app.py

import random

import gradio as gr
import numpy as np
import spaces
import torch
from dataset_viber import CollectorInterface
from diffusers import DiffusionPipeline

dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    image = pipe(
            prompt=prompt,
            width=width,
            height=height,
            num_inference_steps=num_inference_steps,
            generator=generator,
            guidance_scale=0.0
    ).images[0]
    return image

examples = [
    ["a tiny astronaut hatching from an egg on the moon", 0, True, 1024, 1024, 4],
    ["a cat holding a sign that says hello world", 0, True, 1024, 1024, 4],
    ["an anime illustration of a wiener schnitzel", 0, True, 1024, 1024, 4],
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

description = """# 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)]
"""

interface = CollectorInterface(
    fn=infer,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your prompt")
    ],
    outputs=[
        gr.Image(label="Result"),
    ],
    additional_inputs=[
        gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0),
        gr.Checkbox(label="Randomize seed", value=True),
        gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024),
        gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024),
        gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=4),
    ],
    title="FLUX.1 [schnell] - with Dataset Viber data collection",
    description=description,
    examples=examples,
    css=css,
    dataset_name="image-generation-flux1-schnell"
)

interface.launch()