new

app.py

@@ -281,206 +281,206 @@ def image_to_base64(image_path):
     with open(image_path, "rb") as img_file:
         return base64.b64encode(img_file.read()).decode('utf-8')
 
-def main():
-
-    torch.set_grad_enabled(False)
-
-    # Convert the logo image to base64
-    logo_base64 = image_to_base64(LOGO_PATH)
-    # with gr.Blocks() as demo:
-    with gr.Blocks(css="""
-        body {
-            display: flex;
-            justify-content: center;
-            align-items: center;
-            min-height: 100vh;
-            margin: 0;
-            padding: 0;
-        }
-        #col-container { margin: 0px auto; max-width: 200px; } 
-
-
-        .gradio-container {
-            max-width: 1000px;
-            margin: auto;
-            width: 100%;
-        }
-        #center-align-column {
-            display: flex;
-            justify-content: center;
-            align-items: center;
-        }
-        #right-align-column {
-            display: flex;
-            justify-content: flex-end;
-            align-items: center;
-        }
-        h1 {text-align: center;}
-        h2 {text-align: center;}
-        h3 {text-align: center;}
-        p {text-align: center;}
-        img {text-align: right;}
-        .right {
-        display: block;
-        margin-left: auto;
-        }
-        .center {
-        display: block;
-        margin-left: auto;
-        margin-right: auto;
-        width: 50%;
-
-        #content-container {
-            max-width: 1200px;
-            margin: 0 auto;
-        }
-        #example-container {
-            max-width: 300px;
-            margin: 0 auto;
-        }
-    """,elem_id="col-container") as demo:
-        # Header Section
-        # gr.Image(value=LOGO_PATH, width=64, height=64)
-        # gr.Markdown(_HEADER_)
-        with gr.Row(elem_id="content-container"):
-            # with gr.Column(scale=1):
-            #     pass
-            # with gr.Column(scale=1, elem_id="right-align-column"):
-            #     # gr.Image(value=LOGO_PATH, interactive=False, show_label=False, width=64, height=64, elem_id="logo-image")
-            #     # gr.Markdown(f"<img src='{LOGO_PATH}' alt='Logo' style='width:64px;height:64px;border:0;'>")
-            #     # gr.HTML(f"<img src='data:image/png;base64,{logo_base64}' alt='Logo' class='right' style='width:64px;height:64px;border:0;text-align:right;'>")
-            #     pass
-            with gr.Column(scale=7, elem_id="center-align-column"):
-                gr.Markdown(f"""
-                ## Official 🤗 Gradio Demo
-                # Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation""")
-                gr.HTML(f"<img src='data:image/png;base64,{logo_base64}' alt='Logo' class='center' style='width:64px;height:64px;border:0;text-align:center;'>")
-
-                gr.HTML(f"""
-                <div style="display: flex; justify-content: center; align-items: center; gap: 10px;">
-                    <a href="{ARXIV_LINK}" target="_blank">
-                        <img src="https://img.shields.io/badge/arXiv-Link-red" alt="arXiv">
-                    </a>
-                    <a href="{GITHUB_LINK}" target="_blank">
-                        <img src="https://img.shields.io/badge/GitHub-Repo-blue" alt="GitHub">
-                    </a>
-                </div>
-                
-                """)
-
-
-                # gr.HTML(f"""
-                # <div style="display: flex; gap: 10px; align-items: center;"><a href="{ARXIV_LINK}" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/arXiv-Link-red" alt="arXiv"></a>  <a href="{GITHUB_LINK}" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/GitHub-Repo-blue" alt="GitHub"></a></div>
-                # """)
-
-                # gr.Markdown(f"""
-                # [![arXiv](https://img.shields.io/badge/arXiv-Link-red)]({ARXIV_LINK})  [![GitHub](https://img.shields.io/badge/GitHub-Repo-blue)]({GITHUB_LINK})
-                # """, elem_id="title")
-            # with gr.Column(scale=1):
-            #     pass
-                # with gr.Row():
-                #     gr.Markdown(f"[![arXiv](https://img.shields.io/badge/arXiv-Link-red)]({ARXIV_LINK})")
-                #     gr.Markdown(f"[![GitHub](https://img.shields.io/badge/GitHub-Repo-blue)]({GITHUB_LINK})")
-
-        # Tabs Section
-        with gr.Tabs(selected='tab_text_to_3d', elem_id="content-container") as main_tabs:
-            with gr.TabItem('Text-to-3D', id='tab_text_to_3d'):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        prompt = gr.Textbox(value="", label="Input Prompt", lines=4)
-                        seed1 = gr.Number(value=10, label="Seed")
-
-                        with gr.Row(elem_id="example-container"):
-                            gr.Examples(
-                                examples=[
-                                    # ["A tree with red leaves"],
-                                    # ["A dragon with black texture"],
-                                    ["A girl with pink hair"],
-                                    ["A boy playing guitar"],
-
-
-                                    ["A dog wearing a hat"],
-                                    ["A boy playing basketball"],
-                                    # [""],
-                                    # [""],
-                                    # [""],
-
-                                ],
-                                inputs=[prompt],  # 将选中的示例填入 prompt 文本框
-                                label="Example Prompts"
-                            )
-                        btn_text2detailed = gr.Button("Refine to detailed prompt")
-                        detailed_prompt = gr.Textbox(value="", label="Detailed Prompt", placeholder="detailed prompt will be generated here base on your input prompt. You can also edit this prompt", lines=4, interactive=True)
-                        btn_text2img = gr.Button("Generate Images")
-
-                    with gr.Column(scale=1):
-                        output_image1 = gr.Image(label="Generated image", interactive=False)
-
-
-                        # lrm_radius = gr.Number(value=4.15, label="lrm_radius")
-                        # isomer_radius = gr.Number(value=4.5, label="isomer_radius")
-                        # reconstruction_stage1_steps = gr.Number(value=10, label="reconstruction_stage1_steps")
-                        # reconstruction_stage2_steps = gr.Number(value=50, label="reconstruction_stage2_steps")
-
-                        btn_gen_mesh = gr.Button("Generate Mesh")
-                        output_video1 = gr.Video(label="Generated Video", interactive=False, loop=True, autoplay=True)
-                        btn_download1 = gr.Button("Download Mesh")
-
-                        file_output1 = gr.File()
-                        
-            with gr.TabItem('Image-to-3D', id='tab_image_to_3d'):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        image = gr.Image(label="Input Image", type="pil")
-                        
-                        seed2 = gr.Number(value=10, label="Seed (0 for random)")
-
-                        btn_img2mesh_preprocess = gr.Button("Preprocess Image")
-
-                        image_caption = gr.Textbox(value="", label="Image Caption", placeholder="caption will be generated here base on your input image. You can also edit this caption", lines=4, interactive=True)
-                        
-                        output_image2 = gr.Image(label="Generated image", interactive=False)
-                        strength1 = gr.Slider(minimum=0, maximum=1.0, step=0.01, value=0.5, label="strength1")
-                        strength2 = gr.Slider(minimum=0, maximum=1.0, step=0.01, value=0.95, label="strength2")
-                        enable_redux = gr.Checkbox(label="enable redux", value=True)
-                        use_controlnet = gr.Checkbox(label="use controlnet", value=True)
-
-                        btn_img2mesh_main = gr.Button("Generate Mesh")
-
-                    with gr.Column(scale=1):
-
-                        # output_mesh2 = gr.Model3D(label="Generated Mesh", interactive=False)
-                        output_image3 = gr.Image(label="gen save image", interactive=False)
-                        output_video2 = gr.Video(label="Generated Video", interactive=False, loop=True, autoplay=True)
-                        btn_download2 = gr.Button("Download Mesh")
-                        file_output2 = gr.File()
-
-        # Image2
-        btn_img2mesh_preprocess.click(fn=image2mesh_preprocess_, inputs=[image, seed2], outputs=[output_image2, image_caption])
-
-        btn_img2mesh_main.click(fn=image2mesh_main_, inputs=[output_image2, image_caption, seed2, strength1, strength2, enable_redux, use_controlnet], outputs=[output_image3, output_video2])
-
-
-        btn_download2.click(fn=save_cached_mesh, inputs=[], outputs=file_output2)
-
-
-        # Button Click Events
-        # Text2
-        btn_text2detailed.click(fn=text_to_detailed, inputs=[prompt, seed1], outputs=detailed_prompt)
-        btn_text2img.click(fn=text_to_image, inputs=[detailed_prompt, seed1], outputs=output_image1)
-        btn_gen_mesh.click(fn=bundle_image_to_mesh, inputs=[output_image1,], outputs=output_video1)
