Delete di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS_model_card.md

di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS_model_card.md

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-# Model Card for di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS
-
-<!-- Provide a quick summary of what the model is/does. [Optional] -->
-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.
-
-
-
-
-#  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:** F, F, u, s, i, o, n, ., a, i
-- **Shared by [Optional]:** i, d, l, e,  , s, t, o, e, v
-- **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
-
-
-## Downstream Use [Optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged 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." -->
- 
-
-
-
-## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- 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." -->
-
-
-
-
-# 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. -->
-
-i, d, l, e,  , s, t, o, e, v
-
-# Model Card Contact
-
-d, i, @, f, f, u, s, i, o, n, ., a, i
-
-# How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-<details>
-<summary> Click to expand </summary>
-
-More information needed
-
-</details>