Text-to-image finetuning - arpachat/small-stable-diffusion-v0-th-1000

This pipeline was finetuned from OFA-Sys/small-stable-diffusion-v0 on the jwl25b/final_project_dataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ["Tommy Hilfiger men's Long Sleeve Deep Red T-Shirt"]:

val_imgs_grid

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("arpachat/small-stable-diffusion-v0-th-1000", torch_dtype=torch.float16)
prompt = "Tommy Hilfiger men's Long Sleeve Deep Red T-Shirt"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

  • Epochs: 500
  • Learning rate: 1e-05
  • Batch size: 8
  • Gradient accumulation steps: 4
  • Image resolution: 512
  • Mixed-precision: fp16

More information on all the CLI arguments and the environment are available on your wandb run page.

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Dataset used to train arpachat/small-stable-diffusion-v0-th-1000