Update app.py

app.py

@@ -1,65 +1,69 @@
 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)
+gr.load("models/prithivMLmods/SD3.5-Large-Photorealistic-LoRA").launch()
 
-# 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)
+# import gradio as gr
+# import torch
+# from diffusers import StableDiffusion3Pipeline
+# import os
+# from huggingface_hub import login
 
-# 如果有GPU，转移到GPU
-# pipe.to("cuda")
+# # 获取Hugging Face Token
+# hf_token = os.environ.get("HF_TOKEN")
+# login(token=hf_token)
 
-# 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)
+# # 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)
+#     # 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
+#     # 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()
+# # 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()
+# # 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")
+# # 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")
+# #     # 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)
+# #     # 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")
+# #     # 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)
+# #     # 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()
+# # # 启动Gradio应用
+# # demo.launch()