Updated: Aug 19, 2025
style
Wan 2.2 14B V1 whats new:
-This is the same dataset + captions from the 2.1 lora, except trained on both the high and low WAN 2.2 14B models.
-You will get all the benefits of the upgrade from 2.1 to 2.2. Especially movement and camera control is quite nice.
-Warning: I feel its gotten further away from the dataset in terms of style, I will try doing more training later, I think its in good enough state now to release. I wanna move onto something new next, this update was more about learning how to train 2.2. Read below about the training for my process which is new.
What is this lora?
This is a style lora used to recreate the style of the 1995 anime series "Golden Boy". The series has beautiful mid-90s matte painting style backgrounds which came out great in the lora. And the way they draw the girls is awesome and really represents the art style of the period for raunchy comedies. But if you just want a mid-90's retro style anime look in wan then use this lora too, its really great at just doing older style anime in general. And it is perfect at doing detailed environmental shots. Its captioned on bikes, cars, delicious looking food, garbage etc, not just people. Its trained on the T2V model and therefore should also work for I2V.
Trigger word: Goldenboystyle
(You do not need to add any other descriptions for anime or animation style in the prompt, it should make it the style without any other prompting). In fact I would recommend against adding anime keywords to the prompt as it will create more of a bias from the base model which is now trained on anime much better than before. The trigger word may not even be needed but I put it in anyway.
All the characters from the show is in the training data. The blonde woman (Madame President) comes out pretty much if you mention blonde women. If you describe any character from the show it will probably generate them accurately. The main character Kentaro Oe will also come out if prompted but only by description and not by name. The silly faces the characters make are also in the training data. There is naked breasts in the training data but no lower genitals.
Recommended Settings
It can run on default wan workflow just fine, and retains that real nostalgic retro animation style, but I recommend to mix this lora with the following optimization loras. There is 3 settings I recommend and each has their own positives and negative effects.
It's too early to tell whats best aside from default. I am leaning on the 2.1 light lora because 2.2 kills the motion so hard and also changes the style to be less like the show, but still a nice retro feeling.
I think I will make a workflow for my loras and link that from now on. So just download my example workflow for this lora and try it yourself.
See below image to see how it effects the look. For motion check the example generations I provided for these, it has in the comments the settings I used for reference.
LINK TO EXAMPLE WORKFLOWS
https://civitai.com/models/1868641
1.) Default Setting
Just run the lora with no other loras and it will work fine. And it will retain the closest look and feel to the original source material. On a 3090 it takes over 20 mins for a 720p video to generate.
20 steps (10/10), 3.5 CFG, NO NAG
Benefits: Closer to trained data. You get all the 2.2 benefits like motion, quality, camera control etc.
Negatives: Slower, more resource intense
2.) Lightx2V Wan 2.1 Lora Optimization
1.) This lora (golden boy style) (strength 1.0 on both high and low)
7 Steps ( 3 / 4), though you can try 4/4 or 2/2. CFG 1 with NAG
Benefits: Can complete higher resolution with fewer steps. The motion is retained and style closer default than lightning lora.
Negatives: Lightx2V is a Wan 2.1 lora so I think you downgrade the output to look more like 2.1 than 2.2. I feel also that the colors are a bit dark. It adds some weird snow effect sometimes which can be mitigated by increasing strength on the lightx2v loras.
3.) Lightning 1.1 Wan 2.2 Lora Optimization
7 Steps ( 3 / 4), though you can try 4/4 or 2/2.CFG 1 with NAG
1.) This lora (golden boy style) (strength 1.0 on both high and low)
2.) Wan 2.2 Lighting v1.1 loras (strength 1.0 on both high and low)
Benefits: Can complete higher resolution with fewer steps. It kinda makes colors brighter and less saturated if you like that aesthetic. Its a 2.2 lora so you technically get benefits from 2.2 wan but its kind of not working properly.
Negatives: It effects the style heavily, it still looks anime retro but the colors are brighter than the source material. The motion is HEAVILY reduced.
4.) Other 2.1 loras
2.1 Goldenboystyle lora. You can mix this on high or low, maybe low is better.
These above two loras were great for 2.1 version of this. I don't use them because I feel the more 2.1 lora you use the less the output looks like 2.2 and just becomes wan 2.1 again... If they release 2.2 versions of these loras I will edit here.
See the below example to see how each setting effects the output compared to the source.
In the end, I don't think there is a right choice as they all have negatives in some way, its too early to tell the best way to set things. So I'll update this bit in the future if I figure anything out. The motion killed in #3 makes me use #2 most often. And I don't have much patience for option #1. Please tell me if you have a good setting suggestion.
Training Info
Low Lora Model:
[model]
type = 'wan'
ckpt_path = '/data/trainingstuff/wan2.2_base_checkpoint/low_noise_model'
transformer_path = '/data/trainingstuff/wan2.2_base_checkpoint/low_noise_model'
dtype = 'bfloat16'
transformer_dtype = 'float8'
timestep_sample_method = 'logit_normal'
blocks_to_swap = 8
min_t = 0
max_t = 0.875
[adapter]
type = 'lora'
rank = 32
dtype = 'bfloat16'
[optimizer]
type = 'adamw_optimi'
lr = 2e-5
betas = [0.9, 0.99]
weight_decay = 0.01
eps = 1e-8
High Lora Model:
Basically same settings as lora except max/min_t changes 0.875 to 1 range.
type = 'automagic'
lr = 2e-5
weight_decay = 0.00195
lr_bump = 5e-6
eps = 1e-8
Lets talk graphs:
Here is the low lora graph:
You can see it jumps up and down. It tends downward over time. And epoch 65 it was fine, but I trained more. I honestly didnt see much difference between 65 and 106. I couldnt get it lower than 0.8, maybe I can if I try again with proper settings on the training.
Here is the high lora graph
(I cant seem to find my training data, but you get the gist from this earlier screen shot. It trends like this then sort of flat lines. We get much better loss trend on high. # of steps is much lower too.
Sorry I cannot find my training data for this, maybe its deleted (I still have the epochs). Not a big deal since this thing reaches a good state FAST unlike the low lora. I am of the opinion you want high to get a general shape when watching it the preview since its made for motion. Let the low lora get the details in, if its not close enough in shape then the details will look off in the low model.
Note:
I ran an initial run on automagic for the low model and it came out garbage. It wouldn't work without lightx loras and it had ghosting and motion blur. So I did a second run with the settings above with adamw_optimi on the low and it completely fixed all the problems. I can't say for certain but I have theory the low model will train better on default settings with adamw_optimi. High model can do either. The high model trains super fast and doesnt need a lot of steps compared to the low model which drags on and is super erratic in terms of the trend of the loss.
Also, I screwed up the training on the low lora when I resumed on checkpoint after epoch65, I think for some reason it was training only on images for 30 more epochs at our final one. I didnt notice any negative effects, so I will just give the latest epoch. Try the other LOW epoch in the training data which I will include with the captions.
Its hard to test wan 2.2 loras. You basically have to train both and then do the fine-tunning. If you have a 2.1 lora already you can use it for the high model and train the low first but you're mixing 2.1 with 2.2 and I think better to just train the high model before testing heavily. Overall I think this two lora system is not good, there is too many variables to test if something is going wrong. This took a long time to trouble shoot and I had to abandon 10K+ steps worth of trained data.
Big Thanks
There is too many people to thank, I bother so many people with dumb questions in the Banodoco Discord, but they are always kind and put up with me and help me along the way. As always I want to shout out Kijai for his great help, lightx team for their loras, and Seruva19 as his loras and detail into documentation into the process are really what this scene needs. I am kind of just figuring things out along the way and taking bits and pieces of existing info and brute-forcing them together to an output I hope everyone can enjoy.