CivArchive
    GAG - RPG Potions | LoRa XL - v 0.1
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    GAG = Game Asset Generator
    📅 Date: 2023-10-23T12:19:42

    FF_Potion_Generator 🧪⚗️

    • Resolution: 1024x1024

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

    • Network Dim/Rank: 32.0

    • Alpha: 1.0

    • Module: networks.lora

    • Learning Rates:

      • Overall: 0.0005

      • UNet: 0.0005

      • Text Encoder: 5e-05

    • Optimizer: bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)

    • Scheduler: cosine_with_restarts

    • Warmup steps: 0

    • Training Details:

      • Epoch: 15

      • Batches per epoch: 250

      • Gradient accumulation steps: 1

    • Images:

      • Train images: 1000

      • Regularization images: 0

    • Noise & Regularization:

      • 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: 1 Directory with 500 images

    • UNet Weights:

      • Average magnitude: 2.9637

      • Average strength: 0.0109

    • Text Encoder Weights:

      • (1) Magnitude: 1.7253 | Strength: 0.0086

      • (2) Magnitude: 1.7251 | Strength: 0.0067

    🔖 Tags:

    
    potion, painting of one health potion, potion of healing, health potion, magic potions, magical potions, alchemy concept, hyper realistic poison bottle, rendered art, making a potion, potions, fantasy game spell symbol, rpg game item, alchemist bottles, rpg item render, stylized game art, game icon stylized, hyperdetailed scp artifact jar, 3 d icon for mobile game, 3 d render stylized, casting a spell on a potion ... and 904 more!


    Starting my GAG Serries 🤟 🥃

    This one is a Potion LORA for 2.1 SD models.

    Depending on the model you will get quite different results.



    You can use a background remover while generating and get:



    again this is a test LORA and is heavily dependent on the input model.

    LyCORIS version will be up for testing in a few hours.
    And a 2.1 Model is expected in 48-60 hours

    For PIXALISATION try:
    PIXHELL by R6SPY 🤟


    PS: so far you can use it for demonstration purposes (preview version)

    with version 1.0 above (lora and models of GAG series)
    two versions will be available:
    Licensed - can be licensed for in-game usage upon request (using only my work and licensed images from my end)
    Community - using sourced free images and sources but you are responsible is used commercially in your project ¯\_(ツ)_/¯ 🤟 🥃

    Description

    Training data:
    ss_batch_size_per_device: "10",

    ss_bucket_info: "{"buckets": {"0": {"resolution": [384, 576], "count": 2440}, "1": {"resolution": [512, 512], "count": 49520}, "2": {"resolution": [576, 384], "count": 1720}}, "mean_img_ar_error": 0.0}",

    ss_bucket_no_upscale: "True",

    ss_cache_latents: "True",

    ss_caption_dropout_every_n_epochs: "0",

    ss_caption_dropout_rate: "0.0",

    ss_caption_tag_dropout_rate: "0.0",

    ss_clip_skip: "None",

    ss_color_aug: "False",

    ss_enable_bucket: "True",

    ss_epoch: "2",

    ss_face_crop_aug_range: "None",

    ss_flip_aug: "False",

    ss_full_fp16: "False",

    ss_gradient_accumulation_steps: "1",

    ss_gradient_checkpointing: "False",

    ss_keep_tokens: "0",

    ss_learning_rate: "0.0001",

    ss_lowram: "False",

    ss_lr_scheduler: "linear",

    ss_lr_warmup_steps: "1074",

    ss_max_bucket_reso: "1024",

    ss_max_grad_norm: "1.0",

    ss_max_token_length: "225",

    ss_max_train_steps: "10736",

    ss_min_bucket_reso: "256",

    ss_mixed_precision: "fp16",

    ss_network_alpha: "1.0",

    ss_network_args: "{"conv_dim": "1", "conv_alpha": "1"}",

    ss_network_dim: "16",

    ss_network_module: "networks.lora",

    ss_noise_offset: "0.1",

    ss_num_batches_per_epoch: "5368",

    ss_num_epochs: "2",

    ss_num_reg_images: "0",

    ss_num_train_images: "53680",

    ss_optimizer: "lion_pytorch.lion_pytorch.Lion",

    ss_output_name: "idle.Potionsv_qv",

    ss_prior_loss_weight: "1.0",

    ss_random_crop: "False",

    ss_reg_dataset_dirs: "{}",

    ss_resolution: "(512, 512)",

    ss_sd_model_name: "stabilityai/stable-diffusion-2-1",

    ss_sd_scripts_commit_hash: "00f74d271ab015916a75d6cee6f3af9b0b89f764",

    ss_seed: "None",

    ss_session_id: "3030622464",

    ss_shuffle_caption: "True",

    ss_text_encoder_lr: "5e-05",

    ss_total_batch_size: "10",

    ss_training_comment: "None",

    ss_training_finished_at: "1679317172.8840992",

    ss_training_started_at: "1679302821.9685614",

    ss_unet_lr: "0.0001",

    ss_v2: "True"