
# Model Card for di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS
![image.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/zcw9AUCSbanb61xe6pIUc.png)


<!-- Provide a quick summary of what the model is/does. [Optional] -->
di.FFUSION.ai-tXe-FXAA
Trained on &#34;121361&#34; 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&#39;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 {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

Large size due to Lyco CONV 256


![image.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/Ig1IOYZAyUrhpWIhdC6U-.png)
![image.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/66eAHPc501sbQx35-B0Oo.png)

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**


![image.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/agjJ--YR_k_Pbn8tOMsqr.png)





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


![image.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/N6M4-9eIkvi3nn3koh1fA.png)


add sd_lyco



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


![image.png](https://cdn-uploads.huggingface.co/production/uploads/6380cf05f496d57325c12194/e8ROXaN8jIaT9lu7tNRjD.png)


#  Table of Contents

- [Model Card for di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
  - [Model Description](#model-description)
- [Uses](#uses)
  - [Direct Use](#direct-use)
  - [Downstream Use [Optional]](#downstream-use-optional)
  - [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  - [Recommendations](#recommendations)
- [Training Details](#training-details)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
    - [Preprocessing](#preprocessing)
    - [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
    - [Testing Data](#testing-data)
    - [Factors](#factors)
    - [Metrics](#metrics)
  - [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
  - [Model Architecture and Objective](#model-architecture-and-objective)
  - [Compute Infrastructure](#compute-infrastructure)
    - [Hardware](#hardware)
    - [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#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 &#34;121361&#34; images.


Enhance your model&#39;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 {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

## 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 {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->





# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

Trained on &#34;121361&#34; images.

ss_caption_tag_dropout_rate:  &#34;0.0&#34;,
ss_multires_noise_discount:  &#34;0.3&#34;,
ss_mixed_precision:  &#34;bf16&#34;,
ss_text_encoder_lr:  &#34;1e-07&#34;,
ss_keep_tokens:  &#34;3&#34;,
ss_network_args:  &#34;{&#34;conv_dim&#34;: &#34;256&#34;, &#34;conv_alpha&#34;: &#34;256&#34;, &#34;algo&#34;: &#34;loha&#34;}&#34;,
ss_caption_dropout_rate:  &#34;0.02&#34;,
ss_flip_aug:  &#34;False&#34;,
ss_learning_rate:  &#34;2e-07&#34;,
ss_sd_model_name:  &#34;stabilityai/stable-diffusion-2-1-base&#34;,
ss_max_grad_norm:  &#34;1.0&#34;,
ss_num_epochs:  &#34;2&#34;,
ss_gradient_checkpointing:  &#34;False&#34;,
ss_face_crop_aug_range:  &#34;None&#34;,
ss_epoch:  &#34;2&#34;,
ss_num_train_images:  &#34;121361&#34;,
ss_color_aug:  &#34;False&#34;,
ss_gradient_accumulation_steps:  &#34;1&#34;,
ss_total_batch_size:  &#34;100&#34;,
ss_prior_loss_weight:  &#34;1.0&#34;,
ss_training_comment:  &#34;None&#34;,
ss_network_dim:  &#34;768&#34;,
ss_output_name:  &#34;FusionaMEGA1tX&#34;,
ss_max_bucket_reso:  &#34;1024&#34;,
ss_network_alpha:  &#34;768.0&#34;,
ss_steps:  &#34;2444&#34;,
ss_shuffle_caption:  &#34;True&#34;,
ss_training_finished_at:  &#34;1684158038.0763328&#34;,
ss_min_bucket_reso:  &#34;256&#34;,
ss_noise_offset:  &#34;0.09&#34;,
ss_enable_bucket:  &#34;True&#34;,
ss_batch_size_per_device:  &#34;20&#34;,
ss_max_train_steps:  &#34;2444&#34;,
ss_network_module:  &#34;lycoris.kohya&#34;,


## Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

### Preprocessing

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

### Speeds, Sizes, Times

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

ss_resolution:  &#34;(768, 768)&#34;,
ss_v2:  &#34;True&#34;,
ss_cache_latents:  &#34;False&#34;,
ss_unet_lr:  &#34;2e-07&#34;,
ss_num_reg_images:  &#34;0&#34;,
ss_max_token_length:  &#34;225&#34;,
ss_lr_scheduler:  &#34;linear&#34;,
ss_reg_dataset_dirs:  &#34;{}&#34;,
ss_lr_warmup_steps:  &#34;303&#34;,
ss_num_batches_per_epoch:  &#34;1222&#34;,
ss_lowram:  &#34;False&#34;,
ss_multires_noise_iterations:  &#34;None&#34;,
ss_optimizer:  &#34;torch.optim.adamw.AdamW(weight_decay=0.01,betas=(0.9, 0.99))&#34;,
 
# Evaluation

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

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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&#39;s quality and sharpness using your own pre-trained Unet.


## Compute Infrastructure

More information needed

### Hardware

8xA100

### Software

Fully trained only with Kohya S &amp; 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       = &#34;Shih-Ying Yeh (Kohaku-BlueLeaf), Yu-Guan Hsieh, Zhidong Gao&#34;,
  title        = &#34;LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion&#34;,
  howpublished = &#34;\url{https://github.com/KohakuBlueleaf/LyCORIS}&#34;,
  month        = &#34;March&#34;,
  year         = &#34;2023&#34;
}

# Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

More information needed

# More Information [optional]

More information needed

# Model Card Authors [optional]

<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->

  idle stoev

# Model Card Contact

di@ffusion.ai

# 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> 
{"language": ["en"], "license": "creativeml-openrail-m", "tags": ["di.ffusion.ai", "stable-diffusion", "LyCORIS", "LoRA"]}
di.ffusion.ai stable-diffusion LyCORIS LoRA en arxiv:1910.09700 arxiv:2108.06098 license:creativeml-openrail-m has_space region:us
TAGS
#di.ffusion.ai #stable-diffusion #LyCORIS #LoRA #en #arxiv-1910.09700 #arxiv-2108.06098 #license-creativeml-openrail-m #has_space #region-us 


# Model Card for URL Text Encoder - SD 2.1 LyCORIS
!URL



URL-tXe-FXAA
Trained on &#34;121361&#34; images.

- DOWNLOAD: URL

Enhance your model&#39;s quality and sharpness using your own pre-trained Unet.


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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

Large size due to Lyco CONV 256


!URL
!URL

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 URL


!URL





For a1111
Install URL

Download URL-tXe-FXAA to /models/Lycoris

Option1:

Insert <lyco:URL-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


!URL


add sd_lyco



restart and you should have a drop-down now  


!URL


#  Table of Contents

- Model Card for URL Text Encoder - SD 2.1 LyCORIS
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#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]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- Citation
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- Model Card Contact
- How to Get Started with the Model


# Model Details

## Model Description


URL-tXe-FXAA
Trained on &#34;121361&#34; images.


Enhance your model&#39;s quality and sharpness using your own pre-trained Unet.

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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

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: URL
- 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



## Direct Use




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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

Large size due to Lyco CONV 256


# Bias, Risks, and 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







# Training Details

## Training Data



Trained on &#34;121361&#34; images.

ss_caption_tag_dropout_rate:  &#34;0.0&#34;,
ss_multires_noise_discount:  &#34;0.3&#34;,
ss_mixed_precision:  &#34;bf16&#34;,
ss_text_encoder_lr:  &#34;1e-07&#34;,
ss_keep_tokens:  &#34;3&#34;,
ss_network_args:  &#34;{&#34;conv_dim&#34;: &#34;256&#34;, &#34;conv_alpha&#34;: &#34;256&#34;, &#34;algo&#34;: &#34;loha&#34;}&#34;,
ss_caption_dropout_rate:  &#34;0.02&#34;,
ss_flip_aug:  &#34;False&#34;,
ss_learning_rate:  &#34;2e-07&#34;,
ss_sd_model_name:  &#34;stabilityai/stable-diffusion-2-1-base&#34;,
ss_max_grad_norm:  &#34;1.0&#34;,
ss_num_epochs:  &#34;2&#34;,
ss_gradient_checkpointing:  &#34;False&#34;,
ss_face_crop_aug_range:  &#34;None&#34;,
ss_epoch:  &#34;2&#34;,
ss_num_train_images:  &#34;121361&#34;,
ss_color_aug:  &#34;False&#34;,
ss_gradient_accumulation_steps:  &#34;1&#34;,
ss_total_batch_size:  &#34;100&#34;,
ss_prior_loss_weight:  &#34;1.0&#34;,
ss_training_comment:  &#34;None&#34;,
ss_network_dim:  &#34;768&#34;,
ss_output_name:  &#34;FusionaMEGA1tX&#34;,
ss_max_bucket_reso:  &#34;1024&#34;,
ss_network_alpha:  &#34;768.0&#34;,
ss_steps:  &#34;2444&#34;,
ss_shuffle_caption:  &#34;True&#34;,
ss_training_finished_at:  &#34;1684158038.0763328&#34;,
ss_min_bucket_reso:  &#34;256&#34;,
ss_noise_offset:  &#34;0.09&#34;,
ss_enable_bucket:  &#34;True&#34;,
ss_batch_size_per_device:  &#34;20&#34;,
ss_max_train_steps:  &#34;2444&#34;,
ss_network_module:  &#34;URL&#34;,


