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Cant make up my mind about the UI interface color

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DeepBeepMeep 2025-03-19 00:41:39 +01:00
parent f3641bf38e
commit 1406a44a69
2 changed files with 2 additions and 2 deletions
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2
README.md
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@ -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.

2
gradio_server.py
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@ -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: