Update app.py

app.py

@@ -18,6 +18,8 @@ from PIL import Image
 import subprocess
 
 import torch
+import torch.multiprocessing as mp
+mp.set_start_method('spawn', force=True)
 import gradio as gr
 import string
 import random, time, math   
@@ -441,86 +443,88 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             prompt = gr.Textbox(label="Prompt", value="")
-            with gr.Row():
-                target_height = gr.Slider(512, 1024, step=128, value=768, label="Generated Height", info="")
-                target_width = gr.Slider(512, 1024, step=128, value=768, label="Generated Width", info="")
-                cond_size = gr.Slider(256, 384, step=128, value=256, label="Condition Size", info="")
-            with gr.Row():
-                weight_id = gr.Slider(0.1, 5, step=0.1, value=3, label="weight_id")
-                weight_ip = gr.Slider(0.1, 5, step=0.1, value=5, label="weight_ip")
-            with gr.Row():
-                ip_scale_str = gr.Slider(0.5, 1.5, step=0.01, value=0.85, label="latent_lora_scale")
-                vae_lora_scale = gr.Slider(0.5, 1.5, step=0.01, value=1.3, label="vae_lora_scale")
-            with gr.Row():
-                vae_skip_iter_s1 = gr.Slider(0, 1, step=0.01, value=0.05, label="vae_skip_iter_before")
-                vae_skip_iter_s2 = gr.Slider(0, 1, step=0.01, value=0.8, label="vae_skip_iter_after")
-            
-    
-            with gr.Row():
-                weight_id_ip_str = gr.Textbox(
-                    value="0-1:1/3/5",
-                    label="weight_id_ip_str",
-                    interactive=False, visible=False
-                )
-                weight_id.change(
-                    lambda s1, s2: f"0-1:1/{s1}/{s2}",
-                    inputs=[weight_id, weight_ip],
-                    outputs=weight_id_ip_str
-                )
-                weight_ip.change(
-                    lambda s1, s2: f"0-1:1/{s1}/{s2}",
-                    inputs=[weight_id, weight_ip],
-                    outputs=weight_id_ip_str
-                )
-                vae_skip_iter = gr.Textbox(
-                    value="0-0.05:1,0.8-1:1",
-                    label="vae_skip_iter",
-                    interactive=False, visible=False
-                )
-                vae_skip_iter_s1.change(
-                    lambda s1, s2: f"0-{s1}:1,{s2}-1:1",
-                    inputs=[vae_skip_iter_s1, vae_skip_iter_s2],
-                    outputs=vae_skip_iter
-                )
-                vae_skip_iter_s2.change(
-                    lambda s1, s2: f"0-{s1}:1,{s2}-1:1",
-                    inputs=[vae_skip_iter_s1, vae_skip_iter_s2],
-                    outputs=vae_skip_iter
-                )
+            with gr.Tab("Tiger"):
                 
