Datasets:
npz
dict | __key__
stringlengths 4
7
| __url__
stringclasses 6
values |
|---|---|---|
{"crop_ltrb":[1.0,0.0,509.16,1024.0],"latents":[[[13.8828125,11.1015625,13.125,9.4609375,11.8046875,(...TRUNCATED) | 3000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[11.0,0.0,819.8467614533965,1024.0],"latents":[[[-12.5234375,-1.0761719,5.1171875,-6.0,(...TRUNCATED) | 4000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[1.0,0.0,1022.0232558139535,768.0],"latents":[[[19.65625,19.484375,17.84375,17.6875,17.(...TRUNCATED) | 5000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[0.0,17.0,768.0,1006.184],"latents":[[[21.25,18.890625,17.640625,17.46875,17.46875,17.4(...TRUNCATED) | 6000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[0.0,15.0,896.0,1007.4131994261119],"latents":[[[19.015625,18.5625,17.671875,17.375,17.(...TRUNCATED) | 7000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[6.0,0.0,760.8519637462235,1024.0],"latents":[[[20.890625,18.75,17.546875,17.34375,17.3(...TRUNCATED) | 8000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[0.0,0.0,1024.0,1024.0],"latents":[[[2.015625,1.0566406,0.119506836,-1.1894531,-0.09436(...TRUNCATED) | 10000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[0.0,12.0,640.0,1012.0],"latents":[[[3.4863281,-0.48632812,0.46484375,0.6791992,2.25,0.(...TRUNCATED) | 11000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[0.0,8.0,1024.0,759.2],"latents":[[[21.21875,18.03125,17.53125,17.203125,17.21875,17.23(...TRUNCATED) | 12000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
{"crop_ltrb":[0.0,0.0,767.0889679715302,1024.0],"latents":[[[14.4296875,13.4921875,12.7109375,17.265(...TRUNCATED) | 13000 | "hf://datasets/6DammK9/e621_2024-latents-sdxl-1ktar@c21ec2741c70ad4c68288c6fc3b4f12c882f4cec/latents(...TRUNCATED) |
End of preview. Expand
in Data Studio
E621 2024 SDXL VAE latents in 1k tar
- Dedicated dataset to align both NebulaeWis/e621-2024-webp-4Mpixel and deepghs/e621_newest-webp-4Mpixel. "4MP-Focus" for average raw image resolution.
- Latents are ARB with maximum size of 1024x1024 as the recommended setting in kohyas. Major reason is to make sure I can finetune with RTX 3090. VRAM usage will raise drastically after 1024.
- Generated from prepare_buckets_latents_v2.py, modified from prepare_buckets_latents.py.
- Used for kohya-ss/sd-scripts. In theory it may replace
*.webpand*.txtalong with meta_lat.json. Raw data is no longer required. - It took me around 10 days with 4x RTX 3090 to generate (with many PSU trips and I/O deadlocks). Perfect case would be 3-4 days only (18 it/s).
- Download along with webp / txt, and then extract them all to single directory, and then you are good to go. Tags available in 6DammK9/e621_2024-tags-1ktar.
- I still don't know how to work with multigpu trainning in Windows. Ultimately I may need to switch trainer. Use this repo if you are working well already.
- The used VAE: madebyollin/sdxl-vae-fp16-fix
- Most logs are lost. Verify with verify_npz.py. It should take 40 minutes (2000 it/s) if OS is super stable and you have a nice U.2 (Intel P4510 4T) and CPU (Intel Xeon 8358).
Extra: 12.5M Merged dataset for both danbooru and e621
Check out the meta_lat_merged.tar.gz. It is 23.8GB when decompressed.
The keys are casted in such pattern:
#250225: Relative to --train_data_dir="/tmp/dataset"
FOLDER_A = "danbooru/"
FOLDER_B = "e621/"
merged = {}
def cast_a(k):
return f"{FOLDER_A}{k}"
def cast_b(k):
return f"{FOLDER_B}{k}"
- One of the best apporach is create a nested folder like
/tmp/dataset/danbooruand/tmp/dataset/e621. Kohyas (torch.data.DataLoader) will support localized path.
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