-        # btn_gen_mesh.click(fn=bundle_image_to_mesh, inputs=[output_image1, lrm_radius, isomer_radius, reconstruction_stage1_steps, reconstruction_stage2_steps], outputs=output_video1)
-
-        with gr.Row():
-            pass
-        with gr.Row():
-            gr.Markdown(_CITE_)
-
-    # demo.queue(default_concurrency_limit=1)
-    # demo.launch(server_name="0.0.0.0", server_port=9239)
-    # subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
-    demo.launch()
-
+# def main():
+
+torch.set_grad_enabled(False)
+
+# Convert the logo image to base64
+logo_base64 = image_to_base64(LOGO_PATH)
+# with gr.Blocks() as demo:
+with gr.Blocks(css="""
+    body {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        min-height: 100vh;
+        margin: 0;
+        padding: 0;
+    }
+    #col-container { margin: 0px auto; max-width: 200px; } 
+
+
+    .gradio-container {
+        max-width: 1000px;
+        margin: auto;
+        width: 100%;
+    }
+    #center-align-column {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+    }
+    #right-align-column {
+        display: flex;
+        justify-content: flex-end;
+        align-items: center;
+    }
+    h1 {text-align: center;}
+    h2 {text-align: center;}
+    h3 {text-align: center;}
+    p {text-align: center;}
+    img {text-align: right;}
+    .right {
+    display: block;
+    margin-left: auto;
+    }
+    .center {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    width: 50%;
+
+    #content-container {
+        max-width: 1200px;
+        margin: 0 auto;
+    }
+    #example-container {
+        max-width: 300px;
+        margin: 0 auto;
+    }
+""",elem_id="col-container") as demo:
+    # Header Section
+    # gr.Image(value=LOGO_PATH, width=64, height=64)
+    # gr.Markdown(_HEADER_)
+    with gr.Row(elem_id="content-container"):
+        # with gr.Column(scale=1):
+        #     pass
+        # with gr.Column(scale=1, elem_id="right-align-column"):
+        #     # gr.Image(value=LOGO_PATH, interactive=False, show_label=False, width=64, height=64, elem_id="logo-image")
+        #     # gr.Markdown(f"<img src='{LOGO_PATH}' alt='Logo' style='width:64px;height:64px;border:0;'>")
+        #     # gr.HTML(f"<img src='data:image/png;base64,{logo_base64}' alt='Logo' class='right' style='width:64px;height:64px;border:0;text-align:right;'>")
+        #     pass
+        with gr.Column(scale=7, elem_id="center-align-column"):
+            gr.Markdown(f"""
+            ## Official 🤗 Gradio Demo
+            # Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation""")
+            gr.HTML(f"<img src='data:image/png;base64,{logo_base64}' alt='Logo' class='center' style='width:64px;height:64px;border:0;text-align:center;'>")
+
+            gr.HTML(f"""
+            <div style="display: flex; justify-content: center; align-items: center; gap: 10px;">
+                <a href="{ARXIV_LINK}" target="_blank">
+                    <img src="https://img.shields.io/badge/arXiv-Link-red" alt="arXiv">
+                </a>
+                <a href="{GITHUB_LINK}" target="_blank">
+                    <img src="https://img.shields.io/badge/GitHub-Repo-blue" alt="GitHub">
+                </a>
+            </div>
+            
+            """)
+
+
+            # gr.HTML(f"""
+            # <div style="display: flex; gap: 10px; align-items: center;"><a href="{ARXIV_LINK}" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/arXiv-Link-red" alt="arXiv"></a>  <a href="{GITHUB_LINK}" target="_blank" rel="noopener noreferrer"><img src="https://img.shields.io/badge/GitHub-Repo-blue" alt="GitHub"></a></div>
+            # """)
+
+            # gr.Markdown(f"""
+            # [![arXiv](https://img.shields.io/badge/arXiv-Link-red)]({ARXIV_LINK})  [![GitHub](https://img.shields.io/badge/GitHub-Repo-blue)]({GITHUB_LINK})
+            # """, elem_id="title")
+        # with gr.Column(scale=1):
+        #     pass
+            # with gr.Row():
+            #     gr.Markdown(f"[![arXiv](https://img.shields.io/badge/arXiv-Link-red)]({ARXIV_LINK})")
+            #     gr.Markdown(f"[![GitHub](https://img.shields.io/badge/GitHub-Repo-blue)]({GITHUB_LINK})")
+
+    # Tabs Section
+    with gr.Tabs(selected='tab_text_to_3d', elem_id="content-container") as main_tabs:
+        with gr.TabItem('Text-to-3D', id='tab_text_to_3d'):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    prompt = gr.Textbox(value="", label="Input Prompt", lines=4)
+                    seed1 = gr.Number(value=10, label="Seed")
+
+                    with gr.Row(elem_id="example-container"):
+                        gr.Examples(
+                            examples=[
+                                # ["A tree with red leaves"],
