app.py

import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline
import io
import base64
from PIL import Image
import json

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)
    
    # Gerar a imagem
    image = pipe(
        prompt=prompt, 
        width=width,
        height=height,
        num_inference_steps=num_inference_steps, 
        generator=generator,
        guidance_scale=0.0
    ).images[0]
    
    # Converter a imagem para Base64
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    
    # Retornar JSON com Base64 e seed
    return {"image_base64": f"data:image/png;base64,{img_str}", "seed": seed}

# Função para a API personalizada
def api_infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4):
    result = infer(prompt, seed, randomize_seed, width, height, num_inference_steps)
    return result  # Retorna diretamente o JSON

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"""# 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)
        seed_output = gr.Number(label="Seed", show_label=True)
        
        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,
                )
            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=[prompt],
            outputs=[result, seed_output],
            cache_examples=True,
            cache_mode="lazy"
        )

    # Função para formatar a saída para a interface
    def format_output(prompt, seed, randomize_seed, width, height, num_inference_steps):
        output = infer(prompt, seed, randomize_seed, width, height, num_inference_steps)
        return output["image_base64"], output["seed"]

    # Interface Gradio
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=format_output,
        inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
        outputs=[result, seed_output]
    )

    # Endpoint personalizado para a API
    demo.queue(api_name="infer_api").launch()

demo.launch()