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import gradio as gr
from diffusers import StableDiffusion3Pipeline
import torch
import os
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",
revision="main",
torch_dtype=torch.float16)
# 如果模型需要LoRA权重文件，可以手动加载
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)
# 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() |