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A Fresh Approach to SDXL & Pony XL Lora Training

duskfallcrew's Avatar
duskfallcrew
Holiday 2024: 6 lights
Jul 12, 2024
training guide
howtosdxl guidesdxl kohyatraining diariesmetadata
A Fresh Approach to SDXL & Pony XL Lora Training

A Fresh Approach: Opinionated Guide to SDXL Lora Training

Preface

Amidst the ongoing discussions surrounding SD3 and model preferences, I'm sharing my latest approach to training ponyXL. I've archived the original article on Ko-Fi and have a version stored on Discord for reference. The article has been renamed, and more examples plus metadata examples and commentary.

This update features significant rewrites and omits some outdated information, showcasing more recent training methodologies. Please note, this guide reflects my personal opinions and experiences. If you disagree, that's okay—there's room for varied approaches in this field. There are metadata examples of recent SDXL and Pony trains using Xypher's metadata tool linked below.

A thank you:

Thank you to all of the rest of the Creator Program and Civitai Staff for allowing Earthnicity the space to do what we've been trying to do. Without your continued support, we would not have the strength to keep up with what we're doing.

Original Article was written in August of 2023, as stated above the original SDXL version from 2023 is a PDF we uploaded into Ko-Fi, and the previous version of this is copied into our discord. IF you'd like any information, or the JSON files that were removed as of this update: Please let me know. The Json files are in one of the two backup repositories that are linked at the bottom of this article. Also fixed the support links at the bottom. Fixed this because omfg WTF i left out something crucial!

Software Requirements

For offsite training, ensure you have adequate software tools. If your hardware is underpowered (less than 24GB VRAM), consider onsite or cloud-based options for smoother operations.

(*Local & Server)

I have NOT used all of these and PRE WARNING: Colab's AUP is dodgy AF and may lead to slowdowns. And also not all of these may work as intended. Be careful!

Bmaltais repo: https://github.com/bmaltais/kohya_ss

LastBen: https://github.com/TheLastBen/fast-stable-diffusion

Holo's SDXL for Colab: https://github.com/hollowstrawberry/kohya-colab

Derrian's forked for Colab: https://github.com/Jelosus2/LoRA_Easy_Training_Colab_Frontend

SD Web UI Extension: https://github.com/hako-mikan/sd-webui-traintrain

Image Captioning for ComfyUI: https://github.com/LarryJane491/Image-Captioning-in-ComfyUI

Derrian's back end for SErver: https://github.com/derrian-distro/LoRA_Easy_Training_scripts_Backend

ComfyUI Lora Trainer: https://github.com/LarryJane491/Lora-Training-in-Comfy

Train WebUI: https://github.com/Akegarasu/lora-scripts

OneTrainer: https://github.com/Nerogar/OneTrainer

Lora Studio: https://github.com/michaelringholm/lora-studio

Derrian Main scripts: https://github.com/derrian-distro/LoRA_Easy_Training_Scripts

DOCKER TEMPLATES AS OF 2024 are automatically set up, except in the case where some custom content like Derrian's isn't dockerized:

Runpod: https://runpod.io/?ref=yx1lcptf (our Referral)

VastAI: https://cloud.vast.ai/?ref=70354

Xypher's Lora Metadata: https://xypher7.github.io/lora-metadata-viewer/

Our Tools for Conversion & Backup:

SDXL To Diffusers; https://github.com/duskfallcrew/sdxl-model-converter

Huggingface Backup: https://github.com/duskfallcrew/HuggingFace_Backup

Dataset Editor (in progress WIP): https://github.com/duskfallcrew/Dataset-Tools

Training Essentials for SDXL & Pony XL

First and foremost, quality over quantity in your data collection is crucial. Even a small dataset of around 10 images can yield results. Be mindful of ethical considerations, especially concerning copyrighted content.

  • Collect Your Data: Sources like Nijijourney, Midjourney, and various online databases can provide diverse image sets. Respect artist rights and consider potential ethical issues.

  • Upscale as Needed: Aim for images predominantly above 512 pixels, ideally 1024. Onsite, 2048 is the maximum, although consistency is key.

