| 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) | |
| 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) | |
| # 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() |