Instructions to use segmind/SSD-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use segmind/SSD-1B with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Training Steps questions
Hey, great model and thanks for sharing the hyperparams! I'm scaling the learning rate to use with lower batch size and it help my SDXL training. I'm considering to fine-tune with this model soon, but I have some question.
When you mention "Steps: 251000", are those pure steps without considering the batch size and grad accumulation? Or have they already been divided by the batch size/grad steps, like around 1960 steps? Also, it'd probably be helpful if you could also provide the number of epochs.
Thanks!
Hello! Those are pure steps. We overall did roughly 1.5 epochs on the data. Do you have any specific finetunes in mind?
Oh, okay. Thank you for the information. I plan to fine-tune an anime model based on SSD-1B. I am currently gathering 1 million images from Danbooru and curating generated images from Nijijourney. However, I might try training with a smaller dataset first.
Good luck on the finetune!