  • Organize Your Folders: Use structured folder setups like those recommended for BMaltais & Derrian Distro on platforms such as Runpod or local environments. Please note, each of these have their own desired folder setup. Each varies per trainer including One Trainer.

Choosing a Base Model

  • Offsite & Onsite: I primarily use PonyXL due to personal preference. Animagine is another viable option, despite occasional issues with its text encoder. For broader compatibility, consider SDXL base, which supports most SDXL models except PonyXL. Be aware some of these may cost more if you're using onsite training with buzz.

Repeats and Epochs

The number of repeats depends on your desired level of detail and fidelity in your models. Larger datasets may require adjustments like using Adafactor for PonyXL. (For more information on this please refer to example metadata later on)

Learning Rate and Optimization

  • 2024 Update: Base learning rates are set at 5e4, while text encoder settings for concepts and characters can be adjusted to 1e4. Consider turning off the text encoder for simpler styles.

  • Optimization: Adafactor and AdamW8Bit are reliable choices, with Prodigy suitable for smaller datasets due to its limitations with larger volumes. However, as of 2024 AdamW8bit as far as i'm aware is not available for SDXL & PonyXL due to mixed information across communities.

Additional Tips

  • Dataset Organization: Regularly rename files and eliminate duplicates to streamline training. Ensure character-specific datasets remain focused to avoid mixing unrelated elements.

  • Training Tips: Optimal batch sizes range from 2 to 4 to balance efficiency and quality.

  • Latents: Always cache latents to enhance training stability and efficiency.

  • Gradient Checkpointing: Useful for conserving VRAM during training sessions, particularly on lower-spec machines.

Conclusions

SDXL offers advancements over SD 1.5, though hardware compatibility can pose challenges. Remember, training models is akin to navigating nuanced debates—there's no one-size-fits-all solution.

Disclaimer

This guide represents my approach, not definitive rules. Experimentation and adaptation are key to finding what works best for your projects. Removed outdated JSON references and highlighted ongoing updates for clarity and relevance.

EXAMPLE SETTINGS FOR CIVITAI ONSITE

Settings are copied over using: https://xypher7.github.io/lora-metadata-viewer/

Pony XL for a World Morph: https://civitai.com/models/571004/gold-filigree-xl-and-pony

{ 
"ss_output_name": "Gold_Filigree_Style", 
"ss_sd_model_name": "290640.safetensors", 
"ss_network_module": "networks.lora", 
"ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", 
"ss_lr_scheduler": "cosine", 
"ss_clip_skip": 2, 
"ss_network_dim": 32, 
"ss_network_alpha": 32, 
"ss_epoch": 12, "ss_num_epochs": 12, "ss_steps": 3360, 
"ss_max_train_steps": 3360, "ss_learning_rate": 0.0005, 
"ss_text_encoder_lr": 0.00005,
"ss_unet_lr": 0.0005, 
"ss_noise_offset": 0.03, 
"ss_adaptive_noise_scale": "None", 
"ss_min_snr_gamma": 5,  
}

SDXL World Morph:https://civitai.com/models/571004/gold-filigree-xl-and-pony

{
  "ss_output_name": "Gold_Filigree_Style",
  "ss_sd_model_name": "128078.safetensors",
  "ss_network_module": "networks.lora",
  "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)",
  "ss_lr_scheduler": "cosine_with_restarts",
  "ss_clip_skip": 2,
  "ss_network_dim": 32,
  "ss_network_alpha": 16,
  "ss_epoch": 13,
  "ss_num_epochs": 14,
  "ss_steps": 2509,
  "ss_max_train_steps": 2702,
  "ss_learning_rate": 0.0005,
  "ss_text_encoder_lr": 0.00005,
  "ss_unet_lr": 0.0005,
  "ss_noise_offset": 0.1,
  "ss_adaptive_noise_scale": "None",
  "ss_min_snr_gamma": 5,
}

