app.py

import gradio as gr
import torch
from diffusers import StableDiffusion3Pipeline
import os
from huggingface_hub import login
hf_token = os.environ.get("HF_TOKEN")
login(token=hf_token)


# 加载模型并配置
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
pipe.load_lora_weights("prithivMLmods/SD3.5-Large-Photorealistic-LoRA", weight_name="Photorealistic-SD3.5-Large-LoRA.safetensors")
pipe.fuse_lora(lora_scale=1.0)
# pipe.to("cuda")

# 定义图像生成函数，添加种子参数
def generate_image(prompt, seed):
    # 设置种子
    generator = torch.manual_seed(seed)
    
    # 使用模型生成图像
    image = pipe(prompt=prompt,
                 num_inference_steps=24, 
                 guidance_scale=4.0,
                 width=960, height=1280,
                 generator=generator).images[0]
    
    return image

# 创建Gradio界面
with gr.Blocks() as demo:
    gr.Markdown("## Stable Diffusion Image Generation with Seed Control")

    # 输入框：提示文本
    prompt_input = gr.Textbox(label="Prompt", value="Man in the style of dark beige and brown, uhd image, youthful protagonists, nonrepresentational photography")

    # 滑块：种子
    seed_input = gr.Slider(minimum=0, maximum=100000, step=1, label="Seed", value=42)

    # 输出图像
    output_image = gr.Image(type="pil", label="Generated Image")

    # 按钮触发事件
    generate_btn = gr.Button("Generate Image")
    generate_btn.click(generate_image, inputs=[prompt_input, seed_input], outputs=output_image)

# 启动Gradio应用
demo.launch()