app.py

#!/usr/bin/env python

import os
import random
import uuid

import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, KDPM2AncestralDiscreteScheduler, AutoencoderKL

DESCRIPTION = """
# Proteus ```V0.1```
Model by [dataautogpt3](https://huggingface.co/dataautogpt3)
Demo by [ehristoforu](https://huggingface.co/ehristoforu)
"""
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"

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

USE_TORCH_COMPILE = 0
ENABLE_CPU_OFFLOAD = 0

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Lista de modelos disponíveis
available_models = {
    "animagine-xl-3.0": "cagliostrolab/animagine-xl-3.0",
    # Adicione mais modelos conforme necessário
}

# Função para carregar o modelo escolhido
def load_model(model_name):
    if model_name == "animagine-xl-3.0":
        return StableDiffusionXLPipeline.from_pretrained(model_name, use_safetensors=True)
    # Adicione mais modelos conforme necessário
    else:
        raise ValueError(f"Model '{model_name}' not recognized.")

# Parâmetros iniciais
selected_model_name = "animagine-xl-3.0"
pipe = load_model(selected_model_name)

# Dropdown para selecionar o modelo
model_dropdown = gr.Dropdown(
    label="Select Model",
    choices=list(available_models.keys()),
    default=selected_model_name,
)

# Adicione o dropdown à UI
with gr.Blocks(title="Proteus V0.1", css=css) as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=False,
    )
    with gr.Group():
        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.Gallery(label="Result", columns=1, preview=True, show_label=False)
    with gr.Accordion("Advanced options", open=False):
        with gr.Row():
            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
            visible=True
        )
        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
        with gr.Row(visible=True):
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=1536,
                step=8,
                value=768,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=1536,
                step=8,
                value=768,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=20,
                step=0.1,
                value=7.0,
            )

        # Adicione o dropdown ao início da seção avançada
        with gr.Row():
            model_dropdown

    # Restante do código...

# Função para gerar imagens
@spaces.GPU(enable_queue=True)
def generate(
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    randomize_seed: bool = False,
    model_name: str = selected_model_name,  # Novo parâmetro para o modelo
    progress=gr.Progress(track_tqdm=True),
):
    global pipe
    pipe = load_model(model_name)
    
    # Restante do código...

# Restante do código...

# Adicione a seleção do modelo à função de execução
gr.on(
    triggers=[
        prompt.submit,
        negative_prompt.submit,
        run_button.click,
        model_dropdown.change,  # Adicione o dropdown de modelo como um trigger
    ],
    fn=generate,
    inputs=[
        prompt,
        negative_prompt,
        use_negative_prompt,
        seed,
        width,
        height,
        guidance_scale,
        randomize_seed,
        model_dropdown,  # Adicione o dropdown de modelo como uma entrada
    ],
    outputs=[result, seed],
    api_name="run",
)

# ... Restante do código ...

# Lançamento da interface
if __name__ == "__main__":
    demo.queue(max_size=20).launch(show_api=False, debug=False)