Pony XL Anime: https://civitai.com/models/545495/sm-90s-aesthetic

{
  "ss_output_name": "SM90sPDXL_r1",
  "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors",
  "ss_network_module": "networks.lora",
  "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)",
  "ss_lr_scheduler": "cosine",
  "ss_clip_skip": 1,
  "ss_network_dim": 32,
  "ss_network_alpha": 32,
  "ss_epoch": 8,
  "ss_num_epochs": 8,
  "ss_steps": 2560,
  "ss_max_train_steps": 2560,
  "ss_learning_rate": 1,
  "ss_text_encoder_lr": 1,
  "ss_unet_lr": 1,
  "ss_noise_offset": 0.03,
  "ss_adaptive_noise_scale": "None",
  "ss_min_snr_gamma": 5,

}

XL & Pony 2024 Comparison outputs

Some of these are loras, and some are lora baking on a model, but they're left in for comparison on how far things are coming. More training settings using the Metadata viewer are shown after each sample. If you're curious what some of this means we'll try and break it back down later on.

DID VAPORWAVE PDXL

Blonde Hair Concept PDXL

X-men 97 Style:

Lora Bake Example:

Embedding Not Lora but it's using Faeia's model. Which like myself, uses Lora baking techniques.

DPO version of a model upcoming using lora bake technniques

Likely Hellaine Mix Lora Bake, which used Hellaine PDXL as a base style influencer.

Khitli Miqote 2024 PDXLOlder Duotone XL

Line Art PDXL

Flat Vector Art

Phoenix (see Metadata below) PDXL

Niji CGI

Strange Animals SDXL

Fashionable Niji PDXL

This concludes the IMAGE reference section, and in the next final section to conclude this version of the article I'll add some META data examples in which you can use to influence your future trainings

METADATA of Earth & Dusk Loras

We've used Xypher's metadata viewer (linked in several places in this article) and have given some commentary on each of these plus a general preview and link. Note that because we HELP Earthnicity as they're our Business & Queer Platonic Partner - we have access to may of their uploads as we share a huggingface repository. These backup links are listed below the Metadata section.

Earthnicity's Classic Storm (Character) - Text Encoder ON:

{ "ss_output_name": "ClassicSTormPDXL", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 9, "ss_num_epochs": 9, "ss_steps": 2754, "ss_max_train_steps": 2754, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-04-07T01:56:00.334Z", "ss_training_finished_at": "2024-04-07T03:12:40.064Z", "training_time": "1h 16m 39s", "sshs_model_hash": "a888b902d9e90b38ea466848296f2af35379a08c8517a75515db9092de99693e" }

URL:https://civitai.com/models/536896

Preview:

Earthnicity's Kawaii Pride Lora:

{ "ss_output_name": "Earthnicity_Kawaii_LGBTQIA__Plural_Pride_Lora_Pony_XL", "ss_sd_model_name": "290640.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 3160, "ss_max_train_steps": 3160, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-06-21T00:32:11.624Z", "ss_training_finished_at": "2024-06-21T03:02:19.466Z", "training_time": "2h 30m 7s", "sshs_model_hash": "d55f4e26a7d5ec07beeab28196a863f389a94aa47df712d60b4e64e8b340366d" }

URL:https://civitai.com/models/528225

Preview:

Offsite & Colab (Virtual Diffusion Styler) :

Different Text Encoder & Example of Google Colab (this took way longer than it would on a Normal 4090 imho)

{ "ss_output_name": "Virtual_3d_Diffusion_Update", "ss_sd_model_name": "ponyDiffusionV6XL.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.01,betas=[0.9, 0.999],d_coef=2,use_bias_correction=True,safeguard_warmup=True)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": "None", "ss_network_dim": 16, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 8160, "ss_max_train_steps": 8160, "ss_learning_rate": 0.75, "ss_text_encoder_lr": 0.75, "ss_unet_lr": 0.75, "ss_noise_offset": 0.0357, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 8, "ss_training_started_at": "2024-06-08T04:38:10.314Z", "ss_training_finished_at": "2024-06-08T09:21:05.103Z", "training_time": "4h 42m 54s", "sshs_model_hash": "3bd13bb2ca0daabfae31a7fd92579654ab4696c49212c166feb8c2c9c1345d16" }

URL:https://civitai.com/models/532668

Preview:

(Onsite) Niji CGI:

I'm not sure why it keeps saying Text Encoder ONE when i'm positive i didn't train it with text encoder >_>. (other than evidently I did something dumb?)

{ "ss_output_name": "Niji_CGI_Pony_XL_LoRa", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 3570, "ss_max_train_steps": 3570, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-06-18T03:00:58.496Z", "ss_training_finished_at": "2024-06-18T07:27:06.977Z", "training_time": "4h 26m 8s", "sshs_model_hash": "590108cc59937bdc126cf4662a7989ab84a80ef835e4734b6684698d65f7fb69" }

URL:https://civitai.com/models/522405

Preview:

Andy Kubert Comic Book Style:

Please note, I am a "SCIENCE THIS BINCH" type of trainer, and get usually side eyes from my peers (Lovingly) in discord. Anzch will probably peer over this later and ask me why i'm not consistent. Please also note, that sometimes fudging with Noise offset and MIN SNR gamma CAN burn your model without realizing it. I have yet to promise if this works OR NOT, it's meant to help on SD 1.5 - and i promise on SDXL it helps, but sometimes i've noticed that on PonyXL it can be flippant and cause contrast/noise issues. However, if you know more about math/training than I do please go in the comments and let me know how this works and how to correct it.

{ "ss_output_name": "AndyKubertPDXL", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 6, "ss_num_epochs": 6, "ss_steps": 3024, "ss_max_train_steps": 3024, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.1, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-04-07T05:23:12.741Z", "ss_training_finished_at": "2024-04-07T06:37:02.208Z", "training_time": "1h 13m 49s", "sshs_model_hash": "33a1cbfa2da7c6fe2003721cac744a9a20402ffa79d016418cacbd65a0778ac0" }

URL:https://civitai.com/models/388048

Preview:

Alternative Sailor Moon on PonyXL:

Again this is like above, i've been known to fudge the settings and it CAN come out fine, but be aware that sometimes this can cause artifacts and not always work:

{ "ss_output_name": "AltMoonPonyXL", "ss_sd_model_name": "290640.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2440, "ss_max_train_steps": 2440, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.1, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-03-06T02:38:36.724Z", "ss_training_finished_at": "2024-03-06T04:17:52.272Z", "training_time": "1h 39m 15s", "sshs_model_hash": "38aab066bd26344663eaba4b6cd7a42a8fbb61f37e327e231e0cbfb8ac69c46d" }

URL:https://civitai.com/models/434907

Preview:

Upcoming Landscape lora for PonyXL:

Again don't take my metadata examples as "THE BIBLE" - as my settings will vary from month to month. Always remember that you're dealing with someone with mental health things that flavor differently day to day let alone month to month.

{ "ss_output_name": "Cyberpunk_Landscape_Pony_XL", "ss_sd_model_name": "290640.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 3820, "ss_max_train_steps": 3820, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-06-21T01:55:00.735Z", "ss_training_finished_at": "2024-06-21T04:23:59.809Z", "training_time": "2h 28m 59s", "sshs_model_hash": "fc07afe3ece2a5cc19555f035e6381a562d6f1dee92f14cdc06d481924f423fb" }

There are no examples for this one sadly, as i have yet to publish and test it.

25D Style for Pony XL:

This was likely batch trained on Site a couple months ago, so be aware it'll probably tell you i used noise offset like SDXL. Evidently no, and on top of that i did text encoder on accident lol. There is a pure warning on this: TEXT ENCODER is finicky on XL and PonyXL, it doesn't always work it sometimes works. Aka: Styles aren't BAD with TENC trained, just that be aware training the TENC with a lora may produce varied differences vs training Unet only.

{ "ss_output_name": "25DPDXL", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2480, "ss_max_train_steps": 2480, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-04-06T23:50:32.729Z", "ss_training_finished_at": "2024-04-07T01:05:24.217Z", "training_time": "1h 14m 51s", "sshs_model_hash": "c2278d08a7fb6faace958ab7b7eb2b5d93e53918575494b044da9400f5256357" }

URL:https://civitai.com/models/438760

Preview:

Santa Overlords (Christmas 2023):

Let's start sharing more older XL examples to give a wide amount of data!

{ "ss_output_name": "SantaOverlordsXL", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 3, "ss_num_epochs": 3, "ss_steps": 2544, "ss_max_train_steps": 2544, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0.00005, "ss_unet_lr": 0.0005, "ss_noise_offset": "None", "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2023-12-12T07:33:39.601Z", "ss_training_finished_at": "2023-12-12T08:29:52.281Z", "training_time": "0h 56m 12s", "sshs_model_hash": "e80d4b08fc7445c4120e1a67447d321cc4cd20b3f68d804d56f861d50dab7e92" }

URL:https://civitai.com/models/230237

Preview:

Mica X'voor on SDXL:

Please note, that the following is what we personally think is a "BAD EXAMPLE" for training concepts or people with, as at the time on SDXL this bombed horribly. IT was on Tensorart when we were there, but we never brought it back to Civitai, as it wasn't of good quality. Please note everything listed here is always listed in our backup folders. I will link both LORA Backups after the metadata section, and if you scroll back up you can see there is a link to Xypher's lora Metadata tool. This will allow you to download and double check tags, and everything else!

{ "ss_output_name": "MicaXvoorSDXL", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 540, "ss_max_train_steps": 540, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0.00005, "ss_unet_lr": 0.0005, "ss_noise_offset": "None", "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2023-09-21T03:41:43.484Z", "ss_training_finished_at": "2023-09-21T04:10:38.901Z", "training_time": "0h 28m 55s", "sshs_model_hash": "6042158baa3ce03a2bf65648a37170e8c94c8010e5011144fcdca6c9b8c2bbe9" }

Sadly i don't know where this brat's images for SDXL went, and i know we had severe genetic issues in testing lol.

G'raha Tia (First version) on SDXL:

This was when people were telling you NEVER TO TRAIN ABOVE 500-1000 steps, some concepts CAN go under these numbers - and there are times i've burned loras with LESS data. This was not one of those, as you can tell with the TENC and UNET rates, this didn't come out nearly as well as it should have.

{ "ss_output_name": "GrahaSDXL", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 8, "ss_num_epochs": 8, "ss_steps": 856, "ss_max_train_steps": 856, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0.00005, "ss_unet_lr": 0.0005, "ss_noise_offset": "None", "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2023-09-29T04:16:00.300Z", "ss_training_finished_at": "2023-09-29T04:52:16.327Z", "training_time": "0h 36m 16s", "sshs_model_hash": "b2b3669c7c13b47c003f48d4f4e7282ebbca63417693c44bf18d7c8d9e28bbc5" }

URL:https://civitai.com/models/153609

Preview:

80s Fashion White Skin Art Deco Vector on SDXL:

{ "ss_output_name": "80s_Flat_Vector_Fashion_Art", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2920, "ss_max_train_steps": 2920, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-05-02T06:41:26.317Z", "ss_training_finished_at": "2024-05-02T07:42:20.216Z", "training_time": "1h 0m 53s", "sshs_model_hash": "7451255f40a872e117d629ac9947c54ca2ecc3dad21d0f3af34751d658885675" }

Also noting: SDXL AND PONYXL are trained on Clip Skip 2 - However, i'm not going to bother arguing this note, because Clip Skip will start WW6, 7, 8 and 9 -- WIth 10 being SD3 "SKILL ISSUE" arguments on reddit. (We know where that one went lol)

URL:https://civitai.com/models/431427

Preview:

Older Lora of Misuo Fujiwara on SDXL:

{ "ss_output_name": "DUSK_XL_MISUOFUJIWARA_dadapt_cos_1e-7", "ss_sd_model_name": "sd_xl_base_1.0.safetensors", "ss_network_module": "networks.lora", "ss_total_batch_size": 2, "ss_resolution": "(1024, 1024)", "ss_optimizer": "dadaptation.experimental.dadapt_adam_preprint.DAdaptAdamPreprint", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": "None", "ss_network_dim": 64, "ss_network_alpha": 32, "ss_epoch": 5, "ss_num_epochs": 5, "ss_steps": 1000, "ss_max_train_steps": 1000, "ss_learning_rate": 1e-7, "ss_text_encoder_lr": "None", "ss_unet_lr": 1000, "ss_shuffle_caption": "True", "ss_keep_tokens": 0, "ss_flip_aug": "True", "ss_noise_offset": 0.0357, "ss_adaptive_noise_scale": 0.00357, "ss_min_snr_gamma": 5, "ss_training_started_at": "2023-08-13T06:34:35.162Z", "ss_training_finished_at": "2023-08-13T07:07:13.230Z", "training_time": "0h 32m 38s", "sshs_model_hash": "6b3f7ccf27a1cdd7642e78f4222452b198ca6ea85662d5d80c9f8e3d7d2f72ad" }

(The tool couldn't find the images so i'm just doing i straight from the model lol)

URL: https://civitai.com/models/129262

Model Preview:

( Yes, this was again Second Life data, and Anzch and I have jokes about how bad SL "looks" desptie how much I spend on actual photorealistic skins lol. It's all in good fun!)

