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--- |
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license: creativeml-openrail-m |
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language: |
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- en |
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tags: |
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- di.ffusion.ai |
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- stable-diffusion |
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- LyCORIS |
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- LoRA |
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--- |
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# Model Card for di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS |
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<!-- Provide a quick summary of what the model is/does. [Optional] --> |
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di.FFUSION.ai-tXe-FXAA |
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Trained on "121361" images. |
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- **DOWNLOAD:** https://huggingface.co/FFusion/FFUSION.ai-Text-Encoder-LyCORIS-SD-2.1/blob/main/di.FFUSION.ai-tXe-FXAA.safetensors |
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Enhance your model's quality and sharpness using your own pre-trained Unet. |
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The text encoder (without UNET) is wrapped in LyCORIS. Optimizer: torch.optim.adamw.AdamW(weight_decay=0.01, betas=(0.9, 0.99)) |
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Network dimension/rank: 768.0 Alpha: 768.0 Module: lycoris.kohya {'conv_dim': '256', 'conv_alpha': '256', 'algo': 'loha'} |
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Large size due to Lyco CONV 256 |
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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. |
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Note: This is not the text encoder used in the official FFUSION AI model. |
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# SAMPLES |
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**Available also at https://civitai.com/models/83622** |
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For a1111 |
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Install https://github.com/KohakuBlueleaf/a1111-sd-webui-lycoris |
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Download di.FFUSION.ai-tXe-FXAA to /models/Lycoris |
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Option1: |
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Insert <lyco:di.FFUSION.ai-tXe-FXAA:1.0> to prompt |
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No need to split Unet and Text Enc as its only TX encoder there. |
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You can go up to 2x weights |
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Option2: If you need it always ON (ex run a batch from txt file) then you can go to settings / Quicksettings list |
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add sd_lyco |
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restart and you should have a drop-down now 🤟 🥃 |
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# Table of Contents |
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- [Model Card for di.FFUSION.ai Text Encoder - SD 2.1 LyCORIS](#model-card-for--model_id-) |
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- [Table of Contents](#table-of-contents) |
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- [Table of Contents](#table-of-contents-1) |
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- [Model Details](#model-details) |
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- [Model Description](#model-description) |
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- [Uses](#uses) |
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- [Direct Use](#direct-use) |
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- [Downstream Use [Optional]](#downstream-use-optional) |
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- [Out-of-Scope Use](#out-of-scope-use) |
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- [Bias, Risks, and Limitations](#bias-risks-and-limitations) |
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- [Recommendations](#recommendations) |
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- [Training Details](#training-details) |
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- [Training Data](#training-data) |
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- [Training Procedure](#training-procedure) |
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- [Preprocessing](#preprocessing) |
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- [Speeds, Sizes, Times](#speeds-sizes-times) |
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- [Evaluation](#evaluation) |
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- [Testing Data, Factors & Metrics](#testing-data-factors--metrics) |
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- [Testing Data](#testing-data) |
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- [Factors](#factors) |
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- [Metrics](#metrics) |
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- [Results](#results) |
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- [Model Examination](#model-examination) |
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- [Environmental Impact](#environmental-impact) |
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- [Technical Specifications [optional]](#technical-specifications-optional) |
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- [Model Architecture and Objective](#model-architecture-and-objective) |
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- [Compute Infrastructure](#compute-infrastructure) |
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- [Hardware](#hardware) |
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- [Software](#software) |
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- [Citation](#citation) |
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- [Glossary [optional]](#glossary-optional) |
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- [More Information [optional]](#more-information-optional) |
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- [Model Card Authors [optional]](#model-card-authors-optional) |
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- [Model Card Contact](#model-card-contact) |
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- [How to Get Started with the Model](#how-to-get-started-with-the-model) |
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# Model Details |
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## Model Description |
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<!-- Provide a longer summary of what this model is/does. --> |
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di.FFUSION.ai-tXe-FXAA |
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Trained on "121361" images. |
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Enhance your model's quality and sharpness using your own pre-trained Unet. |
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The text encoder (without UNET) is wrapped in LyCORIS. Optimizer: torch.optim.adamw.AdamW(weight_decay=0.01, betas=(0.9, 0.99)) |
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Network dimension/rank: 768.0 Alpha: 768.0 Module: lycoris.kohya {'conv_dim': '256', 'conv_alpha': '256', 'algo': 'loha'} |
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Large size due to Lyco CONV 256 |
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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. |
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Note: This is not the text encoder used in the official FFUSION AI model. |
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- **Developed by:** FFusion.ai |
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- **Shared by [Optional]:** idle stoev |
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- **Model type:** Language model |
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- **Language(s) (NLP):** en |
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- **License:** creativeml-openrail-m |
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- **Parent Model:** More information needed |
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- **Resources for more information:** More information needed |
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# Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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## Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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<!-- 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." --> |
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The text encoder (without UNET) is wrapped in LyCORIS. Optimizer: torch.optim.adamw.AdamW(weight_decay=0.01, betas=(0.9, 0.99)) |
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Network dimension/rank: 768.0 Alpha: 768.0 Module: lycoris.kohya {'conv_dim': '256', 'conv_alpha': '256', 'algo': 'loha'} |
