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  1. app.py +147 -0
  2. requirements.txt +2 -0
app.py ADDED
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+ import gradio as gr
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+ import numpy as np
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+ import os
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+ import random
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+ import requests
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+ from PIL import Image
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+ from io import BytesIO
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+
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+ MAX_SEED = np.iinfo(np.int32).max
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+ MAX_IMAGE_SIZE = 2048
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+
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+ class APIClient:
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+ def __init__(self, api_key=os.getenv("API_KEY"), base_url="inference.prodia.com"):
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+ self.headers = {
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+ "Content-Type": "application/json",
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+ "Accept": "image/jpeg",
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+ "Authorization": f"Bearer {api_key}"
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+ }
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+ self.base_url = f"https://{base_url}"
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+
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+ def _post(self, url, json=None):
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+ r = requests.post(url, headers=self.headers, json=json)
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+ r.raise_for_status()
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+
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+ return Image.open(BytesIO(r.content)).convert("RGBA")
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+
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+ def job(self, config):
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+ body = {"type": "inference.flux.dev.txt2img.v1", "config": config}
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+ return self._post(f"{self.base_url}/v2/job", json=body)
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+
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+
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+ 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)):
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+ if randomize_seed:
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+ seed = random.randint(0, MAX_SEED)
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+ image = generative_api.job({
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+ "prompt": prompt,
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+ "width": width,
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+ "height": height,
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+ "seed": seed,
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+ "num_inference_steps": num_inference_steps,
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+ "guidance_scale": guidance_scale
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+ })
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+ return image, seed
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+
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+ generative_api = APIClient()
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+
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+ examples = [
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+ "a tiny astronaut hatching from an egg on the moon",
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+ "a cat holding a sign that says hello world",
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+ "an anime illustration of a wiener schnitzel",
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+ ]
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+
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+ css="""
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+ #col-container {
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+ margin: 0 auto;
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+ max-width: 520px;
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+ }
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+ """
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+
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+ with gr.Blocks(css=css) as demo:
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+
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+ with gr.Column(elem_id="col-container"):
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+ gr.Markdown(f"""# FLUX.1 [dev]
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+ 12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
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+ [[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)]
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+ """)
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+
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+ with gr.Row():
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+
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+ prompt = gr.Text(
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+ label="Prompt",
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+ show_label=False,
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+ max_lines=1,
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+ placeholder="Enter your prompt",
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+ container=False,
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+ )
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+
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+ run_button = gr.Button("Run", scale=0)
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+
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+ result = gr.Image(label="Result", show_label=False)
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+
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+ with gr.Accordion("Advanced Settings", open=False):
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+
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+ seed = gr.Slider(
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+ label="Seed",
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+ minimum=0,
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+ maximum=MAX_SEED,
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+ step=1,
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+ value=0,
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+ )
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+
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+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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+
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+ with gr.Row():
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+
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+ width = gr.Slider(
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+ label="Width",
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+ minimum=256,
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+ maximum=MAX_IMAGE_SIZE,
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+ step=32,
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+ value=1024,
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+ interactive=False
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+ )
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+
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+ height = gr.Slider(
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+ label="Height",
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+ minimum=256,
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+ maximum=MAX_IMAGE_SIZE,
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+ step=32,
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+ value=1024,
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+ interactive=False
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+ )
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+
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+ with gr.Row():
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+
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+ guidance_scale = gr.Slider(
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+ label="Guidance Scale",
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+ minimum=1,
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+ maximum=15,
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+ step=0.1,
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+ value=3.5,
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+ )
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+
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+ num_inference_steps = gr.Slider(
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+ label="Number of inference steps",
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+ minimum=1,
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+ maximum=50,
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+ step=1,
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+ value=28,
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+ )
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+
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+ gr.Examples(
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+ examples = examples,
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+ fn = infer,
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+ inputs = [prompt],
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+ outputs = [result, seed],
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+ cache_examples="lazy"
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+ )
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+
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+ gr.on(
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+ triggers=[run_button.click, prompt.submit],
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+ fn = infer,
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+ inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
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+ outputs = [result, seed]
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+ )
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+
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+ demo.launch()
requirements.txt ADDED
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+ requests~=2.32.3
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+ numpy~=1.26.4