v2.0
Trained by 60k images from Danbooru and Gelbooru + 8k AI-generated images.
・UNet lr= 5e-6 ~ 3e-6
・Text Encoder 1 Lr = 3e-6 ~ 1e-6
・Text Encoder 2 Lr = 2e-6 ~ 7e~8
To prevent excessive training, the dataset categorized by style sets repetition numbers based on the amount of data.
Prompting Guide
・1girl/1boy/2girls..., characters, copyright, style, general tags, rating, score_9, score_8_up, score_7_up.
Negative Prompts
・score_4, score_5, score_6, source_pony, source_furry, monochrome, realistic, rough sketch, fewer digits, extra digits
Score tags are adjusted so that, average anime illustrations are generated, without the aesthetically tuned styles like in v1.0.
To increase detailing or change styles, you can use prompting similar to 'NovelAI' or use 'lora'.
Some artist tags seem to work. When using artist tags, write the score tags at the end.
Example
v1.0
Introduction
This model is finetuned from Pony Diffusion V6 XL.
Trained by SFW and NSFW images scraped from Danbooru, Gelbooru.
Tagging follows the template of Pony Diffusion.
score, 1girl/1boy/2girls..., characters, copyright, style, general tags, rating
Negative Prompts
score_4, score_5, score_6, source_pony, source_furry, monochrome, 3d, photo, hyperrealistic, realistic, rough sketch, fewer digits, extra digits, signature, artist name
Score tags have been made clearer.
score_9: high quality illustrations
score_8_up: realistic texture
score_7_up: anime-like
Although no training focused on specific characters, some characters may have been learned.
License
Support☕ https://ko-fi.com/sfa837348
Description
FAQ
Comments (15)
Hi can you provide more info on training for each version?
How many images were used? Epochs? Steps?
Sorry, but v2 is a downgrade.
Haven't tried it out yet but could you elaborate what's wrong with it? Is it the style or did the anatomy get worse somehow?
I only tried it out a little bit, but I agree. Hopefully we're both using it wrong :)
@GogetaSSGSS3 the illustration style is taking over the model, so score tags have very little impact on style change unlike 1.0. The color saturation is much stronger and hard to tune down. I feel the model becomes overfitting.
@GogetaSSGSS3 The style takes over like ai_mocha said. It's very hard to manipulate the results.
@ai_mocha I see, I was hoping v2 would've been similar to v1 but with improved anatomy at least. This model has potential tho so hopefully the future versions can fix these issues.
@GogetaSSGSS3 Ok, after the auther updated the training data, changes, and guidelines to create styles, I decided to spend more time with 2.0, and the results are surprisingly good.
Id say 2.0 is an upgrade in terms of replicating styles from artists, that is why illust/key visual style is taking over. I have found some artist tag is not usable in 1.0, but create very good result in 2.0. Simply put artist name of your like in the prompt and see what you get.
@ai_mocha interesting. I have another question. Are you able to get good img2img generations? For example, if you were to generate an image with AutismMix (for good anatomy), would you be able to img2img that image with this model to get a better looking style while keeping the good anatomy?
This is very good, sent you support on ko-fi :)
Hopefully you keep going, best pony finetune so far.
Are you using the SDXL 1.0 VAE in PD for Anime v2? Because it produces color artifacts on all images.
Is there a list of characters and styles the the model knows?
Pls add this model in TensorArt ;w;
really underrated model
will there be another version of it or its dropped?
Details
Files
pdForAnime_v20.safetensors
Mirrors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
pdForAnime_v20.safetensors
Available On (2 platforms)
Same model published on other platforms. May have additional downloads or version variants.

