sexual situations

physical violence

disturbing

male nudity

hanging

hate symbols

nazi party

revealing clothes

weapon violence

female swimwear or underwear

male swimwear or underwear

partial nudity

white supremacy

adult toys

graphic male nudity

illustrated explicit nudity

nudity

graphic violence or gore

graphic female nudity

pg-13

corpses

wide hips

convenient censoring

peeing

oral invitation

emaciated bodies

exposed female nipple

blowjob

female nudity

sexual activity

sexual intent

undressed

male underwear

female swimwear

genitals

female underwear

thick thighs

breasts out

strapless leotard

vore

breast out

one breast out

huge breasts

gigantic breasts

huge butt

covered nipples

hair over breasts

no panties

sitting on face

anal

dildo riding

downblouse

oral

porn

futanari

hentai

nude

lingerie

nsfw

suggestive

child on child

self injury

extremist

hate speech

diapers

urine

incest

scat

sexy

latex clothing

swimwear

bukkake

fellatio

cumshot

implied fellatio

eat_cum

cumdrip

cum in pussy

cum on face

after fellatio

cum on hair

cum on body

cum on tongue

cum on hands

cum in mouth

triple fellatio

autofellatio

fucked silly

cum on pussy

pov fellatio

Simple LoRA for analog/film photo style. Works best at resolutions above 1024x, but I don't have the memory to train above that, so it's not perfect.Example images were generated with 8step lightning lora on q4ks gguf.

Same images, same captions, same settings, same seed. Only change is using 640p + 1328p buckets instead of only 1024p buckets, as the latest musubi-tuner + activation offloading lowered my memory usage by a lot.The dataset is copied to do both resolutions, so the step count is doubled from 1000 to 2000.I didn't save the list of buckets for v1.0, but this version's buckets are:<pre><code>INFO:musubi_tuner.dataset.image_video_dataset:bucket: (512, 800), count: 6
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (528, 768), count: 18
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (544, 752), count: 6
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (560, 720), count: 7
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (576, 704), count: 9
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (624, 656), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (640, 640), count: 14
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (656, 624), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (672, 608), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (704, 576), count: 5
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (720, 560), count: 3
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (752, 544), count: 3
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (768, 528), count: 26
INFO:musubi_tuner.dataset.image_video_dataset:total batches: 100
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1072, 1632), count: 7
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1088, 1616), count: 16
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1104, 1584), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1120, 1568), count: 3
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1136, 1552), count: 3
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1152, 1520), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1168, 1504), count: 4
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1184, 1488), count: 9
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1200, 1456), count: 2
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1296, 1360), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1312, 1344), count: 5
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1328, 1328), count: 6
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1344, 1312), count: 4
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1392, 1264), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1456, 1200), count: 2
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1488, 1184), count: 3
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1504, 1168), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1520, 1152), count: 3
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1552, 1136), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1568, 1120), count: 1
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1616, 1088), count: 17
INFO:musubi_tuner.dataset.image_video_dataset:bucket: (1632, 1072), count: 9
INFO:musubi_tuner.dataset.image_video_dataset:total batches: 100</code></pre>

v1.1

Very simple training config for this. Most are captioned starting with "An analog photo of...", and the rest were only captioned with "An analog photo." Uses 1024*1024 buckets. Next version I will likely remove the trigger words.

v1.0

Analog (Style LoRA) | Qwen Image

1aa80c88615d91ddaf6fbeec7301d554.png

Type	LoRA
Stats	262 0
Reviews	Positive (19)
Published	Sep 15, 2025
Base Model	Qwen
Training	Steps: 1,000 Epochs: 10
Trigger Words	An analog photo. An analog photo of
Hash	AutoV2 949CC62A0D