FF Safe AnimXL (Lora, Lycoris +dataset)版本Safe AnimeXL-Lycoris V.3 (ID: 185126)

? Trained specifically for style mixing on Safe For Work (SFW) dataset.

? Text Encoder (strongly reliant on U-Net architecture):

? Lora Model:

• Trained using the CivitAI trainer.

• Underwent training for 16 epochs.

• Performance: Lora showcases stable diffusion in XL settings.

? Lycoris Models:

• Configured using this preset from LyCORIS, but with our custom tweaks.

• Trained on 8xA6000 GPUs.

• Underwent 20-40 epochs with a batch size of 20.

• Module Type Breakdown:

  • LohaModule: 176

  • LoConModule: 150

  • FullModule: 26

  • LokrModule: 700

? Comparison:

• CivitAI's Lora stands out in our tests, but we've yet to explore its full potential in style mixing with other models.

Feedback, collaboration, and tests are welcome! ? ?

描述:

Mixed training experiment using | lokr lora loha

More balanced dims (lower size)

module type table: {'LohaModule': 176, 'LoConModule': 150, 'FullModule': 26, 'LokrModule': 700}

 @unet_target_module = [



  "Transformer2DModel", 



  "ResnetBlock2D", 



  "Downsample2D", 



  "Upsample2D",



]



unet_target_name = [



  "conv_in",



  "conv_out",



  "time_embedding.linear_1",



  "time_embedding.linear_2",



]



text_encoder_target_module = [



  "CLIPAttention",



  "CLIPMLP",



]



[module_algo_map]

  [module_algo_map.CrossAttention]  # Attention Layer in UNet

    algo = "lokr"



  [module_algo_map.FeedForward]     # MLP Layer in UNet

    algo = "lokr"



  [module_algo_map.ResnetBlock2D]   # ResBlock in UNet

    algo = "lora"



  [module_algo_map.CLIPAttention]   # Attention Layer in TE

    algo = "loha"



  [module_algo_map.CLIPMLP]         # MLP Layer in TE

    algo = "lora"

训练词语: