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A Fresh Approach: Opinionated Guide to SDXL Lora Training
Updating constantly to reflect current trends, based on my learnings and findings - please note that for ease of use for people who are not neurodivergent: I will not be re-translating this to "proper english" because gemini and other LLM's will fabricate information based on incorrect findings. If you don't like my crazy lingo, that's on you to find another article!
Preface
This update features significant rewrites and omits some outdated information, showcasing more recent training methodologies.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 as well as Illustrious Lora trains using Xypher's metadata tool linked below.
Original Article was written in August of 2023. The original SD 1.5 and XL information has since been removed. This is to keep up with clarification and current trends and updates in my own learning.
Software Requirements (OFFSITE)
For offsite training, ensure you have something to use to train, loras do not come out of thin air. I don't have actual preferences on VRAM information for offsite, but i'm aware you can now go down to about 8gb Vram and even use AMD cards (?) for Kohya-SS.
Civitai's trainer is a choice, and it's not that it's bad nor good, but it's expensive long term and some of these links are free. These links are going to be at the BOTTOM of the article not the TOP of the article like before.
Colab is a reasonable offsite, and free option for those, the AUP is not forbidding of Lora training, however it can be stated that the AUP likes to lie to people and be aware that Lora training is hit or miss for some people with it. The last time i used Colab for lora training was mid 2024. I will be returning soon again, as the requirements for VRAM have been lowered for XL base models.
Training Essentials for SDXL Base Models
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, are great synthetic data resources. You can use various tools to scrap Gelbooru, and other sites, as well as Chrome/Firefox extensions to mass download images from places like Pinterest. You can also literally create your own datasets from your own AI creations. Be mindful scarily of limitation downstream: Flux 1.d isn't peachy keen on commercial use, this may effect the downstream use of your models.
Upscale as Needed: Aim for images predominantly above 512 pixels, ideally 1024. Onsite, 2048 is the maximum, although consistency is key. You can go all the way up to 4096 for offsite training, and you ARE NOT limited to 1:1 for resolution.
Organize Your Folders: If you're using the Civitai trainer, you don't need to worry about this. Assuming that is you're not a Mac user. Mac users no matter the platform SHOULD make sure all resource forks are removed. That being said, Civitai's trainer doesn't like to remove resource forks from MacOS based folders. Offsite services usually remove non images or text files from folders before training. If you're preparing something via Bmaltais, Derrian Distro or otherwise please follow the setup depending on which service you're using.
Resolution: Some times doing 1:1 CAN HELP, but it doesn't entirely matter from my experience. If your lora trained employs what's called "BUCKETING" it'll refactor in the sizing of your images that way. SDXL is trained on several different sizes not just 1:1 resolution.
Choosing a Base Model
SD 1.5 Days: This is a fun discussion! I used to beleive in the value of choice back in the day on SD 1.5. There were clear reasons sometimes to use ANYLORA (not really lol) and sometimes clear reasons to use NAI. This used to cause "turf wars" and clearly oh boy you'd see popcorn flying.
Today's choices: This all depends on what you're doing, who's market you're going for and why. I fly between Pony and Illustrious and am going to try NoobAI XL soon, it's just that the Civitai online trainer doesn't have an option for it - and when i tried it, it fried itself. XL still handles a lot of interesting concepts, i still offer it myself. You now even have the option of FLUX and SD 3 models.
My Choices: I tend to stick to what my userbase wants, and mostly it's Animagine + SDXL, IIlustrious/NoobAI & PonyXL. The occasional Flux and old school SD 1.5 slips in.
Peer Discussion: There is professional discussion amongst peers on what models are still viable, and for what reasons. Because i'm still not sure of all the technical jargon, and understanding "WHY" something is "Cooked" or "fried" - it's hard for me to clearly move forward to say yes or no on models. This is for your explorations, if a TENC is "FRIED" (Text Encoder) you'll have to experience this for yourself.
Repeats and Epochs
The number of repeats depends on your desired level of detail and fidelity in your models. This also depends on two things: If you're ON SITE or OFF SITE. Do not use 2023 data of "NO REPEATS" - we can use repeats, you're safe. That being said, it largely depends on the size of your dataset, and if you're using Civitai or other site based trainers. Offsite well, you do you boo - your repeats will up your epochs/steps sometimes and may cause longer training times. Onsite for Civitai? More buzz = More Repeats. How I train is how I train, and i'm starting to try and find OTHER ways of doing things instead of just FSk around and find out - and SD 1.5'ing it until the thing looks burned in.
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 is NOW available - and is NOW if you're using Civitai onsite: Largely one of the better ones to use. That being said if you're using Derrian Distro: there is the "LoraEasyCustomOptimizer.came.CAME" - I have yet personally to test this, but there are plenty of my peers who use this and are totally good with this.
LR Scheduler: The Civitai trainer doesn't have that many options, and most people default to Cosine. Back in the 1.5 days we did constant with restarts sometimes. My peers recommend "LoraEasyCustomOptimizer.RexAnnealingWarmRestarts.RexAnnealingWarmRestarts"
Additional Tips
Dataset Organization: Organize this how you feel comfortable, my Dataset Tools python program is still WIP but is somewhat working, but there are plenty others out there. Be sure to either manually or with aid of a program - check for duplicates.
Training Tips: Optimal batch sizes range from 2 to 4 to balance out the cost on Civitai, and if you're offsite, well I haven't done offsite training in a long time don't ask me.
Latents: "What's a Latent" - I'm kidding, usually we cache these offsite and I don't recall why anymore, but I do recall it being a decent thing to save on memory.
Gradient Checkpointing: Useful for conserving VRAM during training sessions, particularly on lower-spec machines. This is NOT a thing on Civitai's onsite trainer nor other sites, but there is things on Bmaltais and other tools.
Conclusions
SDXL base models (Illustrious, NoobAI XL, PonyXL and SDXL base as well as Animagine and others) are miles ahead of SD 1.5. Gone are (sadly) the days of "FAFO" and "CRUNCHY" sd 1.5 loras and checkpoints. Now we have SDXL base crunchy Fsk around and find out models!
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,
}Rogue (X-men) on Illustrious: https://civitai.com/models/1368221/rogue-x-men-illustrious
{
"ss_output_name": "Rogue_X-men_Illustrious",
"ss_sd_model_name": "128078.safetensors",
"ss_network_module": "networks.lora",
"custom.batch_size": 2,
"ss_gradient_accumulation_steps": 1,
"custom.resolution": "1024x1024",
"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_network_args": "undefined",
"ss_epoch": 5,
"ss_num_epochs": 5,
"ss_steps": 4130,
"ss_max_train_steps": 4130,
"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,
"sshs_model_hash": "a6f029d23c23e34637f4f6384995f6ee490c0e3788a3d5c280f658c77df74976",
"custom.training_time": "2h 14m 42s",
"custom.training_start_time": "2025-03-17T22:35:23.443Z",
"custom.training_end_time": "2025-03-18T00:50:05.869Z",
"custom.lora_hash": "7876a9dbfc",
"custom.lora_hash_type": "AutoV2",
"custom.civitai_url": "https://civitai.com/models/1368221?modelVersionId=1545757",
"ss_dataset_dirs": {
"img": {
"n_repeats": 4,
"img_count": 413
}
},
"civitai.trainedWords": [
"R0GU3",
"two-tone hair, auburn hair, white hair, green eyes",
"long hair, curly hair, fluffy hair",
" leather jacket, green and yellow bodysuit, yellow gloves, leather, skin tight",
"yellow crop top, torn clothes, green bikini, shorts, leather shorts, yellow gloves"
]
}XL , Illustrious & Pony 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 PDXL
Older 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 - I'll try to include Illustrious in this time.
METADATA of Earth & Dusk Loras
Some of these are Xypher's output for our loras and some are the training data we can find in the actual model card . It turns out that most of the time the only time i've been "ABLE" to get onsite metadata is when I was actually finished training something. Sadly, I have not been keeping tabs on these so i'll not be downloading too many of these for reference. Xypher's tool is linked in the resources below.
Aion MMORPG Illustrious:
{
"ss_output_name": "Aion_MMORPG_Illustrious",
"ss_sd_model_name": "889818.safetensors",
"ss_network_module": "networks.lora",
"custom.batch_size": 2,
"ss_gradient_accumulation_steps": 1,
"custom.resolution": "1024x1024",
"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_network_args": "undefined",
"ss_epoch": 10,
"ss_num_epochs": 10,
"ss_steps": 4360,
"ss_max_train_steps": 4360,
"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,
"sshs_model_hash": "7677e7a3b1a14da6a153548f3f6f509f52fd029572467740ed7e03a18d9e305a",
"custom.training_time": "2h 36m 46s",
"custom.training_start_time": "2025-03-15T03:45:55.947Z",
"custom.training_end_time": "2025-03-15T06:22:42.216Z",
"custom.lora_hash": "b8acbfa597",
"custom.lora_hash_type": "AutoV2",
"custom.civitai_url": "https://civitai.com/models/1358301?modelVersionId=1534380",
"ss_dataset_dirs": {
"img": {
"n_repeats": 4,
"img_count": 218
}
},
"civitai.trainedWords": [
"End_Aion"
]
}
https://civitai.com/models/1358301
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" } Preview:
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:
Software Github & Huggingface Repository Links
(*Local & Server)
I have NOT used all of these and PRE WARNING: Colab's AUP does support LORA training, but because it's AUP is AI moderated - it may assume the worst and slow you down or otherwise. This may not be an AUP thing, this may be due to resources an distribution on their end.
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
Jelosus2's Derrian Colab: https://github.com/Jelosus2/LoRA_Easy_Training_Colab
AndroidXL's Fork: https://colab.research.google.com/github/AndroidXXL/Android-Forge-UI/blob/main/Adapted_Easy_Training_Colab.ipynb
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/Ktiseos-Nyx/sdxl-model-converter
Huggingface Backup: https://github.com/Ktiseos-Nyx/HuggingFace_Backup
Dataset Editor (in progress WIP): https://github.com/Ktiseos-Nyx/Dataset-Tools
WIP Gradio XL to Diffusers: https://github.com/Ktiseos-Nyx/Gradio-SDXL-Diffusers
Contact Us:
We're a Neurodivergent nightmare your Lora Trainer Warned you about! We do this to make space for others like ourselves, but it's been difficult to keep up for both ends of the spectrum here. If you're interested in what we do take a look:
Arc En Ciel: https://arcenciel.io/users/77
Our Discord: https://discord.gg/HhBSvM9gBY
Earth & Dusk Media https://discord.gg/5t2kYxt7An
Backups: https://huggingface.co/EarthnDusk
Send a Pizza: https://ko-fi.com/duskfallcrew/
WE ARE PROUDLY SUPPORTED BY: https://yodayo.com/ / https://moescape.ai/
JOIN OUR DA GROUP: https://www.deviantart.com/diffusionai
JOIN OUR SUBREDDIT: https://www.reddit.com/r/earthndusk/