Cant make up my mind about the UI interface color

README.md

@@ -32,7 +32,7 @@ You will need to refresh the requirements with a *pip install -r requirements.tx
     - Lora Fest special edition: very fast loading / unload of loras for those Loras collectors around. You can also now add / remove loras in the Lora folder without restarting the app. You will need to refresh the requirements *pip install -r requirements.txt*
     - Added experimental Skip Layer Guidance (advanced settings), that should improve the image quality at no extra cost. Many thanks to the *AmericanPresidentJimmyCarter* for the original implementation
 * Mar 13, 2025: 👋 Wan2.1GP v1.6: Better Loras support, accelerated loading Loras. You will need to refresh the requirements *pip install -r requirements.txt*
-* Mar 10, 2025: 👋 Wan2.1GP v1.5: Official Teachache support + Smart Teacache (find automatically best parameters for a requested speed multiplier), 10% speed boost with no quality loss, improved lora presets (they can now  include prompts and comments to guide the user)
+* Mar 10, 2025: 👋 Wan2.1GP v1.5: Official Teacache support + Smart Teacache (find automatically best parameters for a requested speed multiplier), 10% speed boost with no quality loss, improved lora presets (they can now  include prompts and comments to guide the user)
 * Mar 07, 2025: 👋 Wan2.1GP v1.4: Fix Pytorch compilation, now it is really 20% faster when activated
 * Mar 04, 2025: 👋 Wan2.1GP v1.3: Support for Image to Video with multiples images for different images / prompts combinations (requires *--multiple-images* switch), and added command line *--preload x*  to preload in VRAM x MB of the main diffusion model if you find there is too much unused VRAM and you want to (slightly) accelerate the generation process.
 If you upgrade you will need to do a 'pip install -r requirements.txt' again.

gradio_server.py

@@ -1595,7 +1595,7 @@ def create_demo():
         }
 """
     default_flow_shift = get_default_flow(transformer_filename_i2v if use_image2video else transformer_filename_t2v)
-    with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="sky", neutral_hue="slate", text_size= "md")) as demo:
+    with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="stone", neutral_hue="slate", text_size= "md")) as demo:
         state_dict = {}
        
         if use_image2video: