seed and path

app.py

@@ -32,7 +32,6 @@ from nag import NAGStableDiffusion3Pipeline
 nag_pipe = NAGStableDiffusion3Pipeline.from_pretrained(
     model_id,
     torch_dtype=torch.bfloat16,
-    token="hf_token",
 )
 
 
@@ -42,9 +41,9 @@ from PIL import Image
 import json
 from datetime import datetime
 from pathlib import Path
-from uuid import uuid4
+from uuid import uuid4 
 
-IMAGE_DATASET_DIR = Path("image_dataset") / f"train-{uuid4()}"
+IMAGE_DATASET_DIR = Path("image_dataset") / f"train/{uuid4()}"
 IMAGE_DATASET_DIR.mkdir(parents=True, exist_ok=True)
 IMAGE_JSONL_PATH = IMAGE_DATASET_DIR / "metadata.jsonl"
 
@@ -207,6 +206,8 @@ with gr.Blocks(title="Value Sign Flip SD3.5 Demo") as demo:
         bias = gr.Slider(0, 0.5, step=0.01, label="Bias", value=0.1)
         step = gr.Slider(4, 15, step=1, label="Step", value=8)
         seed = gr.Number(label="Seed", value=0, precision=0)
+        randomize_seed = gr.Button("Randomize Seed")
+        randomize_seed.click(fn=lambda : np.random.randint(0, 1000000), inputs=[], outputs=[seed])
         set_strong_vsf = gr.Button("Set to VSF Strong Settings")
         set_strong_vsf.click(fn=lambda : (3.8, 0.2), inputs=[], outputs=[guidance, bias])
         set_mild_vsf = gr.Button("Set to VSF Quality Settings")