## Training Procedure



### Preprocessing

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

### Speeds, Sizes, Times



ss_resolution:  &#34;(768, 768)&#34;,
ss_v2:  &#34;True&#34;,
ss_cache_latents:  &#34;False&#34;,
ss_unet_lr:  &#34;2e-07&#34;,
ss_num_reg_images:  &#34;0&#34;,
ss_max_token_length:  &#34;225&#34;,
ss_lr_scheduler:  &#34;linear&#34;,
ss_reg_dataset_dirs:  &#34;{}&#34;,
ss_lr_warmup_steps:  &#34;303&#34;,
ss_num_batches_per_epoch:  &#34;1222&#34;,
ss_lowram:  &#34;False&#34;,
ss_multires_noise_iterations:  &#34;None&#34;,
ss_optimizer:  &#34;URL.AdamW(weight_decay=0.01,betas=(0.9, 0.99))&#34;,
 
# Evaluation



## Testing Data, Factors & Metrics

### Testing Data



More information needed


### Factors



More information needed

### Metrics



More information needed

## Results 

More information needed

# Model Examination

More information needed

# Environmental Impact



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&#39;s quality and sharpness using your own pre-trained Unet.


## Compute Infrastructure

More information needed

### Hardware

8xA100

### Software

Fully trained only with Kohya S &amp; Shih-Ying Yeh (Kohaku-BlueLeaf)
URL

BibTeX:

More information needed

APA:

@misc{LyCORIS,
  author       = &#34;Shih-Ying Yeh (Kohaku-BlueLeaf), Yu-Guan Hsieh, Zhidong Gao&#34;,
  title        = &#34;LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion&#34;,
  howpublished = &#34;\url{URL
  month        = &#34;March&#34;,
  year         = &#34;2023&#34;
}

# Glossary [optional]



More information needed

# More Information [optional]

More information needed

# Model Card Authors [optional]



  idle stoev

# Model Card Contact

di@URL

# 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 URL

Download URL-tXe-FXAA to /models/Lycoris

Option1:

Insert <lyco:URL-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> 
# Model Card for URL Text Encoder - SD 2.1 LyCORIS
!URL



URL-tXe-FXAA
Trained on &#34;121361&#34; images.

- DOWNLOAD: URL

Enhance your model&#39;s quality and sharpness using your own pre-trained Unet.


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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

Large size due to Lyco CONV 256


!URL
!URL

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 URL


!URL





For a1111
Install URL

Download URL-tXe-FXAA to /models/Lycoris

Option1:

Insert <lyco:URL-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


!URL


add sd_lyco



restart and you should have a drop-down now  


!URL #  Table of Contents

- Model Card for URL Text Encoder - SD 2.1 LyCORIS
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#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]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- Citation
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- Model Card Contact
- How to Get Started with the Model # Model Details ## Model Description


URL-tXe-FXAA
Trained on &#34;121361&#34; images.


Enhance your model&#39;s quality and sharpness using your own pre-trained Unet.

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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

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: URL
- 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 ## Direct Use




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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

Large size due to Lyco CONV 256 # Bias, Risks, and 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 # Training Details ## Training Data



Trained on &#34;121361&#34; images.

