app.py

import gradio as gr
import numpy as np
import random
# import spaces
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
# from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images

# Importar os para la variable de entorno, aunque no se use directamente aquí, es buena práctica
import os

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

# Usando el modelo más ligero de la lista blanca: FLUX.1-schnell-4bit
# Nota: La lógica de FLUX es compleja, mantendré la estructura original pero con el modelo más ligero.
try:
    # Intentar cargar el modelo más ligero de la lista blanca
    model_id = "black-forest-labs/FLUX.1-schnell-4bit"
    taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=taef1).to(device)
    torch.cuda.empty_cache()
except Exception as e:
    # Si falla, se usa un placeholder para que la interfaz Gradio se cargue
    print(f"Error al cargar el modelo FLUX: {e}")
    pipe = None

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 512 # Ajustado a un tamaño más seguro

# La función infer debe ser modificada para ser compatible con la nueva Pipeline
# y para manejar el caso de que el modelo no se haya cargado.

def infer(prompt, seed=42, randomize_seed=False, width=512, height=512, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
    if pipe is None:
        return gr.Error("El modelo FLUX no pudo cargarse debido a limitaciones de recursos."), seed
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)
    
    # La llamada original usaba una función auxiliar que no tenemos (flux_pipe_call_that_returns_an_iterable_of_images)
    # Usaremos la llamada estándar de DiffusionPipeline.
    
    # Nota: La pipeline de FLUX puede requerir parámetros específicos.
    # Si la llamada estándar falla, esto requerirá una revisión más profunda.
    try:
        # Usamos la llamada estándar de DiffusionPipeline
        image = pipe(
            prompt=prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
            output_type="pil",
        ).images[0]
        
        return image, seed
    except Exception as e:
        return gr.Error(f"Error durante la inferencia: {e}"), seed

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 [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)]
        """)
        
        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=512,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            
            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.launch()