segmind/tiny-sd

license: creativeml-openrail-m basemodel: SG161222/RealisticVision_V4.0 datasets:

• —recastai/LAION-art-EN-improved-captions tags:
• —stable-diffusion
• —stable-diffusion-diffusers
• —text-to-image
• —diffusers inference: true ---

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.

[image]

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", 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

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.

[image] [image]

Here is the code for benchmarking the speeds.