-            
-            with gr.Row():
-                db_latent_lora_scale_str = gr.Textbox(
-                    value="0-1:0.85",
-                    label="db_latent_lora_scale_str",
-                    interactive=False, visible=False
-                )
-                sb_latent_lora_scale_str = gr.Textbox(
-                    value="0-1:0.85",
-                    label="sb_latent_lora_scale_str",
-                    interactive=False, visible=False
-                )
-                vae_lora_scale_str = gr.Textbox(
-                    value="0-1:1.3",
-                    label="vae_lora_scale_str",
-                    interactive=False, visible=False
-                )
-                vae_lora_scale.change(
-                        lambda s: f"0-1:{s}",
-                        inputs=vae_lora_scale,
-                        outputs=vae_lora_scale_str
+                with gr.Row():
+                    target_height = gr.Slider(512, 1024, step=128, value=768, label="Generated Height", info="")
+                    target_width = gr.Slider(512, 1024, step=128, value=768, label="Generated Width", info="")
+                    cond_size = gr.Slider(256, 384, step=128, value=256, label="Condition Size", info="")
+                with gr.Row():
+                    weight_id = gr.Slider(0.1, 5, step=0.1, value=3, label="weight_id")
+                    weight_ip = gr.Slider(0.1, 5, step=0.1, value=5, label="weight_ip")
+                with gr.Row():
+                    ip_scale_str = gr.Slider(0.5, 1.5, step=0.01, value=0.85, label="latent_lora_scale")
+                    vae_lora_scale = gr.Slider(0.5, 1.5, step=0.01, value=1.3, label="vae_lora_scale")
+                with gr.Row():
+                    vae_skip_iter_s1 = gr.Slider(0, 1, step=0.01, value=0.05, label="vae_skip_iter_before")
+                    vae_skip_iter_s2 = gr.Slider(0, 1, step=0.01, value=0.8, label="vae_skip_iter_after")
+                
+        
+                with gr.Row():
+                    weight_id_ip_str = gr.Textbox(
+                        value="0-1:1/3/5",
+                        label="weight_id_ip_str",
+                        interactive=False, visible=False
                     )
-                ip_scale_str.change(
-                        lambda s: [f"0-1:{s}", f"0-1:{s}"],
-                        inputs=ip_scale_str,
-                        outputs=[db_latent_lora_scale_str, sb_latent_lora_scale_str]
+                    weight_id.change(
+                        lambda s1, s2: f"0-1:1/{s1}/{s2}",
+                        inputs=[weight_id, weight_ip],
+                        outputs=weight_id_ip_str
                     )
+                    weight_ip.change(
+                        lambda s1, s2: f"0-1:1/{s1}/{s2}",
+                        inputs=[weight_id, weight_ip],
+                        outputs=weight_id_ip_str
+                    )
+                    vae_skip_iter = gr.Textbox(
+                        value="0-0.05:1,0.8-1:1",
+                        label="vae_skip_iter",
+                        interactive=False, visible=False
+                    )
+                    vae_skip_iter_s1.change(
+                        lambda s1, s2: f"0-{s1}:1,{s2}-1:1",
+                        inputs=[vae_skip_iter_s1, vae_skip_iter_s2],
+                        outputs=vae_skip_iter
+                    )
+                    vae_skip_iter_s2.change(
+                        lambda s1, s2: f"0-{s1}:1,{s2}-1:1",
+                        inputs=[vae_skip_iter_s1, vae_skip_iter_s2],
+                        outputs=vae_skip_iter
+                    )
+                    
+                
+                with gr.Row():
+                    db_latent_lora_scale_str = gr.Textbox(
+                        value="0-1:0.85",
+                        label="db_latent_lora_scale_str",
+                        interactive=False, visible=False
+                    )
+                    sb_latent_lora_scale_str = gr.Textbox(
+                        value="0-1:0.85",
+                        label="sb_latent_lora_scale_str",
+                        interactive=False, visible=False
+                    )
+                    vae_lora_scale_str = gr.Textbox(
+                        value="0-1:1.3",
+                        label="vae_lora_scale_str",
+                        interactive=False, visible=False
+                    )
+                    vae_lora_scale.change(
+                            lambda s: f"0-1:{s}",
+                            inputs=vae_lora_scale,
+                            outputs=vae_lora_scale_str
+                        )
+                    ip_scale_str.change(
+                            lambda s: [f"0-1:{s}", f"0-1:{s}"],
+                            inputs=ip_scale_str,
+                            outputs=[db_latent_lora_scale_str, sb_latent_lora_scale_str]
+                        )
+    
+                with gr.Row():
+                    double_attention = gr.Checkbox(value=False, label="Double Attention", visible=False)
+                    single_attention = gr.Checkbox(value=True, label="Single Attention", visible=False)            
 
-            with gr.Row():
-                double_attention = gr.Checkbox(value=False, label="Double Attention", visible=False)
-                single_attention = gr.Checkbox(value=True, label="Single Attention", visible=False)            
-
-            clear_btn = gr.Button("清空输入图像")
+                clear_btn = gr.Button("清空输入图像")
             with gr.Row():
                 for i in range(num_inputs):
                     image, caption, face_btn, det_btn, vlm_btn, accordion_state, accordion, id_ip_checkbox = create_image_input(i, open=i<2, indexs_state=indexs_state)