Text-to-image Distillation
This pipeline was distilled from SG161222/Realistic_Vision_V4.0 on a Subset of recastai/LAION-art-EN-improved-captions dataset. Below are some example images generated with the tiny-sd model.
This Pipeline is based upon the paper. Training Code can be found here.
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("segmind/tiny-sd-mxtune", torch_dtype=torch.float16)
prompt = "Portrait of a pretty girl"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Steps: 125000
- Learning rate: 1e-4
- Batch size: 32
- Gradient accumulation steps: 4
- Image resolution: 512
- Mixed-precision: fp16
Finetune info
These are the key hyperparameters used during training:
- Steps: 8000 / 100000
- Learning rate: 1e-5
- Batch size: 24
- Gradient accumulation steps: 1
- Image resolution: 768
- Mixed-precision: fp16
Speed Comparision
We have observed that the distilled models are upto 80% faster than the Base SD1.5 Models. Below is a comparision on an A100 80GB.
Below is the code for benchmarking the speeds