app.py

import gradio as gr
import torch
from diffusers import DiffusionPipeline

# Carregar o modelo base
base_model = "stabilityai/stable-diffusion-2-1"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)

# Carregar o LoRA a partir do repositório fornecido
lora_repo = "Shakker-Labs/FLUX.1-dev-LoRA-blended-realistic-illustration"
pipe.load_lora_weights(lora_repo)

pipe.to("cuda")

MAX_SEED = 2**32 - 1

def generate_image(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device="cuda").manual_seed(seed)
    
    image = pipe(
        prompt=prompt,
        num_inference_steps=steps,
        guidance_scale=cfg_scale,
        width=width,
        height=height,
        generator=generator,
        joint_attention_kwargs={"scale": lora_scale},
    ).images[0]
    
    return image, seed

with gr.Blocks() as app:
    gr.Markdown("# Flux RealismLora Image Generator")
    
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.TextArea(label="Prompt", placeholder="Digite o prompt", lines=5)
            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, máximo=20, passo=0.5, valor=7.5)
            steps = gr.Slider(label="Steps", mínimo=1, máximo=100, passo=1, valor=50)
            width = gr.Slider(label="Width", mínimo=256, máximo=1536, passo=64, valor=768)
            height = gr.Slider(label="Height", mínimo=256, máximo=1536, passo=64, valor=768)
            randomize_seed = gr.Checkbox(False, label="Randomize seed")
            seed = gr.Slider(label="Seed", mínimo=0, máximo=MAX_SEED, passo=1, valor=42)
            lora_scale = gr.Slider(label="LoRA Scale", mínimo=0, máximo=1, passo=0.01, valor=0.85)
            generate_button = gr.Button("Generate")
        
        with gr.Column(scale=1):
            result = gr.Image(label="Generated Image")
            gr.Markdown("Gere imagens usando RealismLora com um prompt de texto.")
    
    generate_button.click(
        generate_image,
        inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
        outputs=[result, seed]
    )

app.queue()
app.launch()