app.py

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

# ตั้งค่าอุปกรณ์และโมเดล
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"

torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

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

# ฟังก์ชันหลัก
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().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

# อินเทอร์เฟซ Gradio ที่รองรับ API
iface = gr.Interface(
    fn=infer,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Textbox(label="Negative Prompt", value=""),
        gr.Slider(0, MAX_SEED, step=1, value=0, label="Seed"),
        gr.Checkbox(label="Randomize Seed", value=True),
        gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=1024, label="Width"),
        gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=1024, label="Height"),
        gr.Slider(0.0, 10.0, step=0.1, value=0.0, label="Guidance Scale"),
        gr.Slider(1, 50, step=1, value=2, label="Inference Steps"),
    ],
    outputs=[
        gr.Image(label="Generated Image"),
        gr.Number(label="Used Seed"),
    ],
    title="Text-to-Image API with SDXL-Turbo",
    description="Enter a prompt to generate an image. Works with API too!"
)

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