Miso-diffusion-m
Collection
4 items • Updated • 2
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("suzushi/miso-diffusion-2.1", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]
Change in model behavior
Trained with brand new dataset this time, it might take some time or more epoch before the model can properly adapt, please be aware that some older prompts might degrade or create some artifact.
Version History
| Version | Base Training | Aesthetic Training | Total Epochs |
|---|---|---|---|
| alpha | 250K images | 0 images | 1 |
| beta | 160K images | 0 images | 3 |
| 1.0 | 600k images | 0 images | 2 + (3 from beta) |
| 1.1 | 710k images | 0 images | 5 |
| 2.0 | 1.08M images | 0 images | 5 |
| 2.1 | 1.32M images | 0 images | 5 |
Training Methodology
Training is done on gh200 with 96gb vram, now that prior training shows decent results, I am slightly increasing learning rate.
Training setting: Adafactor with a batchsize of 40, lr_scheduler: cosine SD3.5 Specific setting: enable_scaled_pos_embed = true
pos_emb_random_crop_rate = 0.2
weighting_scheme = "flow" learning_rate = 8e-6
learning_rate_te1 = 5e-6
learning_rate_te2 = 5e-6
Train Clip: true, Train t5xxl: false
Support Me
At the moment training an epoch cost around 130 dollars. If you like my project please consider supporting me: https://ko-fi.com/suzushi2024
Lastly, huge thanks to meg who has been supporting this project, without him this project would not have been possible !
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Model tree for suzushi/miso-diffusion-2.1
Base model
stabilityai/stable-diffusion-3.5-medium