Text-to-image finetuning - arpachat/small-stable-diffusion-v0-th-800-e5-g16-bs16
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 Regular Fit Round Logo Grey Polo"]:
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-800-e5-g16-bs16", torch_dtype=torch.float16)
prompt = "Tommy Hilfiger men's Regular Fit Round Logo Grey Polo"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 800
- Learning rate: 1e-05
- Batch size: 16
- Gradient accumulation steps: 16
- 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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