I do use over 2048 pics in these, but onsite will NOW resize them in bucketing, before it didn't - this is a good thing because I am positive Kitch's Lora is like farking 2gb just for 600 pics lol.

Older Lora Kawaii Devices on SDXL:

{ "ss_output_name": "KAwaiiDEvicesSDXL", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 16, "ss_network_alpha": 8, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2580, "ss_max_train_steps": 2580, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0.00005, "ss_unet_lr": 0.0005, "ss_noise_offset": "None", "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2023-10-14T06:50:44.056Z", "ss_training_finished_at": "2023-10-14T07:46:57.512Z", "training_time": "0h 56m 13s", "sshs_model_hash": "39079bfe92fa2b1c851dd54e36e59d82877ee126a48a75882f3aff7696a7aa56" }

Mike Weiringo on SDXL (Rest in Peace):

{ "ss_output_name": "RingoStyleXL", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 6, "ss_num_epochs": 6, "ss_steps": 2940, "ss_max_train_steps": 2940, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": "None", "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-01-30T04:00:22.690Z", "ss_training_finished_at": "2024-01-30T04:52:34.250Z", "training_time": "0h 52m 11s", "sshs_model_hash": "fc0599598aace436a4a47386ea8e80245f1d3fef7eb276e6e05177e9d56097a9" }

URL:https://civitai.com/models/284492

Preview:

PDXL Classic Rogue (not the recent retrain):

{ "ss_output_name": "ClassicRoguePonyXL", "ss_sd_model_name": "290640.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 6, "ss_num_epochs": 6, "ss_steps": 2562, "ss_max_train_steps": 2562, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.1, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-03-06T21:54:42.240Z", "ss_training_finished_at": "2024-03-06T22:51:07.337Z", "training_time": "0h 56m 25s", "sshs_model_hash": "594c8f3c73beb65c4c6a42e9ee95a82faeb49b95d4c7bcbdee82896c9b9e07a9" }

URL:https://civitai.com/models/342585

Preview:

DID VAPORWAVE VISIONS (SDXL):

{ "ss_output_name": "D.I.D._Vaporwave_Visions_SDXL", "ss_sd_model_name": "sdXL_v10VAEFix.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2920, "ss_max_train_steps": 2920, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.1, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-05-24T11:48:34.289Z", "ss_training_finished_at": "2024-05-24T13:27:30.110Z", "training_time": "1h 38m 55s", "sshs_model_hash": "b52a6673093cb6e6f7e87ff1960ea77dfcf7c8ce8e001180d5b69b843a9544ee" }

URL:https://civitai.com/models/473185

Preview:

Phoenix Montoya (PDXL):

We HAVE an SDXL version and it's probably on Civitai, but the quality is lacking due to a larger dataset and poor training settings... so instead you get the PDXL settings:

{ "ss_output_name": "Phoenix_Montoya_Rodriguez", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 7, "ss_num_epochs": 7, "ss_steps": 2716, "ss_max_train_steps": 2716, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-05-08T23:43:02.113Z", "ss_training_finished_at": "2024-05-09T00:53:59.293Z", "training_time": "1h 10m 57s", "sshs_model_hash": "b831c17749308f41dc2e9d75caed4d858bf6a5bcfaf8f65d3424e31fb7a03963" }

URL:https://civitai.com/models/444188

Preview:

Deadpool Movie style (DPvsWOLV/DP3) MCU:

Example of LARGER dataset poor training on SDXL:

