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wuerstchen-ancient-coins

Text-to-Image
Diffusers
wuerstchen
diffusers-training
lora
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metadata
license: mit
base_model: warp-ai/wuerstchen-prior
datasets:
  - Aff4n20/ancient-coin-dataset
tags:
  - wuerstchen
  - text-to-image
  - diffusers
  - diffusers-training
  - lora
inference: true

LoRA Finetuning - Aff4n20/wuerstchen-ancient-coins

This pipeline was finetuned from warp-ai/wuerstchen-prior on the Aff4n20/ancient-coin-dataset dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['inscription, IMP AVG DIVI F; bare head of Augustus left; in front palm; behind, winged caduceus']:

val_imgs_grid

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
                "warp-ai/wuerstchen", torch_dtype=torch.float16
            )
# load lora weights from folder:
pipeline.prior_pipe.load_lora_weights("Aff4n20/wuerstchen-ancient-coins", torch_dtype=torch.float16)

image = pipeline(prompt=prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

  • LoRA rank: 4
  • Epochs: 19
  • Learning rate: 0.0001
  • Batch size: 1
  • 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.