di.FFUSION.ai-tXe-FXAA Trained on "121361" images.

Enhance your model's quality and sharpness using your own pre-trained Unet.

The text encoder (without UNET) is wrapped in LyCORIS. Optimizer: torch.optim.adamw.AdamW(weight_decay=0.01, betas=(0.9, 0.99))

Network dimension/rank: 768.0 Alpha: 768.0 Module: lycoris.kohya {'conv_dim': '256', 'conv_alpha': '256', 'algo': 'loha'}

Large size due to Lyco CONV 256

This is a heavy experimental version we used to test even with sloppy captions (quick WD tags and terrible clip), yet the results were satisfying.

Note: This is not the text encoder used in the official FFUSION AI model.

For a1111 Install https://github.com/KohakuBlueleaf/a1111-sd-webui-lycoris

Download di.FFUSION.ai-tXe-FXAA to /models/Lycoris

Option1:

Insert lyco:di.FFUSION.ai-tXe-FXAA:1.0 to prompt No need to split Unet and Text Enc as its only TX encoder there.

You can go up to 2x weights

Option2: If you need it always ON (ex run a batch from txt file) then you can go to settings / Quicksettings list

add sd_lyco

restart and you should have a drop-down now 🤟 🥃

di.FFUSION.ai-tXe-FXAA Trained on "121361" images.

Enhance your model's quality and sharpness using your own pre-trained Unet.

The text encoder (without UNET) is wrapped in LyCORIS. Optimizer: torch.optim.adamw.AdamW(weight_decay=0.01, betas=(0.9, 0.99))

Network dimension/rank: 768.0 Alpha: 768.0 Module: lycoris.kohya {'conv_dim': '256', 'conv_alpha': '256', 'algo': 'loha'}

Large size due to Lyco CONV 256

This is a heavy experimental version we used to test even with sloppy captions (quick WD tags and terrible clip), yet the results were satisfying.

Note: This is not the text encoder used in the official FFUSION AI model.

The text encoder (without UNET) is wrapped in LyCORIS. Optimizer: torch.optim.adamw.AdamW(weight_decay=0.01, betas=(0.9, 0.99))

Network dimension/rank: 768.0 Alpha: 768.0 Module: lycoris.kohya {'conv_dim': '256', 'conv_alpha': '256', 'algo': 'loha'}

Large size due to Lyco CONV 256

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Trained on "121361" images.

ss_caption_tag_dropout_rate: "0.0", ss_multires_noise_discount: "0.3", ss_mixed_precision: "bf16", ss_text_encoder_lr: "1e-07", ss_keep_tokens: "3", ss_network_args: "{"conv_dim": "256", "conv_alpha": "256", "algo": "loha"}", ss_caption_dropout_rate: "0.02", ss_flip_aug: "False", ss_learning_rate: "2e-07", ss_sd_model_name: "stabilityai/stable-diffusion-2-1-base", ss_max_grad_norm: "1.0", ss_num_epochs: "2", ss_gradient_checkpointing: "False", ss_face_crop_aug_range: "None", ss_epoch: "2", ss_num_train_images: "121361", ss_color_aug: "False", ss_gradient_accumulation_steps: "1", ss_total_batch_size: "100", ss_prior_loss_weight: "1.0", ss_training_comment: "None", ss_network_dim: "768", ss_output_name: "FusionaMEGA1tX", ss_max_bucket_reso: "1024", ss_network_alpha: "768.0", ss_steps: "2444", ss_shuffle_caption: "True", ss_training_finished_at: "1684158038.0763328", ss_min_bucket_reso: "256", ss_noise_offset: "0.09", ss_enable_bucket: "True", ss_batch_size_per_device: "20", ss_max_train_steps: "2444", ss_network_module: "lycoris.kohya",

