license: apache-2.0
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- VINZING-TINY inference: true
DEVELOPER
DEVELOPER NAME : MUHAMMAD TAQI
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("muhammad-taqi512/VINZING-TINY", 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
Here is the code for benchmarking the speeds.