FFUSION.ai-Text-Encoder-LyCORIS-SD-2.1

Model Card for di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS

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

• DOWNLOAD: https://huggingface.co/FFusion/FFUSION.ai-Text-Encoder-LyCORIS-SD-2.1/blob/main/di.FFUSION.ai-tXe-FXAA.safetensors

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.

SAMPLES

Available also at https://civitai.com/models/83622

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 🤟 🥃

Table of Contents

• Model Card for di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS
• Table of Contents
• Table of Contents
• Model Details
  • Model Description
• Uses
  • Direct Use
  • Downstream Use [Optional]
  • Out-of-Scope Use
• Bias, Risks, and Limitations
  • Recommendations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data, Factors & Metrics
    • Testing Data
    • Factors
    • Metrics
  • Results
• Model Examination
• Environmental Impact
• Technical Specifications [optional]
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
• Citation
• Glossary [optional]
• More Information [optional]
• Model Card Authors [optional]
• Model Card Contact
• How to Get Started with the Model

Model Details

Model Description

<!-- Provide a longer summary of what this model is/does. -->

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.

• Developed by: FFusion.ai
• Shared by [Optional]: idle stoev
• Model type: Language model
• Language(s) (NLP): en
• License: creativeml-openrail-m
• Parent Model: More information needed
• Resources for more information: More information needed

Uses

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Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->

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

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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.

Recommendations

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Training Details

Training Data

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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",

Training Procedure

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Preprocessing

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Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

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))",

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Testing Data, Factors & Metrics

Testing Data

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More information needed

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

More information needed

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

More information needed

Results

More information needed

Model Examination

More information needed

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

• Hardware Type: 8xA100
• Hours used: 64
• Cloud Provider: CoreWeave
• Compute Region: US Main
• Carbon Emitted: 6.72

Technical Specifications [optional]

Model Architecture and Objective

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

Compute Infrastructure

More information needed

Hardware

8xA100

Software

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

Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

BibTeX:

More information needed

APA:

@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}&#34;, month = "March", year = "2023" }

Glossary [optional]

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More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

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idle stoev

Model Card Contact

[email protected]

How to Get Started with the Model

Use the code below to get started with the model.

<details> <summary> Click to expand </summary>

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 🤟 🥃

</details>