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Large size due to Lyco CONV 256 |
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# Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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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. |
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## Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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# Training Details |
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## Training Data |
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<!-- 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. --> |
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Trained on "121361" images. |
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ss_caption_tag_dropout_rate: "0.0", |
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ss_multires_noise_discount: "0.3", |
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ss_mixed_precision: "bf16", |
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ss_text_encoder_lr: "1e-07", |
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ss_keep_tokens: "3", |
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ss_network_args: "{"conv_dim": "256", "conv_alpha": "256", "algo": "loha"}", |
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ss_caption_dropout_rate: "0.02", |
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ss_flip_aug: "False", |
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ss_learning_rate: "2e-07", |
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ss_sd_model_name: "stabilityai/stable-diffusion-2-1-base", |
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ss_max_grad_norm: "1.0", |
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ss_num_epochs: "2", |
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ss_gradient_checkpointing: "False", |
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ss_face_crop_aug_range: "None", |
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ss_epoch: "2", |
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ss_num_train_images: "121361", |
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ss_color_aug: "False", |
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ss_gradient_accumulation_steps: "1", |
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ss_total_batch_size: "100", |
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ss_prior_loss_weight: "1.0", |
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ss_training_comment: "None", |
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ss_network_dim: "768", |
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ss_output_name: "FusionaMEGA1tX", |
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ss_max_bucket_reso: "1024", |
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ss_network_alpha: "768.0", |
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ss_steps: "2444", |
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ss_shuffle_caption: "True", |
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ss_training_finished_at: "1684158038.0763328", |
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ss_min_bucket_reso: "256", |
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ss_noise_offset: "0.09", |
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ss_enable_bucket: "True", |
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ss_batch_size_per_device: "20", |
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ss_max_train_steps: "2444", |
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ss_network_module: "lycoris.kohya", |
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## Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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### Preprocessing |
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"{"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}", |
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### Speeds, Sizes, Times |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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ss_resolution: "(768, 768)", |
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ss_v2: "True", |
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ss_cache_latents: "False", |
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ss_unet_lr: "2e-07", |
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ss_num_reg_images: "0", |
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ss_max_token_length: "225", |
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ss_lr_scheduler: "linear", |
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ss_reg_dataset_dirs: "{}", |
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ss_lr_warmup_steps: "303", |
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ss_num_batches_per_epoch: "1222", |
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ss_lowram: "False", |
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ss_multires_noise_iterations: "None", |
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ss_optimizer: "torch.optim.adamw.AdamW(weight_decay=0.01,betas=(0.9, 0.99))", |
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# Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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More information needed |
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### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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More information needed |
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### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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More information needed |
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## Results |
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More information needed |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** 8xA100 |
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- **Hours used:** 64 |
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- **Cloud Provider:** CoreWeave |
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- **Compute Region:** US Main |
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- **Carbon Emitted:** 6.72 |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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Enhance your model's quality and sharpness using your own pre-trained Unet. |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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8xA100 |
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### Software |
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Fully trained only with Kohya S & Shih-Ying Yeh (Kohaku-BlueLeaf) |
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https://arxiv.org/abs/2108.06098 |
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# Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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More information needed |
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**APA:** |
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@misc{LyCORIS, |
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author = "Shih-Ying Yeh (Kohaku-BlueLeaf), Yu-Guan Hsieh, Zhidong Gao", |
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title = "LyCORIS - Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion", |
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howpublished = "\url{https://github.com/KohakuBlueleaf/LyCORIS}", |
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month = "March", |
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year = "2023" |
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} |
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# Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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<!-- 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. --> |
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idle stoev |
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# Model Card Contact |
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di@ffusion.ai |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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For a1111 |
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Install https://github.com/KohakuBlueleaf/a1111-sd-webui-lycoris |
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Download di.FFUSION.ai-tXe-FXAA to /models/Lycoris |
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Option1: |
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Insert <lyco:di.FFUSION.ai-tXe-FXAA:1.0> to prompt |
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No need to split Unet and Text Enc as its only TX encoder there. |
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You can go up to 2x weights |
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Option2: If you need it always ON (ex run a batch from txt file) then you can go to settings / Quicksettings list |
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add sd_lyco |
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restart and you should have a drop-down now 🤟 🥃 |
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</details> |
