app.py

import gradio as gr
from diffusers import StableDiffusion3Pipeline
import torch

from huggingface_hub import login

# 通过环境变量获取 Token
hf_token = os.getenv("HF_TOKEN")

# 使用 Hugging Face Token 登录
login(token=hf_token)

# 加载模型
pipe = StableDiffusion3Pipeline.from_pretrained("prithivMLmods/SD3.5-Large-Photorealistic-LoRA", torch_dtype=torch.float16)

# 定义图像生成函数
def generate_image(prompt):
    print(prompt)
    return pipe(prompt).images[0]

# 创建 Gradio 界面
iface = gr.Interface(fn=generate_image, inputs="text", outputs="image")

# 启动界面
iface.launch()


# import gradio as gr

# gr.load("models/prithivMLmods/SD3.5-Large-Photorealistic-LoRA").launch()



# import gradio as gr
# import torch
# from diffusers import StableDiffusion3Pipeline
# import os
# from huggingface_hub import login

# # 获取Hugging Face Token
# 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)

# # 如果有GPU，转移到GPU
# # 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
#     result = pipe(prompt=prompt,
#                   num_inference_steps=24, 
#                   guidance_scale=4.0,
#                   width=960, height=1280,
#                   generator=generator)
    
#     # 确保返回 PIL 图像
#     image = result.images[0]
#     print(type(image))
#     return image

# # 创建Gradio界面（使用 Interface）
# def gradio_interface():
#     with gr.Interface(fn=generate_image, 
#                       inputs=[gr.Textbox(label="Prompt", value="Man in the style of dark beige and brown, uhd image, youthful protagonists, nonrepresentational photography"),
#                               gr.Slider(minimum=0, maximum=100000, step=1, label="Seed", value=42)], 
#                       outputs=gr.Image(type="pil", label="Generated Image")) as demo:
#         demo.launch()

# # 启动Gradio应用
# gradio_interface()
    
# # 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(fn=generate_image, inputs=[prompt_input, seed_input], outputs=output_image)

# # # 启动Gradio应用
# # demo.launch()