+                                # ["A dragon with black texture"],
+                                ["A girl with pink hair"],
+                                ["A boy playing guitar"],
+
+
+                                ["A dog wearing a hat"],
+                                ["A boy playing basketball"],
+                                # [""],
+                                # [""],
+                                # [""],
+
+                            ],
+                            inputs=[prompt],  # 将选中的示例填入 prompt 文本框
+                            label="Example Prompts"
+                        )
+                    btn_text2detailed = gr.Button("Refine to detailed prompt")
+                    detailed_prompt = gr.Textbox(value="", label="Detailed Prompt", placeholder="detailed prompt will be generated here base on your input prompt. You can also edit this prompt", lines=4, interactive=True)
+                    btn_text2img = gr.Button("Generate Images")
+
+                with gr.Column(scale=1):
+                    output_image1 = gr.Image(label="Generated image", interactive=False)
+
+
+                    # lrm_radius = gr.Number(value=4.15, label="lrm_radius")
+                    # isomer_radius = gr.Number(value=4.5, label="isomer_radius")
+                    # reconstruction_stage1_steps = gr.Number(value=10, label="reconstruction_stage1_steps")
+                    # reconstruction_stage2_steps = gr.Number(value=50, label="reconstruction_stage2_steps")
+
+                    btn_gen_mesh = gr.Button("Generate Mesh")
+                    output_video1 = gr.Video(label="Generated Video", interactive=False, loop=True, autoplay=True)
+                    btn_download1 = gr.Button("Download Mesh")
+
+                    file_output1 = gr.File()
+                    
+        with gr.TabItem('Image-to-3D', id='tab_image_to_3d'):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    image = gr.Image(label="Input Image", type="pil")
+                    
+                    seed2 = gr.Number(value=10, label="Seed (0 for random)")
+
+                    btn_img2mesh_preprocess = gr.Button("Preprocess Image")
+
+                    image_caption = gr.Textbox(value="", label="Image Caption", placeholder="caption will be generated here base on your input image. You can also edit this caption", lines=4, interactive=True)
+                    
+                    output_image2 = gr.Image(label="Generated image", interactive=False)
+                    strength1 = gr.Slider(minimum=0, maximum=1.0, step=0.01, value=0.5, label="strength1")
+                    strength2 = gr.Slider(minimum=0, maximum=1.0, step=0.01, value=0.95, label="strength2")
+                    enable_redux = gr.Checkbox(label="enable redux", value=True)
+                    use_controlnet = gr.Checkbox(label="use controlnet", value=True)
+
+                    btn_img2mesh_main = gr.Button("Generate Mesh")
+
+                with gr.Column(scale=1):
+
+                    # output_mesh2 = gr.Model3D(label="Generated Mesh", interactive=False)
+                    output_image3 = gr.Image(label="gen save image", interactive=False)
+                    output_video2 = gr.Video(label="Generated Video", interactive=False, loop=True, autoplay=True)
+                    btn_download2 = gr.Button("Download Mesh")
+                    file_output2 = gr.File()
+
+    # Image2
+    btn_img2mesh_preprocess.click(fn=image2mesh_preprocess_, inputs=[image, seed2], outputs=[output_image2, image_caption])
+
+    btn_img2mesh_main.click(fn=image2mesh_main_, inputs=[output_image2, image_caption, seed2, strength1, strength2, enable_redux, use_controlnet], outputs=[output_image3, output_video2])
+
+
+    btn_download2.click(fn=save_cached_mesh, inputs=[], outputs=file_output2)
+
+
+    # Button Click Events
+    # Text2
+    btn_text2detailed.click(fn=text_to_detailed, inputs=[prompt, seed1], outputs=detailed_prompt)
+    btn_text2img.click(fn=text_to_image, inputs=[detailed_prompt, seed1], outputs=output_image1)
+    btn_gen_mesh.click(fn=bundle_image_to_mesh, inputs=[output_image1,], outputs=output_video1)
+    # btn_gen_mesh.click(fn=bundle_image_to_mesh, inputs=[output_image1, lrm_radius, isomer_radius, reconstruction_stage1_steps, reconstruction_stage2_steps], outputs=output_video1)
+
+    with gr.Row():
+        pass
+    with gr.Row():
+        gr.Markdown(_CITE_)
+
+# demo.queue(default_concurrency_limit=1)
+# demo.launch(server_name="0.0.0.0", server_port=9239)
+# subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
+demo.launch()
+
 
-if __name__ == "__main__":
-    main()
+# if __name__ == "__main__":
+#     main()