"{"buckets": {"0": {"resolution": [192, 256], "count": 1}, "1": {"resolution": [192, 320], "count": 1}, "2": {"resolution": [256, 384], "count": 1}, "3": {"resolution": [256, 512], "count": 1}, "4": {"resolution": [384, 576], "count": 2}, "5": {"resolution": [384, 640], "count": 2}, "6": {"resolution": [384, 704], "count": 1}, "7": {"resolution": [384, 1088], "count": 15}, "8": {"resolution": [448, 448], "count": 5}, "9": {"resolution": [448, 576], "count": 1}, "10": {"resolution": [448, 640], "count": 1}, "11": {"resolution": [448, 768], "count": 1}, "12": {"resolution": [448, 832], "count": 1}, "13": {"resolution": [448, 1088], "count": 25}, "14": {"resolution": [448, 1216], "count": 1}, "15": {"resolution": [512, 640], "count": 2}, "16": {"resolution": [512, 768], "count": 10}, "17": {"resolution": [512, 832], "count": 3}, "18": {"resolution": [512, 896], "count": 1525}, "19": {"resolution": [512, 960], "count": 2}, "20": {"resolution": [512, 1024], "count": 665}, "21": {"resolution": [512, 1088], "count": 8}, "22": {"resolution": [576, 576], "count": 5}, "23": {"resolution": [576, 768], "count": 1}, "24": {"resolution": [576, 832], "count": 667}, "25": {"resolution": [576, 896], "count": 9601}, "26": {"resolution": [576, 960], "count": 872}, "27": {"resolution": [576, 1024], "count": 17}, "28": {"resolution": [640, 640], "count": 3}, "29": {"resolution": [640, 768], "count": 7}, "30": {"resolution": [640, 832], "count": 608}, "31": {"resolution": [640, 896], "count": 90}, "32": {"resolution": [704, 640], "count": 1}, "33": {"resolution": [704, 704], "count": 11}, "34": {"resolution": [704, 768], "count": 1}, "35": {"resolution": [704, 832], "count": 1}, "36": {"resolution": [768, 640], "count": 225}, "37": {"resolution": [768, 704], "count": 6}, "38": {"resolution": [768, 768], "count": 74442}, "39": {"resolution": [832, 576], "count": 23784}, "40": {"resolution": [832, 640], "count": 554}, "41": {"resolution": [896, 512], "count": 1235}, "42": {"resolution": [896, 576], "count": 50}, "43": {"resolution": [896, 640], "count": 88}, "44": {"resolution": [960, 512], "count": 165}, "45": {"resolution": [960, 576], "count": 5246}, "46": {"resolution": [1024, 448], "count": 5}, "47": {"resolution": [1024, 512], "count": 1187}, "48": {"resolution": [1024, 576], "count": 40}, "49": {"resolution": [1088, 384], "count": 70}, "50": {"resolution": [1088, 448], "count": 36}, "51": {"resolution": [1088, 512], "count": 3}, "52": {"resolution": [1216, 448], "count": 36}, "53": {"resolution": [1344, 320], "count": 29}, "54": {"resolution": [1536, 384], "count": 1}}, "mean_img_ar_error": 0.01693107810697896}",

ss_resolution: "(768, 768)", ss_v2: "True", ss_cache_latents: "False", ss_unet_lr: "2e-07", ss_num_reg_images: "0", ss_max_token_length: "225", ss_lr_scheduler: "linear", ss_reg_dataset_dirs: "{}", ss_lr_warmup_steps: "303", ss_num_batches_per_epoch: "1222", ss_lowram: "False", ss_multires_noise_iterations: "None", ss_optimizer: "torch.optim.adamw.AdamW(weight_decay=0.01,betas=(0.9, 0.99))",

More information needed

More information needed

More information needed

More information needed

More information needed

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Enhance your model's quality and sharpness using your own pre-trained Unet.

More information needed

8xA100

Fully trained only with Kohya S & Shih-Ying Yeh (Kohaku-BlueLeaf) https://arxiv.org/abs/2108.06098

More information needed

@misc{LyCORIS, author = "Shih-Ying Yeh (Kohaku-BlueLeaf), Yu-Guan Hsieh, Zhidong Gao", title = "LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion", howpublished = "\url{https://github.com/KohakuBlueleaf/LyCORIS}", month = "March", year = "2023" }

More information needed

More information needed

idle stoev

Use the code below to get started with the model.