CivArchive
    Preview 3681174
    Preview 3681180
    Preview 3681171
    Preview 3681175
    Preview 3681173
    Preview 3681179
    Preview 3681176
    Preview 3681178
    Preview 3681177
    Preview 3681186
    Preview 3681172
    Preview 3681181
    Preview 3681189
    Preview 3681183
    Preview 3681185
    Preview 3681184
    Preview 3681188
    Preview 3681192
    Preview 3681191

    CivitAI Style FusionπŸ†LoRAs

    Last update: πŸš€ CivitAI Lora5 32DIM Notebook with dataset

    Last update: πŸš€ CivitAI Lora3 Configuration - Trained with CivitAI Trainer

    πŸš€ Date: 2023-11-10 | Title: CivitAI_64_ALL

    πŸ” Key Specifications:

    • Resolution: 1024x1024

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

    • Network Dim/Rank: 64.0, Alpha: 1.0

    • Module: networks.lora

    • Learning Rates: UNet LR & TE LR set to optimal levels

    • Optimizer: Advanced AdamW8bit

    • Epochs & Training: Intensive 10 epochs with 576 batches

    πŸ“Š Model Stats:

    • UNet Weight: Mag - 7.602, Str - 0.0187

    Resolution: 1024x1024 Architecture: stable-diffusion-xl-v1-base/lora
    Network Dim/Rank: 64.0 Alpha: 1.0
    Module: networks.lora
    Learning Rate (LR): 0.0005 UNet LR: 0.0005 TE LR: 5e-05
    Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)
    Scheduler: constant  Warmup steps: 0
    Epoch: 10 Batches per epoch: 576 Gradient accumulation steps: 1
    Train images: 2304 Regularization images: 0
    Multires noise iterations: 6.0 Multires noise discount: 0.3
    Min SNR gamma: 5.0 Zero terminal SNR: True Max grad norm: 1.0  Clip skip: 1
    Dataset dirs: 1
            [img] 576 images
    UNet weight average magnitude: 7.602270778898858
    UNet weight average strength: 0.018722912685324843
    Text Encoder (1) weight average magnitude: 2.7649271326702607
    Text Encoder (1) weight average strength: 0.009535635958680934
    Text Encoder (2) weight average magnitude: 2.6905091182810352
    Text Encoder (2) weight average strength: 0.007233532415344915

    Delve into FFusionAI's approach to AI-driven style synthesis with our newly released LoRA models. Each model has been developed using CivitAI's official trainer, ensuring precision and quality.

    πŸ› οΈ LoRA Model Overview:

    • LoRA 1 - Lite Version: Designed for quick testing, this model utilizes a small dataset for swift style generation, operating with a 32-dimension capacity.

    • LoRA 2 - Community Fusion: A robust model developed from over 500+ images, submitted by various users for the CivitAI contest. This iteration also features a 32-dimension capacity.

    • LoRA 3 - Enhanced Fidelity: Building upon LoRA 2, this model is further trained with higher dimensions, focusing on improving the overall image quality.

    • LoRA 4 - Comprehensive Style Mash: Our expansive dataset of 1400 images represents a confluence of all FFusionAI submissions. This model undergoes additional UNet training to refine and diversify the generated styles.

    1. FFusionAI Style Capture & Fusion Showdown LoRA

    🎨 Dataset and Training:

    Included within the package are curated collections accessible at CivitAI Collections. The training prompts have been crafted with BLIP-2, FLAN-T5-XL, and ViT-H-14.

    Please note, original prompts were not utilized for training. Instead, intentional modifications were made using blip2-flan-t5-xl & ViT-H-14/laion2b_s32b_b79k to adjust and enhance the training dataset, which can be reviewed here.

    πŸ”„ Further Information:

    For a detailed examination of the training datasets, parameters, and model specifications, professionals and enthusiasts are encouraged to explore the metadata provided within the collection.

    • LORA 2

      πŸš€ CivitAI Configuration Overview - 2023-11-10

    πŸš€ Trained with the Official CivitAI Trainer

    πŸ“… Date: 2023-11-10

    πŸ–ΌοΈ Title: CivitAI_ALL

    πŸ” Resolution: 1024x1024

    πŸ—οΈ Architecture: stable-diffusion-xl-v1-base/lora

    βš™οΈ Key Settings:

    • Network Dim/Rank: 32.0

    • Alpha: 1.0

    • Module: networks.lora

    • Learning Rates: UNet LR - 0.0005, TE LR - 5e-05

    • Optimizer: AdamW8bit (weight_decay=0.1)

    • Epochs & Batches: 10 epochs, 167 batches/epoch

    • Train Images: 576

    πŸ“Š Model Stats:

    • UNet Weight: Mag - 3.755, Str - 0.0135

    • Text Encoder (1): Mag - 1.833, Str - 0.0091

    • Text Encoder (2): Mag - 1.836, Str - 0.0071

    🏷️ Prominent Tags:

    • Fusion styles, Artgerm, Beeple

    • Dark fantasy, Official artwork, Pinup art

    • Digital illustration, Fantasy & Sci-fi

    • ...and over 4500 more!

    🌐 FFusion.ai Contact Information

    Proudly maintained by Source Code Bulgaria Ltd & Black Swan Technologies.

    • πŸ“§ For collaborations, inquiries, or support: [email protected]

    • 🌍 Locations: Sofia | Istanbul | London

    Connect with Us:

    Our Websites:

    Description

    Trained on Civitai Trainer
    An attempt to expand the book image.

    https://civitai.com/images/3507510

    32dim ver

    {
      "unetLR": 0.0005,
      "clipSkip": 1,
      "loraType": "lora",
      "keepTokens": 0,
      "networkDim": 32,
      "numRepeats": 4,
      "resolution": 1024,
      "lrScheduler": "cosine_with_restarts",
      "minSnrGamma": 4,
      "targetSteps": 1920,
      "enableBucket": true,
      "networkAlpha": 1,
      "optimizerArgs": "weight_decay=0.1",
      "optimizerType": "AdamW8Bit",
      "textEncoderLR": 0.00005,
      "maxTrainEpochs": 10,
      "shuffleCaption": false,
      "trainBatchSize": 4,
      "flipAugmentation": false,
      "lrSchedulerNumCycles": 3
    }

    FAQ

    LORA
    SDXL 1.0
    by idle

    Details

    Downloads
    100
    Platform
    CivitAI
    Platform Status
    Available
    Created
    11/17/2023
    Updated
    5/12/2026
    Deleted
    -
    Trigger Words:
    notebook
    book

    Files

    226661_training_data.zip

    Mirrors

    CivitAI (1 mirrors)

    Book_FFusion_32.safetensors

    Mirrors

    CivitAI (1 mirrors)

    Available On (1 platform)

    Same model published on other platforms. May have additional downloads or version variants.