metadata

license: mit
base_model: warp-ai/wuerstchen-prior
datasets:
  - dongOi071102/meme-pretreatment-dataset-100rows
tags:
  - wuerstchen
  - text-to-image
  - diffusers
  - diffusers-training
  - lora
inference: true

LoRA Finetuning - dongOi071102/wuerstchen-prior-naruto-lora-2

This pipeline was finetuned from warp-ai/wuerstchen-prior on the dongOi071102/meme-pretreatment-dataset-100rows dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a catton cat with angry face']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
                "warp-ai/wuerstchen", torch_dtype=float32
            )
# load lora weights from folder:
pipeline.prior_pipe.load_lora_weights("dongOi071102/wuerstchen-prior-naruto-lora-2", torch_dtype=float32)

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: 5
Learning rate: 0.0002
Batch size: 8
Gradient accumulation steps: 1
Image resolution: 512
Mixed-precision: fp16

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