dongOi071102/wuerstchen-prior-meme-lora-epochs-10

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-meme-lora-epochs-10

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 cartoon character with a big smile']:

[image]

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-meme-lora-epochs-10", 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: 8
• —Epochs: 10
• —Learning rate: 1e-05
• —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.