ss_caption_tag_dropout_rate:  &#34;0.0&#34;,
ss_multires_noise_discount:  &#34;0.3&#34;,
ss_mixed_precision:  &#34;bf16&#34;,
ss_text_encoder_lr:  &#34;1e-07&#34;,
ss_keep_tokens:  &#34;3&#34;,
ss_network_args:  &#34;{&#34;conv_dim&#34;: &#34;256&#34;, &#34;conv_alpha&#34;: &#34;256&#34;, &#34;algo&#34;: &#34;loha&#34;}&#34;,
ss_caption_dropout_rate:  &#34;0.02&#34;,
ss_flip_aug:  &#34;False&#34;,
ss_learning_rate:  &#34;2e-07&#34;,
ss_sd_model_name:  &#34;stabilityai/stable-diffusion-2-1-base&#34;,
ss_max_grad_norm:  &#34;1.0&#34;,
ss_num_epochs:  &#34;2&#34;,
ss_gradient_checkpointing:  &#34;False&#34;,
ss_face_crop_aug_range:  &#34;None&#34;,
ss_epoch:  &#34;2&#34;,
ss_num_train_images:  &#34;121361&#34;,
ss_color_aug:  &#34;False&#34;,
ss_gradient_accumulation_steps:  &#34;1&#34;,
ss_total_batch_size:  &#34;100&#34;,
ss_prior_loss_weight:  &#34;1.0&#34;,
ss_training_comment:  &#34;None&#34;,
ss_network_dim:  &#34;768&#34;,
ss_output_name:  &#34;FusionaMEGA1tX&#34;,
ss_max_bucket_reso:  &#34;1024&#34;,
ss_network_alpha:  &#34;768.0&#34;,
ss_steps:  &#34;2444&#34;,
ss_shuffle_caption:  &#34;True&#34;,
ss_training_finished_at:  &#34;1684158038.0763328&#34;,
ss_min_bucket_reso:  &#34;256&#34;,
ss_noise_offset:  &#34;0.09&#34;,
ss_enable_bucket:  &#34;True&#34;,
ss_batch_size_per_device:  &#34;20&#34;,
ss_max_train_steps:  &#34;2444&#34;,
ss_network_module:  &#34;URL&#34;, ## Training Procedure ### Preprocessing

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



ss_resolution:  &#34;(768, 768)&#34;,
ss_v2:  &#34;True&#34;,
ss_cache_latents:  &#34;False&#34;,
ss_unet_lr:  &#34;2e-07&#34;,
ss_num_reg_images:  &#34;0&#34;,
ss_max_token_length:  &#34;225&#34;,
ss_lr_scheduler:  &#34;linear&#34;,
ss_reg_dataset_dirs:  &#34;{}&#34;,
ss_lr_warmup_steps:  &#34;303&#34;,
ss_num_batches_per_epoch:  &#34;1222&#34;,
ss_lowram:  &#34;False&#34;,
ss_multires_noise_iterations:  &#34;None&#34;,
ss_optimizer:  &#34;URL.AdamW(weight_decay=0.01,betas=(0.9, 0.99))&#34;, # Evaluation ## Testing Data, Factors & Metrics ### Testing Data



More information needed ### Factors



More information needed ### Metrics



More information needed ## Results 

More information needed # Model Examination

More information needed # Environmental Impact



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&#39;s quality and sharpness using your own pre-trained Unet. ## Compute Infrastructure

More information needed ### Hardware

8xA100 ### Software

Fully trained only with Kohya S &amp; Shih-Ying Yeh (Kohaku-BlueLeaf)
URL

BibTeX:

More information needed

APA:

@misc{LyCORIS,
  author       = &#34;Shih-Ying Yeh (Kohaku-BlueLeaf), Yu-Guan Hsieh, Zhidong Gao&#34;,
  title        = &#34;LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion&#34;,
  howpublished = &#34;\url{URL
  month        = &#34;March&#34;,
  year         = &#34;2023&#34;
} # Glossary [optional]



More information needed # More Information [optional]

More information needed # Model Card Authors [optional]



  idle stoev # Model Card Contact

di@URL # 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 URL

Download URL-tXe-FXAA to /models/Lycoris

Option1:

Insert <lyco:URL-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>
TAGS
#di.ffusion.ai #stable-diffusion #LyCORIS #LoRA #en #arxiv-1910.09700 #arxiv-2108.06098 #license-creativeml-openrail-m #has_space #region-us 
 # Model Card for URL Text Encoder - SD 2.1 LyCORIS
!URL



URL-tXe-FXAA
Trained on &#34;121361&#34; images.

- DOWNLOAD: URL

Enhance your model&#39;s quality and sharpness using your own pre-trained Unet.


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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

Large size due to Lyco CONV 256


!URL
!URL

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 URL


!URL





For a1111
Install URL

Download URL-tXe-FXAA to /models/Lycoris

Option1:

Insert <lyco:URL-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


!URL


add sd_lyco



restart and you should have a drop-down now  


!URL #  Table of Contents

- Model Card for URL Text Encoder - SD 2.1 LyCORIS
- Table of Contents
- Table of Contents
- Model Details
  - Model Description
- Uses
  - Direct Use
  - [Downstream Use [Optional]](#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]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- Citation
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- Model Card Contact
- How to Get Started with the Model # Model Details ## Model Description


URL-tXe-FXAA
Trained on &#34;121361&#34; images.


Enhance your model&#39;s quality and sharpness using your own pre-trained Unet.