{ "ss_output_name": "MCU-DeadpoolMovieStyle", "ss_sd_model_name": "sdXL_v10VAEFix.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 800, "ss_max_train_steps": 800, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0.00005, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.1, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-04-14T22:35:01.686Z", "ss_training_finished_at": "2024-04-14T23:05:44.752Z", "training_time": "0h 30m 43s", "sshs_model_hash": "b743391ba457aa0530a8d080bce10bb554b43d0dfef0904b4b5d7d8bee08f09d" }

URL:https://civitai.com/models/474750

Preview:

Please note that this was 800 steps, and it has some hilarious artifacting, it's NOT BAD overall, but it's hilarious in that it will add RANDOM BLOBS. Note I didn't even turn off the text encoder, i just remember throwing in whatever from the screencap set i made for Capsekai on Tumblr (which has been ignored in the last two months because i'm an idiot)

Earthnicity's MJ Manga on SDXL:

Animated Datasets such as this are a hard call, if anything the lack of steps explains a lot of the issues we faced in testing it. It produces a lot of artifacts, and has issues with hands and body proportions - yet it's one of my partner's most popular loras.

{ "ss_output_name": "MJMangaSDXL", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 570, "ss_max_train_steps": 570, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0.00005, "ss_unet_lr": 0.0005, "ss_noise_offset": "None", "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2023-09-23T03:06:18.127Z", "ss_training_finished_at": "2023-09-23T03:39:37.156Z", "training_time": "0h 33m 19s", "sshs_model_hash": "9b8630f11b7ab6c490ef53308b35523430e46f47d2b2ed52d9b0a91592bc3965" }

URL:https://civitai.com/models/185798

Preview:

Earthnicity's Pony XL version of Kitch X'voor:

This was because while Kitch is related in theory to many of the X'voor line in our system - she's really in Earthnicity's system (See our Bio sections, we're both plural systems they haev a UDD dx and we have a DID dx).

{ "ss_output_name": "Kitch_Xvoor_PDXL", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 3510, "ss_max_train_steps": 3510, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-05-08T03:34:07.815Z", "ss_training_finished_at": "2024-05-08T05:06:19.651Z", "training_time": "1h 32m 11s", "sshs_model_hash": "3ecfa7d609e4a7c8b2861bc82972f18931af628ae76cd0f42ef92d4a68f93799" }

There are SOME issues with proportions, so there is talk that the Datasets we've been creating may have quality issues - I need to speak in length with peers on what this may be. It wouldn't be nesscarily the subject quality as more lazyness on my part with my partner in not pre cropping or subject cropping. (And I mean that sincerely we don't tend to pre-crop, we just dump and go lol)

Note: Evidently WE DID NOT UPLOAD THIS, and i'm not sharing samples until i figure out WHY, you're welcome to download it via backups, but somehow this never got uploaded. (Despite that i somehow remember helping earthnicity upload this.... )

Female Hrothgar (DAWNTRAIL FFXIV):

Yep. Cat GIrls:

{ "ss_output_name": "FemHrothgarPonyXL", "ss_sd_model_name": "290640.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2790, "ss_max_train_steps": 2790, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.1, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-03-05T23:39:27.921Z", "ss_training_finished_at": "2024-03-06T00:44:52.815Z", "training_time": "1h 5m 24s", "sshs_model_hash": "1ee21226c7689ce48457f7e9b85d73a8a274f2e20b1dedf1cdbb60f9459cf1a9" }

URL:https://civitai.com/models/336350

Preview:Experimentation Lora:

{ "ss_output_name": "Lora_Trained_with_Whatever_was_From_my_Phone_Pony_XL", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2750, "ss_max_train_steps": 2750, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-05-17T02:25:46.548Z", "ss_training_finished_at": "2024-05-17T04:04:34.622Z", "training_time": "1h 38m 48s", "sshs_model_hash": "f0d70969bd5198487029961eac3732b75b3d7167b383ca7f9c8b5ef324f4a303" }

Description: This was a dataset we'd devised somehow from our phone, and I don't recall if it worked to do it FROM phone - because sometimes this doesn't work. You'd have to check what i said in the model card.

