app.py

import gradio as gr
import numpy as np
import random
import torch
from diffusers import DiffusionPipeline, AutoencoderKL

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

# Базовая модель (обязательно та же, на которой ты обучал LoRA)
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

pipe = DiffusionPipeline.from_pretrained(
    base_model_id,
    vae=vae,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    use_safetensors=True,
    variant="fp16"
)

# Загружаем твой LoRA
pipe.load_lora_weights("Bexiiii/Inspira_v_1")
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(seed)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]
    return image, seed

examples = [
    "A creative Instagram ad for brand TOK, featuring a cute dog wearing stylish accessories",
    "Ultra-detailed vibrant cinematic photo of a corgi mascot for Inspira",
]

with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# Inspira_v_1 — SDXL + LoRA")

        with gr.Row():
            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt")
            run_button = gr.Button("Generate")

        result = gr.Image(label="Result")
        seed = gr.Number(label="Seed", value=0)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Textbox(label="Negative prompt")
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            width = gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=1024, label="Width")
            height = gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=1024, label="Height")
            guidance_scale = gr.Slider(0, 10, step=0.1, value=7.5, label="Guidance scale")
            num_inference_steps = gr.Slider(1, 50, step=1, value=30, label="Inference steps")

        gr.Examples(examples=examples, inputs=[prompt])

        run_button.click(
            fn=infer,
            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
            outputs=[result, seed]
        )

if __name__ == "__main__":
    demo.launch()