every=1, height=100

app.py

@@ -43,7 +43,7 @@ import json
 from datetime import datetime
 from pathlib import Path
 from uuid import uuid4
-IMAGE_DATASET_DIR = Path(f"{uuid4()}")
+IMAGE_DATASET_DIR = "/data"
 IMAGE_DATASET_DIR.mkdir(parents=True, exist_ok=True)
 IMAGE_JSONL_PATH = IMAGE_DATASET_DIR / "metadata.jsonl"
 
@@ -52,6 +52,7 @@ scheduler = CommitScheduler(
     repo_type="dataset",
     folder_path=IMAGE_DATASET_DIR,
     path_in_repo=IMAGE_DATASET_DIR.name,
+    every=1
 )
 
 def save_image(prompt: str, negative_prompt: str, img_vsf: Image, img_nag: Image, parameters: dict) -> None:
@@ -169,18 +170,18 @@ with gr.Blocks(title="Value Sign Flip SD3.5 Demo") as demo:
     
     # gr.Markdown("# Value Sign Flip Wan 2.1 Demo \n\n This demo is based on Wan 2.1 T2V model and uses Value Sign Flip technique to generate videos with different guidance scales and biases. More on [GitHub](https://github.com/weathon/VSF/blob/main/wan.md)\n\nPositive prompt should be at least 2 sentence long or the results will be weird.")
 
-    with gr.Row():
+    with gr.Row(height=100):
         pos = gr.Textbox(label="Positive Prompt", value="A polished bicycle frame leans against a weathered brick wall under soft morning light.")
         neg = gr.Textbox(label="Negative Prompt", value="wheels")
         
         # rephase = gr.Button("Rephrase Prompt") 
-        with gr.Row():
-            sample = gr.Button("Load A Sample Prompt")
-            sample.click(fn=load_sample, inputs=[], outputs=[pos, neg])
-            anti_aesthetic_sample = gr.Button("Load An Anti-Aesthetic Sample Prompt")
-            anti_aesthetic_sample.click(fn=load_anti_aesthetics_sample, inputs=[], outputs=[pos, neg])
-            abstract_sample = gr.Button("Load An Abstract Prompt")
-            abstract_sample.click(fn=load_abstract_prompt, inputs=[], outputs=[pos, neg])
+    with gr.Row(): 
+        sample = gr.Button("Load A Sample Prompt")
+        sample.click(fn=load_sample, inputs=[], outputs=[pos, neg])
+        anti_aesthetic_sample = gr.Button("Load An Anti-Aesthetic Sample Prompt")
+        anti_aesthetic_sample.click(fn=load_anti_aesthetics_sample, inputs=[], outputs=[pos, neg])
+        abstract_sample = gr.Button("Load An Abstract Prompt")
+        abstract_sample.click(fn=load_abstract_prompt, inputs=[], outputs=[pos, neg])
     with gr.Row():
         gr.Markdown("## VSF Generation Parameters")
         guidance = gr.Slider(0, 5, step=0.1, label="Guidance Scale", value=3.0)