URL:https://civitai.com/models/459570

Preview:

Niji Vs MJ Realism:

{ "ss_output_name": "Niji-Mj_Almost_Realism", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 4, "ss_num_epochs": 4, "ss_steps": 3448, "ss_max_train_steps": 3448, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-05-11T06:51:09.229Z", "ss_training_finished_at": "2024-05-11T08:47:19.047Z", "training_time": "1h 56m 9s", "sshs_model_hash": "755641c2363e7d6c461c983afdef004ace5e149265b375d7e7f99947d5ab7d25" }

URL:https://civitai.com/models/448441

Preview:

Cybercore SDXL:

{ "ss_output_name": "Cybercore", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "bitsandbytes.optim.adamw.AdamW8bit(weight_decay=0.1)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 2360, "ss_max_train_steps": 2360, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": "None", "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-02-25T01:18:36.723Z", "ss_training_finished_at": "2024-02-25T02:02:46.567Z", "training_time": "0h 44m 9s", "sshs_model_hash": "d311708b003d0297d3c31372374e4c911b4d4954ff80e6e0505a4daabfa6d793" }

URL:https://civitai.com/models/320527

Preview:

Osenayan Style PDXL:

{ "ss_output_name": "Original_Osenayan_Mix_Style_for_PDXL", "ss_sd_model_name": "ponyDiffusionV6XL_v6StartWithThisOne.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "prodigyopt.prodigy.Prodigy(decouple=True,weight_decay=0.5,betas=(0.9, 0.99),use_bias_correction=False)", "ss_lr_scheduler": "cosine", "ss_clip_skip": 1, "ss_network_dim": 32, "ss_network_alpha": 32, "ss_epoch": 15, "ss_num_epochs": 15, "ss_steps": 2925, "ss_max_train_steps": 2925, "ss_learning_rate": 1, "ss_text_encoder_lr": 1, "ss_unet_lr": 1, "ss_noise_offset": 0.03, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-05-13T02:02:28.455Z", "ss_training_finished_at": "2024-05-13T03:30:28.802Z", "training_time": "1h 28m 0s", "sshs_model_hash": "928cee6fe06454394ce1ed0f1df8d1a608527d7dc0335387ffef32256557deb7" }

URL:https://civitai.com/models/451791

Preview:

Matariki Aotearoa XL:

{ "ss_output_name": "MatarikiAotearoaXL", "ss_sd_model_name": "128078.safetensors", "ss_network_module": "networks.lora", "ss_optimizer": "transformers.optimization.Adafactor(scale_parameter=False,relative_step=False,warmup_init=False)", "ss_lr_scheduler": "cosine_with_restarts", "ss_clip_skip": 2, "ss_network_dim": 32, "ss_network_alpha": 16, "ss_epoch": 10, "ss_num_epochs": 10, "ss_steps": 3050, "ss_max_train_steps": 3050, "ss_learning_rate": 0.0005, "ss_text_encoder_lr": 0, "ss_unet_lr": 0.0005, "ss_noise_offset": 0.1, "ss_adaptive_noise_scale": "None", "ss_min_snr_gamma": 5, "ss_training_started_at": "2024-03-08T06:08:43.399Z", "ss_training_finished_at": "2024-03-08T07:44:40.596Z", "training_time": "1h 35m 57s", "sshs_model_hash": "ad638e9ac0e198efef70822932b9fa3d89012d975d41a50730f5d6fc3b4510ce" }

URL:https://civitai.com/models/339317

Preview:

About Us

We are the Duskfall Portal Crew, a DID system with over 300 alters, navigating life with DID, ADHD, Autism, and CPTSD. We believe in AI’s potential to break down barriers and enhance mental health, despite its challenges. Join us on our creative journey exploring identity and expression.

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Community Groups:

Embeddings to Improve Quality

Negative Embeddings: Use scenario-specific embeddings to refine outputs.

Positive Embeddings: Enhance image quality with these embeddings.

Extensions

  • ADetailer: ADetailer GitHub

    • Usage: Use this extension to enhance and refine images, but use sparingly to avoid over-processing with SDXL.

  • Batchlinks: Batchlinks for A1111

    • Description: Manage multiple links when running A1111 locally or on a server.

    • Addon: @nocrypt Addon (The link is broken for now i'll find it later OOPS)

Additional Extensions:

Backups for Loras on SDXL & Pony XL:

273

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