这破COS模型谁爱炼谁炼吧，我再炼就是傻逼，这傻逼高P图炼丹本身就是无稽之谈

还有，这模型训练量严重不够，有训练更多的在Q群发过，也烂的要死，有时间整个踩坑记录

Thank RumbleXL for helping me verify the feasibility of doing so.

This model is specifically used to generate Cos graphs.Since it is trained from the animation base model, the body and hand effects of generating images are relatively poor. If you want a better effect, you can try MBW processing.I also tried to use a real human model to train, but the hair will always be black, can not achieve the desired effect, so give up, can only use anime base model training.

Train

CosXL is trained using 6000 high quality cos diagrams, using Lokr training with the base model AnimagineV3, the difference between A and B is that they use different LBW.

dataset

The training dataset used consists of over 6000 randomly selected images from Cosbooru, sourced from an adult cosplay website. Among the selected 6000+ images, 95% of them feature cosers from East Asia. The training images utilized are a collection of images after excluding NSFW content.

Training dependent parameter:

Cosplay Model XL
GPU                  1 x RTX4090
dataset              6450
Num Epoch            34
Learning Rate        5e-3
TE Lr                1e-5
Effective Bs         2 x 64
Optimizer            AdamW
Lr_scheduler         cosine_with_restarts
Lr_warmup_steps      50

Picture size:

(768   1312), count: 36                                  
(768   1344), count: 111                                  
(800   1280), count: 45          
(832   1216), count: 159                                 
(832   1248), count: 4404                               
(864   1184), count: 198                                  
(896   1152), count: 21                                   
(928   1120), count: 3                                   
(1024  1024), count: 12                                 
(1184   864), count: 24                                 
(1216   832), count: 24                                 
(1248   832), count: 1272                              
(1280   800), count: 39                                
(1344   768), count: 60

LoRa Block Weights:

CosXL_A:
0.5,0.8,0.3,0.3,0,0.3,0.3,0,0,0.5,0.5,0,0,0.5,0.3,0.3,0.5,0.6,0.6,0.5
CosXL_B:
0.8,0.3,0.3,0.0,0.3,0.3,0,0,0.5,0.5,0,0,0.5,0.3,0.3,0.5,0.6,0.6,0.5,0

Recommended settings

To guide the model towards generating high-aesthetic images, use negative prompts like (Please do not use the artist's name as a prompt):

nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]

Negative tags can include common negative tags, but it is best not to assign too high of a weight to their content, for example (ugly:2.8).

Most of the generation parameters of the example graph are:

euler_a | 20steps | no hires fix | CFG7

Acknowledgements 

The development and release of CosXL would not have been possible without the invaluable contributions and support from the following individuals and organizations:

1. TusiArt:Our collaboration partner and sponsor.

2. HCP-diffusion:The previous trainer

3. 秋葉aaaki:This time use the training UI

4. Kohya SS: For their comprehensive training scripts.

Disclaimer:

All images generated by the model are created by the users themselves, and the model author cannot control the images generated by the users. The model author will not be held responsible for any potential copyright infringement or unsafe images.

License:

CosXL now uses the Fair AI Public License 1.0-SD, compatible with Stable Diffusion models. Key points:

• Modification Sharing: If you modify CosXL, you must share both your changes and the original license.

• Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too.

• Distribution Terms: Any distribution must be under this license or another with similar rules.

• Compliance: Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence toopen-sourcevalues