column(elem_id="col-container"): | |
| gr.Markdown(f"""# Fast FLUX.1 [schnell] | |
| 12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation | |
| [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)] | |
| """) | |
| 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): | |
| 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(): | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=10, | |
| 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.queue(default_concurrency_limit=4, max_size=5, api_open=False).launch(max_threads=256, show_api=False) |