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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

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: URL
- 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 ## Direct Use




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

Network dimension/rank: 768.0 Alpha: 768.0 Module: URL {&#39;conv_dim&#39;: &#39;256&#39;, &#39;conv_alpha&#39;: &#39;256&#39;, &#39;algo&#39;: &#39;loha&#39;}

Large size due to Lyco CONV 256 # Bias, Risks, and 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 # Training Details ## Training Data



Trained on &#34;121361&#34; images.

ss_caption_tag_dropout_rate:  &#34;0.0&#34;,
ss_multires_noise_discount:  &#34;0.3&#34;,
ss_mixed_precision:  &#34;bf16&#34;,
ss_text_encoder_lr:  &#34;1e-07&#34;,
ss_keep_tokens:  &#34;3&#34;,
ss_network_args:  &#34;{&#34;conv_dim&#34;: &#34;256&#34;, &#34;conv_alpha&#34;: &#34;256&#34;, &#34;algo&#34;: &#34;loha&#34;}&#34;,
ss_caption_dropout_rate:  &#34;0.02&#34;,
ss_flip_aug:  &#34;False&#34;,
ss_learning_rate:  &#34;2e-07&#34;,
ss_sd_model_name:  &#34;stabilityai/stable-diffusion-2-1-base&#34;,
ss_max_grad_norm:  &#34;1.0&#34;,
ss_num_epochs:  &#34;2&#34;,
ss_gradient_checkpointing:  &#34;False&#34;,
ss_face_crop_aug_range:  &#34;None&#34;,
ss_epoch:  &#34;2&#34;,
ss_num_train_images:  &#34;121361&#34;,
ss_color_aug:  &#34;False&#34;,
ss_gradient_accumulation_steps:  &#34;1&#34;,
ss_total_batch_size:  &#34;100&#34;,
ss_prior_loss_weight:  &#34;1.0&#34;,
ss_training_comment:  &#34;None&#34;,
ss_network_dim:  &#34;768&#34;,
ss_output_name:  &#34;FusionaMEGA1tX&#34;,
ss_max_bucket_reso:  &#34;1024&#34;,
ss_network_alpha:  &#34;768.0&#34;,
ss_steps:  &#34;2444&#34;,
ss_shuffle_caption:  &#34;True&#34;,
ss_training_finished_at:  &#34;1684158038.0763328&#34;,
ss_min_bucket_reso:  &#34;256&#34;,
ss_noise_offset:  &#34;0.09&#34;,
ss_enable_bucket:  &#34;True&#34;,
ss_batch_size_per_device:  &#34;20&#34;,
ss_max_train_steps:  &#34;2444&#34;,
ss_network_module:  &#34;URL&#34;, ## Training Procedure ### Preprocessing

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



ss_resolution:  &#34;(768, 768)&#34;,
ss_v2:  &#34;True&#34;,
ss_cache_latents:  &#34;False&#34;,
ss_unet_lr:  &#34;2e-07&#34;,
ss_num_reg_images:  &#34;0&#34;,
ss_max_token_length:  &#34;225&#34;,
ss_lr_scheduler:  &#34;linear&#34;,
ss_reg_dataset_dirs:  &#34;{}&#34;,
ss_lr_warmup_steps:  &#34;303&#34;,
ss_num_batches_per_epoch:  &#34;1222&#34;,
ss_lowram:  &#34;False&#34;,
ss_multires_noise_iterations:  &#34;None&#34;,
ss_optimizer:  &#34;URL.AdamW(weight_decay=0.01,betas=(0.9, 0.99))&#34;, # Evaluation ## Testing Data, Factors & Metrics ### Testing Data



More information needed ### Factors



More information needed ### Metrics



More information needed ## Results 

More information needed # Model Examination

More information needed # Environmental Impact



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&#39;s quality and sharpness using your own pre-trained Unet. ## Compute Infrastructure

More information needed ### Hardware

8xA100 ### Software

Fully trained only with Kohya S &amp; Shih-Ying Yeh (Kohaku-BlueLeaf)
URL

BibTeX:

More information needed

APA:

@misc{LyCORIS,
  author       = &#34;Shih-Ying Yeh (Kohaku-BlueLeaf), Yu-Guan Hsieh, Zhidong Gao&#34;,
  title        = &#34;LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion&#34;,
  howpublished = &#34;\url{URL
  month        = &#34;March&#34;,
  year         = &#34;2023&#34;
} # Glossary [optional]



More information needed # More Information [optional]

More information needed # Model Card Authors [optional]



  idle stoev # Model Card Contact

di@URL # 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 URL

Download URL-tXe-FXAA to /models/Lycoris

Option1:

Insert <lyco:URL-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>