MOSS Image LoRA Training
Collection
A collection of experimental LoRAs trained for MOSS Image • 7 items • Updated
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("OpenMOSS-Team/MOSS-Image-Base", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("SimpleTuner/moss-image-base-domokun-512")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]MOSS-Image BASE Domokun, 512px
Part of the MOSS-Image LoRA experiments.
Training status: completed, 10,000 / 10,000 observed updates. Captions: original filename captions. Each run starts fresh from its released checkpoint, without an assistant LoRA. Resolution is 512px equivalent image area with aspect ratios preserved.
Rank-32 BF16 LoRA, batch 4, learning rate 0.0001, seed 42, REPA, one H100 80 GB. Validation uses 50 sampling steps, CFG 3, seed 42, and four prompts every 500 updates.
Recipe
The training config, dataset config, prompts,
and dataset manifest record the run. Publishing is handled by an external
checkpoint collector; the training config retains the in-process Hub flags used by training.
The dataset is RareConcepts/Domokun, revision
2e4831a72f2a701c080e0046c8f8ec41cb1ccbcf, containing 29 images. Dataset files are not redistributed here.
Checkpoints
500, 1,000, 1,500, 2,000, 2,500, 3,000, 3,500, 4,000, 4,500, 5,000, 5,500, 6,000, 6,500, 7,000, 7,500, 8,000, 8,500, 9,000, 9,500, 10,000
Latest published adapter: update 10,000.
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Base model
OpenMOSS-Team/MOSS-Image-Base