FFusion - AnimAI Planet XL

FFAI 🦁🐶🐱 AnimAI Planet Demonstrations

A normal LyCORIS - algo: LoRA was used for the training.

🎯 Models at a Glance:

1. General LyCORIS Training

📌 Highlights:

'algo': 'lora'

📊 Specifications:

Date: 2023-08-26T23:08:56

Resolution: 1024x1024

Architecture: stable-diffusion-xl-v1-base/lora

Network Dim/Rank: 16.0

Alpha: 64.0

2. Text Encoder LyCORIS

📌 Highlights:

TEXT ENCODER ONLY 'algo': 'lora'

📊 Specifications:

Date: 2023-10-01T07:31:55

Resolution: 1024x1024

Architecture: stable-diffusion-xl-v1-base/lora

Network Dim/Rank: 16.0

Alpha: 32.0

3. General LyCORIS Training

📌 Highlights:

{'conv_dim': '32', 'conv_alpha': '64', 'algo': 'lora'}

📊 Specifications:

Date: 2023-10-02T08:37:35

Resolution: 1024x1024

Architecture: stable-diffusion-xl-v1-base/lora

Network Dim/Rank: 32.0

Alpha: 64.0

4. General LyCORIS Training

📌 Highlights:

{'conv_dim': '64', 'conv_alpha': '64', 'algo': 'lora'}

📊 Specifications:

Date: 2023-10-02T11:40:06

Resolution: 1024x1024

Architecture: stable-diffusion-xl-v1-base/lora

Network Dim/Rank: 64.0

Alpha: 64.0

1.AnimAl P FFusion.safetensors Date: 2023-10-01T05:08:45 Title: AnimAl P FFusion Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora Network Dim/Rank: 16.0 Alpha: 64.0 Module: lycoris.kohya : {'conv_dim': '16', 'conv_alpha': '64', 'algo': 'lora'}

UNet weight average magnitude: 1.5612285654851892 UNet weight average strength: 0.00855100617083033 UNet Conv weight average magnitude: 2.3661269530966935 UNet Conv weight average strength: 0.005806554066645475 Text Encoder (1) weight average magnitude: 1.4098021807770307 Text Encoder (1) weight average strength: 0.009792171967200777 Text Encoder (2) weight average magnitude: 1.5160455204089474 Text Encoder (2) weight average strength: 0.008057038737473559

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2.AnimAl P FFusion (TX Encoder).safetensors Date: 2023-10-01T07:31:55 Title: AnimAl P FFusion Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora Network Dim/Rank: 16.0 Alpha: 32.0 Module: lycoris.kohya : {'conv_dim': '16', 'conv_alpha': '32', 'algo': 'lora'}

Text Encoder (1) weight average magnitude: 2.5523402023430064 Text Encoder (1) weight average strength: 0.016584716454285692 Text Encoder (2) weight average magnitude: 3.2502294766067283 Text Encoder (2) weight average strength: 0.01608869545658079 No UNet found in this LoRA

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3.AnimAl P FFusion v2-ep2.safetensors Date: 2023-10-02T08:37:35 Title: AnimAl P FFusion v2-ep2 Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora Network Dim/Rank: 32.0 Alpha: 64.0 Module: lycoris.kohya : {'conv_dim': '32', 'conv_alpha': '64', 'algo': 'lora'}

UNet weight average magnitude: 2.0946387136587896 UNet weight average strength: 0.008093249212950576 UNet Conv weight average magnitude: 3.5111623881034153 UNet Conv weight average strength: 0.006158271377234372 Text Encoder (1) weight average magnitude: 1.9438021141701276 Text Encoder (1) weight average strength: 0.009529127702349835 Text Encoder (2) weight average magnitude: 2.0788744162733246 Text Encoder (2) weight average strength: 0.007869903389610624

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4.AnimAl P FFusion v2-ep2.4.safetensors Date: 2023-10-02T11:40:06 Title: AnimAl P FFusion v2-ep2.4 Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora Network Dim/Rank: 64.0 Alpha: 64.0 Module: lycoris.kohya : {'conv_dim': '64', 'conv_alpha': '64', 'algo': 'lora'}

UNet weight average magnitude: 3.58799512097297 UNet weight average strength: 0.00944065170718396 UNet Conv weight average magnitude: 7.605353630925971 UNet Conv weight average strength: 0.008435387484832527 Text Encoder (1) weight average magnitude: 3.167086568188093 Text Encoder (1) weight average strength: 0.01076880155542553 Text Encoder (2) weight average magnitude: 3.525044115169337 Text Encoder (2) weight average strength: 0.009155737913150608

🎨 Readme Crafted by: 🤖 & FFusionAI 🚀

🌐 Contact Information

The FFusion.ai project is proudly maintained by Source Code Bulgaria Ltd & Black Swan Technologies .

📧 Reach us at [email protected] for any inquiries or support.

🌌 Find us on:

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